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Implementation of chronic care model for diabetes self-management: a quantitative analysis.

1. Introduction

2. application of chronic care model in pakistan, 2.1. self-management support (sms), 2.2. delivery system design (dsd), 3. material and methods, 3.1. study design, 3.2. research participants, 3.3. statistical analysis, 3.4. data collection, 4.1. quantitative analysis, 4.2. application of the two components of ccm, 4.2.1. baseline analysis of hba1c, 4.2.2. after the 3-months intervention of ccm components, 4.2.3. after the 6-month intervention of ccm components, 5. discussion, 6. strength and limitations, 7. relevance to clinical practice, 8. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

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Ansari, R.M.; Harris, M.F.; Hosseinzadeh, H.; Zwar, N. Implementation of Chronic Care Model for Diabetes Self-Management: A Quantitative Analysis. Diabetology 2022 , 3 , 407-422. https://doi.org/10.3390/diabetology3030031

Ansari RM, Harris MF, Hosseinzadeh H, Zwar N. Implementation of Chronic Care Model for Diabetes Self-Management: A Quantitative Analysis. Diabetology . 2022; 3(3):407-422. https://doi.org/10.3390/diabetology3030031

Ansari, Rashid M., Mark F. Harris, Hassan Hosseinzadeh, and Nicholas Zwar. 2022. "Implementation of Chronic Care Model for Diabetes Self-Management: A Quantitative Analysis" Diabetology 3, no. 3: 407-422. https://doi.org/10.3390/diabetology3030031

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Open Access

Peer-reviewed

Research Article

Enablers and barriers to effective diabetes self-management: A multi-national investigation

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Writing – original draft

* E-mail: [email protected] , [email protected]

Affiliation College of Medicine and Dentistry, James Cook University, Townsville, Australia

Roles Conceptualization, Methodology, Supervision, Writing – review & editing

Affiliation College of Public Health, Medical and Veterinary Sciences, James Cook University, Townsville, Australia

Roles Conceptualization, Methodology, Project administration, Supervision, Validation, Writing – review & editing

• Mary D. Adu, 
• Usman H. Malabu, 
• Aduli E. O. Malau-Aduli, 
• Bunmi S. Malau-Aduli

• Published: June 5, 2019
• https://doi.org/10.1371/journal.pone.0217771
• Reader Comments

The study aimed to identify the common gaps in skills and self-efficacy for diabetes self-management and explore other factors which serve as enablers of, and barriers to, achieving optimal diabetes self-management. The information gathered could provide health professionals with valuable insights to achieving better health outcomes with self-management education and support for diabetes patients.

International online survey and telephone interviews were conducted on adults who have type 1 or type 2 diabetes. The survey inquired about their skills and self-efficacy in diabetes self-management, while the interviews assessed other enablers of, and barriers to, diabetes self-management. Surveys were analysed using descriptive and inferential statistics. Interviews were analysed using inductive thematic analysis.

Survey participants (N = 217) had type 1 diabetes (38.2%) or type 2 diabetes (61.8%), with a mean age of 44.56 SD 11.51 and were from 4 continents (Europe, Australia, Asia, America). Identified gaps in diabetes self-management skills included the ability to: recognize and manage the impact of stress on diabetes, exercise planning to avoid hypoglycemia and interpreting blood glucose pattern levels. Self-efficacy for healthy coping with stress and adjusting medications or food intake to reach ideal blood glucose levels were minimal. Sixteen participants were interviewed. Common enablers of diabetes self-management included: (i) the will to prevent the development of diabetes complications and (ii) the use of technological devices. Issues regarding: (i) frustration due to dynamic and chronic nature of diabetes (ii) financial constraints (iii) unrealistic expectations and (iv) work and environment-related factors limited patients’ effective self-management of diabetes.

Conclusions

Educational reinforcement using technological devices such as mobile application has been highlighted as an enabler of diabetes self-management and it could be employed as an intervention to alleviate identified gaps in diabetes self-management. Furthermore, improved approaches that address financial burden, work and environment-related factors as well as diabetes distress are essential for enhancing diabetes self-management.

Citation: Adu MD, Malabu UH, Malau-Aduli AEO, Malau-Aduli BS (2019) Enablers and barriers to effective diabetes self-management: A multi-national investigation. PLoS ONE 14(6): e0217771. https://doi.org/10.1371/journal.pone.0217771

Editor: Simone Rodda, University of Auckland, NEW ZEALAND

Received: December 11, 2018; Accepted: May 19, 2019; Published: June 5, 2019

Copyright: © 2019 Adu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: The first author of this study (MDA) is funded by the Australian Government International Research Training Program Scholarship. The funder had no role in the study design, data collection and analysis, decision to publish or preparation of manuscript.

Competing interests: The authors have declared that no competing interest exist.

Abbreviations: SD, Standard deviation; T1D, Type 1 Diabetes mellitus; T2D, Type 2 Diabetes mellitus

Introduction

Diabetes mellitus is a major public health problem with rapidly increasing prevalence. In 2017, the global prevalence of diabetes among people aged 20–79 years was 425 million, mainly comprising type 1 or type 2 [ 1 ]. Diabetes is one of the top 10 global causes of mortality. In 2015, it was responsible for 1.6 million deaths, indicating a 60% increase in 15 years from less than 1 million in 2000 [ 2 ]. International audits have found that regimen adherence is less than optimal in both types 1 and 2 diabetes patients [ 3 ]. As a consequence, the majority of these patients are at risk of serious health complications that endanger life [ 1 , 4 ] and impose great economic burden on affected individuals and the health care system [ 1 ].

Consistent engagement in diabetes self-management has been found to be correlated with the attainment of health outcomes in terms of good blood glucose control, fewer complications [ 1 , 5 ], improved quality of life [ 6 , 7 ] and reduction in diabetes-related death risks [ 8 ]. The term “self-management” refers to day to day activities or actions an individual must undertake to control or reduce the impact of disease on their health and wellbeing [ 9 ] in order to prevent further illness [ 10 ]. Diabetes self-management actions involve engagement in recommended behavioural activities such as healthy eating, medication adherence, being active, monitoring, reducing risks, problem-solving and healthy coping, which are all necessary for the successful management of the disease [ 11 ]. Level of adherence to diabetes self-management differs in patients, which implies that decision-making processes for self-management are influenced by various factors, which could either serve as enablers or barriers.

Enablers of self-management

Enablers of self-management are mechanisms or factors that foster the ability of patients to undertake their recommended self-management regimen. Such factors are diverse and include effective social support with assistance and encouragement from family members [ 12 , 13 ] or peers who have diabetes or close relatives familiar with its management [ 14 ]. Likewise, individual resolution to prevent or reduce the risk of developing diabetes complications helps with the determination to engage in self-management [ 12 , 15 ]. Studies have also noted positive decision making about diabetes self-management as a result of effective health care provider-patient communication [ 16 ], characterized by trust, respect and shared decision-making in planning health goals [ 17 , 18 ]. In addition, patient support with the use of health technological interventions such as mobile phone applications [ 19 ] and self-management education [ 20 , 21 ], facilitate efective diabetes management. Individual factors, particularly higher educational level [ 22 , 23 ] and gender [ 24 ], also contribute to patients’ ability to care for their diabetes.

More importantly, adequate self-management skills [ 25 ] and self-efficacy (confidence) [ 26 ] to perform these skills are major enabling factors for engagement in diabetes self-management. This is because skills and self-efficacy operate in tandem to foster full engagement with self-management. Self-management skills result from knowledge about the disease [ 25 ], and understanding the interrelationships between different self-management activities and their impact on health outcomes [ 27 ]. On the other hand, self-efficacy refers to ‘‘one’s belief in his/her own innate ability to perform specific tasks required to reach a desired goal” [ 28 ]. Unless people believe they can produce desired effects by their action, they have little incentive to act [ 29 ], regardless of other enabling factors which may be available to them. In diabetes management, patients’ level of self-efficacy is influenced by their level of skills for self-management. Hence, patients with adequate skills and efficacy have more likelihood to adhere to prescribed behavioural regimen necessary to attain optimal health [ 25 , 30 – 32 ]. Acquiring diabetes self-management skills and efficacy is an ongoing learning process [ 20 , 25 ]. While some skills and efficacy are easily acquired, others are often difficult to attain. Further research is therefore needed to adequately identify gaps in diabetes patients’ skills set and self-efficacy levels for self-management of their health issues. Information on identified gaps will guide health care providers in their development of educational support programs that foster self-management among diabetes patients.

Barriers to self-management

Non-adherence to recommended diabetes self-management regimen is influenced by barriers encountered by patients. These barriers make managing the disease more difficult. Only few studies have examined patients’ perceived barriers to general diabetes self-management from a global perspective. An international study identified diabetes related distress as a major factor responsible for poor adherence to self-management in patients [ 3 ]. Local studies reported that difficulty in making lifestyle changes [ 33 ] and inadequate health care system communication interface [ 34 ] were related to poor diabetes self-management. In addition, financial constraints resulted in patients’ inability to access diabetes clinical supplies and eat in line with appropriate dietary recommendations [ 35 – 37 ]. Other studies have examined barriers to some specific areas of diabetes self-management. Nagelkerk et al., [ 16 ] and Ghimire [ 38 ] reported that patients’ lack of knowledge of a specific diet plan and perceived belief in social unacceptability of healthy behaviours hindered healthy eating and participation in physical exercise. Furthermore, depressive symptoms and personal belief about medication were observed to be associated with lower adherence to diabetes medications [ 39 ].

The empirical and conceptual research findings mentioned above are not exhaustive because only a few have an international focus [ 3 ]. Additionally, the studies are mostly focused on barriers to self-management in patients with type 2 diabetes only [ 33 – 36 , 38 , 40 ], older populations [ 35 ], those from low income background without indicating the type of diabetes the respondents had [ 41 ], or few areas of diabetes self-management [ 38 – 40 ]. The above limitations in previous studies emphasize the need for further and detailed exploration of factors serving as barriers to self-management in both types 1 and 2 diabetes patients. This will provide strategies that adequately address such challenges and foster better adherence to self-management for better health outcomes in both patient groups.

There is diversity in the level of self-management between patients. The ability to self-manage diabetes is influenced by various factors that can either serve as enablers or barriers. However, to the best our knowledge, global perspectives on the crucial enablers of self-management in terms of skills and self-efficacy, among types 1 and 2 diabetes patients is relatively scarce. Likewise, studies on other enablers and potential barriers to general self-management as perceived by these patient groups is scanty in the published literature. There is special interest in elucidating this information from an international perspective because issues encountered in self-management by both patient groups are likely to include common experiences and challenges. Identifying these commonalities could provide health professionals with an in-depth understanding of patients’ experiences and help guide the development and enhancement of intervention strategies to improve patients’ self-management of diabetes. Therefore, this study aimed to: i) identify the common gaps in skills and self-efficacy for self-management among individuals with type 1 or type 2 diabetes; ii) examine factors associated with self-management skills and self-efficacy; iii) explore other factors which serve as enablers of, and barriers to, achieving optimum diabetes self-management.

Recruitment procedure

A maximum variation purposive sampling technique was employed in recruiting participants aged ≥ 18 years who had type 1 or type 2 diabetes. Participants were recruited globally using diverse recruitment strategies. The aim of this sampling method was to obtain a mix of participants with diverse experiences and identify common patterns that cut across the population sample with regards to the subject of interest [ 42 ]. Officially approved advertisement for the study was placed on various health organizations’ websites. These websites included Diabetes UK and Diabetes Australia. In addition, the advertisement was placed in local digital newspapers, Twitter and Facebook pages focusing on diabetes support. Data collection was conducted between November 2017 and June 2018. There was no limit to sample size in order to capture the maximum number of people with type 1 or type 2 diabetes. The study requested participants’ socio demographic characteristics of age, gender, educational level and geographic location. Details of the recruitment strategy and participants’ characteristics have been fully described in our previous publication [ 43 ].

Study design

A sequential mixed methods approach was used; comprising quantitative and qualitative data collection methods [ 44 ]. The quantitative phase of the study involved a cross sectional survey and data analysis. This was followed by qualitative telephone interviews of a subsample of the participants in order to provide a more complete and comprehensive understanding of the results which were integrated into the data interpretative phase [ 44 ]. Quantitative data were obtained through an online survey that focused on assessing participants’ self-reported skills and self-efficacy (confidence) as part of the factors that enable diabetes self-management. Qualitative data were collected through individual telephone interviews which further explored additional factors that serve as enablers and barriers to diabetes self-management.

Quantitative measures–survey.

The survey questions were divided into two parts. First, the following health characteristics which were likely to influence skills and self-efficacy for diabetes management were assessed: type of diabetes, duration of diagnosis and whether participants had recently received (within the previous 12 months) diabetes self-management education (DSME) from a member of their health care team.

Second, novel LMC Skills, Confidence and Preparedness Index (SCPI) tool was used to assess skills and self-efficacy in core behaviours central to diabetes self-management such as healthy eating, blood glucose monitoring, being active, healthy coping, medication adherence, problem solving and reducing risk [ 11 , 45 ]. The SCPI tool had been previously validated, where its construct validity for different ages, ethnicity, gender and level of education was established [ 32 ]. Additionally, the validity of the tool for use in different settings is established by the fact that, as a new tool, the questions reflect the current recommended self-management regimen for diabetes patients, and this has not been fully explored by previous tools [ 45 ]. It has excellent readability and reliability. Permission was obtained to use the tool. The SCPI tool consists of three subscales: skills, confidence and preparedness. The skills subscale was used to assess perceived ability to perform the self-management activities mentioned above. The confidence subscale was used to assess self-efficacy in being able to perform the skills. The preparedness scale was not used in this study because this subscale assesses the readiness of patients to implement behavioural changes following an educational session; which was not applicable in the present study.

The skills and confidence domains consist of nine (9) and eight (8) items respectively. Two of these items focus on skills and confidence to use insulin. These skills were adapted to accommodate participants who have type 2 diabetes but do not use insulin/other medications as part of their treatment regimen. All items were rated using a visual analogue scale, with scores between 1 and 10. Each of the items in the domains produced its own score out of 10. The total score was the mean score in each of the subscales, where higher scores denoted better skills and confidence. The scoring process is not affected by demographic factors such as age, gender, level of education or ethnicity [ 45 ], hence, its’ applicability for use in study populations with diverse social and health characteristics. The instrument was administered in English Language.

Qualitative measures–phone interviews.

Through online survey, all participants were invited to an individual telephone interview session. They were requested to indicate interest by providing their best contact number and availability. A single independent resource person (male) who is an experienced researcher in qualitative studies conducted all interviews. The interviewer was trained on the aims of the study and the interview guide by the first author of this study (MDA). The guide was then pilot tested between the interviewer and MDA before actual use. Additionally, MDA was present in the first three interviews to ensure appropriateness of data collection. While the interviews were used to reflect on the interview guide, no changes were made to the guide afterwards. There was no interaction or previous relationship between MDA and the participants. The interviewer was located in a private office at James Cook University, Townsville, Australia. Prior to the commencement of the interview, each respondent was asked if they were located in a comfortable place for an interview, and were briefly presented with the general idea of the study and key diabetes self-management activities. The interviewer did not have prior relationship with the participants. Each Interview was audio recorded and lasted between 7 and 20 minutes in duration. Data saturation was achieved through recurring explicit ideas [ 46 ] after completing the 14 th interview. However, the interview was conducted for the remaining two participants who had indicated interest in order to ensure that no main idea was unintentionally discarded. Repeat interviews were not required and due to the remoteness of the study participants, there was no post interview debriefing. The semi-structured interview guide was developed by the research team. Topics covered in the interview included open ended questions and probes to facilitate discussion (See S1 Appendix for details of the interview questions).

Ethics and consent

The study procedures (registration number: H7087) were approved by James Cook University’s Human Research Ethics Committee. The protocol contained detailed information on the ethical obligations of researchers toward participants engaging in online research activities. Essentially, these obligations included confidentiality, anonymity, scientific value, maximising benefits, minimizing harms, and informed consent [ 47 ]. All these obligations were strictly adhered to during the research process. Furthermore, as part of the application process for advertisement of the study on the website of health organisations, the ethics approval document was made available to the appropriate and designated officials of these organisations. All prospective study participants were provided with the study information along with the privacy policy prior to the survey. Therefore, participants were informed about the use of their answers for analysis under anonymity. Informed consent was implied by submission of the online survey, while all telephone interviewees provided verbal consent.

Data analyses

SPSS (Version 23) was used for quantitative data analysis. Cronbach’s alpha of the subscales of measures used in this study were acceptable (.92 and .91 for skills and confidence scales respectively). Participants’ demographics and health variables were presented using descriptive statistics. Items in the skills and self-efficacy domains were reported as means and standard deviations (SD). For the purpose of explaining and discussing the results, scores were graded as high (≥ 7), moderate (4–6) or poor (≤ 3). Mean scores were calculated for demographic and health variable subgroups. Bivariate analyses were performed using Independent sample t-test and Analysis of Variance (ANOVA) to test the relationship between participants’ subgroups and level of skills and confidence. Specifically, t-test was used for variables with two categories (i.e. type of diabetes, received DSME or not, gender) while ANOVA was used for variables with three or more categories (i.e. educational status, duration of diagnosis, geographic location, age range). Effect sizes were calculated using Eta squared values to show the magnitude of difference in mean scores between categories within each variable. Pearson correlation coefficients were used to estimate the strength of association between skills and self-efficacy scores. Additionally, multiple regression analysis was used to estimate the contributions of the different independent variables to participants’ reported skills levels. Significant variables in the bivariate analysis were included in the regression. In all statistical analysis, values were considered statistically significant at p < 0.05 (two tailed).

For qualitative data analysis, audio recordings were transcribed verbatim by an independent professional transcriber and reviewed by the first author (MDA) for accuracy. The transcripts were uploaded into a qualitative data analysis software (QSR NVivo 11). Emerging themes were identified using in-depth inductive thematic analysis [ 48 ] undertaken in six steps: (i) re-reading of data line by line to ensure familiarization (ii) identification of patterns within data and organization into codes (iii) grouping of initial codes through constant comparison to identify emerging themes (iv) grouping and review of identified themes into general themes (v) refining themes and (vi) selection of representative quotes to support themes [ 48 ]. The first coding and generation of themes was done by MDA. In order to enhance result credibility and validity, raw data transcripts, coded data and themes were independently reviewed by the last-named author (BMA). Data were cross-checked in a consensus meeting and there was 90% degree of congruence between both authors’ coding, themes and classifications. Discrepancies were resolved through discussion and mutual agreement. Both MDA and BMA have experience in qualitative research methods. The remaining two researchers (UMA and AEOMA) checked the quotes and themes to ensure consistency. Key themes were reported along with relevant quotes affixed with an assigned number code and the type of diabetes the respondent has (for instance P3, T2D). The final manuscript was subjected to COREQ checklist for consolidated criteria for reporting qualitative research (See S1 Checklist ) [ 49 ].

Socio-demographic and health characteristics

A total of 217 complete responses to the online survey was received. Respondents were located in four geographic regions; namely, Europe (35%), Australia (34.6%), Asia (29.5%) and America (0.9%). The mean age of respondents was 44.65 ± 14.0 years (range 18–76 years) and 56.7% of them were females. More than half of the respondents had type 2 diabetes (61.8%) and had received DSME in the previous 12 months prior to the study (64.1%). About half of them were diagnosed in the last 5 years (52.5%) while 20.3% were diagnosed 6–10 years ago and the remaining 27.2% over 10 years. Over half of the respondents (56.2%) reported having a minimum of bachelor’s degree, 20.3% completed high school, while 18.9% completed technical college and 4.6% attained other forms of education.

A total of 31 respondents (14.3%) expressed interest to participate in the telephone interview. However, about half of them declined at time of interview or never responded to phone calls, leaving a final respondent number of 16 individuals who were interviewed. The participants were mostly males; 56.2% (9/16), had type 1 diabetes; 62.5% (10/16) and lived in Australia; 87.5% (14/16), with age ranging from 26 to 61 years [mean age of 44.56 (SD 11.51)].

Diabetes self-management skills and self-efficacy (confidence)

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https://doi.org/10.1371/journal.pone.0217771.t001

There was a strong positive correlation between the scores in the two domains, r = .906, p<0 .001, where higher levels of perceived skills were associated with higher levels of perceived self-efficacy. Coefficient of determination (R 2 ) indicates that level of skills explained 82% of the variation in respondents’ scores on self-efficacy.

Relationship between participants’ characteristics and levels of skills and self-efficacy.

Table 2 shows the relationship between demographic and health characteristics and the levels of skills and self-efficacy for diabetes management in participants. All demographic characteristics except geographic location, gender and age, were significantly associated with perceived skills and self-efficacy.

https://doi.org/10.1371/journal.pone.0217771.t002

Participants who had type 1 diabetes had higher levels of skills compared to those with type 2 diabetes, t (215) = 17.41, p < 0.001, eta squared = 0.123. Additionally, receiving DSME within the past 12 months prior to participating in the study had a moderate but significant association with level of skills, t (215) = 2.01, p = .045, eta squared = 0.018. There was a significant difference in duration of diabetes diagnosis, F (4, 215) = 5.59, p <0.001, eta squared = 0.095. Skill scores were significantly higher in the >15 years ( M = 8.28, SD = 1.22) when compared to <1 year ( M = 6.28, SD = 1.82), 1–5 years ( M = 6.98, SD = 2.08) and 6–10 years ( M = 6.97, SD = 2.14) of diabetes diagnosis. There was no significant difference for those with 10–15 years of diagnosis ( M = 7.00, SD = 1.58). In addition, level of educational qualification significantly influenced the level of skills, F (4, 215) = 7.87, p <0.001, eta squared = 0.132. Skill scores were significantly higher among postgraduate degree holders ( M = 7.76, SD = 1.12) in comparison to high school ( M = 6.13, SD = 2.21) and technical school ( M = 6.43, SD = 2.25) certificate holders. No significant difference was observed when compared to those with bachelor’s degree ( M = 7.76, SD = 1.53).

For self-efficacy (confidence), type 1 diabetes participants had higher confidence levels compared to their type 2 counterparts, t (215) = 5.46, p = 0.02, eta squared = 0.051. Furthermore, confidence score was significantly associated with duration of diagnosis, F (4, 215) = 3.23, p = 0.013, eta squared = 0.057. Confidence was significantly higher in the >15 years ( M = 7.95, SD = 1.30) when compared to <1 year ( M = 6.50, SD = 1.68) only. Furthermore, level of educational qualification significantly influenced confidence level, F (4, 215) = 6.77, p <0.001, eta squared = 0.11. Participants with postgraduate degree had significantly higher confidence ( M = 7.71, SD = 1.55) in comparison to those with high school ( M = 6.42, SD = 1.98) and technical school ( M = 6.47, SD = 2.11) certificates. No significant difference was observed for those with bachelor’s degree ( M = 7.48, SD = 1.39).

Multiple regression analysis identified the simultaneous contributions of duration of diagnosis, type of diabetes, educational qualification and receiving DSME within 12 months prior to the study on participants’ level of skills. These variables predicted 22% of the variation in level of skills F (2, 216) = 14.815, p <0.001, R 2 = .218. All variables, except receiving DSME, were statistical significant at p < .05.

Other enablers of self-management

Two major themes were identified as factors which could facilitate diabetes self-management. These were patients’ determination to prevent the development of complications and the use of health technological devices or software.

1. Determination to prevent diabetes complications.

The decision to regularly engage in self-management was fostered by participants’ resolution to prevent the development of diabetes complications. Participants ensured that they engaged in the necessary lifestyle behavioural activities due to their determination to maintain better quality of life and thereby avoid what was observed in their peers who had already developed some form of diabetes complications:

‘‘I see a lot of other people who already have diabetes talking about their diabetes on social media . Looking at others who are worse off than me and the problems they struggle with , I guess is keeping me in check saying , hell no , I’m not going down that path” . [P6, T2D]

Furthermore, the determination to prevent diabetes complication was expressed by refusal to purchase certain foods which participants believed could increase the risk of progressing type 2 diabetes management into requiring the use of insulin injection:

‘‘It’s just the fact that I don’t want to get to the stage of having injections many times a day… . I have to remind myself of that always . I’m quite happy to walk past some chocolate… . knowing fully well that whilst I might enjoy a ** (name of a chocolate brand) , … . then I get an injection at the end , which I don’t want , which mean I will leave (name of a chocolate brand) alone” . [P3, T2D]

Respondents acknowledged that having good knowledge and problem solving skills in diabetes has proven useful to aid their self-management. Awareness of how foods impact their health was reported as highly essential:

‘‘I think having knowledge of the foods and the type of foods and diet and portion sizes are very important . Also , I found understanding what hypo or hyper , and understanding how my body reacts and how I can resolve that has been very useful in managing my diabetes . ” [P15, T1D]

2. Use of health technological software and devices.

Participants mentioned the use of mobile technological devices specifically, smart phone application (apps), insulin pump and continuous glucose monitors (CGM) as supporting tools which have enhanced their self-management.

2.1 APPS: Some of the participants use smart phone apps to record their blood glucose data. They noted that having access to such previously stored data on their phones gave them insight into the best self-management strategy which had assisted in adequate glycemic control:

‘‘I have been diagnosed for a long time and back in the days I used to write it on a note book . But in these days , I record it using a smart phone app , which allows me to search . So it allows me to access the data quickly and make a sort of best guess for now based on what happened in the past . If it’s not working , as it has done recently , I can go back to strategies that I might have been using years ago , that seems to work then “ . [P14, T1D]

Reminder feature in apps were found useful to give alert for recurring tasks such as taking medications thereby improving medication taking behaviour especially during busy schedules:

‘‘I’m only kind of new to this (newly diagnosed) , so I am actually looking for ways to remind myself of the tablets (medication) am meant to be taking . When I get really busy I forgot ‥ so my app pings at me a certain time of the day…just to kind of prompt me” [P3, T2D]

Also, motivations and encouragements were received through the use of app especially in the event of unstable blood glucose control. Participants stated that whenever their blood glucose level fluctuated and differed from the prescribed limits despite all efforts to stabilize it, looking at good data previously stored in apps provided an assurance that their blood glucose levels will not always be unstable:

‘‘Sometimes it is simple as realizing it’s not all terrible . Being able to flip back on my smart phone . If you’ve had a rough four or five days , it can feel like it’s a long time since you’ve seen numbers that felt like relatively stable or in range . You can get disheartened but if you can just check back you and see , actually no , it’s fine because two weeks ago it was all right , so I’ll be able to get back to that again . So having access to that sort of information storage allows me to be a little bit more relaxed when inevitable things start to wobble and go adrift again” . [P14, T1D]

2.2 INSULIN PUMP AND CGM: Participants with type 1 diabetes reported the use of insulin pump or continuous glucose monitor (CGM) as external aids which made it easier for them to manage and effectively monitor their health. In this regard, one participant stated that:

‘‘With the insulin pump, I find it easier to manage. Also, I’ve got the CGM and I can see what my sugar is on the screen all the time….you know that changed my life”. [P1, T1D]

Participants also indicated that use of insulin pump provided additional support and relief from pains experienced while using needles:

‘‘ I’m quite a thin bloke…… and have no body fat so inserting needles really hurt . My insulin pump definitely helps . So the best way I’ve managed my diabetes is through the insulin pump” . [P12, T1D]

In spite of the factors that foster effective self-management of diabetes, the key themes that emerged from the interview indicated that people with diabetes encountered diverse challenges in performing their self-management due to the: i) dynamic and chronic nature of diabetes; ii) financial constraints iii) work and environment related factors; and (iv) unrealistic expectations

Theme 1: Dynamic and chronic nature of diabetes.

The most common complaint reported by participants was the dynamic and chronic nature of diabetes and how these attributes make diabetes self-management require multiple needs. Participants felt there were many reasons including environmental conditions, which may demand an adjustment in their self-management even within short time periods. They believed the constant requirement to modify needs of the condition denoted certain things they were not doing right in their self-management and they always had to put in great effort to meet up with their health requirements:

‘‘Because I live with type 1 diabetes I have to do a complete insulin replacement , which involves balancing for activity , ambient temperature , stress levels , insulin sensitivity of my body . It could be so much easier if you could just work out what your insulin to carb sensitivity portion is , work out how to behave around exercise , work out correction factors and that would be all . But no , my experience is that that’s it for a week and then your basal requirement would have changed . Then the weather get warmer , you may need to re-evaluate your insulin sensitivity and carb ratio . So it’s just- you are never getting it right and you’re just always constantly trying to play catch up” . [P14 T1D]

Likewise, the effects of self-management on diabetes outcome was referred to as a system which could not be automatically controlled. Participants described how similar behavioral activity such as eating the same diet over time could impact their health differently.

‘‘It is a dynamic disease . I mean what works today doesn’t work tomorrow . You can eat something today and you can be okay , eat something tomorrow and it can be completely different . So you can never just put it on a cruise control and away you go” . [P2, T1D]

The weariness about the never-ending need for self-management because diabetes is a lifetime disease was expressed:

‘‘The biggest thing that fazes me is just the fact that it’s something that you have to do 24 hours a day , seven days a week and nothing ever going to change that” . [P4, T1D]

Participants were sometimes unwilling to undertake their self-management because they felt it is not a permanent cure for the disease, diabetes is chronic, so what is the point?:

‘‘ ‥ Probably my mind frame , in just getting yourself down to the fact that it’s never going to ‥ I’m always going to have it . So you sort of question what’s the point (of management) ? It’s hard to comprehend” . [P11, T1D]

The presence of other diabetes related complications or health problems such as neuropathy and depression in some participants limited their ability to actively engage in behavioral activities especially physical exercise or healthy eating:

‘ ‘Physical exercise is difficult…Yeah , I have peripheral neuropathy of the leg , a collapse in the foot and yeah , problems with the other foot” . [P10, T2D] ‘‘Nutrition is something that is hard to keep on top of . I suffer from a major depressive disorder , so I have a lot more trouble following my optimum diet” . [P7, T1D]

Theme 2: Financial burden.

The difficulty in meeting the financial cost for some diabetes medical tests and other treatment requirements was also identified as a barrier. Participants voiced out the financial burden they experienced by citing the need to pay for some clinical tests and diabetes supplies which are not covered by their health insurance such as the glycosylated hemoglobin (HbA1c) test and continuous glucose monitor. They expressed the desire to receive more support from the government:

‘‘I manage my diabetes fairly closely and I pay for HbA1c , you know …the financial cost is quite large . In Australia , our health system’s pretty good but you still have to pay for a lot of equipment which the government doesn’t seem to agree necessarily . Continuous Glucose Monitor should be government funded for over 21s for Christ sake” . [P2, T1D]

Another participant based in the United Kingdom (UK) stated:

‘‘I don’t have unimpeded access to Continuous Glucose Monitor (CGM) . I mean ‥ the situation of health care in UK is that it’s (CGM) not often funded by National Health Service (NHS) apart from people that are in quite profound need . I don’t get that assistance … So that’s a challenge and access issue” . [P14, T1D]

Theme 3: Work and environment-related conditions.

3.1: Occupation : Job requirements especially those involving a lot of travelling serves as deterrent to maintaining a healthy diet. Participants stated that the inability to get healthy choices of foods in most restaurants or public places when unavoidably required to eat out due to travelling long distances to fulfill their job requirements:

‘‘My work requires a lot of travelling . If you are actually going to eat something that is actually not good and could put you in the circumstance where you know… Like I had a 16 hour travelling the other day and everywhere I turned , I couldn’t touch any of it . I had some but I had to acknowledge that it was not what I really needed to eat” [P3, T2D]

Work related stress was also reported as a hindrance to attaining optimal blood sugar levels:

‘‘With me personally , it’s stress . I’m an electrician , and I’m full time employed , so stress gets me . When I get stressed , my blood sugar level goes downhill” [P13, T1D]

3.2: Weather condition : Participants find it difficult to engage in physical exercise in hot weather conditions:

‘‘ ‥ Exercise is something I have trouble getting around to doing . Like during the summer , the heat hits me big time . So I’m loving the cooler weather we’re starting to have because I can start to work a bit more , but during the heat , I cannot do it” . [P2, T1D]

Theme 4: Unrealistic demands.

Unrealistic expectations and advice about self-management from family or friends especially those not diagnosed with diabetes could be a hindrance to effective care. Participants’ found such wrong advice irritating as evident in the following comment:

‘‘You know I don’t think a lot of non-diabetic actually get to know how much it can take to actually manage a high or a low (Blood sugar) potentially . You know , you get comments from people that you’re low and they know you are diabetic saying , oh , should you be eating that ? Well , I’m going to say this nicely , you want me to die now or not or to go into coma ? Because I need to eat this . They go oh , you didn’t need to say it like that . You go well , stop asking a stupid question that you don’t know anything about” . [P4, T1D]

Additionally, discrepancy between patients and their health professionals’ (HP) perception of care could be a barrier to self-management. Participants felt that some recommendations from HPs were contrary to their opinions on what their diabetes self-management should entail:

‘ ‘My doctor doesn’t feel I need to be using a glucose meter to monitor my sugar levels and the diabetes educator doesn’t think I need to be on any sort of diet , even though I’ve had increases in diabetes medications” . [P10, T2D]

To the best of our knowledge, this is the first mixed methods study that has investigated enablers and barriers to general self-management among a multinational audience of people who have type 1 or type 2 diabetes. Most importantly, our findings emphasise the consequential impact of currency of exposure to DSME (within the previous 12 months), duration of diagnosis, level of educational qualification and use of technological devices on self-management skills and self-efficacy, regardless of geographical location or ethnicity. This implies that provision of ongoing self-management education/support through the use of mobile phones may help address the various difficulties (including time/financial constraint, diabetes distress, and limited access to care providers) encountered by patients and foster adherence to recommended self-management activities, which are necessary to prevent the risk of developing diabetes complications. Furthermore, this study presents an in-depth understanding of the experiences of diabetic patients and provides useful insights to health professionals and researchers on how to improve the frequency and quality of self-management support provided to diabetic patients to achieve better health outcomes.

Skills and self-efficacy for diabetes self-management

The overall skills score was found to be high and many participants reported good level of ability for self-management. This is specifically in the area of accurate monitoring to assess the impact of diet, medication or physical activities on blood glucose levels. Similar findings were observed in a previous study [ 25 ]. Accurate monitoring of blood glucose in relation to foods consumed and physical activities are important because they predict good outcomes in diabetes management [ 50 ].

Although the participants in this study scored high in their ability to monitor blood glucose, their capacity to interpret their blood glucose patterns over time was only moderate. Self-monitoring of blood glucose is important to assess glycemic pattern, hence accurate interpretation of these patterns is highly important to ensure effective management of glycaemia related problems encountered in diabetes management [ 51 ]. More emphasis should be laid on glucose pattern management during diabetes self-management educational sessions in order to expatiate patients’ skills on effective monitoring and interpretation of blood glucose data and the resulting health implications.

Participants in this study possessed lower skills related to planning for physical exercise in order to avoid hypoglycemia and adjusting medication to reach targeted blood glucose levels. This result corroborates previous findings [ 52 ]. The ability to manage and make appropriate adjustment to multiple regimens often determines success with other core areas of diabetes self-management and glycemic control [ 51 ]. For instance, studies have reported that due to the fear of hypoglycemia, patients have resorted to unhealthy behaviours (such as reducing or eliminating medication dose, inappropriate food choices and /or avoiding physical activities) that increase glucose levels [ 53 ]. Diabetic patients have an increased risk of developing hypoglycemia particularly when treated with insulin or insulin secretagogues [ 53 ]. Hence, they should be provided with regular refresher courses and continuous training on blood glucose levels awareness and strategies to balance exercise which could promote glycemic control and adherence to self-management.

Healthy coping strategies to identify and manage the impact of stress on diabetes management may be a difficult aspect of diabetes care because the participants in this study scored lowest in this area for both the skills and self-efficacy domains. All forms of stress either physical or mental, negatively impact blood glucose levels in those with diabetes [ 54 ] and it is a potential obstacle to attaining effective self-management and optimal health outcomes [ 55 ]. Patients’ understanding of dimensions of diabetes related stress is a clinically important factor and forms of stress that are potentially modifiable should be prioritized to guide clinical and educational interventions. This can include regular educational information on the impact of stress on health of diabetes patients and suggestions to reduce it.

Contrary to the findings of a previous study [ 56 ] that reported people with type 1 diabetes as having poorer self-management; our study participants who had type 1 diabetes scored higher than those with type 2 diabetes in skills and self-efficacy to care for their diabetes. Additionally, there was a significant positive relationship between the duration of diabetes and both skills and confidence for self-management. Patients with type 1 diabetes are typically diagnosed at an early age that may correspond to longer duration of diabetes. This pattern might have afforded them prolonged and regular exposure to health education, which is a significant predictor of successful diabetes self-management [ 20 ].

Overall, the strong correlation between the level of skills and self-efficacy found in this study strengthens the body of evidence supporting this link [ 32 ]. This pattern may be related to high level of education among most of the study respondents as also observed in a previous study [ 57 ]. Patients who possess higher skills usually have higher perceived level of efficacy and are most likely to actually engage in their self-management [ 25 , 32 ]. Building patients’ skills and confidence in their ability to self-manage diabetes is therefore imperative. Regular encouragement which could either be provided verbally or through other means of contact (e.g text messages through phones or emails) could be beneficial to patients [ 58 ]. While for those with limited educational backgrounds, the use of clear and simple communication styles when providing diabetes education to them will be essential to foster their skills and confidence [ 57 ].

Based on the results of the interviews, the most commonly perceived factor that fostered regular self-management was the will to prevent the development of diabetes complications. This result corroborates previous findings [ 12 , 59 ] and indicates that the participants in this study took responsibility for their choices and respective consequences. Discipline and proactive approaches to self-management are essential to reducing or preventing the development of diabetes complications. Regular reinforcement of education and motivation of patients could provide in-depth information about the disease and foster the will to mitigate its’ clinical course.

Furthermore, our study findings confirm those of other studies that the use of mobile technologies such as smartphone applications [ 19 ], insulin pump [ 60 ] and continuous glucose monitor [ 61 ] could enhance diabetes self-management in patients. Technology interventions have positive impact on diabetes outcomes such as adherence to self-management activities, glycosylated hemoglobin and diabetes self-efficacy [ 19 ]. Therefore, health professionals could recommend the use of mobile health technologies to patients who are capable of using them as they benefit from them.

The lack of enthusiasm towards regular self-management due to the chronic and dynamic nature of diabetes was not entirely unexpected. This phenomenon could be referred to as diabetes distress which is the emotional stress resulting from living with diabetes and the ‘‘burden of relentless management” [ 62 ]. High diabetes distress results in sub-optimal diabetes management and compromised quality of life [ 3 , 63 ]. Diabetes distress is common among patients and impacts on their self-management and health outcomes. Therefore, the importance of providing appropriate regular support to all patients in this regard cannot be overemphasized. Health professionals could ask patients at every consultation about how they are coping with diabetes, encourage them to express particular diabetes related issues causing them distress and offer encouragement and suggestions on ways to deal with it on a daily basis.

For many of the respondents in this study, the need to meet up with job requirements especially frequent travelling, makes adherence to healthy eating difficult. Additionally, work related stress impacts greatly on their blood glucose levels. These findings echo the results of Chao et al. [ 39 ]. Recommendations to patients to engage in creative planning and social support are strategies to help address this barrier. Social support from families are essential. Families should be encouraged to attend educational training sessions with patients so as to offer appropriate support which can assist patients to make healthy food choices and decisions regarding their diabetes management [ 12 , 13 ].

Furthermore, financial burden associated with diabetes could be a hindrance to self-management especially those associated with out-of-pocket expenditure for medical needs. Campbell et al., (2017) observed that the predominant area of management where patients experience financial burdens are medications, diabetes supply and healthy food [ 37 ]. People with diabetes require regular self-management and clinical monitoring to prevent the development of complications and foster optimal health outcomes; hence the associated financial demand. Health care providers could inform patients about resources available to them to buffer financial constraints that limit adherence to treatment plans. Such resources may include referring patients to specific social programs or compassionate relief programs to support financial burdens and enable easier access to necessary services.

Differences in patients’ and health care professionals’ (HCP) views of what constitutes the best approach to care was also identified as a barrier to self-management. This may be due to gaps in the way treatment recommendations were communicated to patients. Often times, HCPs’ view of good care are based on adhering to stipulated biomedical care model, structured communication and central decision making [ 64 ], whereas patients perceived quality health care is how the scientific knowledge of HCPs’ aligns with their own experiential knowledge and personal preferences [ 65 ]. Therefore, patients are always seeking exhaustive information about their diagnosis and treatment [ 65 ]. There is responsibility on the part of HCP’s to advice and educate their patients on different treatment options and the reasons they are placed on a particular option and not the other. This patient centered-approach will empower patients and foster their health outcomes.

Integration of findings and recommendations for future interventions

The survey results show that many patients have limited capacity for healthy coping strategies to identify and manage the impact of diabetes related stress. This finding was confirmed in the interviews where diabetes distress was reported as a major barrier to self-management. Given that stress is a potential contributor to chronic elevated blood glucose levels, it is essential for health care professionals to assist patients with identifying approaches to reducing diabetes distress. Additionally, increased access to healthcare providers through expanded clinic hours could be a means of easing the burden of diabetes diagnosis [ 41 ].

The quantitative data also showed that higher educational level was the strongest predictor of better self-management skills in patients and this was affirmed by the highly skilled interviewees who identified the use of technological devices as an enabler to their self-management. This corroborates that higher educational level is a good predictor of eHealth usage [ 66 ]. In addition, in accordance with previous literature [ 67 ], good overall self-efficacy level observed in the survey might have influenced the positive report on the usefulness of technology in diabetes management. Therefore, given that use of health technologies provides both short and long term health improvement in diabetes patients [ 68 ], active usage should be encouraged where necessary especially among patients who are educated and have the ability to engage with them. Furthermore, it is important to device avenues to improve patients’ self-efficacy in their ability to manage the disease as this could increase their likelihood of engaging with technology for their self-management [ 69 ].

The interviews revealed that determination to prevent development of complications is one of the major enablers to diabetes-self-management. This might explain the overall high score in skills and self-efficacy observed in the survey. Therefore, we suggest that educators could focus on improving patients’ skills and self-efficacy for diabetes self-management thereby raising patients’ awareness of the negative effect of diabetes. This approach could in turn stimulate the patients’ determination to engage in diabetes self-management and thereby reduce their risk of developing complications.

A unique perspective from the qualitative results revealed that patients and HCPs have divergent views/opinions about what should constitute patient care. It is therefore, imperative that HCPs ensure that patients understand the reasons for the recommended treatments and engage them in shared decision making which is essential for patients’ satisfaction and engagement in self-management practices [ 70 ].

Lastly, it has been advocated that people with diabetes should receive self-management education and support in an ongoing and consistent manner [ 71 ], but the reality of facilitating face-to-face diabetes education between patients and HCPs on an ongoing basis is low due to limited human and organisational resources. Health behavioural treatments and therapies such as diabetes self-management education/support could be provided to patients on an on-going daily basis outside the clinical setting through the use of ecological momentary interventions such as mobile technologies [ 72 ]. Apart from the fact that apps were opined by patients to enable self-management in this study, the World Health Organisation (WHO) also confirmed that the use of mobile technologies (such as apps) can support attainment of health outcomes which could transform health service delivery globally [ 73 ]. Considering that Apps are cost effective avenues for providing ongoing delivery of care to patients outside the clinical environment [ 74 ], diabetes self-management educational (DSME) messages could be developed and integrated into apps for patients. Such DSME should be targeted at improving patients’ skills and self-efficacy capacity for effective self-management.

Strengths and limitations

The strength of this work is that it provides a multinational picture of skills and confidence for self-management in people with type 1 or type 2 diabetes. Such an elaborate and international approach to assessing the capacity and confidence levels for self-management is scanty in the literature. In addition, the data identified a number of factors serving as enablers and barriers to diabetes self-management emanated from patients’ perspectives and their lived experiences. Therefore, the results are tenable for providing immense insights into improved strategies for supporting patients in their self-management.

There are some limitations to this study. Firstly, the reliability and validity of the quantitative tool used have not been previously demonstrated at multinational/multicultural levels, therefore, this may limit the interpretation of our findings. Although, in a previous study [ 32 ], the construct validity of the scale was tested among type 1 and type 2 diabetic patients who were from different ethnic backgrounds (Asians, Caribbeans, Caucasians etc.), but living in the same regional location. The study reported that the scale was not influenced by ethnicity. Secondly, the small sample size/groups for the survey which mainly comprised of participants from three continents, may limit the generalization of our findings to other settings. Thirdly, the quantitative data were self-reported and therefore susceptible to bias, which may not reflect participants’ actual skills and confidence levels for self-management. Hence, under or over reporting could result in inaccurate identification of common gaps in skills and confidence requiring intervention. Nevertheless, self-report can be made more reliable when questions are asked in a non-judgmental manner as obtained in the SCPI tool used in this study. Lastly, the small number of interview participants is also acknowledged and the interview sessions were brief because additional compensation was not offered to interviewees. Short interview duration was utilised to foster increased participant numbers because long interviews may not be justifiable for participants’ time involvement in the study. Published literature has shown that the anonymity of telephone interview reduces interviewer bias which makes the interview setting more calming and forthcoming, thus fostering a more accurate and truthful data collection [ 75 ].

This study identified common gaps in the skills and self-efficacy of people with type 1 or type 2 diabetes mellitus as well as other perceived enablers of, and barriers to, self-management in this population. Diabetes health care stakeholders may consider strategies for regular educational reinforcement in patients in order to foster healthy coping with diabetes stress, exercise planning to avoid hypoglycemia, interpreting blood glucose patterns and adjusting medications or foods to reach the targeted blood glucose levels. Furthermore, designing of interventions that capitalize on how to improve patients’ desire to reduce the progression of diabetes and the use of relevant technological devices could enhance diabetes self-management. Improved approaches to address diabetes distress, financial burden, discrepancy between patients and their health professionals’ perception of care as well as work and environment related factors are essential to foster improved self-management in patients. Finally, attention should be paid to type of diabetes, level of education and duration of diagnosis when counselling patients on diabetes self-management. Consideration of these areas of educational reinforcement and interventions could enhance self-management in patients and consequently improve their health outcomes.

Supporting information

S1 checklist. coreq checklist..

https://doi.org/10.1371/journal.pone.0217771.s001

S1 Appendix. Interview guide.

https://doi.org/10.1371/journal.pone.0217771.s002

Acknowledgments

The authors would like to thank Mr Aaron Drovandi for his assistance with the study interview, and the health organisations who helped with the study advertisement. In addition, we sincerely appreciate the participants of this study.

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• Open access
• Published: 24 May 2022

The experiences of patients with diabetes and strategies for their management during the first COVID-19 lockdown: a qualitative study

• Mireia Vilafranca Cartagena   ORCID: orcid.org/0000-0003-2953-3196 1 , 2 ,
• Glòria Tort-Nasarre   ORCID: orcid.org/0000-0001-5270-821X 3 , 4 , 5 ,
• Maria Romeu-Labayen   ORCID: orcid.org/0000-0001-9482-9474 5 , 6 &
• Josep Vidal-Alaball   ORCID: orcid.org/0000-0002-3527-4242 7 , 8  

BMC Nursing volume  21 , Article number:  124 ( 2022 ) Cite this article

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During the pandemic, primary care systems prioritised attention to COVID-19 patients; chronically ill patients, such as people with Type 2 Diabetes were obliged to take more responsibility for their own care. We aimed to analyse the experiences of patients with Type 2 Diabetes Mellitus during the stay-at-home order that was in place during the first wave of the COVID-19 pandemic and identify the strategies and resources used in managing their care.

We conducted a qualitative descriptive study. The participants were ten patients with type 2 Diabetes Mellitus who experienced strict lockdown during the first wave of the COVID-19 pandemic in Catalonia, Spain, selected using intentional sampling. We recorded semi-structured interviews with the participants and conducted thematic analysis.

We identified 14 subthemes, which we then grouped into three overarching themes: 1) anxiety, fear, and vulnerability (anxiety, fear, vulnerability, rethinking life, loneliness, sadness), 2) insufficient diabetes monitoring by the health system (health care received, glycaemic control, view of treatment by health providers) and proactive self-care (changes in daily routine, diet, physical activity, medication, personal protective equipment & social distancing).

Despite the exceptional nature of the situation and the stress, worry, and changes in their daily lives, many respondents reported that they had successfully modified their lifestyles. Self-care was effective during confinement and was based on a process of adaptation using the resources available, without face-to-face contact with primary care health staff.

Relevance to clinical practice

These results can help to guide the design and implementation of self-care-focused strategies and also to explore new ways of empowering patients without access to health care personnel.

Peer Review reports

Introduction

Novel coronavirus disease (COVID-19) is a highly transmissible, rapidly spreading disease which has had a dramatic impact all over the globe. Although the overall mortality rate due to COVID-19 is relatively low [ 1 ], diabetes has emerged as a prominent comorbidity, associated with a severe and acute picture of respiratory distress and increased mortality. Thus, patients with chronic diseases such as type 2 diabetes (T2D) appear to be particularly vulnerable to the effects of the virus, and T2D is a major risk factor for poor prognosis in COVID-19 infection [ 2 ].

With the outbreak of the pandemic, governments imposed policies to reduce the transmission of the virus, including quarantine, isolation, social distancing and stay-at-home orders. These exceptional measures had a direct effect on the health behaviours of patients with chronic pathologies such as T2D [ 3 ]. Many patients with diabetes have encountered barriers to care due to the policies introduced to combat COVID-19, although maintaining good blood glucose control in these patients has proved to be an effective measure in preventing the transmission of the virus [ 4 ].

Type 2 diabetes cannot be cured, but lifestyle changes such as following a healthy diet, regular physical activity, and maintaining normal body weight can slow the progression of the disease and reverse its effects [ 5 ]. However, previous studies have shown that long-term maintenance of weight loss and complete adherence to diet and physical exercise recommendations is rare, especially in the adult population. Understandably, during the pandemic, many patients with T2D have found it particularly difficult to adhere to these lifestyle recommendations due to the restrictions on their access to health services and the problems in obtaining fresh food and in exercising [ 6 ].

In Catalonia (Spain), a stay-at-home order took place during the first wave of COVID-19, March 14 to May 2, 2020, at which time the measures were progressively relaxed. Stay-at-home orders (or “lockdown”) are implemented when quarantine for exposed patients and isolation for infected patients are insufficient to contain the spread of a disease [ 7 ]. During the seven weeks of strict lockdown in Spain, people were only allowed to leave home to receive medical treatment, buy food or work as an essential worker. Leaving home for exercise was prohibited, and non-essential businesses were shuttered.

Early research shows mixed effects of COVID-19 lockdowns on patients with diabetes. [ 8 ]) show that while glucose levels for type 1 diabetes patients improved significantly, those for T2D worsened in the short term. Makki et al. [ 9 ] show that patients with T2D had better glycaemic control during lockdown, but they do not specify whether the lockdown conditions were as strict as those in Spain.

Nursing professionals have a vital role to play in educating patients about the need to adapt their lifestyles and in helping them to modify their behaviour with respect to their health [ 10 ]. During the pandemic, primary care nurses have been obliged to prioritise care for COVID-19 patients [ 11 ], and as a result they have had to postpone the care of the chronically ill [ 12 ]. In this scenario, innovative strategies are needed to monitor and motivate diabetic patients who have had to take on more responsibility for their care [ 13 ].

Qualitative research on the experience of patients with COVID-19 has provided valuable information [ 4 ]. However, few qualitative studies have addressed the experiences of patients with chronic pathologies during the pandemic, and even fewer in patients with T2D. People with chronic conditions experienced a confluence of the COVID-19 pandemic and chronic diseases in the context of difficulty in accessing healthcare, sedentary lifestyle and increased stress and anxiety [ 14 ]. Shi et al. describes the perceived barriers to diabetes self-management of people with T2D during the pandemic: inadequate knowledge and behavioural beliefs, shortage of resources, health problems, negative emotions and lack of support [ 15 ]. A structured analysis of the experiences of these patients would provide a valuable tool for organising the community and human resources needed in similar situations.

The aim of the present study is to analyse the experiences of patients with T2D that were under a stay-at-home order during the first wave of the COVID-19 pandemic and to identify the strategies and resources used in the management of T2D in this new situation.

We conducted a qualitative descriptive study, a design that is suited to arriving at a deeper understanding of practice in applied disciplines and is especially pertinent when the goal is to understand participants’ perspective and experience [ 16 ]. We began with a deductive approach to develop the interview guide and then conducted an inductive analysis of the resulting data. The study is part of an ongoing project about diabetes and physical activity, which was underway when the pandemic began (Authors, in progress).

Participants

Sampling was intentional [ 17 ]. The participants were the ten patients with T2D from four different primary health centres in central Catalonia (Spain) that were participating in our ongoing study about diabetes and physical activity. The inclusion criteria were adults aged 55 to 79 years diagnosed with T2D at least two years previously. We chose this age range because 55 is the age at which the prevalence of T2D begins to increase rapidly in the population, and a cut-off at 79 allowed us to ensure that participants were young enough to conduct physical activity [ 18 ]). Additional inclusion criteria were having no complications associated with T2D, having good metabolic control (hbA1c < 7), and showing good adherence to T2D treatment (defined as adherence to prescribed medication for T2D, physical activity > 150 min/week, and healthy diet). The exclusion criteria were gestational diabetes or type 1 diabetes, cognitive impairment, or admission to hospital during confinement. All ten participants from our initial study agreed to a follow-up interview about their experiences of COVID-19. Data saturation [ 19 ] was reached by the tenth interview, when we detected that no relevant new information was emerging.

Data collection

Data were collected through a semi-structured interview. The research team developed a set of interview questions relevant to the study objectives, based on the researchers’ clinical experience and a review of the scarce existing literature about patients with chronic illness during the pandemic: How is the COVID-19 pandemic affecting you as a person with diabetes? Can you describe the effect of the stay-at-home order on you at a personal, family, and professional level? Describe to me the care you received for your T2D during the stay-at-home order. How did your lifestyle change (In what sense? Can you tell me?). During the interview, follow-up questions were asked to encourage participants to provide additional details about their perspective.

The interviews were conducted by the principal investigator (PI) between July 2020 and January 2021. In the initial interviews for the ongoing study about T2D and physical activity, the PI had conducted interviews with the participants lasting approximately 45 min. When the pandemic broke out, the team devised a second phase of the study, and the PI invited the participants to a follow-up interview about their COVID-19 experiences. All ten agreed to participate and gave their informed consent. We opted for telephone interviews because we thought it would be easier for participants than video conferencing. We suggested that participants conduct the interview from a quiet place in which they wouldn’t be interrupted. This second interview lasted between 15 and 35 min, meaning that for each participant we have a total of between 60 and 80 min of recorded data. Participants’ confidentiality was protected by giving them pseudonyms. The voice files and transcriptions were encrypted and stored on a computer protected with an encrypted password. The interviews were performed and transcribed in Catalan or Spanish, depending on the preference of the participant. Later, the transcribed interviews were returned to participants for their approval. All participants accepted their transcribed interviews without changes.

Data analysis

Data were analysed using thematic analysis [ 20 ] by ATLAS ti ®vs 9 support. We identified and reported patterns that emerged from the data and arranged them systematically to shed light on the research questions, while trying to keep faithful to the perspectives expressed by participants [ 16 ]. We conducted the analysis in the following phases:

Phase 1 Become familiar with the data by listening to recordings, transcribing them, and reading and rereading the transcripts. Entering transcripts into software Atlas-ti vs 9. Author 1 (MCV) participated in this phase.

Phase 2: Segmenting the meaning units in the transcripts and inductively grouped them to create subthemes and identify relationships among them. Author 1 participated in this phase.

Phase 3: Group the meaning units and abstracted the subthemes. Define the parameters of each subtopic. 14 subtopics have been tagged. Author 1 participated in this phase.

Phase 4: Group the subthemes into overarching themes (which became the primary structure for our analysis). Which in turn we grouped into three themes. Devise a glossary of themes. Author 1 participated in this phase.

Phase 5: Revised, discussed and agreed upon the subthemes and themes while returning to the data to verify the analysis. Authors 1, 2 (MRL) and 4 (GTN) participated in this phase.

Phase 6: Write the research report. Authors 1, 2 and 4 participated in this phase. Author 3 (JVA) examined both the processing and product of the research study.

Rigour, reflexivity and quality criteria

The trustworthiness of data was determined by Credibility, Dependability, Conformability, Transferability [ 21 ].

Credibility has been achieved thanks to the analyst triangulation, to undertook constant revisions of the themes, subthemes and units of analysis and evaluation, ensuring qualitative validity by authors 1, 2 and 4. Transferability has been achieved by describing a phenomenon in sufficient detail to transferable to other settings and people. Dependability was ensured in this study thanks to the review by the third researcher who examined both the processing and product of the research study. Confirmability was achieved through the reflective effort of each researcher to be aware of and try to limit the influence of their own positionality on their analysis. As well as a transparent description of the research steps taken from the start of a research project. All methods were carried out by relevant guidelines and regulations.

The research team have experience with qualitative research and resolved disagreements by consensus, and complied with the Consolidated Criteria for Reporting Qualitative Research [ 22 ].

Ten patients with T2D from four primary care centres in central Catalonia (Spain) participated in the study. Table 1 displays the participants’ main sociodemographic characteristics. Ages ranged from 58 to 79 years, and 60% of participants had had T2D for more than 10 years; most also had a past history of pathology other than T2D.

In our inductive analysis, we identified 14 subthemes, which we grouped into three themes: 1) anxiety, fear and vulnerability, 2) insufficient diabetes monitoring by the health system, and 3) proactive self-care. Table 2 shows an example of the final themes, the codes from which they are built, and an example of a meaning unit from each code.

Anxiety, fear and vulnerability

The context of pandemic and confinement had a strong emotional impact on participants, and the most-expressed emotions were anxiety, fear, and vulnerability. Participants described the lockdown during first wave of the pandemic as something that was totally abnormal and hard to believe; they were shocked to hear the news of the number of deaths in Spain every day:

I thought I was dreaming. I thought this shouldn’t be happening in this day and age 3: 1 (P3).

One issue that respondents mentioned was the fear of infecting others, despite all the protective measures they used. For example, one participant, a health centre worker, was afraid of contagion in spite of the measures she took with her family:

In fact, at first I was worried that I might pass it on to them; I was working, I think the worst time was before [the state of emergency] (…). I got a room ready in case I had to isolate 5:13 (P5).

They also reported negative emotions, such as anxiety and worry:

I have anxiety problems, what`s been getting me down is the fact that I’m feeling a little agoraphobic 8:10 (P8).

It was the anguish of being locked away, of thinking you couldn’t see my 5-year-old granddaughter. My brother …. the family … my daughter and my son… 4:3 (P4).

Others felt fear at seeing so many COVID-19 infections at close range:

We’re all a bit scared. My children have all been through it, three of my four grandchildren. My daughter-in-law has had some awful aftereffects 3: 8 (P3).

Or at living close to death:

Scared. Because you see that the people who started to fall ill were mainly over 55 years old and it really hits you … 6: 1 (P6).

It was made worse by the experience of the loss of friends and family, or by news of acquaintances being admitted to the ICU:

I felt very sad to think of all the people who … I have relatives who have died and … it affected me a lot … not being able to be there … not being able to be with them 3.3 (P3).

On the other hand, some of them managed to keep these feelings of sadness at bay, thanks to their contacts with family, mainly through social media and video conferencing.

I saw them on the phone … and that kept me happy 7:16 (P7).

This feeling of social isolation was extremely negative:

I took it badly because I couldn’t leave the house, I couldn’t see my friends… 9.1 (P9).

For some, it was a negative experience because it disrupted their everyday routines and their self-care.

I felt terrible, it disrupted everything for me. I go to the pool for my water aerobics class, and everything was closed (…). I felt really bad having to spend all day at home 1: 1 (P1).

I used to walk two hours a day, when I was confined because I stayed at home, and I started to put on weight again … 6.4 (P6).

On the other hand, some respondents reported that the confinement and the change in their daily routines was an opportunity for reflection and thinking about their lives:

Three months, locked up at home without singing, without walking, without exercising … I mean, it practically gives you a vision of yourself, the experience of being alone for so many days, it’s a bit like being in a monastery (…). From this point of view the confinement was quite interesting 10.5 (P10).

Insufficient diabetes monitoring by the health system

During the pandemic, health centres prioritised attention to COVID-19 patients, and on-site care of chronic diseases was postponed. Patients reported that their analyses and tests were cancelled:

During the pandemic no diabetes care was available. And even now, there are people who are being told over the phone that it isn’t important … they’re told not to come because no tests are being done 5.6 (P5).

Nonetheless, medication and supplies for diabetics were provided:

At the beginning of the pandemic, I went to look for supplies for diabetes and they gave me enough for three or four months 7.9 (P7).

Some respondents felt abandoned by the health staff who normally cared for them:

Abandoned… (silence) … The normal monthly check-up with the nurse to look at everything (…) didn’t happen. I also have blood tests every three or six months to check my sugar level… (…) but they didn’t happen either 8.2 (P8).

Some participants expressed not understanding the reason for the restriction:

Why can children go to school in a group, in a class, but a doctor can’t see you, they can only talk to you by phone … even though when you go for an appointment there’s a separation between you, the desk, you’re at least a yard away … and wearing their masks … and it turns out they can’t see you … well, a lack of personal protection … yes, you really notice it, because there has been a lot of neglect 8.5 (P8).

But others expressed more understanding of the situation even though they were not seen by health staff:

If you put yourself in their shoes, you realise they couldn't have done any more … 3.5 (P3).

Some patients realised that they had to take control of their disease, because no one else could help them; they ended up accepting the situation:

Well, you realise you’ve got to take care of yourself. And in all, a little self-discipline. Because I didn’t have anyone else to depend on, it was only me, there was no one else (P8).

Others stated that this situation did not affect them because they were already used to a patient-centred model and that the maximum responsibility for their care lay with them:

What sort of care do you expect? We have to care for ourselves … no matter how much they call me and ask me if I’m following my diet, if I’m eating properly, if I’m walking … no matter how much they call … it's up to you …. it's not an injury that you need someone to come and treat you, this is something that’s your own decision 4.5 (P4).

Most participants monitored their blood glucose:

Because I knew I had to check my glucose, I checked it every day and no problem 7.11 (P7).

Proactive self-care

In the management of their disease during lockdown, patients with T2D introduced changes in terms of their physical activity, diet, and medication. Given the impossibility of going outside to exercise, many adapted their physical activity to their home space:

Well, being at home, I coped quite well. I went out onto the rooftop, where I was able to move around and pass the time. I walked up the stairs two or three times. So, I coped quite well 2.2 (P2).

Many participants established routines and did their regular activities, at different levels of intensity:

Every day, every day, every day, and it started … first I started 15 min a day, and then went up to 45 min every day and more intense; I walked fast, then I ran, faster and faster until I got a sore back 4.4 (P4).

This change in physical activity was regarded as a problem by some, but not by others:

I would open all the doors of the apartment and go around until I got tired, and when I got tired, I stopped. It was very boring 1: 5 (P1).

As there was time for everything (…), establishing a routine of walking one hour in the morning and one in the afternoon was not too hard 10: 7 (P10).

However, others abruptly stopped taking exercise:

I didn't do any physical activity while the stay-at-home order lasted 6.6 (P6).

All participants had access to fresh food and their normal diet, since the food shops stayed open during lockdown.

The shops where I go have got everything, fish, meat, chicken, everything 11.9 (P1).

Most reported good adherence to their regular diet:

Well, I saw that I couldn’t … do anything else, or go out … well, it's better to take care of yourself a little, isn’t it? This is also unconscious because I don't think about being diabetic … it’s something I’ve just accepted …. 13.7 (P4).

Others ate between meals, out of stress or boredom:

When I’m nervous, when I’m anxious … there are people whose stomachs close up, but I’m the opposite. I have snacks even though I’m not hungry 12.2 (P3).

None of the respondents had trouble getting their usual medication, and they followed their prescriptions, although they stressed that they were taking the medication without any medical supervision:

What I did is what I always did, there was no change. I went to the pharmacy every month to get my medication 11.9 (P1).

Most participants complied with the recommendations regarding personal protective equipment, hand washing, disinfection, ventilation of the home, and social distancing.

I was careful with my mask, I washed my hands a lot, and cleaned the flat 3.11 (P3).

Some participants applied specific protective measures in their homes:

At the door everyone took off their shoes, and they left their coats in a separate room, they sprayed their hands continuously, and every other day I changed the bed linen, ventilated the flat, cleaned everything. (…). Every time I went to the bathroom I pulled the chain with the lid down, and then cleaned my hands with disinfectant and the toilet as well 4:14 (P4).

Others reported taking particularly strict protective measures, due to their condition:

I took much more care (than other people) because I’m diabetic 8:12 (P8).

Some participants reported that they kept their distance from others, due to their diabetes:

I kept away because I thought I was much more likely to infect them than they were to infect me … so to avoid contagion I kept away from them 8:17 (P8).

Or that their families imposed this distancing on them, in order to protect them:

I asked her [the participant’s granddaughter] to give me a kiss because I needed one, but she said, “No, grandma, I have to go to school, and I don't want to” … And I said, “I’ll just give you a little kiss on your head” and she said “No, no, no!” She wouldn’t let me … 3: 9 (P3).

We have analysed the experiences of patients with T2D in lockdown during the first wave of the COVID-19 pandemic in Catalonia, Spain, and their strategies for managing their disease. Patients with diabetes felt especially vulnerable to infection, and presented emotional difficulties similar to those recorded in patients with COVID-19 at home or with other chronic conditions [ 6 ]. However, despite the changes they experienced in their daily lives and the barriers to accessing chronic care follow-up in primary care centres, they were able to establish routines for self-care.

Fear, anxiety, and vulnerability

Global guidelines on containment measures for the prevention of COVID-19 place special emphasis on vulnerable populations, including people with diabetes [ 23 ]. Our results show that when patients were aware of the risk of contracting COVID-19 due to their T2D status they felt particularly vulnerable and fearful of falling ill. Our data are in line studies showing that having a chronic illness (including T2D), belonging to a risk group, or the death of a family member due to COVID-19 are positively associated with fear of COVID-19 [ 24 ]. The emotional impact of the pandemic was considerable, as the necessary lifestyle changes caused feelings of anxiety among many patients. Elsewhere, the pandemic has been associated with increased stress in general populations, and external stress may reduce physical activity and lead to a poorer diet [ 25 ].

The participants engaged in social distancing due to their fear of infecting others but found the experience to be emotionally challenging. Indeed, due to the high mortality related to COVID and the frequency of near-death experiences, an increased awareness of mortality has been reported during lockdown [ 25 ]. Not only diabetic patients have this perception: other patients with chronic and immunocompromised diseases such as cancer, rheumatoid arthritis, asthma, Crohn’s disease, hypertension, and cardiovascular disease also felt anxiety and fear during the pandemic [ 26 ].

Despite the negative emotional experience of most, for some participants, the suspension of everyday life routines represented an opportunity for reflecting on what was most important to them.

In contrast to reports in other countries [ 27 ], our participants had no difficulty accessing medication or blood glucose control equipment such as glucose strips, needles, or glucometers. However, all of them encountered barriers to accessing primary care. Although they expressed understanding of the pandemic situation, many felt abandoned by the health care system, as other researchers have reported [ 28 ].

Our data suggest that the use of telemedicine and an e-Health model could achieve satisfactory levels of self-care especially in patients with an hb1Ac greater than 6. The popularisation of the Internet and the use of smartphones and emerging fifth-generation networks have allowed patients to attend medical appointments remotely instead of coming to the hospital during the COVID-19 outbreak.

Our results also provide relevant data regarding blood glucose control during the COVID-19 pandemic. Although diabetes is a primary risk factor for the development of severe and septic pneumonia due to infection, patients do not generally intensify their metabolic controls [ 29 ]. This may be due to a lack of information received from professionals monitoring the chronicity of primary pare or due to the lack of protocols or clinical practice guidelines adapted to the situation. In the study by [ 30 ], medication intake was significantly reduced during the pandemic, although in our study compliance with medication intake remained good.

Changes in the provision of health care due to the pandemic have created the need for greater attention to emotional and psychosocial health of patients with T2D. [ 31 ].

The measures imposed by the authorities affected the daily life of the general population as well as that of patients with T2D. In general, this situation was experienced negatively, given that it caused social and family isolation.

The restrictions introduced by the authorities to prevent or reduce the risk of virus transmission led to significant changes in diabetes control. One of the nursing strategies applied to address the needs of patients with T2D in primary care was to promote self-care. Self-care-focused nursing interventions can achieve significant improvements in responsibility for health, physical activity, nutrition, and stress management [ 32 ].

All patients had access to fresh food, since food shops remained open during the lockdown and most patients continued with their usual diets. Most already had a good adherence to diet, although some reported eating between meals out of boredom. Although our participants had access to medication and food, the pandemic made it more difficult to manage their diabetes [ 31 ].

The results show that the stay-at-home order forced patients with T2D to limit their activities, including physical activity. Barone et al. [ 33 ] found that physical activity in diabetic patients fell by 59.5% during the COVID-19 pandemic and suggested that this variable be closely monitored due to its potential negative consequences on metabolic and cardiovascular health. As regards physical exercise, some patients reported decreased activity; others adapted their routines at home to be able to carry out physical activities recommended for a healthy lifestyle, such as walking, running, and going up and down stairs. A few participants performed no physical activity during lockdown, and some achieved optimal T2D risk prevention values, by brisk walking or by observing the current recommendation of 150 min/week of moderate aerobic activity or 30 min/day for 5 days/week [ 34 ]. Despite these changes in behaviour, however, the amount of time devoted to exercise was not optimal for preventing the risks caused by diabetes. The emotional and social impact on certain patients may also be related to the reduction in physical activity, as regular exercise is acknowledged to improve the mental and social health of patients with T2D [ 35 ].

Limitations

This study has several limitations. The first is that the results can only be extrapolated to similar clinical contexts and similar users. The sample is small, and therefore is not representative of all T2D patients with a similar profile. This study can be a launch point, useful for comparison with larger studies in other contexts, to identify best practices in caring for people with T2D during a health crisis.

Second, the context in which the study was carried out was limited to primary care centres in Catalonia with specific sociodemographic characteristics and with good adherence to their prescribed T2D care. Including other types of patients from other geographic areas could provide different results.

Finally, our study includes only the perspective of patients. A fuller picture would emerge if the perspective of nurses monitoring diabetic patients were also included.

Conclusions

This study has provided information on the experiences and emotional responses of patients with T2D during home confinement and on the adaptation of the management of their pathology in Catalonia, Spain. All participants were diabetic patients with good adherence to treatment prior to the pandemic. Due to their health status, patients reported feeling highly vulnerable and fearful of infection. Despite this, patients with T2D were able to establish self-care routines for physical activity and nutrition. In some cases, the lack of access to their normal care at primary care centres made them feel abandoned, although the fact that they were well and that their blood sugar levels were within the recommended levels meant that they did not feel particularly anxious; in general, they were sympathetic to the situation of the health workers. A silver lining of the pandemic may be the way it allowed patients to take control of their disease. This pro-activity on the part of patients should be considered in preparation for future health crises.

Availability of data and materials

The interviews were conducted by the lead author and she is the only researcher who knew the identity of the participants. Her record of interviewees’ names and other personal information will be deleted after publication. Data will be provided upon reasonable request.

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Acknowledgements

The research team would like to thank all participants for their collaboration. We would also like to thank the expert Dr. Susan Frekko.

This research received no external funding.

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The research team was formed by three nurses (authors 1, 2 and 4) and a medical doctor (author 3). All the authors have experience with qualitative research, but authors 2 and 4 have long experience. Author 4 proposed the study, contributed to its design and to data analysis and supervised the project. Author 2 contributed to analysis. Author 3 examined both the processing and product of the research study. Author 1, who is also the PI, conducted the interviews and contributed to the analysis and to writing the discussion. The author(s) read and approved the final manuscript.

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The study was approved by the ethics and research committee “Fundació Unió Catalana d'Hospitals” (code nº 19/45) and also complied with the principles of the Helsinki Declaration. Participants received verbal and written information explaining that their participation was voluntary and that they could withdraw from the project at any time. All participants provided informed consent. All interviews were anonymised by assigning an alphanumeric code in observance of the Spanish legislation on personal data protection of 2018.

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Vilafranca Cartagena , M., Tort-Nasarre, G., Romeu-Labayen, M. et al. The experiences of patients with diabetes and strategies for their management during the first COVID-19 lockdown: a qualitative study. BMC Nurs 21 , 124 (2022). https://doi.org/10.1186/s12912-022-00911-4

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Prevention of Type 2 Diabetes by Lifestyle Changes: A Systematic Review and Meta-Analysis

Matti uusitupa.

1 Institute of Public Health and Clinical Nutrition, School of Medicine, University of Eastern Finland, P.O. Box 1627, 70211 Kuopio, Finland; [email protected]

Tauseef A. Khan

2 Toronto 3D Knowledge Synthesis and Clinical Trials Unit, Clinical Nutrition and Risk Factor Modification Centre, St. Michael’s hospital, Toronto, ON M5B 1W8, Canada; [email protected] (T.A.K.); [email protected] (E.V.); [email protected] (J.L.S.)

3 Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Toronto, ON M5S 1A1, Canada

Effie Viguiliouk

Hana kahleova.

4 Physicians Committee for Responsible Medicine, Washington, DC 20016, USA; [email protected]

5 Institute for Clinical and Experimental Medicine, 140 21 Prague, Czech Republic

Angela A Rivellese

6 Department of Clinical Medicine and Surgery, Federico II University, 80138 Naples, Italy; ti.atinu@sellevir

Kjeld Hermansen

7 Department of Endocrinology and Internal Medicine, Aarhus University Hospital, 8200 Aarhus N, Denmark; [email protected]

Andreas Pfeiffer

8 German Institute of Human Nutrition Potsdam-Rehbrücke, Clinical Nutrition-DZD, Arthur-Scheunert-Allee 114-116, D-14558 Nuthetal, Germany; [email protected]

9 Department of Endocrinology, Charité University Medicine, Diabetes and Nutrition, Campus Benjamin Franklin, Hindenburgdamm 30, D-12203 Berlin, Germany

10 German Center for Diabetes Research (DZD), 85764 München-Neuherberg, Germany

Anastasia Thanopoulou

11 Diabetes Center, 2nd Department of Internal Medicine, Medical School, National and Kapodistrian University of Athens, Hippokration General Hospital of Athens, 157 72 Athens, Greece; moc.liamtoh@uoluoponaht_a

Jordi Salas-Salvadó

12 Human Nutrition Unit, University Hospital of Sant Joan de Reus, Department of Biochemistry and Biotechnology, Faculty of Medicine and Health Sciences, Institut d’Investigació Sanitària Pere Virgili, Rovira i Virgili University, 43201 Reus, Spain; [email protected]

13 Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Institute of Health Carlos III, 28029 Madrid, Spain

Ursula Schwab

14 Department of Medicine, Endocrinology and Clinical Nutrition, Kuopio University Hospital, 70210 Kuopio, Finland

John L. Sievenpiper

15 Division of Endocrinology and Metabolism, Department of Medicine, St. Michael’s Hospital, Toronto, M5B 1W8 ON, Canada

16 Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Toronto, M5B 1T8 ON, Canada

Prevention of type 2 diabetes (T2D) is a great challenge worldwide. The aim of this evidence synthesis was to summarize the available evidence in order to update the European Association for the Study of Diabetes (EASD) clinical practice guidelines for nutrition therapy. We conducted a systematic review and, where appropriate, meta-analyses of randomized controlled trials (RCTs) carried out in people with impaired glucose tolerance (IGT) (six studies) or dysmetabolism (one study) to answer the following questions: What is the evidence that T2D is preventable by lifestyle changes? What is the optimal diet (with a particular focus on diet quality) for prevention, and does the prevention of T2D result in a lower risk of late complications of T2D? The Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach was applied to assess the certainty of the trial evidence. Altogether seven RCTs (N = 4090) fulfilled the eligibility criteria and were included in the meta-analysis. The diagnosis of incident diabetes was based on an oral glucose tolerance test (OGTT). The overall risk reduction of T2D by the lifestyle interventions was 0.53 (95% CI 0.41; 0.67). Most of the trials aimed to reduce weight, increase physical activity, and apply a diet relatively low in saturated fat and high in fiber. The PREDIMED trial that did not meet eligibility criteria for inclusion in the meta-analysis was used in the final assessment of diet quality. We conclude that T2D is preventable by changing lifestyle and the risk reduction is sustained for many years after the active intervention (high certainty of evidence). Healthy dietary changes based on the current recommendations and the Mediterranean dietary pattern can be recommended for the long-term prevention of diabetes. There is limited or insufficient data to show that prevention of T2D by lifestyle changes results in a lower risk of cardiovascular and microvascular complications.

1. Introduction

Both the prevalence and incidence of type 2 diabetes (T2D) are increasing rapidly worldwide. Worldwide, in 2017, approximately 425 million people had diabetes. This figure may rise to 629 million by 2045. However, the figures for different European countries are not as dramatic as the figures in America and in many low- and middle-income countries. In Europe, the prevalence of T2D is also increasing in parallel to the obesity epidemic. In 2017, the number of patients with diabetes in Europe was 66 million (prevalence 9.1%) and it is estimated to be 81 million by 2045. [ 1 , 2 ]. T2D is a potent risk factor for cardiovascular diseases, but also for blindness, renal failure, and lower limb amputation, decreasing the quality of life of people affected. The burden of diabetes is not only a public health issue, but it also has marked economic consequences. More specifically, the expenses for the treatment of diabetes are increasing mostly due to its long-term complications but also modern drug treatment options [ 3 ]. Furthermore, bariatric surgery is becoming more popular for markedly obese patients with T2D due to its significant beneficial effects on metabolic control, long-term complications, and prognosis of T2D [ 4 , 5 ].

The interest in preventing diabetes through lifestyle changes was already present in the 1980s [ 6 ], and the opportunity to prevent T2D through lifestyle changes was re-emphasized in the 2004 recommendations of the Diabetes and Nutrition Study Group (DNSG) of the European Association for the Study of Diabetes (EASD) [ 7 ]. Since then, a number of randomized controlled trials (RCTs) have been published that show that T2D is preventable, or its onset can be markedly postponed, by increasing physical activity, reducing weight, and changing dietary habits.

To update the evidence for the EASD clinical practice guidelines for nutrition therapy, we conducted a systematic review and, where appropriate, meta-analyses of the available randomized controlled trials assessing lifestyle interventions in the prevention of T2D with the aim of answering the following questions:

• (a) What is the evidence that T2D is preventable by lifestyle changes in adults with impaired glucose tolerance (IGT) and (b) what are the long-term results on the prevention of T2D?
• What is the evidence that the lifestyle changes aimed to prevent T2D also modify the risk of cardiovascular disease and microvascular complications in people with IGT?
• What is the optimal dietary composition for the prevention of T2D in people with IGT?

A systematic review and meta-analysis of randomized controlled trials (RCTs) was conducted to assess the role of lifestyle changes on the prevention of T2D using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach. In addition, we discuss the lifestyle including dietary changes that have been successfully used for the prevention of T2D and summarize the long-term follow-up results after the active intervention periods from the major T2D prevention trials on the incidence of T2D and micro- and macrovascular diseases, and finally make the conclusions regarding the three study questions.

We attempt to answer these three questions in turn, summarizing the evidence following by making conclusions at the end of the paper.

2. Evidence That T2D Is Preventable by Changing Lifestyles

A systematic review and meta-analysis of randomized controlled trials (RCTs) was conducted to assess the role of lifestyle changes on the prevention of T2D using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach.

3.1. Search Strategy and Study Selection

We conducted our systematic review and meta-analysis according to the Cochrane Handbook for Systematic Reviews of Interventions [ 8 ], and reported the results according to the PRISMA guidelines ( www.prisma-statement.org ). We conducted standard literature searches of PubMed (MEDLINE), EMBASE, and Cochrane Library through 21 June 2019 to identify both original RCTs and recent systematic reviews [ 9 , 10 , 11 , 12 ] that have examined the association of lifestyle intervention with T2D. The following key words were used in selecting original RCTs for this search: type 2 diabetes, RCT, prevention, systematic reviews, impaired glucose tolerance (IGT), diet, dietary pattern, physical activity, and lifestyle. We supplemented the systematic search with a manual search of reference lists. We selected RCTs comparing the effect of lifestyle intervention (exercise-plus-diet or exercise-plus-diet-plus-weight loss) versus control (no lifestyle intervention) on incident T2D defined using study-specific criteria based on a 2 h oral glucose tolerance test (OGTT) in all populations in an outpatient setting with a minimum follow-up of 1 year. We included studies that were conducted in a high-risk population including those with IGT and metabolic syndrome. Studies that only assessed exercise intervention without diet or weight-loss, used a drug(s) as part of the lifestyle intervention, or only reported observational cohort studies were excluded. In case of the multiple publication of the same trial, we used the one with the end-trial data.

3.2. Data Extraction

Two investigators (EV and TAK) independently reviewed and extracted relevant data from each included report. A standardized form was used to extract data on sample size, participant characteristics, study setting and design, level of monitoring of eating habits, intervention and control arm, macronutrient composition of diets, energy balance, follow-up duration, funding source and outcome data. All discrepancies and disagreements were resolved through consensus.

3.3. Risk of Bias Assessment

Included trials were independently assessed by two investigators (EV and TAK) for the risk of bias using the Cochrane Risk of Bias Tool [ 8 ]. An assessment was performed across 5 domains of bias (sequence generation, allocation concealment, blinding, incomplete outcome data and selective reporting). The risk of bias was assessed as either low (proper methods taken to reduce bias), high (improper methods creating bias) or unclear (insufficient information provided to determine the bias level). All discrepancies and disagreements were resolved through consensus or, where necessary, by a third author (JLS). The methods applied are described in the individual publications [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ].

3.4. Data Syntheses

All analyses were conducted using Stata 16 ((StataCorp, College Station, TX, USA). Data were expressed as risk ratios (RRs) with 95% confidence intervals (CIs) and pooled using the restricted maximum likelihood (REML) random-effects models [ 29 ]. A random-effects model assumes that study estimates are estimating different, yet related, intervention effects and thus incorporates heterogeneity among studies. This is a more appropriate method to pool studies that may differ slightly in distribution of risk factors, population, size, and outcomes [ 30 ]. Heterogeneity was assessed using the Cochran Q statistic and quantified using the I 2 statistic. Significance for heterogeneity was set at p < 0.10, with an I 2 > 50% considered to be evidence of substantial heterogeneity [ 15 ]. Sources of heterogeneity were explored using sensitivity and subgroup analyses. Sensitivity analyses were performed in which each individual trial was removed from the meta-analysis and the effect size recalculated to determine whether a single trial exerted an undue influence. If ≥10 trials were available, then a priori subgroup analyses were conducted using meta-regression by baseline values, study design, follow-up, comparator arm, risk of bias and diabetes duration [ 16 ]. If ≥10 trials were available, then we also assessed publication bias by visual inspection of funnel plots and formal testing by the Egger and Begg tests [ 17 ].

3.5. Grading of the Evidence

The GRADE approach was used to assess the certainty of the evidence [ 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ]. The certainty of the evidence was graded as high, moderate, low, or very low. Randomized controlled trials receive an initial grade of high by default and are downgraded based on the following pre-specified criteria: risk of bias (weight of trials showing risk of bias by the Cochrane Risk of Bias Tool), inconsistency (substantial unexplained inter-study heterogeneity, I 2 > 50% and p < 0.10), indirectness (presence of factors that limit the generalizability of the results), imprecision (the 95% CI for effect estimates were wide or cross minimally important differences (MIDs) for benefit or harm), and publication bias (significant evidence of small-study effects). The MID for T2D was set at 5 percent based on increased cardiovascular disease risk [ 31 ].

4.1. Search Results

Figure 1 outlines our systematic search. We identified 5286 articles from PubMed (MEDLINE), EMBASE, and Cochrane Library.

Flow diagram outlining the systematic search and article selection process.

4.2. Randomized Controlled Trials

We identified seven RCTs comprising 4090 study participants and 2466 incident type 2 diabetes cases [ 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 ] (see Table 1 and Figure 1 ). Except for the study by Bo et al. [ 38 , 39 ] (which was conducted in people with dysmetabolism), all studies were carried out in people with impaired glucose tolerance (IGT) based on an OGTT, and the diagnosis of incident diabetes was confirmed by OGTT applying contemporary WHO criteria for diabetes mellitus. Detailed data on the intervention measures and the follow-up of the control groups have been reported in individual publications and summarized in Table 1 .

Summary results on the randomized controlled trials aimed to prevent type 2 diabetes in people with impaired glucose tolerance or in people at high increased risk for diabetes.

IGT = impaired glucose tolerance based on OGTT, CHO = carbohydrates, prot = protein, SFA = saturated fatty acids, PUFA = polyunsaturated fatty acids, intervention = intervention group, control = control group, minus = reduction from baseline, NA = not available, and NS = not significant, LSM = lifestyle modification, Met = Metformin. Da Qing IGT: The Da Qing IGT and Diabetes Study; FDPS: Finnish Diabetes Prevention Study; DPP: The Diabetes Prevention Program; IDDP-1: The Indian Diabetes Prevention Programme; EDIPS: European Diabetes Prevention Study; LSM: lifestyle modification; Met: metformin; yrs: years; IGT: Impaired glucose tolerance.

4.3. Risk of Bias

Figure 2 shows the individual Cochrane Risk of Bias assessments of seven trials included in the current meta-analysis (see Figure 1 and Table 1 for details). The majority of trials were judged as having unclear or low risk of bias across domains. No evidence of a serious risk of bias was detected.

Risk of bias assessment.

4.4. Effect of Lifestyle Changes on Type 2 Diabetes Risk

Figure 3 shows the effect of lifestyle changes on T2D risk based on the meta-analysis. In seven trials involving 4090 participants [ 32 , 33 , 34 , 36 , 37 , 38 , 40 ], lifestyle intervention significantly decreased T2D risk compared to control groups (RR = 0.53 (95% CI: 0.41, 0.67), p < 0.001), with evidence of substantial inter-study heterogeneity (I 2 = 63%, p = 0.01).

Forest plot of randomized controlled trials investigating the effect of lifestyle changes on type 2 diabetes risk (T2D). The pooled effect estimate for the overall effect is represented by the green diamond. Data are expressed as weighted risk ratios with 95% confidence intervals (CIs) using the restricted maximum likelihood (REML) random-effects model. Inter-study heterogeneity was tested by the Cochrane Q-statistic at a significance level of p < 0.10 and quantified by I 2 , where a level of ≥50% represented substantial heterogeneity.

4.5. Sensitivity and Subgroup Analyses

Table 2 shows selected sensitivity analyses in which the systematic removal of individual trials altered the results. The evidence of substantial heterogeneity was partially explained by the removal of Knowler et al. [ 34 ], which changed the evidence for heterogeneity from significant (I 2 = 65%, p = 0.009) to non-significant (I 2 = 43%, p = 0.16). However, this did not appreciably change the overall effect estimate (RR = 0.49 (95% CI: 0.37, 0.64), p < 0.001). Subgroup analyses were not conducted for any outcome as <10 trials were available.

Influence analysis assessment for the effect of lifestyle changes on T2D risk.

CI = confidence interval.

4.6. Publication Bias

Publication bias was not assessed for any outcome as <10 trials were available.

4.7. GRADE Assessment

Table 3 shows a summary of the GRADE assessments of the overall certainty of the effect of lifestyle changes on the risk of transition from IGT to T2D. The evidence was graded as high for the effect of lifestyle intervention on T2D risk reduction without any downgrading for risk of bias, inconsistency, indirectness, imprecision, or other considerations.

GRADE assessment for the effect of lifestyle changes on T2D risk.

CI = confidence interval; GRADE = grading of recommendations assessment, development, and evaluation; RR = risk ratio; T2D = type 2 diabetes. a Although there was significant heterogeneity (I 2 = 65%, p = 0.01), the removal of one study [ 34 ] explained some of the heterogeneity, which changed it from significant to non-significant (I 2 = 36%, p = 0.16). However, the estimate of effect did not change appreciably. Furthermore, this inconsistency was not considered serious as the magnitude of effect remained large and in the same direction across all the studies (RR < 0.72).

5. Discussion on the Systematic Review and Meta-Analysis

We conducted a systematic review and meta-analysis of seven randomized controlled trials involving 4090 predominantly middle-aged participants with glucose impairment (IGT or dysmetabolism), which showed that lifestyle modification including improved diet and physical activity reduced the risk of type 2 diabetes by 47 percent.

5.1. Results in the Context of Existing Literature

Recent systematic reviews published on the prevention of T2D in high-risk groups uniformly conclude that the onset of T2D can be delayed or prevented with lifestyle changes. Furthermore, these systematic reviews conclude that lifestyle changes may result in the sustained reduction of T2D [ 9 , 10 , 11 , 12 ]. On the other hand, a recent Cochrane review concluded that the evidence took into account only the combined effect of physical activity and dietary changes, and the evidence on the effect of diet or physical activity alone is insufficient [ 12 ].

A brief discussion of the included studies and other literature is helpful here as these will also be referred to in the subsequent sections of this paper. The Chinese Da Qing study [ 32 ] had altogether 577 IGT individuals in 33 study clinics that were randomized to control, exercise, healthy diet, and healthy diet plus exercise clinics, with a follow-up of 6 years. The risk of diabetes was reduced by 33% in the diet-only group, 47% in the exercise-only group and 38% in the diet-plus-exercise group as compared to the control group, without significant differences between the intervention groups. The study individuals were normal weight or overweight at baseline, and the reduction in total energy intake was 100–240 kcal depending on the intervention ( Table 1 ).

In the Finnish Diabetes Prevention Study (FDPS) [ 33 ], 522 individuals with IGT were randomized into a control or lifestyle intervention group (healthy diet and physical activity promotion). The diagnosis of T2D was based on repeated OGTT. After 3.2 years of follow-up, there was a significant decrease in the incidence of T2D, and the trial was prematurely stopped based on the decision of the independent advisory committee. The risk reduction was 58% in the intervention group compared to the control group. Weight loss was larger in the intervention group: the difference in weight reduction between the groups was 3.5 and 2.6 kg at 1 and 3 years, respectively. The intervention group also showed an increase in physical activity and the number of sedentary people was smaller in the intervention (17%) than in the control group (29%).

In the Diabetes Prevention Program (DPP) study conducted in the USA [ 34 ], altogether, 3234 individuals with IGT in 27 centers were randomized into the lifestyle intervention, metformin or control groups. The mean follow-up was 2.8 years. The risk of T2D was reduced by 58% in the lifestyle intervention group as compared to the control group. In the metformin group, the risk of diabetes was 31% lower than in the control group. At year 1, weight reduction in the intervention group was 5.6 kg and 0.1 kg in the control group. No detailed changes in physical activity were reported. It is of note that the initial BMI in the DPP was 34 kg/m 2 when in the FDPS it was 30–31 kg/m 2 .

In a Japanese study on 458 men with IGT [ 36 ], compared to the control group, a remarkable relative risk reduction of 67.4% was found in the intervention group that aimed for weight reduction, increased vegetable intake and physical activity during the 4 year follow-up. The BMI goal was 22 kg/m 2 and the majority of participants had either normal BMI or they were overweight with IGT. Still, the average weight loss was 2.2 kg in the intervention group.

In the Indian Diabetes Prevention Programme (IDPP-1) study [ 37 ], consisting of 531 subjects with IGT, there was a 28.5% reduction in the risk of T2D after 3 years of follow-up in the lifestyle modification group (LSM) compared to the control group, 28.2% reduction in the LSM-plus-metformin (Met) group and 26.4% reduction in the Met group. No significant group differences were found in the preventative effect with regard to LSM, Met and LSM-plus-Met groups. This study did not report significant changes in body weight.

Bo et al. in Italy carried out a lifestyle intervention aimed at the prevention of metabolic syndrome (MetS) in 335 subjects with dysmetabolism. This group included subjects with metabolic syndrome together with those having only two components of metabolic syndrome plus high hs-CRP values. In addition to an effect on metabolic syndrome, this study also reported 1 and 4 year results on the incidence of T2D [ 38 , 39 ]. After one year, there was a marked risk reduction in the incidence of T2D [OR 0.23; 95% CI 0.06–0.85]. The difference in weight reduction between the intervention and control groups was approximately 2.3 kg. After 4 years, the incidence of T2D was 5.4% in the intervention group and 10.2% in the control group.

In the Newcastle arm of the European Diabetes Prevention Study (EDIPS) study [ 40 ] consisting of 102 subjects with IGT, after 3 years of lifestyle intervention following mostly principles of the FDPS, the incidence of T2D was 5.0% and 11.1% in the intervention and the control groups, respectively. The average weight loss was 2.5 kg in the intervention group and sustained beneficial changes in lifestyles predicted better outcome in the T2D risk.

Before the above randomized trials that are included in the meta-analysis, Eriksson and Lindgarde reported in 1991 [ 41 ] that a 6 month sequential intervention of dietary change or increased physical activity may have prevented the development of T2D in 181 Swedish men who volunteered to take part in the lifestyle intervention compared to those who did not volunteer to participate.

In a smaller study of 88 subjects (the SLIM Study) [ 35 ], with 2 years of lifestyle intervention, not included in the current meta-analysis because it did not fulfill the inclusion criteria, there was a significant improvement in 2 h glucose values in the active intervention group. The beneficial changes could be ascribed to moderate weight loss and dietary changes (i.e., reduction in saturated fat intake) in combination with increased physical activity. Incidence data on T2D after 3 years were included in the European Diabetes Prevention Study RCT [ 42 ], where the preventative effect of ≥5% weight loss was particularly high, especially if maintained for 3 years.

Two post-hoc reports from the PREDIMED study also suggest that it is possible to prevent T2D even without significant weight loss in individuals at high risk for cardiovascular disease (CVD), using the Mediterranean diet including extra virgin olive oil or nuts. The risk reduction using the Mediterranean diet intervention, either supplemented with virgin olive oil or nuts, compared to the control group was 30% to 50% depending on the baseline population [ 43 , 44 ]. These studies are discussed in greater detail later in the manuscript with regard to the optimal diet for the prevention of T2D and cardiovascular disease.

5.2. Strengths and Limitations

Our systematic review and meta-analysis have several strengths. These include a rigorous search and selection strategy that identified all available randomized controlled trials examining the effect of lifestyle modification on T2D in individuals; the inclusion of predominantly high-quality randomized controlled trials, which give the greatest protection against bias; the use of the REML random-effects model, which is robust to non-normal distributions and has been recommended for use in meta-analyses over other random-effects estimators [ 29 ]; and the assessment of the overall certainty of the evidence using the GRADE approach.

There were no major limitations of our systematic review and meta-analysis. There was an issue of high heterogeneity, but we did not downgrade for the observed inconsistency. We did not consider the statistical heterogeneity to be a limitation as our meta-analysis included large studies with narrow confidence intervals and similar estimates in the same direction. Therefore, this apparent inconsistency was an artefact of non-overlapping narrow CIs rather than a limitation of the certainty of the overall estimate [ 23 , 45 ]. Balancing the strengths and limitations, the evidence as assessed using GRADE was of high certainty for the effect of lifestyle modification on the reduction of T2D.

6. Long-Term Results on the Prevention of Type 2 Diabetes

Three follow-up studies, the Da Qing Chinese study [ 46 ], FDPS [ 47 , 48 ] and DPP [ 49 ], showed that the beneficial lifestyle changes achieved in the prevention of T2D trials resulted in a sustained risk reduction of T2D over 10 years of follow-up ( Table 4 ).

Long-term post-intervention preventative effect on the incidence of type 2 diabetes in the former intervention groups compared to control groups in three randomized controlled lifestyle intervention studies.

Figure 4 shows the effect of lifestyle changes on the T2D risk based on the meta-analysis of the selected trials that had the long-term follow-up after the lifestyle intervention phase. In three trials consisting a total of 3855 participants with a median follow-up of 13 years [ 46 , 47 , 49 ], lifestyle intervention was associated with significantly lower T2D risk compared to control groups (RR = 0.63 [95% CI: 0.54, 0.74], p < 0.001) with no evidence of inter-study heterogeneity (I 2 = 0%, p = 0.76).

Forest plot of randomized controlled trials investigating the long-term post-intervention effect of lifestyle changes on type 2 diabetes risk. The pooled effect estimate for the overall effect is represented by the green diamond. Data are expressed as weighted risk ratios with 95% confidence intervals (CIs) using the REML random-effects model. Inter-study heterogeneity was tested by the Cochrane Q-statistic at a significance level of p < 0.10 and quantified by I 2 , where a level of ≥50% represented substantial heterogeneity.

Based on the results from FDPS [ 47 , 48 ], 22 subjects with IGT must be treated for one year or 5 subjects for five years to prevent one case of diabetes. Accordingly, in DPP [ 49 ], the respective figure was 6.9 subjects for a 3 year intervention.

7. Evidence That the Prevention of T2D in High-Risk Individuals Results in a Lower Risk of Cardiovascular Disease (CVD) and Microvascular Complications

Among the selected intervention trials, three follow-up post-intervention studies reported cardiovascular and/or microvascular complications ( Table 5 ). Furthermore, we considered the PREDIMED intervention trial results for this question as this study was carried out in high-risk individuals [ 43 , 44 ].

Long-term post-intervention data on mortality, cardiovascular (CVD) mortality and microvascular complications in the former intervention groups compared to the control groups in three randomized controlled lifestyle intervention studies.

NA: Not available.

This question is of particular importance, since the ultimate goal of the prevention and treatment of diabetes is the prevention of the long-term complications of diabetes associated with long-term hyperglycemia, dyslipidemias, hypertension, and other metabolic abnormalities, including low-grade inflammation [ 50 ]. Indeed, long-term intervention trials on the prevention of T2D have shown that besides improved glycemia, due to the correction of insulin resistance and possibly the preservation of beta-cell capacity [ 33 , 34 , 51 ], many of the well-known cardiovascular risk factors and characteristics of metabolic syndrome are corrected by changing to a healthier diet, increasing physical activity and losing weight [ 43 , 44 , 51 , 52 , 53 ]. However, there has been little evidence that the incidence of CVD or microvascular complications can be postponed or prevented by changing lifestyles. Recent data from the Da Qing Diabetes Prevention Outcome study reported results for both mortality and morbidity that suggest long-term benefits as a result of changing lifestyle habits. To summarize, there was a significant reduction in all cause deaths (26%), CVD deaths (33%) and total CVD events (26%) in the combined intervention groups as compared to the control group. Furthermore, composite microvascular diseases (35%) and the incidence of any retinopathy (40%) were significantly lower in the combined intervention groups in this cohort [ 54 ].

Furthermore, the PREDIMED study reported a significant reduction in combined stroke and all cardiovascular events in individuals randomized to the Mediterranean diet (MedDiet) plus extra-virgin olive oil or MedDiet plus nuts group [ 58 ]. Recently, the incidence of retinopathy was reported to be lower in the PREDIMED study in individuals randomized to MedDiet plus extra-virgin olive oil group (RR 0.56; 95%CI 0.32–0.97) or MedDiet plus nuts group (0.63; 95% CI 0.35–1.11). By contrast, no effect of the Mediterranean diet interventions on diabetic nephropathy was reported in the PREDIMED [ 59 ]. In the DPP follow-up study [ 55 ], retinopathic changes in women were lower in the former lifestyle intervention group than in the control group. Similarly, individuals who developed T2D had higher incidence of retinopathy than those who were non-diabetic after a long follow-up period ( Table 5 ). In FDPS, no difference was found in CVD morbidity or mortality between the intervention and control groups after 10 years, but incident cases remained low in both intervention and control groups [ 56 ]. In a sub-group analysis, the occurrence of retinopathy (microaneurysms) was significantly higher in the control (37/98, 38%) than in the intervention group (27/113, 24%; p = 0.026, see Table 4 for adjusted results) of the former FDPS participants [ 56 ].

An original report from the Look AHEAD trial showed no benefit of lifestyle intervention for the prevention of cardiovascular disease in patients with T2D, but a post-hoc analysis showed a 21% risk reduction in combined cardiovascular events in individuals who were able lose at least 10 kg of body weight as compared to patients with a stable body weight or long-term weight gain [ 60 ].

A recent systematic review and meta-analysis of prospective cohort studies and randomized clinical trials suggests that MedDiet has a beneficial role on the CVD prevention in populations inclusive of the individuals with T2D [ 61 ].

Discussion on Macro- and Microvascular Risk Reduction in the T2D Prevention Trials

Among the diabetes prevention trials which have examined follow-up data, only the Chinese Da Qing Diabetes Prevention Outcome Study has reported lower mortality and morbidity from any cause and cardiovascular disease in the people with IGT randomized into lifestyle intervention groups ( Table 5 ). Furthermore, the Chinese study found a clear decrease in composite microvascular diseases and retinopathy [ 54 ]. Indeed, these long-term results are of particular interest, since one long-term goal of the prevention of T2D is to prevent its complications as well. A longer follow-up of a relatively younger age cohort that is also less obese is a possible reason why significant risk reduction in CVD mortality and morbidity is only seen in the Chinese study and not in the American DPP Outcome Study [ 55 ] or in the FDPS [ 56 ]. After the active intervention phase, both the American and Finnish study participants, on average, remained relatively obese compared to the Chinese study. There may also be genetic or ethnic differences between the study populations, resulting in different distributions of the risk factors for T2D and of T2D rate itself [ 3 ]. For example, smoking was particularly common among the Chinese study participants [ 54 ]. Furthermore, the management of the main risk factors and health care resources available may offer other explanations for divergent results. In terms of microvascular complications, which are closely associated to hyperglycemia, the Chinese study results were encouraging with a 35% reduction in composite microvascular complications and 40% reduction in any retinopathy in the intervention groups. The results from both the DPP Outcome Study and the FDPS supported the long-term benefit achieved by changing lifestyles with regard to incident retinopathy [ 55 , 57 ]. Finally, it should be emphasized that the statistical power of the intervention studies on the prevention of T2D may not be sufficient to show significant differences in CVD outcomes between the intervention and the control groups [ 62 ].

8. Discussion on the Factors Explaining the Risk Reduction of T2D Including the Optimal Dietary Composition for the Prevention of T2D

8.1. what are the factors explaining the risk reduction of t2d in randomized controlled trials.

This question is of particular importance as it is related to strategies in preventing T2D. The Da Qing IGT study is the only study with both diet and physical activity arms randomized by clinic [ 32 ], and the PREDIMED trial is the only study testing the effect of a food pattern enriched with key foods (nuts or virgin olive oil) without physical activity or energy restriction [ 43 , 44 ]. All other lifestyle intervention studies combine dietary changes, weight reduction for overweight or obese people, and physical activity. It is of note that Chinese people with IGT in the Da Qing study [ 32 ], Japanese men with IGT [ 36 ], and individuals in the Indian IDD-1 study [ 37 ] had a much lower BMI than in study populations carried out in Europe or in the U.S.A.

8.2. Weight Reduction

Based on secondary analyses of randomized controlled trials, it can be concluded that a better adherence to lifestyle changes in general results in the better long-term prevention of T2D [ 33 , 48 , 49 ]. Furthermore, based on the evidence coming from observational studies on T2D risk factors [ 2 , 63 ] and the remarkable beneficial effects of weight reduction on glucose metabolism [ 51 , 64 , 65 , 66 ], weight reduction has been considered as a cornerstone in the prevention of T2D; with larger weight reductions associated with a lower risk of T2D. In the EDIPS study on 771 participants with IGT combining data from the FDPS, and SLIM and Newcastle studies, the risk of T2D was 89% lower in individuals who were able to sustain weight loss of at least 5% over 3 years than in individuals without significant weight changes [ 42 ]. Nevertheless, it is impossible to conclude that weight reduction is the only means to reduce the risk of T2D in overweight and obese people with impaired glucose metabolism, since weight loss is almost always associated with simultaneous changes in physical activity and/or diet. Indeed, the studies in people with Asian origin suggest that changing diet and increasing physical activity also seem to play a significant role in the prevention of T2D in individuals at risk for T2D with both normal body weight and over-weight people [ 32 , 36 , 37 ]. The importance of weight reduction in T2D can be gauged from a recent weight-management trial, in which 306 individuals with T2D in 39 primary care practices demonstrated a remission rate of 86% in individuals who lost 15 kg or more (24% of participants) [ 67 ]; an overall weight-loss difference of 9 kg resulted in a remission rate of 46% in the intervention group versus 4% in the control group in the full study.

8.3. Optimal Diet

8.3.1. individual nutrients and foods.

Several observational studies have been conducted to analyze the associations between food groups or nutrient consumption and T2D incidence. Ley et al. [ 68 ] conducted a series of meta-analyses of prospective cohort studies on food and beverage intake and T2D risk. Processed and unprocessed red meat, white rice, and sugar-sweetened beverages have shown a consistent positive relation with T2D, whereas green leafy vegetables, total dairy products, whole grains, alcohol in moderation in women, and coffee have been inversely associated with T2D. The consumption of berries and fruits rich in anthocyanins, such as bilberries, blueberries, grapes, apples, and pears, has also been associated with a lower risk of T2D [ 69 ]. Recent evidence also shows that yogurt intake [ 70 ] and nut intake (in women) is inversely associated with T2D. Legumes are another food group with cardiometabolic benefits [ 71 , 72 , 73 , 74 , 75 , 76 , 77 ] and legumes show an inverse association with the risk of diabetes and gestational diabetes [ 77 , 78 ]. In the same meta-analysis of prospective studies by Ley et al. [ 68 ], heme-iron, glycemic index and glycemic load of the diet were directly associated with T2D incidence, whereas total magnesium and vitamin D in the diet, as well as cereal fiber, were inversely related to T2D. A recent review based on meta-analyses and earlier reviews emphasize the preventive effect of whole grains and dietary fiber on the incidence of T2D [ 79 ].

8.3.2. Dietary Patterns

In addition to individual nutrients and foods, several studies have looked at dietary patterns and prevention of T2D. A Western dietary pattern, which is high in sugar-sweetened soft drinks, refined grains, diet soft drinks, and processed meat, was associated with an increased risk of diabetes in the Nurses Health Study (NHS) I and NHS II studies [ 80 ].

In contrast, some prospective cohort studies have demonstrated that adherence to plant-based dietary patterns, such as Mediterranean [ 81 , 82 ] DASH (Dietary Approaches to Stop Hypertension) or vegetarian dietary patterns [ 82 , 83 , 84 , 85 ], are associated with a lower risk of T2D incidence. In two prospective studies, a Mediterranean-type or healthy dietary pattern has also been inversely related to gestational diabetes [ 78 , 86 ].

Meal frequency and timing may also have a role in the T2D risk. Skipping breakfast and snacking have been associated with increased risk of T2D in both men and women [ 87 , 88 ]. Based on limited evidence, consuming breakfast regularly and not eating snacks between main meals may also be a strategy to reduce the risk of T2D [ 89 ].

8.3.3. Diet and Weight Loss

Current evidence from randomized intervention trials ( Table 1 ) suggests that weight loss by means of a healthy diet with lower saturated fat intake, but rich in vegetables, fruit, and whole grain products is beneficial in the prevention of T2D, especially when combined with physical activity. Indeed, all of the seven randomized lifestyle intervention studies in our systematic review and meta-analysis applied this kind of dietary approach. In FDPS, the best results in the prevention of T2D were achieved in IGT individuals with high fiber but moderate fat intake [ 47 , 90 ]. Similarly, in the American DPP study, 1 year weight loss success was associated with a high carbohydrate, high fiber, but a rather low total and saturated fat diet intake [ 91 ]. Regarding the quality of dietary fat, current evidence suggests that unsaturated fatty acids may have beneficial effects on insulin sensitivity and it is suggested to lower the risk of T2D [ 92 , 93 ].

In the PREDIMED trial, the Mediterranean diet enriched in nuts or extra virgin olive oil, resulted in a significant reduction in the incidence of T2D independent of weight loss or physical activity changes. This suggests that the quality of the diet may play a role in the prevention of T2D independent of weight changes [ 43 , 44 ]. However, these results are based on post-hoc analyses of a population at high cardiovascular risk and may not be extrapolated to healthy populations. In the SLIM and Newcastle studies, better adherence to the diet also predicted lower T2D risk [ 42 ]. To conclude, a diet with low consumption of red and processed meat, sugar, and sugar-sweetened beverages, but rich in vegetables, fruit, legumes, and whole grain products seems to be beneficial in the prevention of T2D.

8.3.4. Physical Activity

The Chinese Da Qing study [ 32 ] is the only intervention study that has examined the effect of exercise without weight loss or dietary changes. In the physical activity clinics, the risk of T2D was reduced by 47% as compared to clinics serving as control clinics, but no significant differences were observed between different randomization groups ( Table 1 ). There are no other long-term controlled intervention trials in this field. In FDPS, the impact of physical activity was examined as a secondary analysis taking into account the effect of diet and weight reduction. Based on different criteria used to evaluate physical activity, it was concluded that being physically active may reduce T2D risk by approximately 50% [ 94 ]. The recommendations to increase physical activity are strongly grounded by short-term controlled interventions that show improved glucose metabolism after increasing physical activity. Furthermore, epidemiological and trial evidence support the view that physical inactivity/sedentary lifestyle, along with being overweight and/or obese, are important risk factors for T2D and contribute to the current epidemic of T2D [ 1 , 2 , 95 , 96 ]. A recent PREDIMED-Plus Trial on overweight/obese individuals with metabolic syndrome who combined an energy-reduced Mediterranean-type diet and exercise promotion showed significant weight reduction (3.2 vs. 0.7 kg) and improvements in glucose metabolism, serum concentrations of triglycerides, HDL-cholesterol, and some inflammatory factors, compared to controls. These results confirm that a multifactorial approach, including physical activity, is successful in the prevention and treatment of disturbances in glucose metabolism [ 52 ].

9. Conclusions

• We have a high certainty of evidence that T2D is preventable by changing lifestyle, i.e., weight reduction by diet change according to the current recommendations in terms of quality of fat, fiber intake, increased use of whole grain products, fruit, and vegetables, and increasing physical activity. The risk reduction of T2D is strongly related to the degree of long-term weight loss and adherence to lifestyle changes, and this preventive effect has been demonstrated to sustain for many years after active intervention.
• Additional well-controlled intervention studies are needed to identify the optimal diet to prevent T2D. Currently, a diet moderate in fat, low in saturated fat intake, rich in fiber, whole grains, and fruit and vegetables, as well as a Mediterranean-type diet, may be recommended for the prevention of T2D in prediabetes.
• There is still limited/insufficient evidence that the prevention of T2D by changing lifestyle may also prevent CVD or microvascular diseases.

Author Contributions

Conceptualization, M.U., H.K., A.A.R., K.H., A.P., A.T., J.S.-S., U.S. and J.L.S.; Methodology, M.U., T.A.K., E.V. and J.L.S.; Software, T.A.K. and E.V.; Validation, M.U., E.V. and T.A.K.; Formal Analysis, E.V., T.A.K.; Investigation, M.U., E.V., T.A.K. and J.L.S.; Resources, M.U., E.V., T.A.K., and J.L.S.; Data Curation, M.U., E.V. and T.A.K.; Writing—Original Draft Preparation, M.U., E.V., T.A.K., and J.L.S.; Writing—Review & Editing, M.U., T.A.K., E.V., H.K., A.A.R., K.H., A.P., A.T., J.S.-S., U.S. and J.L.S.; Visualization, M.U. and T.A.K.; Supervision, M.U. and J.L.S.; Project Administration, M.U. and J.L.S.; Funding Acquisition, M.U. and J.L.S.

The Diabetes and Nutrition Study Group (DNSG) of the European Association for the Study of Diabetes (EASD) commissioned this systematic review and meta-analysis and provided funding and logistical support for meetings as part of the development of the EASD Clinical Practice Guidelines for Nutrition Therapy. This work was also supported by the Canadian Institutes of Health Research [funding reference number, 129920] through the Canada-wide Human Nutrition Trialists’ Network (NTN). The Diet, Digestive tract, and Disease (3-D) Centre, funded through the Canada Foundation for Innovation (CFI) and the Ministry of Research and Innovation’s Ontario Research Fund (ORF), provided the infrastructure for the conduct of this project. Effie Viguiliouk was supported by a Toronto 3D Knowledge Synthesis and Clinical Trials foundation Internship Award. John L Sievenpiper was funded by a Diabetes Canada Clinician Scientist award. With the exception of the Clinical Practice Guidelines Committee of the DNSG of the EASD, none of the sponsors had a role in any aspect of the present study, including the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, approval of the manuscript or decision to publish.

Conflicts of Interest

JSS serves on the board of, and has received a grant through, his institution from the International Nut and Dried Fruit Council and the Eroski Foundation. He serves on the Executive Committee of the Instituto Danone Spain and on the Scientific Committee of the Danone International Institute. He has received research support from the Instituto de Salud Carlos III, Spain; the Ministerio de Educación y Ciencia, Spain; the Departament de Salut Pública de la Generalitat de Catalunya, Catalonia, Spain; and the European Commission. Further research support has come from the California Walnut Commission, Sacramento CA, USA; the Patrimonio Comunal Olivarero, Spain; the La Morella Nuts, Spain; and Borges S.A., Spain. He reports receiving consulting fees or travel expenses from Danone; California Walnut Commission, the Eroski Foundation, the Instituto Danone–Spain, Nuts for Life, Australian Nut Industry Council, Nestlé, Abbot Laboratories, and Font Vella Lanjarón. He is on the Clinical Practice Guidelines Expert Committee of the European Association for the study of Diabetes (EASD) and has served on the Scientific Committee of the Spanish Food and Safety Agency, and the Spanish Federation of the Scientific Societies of Food, Nutrition and Dietetics. He is an Executive Board Member of the Diabetes and Nutrition Study Group [DNSG] of the EASD. JLS has received research support from the Canadian Foundation for Innovation, Ontario Research Fund, Province of Ontario Ministry of Research and Innovation and Science, Canadian Institutes of health Research (CIHR), Diabetes Canada, PSI Foundation, Banting and Best Diabetes Centre (BBDC), American Society for Nutrition (ASN), INC International Nut and Dried Fruit Council Foundation, National Dried Fruit Trade Association, The Tate and Lyle Nutritional Research Fund at the University of Toronto, The Glycemic Control and Cardiovascular Disease in Type 2 Diabetes Fund at the University of Toronto (a fund established by the Alberta Pulse Growers), and the Nutrition Trialists Fund at the University of Toronto (a fund established by an inaugural donation from the Calorie Control Council). He has received in-kind food donations to support a randomized controlled trial from the Almond Board of California, California Walnut Commission, American Peanut Council, Barilla, Unilever, Unico/Primo, Loblaw Companies, Quaker, Kellogg Canada, and WhiteWave Foods. He has received travel support, speaker fees and/or honoraria from Diabetes Canada, Mott’s LLP, Dairy Farmers of Canada, FoodMinds LLC, International Sweeteners Association, Nestlé, Pulse Canada, Canadian Society for Endocrinology and Metabolism (CSEM), GI Foundation, Abbott, Biofortis, ASN, Northern Ontario School of Medicine, INC Nutrition Research & Education Foundation, European Food Safety Authority (EFSA), Comité Européen des Fabricants de Sucre (CEFS), and Physicians Committee for Responsible Medicine. He has or has had ad hoc consulting arrangements with Perkins Coie LLP, Tate & Lyle, and Wirtschaftliche Vereinigung Zucker e.V. He is a member of the European Fruit Juice Association Scientific Expert Panel. He is on the Clinical Practice Guidelines Expert Committees of Diabetes Canada, European Association for the study of Diabetes (EASD), Canadian Cardiovascular Society (CCS), and Obesity Canada. He serves or has served as an unpaid scientific advisor for the Food, Nutrition, and Safety Program (FNSP) and the Technical Committee on Carbohydrates of the International Life Science Institute (ILSI) North America. He is a member of the International Carbohydrate Quality Consortium (ICQC), Executive Board Member of the Diabetes and Nutrition Study Group (DNSG) of the EASD, and Director of the Toronto 3D Knowledge Synthesis and Clinical Trials foundation. His wife is an employee of Sobeys Inc. TAK has received research support from the Canadian Institutes of Health Research (CIHR) and an unrestricted travel donation from Bee Maid Honey Ltd. He was an invited speaker at a Calorie Control Council annual general meeting for which he received an honorarium. No competing interests were declared by the other authors (MU, EV, HK, AAR, KH, AP, AT, US).

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• Risk models and scores...

Risk models and scores for type 2 diabetes: systematic review

• Related content
• Peer review
• Douglas Noble , lecturer 1 ,
• Rohini Mathur , research fellow 1 ,
• Tom Dent , consultant 2 ,
• Catherine Meads , senior lecturer 1 ,
• Trisha Greenhalgh , professor 1
• 1 Centre for Primary Care and Public Health, Barts and the London School of Medicine and Dentistry, London E1 2AT, UK
• 2 PHG Foundation, Cambridge, UK
• Correspondence to: D Noble d.noble{at}qmul.ac.uk
• Accepted 5 October 2011

Objective To evaluate current risk models and scores for type 2 diabetes and inform selection and implementation of these in practice.

Design Systematic review using standard (quantitative) and realist (mainly qualitative) methodology.

Inclusion criteria Papers in any language describing the development or external validation, or both, of models and scores to predict the risk of an adult developing type 2 diabetes.

Data sources Medline, PreMedline, Embase, and Cochrane databases were searched. Included studies were citation tracked in Google Scholar to identify follow-on studies of usability or impact.

Data extraction Data were extracted on statistical properties of models, details of internal or external validation, and use of risk scores beyond the studies that developed them. Quantitative data were tabulated to compare model components and statistical properties. Qualitative data were analysed thematically to identify mechanisms by which use of the risk model or score might improve patient outcomes.

Results 8864 titles were scanned, 115 full text papers considered, and 43 papers included in the final sample. These described the prospective development or validation, or both, of 145 risk prediction models and scores, 94 of which were studied in detail here. They had been tested on 6.88 million participants followed for up to 28 years. Heterogeneity of primary studies precluded meta-analysis. Some but not all risk models or scores had robust statistical properties (for example, good discrimination and calibration) and had been externally validated on a different population. Genetic markers added nothing to models over clinical and sociodemographic factors. Most authors described their score as “simple” or “easily implemented,” although few were specific about the intended users and under what circumstances. Ten mechanisms were identified by which measuring diabetes risk might improve outcomes. Follow-on studies that applied a risk score as part of an intervention aimed at reducing actual risk in people were sparse.

Conclusion Much work has been done to develop diabetes risk models and scores, but most are rarely used because they require tests not routinely available or they were developed without a specific user or clear use in mind. Encouragingly, recent research has begun to tackle usability and the impact of diabetes risk scores. Two promising areas for further research are interventions that prompt lay people to check their own diabetes risk and use of risk scores on population datasets to identify high risk “hotspots” for targeted public health interventions.

Introduction

The prevalence of diabetes is rising rapidly throughout the world. 1 By 2010 its prevalence in the adult populations of the United Kingdom, the United States, mainland China, and the United Arab Emirates had exceeded 7%, 2 11%, 3 15%, 4 and 17%, 5 respectively. Americans born in 2000 or later have a lifetime risk of more than one in three of developing diabetes. 6 Type 2 diabetes (which accounts for over 95% of diabetes worldwide) results from a complex gene-environment interaction for which several risk factors, such as age, sex, ethnicity, family history, obesity, and hypertension, are well documented. The precise interaction of these and other risk factors with one another is, however, a complex process that varies both within and across populations. 7 8 9 10 11 Epidemiologists and statisticians are striving to produce weighted models that can be presented as scores to reflect this complexity but which at the same time are perceived as sufficiently simple, plausible, affordable, and widely implementable in clinical practice. 12 13

Cohort studies have shown that early detection of established diabetes improves outcome, although the evidence base for screening the entire population is weak. 14 15 The proportion of cases of incident type 2 diabetes in people with impaired glucose tolerance or impaired fasting glucose levels was reduced in landmark trials from China, 16 Finland, 17 and the United States 18 by up to 33%, 50%, and 58%, respectively, through lifestyle changes (increased exercise, weight loss) or pharmacotherapy, or both, although changes may be more modest in a non-trial population. Some have argued that because combining known risk factors predicts incident diabetes at least as effectively as impaired glucose metabolism, a diabetes risk score may be a better and more practical means of identifying people for preventive interventions than either a glucose tolerance test or a fasting blood glucose level. 19 Others favour targeting the assessment of diabetes risk in those with established impaired glucose metabolism on the basis that interventions in this group are particularly effective. 20

Risk models and scores first emerged for cardiovascular disease, and these are widely used in clinical and public health practice. In the United Kingdom, for example, all electronic patient record systems in general practice offer the facility to calculate the Framingham score, a patient’s risk of a cardiovascular event within 10 years. This risk score features in many guidelines and decision pathways (such as the cut-off for statin therapy 21 ), and general practitioners receive financial rewards for calculating it. 22 In contrast, although numerous models and scores have been developed for diabetes risk, we found limited evidence for use of these as part of a formal health policy, guideline, or incentive scheme for practitioners in any country (one Australian scheme incentivises general practitioners’ measurement of the risk of diabetes in adults aged 40-49 23 ). This is perhaps surprising, given that morbidity and mortality from cardiovascular disease has been decreasing in many countries since the 1970s, 24 whereas those from diabetes continue to increase. 3

A diabetes risk score is an example of a prognostic model. 25 Such scores should ideally be developed by taking a large, age defined population cohort of people without diabetes, measuring baseline risk factors, and following the cohort for a sufficiently long time to see who develops diabetes. 26 Although prospective longitudinal designs in specially assembled cohorts are expensive, difficult, and time consuming to execute, cross sectional designs in which risk factors are measured in a population including people both with and without diabetes are methodologically inferior. They use prevalence as a proxy for incidence and conflate characteristics of people with diabetes with risk factors in those without diabetes, and thus are incapable of showing that a putative risk factor predated the development of diabetes. In practice, researchers tend to take one of two approaches: they either study a cohort of people without diabetes, which was assembled some years previously with relevant baseline metrics for some other purpose (for example, the British Regional Heart Study 27 ), or they analyse routinely available data, such as electronic patient records. 8 Both approaches are potentially susceptible to bias.

Some diabetes risk scores are intended to be self administered using questions such as “have you ever been told you have high blood pressure?” Scores that rely entirely on such questions may be hosted on the internet (see for example www.diabetes.org.uk/riskscore ). Some researchers have used self completion postal questionnaires as the first part of a stepwise detection programme. 28 To the extent that these instruments are valid, they can identify two types of people: those who already have diabetes whether or not they know it (hence the questionnaire may serve as a self administered screening tool for undiagnosed diabetes) and those at high risk of developing diabetes—that is, it may also serve as a prediction tool for future diabetes. Prevalence rates for diabetes derived from self assessment studies thus cannot be compared directly with the rate of incident diabetes in a prospective longitudinal sample from which those testing positive for diabetes at baseline have been excluded.

A good risk score is usually defined as one that accurately estimates individuals’ risk—that is, predictions based on the score closely match what is observed (calibration); the score distinguishes reliably between high risk people, who are likely to go on to develop the condition, and low risk people, who are less likely to develop the condition (discrimination); and it performs well in new populations (generalisability). Validating a risk model or score means testing its calibration and discrimination either internally (by splitting the original sample, developing the score on one part and testing it on another), temporally (re-running the score on the same or a similar sample after a time period), or, preferably, externally (running the score on a new population with similar but not identical characteristics from the one on which it was developed). 26 29 Caution is needed when extrapolating a risk model or score developed in one population or setting to a different one—for example, secondary to primary care, adults to children, or one ethnic group to another. 30

Risk scores and other prognostic models should be subject to “impact studies”—that is, studies of the extent to which the score is actually used and leads to improved outcomes. Although most authors emphasise quantitative evaluation of impact such as through cluster randomised controlled trials, 30 much might also be learnt from qualitative studies of the process of using the score, either alone or as an adjunct to experimental trials. One such methodology is realist evaluation, which considers the interplay between context, mechanism (how the intervention is perceived and taken up by practitioners), and outcome. 31 In practice, however, neither quantitative nor qualitative studies of impact are common in the assessment of risk scores. 30

We sought to identify, classify, and evaluate risk models and scores for diabetes and inform their selection and implementation in practice. We wanted to determine the key statistical properties of published scores for predicting type 2 diabetes in adults and how they perform in practice. Hence we were particularly interested in highlighting those characteristics of a risk score that would make it fit for purpose in different situations and settings. To that end we reviewed the literature on development, validation, and use of such scores, using both quantitative data on demographics of populations and statistical properties of models and qualitative data on how risk scores were perceived and used by practitioners, policy makers, and others in a range of contexts and systems.

Theoretical and methodological approach

We followed standard methodology for systematic reviews, summarised in guidance from a previous study and the York Centre for Reviews and Dissemination. 32 33 The process was later extended by drawing on the principles of realist review, an established form of systematic literature review that uses mainly qualitative methods to produce insights into the interaction between context, mechanism, and outcome, hence explaining instances of both success and failure. 34 Our team is leading an international collaborative study, the Realist and Meta-narrative Evidence Synthesis: Evolving Standards (RAMESES) to develop methodological guidance and publication standards for realist review. 35

Search strategy

We identified all peer reviewed cohort studies in adults over age 18 that had derived or validated, or both, a statistically weighted risk model for type 2 diabetes in a population not preselected for known risk factors or disease, and which could be applied to another population. Studies were included that had developed a new risk model based on risk factors and that used regression techniques to weight risk factors appropriately, or validated an existing model on a new population, or did both. Exclusion criteria were cross sectional designs, studies that had not finished recruiting, studies on populations preselected for risk factors or disease, studies that did not link multiple risk factors to form a scoring system or weighted model, screening or early detection studies, genetic studies, case series, studies on under 18s, animal studies, and studies that applied a known risk model or score to a population but did not evaluate its statistical potential.

In January 2011 we undertook a scoping search, beginning with sources known to the research team and those recommended by colleagues. We used the 29 papers from this search to develop the definitive protocol, including search terms and inclusion and exclusion criteria. In February 2011 a specialist librarian designed a search strategy (see web extra) with assistance from the research team. Key words were predict, screen, risk, score, [type two] diabetes, model, regression, risk assessment, risk factor, calculator, analysis, sensitivity and specificity, ROC and odds ratio. Both MESH terms and text words were used. Titles and abstracts were searched in Medline, PreMedline, Embase, and relevant databases in the Cochrane Library from inception to February 2011, with no language restrictions.

Search results from the different databases were combined in an endnote file and duplicates removed electronically and manually. In February and March 2011 two researchers independently scanned titles and abstracts and flagged potentially relevant papers for full text analysis.

Two researchers independently read the interim dataset of full text papers and reduced this to a final dataset of studies, resolving disagreements by discussion. Bilingual academic colleagues translated non-English papers and extracted data in collaboration with one of the research team. To identify recently published papers two researchers independently citation tracked the final dataset of studies in Google Scholar. Reference lists of the final dataset and other key references were also scanned.

Quantitative data extraction and analysis

Properties of included studies were tabulated on an Excel spreadsheet. A second researcher independently double checked the extraction of primary data from every study. Discrepancies were resolved by discussion. Where studies trialled multiple models with minimal difference in the number of risk factors, a judgment was made to extract data from the authors’ preferred models or (if no preferences were stated in the paper) the ones judged by two researchers to be the most complete in presentation of data or statistical robustness. Data extraction covered characteristics of the population (age, sex, ethnicity, etc), size and duration of study, completeness of follow-up, method of diagnosing diabetes, details of internal or external validation, or both, and the components and metrics used by authors of these studies to express the properties of the score, especially their calibration and discrimination—for example, observed to predicted ratios, sensitivity and specificity, area under the receiver operating characteristic curve. We aimed to use statistical meta-analysis where appropriate and presented heterogeneous data in disaggregated form.

Qualitative data extraction and analysis

For the realist component of the review we extracted data and entered these on a spreadsheet under seven headings (box 1).

Box 1: Categories for data entry

Intended users.

Authors’ assumptions (if any) about who would use the risk score, on which subgroups or populations

Proposed action based on the score result

Authors’ assumptions (if any) on what would be offered to people who score above the designated cut-off for high risk

Authors’ hypothesised (or implied) mechanism by which use of the score might improve outcomes for patients

Authors’ adjectives to describe their risk model or score

Relative advantage

Authors’ claims for how and in what circumstances their model or score outperforms previous ones

Authors’ stated concerns about their model or score

Real world use, including citation tracking

Actual data in this paper or papers citing it on use of the score in the real world

One researcher extracted these data from our final sample of papers and another checked a one third sample of these. Our research team discussed context-mechanism-outcome interactions hypothesised or implied by authors and reread the full sample of papers with all emerging mechanisms in mind to explore these further.

Impact analysis

We assessed the impact of each risk score in our final sample using three criteria: any description in the paper of use of the score beyond the population for whom it was developed and validated; number of citations of the paper in Google Scholar and number of these that described use of the score in an impact study; and critical appraisal of any impact studies identified on this citation track. In this phase we were guided by the question: what is the evidence that this risk score has been used in an intervention which improved (or sought to improve) outcomes for individuals at high risk of diabetes?

Prioritising papers for reporting

Given the large number of papers, statistical models, and risk scores in our final sample, we decided for clarity to highlight a small number of scores that might be useful to practising clinicians, public health specialists, or lay people. Adapting previous quality criteria for risk scores, 26 we favoured those that had external validation by a separate research team on a different population (generalisability), statistically significant calibration, a discrimination greater than 0.70, and 10 or fewer components (usability).

Figure 1 ⇓ shows the flow of studies through the review. One hundred and fifteen papers were analysed in detail to produce a final sample of 43. Of these 43 papers, 18 described the development of one or more risk models or scores, 8 27 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 17 described external validation of one or more models or scores on new populations, 9 10 19 52 53 54 55 56 57 58 59 60 61 62 63 64 65 and eight did both. 7 66 67 68 69 70 71 72 In all, the 43 papers described 145 risk models and scores, of which 94 were selected for extraction of full data (the other 51 were minimally different, were not the authors’ preferred model, or lacked detail or statistical robustness). Of the final sample of 94 risk models, 55 were derivations of risk models on a base population and 39 were external validations (of 14 different models) on new populations. Studies were published between 1993 and 2011, but most appeared in 2008-11 (fig 2 ⇓ ). Indeed, even given that weaker cross sectional designs had been excluded, the findings suggest that new risk models and scores for diabetes are currently being published at a rate of about one every three weeks.

Fig 1  Flow of studies through review

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Fig 2  Publication of diabetes risk models and scores 1990-2010. Eleven new risk models and scores had been published in the first five months of 2011

Table 1 ⇓ gives full details of the studies in the sample, including the origin of the study, setting, population, methodological approach, duration, and how diabetes was diagnosed. The studies were highly heterogeneous. Models were developed and validated in 17 countries representing six continents (30 in Europe, 25 in North America, 21 in Asia, 8 in Australasia, 8 in the Middle East, 1 in South America, and 1 in Africa).

 Summary of 43 papers from which 94 diabetes risk models or scores were identified for systematic review

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Comparisons across studies were problematic owing to heterogeneity of data and highly variable methodology, presentation techniques, and missing data. Cohorts ranged in size from 399 to 2.54 million. The same data and participants were often included in several different models in the same paper. Ten research populations were used more than once in different papers. 9 10 27 37 41 42 44 46 47 48 49 51 52 53 54 55 56 63 64 65 66 70 71 In total, risk models were tested on 6.88 million participants, although this figure includes duplicate tests on the same dataset. Participants aged 18 to 98 were studied for periods ranging from 3.15 to 28 years. Completeness of follow-up ranged from 54% to 99% and incidence of diabetes across the time periods studied ranged from 1.3% to 20.9%.

None of the models in the sample was developed on a cohort recruited prospectively for the express purpose of devising it. Rather, all authors used the more pragmatic approach of retrospectively studying a research dataset that had been assembled some years previously for a different purpose. Forty two studies excluded known diabetes in the inception cohort. Diagnosis of diabetes in a cohort at inception and completion of the study was done in different ways, including self report, patient questionnaires, clinician diagnosis, electronic code, codes from the International Classification of Diseases , disease or drug registers, diabetes drugs, dietary treatment, fasting plasma glucose levels, oral glucose tolerance test, and measurement of haemoglobin A 1c . In some studies the method was not stated. Half the studies used different diagnostic tests at inception and completion of the study.

One third of the papers focused almost exclusively on the statistical properties of the models. Many of the remainder had a clinician (diabetologist or general practitioner) as coauthor and included an (often short and speculative) discussion on how the findings might be applied in clinical practice. Three described their score as a “clinical prediction rule.” 45 51 59

Quantitative findings

Table 2 ⇓ gives details of the components of the 94 risk models included in the final sample and their statistical properties—including (where reported) their discrimination, calibration, sensitivity, specificity, positive and negative predictive value, and area under the receiver operating characteristic curve. Many papers offered additional sophisticated statistical analysis, although there was no consistency in the approach used or statistical tests. Heterogeneity of data (especially demographic and ethnic diversity of validation cohorts and different score components) in the primary studies precluded formal meta-analysis.

 Key characteristics of 94 diabetes risk models or scores included in systematic review

All 94 models presented a combination of risk factors as significant in the final model, and different models weighted different components differently. The number of components in a single risk score varied from 3 to 14 (n=84, mean 7.8, SD 2.6). The seven risk scores that were classified as having high potential for use in practice offered broadly similar components and had similar discriminatory properties (area under receiver operating characteristic curve 0.74-0.85, table 4). Overall, the areas under the receiver operating characteristic curve ranged from 0.60 to 0.91. Certain components used in some models (for example, biomarkers) are rarely available in some pathology laboratories and potentially too expensive for routine use. Some models that exhibited good calibration and discrimination on the internal validation cohort performed much less well when tested on an external cohort, 62 67 suggesting that the initial model may have been over-fitted by inclusion of too many variables that had only minor contributions to the total risk. 73 Although this study did not seek out genetic components, those studies that had included genetic markers alongside sociodemographic and clinical data all found that the genetic markers added little or nothing to the overall model. 9 10 36 50

Reporting of statistical data in some studies was incomplete—for example, only 40 of the 94 models quantified any form of calibration statistic. Forty three presented sensitivity and specificity, 27 justified the rationale for cut-off points, 22 presented a positive predictive value, 19 presented a negative predictive value, and 26 made some attempt to indicate the percentage of the population that would need clinical follow-up or testing if they scored as “high risk.” Some models performed poorly—for example, there was a substantial gap between expected and observed numbers of participants who developed diabetes over the follow-up period. The false positive and false negative rates in many risk scores raised questions about their utility in clinical practice (for example, positive predictive values ranged from 5% to 42%, negative predictive values from 88% to 99%). However, some scores were designed as non-invasive preliminary instruments, with a recommended second phase involving a blood test. 7 43 52 53 55 58 65

Risk models and scores tended to “morph” when they were externally validated because research teams dropped components from the original (for example, if data on these were not available), added additional components (for example, to compensate for missing categories), or modified what counted in a particular category (for example, changing how ethnicity was classified); in some cases these modifications were not clarified. A key dimension of implementation is appropriate adaptation to a new context. It was considered that this did not negate the external validation.

Qualitative findings

Table 3 ⇓ provides the qualitative findings from the risk scores. Of the 43 papers in the full sample, three did not recommend use of the model tested because the authors believed it had no advantage over existing ones. 50 56 60 Authors of the other 40 papers considered that at least one of their scores should be adopted and used, and to justify this made various claims. The commonest adjective used by authors to describe their score was “simple” (26 of 43); others mentioned “low cost,” “easily implemented,” “feasible,” and “convenient.”

 Summary of authors’ assumptions and claims about their diabetes risk models or scores

Sixteen of the 43 studies that recommended use of a particular risk model or score did not designate an intended user for it. Some authors assigned agency to a risk score—that is, they stated, perhaps inadvertently, that the score itself had the potential to prevent diabetes, change behaviour, or reduce health inequalities. Although most authors did state an intended target group, this was usually given in vague terms, such as “the general population” or “individuals who are likely to develop diabetes.” Eleven of the 43 papers gave a clear statement of what intervention might be offered, by whom, to people who scored above the cut-off for high risk; the other papers made no comment on this or used vague terms such as “preventive measures,” without specifying by whom these would be delivered.

In all, authors of the papers in the full sample either explicitly identified or appeared to presume 10 mechanisms (box 2) by which, singly or in combination, use of the diabetes risk score might lead to improved patient outcomes (see table 3).

Box 2: 10 suggested mechanisms by which diabetes risk scores could help improve patient outcomes

Direct impact —clinicians will pick up high risk patients during consultations and offer advice that leads to change in patients’ behaviour and lifestyle

Indirect impact —routine use of the score increases clinicians’ awareness of risk for diabetes and motivation to manage it

Self assessment

Direct impact —people are alerted by assessing their own risk (for example, using an online tool), directly leading to change in lifestyle

Indirect impact —people, having assessed their own risk, are prompted to consult a clinician to seek further tests or advice on prevention

Technological

Individual impact —a risk model programmed into the electronic patient record generates a point of care prompt in the clinical encounter

Population impact —a risk model programmed into the electronic patient record generates aggregated data on risk groups, which will inform a public health intervention

Public health

Planners and commissioners use patterns of risk to direct resources into preventive healthcare for certain subgroups

Administrative

An administrator or healthcare assistant collects data on risk and enters these onto the patients’ records, which subsequently triggers the technological, clinical, or public health mechanisms

Research into practice

Use of the risk score leads to improved understanding of risk for diabetes or its management by academics, leading indirectly to changes in clinical practice and hence to benefits for patients

Future research

Use of the risk score identifies focused subpopulations for further research (with the possibility of benefit to patients in later years)

Risk models and scores had been developed in a range of health systems. Differences in components could be explained partly in terms of their intended context of use. For example, the QDScore, intended for use by general practitioners, was developed using a database of electronic records of a nationally representative sample of the UK general practice population comprising 2.5 million people. The QDScore is composed entirely of data items that are routinely recorded on general practice electronic records (including self assigned ethnicity, a deprivation score derived from the patient’s postcode, and clinical and laboratory values). 8 Another score, also intended to be derived from electronic records but in a US health maintenance organisation (covering people of working age who are in work), has similar components to the QDScore except that ethnicity and socioeconomic deprivation are not included. In contrast, the FINDRISC score was developed as a population screening tool intended for use directly by lay people; it consists of questions on sociodemographic factors and personal history along with waist circumference but does not include clinical or laboratory data; high scorers are prompted to seek further advice from a clinician. 52 Such a score makes sense in many parts of Finland and also in the Netherlands where health and information literacy rates are high, but would be less fit for purpose in a setting where these were low.

Prioritising scores for practising clinicians

Table 4 ⇓ summarises the properties of seven validated diabetes risk scores which we judged to be the most promising for use in clinical or public health practice. The judgments on which this selection was based were pragmatic; other scores not listed in table 4 (also see tables 1 and 2) will prove more fit for purpose in certain situations and settings. One score that has not yet been externally validated was included in table 4 because it is the only score already being incentivised in a national diabetes prevention policy. 23

 Components of seven diabetes risk models or scores with potential for adaptation for use in routine clinical practice

Studies of impact of risk scores on patient outcomes

None of the 43 papers that validated one or more risk scores described the actual use of that score in an intervention phase. Furthermore, although these papers had been cited by a total of 1883 (range 0-343, median 12) subsequent papers, only nine of those 1883 papers (table 5 ⇓ ) described application and use of the risk score as part of an impact study aimed at changing patient outcomes. These covered seven studies, of which (to date) three have reported definitive results. All three reported positive changes in individual risk factors, but surprisingly none recalculated participants’ risk scores after the intervention period to see if they had changed. While one report on the ongoing FIN-D2D study suggests that incident diabetes has been reduced in “real world” (non-trial) participants who were picked up using a diabetes risk score and offered a package of preventive care, 74 this is a preliminary and indirect finding based on drug reimbursement claims, and no actual data are given in the paper. With that exception, no published impact study on a diabetes risk score has yet shown a reduction in incident diabetes.

 Results of impact citation search (studies using diabetes risk models or scores as part of an intervention to improve outcomes)

Numerous diabetes risk scores now exist based on readily available data and provide a good but not perfect estimate of the chance of an adult developing diabetes in the medium term future. A few research teams have undertaken exemplary development and validation of a robust model, reported its statistical properties thoroughly, and followed through with studies of impact in the real world.

Limitations of included studies

We excluded less robust designs (especially cross sectional studies). Nevertheless, included studies were not entirely free from bias and confounding. This is because the “pragmatic” use of a previously assembled database or cohort brings an inherent selection bias (for example, the British Regional Heart Study cohort was selected to meet the inclusion criteria for age and comorbidity defined by its original research team and oriented to research questions around cardiovascular disease; the population for the QDScore is drawn from general practice records and hence excludes those not registered with a general practitioner).

Most papers in our sample had one or more additional limitations. They reported models or scores that required collection of data not routinely available in the relevant health system; omitted key statistical properties such as calibration and positive and negative predictive values that would allow a clinician or public health commissioner to judge the practical value of the score; or omitted to consider who would use the score, on whom, and in what circumstances. We identified a mismatch between the common assumption of authors who develop a risk model (that their “simple” model can now be taken up and used) and the actual uptake and use of such models (which seems to happen very rarely). However, there has recently been an encouraging—if limited—shift in emphasis from the exclusive pursuit of statistical elegance (for example, maximising area under the receiver operating curve) to undertaking applied research on the practicalities and outcomes of using diabetes risk scores in real world prevention programmes.

Strengths and limitations of the review

The strengths of this review are our use of mixed methodology, orientation to patient relevant outcomes, extraction and double checking of data by five researchers, and inclusion of a citation track to identify recently published studies and studies of impact. We applied both standard systematic review methods (to undertake a systematic and comprehensive search, translate all non-English texts, and extract and analyse quantitative data) and realist methods (to consider the relation between the components of the risk score, the context in which it was intended to be used, and the mechanism by which it might improve outcomes for patients).

The main limitation of this review is that data techniques and presentation in the primary studies varied so much that it was problematic to determine reasonable numerators and denominators for many of the calculations. This required us to make pragmatic decisions to collate and present data as fairly and robustly as possible while also seeking to make sense of the vast array of available risk scores to the general medical reader. We recognise that the final judgment on which risk scores are, in reality, easy to use will lie with the end user in any particular setting. Secondly, authors of some of the primary studies included in this review were developing a local tool for local use and made few or no claims that their score should be generalised elsewhere. Yet, the pioneers of early well known risk scores 49 68 have occasionally found their score being applied to other populations (perhaps ethnically and demographically different from the original validation cohort), their selection of risk factors being altered to fit the available categories in other datasets, and their models being recalibrated to provide better goodness of fit. All this revision and recalibration to produce “new” scores makes the systematic review of such scores at best an inexact science.

Why did we not recommend a “best” risk score?

We have deliberately not selected a single, preferred diabetes risk score. There is no universal ideal risk score, as the utility of any score depends not merely on its statistical properties but also on its context of use, which will also determine which types of data are available to be included. 75 76 Even when a risk model has excellent discrimination (and especially when it does not) the trade-off between sensitivity and specificity plays out differently depending on context. Box 3 provides some questions to ask when selecting a diabetes risk score.

Box 3: Questions to ask when selecting a diabetes risk score, and examples of intended use

What is the intended use case for the score.

If intended for use:

In clinical consultations, score should be based on data on the medical record

For self assessment by lay people, score should be based on things a layperson would know or be able to measure

In prevention planning, score should be based on public health data

What is the target population?

If intended for use in high ethnic and social diversity, a score that includes these variables may be more discriminatory

What is expected of the user of the score?

If for opportunistic use in clinical encounters, the score must align with the structure and timeframe of such encounters and competencies of the clinician, and (ideally) be linked to an appropriate point of care prompt. Work expected from the intended user of the score may need to be incentivised or remunerated, or both

What is expected of the participants?

If to be completed by laypeople, the score must reflect the functional health literacy of the target population

What are the consequences of false positive and false negative classifications?

In self completion scores, low sensitivity may falsely reassure large numbers of people at risk and deter them from seeking further advice

What is the completeness and accuracy of the data from which the score will be derived?

A score based on automated analysis of electronic patient records may include multiple components but must be composed entirely of data that are routinely and reliably entered on the record in coded form, and readily searchable (thus, such scores are only likely to be useful in areas where data quality in general practice records is high)

What resource implications are there?

If the budget for implementing the score and analysing data is fixed, the cost of use must fall within this budget

Given the above, what would be the ideal statistical and other properties of the score in this context of use?

What trade-offs should be made (sensitivity v specificity, brevity v comprehensiveness, one stage v two stage process)?

Risk scores as complex interventions

Our finding that diabetes risk scores seem to be used rarely can be considered in the light of the theoretical literature on diffusion of innovation. As well as being a statistical model, a risk score can be thought of as a complex, technology based innovation, the incorporation of which into business as usual (or not) is influenced by multiple contextual factors including the attributes of the risk score in the eyes of potential adopters (relative advantage, simplicity, and ease of use); adopters’ concerns (including implications for personal workload and how to manage a positive score); their skills (ability to use and interpret the technology); communication and influence (for example, whether key opinion leaders endorse it); system antecedents (including a healthcare organisation’s capacity to embrace new technologies, workflows, and ways of working); and external influences (including policy drivers, incentive structures, and competing priorities). 77 78

Challenges associated with risk scores in use

While the developers of most diabetes risk scores are in little doubt about their score’s positive attributes, this confidence seems not to be shared by practitioners, who may doubt the accuracy of the score or the efficacy of risk modification strategies, or both. Measuring diabetes risk competes for practitioners’ attention with a host of other tasks, some of which bring financial and other rewards. At the time of writing, few opinion leaders in diabetes seem to be promoting particular scores or the estimation of diabetes risk generally—perhaps because, cognisant of the limited impacts shown to date (summarised in table 5), they are waiting for further evidence of whether and how use of the risk score improves outcomes. Indeed, the utility of measuring diabetes risk in addition to cardiovascular risk is contested within the diabetes research community. 79 In the United Kingdom, the imminent inclusion of an application for calculating QDScore on EMIS, the country’s most widely used general practice computer system, may encourage its use in the clinical encounter. But unless the assessment of diabetes risk becomes part of the UK Quality and Outcomes Framework, this task may continue to be perceived as low priority by most general practitioners. Given current evidence, perhaps this judgment is correct. Furthermore, the low positive predictive values may spell trouble for commissioners. Identifying someone as “[possibly] high risk” will inevitably entail a significant cost in clinical review, blood tests, and (possibly) intervention and follow-up. Pending the results of ongoing impact studies, this may not be the best use of scarce resources.

Delivering diabetes prevention in people without any disease requires skills that traditionally trained clinicians may not possess. 80 We know almost nothing about the reach, uptake, practical challenges, acceptability, and cost of preventive interventions in high risk groups in different settings. 12 The relative benefit of detecting and targeting high risk people rather than implementing population-wide diabetes prevention strategies is unknown. 13 Effective prevention and early detection of diabetes are likely to require strengthening of health systems and development of new partnerships among the clinicians, community based lifestyle programmes, and healthcare funders. 81

Mechanisms by which risk scores might have impact

Although most authors of papers describing diabetes risk scores have hypothesised (or seem to have assumed) a clinical mechanism of action (that the score would be used by the individual’s clinician to target individual assessment and advice), the limited data available on impact studies (see table 5) suggest that a particularly promising area for further research is interventions that prompt self assessment—that is, laypeople measuring their own risk of diabetes. The preliminary findings from the impact studies covered in this review also suggest that not everyone at high risk is interested in coming forward for individual preventive input, nor will they necessarily stay the course of such input. It follows that in areas where aggregated data from electronic patient records are available, the diabetes risk scores may be used as a population prediction tool—for example, to produce small area statistics (perhaps as pictorial maps) of diabetes risk across a population, thereby allowing targeted design and implementation of community level public health interventions. 82 Small area mapping of diabetes risk may be a way of operationalising the recently published guidance on diabetes prevention from the National Institute for Health and Clinical Excellence, which recommends the use of “local and national tools . . . to identify local communities at high risk of developing diabetes to assess their specific needs.” 83

Towards an impact oriented research agenda for risk scores

We recommend that funding bodies and journal editors help take this agenda forward by viewing the risk score in use as a complex intervention and encouraging more applied research studies in which real people identified as at “high risk” using a particular risk score are offered real interventions; success in risk score development is measured in terms of patient relevant intermediate outcomes (for example, change in risk score) and final outcomes (incident diabetes and related morbidity) rather than in terms of the statistical properties of the tool; a qualitative component (for example, process evaluation, organisational case study, patient’s experience of lifestyle modification) explores both facilitators and barriers of using the score in a real world setting; and an economic component evaluates cost and cost effectiveness.

Millions of participants across the world have already participated in epidemiological studies aimed at developing a diabetes risk score. An extensive menu of possible scores are now available to those who seek to use them clinically or to validate them in new populations, none of which is perfect but all of which have strengths. Nevertheless, despite the growing public health importance of type 2 diabetes and the enticing possibility of prevention for those at high risk of developing it, questions remain about how best to undertake risk prediction and what to do with the results. Appropriately, the balance of research effort is now shifting from devising new risk scores to exploring how best to use those we already have.

What is already known on this topic

The many known risk factors for type 2 diabetes can be combined in statistical models to produce risk scores

What this study adds

Dozens of risk models and scores for diabetes have been developed and validated in different settings

Sociodemographic and clinical data were much better predictors of diabetes risk than genetic markers

Research on this topic is beginning to shift from developing new statistical risk models to considering the use and impact of risk scores in the real world

Cite this as: BMJ 2011;343:d7163

We thank Helen Elwell, librarian at the British Medical Association Library, for help with the literature search; Samuel Rigby for manually removing duplicates; and Sietse Wieringa, Kaveh Memarzadeh, and Nicholas Swetenham for help with translation of non-English papers. BMJ reviewers Wendy Hu and John Furler provided helpful comments on an earlier draft.

Contributors: DN conceptualised the study, managed the project, briefed and supported all researchers, assisted with developing the search strategy and ran the search, scanned all titles and abstracts, extracted quantitative data on half the papers, citation tracked all papers, checked a one third sample of the qualitative data extraction, and cowrote the paper. TG conceptualised the qualitative component of the study, extracted qualitative data on all papers, independently citation tracked all papers, and led on writing the paper. RM independently scanned all titles and abstracts of the electronic search, extracted quantitative data from some papers, assisted with other double checking, and helped revise drafts of the paper. TD helped revise and refine the study aims, independently double checked quantitative data extraction from all papers, and helped revise drafts of the paper. CM advised on systematic review methodology, helped develop the search strategy, extracted quantitative data from some papers, and helped revise drafts of the paper. TG acts as guarantor.

Funding: This study was funded by grants from Tower Hamlets, Newham, and City and Hackney primary care trusts, by a National Institute of Health Research senior investigator award for TG, and by internal funding for staff time from Barts and the London School of Medicine and Dentistry. The funders had no input into the selection or analysis of data or the content of the final manuscript.

Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf (available on request from the corresponding author) and declare: no support from any organisation for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; and no other relationships or activities that could appear to have influenced the submitted work.

Ethical approval: Not required.

Data sharing: No additional data available.

This is an open-access article distributed under the terms of the Creative Commons Attribution Non-commercial License, which permits use, distribution, and reproduction in any medium, provided the original work is properly cited, the use is non commercial and is otherwise in compliance with the license. See: http://creativecommons.org/licenses/by-nc/2.0/ and http://creativecommons.org/licenses/by-nc/2.0/legalcode .

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Article information, quantitative trait analysis of type 2 diabetes susceptibility loci identified from whole genome association studies in the insulin resistance atherosclerosis family study.

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Nicholette D. Palmer , Mark O. Goodarzi , Carl D. Langefeld , Julie Ziegler , Jill M. Norris , Steven M. Haffner , Michael Bryer-Ash , Richard N. Bergman , Lynne E. Wagenknecht , Kent D. Taylor , Jerome I. Rotter , Donald W. Bowden; Quantitative Trait Analysis of Type 2 Diabetes Susceptibility Loci Identified From Whole Genome Association Studies in the Insulin Resistance Atherosclerosis Family Study. Diabetes 1 April 2008; 57 (4): 1093–1100. https://doi.org/10.2337/db07-1169

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OBJECTIVE— Evaluate type 2 diabetes susceptibility variants identified from genome-wide association studies in Hispanic Americans and African Americans from the Insulin Resistance Atherosclerosis Family Study (IRAS-FS) for association with quantitative measures of glucose homeostasis and determine their biological role in vivo.

RESEARCH DESIGN AND METHODS— Seventeen type 2 diabetes–associated single nucleotide polymorphisms (SNPs) were genotyped in 1,268 Hispanic- and 581 African-American participants from the IRAS-FS. SNPs were tested for association with quantitative measures of glucose homeostasis, including insulin sensitivity index ( S I ), acute insulin response (AIR), and disposition index.

RESULTS— Previously identified risk variants in cyclin-dependent kinase 5 regulatory subunit associated protein 1-like 1 ( CDKAL1 ) were associated with reduced AIR ( P < 0.0046) in Hispanic Americans. Additionally in Hispanic Americans, the variant in a hypothetical gene (chromosome 11; LOC387761 ) was significantly associated with AIR ( P = 0.0046) with the risk allele showing protective effects, i.e., increased AIR. In both Hispanic- and African-American populations, risk variants at the solute carrier family 30, member 8 ( SLC30A8 ) locus were nominally associated with decreased disposition index ( P < 0.078). Risk variants in the insulin-like growth factor 2 mRNA-binding protein 2 ( IGF2BP2 ) locus were associated with a decreased disposition index ( P = 0.011) exclusively in Hispanic Americans.

CONCLUSIONS— These data indicate a distinct, limited number of diabetes-related genes, more specifically the SNPs in the genes identified in European-derived populations, with modest evidence for association with glucose homeostasis traits in Hispanic Americans and African Americans. We observe evidence that diabetes risk for CDKAL1 , SLC30A8 , IGF2BP2 , and LOC387761 is specifically mediated through defects in insulin secretion. The mechanisms of other predisposing genes remain to be elucidated.

Type 2 diabetes is a complex disease whose pathophysiology can be characterized by peripheral insulin resistance and reduced insulin secretion. Type 2 diabetes is a heritable disease ( 1 ), with multiple variants conferring modest risk to its polygenic inheritance ( 2 ). Prior investigations of the genetic determinants of type 2 diabetes have identified loci, each with relatively modest impacts on disease risk and whose impact has been difficult to replicate across studies ( 2 ).

Recent technical advances have facilitated genome-wide association (GWA) studies which can systematically and more comprehensively search the genome for disease susceptibility loci. Using this technique, novel etiological pathways of type 2 diabetes risk have been elucidated. Recently, four type 2 diabetes GWA studies have been reported ( 3 – 6 ). Taken together, these studies have identified 11 novel loci for involvement in type 2 diabetes susceptibility in European-derived populations. Of these 11 loci, 8 have been replicated across studies.

The purpose of this study was to evaluate variants within the 11 novel type 2 diabetes susceptibility loci identified from GWA studies in a large cohort of Hispanic Americans and African Americans from the Insulin Resistance Atherosclerosis Family Study (IRAS-FS). Quantitative trait analysis was performed to assess the impact of type 2 diabetes susceptibility variants identified in European-derived populations in these two ethnic minority populations. This analysis would enable the assessment of the metabolic pathway (i.e., insulin sensitivity or insulin secretion) through which these susceptibility genes act.

Recruitment.

Study design, recruitment, and phenotyping for IRAS-FS have been described previously in detail ( 7 ). Briefly, the IRAS-FS is a multicenter study designed to identify the genetic determinants of quantitative measures of glucose homeostasis. Members of large families of self-reported Hispanic ancestry ( n = 1,268 individuals in 92 pedigrees from San Antonio, Texas, and San Luis Valley, Colorado) and African Americans ( n = 581 individuals in 42 pedigrees from Los Angeles, California) were recruited. A clinical examination was performed that included an interview, a frequently sampled intravenous glucose tolerance test (FSIGT), anthropometric measurements, and blood collection. Specific to this report, measures of glucose homeostasis included those from the FSIGT using the reduced sampling protocol ( 8 – 10 ) calculated by mathematical modeling methods (MINMOD) ( 11 ): insulin sensitivity index ( S I ), acute insulin response (AIR), and disposition index. Distributions of the primary phenotypes are listed in Table 1 .

Genotyping.

Seventeen single nucleotide polymorphisms (SNPs) from 11 unique loci identified from type 2 diabetes GWA studies ( 3 – 6 ) were selected for analysis. Genotyping was performed on the Sequenom MassArray Genotyping System. Seventy blind duplicates were included to evaluate genotyping accuracy.

Statistical analysis.

Initially, each SNP was examined for Mendelian inconsistencies using PedCheck ( 12 ). Genotypes inconsistent with Mendelian inheritance were converted to missing. Maximum likelihood estimates of allele frequencies were computed using the largest set of unrelated Hispanic- and African-American individuals ( n = 229 and 58, respectively), and then genotypes were tested for departures from Hardy-Weinberg proportions.

To test for association between individual SNPs and each quantitative phenotype, variance component analysis was performed as implemented in SOLAR ( 13 ). When necessary, quantitative traits were transformed to best approximate the distributional assumptions of the test and minimize heterogeneity of the variance. For each phenotype, the 2 degrees of freedom test of genotypic association was performed. In addition, three individual contrasts defined by a priori genetic models (dominant, additive, and recessive) were computed (i.e., dominant model contrasts those with the polymorphism versus those without, additive model tests for a dose effect in the number of alleles, and recessive model contrasts individuals homozygous for the polymorphisms versus not). If the overall genotypic association was significant, the a priori contrasts were examined directly. If the overall genotypic association was not significant, the a priori contrasts were examined after adjusting for the three comparisons using a Bonferroni adjustment. This approach is consistent with the Fisher's protected least significant difference multiple comparisons procedure. Tests reported here were computed adjusting for age, sex, recruitment center, and BMI. Adjustments for multiple comparison tests were not performed because of selection of SNPs based on a priori hypotheses.

To examine the joint effect of these polymorphisms and their explanatory power for continuous traits, the model R 2 was computed. The R 2 statistic was calculated over just the covariates (i.e., age, sex, recruitment center, and BMI) and then with the inclusion of individual SNPs. In addition, stepwise model building was computed (i.e., forward selection with backward elimination) but did not provide additional explanatory information for these traits and SNPs (data not shown). Subjects with type 2 diabetes were excluded from the analysis of glucose homeostasis traits because overt diabetes and its treatment cause secondary changes in glycemic traits that obscure their underlying genetic determinants. SNP alleles were defined as “risk” or “protective” based on previous association studies of type 2 diabetes in European-derived populations ( 3 – 6 ).

This study evaluated 1,849 IRAS-FS participants, 1,268 Hispanic Americans and 581 African Americans. Table 1 summarizes descriptive statistics by ethnicity. On average, the Hispanic- and African-American participants had a similar proportion of women and comparable age and BMI values. Compared with African Americans, Hispanic Americans were more insulin sensitive ( S I 2.15 vs. 1.63 × 10 −5 min −1 /[pmol/l]; P = 0.013), had reduced insulin secretion (AIR 760 vs. 1,006 pmol/l; P < 0.001), and had a reduced disposition index (1,317 vs. 1,426 × 10 −5 min −1 ; P = 0.004). Marker genotyping success rates were 93.3–95.4% for the 17 SNPs examined, and blind duplicates were concordant. PedCheck analysis resulted in the exclusion of 11 of 34,527 genotypes. All SNPs were consistent with Hardy-Weinberg proportions in the Hispanic- and African-American populations.

The results of the quantitative trait analyses in Hispanic Americans are summarized in Table 2 and compared with the results of previous GWA studies from European-derived populations ( 3 – 6 ) in Table 3 . The strongest evidence for association was observed with two SNPs (rs7754840, P = 0.0043, and rs10946398, P = 0.0046) in the intronic region of the cyclin-dependent kinase 5 regulatory subunit associated protein 1-like 1 ( CDKAL1 ) gene with AIR. These SNPs showed the strongest evidence of association in the dominant model (Supplemental Table 1A, which is detailed in the online appendix [available at http://dx.doi.org/10.2337/db07-1169 ]; P = 0.0010 and 0.0011, respectively) with an 18.1% average decrease corresponding to 151 pmol/l insulin in the genotypic mean for AIR associated with the presence of the “risk” alleles C. The next strongest association was also observed with AIR for rs7480010 ( P = 0.0046) in a hypothetical gene (chromosome 11; LOC387761 ). This SNP showed the strongest evidence of association in the additive model (Supplemental Table 1A; P = 0.0011) with an increase of 100 pmol/l (14.2%; genotype A/G) and 307 pmol/l (43.7%; genotype G/G) in the genotypic mean for AIR associated with the increasing copy number of the G allele. Notably, the G allele was previously denoted the “risk” allele in type 2 diabetes studies of European-derived populations because of increased prevalence of the allele in type 2 diabetes cases versus controls ( 5 ). Association at this locus was also seen with disposition index ( P = 0.036) following an additive model (Supplemental Table 1A; P = 0.011) with an increase of 50 × 10 −5 min −1 (3.9%; genotype A/G) and 353 × 10 −5 min −1 (27.5%; genotype G/G) in the genotypic mean for disposition index associated with the increasing number of G alleles. In addition, SNP rs4402960 in the intronic region of insulin-like growth factor 2 mRNA-binding protein 2 ( IGF2BP2 ) was associated with disposition index ( P = 0.011). This SNP showed the strongest association in the additive model (Supplemental Table 1A; P = 0.0031) with a decrease of 221 × 10 −5 min −1 (15.3%; genotype G/T) and 383 × 10 −5 min −1 (26.6%; genotype T/T) in the genotypic mean for disposition index associated with number of the “risk” allele T. The SNPs evaluated explained, on average, <1% of the variance ( R 2 ) for the three quantitative traits examined ( R 2 ; Table 2 ). Analysis of SNPs in the other eight loci, protein kinase N2 ( PKN2 ), a hypothetical gene ( FLJ39370 ), solute carrier family 30, member 8 ( SLC30A8 ), cyclin-dependent kinase inhibitor 2A/B ( CDKN2B / CDKN2A ), the insulin-degrading enzyme ( IDE )/kinesin family member 11 ( KIF11 )/hematopoietically expressed homeobox ( HHEX ) gene cluster, an intragenic region on chromosome 11, the exostosin 2 ( EXT2 )/aristaless-like 4 ( ALX4 ) gene region, and fat mass- and obesity-associated ( FTO ), did not show any evidence of association in the Hispanic-American subjects.

In African Americans ( Tables 4 and 5 , study comparisons), the strongest evidence for association was observed between two SNPs (rs7754840, P = 0.049, and rs10946398, P = 0.063) in the CDKAL1 gene and S I . These SNPs showed the strongest evidence of association in the additive model (Supplemental Table 1B; P = 0.016 and 0.027, respectively) with an average decrease of 0.24 × 10 −5 min −1 /[pmol/l] (12.9%; genotype G/C) and 0.33 × 10 −5 min −1 /[pmol/l] (17.5%; genotype C/C) in the genotypic mean for S I associated with the number of “risk” alleles C. Two additional loci showed evidence for association with disposition index. A nonsynonymous SNP, rs13266634 ( P = 0.050), in the SLC30A8 gene was associated with disposition index following an additive model (Supplemental Table 1B; P = 0.021). The “risk” allele C was associated with a 1,011 × 10 −5 min −1 (38.9%; genotype T/C) and 1,236 × 10 −5 min −1 (47.5%; genotype C/C) decrease in disposition index associated with the number of “risk” alleles. SNP rs7923837, downstream of the IDE / KIF11 / HHEX gene cluster, was modestly associated with disposition index ( P = 0.045) following an additive model (Supplemental Table 1B; P = 0.024). The “risk” allele G was associated with a 505 × 10 −5 min −1 (64.4%; genotype A/G) and 664 × 10 −5 min −1 (84.7%; genotype G/G) increase in disposition index associated with the number of risk alleles. The SNPs evaluated explained, on average, <1% of the variance ( R 2 ) for the three quantitative traits examined ( R 2 ; Table 4 ). Analysis of SNPs in the other eight genes, PKN2 , IGF2BP2 , FLJ39370 , CDKN2B / CDKN2A , LOC387761 , intragenic region on chromosome 11, EXT2 / ALX4 , and FTO , did not show any evidence of association in the African-American subjects.

Quantitative trait analysis results of glucose homeostasis phenotypes differed dramatically between the two populations examined ( Tables 2 and 4 ). The most striking associations observed in the Hispanic American population were at the CDKAL1 locus. Two highly correlated SNPs (rs7754840 and rs10946398; r 2 = 1.0) were associated significantly with β-cell function as measured by AIR ( P < 0.0046). Genotypic means for AIR were consistent with the “risk” alleles C having a reduced AIR following a dominant model ( P < 0.0011). In African Americans, there was a dramatic difference in minor allele frequency (MAF) for these SNPs (C allele; 0.63 vs. 0.34 in Hispanic Americans), and associations at this locus were limited to nominal association with S I ( P < 0.063). Genotypic means associated with the “risk” allele C had a decreased S I following an additive model ( P < 0.027), which is consistent with previous reports ( 3 , 14 ). This difference in trait association may reflect the significant biological differences observed between the African- and Hispanic-American subjects with regard to S I and AIR, as seen in Table 1 . Results of this association could also reflect pleiotropy, however, the genetic correlation between S I and AIR in the African American subjects is −0.09 ± 0.23, which is inconsistent with this hypothesis.

Similar to the results of association analysis with CDKAL1 , a variant in a hypothetical locus ( LOC387761 ) was associated with different phenotypes in the two populations examined. In Hispanic Americans, the previously identified “risk” allele G of rs7480010 was significantly associated with an increased AIR ( P = 0.0046) and modestly associated with an increased disposition index ( P = 0.036). These traits are mathematically related (disposition index = S I × AIR) and have a genetic correlation in these Hispanic-American subjects of 0.68 ± 0.07. In African Americans, there is a trend toward association at this locus with decreased S I ( P = 0.068) corresponding to the “risk” allele. The confounding associations observed at the CDKAL1 and LOC387761 loci could be attributed partially to the dramatic difference in MAF between Hispanic Americans and African Americans which raises the possibility that the identified susceptibility variant is not causal but exhibits effects via linkage disequilibrium, patterns which are different between populations. These are the only two loci that were associated with quantitative measures of glucose homeostasis in both populations in this study, although they have contrasting evidence of phenotypic association across populations. In addition, it is worth noting that the Hispanic- and African-American cohorts examined are phenotypically diverse in terms of glucose homeostasis parameters with African Americans having a significantly lower S I ( P = 0.013) and higher AIR ( P < 0.001). Therefore, the lack of a compensatory increase in AIR observed in the African-American cohort in the presence of a significantly decreased S I could be attributed to an already increased baseline AIR.

At the SLC30A8 locus, a nonsynonymous variant (R325W; rs13266634) was associated with variation in the disposition index in the African-American cohorts ( P = 0.050) and more modestly in the Hispanic-American ( P = 0.078) cohorts. The “risk” allele C, identified and replicated across all four GWA studies ( 3 – 6 ), was associated with a reduced disposition index ( P = 0.05) following an additive genetic model ( P = 0.021). Of the loci examined, variation at the SLC30A8 locus represents the only evidence of consistent association with the GWA reports ( 3 – 6 ) as to the direction of “risk” and consistent findings in the two non–European-origin populations examined herein.

Association observed at the IGF2BP2 locus was limited to the Hispanic-American cohort. SNP rs4402960 was associated with alteration of the disposition index with the “risk” allele T, as determined from the GWA reports ( 3 – 6 ), at a comparable frequency compared with estimates from the European-derived populations and associated with reduced disposition index ( P = 0.011) following an additive model ( P = 0.0031). Lack of association with glucose homeostasis phenotypes in the African-American population could be attributed to a substantially increased diabetes “risk” allele frequency (MAF = 0.50) and linkage disequilibrium block boundaries, which differ between the European-American and African populations as suggested from HapMap data.

In addition, evidence for association of variants located downstream of the IDE / KIF11 / HHEX gene cluster was limited to a single SNP (rs7923837) associated modestly with disposition index ( P = 0.045) exclusively in the African American population. Proposed susceptibility variants in PKN2 , FLJ39370 , CDKN2A / CDKN2B , an intragenic region on chromosome 11, EXT2 / ALX4 , and FTO failed to show evidence of association with the measures of glucose homeostasis evaluated in either ethnic group.

Although the IRAS-FS was designed to study quantitative traits related to glucose homeostasis ( 7 ), there were additional subjects with type 2 diabetes in these families, which allowed us to perform association analysis of the 17 GWA SNPs with type 2 diabetes as a qualitative trait. Likely reflecting the relatively modest numbers of those affected by type 2 diabetes in IRAS-FS (181 Hispanic Americans and 71 African Americans), results were inconsistent and largely nonsignificant (Supplemental Tables 2A and 2B). In Hispanic Americans, a single SNP (rs9300039) in an intragenic region on chromosome 11 was found to be associated with type 2 diabetes ( P = 0.039; Supplemental Table 2A). The A allele of SNP rs9300039 had an odds ratio (OR) of 0.49 (95% CI 0.25–0.96) and therefore was found to be associated with protection from type 2 diabetes. Scott et al. ( 4 ) found association of the C allele with type 2 diabetes “risk”, 1.48 (1.28–1.71), but this result failed to replicate in the companion publications ( 3 , 6 ). In the African American population, a single SNP (rs4402960) in IGF2BP2 was significantly associated with type 2 diabetes as a qualitative trait ( P = 0.021; Supplemental Table 2B). The T allele of SNP rs4402960 was associated with protection from type 2 diabetes, 0.59 (0.38–0.92). This finding is inconsistent with three GWA publications ( 3 , 4 , 6 ), which found this allele to be associated with type 2 diabetes “risk” (meta analysis OR 1.14). The difference in directionality of association could be due to a marked difference in MAF between African-American (MAF = 0.50) and European-derived (MAF = 0.30) populations, which is consistent with HapMap estimates of allele frequency and linkage disequilibrium structural differences. As noted above, there is modest power given the sample size (Supplemental Table 3), nominal P values, and limited evidence that these SNPs contribute to differential type 2 diabetes risk in an independent African-American type 2 diabetes case/control sample (J. Lewis, personal communication). These results suggest these type 2 diabetes results should be viewed as preliminary findings in these populations and any conclusions on the genetic basis of clinical diabetes are not warranted from these data alone.

The IRAS-FS was designed to determine the underlying genetic and environmental contributors to insulin resistance and more broadly glucose homeostasis through quantitative trait analysis. The availability of high-quality metabolic testing in the IRAS-FS, which few studies have, facilitates interrogation of metabolic pathways through which loci implicated in type 2 diabetes susceptibility may influence glucose metabolism. This, taken together with the recruitment of multigenerational pedigrees, with attendant significant increase in power over a sibpair study design, enhance the ability of IRAS-FS to detect and comprehensively evaluate genes related to glucose homeostasis and, in turn, type 2 diabetes. Taken together, the results of the association analyses reported here suggest that a small number of type 2 diabetes susceptibility loci, CDKAL1 , LOC387761 , SLC30A8 , and IGF2BP2 , identified from studies in European-derived type 2 diabetes populations, contribute modestly to variation in glucose homeostasis in Hispanic Americans and African Americans. The balance of the associations with measures of glucose homeostasis suggest that the CDKAL1 , LOC387761 , SLC30A8 , and IGF2BP2 loci are contributing to diabetes susceptibility primarily through effects on insulin secretion as measured by AIR or through homeostatic regulation of the balance of insulin secretion and insulin sensitivity. Any strong evidence for association with insulin sensitivity, a primary component of diabetes susceptibility, is strikingly absent. Therefore, further research for genes effecting insulin sensitivity is in order.

Demographic summary of IRAS-FS Hispanic-American and African-American participants

Quantitative trait analysis using the 2 degrees of freedom test for type 2 diabetes susceptibility loci with glucose homeostasis phenotypes in the IRAS-FS Hispanic-American cohort

Data are means ± SD ( n ).

Major/minor alleles determined from the maximal set of unrelated individuals ( n = 229). Risk allele, identified from previous studies ( 3 – 6 ), is underlined.

Variance proportion over baseline of the quantitative trait explained by inclusion of the SNP in the model.

Comparison of significant findings from the IRAS-FS Hispanic-American population with previous studies in European-derived populations ( 3 – 6 )

Data are OR, OR (95% CI), or means ± SD ( n ). 2df, 2 degrees of freedom.

Hapmap CEU MAFs.

Ref. 5 ( n = 2,617 case subjects/2,894 control subjects).

Ref. 3 ( n = 6,529 case subjects/7,252 control subjects).

Ref. 4 ( n = 2,376 case subjects/2,432 control subjects.

Ref. 6 ( n = 5,681 case subjects/8,284 control subjects).

Quantitative trait analysis using the 2 degrees of freedom test for type 2 diabetes susceptibility loci with glucose homeostasis phenotypes in the IRAS-FS African-American cohort

Comparison of significant findings from the IRAS-FS African-American population with previous studies in European-derived populations ( 3 – 6 )

Data are OR, OR (95% CI), or means ± SD. 2df, 2 degrees of freedom.

Ref. 4 ( n = 2,376 case subjects/2,432 control subjects).

Published ahead of print at http://diabetes.diabetesjournals.org on 5 February 2008. DOI: 10.2337/db07-1169.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked “advertisement” in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

This research was supported in part by NIH grants HL060894, HL060931, HL060944, HL061019, and HL061210.

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• Published: 15 November 2023

Quasi-experimental evaluation of a nationwide diabetes prevention programme

• Julia M. Lemp   ORCID: orcid.org/0000-0002-1524-3641 1 , 2 ,
• Christian Bommer 1 , 3 ,
• Min Xie   ORCID: orcid.org/0000-0003-4835-7060 1 , 2 ,
• Felix Michalik   ORCID: orcid.org/0000-0001-5589-5700 1 , 2 ,
• Anant Jani   ORCID: orcid.org/0000-0002-7046-6768 1 , 4 ,
• Justine I. Davies   ORCID: orcid.org/0000-0001-6834-1838 5 , 6 , 7 ,
• Till Bärnighausen 1 , 8 , 9 ,
• Sebastian Vollmer   ORCID: orcid.org/0000-0002-7863-0462 3 &
• Pascal Geldsetzer   ORCID: orcid.org/0000-0002-8878-5505 2 , 10 , 11  

Nature volume  624 ,  pages 138–144 ( 2023 ) Cite this article

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• Health services
• Lifestyle modification
• Risk factors
• Type 2 diabetes

Diabetes is a leading cause of morbidity, mortality and cost of illness 1 , 2 . Health behaviours, particularly those related to nutrition and physical activity, play a key role in the development of type 2 diabetes mellitus 3 . Whereas behaviour change programmes (also known as lifestyle interventions or similar) have been found efficacious in controlled clinical trials 4 , 5 , there remains controversy about whether targeting health behaviours at the individual level is an effective preventive strategy for type 2 diabetes mellitus 6 and doubt among clinicians that lifestyle advice and counselling provided in the routine health system can achieve improvements in health 7 , 8 , 9 . Here we show that being referred to the largest behaviour change programme for prediabetes globally (the English Diabetes Prevention Programme) is effective in improving key cardiovascular risk factors, including glycated haemoglobin (HbA1c), excess body weight and serum lipid levels. We do so by using a regression discontinuity design 10 , which uses the eligibility threshold in HbA1c for referral to the behaviour change programme, in electronic health data from about one-fifth of all primary care practices in England. We confirm our main finding, the improvement of HbA1c, using two other quasi-experimental approaches: difference-in-differences analysis exploiting the phased roll-out of the programme and instrumental variable estimation exploiting regional variation in programme coverage. This analysis provides causal, rather than associational, evidence that lifestyle advice and counselling implemented at scale in a national health system can achieve important health improvements.

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Data availability.

This study used data from the CPRD Aurum and NHS England HES APC database. The data are available from CPRD ( https://cprd.com ) but restrictions apply to the availability of these data, which were used under license for the current study and so are not publicly available. Owing to CPRD license restrictions, we are unable to share data.

Code availability

All medical codes and algorithms to define variables and R analysis code are available in the  Supplementary Information or at the OSF repository ( https://osf.io/rqz6x/?view_only=abc4c7a3abcb457596cec9fe2664f542 ).

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Acknowledgements

This study is based on data from the Clinical Practice Research Datalink obtained under license from the UK Medicines and Healthcare products Regulatory Agency. The data are provided by patients and collected by the NHS as part of their care and support. The interpretation and conclusions contained in this study are those of the authors alone. This work was supported by the Alexander von Humboldt Foundation through the Alexander von Humboldt Professorship awarded to T.B. Data storage and computing resources used in this work were supported by the Ministry of Science, Research and the Arts Baden-Wuerttemberg, Germany, German Research Foundation, the state of Baden-Wuerttemberg, Germany and the German Research Foundation grant no. INST 35/1314-1 FUGG. P.G. was supported by the National Institute of Allergy and Infectious Diseases (1DP2AI171011) and the Chan Zuckerberg Biohub investigator award. J.M.L. acknowledges support from the German Academic Scholarship Foundation.

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Julia M. Lemp, Christian Bommer, Min Xie, Felix Michalik, Anant Jani & Till Bärnighausen

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J.M.L., C.B., A.J., J.I.D., S.V. and P.G. conceived of the research. T.B., S.V. and P.G. acquired funding for the research project. J.M.L., M.X. and F.M. curated the data. J.M.L., M.X., C.B. and P.G. designed the statistical analyses with consults from F.M., T.B. and S.V. J.M.L. and M.X. analysed the data. C.B. and P.G. supervised the analysis. J.M.L. and P.G. wrote the paper with edits from C.B., M.X., F.M., A.J., J.I.D., T.B. and S.V. All authors approved the final manuscript.

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Extended data figures and tables

Extended data fig. 1 trends in glycated haemoglobin (hba1c) before, during and after programme roll-out..

Weighted average HbA1c in one-year intervals from April 2015 to March 2020, for ( a ) wave 1 and ( b ) wave 2 practices (intervention) compared to wave 3 practices (control). The y-axis does not start from 0, weighting by number of individuals for each practice, by year. The roll-out of the NHS DPP started in June 2016 for wave 1, in April 2017 for wave 2 and in April 2018 for wave 3.

Extended Data Fig. 2 Regional and temporal variation in NHS DPP programme implementation.

( a ) Share of patients eligible for NHS DPP derived via official practice eligibility by roll-out wave and ( b ) share of patients referred to NHS DPP in each of the nine Strategic Health Authorities.

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Lemp, J.M., Bommer, C., Xie, M. et al. Quasi-experimental evaluation of a nationwide diabetes prevention programme. Nature 624 , 138–144 (2023). https://doi.org/10.1038/s41586-023-06756-4

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Using telehealth for diabetes self-management in underserved populations

Ju, Hsiao-Hui DNP, RN, FNP-BC

Hsiao-Hui Ju is an assistant professor at the Cizik School of Nursing at UTHealth, The University of Texas Health Science Center at Houston.

The author and planners have disclosed no potential conflicts of interests, financial or otherwise.

Copyright © 2020 The Author(s). Published by Wolters Kluwer Health, Inc. This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.

Diabetes self-management is a complex process and central to well-being of patients with this chronic disorder. A patient-centered telehealth program may potentially meet needs of those in underserved populations to reduce socioeconomic disparities. Achieving this goal requires a focused concentration on health behaviors and practices of individuals in underserved populations.

Advancements in information technologies have introduced innovative telehealth interventions, which are useful and acceptable for diabetes self-management education and support (DSMES) for patients and providers. 1 Telehealth is the use of electronic information and telecommunication technologies to support and promote long-distance clinical healthcare and patient education. 2 Before designing a telehealth program, NPs should first determine patients' needs. This article will assess the feasibility of using telehealth in low-socioeconomic status (LSES) populations for diabetes self-management. With an understanding of the health behaviors and practices of those in LSES populations, telehealth programs may offer solutions to reduce disparity in diabetes care by increasing access and education.

Diabetes affects about 34.2 million people in the US or 10.5% of the US population. 3 In the last 20 years, the number of adults diagnosed with diabetes has more than doubled. 4 The prevalence of diabetes has increased among LSES populations. 5 These populations are often underinsured or uninsured and reside in medically underserved areas where they can face economic, cultural, and/or linguistic barriers that lead to a shortage of healthcare services. 6 With high rates of poverty and reduced access to care, these groups are also at a higher risk for diabetes complications and poor health outcomes.

Recently, diabetes became a national priority when it was identified as the seventh-leading cause of death in the US. 4 Studies have shown that those with low levels of education and low income have increased susceptibility to developing diabetes-related complications and mortality due to obesity, poor glycemic control, reduced access to care, underinsured or uninsured status, and failure to adhere to other complex diabetes self-management tasks. 3,7,8 Support systems should also be considered as valuable contributors to patients' psychosocial well-being when dealing with the stressors associated with the chronic nature of diabetes. Because access to care and self-management abilities are key components to the outcomes of diabetes, LSES may play a significant role in the disease management process. 8

Review of the literature

The literature search aimed to identify articles that discuss diabetes and self-management behaviors among LSES populations. The following databases were searched: Scopus and Ovid MEDLINE with the use of Medical Subject Headings (MeSH). The keywords used for searching “diabetes” included: diabetes mellitus; diabetes mellitus, type 1; and diabetes mellitus, type 2 . Keywords used for searching “self-management behaviors” included: self-management; self-monitoring; self-care; and behaviors . Keywords used for searching “low socioeconomic status” included: low-income; low socioeconomic; poverty; indigent; impoverished; underinsured; uninsured; and underserved . The search looked for the occurrence of these terms in the article title, abstract, or keywords for each list. The search results were then combined into one search. The literature search identified 273 results in Scopus and 113 articles in Ovid MEDLINE. The following inclusion criteria were used to determine eligibility: 1) participants aged 18 or older with type 1 or type 2 diabetes mellitus; 2) participants from underserved, low-income, or low-socioeconomic groups in the US; 3) discussion of diabetes self-management or diabetes management behaviors. Articles that did not focus on a low-income or low-socioeconomic group in the US were excluded. Studies completed in countries other than the US were excluded. Articles covering gestational diabetes were excluded.

Review of the studies followed guidelines to assess rigor. 9 Study design was evaluated, including sampling methods; biases and threats to validity; reliability and validity of the data; mentions of attrition; narrative descriptions in qualitative studies; and description of the methods, interventions, and findings. 9

Eleven studies met inclusion criteria and had an adequate design. These articles examined diabetes self-management behaviors or perceptions among LSES populations. Two of the studies were cross-sectional descriptive quantitative studies. 10,11 One study was a quantitative study from a sample that was part of another larger randomized study. 12 One was a qualitative study from a randomized controlled pilot study. 13 Another study used a quantitative randomized pretest/posttest control group design. 14 Two were qualitative studies that used a grounded theory approach. 8,15 Four used qualitative studies with a phenomenologic approach. 16,17,18,19 Sample size varied from 10 to 84 in the qualitative studies and from 83 to 314 in the quantitative studies.

The studies were implemented in primary care clinics, university-affiliated health clinics, local community clinics, and hospitals in both rural and urban areas of states spread throughout all the different regions of the US from California, Illinois, Iowa, Kentucky, Maryland, Michigan, North Carolina, Ohio, Tennessee, and Texas. The populations studied in the articles were diverse, including nine examining Hispanics or Latinos, 10-15,17-19 seven evaluating Black Americans, 8,10,11,15,16,18,19 four examining White Americans, 8,11,18,19 as well as two investigating or including Native Americans, 18,19 and two including other races in the study samples. 11,18 Participants' ages ranged from 25 to 86 years old with an average age of approximately 57 years old. Study participants were from LSES populations with low income who were unemployed, uninsured or underinsured, or met financial eligibility for assistance.

Seven studies examined self-care behaviors for medication management. 8,11,13,15,16,18,19 Five evaluated self-monitoring and management of blood glucose levels. 8,10,14,16,19 Eight articles discussed dietary aspects of diabetes management. 8,10,11,15-19 Seven studied exercise or weight management behaviors. 8,10,11,15-17,19 Six reported on psychosocial barriers related to diabetes self-management. 10,12,15,17-19 All studies focused on low-income and underserved populations in the US. This review covered a range of methods, including the use of in-depth focus groups, face-to-face interviews, telephone interviews and surveys, and medical records abstraction. This allowed for a comprehensive interpretation of findings, which is important in qualitative studies.

Synthesis of the literature

Many participants described difficulty keeping up with appointments and carrying out preventive measures such as foot and eye examinations. According to two studies, participants performed foot care on an average of 4 to 5 days a week. 10,19 Most individuals also did not fully understand the meaning of their blood glucose or lab results. Additionally, participants cited that their financial burden limited their ability to utilize healthcare services and engage in certain costly self-care activities such as obtaining glucose-monitoring equipment. 10,15,18 Many participants also reported feeling socially isolated due to their diet restrictions from the diabetes. Psychologically, participants felt uncertain about the future and expressed feelings of fear, anxiety, stress, and depression affecting their blood glucose levels. 10,12,15,18,19 Additionally, many lacked a support system to help cope with and overcome the challenges faced due to this chronic disorder. 15,17,19 Thus, healthcare provider feedback and support were crucial to these patients especially during difficult times. 8,18

Medication management

Out of the self-management methods examined, taking medications as prescribed seemed to have the highest overall adherence. However, three studies shared that some individuals modified their medications based on their own health beliefs or withheld medications due to concern about negative medication adverse reactions. 13,15,18 In another study, one patient did not understand why her blood glucose fluctuated despite taking her medication. 16 Similarly, another participant shared that he did not observe any therapeutic effects of the medication. 13 These experiences led the participants to incorrectly assume that the medications were not working or were not necessary. 13,16 According to one of the studies, medication adherence may be the most important self-care behavior for achieving glycemic control in LSES populations. 11 However, some individuals had trouble accessing medications due to ongoing difficulties, including high cost, lack of insurance, and problems navigating the healthcare system. 13,15,18 Another participant used occasional free medication samples and reused her syringes multiple times to manage the condition. 8 Although there were some misunderstandings and adherence concerns regarding medications, researchers noted that most of the participants felt medications were an essential part of their diabetes management and followed medication recommendations 6 out of 7 days. 13,19 High cost, lack of insurance, difficulty accessing healthcare, medication adverse reactions, and simply forgetting were the main barriers reported on medication adherence. 13,15,18

Self-monitoring of blood glucose

Self-monitoring of blood glucose (SMBG) was another challenge for the LSES population as most of the participants were unaware of a recommended blood glucose goal, and some described their management regimens as “unguided trial and error” when they followed advice from others in their social networks. 8,16 The frequency of blood glucose monitoring also varied widely; one study reported that some patients checked their levels about 4 days of a week while another article noted some only monitored their blood glucose levels when they did not feel well. 8,19 Additionally, some patients faced challenges with SMBG due to the cost of test equipment and supplies. 10,11,19 In another study, researchers reported that SMBG rates remained low despite access to free glucometers and test strips possibly due to unexpected environmental challenges related to severe flood events. 14 Although participants knew how to monitor glucose levels, the ability to interpret their blood glucose patterns varied. 16 Thus, structured testing and interpretation instructions from providers may be necessary. 14 While patients understood SMBG was an important tool in diabetes management, many did not actively monitor their blood glucose levels due to stress, fatigue, or high cost. 10,14,19

Dietary aspects

Dietary aspects of diabetes self-management proved to be the most difficult for participant adherence. Some individuals could not verbalize specific dietary modifications while others could not afford or adhere to a strict diet to control their glucose levels. 15,17-19 Other participants struggled to cook healthy meals due to time constraints and only managed their condition through medications. 8 Participants tried to adjust their diets, but many faced difficulties understanding dietary recommendations such as comprehending information on food labels, trouble counting calories, and confusion about portion sizes. 16 In a recent study, a patient mistakenly tried an all-liquid fruit juice diet before becoming ill and realizing its negative effect on her blood glucose levels. 8 Additionally, family and cultural preferences for a high-carbohydrate diet sometimes negatively influenced food choices and dietary habits, which made it more difficult to make changes. 17 Barriers to dietary modifications included stress, fear of social isolation, limited time and accessibility, and increased cost to obtain the necessary foods to maintain a diabetes-friendly diet. 8,11,17-19 In some cases, comorbidities such as advanced kidney disease (necessitating limiting the amount of protein and potassium consumed) and cardiac conditions (requiring salt restrictions) further narrowed their dietary options. 18

Participants varied widely in their levels and frequency of exercise. Some reported exercising infrequently—2 to 3 days out of 7. 10,19 In another study, many participants held different ideas of what constituted exercise. 15 Most reported mild daily activity such as walking or cleaning around the house, but some did engage in more rigorous exercise through labor-intensive jobs. 15,17,19 While patients understood the importance of exercise, some did not understand the level or type of physical activity that would help manage their diabetes. 15 Providers may need to offer tailored instructions on type, intensity, and duration of physical activity to bridge these knowledge gaps. 10,15 Many participants expressed a desire to increase their physical activity level; however, deterrents such as arthritis, injuries, climate extremes, high-crime neighborhoods, lack of motivation, disability, and limited access to exercise facilities impeded their ability to exercise. 8,11,16,19 According to another study, lower engagement in physical activity was seen with increasing participant age, higher body mass index scores, and less favorable psychosocial well-being. 10 Additionally, depressive symptoms were associated with less exercise. 12 Participants also noted that changing established habits to begin exercising was difficult. 8,19

Strengths and limitations of the research

Some limitations should be considered in this review due to the nature of qualitative studies. A few studies were only able to focus on the participants that were more willing to disclose their attitudes and behaviors toward diabetes self-management. 8,16 Their views may not necessarily truly represent an entire LSES population. Also, only English-language articles published in the US were included. Despite the limitations, this review presented evidence that patients of LSES face unique barriers when managing their diabetes, particularly with adherence to dietary and exercise recommendations. NPs and other healthcare professionals would benefit from understanding the difficulties patients of LSES face regarding diabetes self-management in order to gauge the feasibility of potential telehealth interventions for this unique population.

Technologic advances

One of the main innovations in nursing and the healthcare system is the implementation of technological advances. This encompasses the growing use of electronic health records in clinics and hospitals, the spread of health information through the internet, social media, mobile applications, and the ability to connect to various areas using telehealth systems. The convenience of mobile applications and the internet allows patients to search for information concerning their conditions, medications, and treatments. Advanced technology has made significant impact on the delivery of healthcare and will continue to expand as telehealth services offer the opportunity to improve patient outcomes and access to care. 20

Access to technology

According to a Pew Research Center report from June 2019, smartphone ownership in America increased from 35% in 2011 to 81% in 2019. 21 Overall, it is estimated that 84% of US households own desktop or laptop computers. 22 Although mobile technology use increased widely, 75% of patients living in urban areas have a broadband internet connection at home compared with 63% in rural areas. 23 A small study in an urban clinic for a medically underserved population found that 72% of participants had access to the internet through either computers or cell phones. 24 In contrast, a large study in an urban ED for an underserved population indicated that 96% of the study participants had access to the internet via mobile phone use with internet capability and many utilized the internet for health information. 25 The innovation in technology such as telehealth services could potentially mitigate geographic disparities and increase access to care in underserved areas. 26 With the growth of technology usage, NPs are using a wide variety of telehealth tools, such as real-time video and audio with patients through smartphones and computers. 27 Telehealth calls usually consist of both standardized and personal care. 27 Standardized care checks on the patient's condition, current status, and treatment adherence such as SMBG. Personalized care includes discussing a patient's psychosocial well-being and individual concerns. To help deliver successful telehealth services, NPs need to be familiar with the many telehealth devices such as computers, handheld devices, smartphones, and any preconceived attitudes about using technology in patient care. 27 By incorporating technology into patient care, NPs have the opportunity to provide personalized, meaningful, and high-quality healthcare. Ultimately, patients are the center of healthcare and influence the way healthcare services will be delivered. The indispensable role of the internet, computers, and personal portable technological devices will support a wide range of technology tools. As a result of the advancing technology, telehealth services will also continue to evolve based on patient needs, user preferences, and overall healthcare experiences.

Recommendations

Diabetes self-management is central to the health and well-being of patients. Telehealth interventions could offer synchronous videoconferencing to provide diabetes education to LSES populations. An online video conferencing option would allow these patients and families more access and flexibility. According to the CDC, a telehealth program that offered diabetes care in rural communities in Montana demonstrated positive patient outcomes in terms of self-reported blood glucose monitoring and dietary adherence. 28 Additionally, several studies have already shown a positive correlation between the use of mobile technology and effective weight management, so telehealth videoconferencing that helps increase access to healthcare services may be a viable strategy for improving health outcomes for patients in LSES populations. 29

In addition, advancements in smartphone technology have allowed users to couple fitness trackers with applications on their phones to devise self-monitoring fitness programs. The use of fitness trackers has gradually gained acceptance among both the younger and older generations. 30 With the ownership and usage of smartphones exponentially rising across all age groups, the ability to videoconference is quickly becoming available to a wider population. 21 Telehealth technology could offer supervised and personalized home exercise programs. Healthcare providers must be cognizant that many patients of low-income populations are aware of the importance of diabetes management but are hampered by various challenges. Thus, healthcare providers should consider the unique individual and community barriers and embrace new technologies such as videoconferencing and smartphone applications to help patients with LSES manage diabetes to reach optimal health outcomes.

In response to the public health emergency of coronavirus disease 2019 (COVID-19), the Centers for Medicare and Medicaid Services temporarily expanded telehealth services to allow broader use of technology. 31 NPs are approved Medicare providers for providing telehealth services for rural or underserved areas. 31 Interactive audio and video telecommunications technology must be used that allows for real-time communication between the Medicare beneficiary at the originating site and provider at the distant site. 31 As a result of the temporary expansion, patients can access healthcare professionals using smartphones and other devices with audio and visual capabilities; otherwise, patients must go to authorized originating sites. 31 Medicare offers telehealth reimbursement opportunities for office or other outpatient visits, diabetes self-management training services, pharmacologic management, nutrition therapy, and other preventive services through professional service Current Procedural Terminology (CPT) or Healthcare Common Procedure Coding System (HCPCS) codes. 31

Conclusions

In conclusion, this review revealed that low-income and underserved populations face many barriers to diabetes self-management practices. Participants reported limited access to healthcare services as well as difficulty obtaining the means to healthy nutrition due to social and psychological constraints. Strong self-discipline is often required to maintain appropriate self-care behaviors, and many of the participants felt overwhelmed and stressed. With limited resources and financial hardship, patients of LSES with diabetes may prioritize basic life necessities rather than monitoring blood glucose levels. Issues such as housing instability and living in medically underserved areas also exacerbate stress levels making it more difficult to receive adequate care and implement healthy lifestyle changes.

The prevalence of diabetes is increasing in low-socioeconomic groups, and it is a chronic condition that has many serious complications. 5 In order to improve diabetes outcomes among patients of LSES, telehealth interventions and innovative technologies can be adapted to the needs of these individuals and their local communities. With increased access to care and education using advanced technologies, patients may better understand their health conditions and utilize their healthcare resources to develop optimal diabetes self-management behaviors to manage this lifelong condition. More research is needed to introduce innovative telehealth interventions that provide patient-centered and individualized programs unique to the needs of those in LSES to reduce the socioeconomic disparities in diabetes management. NPs and other healthcare professionals can use the information to better understand the unique barriers that patients of LSES face when planning for diabetes telehealth education in order to improve health outcomes in this population.

With the growth of telehealth services, NPs play pivotal roles in the adoption and integration of technologic advances in healthcare delivery as strategies to improve care in underserved areas. Telehealth allows for health services to be more widely available and are powerful tools for health information exchange between patients and the healthcare team. Patients are encouraged to be active participants and partners in the management of their health. Using a holistic approach, NPs can use telehealth to facilitate partnerships with patients to promote positive lifestyle changes, personalized self-care measures, and evidence-based health interventions in the management of diabetes. Telehealth is a promising approach to positively impact diabetes self-management behaviors in LSES populations.

advanced registered nurse practitioners (ARNPs); independent contractors; Medicaid; Medicare; payment parity; practice owners; private insurance; reimbursement

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Implementation of a Diabetes Self-Management Education and Support Intervention in Rural Guatemala: A Mixed-Methods Evaluation Using the RE-AIM Framework

ORIGINAL RESEARCH — Volume 18 — December 9, 2021

Scott Tschida, MPhil 1 ; David Flood, MD, MSc 1 ,2 ; Magdalena Guarchaj, BS 1 ; Juanita Milian, MS 3 ; Andrea Aguilar, BA 1 ; Meredith P. Fort, PhD, MPH 4 ; Timothy Guetterman, PhD, MA 5 ; Carlos Mendoza Montano, PhD, MS 6 ; Ann Miller, PhD, MPH 7 ; Lidia Morales, MD, MSc 3 ; Peter Rohloff, MD, PhD 1 ,8 ( View author affiliations )

Suggested citation for this article: Tschida S, Flood D, Guarchaj M, Milian J, Aguilar A, Fort MP, et al. Implementation of a Diabetes Self-Management Education and Support Intervention in Rural Guatemala: A Mixed-Methods Evaluation Using the RE-AIM Framework. Prev Chronic Dis 2021;18:210259. DOI: http://dx.doi.org/10.5888/pcd18.210259 .

PEER REVIEWED

Introduction

Acknowledgments, author information.

What is already known on this topic?

The burden of diabetes is large and growing in low- and middle-income countries. A significant gap exists in how to optimally incorporate lifestyle counseling interventions into health systems in these countries.

What is added by this report?

We assessed implementation of a large diabetes self-management education and support (DSMES) program in rural Guatemala. This report highlights information on implementation barriers and facilitators that will be useful to implementers and policy makers who work to scale up DSMES in resource-limited health systems.

What are the implications for public health practice?

Rigorous DSMES interventions can be successfully implemented in rural public health systems in low- and middle-income countries, although challenges include enrollment of men, additional work for overburdened health workers, and sustainability.

To address the global diabetes epidemic, lifestyle counseling on diet, physical activity, and weight loss is essential. This study assessed the implementation of a diabetes self-management education and support (DSMES) intervention using a mixed-methods evaluation framework.

We implemented a culturally adapted, home-based DSMES intervention in rural Indigenous Maya towns in Guatemala from 2018 through 2020. We used a pretest–posttest design and a mixed-methods evaluation approach guided by the RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) framework. Quantitative data included baseline characteristics, implementation metrics, effectiveness outcomes, and costs. Qualitative data consisted of semistructured interviews with 3 groups of stakeholders.

Of 738 participants screened, 627 participants were enrolled, and 478 participants completed the study. Adjusted mean change in glycated hemoglobin A 1c was −0.4% (95% CI, −0.6% to −0.3%; P < .001), change in systolic blood pressure was −5.0 mm Hg (95% CI, −6.4 to −3.7 mm Hg; P < .001), change in diastolic blood pressure was −2.6 mm Hg (95% CI, −3.4 to −1.9 mm Hg; P < .001), and change in body mass index was 0.5 (95% CI, 0.3 to 0.6; P < .001). We observed improvements in diabetes knowledge, distress, and most self-care activities. Key implementation factors included 1) recruitment barriers for men, 2) importance of patient-centered care, 3) role of research staff in catalyzing health worker involvement, 4) tradeoffs between home and telephone visits, and 5) sustainability challenges.

A community health worker–led DSMES intervention was successfully implemented in the public health system in rural Guatemala and resulted in significant improvements in most clinical and psychometric outcomes. Scaling up sustainable DSMES in health systems in rural settings requires careful consideration of local barriers and facilitators.

The number of adults with diabetes is estimated to grow worldwide from 463 million in 2019 to 700 million in 2045 (1). More than 80% of the diabetes burden is in low- and middle-income countries (2). This epidemic requires a multifaceted response, including the delivery of medications and effective lifestyle counseling (3). In low- and middle-income countries, only 36% of people with diabetes receive medication to lower glucose and 19% receive lifestyle counseling (4).

This study investigates the implementation of a diabetes self-management education and support (DSMES) intervention in the public health system in rural Guatemala. Guatemala is the most populous country in the Central America region and has an estimated diabetes prevalence of 9% to 10% (5,6). Diabetes has strained the public health system, which serves more than 70% of the population (7), and has particularly affected rural Indigenous communities (8).

We previously conducted a pilot feasibility study of a culturally tailored, home-based DSMES intervention for Indigenous Maya people (9). DSMES interventions are recommended in Guatemalan primary care guidelines (10) and are effective in ethnic minority groups in high-income countries (11). The pilot intervention used tailored communication theory (12). We subsequently received funding to scale up the DSMES pilot into routine primary public health care centers. The objective of this study was to assess the effectiveness of the DSMES intervention and evaluate this implementation through mixed methods and the RE-AIM framework (13).

We prepared this article according to TREND (Transparent Reporting of Evaluations with Nonrandomized Designs) (14) and STARI (Standards for Reporting Implementation Studies) guidelines (15). Checklists are available elsewhere (Appendixes 1 and 2 [16]). This study was approved by the institutional review boards of Maya Health Alliance and the Institute of Nutrition of Central America and Panama.

Study design and setting

Our DSMES intervention used a pretest–posttest design and was implemented in rural Guatemala from November 2018 through December 2020. The study was conducted by Maya Health Alliance, the Inclusive Health Institute, and the Institute of Nutrition of Central America and Panama. This was a pragmatic study that focused on evaluating DSMES in real-world routine conditions; as a result, we did not perform a sample size calculation.

The study was conducted in 8 rural municipalities in a single province (Chimaltenango) in the Central Highlands region. We chose this province because it is where Maya Health Alliance’s main office is located. The population is predominantly Indigenous Maya (17), and most live below the national poverty line (18). Each municipality has a public health district operated by the Ministry of Health, as well as private and nongovernmental biomedical clinics and nonbiomedical traditional healers. In the public health sector, diabetes care is delivered at a physician-staffed health center. Free services at health centers include blood glucose monitoring and oral glucose-lowering drugs. Patients requiring laboratory assessments, insulin therapy, or specialist management are referred to regional referral hospitals. Delivery of DSMES is limited in this system (8).

Eligibility and recruitment

We used broad participant inclusion criteria: 1) being aged 18 years or older and 2) having a glycated hemoglobin (HbA 1c ) ≥6.5% or diagnosis of diabetes within the preceding 12 months. We excluded individuals who were pregnant or had type 1 diabetes.

Any health facility in included municipalities was eligible to refer patients. In each of the 8 municipalities, we approached all public health centers and selected public hospitals, private clinics, nongovernmental clinics, and pharmacies. At public health centers, study staff also actively recruited patients from diabetes peer-group meetings. Other recruitment activities included approaching known patients from Maya Health Alliance, word-of-mouth from enrolled participants, door-to-door visits, and public fliers.

Intervention

The intervention was based on our previous pilot and delivered by community health workers at the participants’ homes (9). The intervention was a public–private partnership whereby community health workers paid by Maya Health Alliance worked within the public health system. The curriculum was adapted for low-literacy Mayan-speaking populations and based on a Guatemalan version of the US National Heart, Lung, and Blood Institute’s Salud Para Su Corazón (Health for Your Heart) model for Latinx populations (19,20). Prior adaptions by our group to this curriculum included diabetes-specific content, home visits with family participation, minimal written text, and culturally relevant drawings, props, and games (9). The intervention consists of a screening visit, 6 monthly education visits, and a closing visit ( Box ). The curriculum focuses on the “4 pillars” of diabetes control: 1) regular medical appointments, 2) adherence to medications prescribed by health care providers, 3) regular physical activity, and 4) a healthy diet that reduces intake of carbohydrates. At each visit, study educators review achievements from prior visits, assess individualized milestones, and use motivational interviewing to guide participants on overcoming barriers to behavior change. We expected that the total intervention time per participant would be 8 months with each monthly visit lasting 1 hour. We did not provide any incentives to increase participation or adherence.

Box. Structure and Content of a Diabetes Self-Management Education and Support Intervention in Rural Guatemala, 2018–2020

Screening visit

Baseline data collected

“4 Pillars” of type 2 diabetes control: 1) medical visits, 2) medication adherence, 3) diet, and 4) exercise

Normal blood glucose levels

Diabetes symptoms

Diabetes complications

Causes of diabetes

Basic food groups

Carbohydrate portions

Activity (does this increase blood glucose?)

Activity (make a healthy plate)

Sugary drinks

Strategies to eat well at parties

Alcohol consumption

Blood pressure and salt consumption

Benefits of exercise

Types of physical activity

Activity (“a day in the life . . .”)

Importance of family support, diet, and exercise

Guatemalan beliefs about diabetes

Importance of medications

Importance of medical check

Activity (I can control my diabetes!)

Participant-led review activity

Individualized challenges and success (4 pillars of control)

Closing visit

End-point data collected

Full intervention materials (facilitator guide and patient visual materials) are available in Spanish at https://doi.org/10.7910/DVN/CUSI4E.

Impact of COVID-19 . We halted study enrollment in March 2020 when community-based transmission of COVID-19 was reported. Because of safety concerns and mandated travel restrictions, the study transitioned to telephone visits. Participants who had not finished before March 15, 2020, had end-point psychometric data but no end-point clinical data collected.

Data sources and data collection

We conducted an explanatory sequential mixed-methods evaluation (21) guided by the RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) Qualitative Evaluation for Systematic Translation (RE-AIM QuEST) mixed-methods framework (13). The project evaluation plan and data sources are available elsewhere (Appendixes 3–4 [16]).

Quantitative data were entered in real time using smartphones and data capture software (REDCap). Quantitative data included participant sociodemographic characteristics, clinical and psychometric outcomes, implementation metrics, and costs. All quantitative data were collected in participants’ homes by 1 trained research assistant. Qualitative data consisted of semistructured interviews with study participants (n = 12), intervention staff (n = 5), and staff members at health centers (n = 6). All intervention staff members and staff members from 6 of 8 health centers were interviewed; 2 health center staff members did not respond. We purposefully sampled interview participants with low and high effect size (change in HbA 1c ) and engagement (average visit time), including at least 1 participant of each sex in each group. The interview guides were designed to explore elements of the quantitative analysis, following dimensions of the RE-AIM framework tailored to each group (13). Example probes included perceptions of project utility, determinants of clinical benefit, impact of COVID-19 and transition to telephone visits, role of sex and other determinants of participation, and family involvement. The full interview guide is available elsewhere (Appendix 5 [16]). Interviews in Spanish lasted approximately 30 minutes. Interviews in Maya Kaqchikel used an interpreter and lasted approximately 1 hour. Interviews were recorded and then translated and transcribed in Spanish. All qualitative data were collected by 1 study author (A.A.), a trained anthropologist, and were conducted via telephone because of COVID-19.

Reach. Reach references the absolute number, proportion, and representativeness of study participants. Quantitatively, we examined lost-to-follow-up and compared the characteristics of study participants with the characteristics of participants with diabetes in a contemporaneous population-representative chronic disease survey conducted by the authors in 2018 and 2019 in one of the study’s municipalities (22). Qualitatively, we focused on barriers to enrollment, especially for men.

Effectiveness. Effectiveness references the effect of the intervention on study outcomes. Primary clinical outcomes were HbA 1c , systolic blood pressure, diastolic blood pressure, and body mass index (BMI). HbA 1c was assessed with a point-of-care device (A1CNow, PTS Diagnostics). Seated arterial blood pressure was assessed in triplicate after 15 minutes with an Omron 7 digital cuff and estimated as the mean of 3 measurements. Secondary psychometric outcomes were diabetes knowledge, diabetes distress, and self-management. Diabetes knowledge was measured by the Diabetes Knowledge Questionnaire (DKQ-24); scores range from 0 to 24, with higher scores indicating more knowledge (23). Diabetes distress was measured by the Diabetes Distress Scale (DDS); scores range from 1 to 6, with higher scores indicating more distress (24). Self-management was measured by selected questions from the Summary of Diabetes Self-Care Activities instrument (SDSCA) (25). We previously validated the DKQ-24 and SDSCA during our pilot (9). Qualitatively, we investigated the mechanisms influencing effectiveness and potential explanations of differences between participants.

Adoption. Adoption references the absolute number, proportion, and representativeness of staff, providers, and organizations. We calculated the proportion of participants who were enrolled by type of health facility (public facilities, private clinics, nongovernmental clinics, pharmacies). We also examined the proportion of participating versus invited facilities. Qualitatively, our interviews explored factors that affected facility participation.

Implementation. Implementation references how accurately and consistently the intervention was carried out, including adaptations and cost. We examined total time between screening and closing visits, between first and last education visits, average visit duration, and costs. We also calculated the proportion of the suggested curriculum that was completed during each visit. Qualitatively, we explored barriers to fidelity, strategies to overcome barriers, and intervention modifications.

Maintenance. Maintenance references the extent that the intervention and intervention outcomes are sustained after the study. In interviews we explored factors leading to high or low levels of engagement and intent to continue.

Data analysis

Quantitative. We used Stata version 16 (StataCorp LLC) for analyses. We compared participant baseline characteristics with the population-representative sample using the Student t test for continuous data and the proportion test for categorical data. Baseline participant characteristics of retained study participants and participants lost to follow-up were also compared using the Student t test and proportion test. For clinical outcomes, we constructed multilevel mixed-effects models for HbA 1c , blood pressure, BMI, the DKQ-24, the DDS, and the SDSCA. Models were prespecified to include random effects for study participant and fixed effects for age, sex, ethnicity, education level, time since diagnosis, difficulty paying for medications, and baseline value.

To investigate the impact of missing data due to COVID-19 and other causes, we conducted a sensitivity analysis using multiple imputation with chained equations and 100 imputations (26). We conducted a second sensitivity analysis of the impact of conducting the intervention virtually during the COVID-19 pandemic on psychometric outcomes.

Qualitative. We analyzed interviews using Dedoose (Sociocultural Research Consultants). We conducted a thematic framework analysis using an inductive approach. We first developed a codebook by analyzing 2 interviews from each group. Responses were then coded by 2 authors (S.T. and D.F.), and grouped by RE-AIM dimensions. Differences were resolved through consensus. The final coded interviews added less than 5% new information, indicating thematic saturation (27).

Mixed methods. We integrated our findings using a joint display of quantitative and qualitative findings and meta-inferences (21). Meta-inferences are interpretations that emerge from the integrated analysis of the quantitative and qualitative data. They were generated iteratively by discussion among the team (27).

Of 738 participants screened, 111 did not meet inclusion criteria. In total, 627 participants were enrolled ( Figure ). Of all enrolled participants, end-point data were not collected for 23.8% (149 of 627), most of whom had no working telephone number during the COVID-19 lockdown. Psychometric end-point data were collected for 74.6% (468 of 627). Clinical end-point data were collected for 40.0% (251 of 627) .

Quantitative. In the comparison of baseline characteristics of participants and the total diabetes population, important differences included the overrepresentation of women, greater preference for a Mayan language, lower levels of education, and higher baseline values for HbA 1c and blood pressure ( Table 1 ). Compared with participants who completed the study, participants lost to follow-up were more likely to speak a Mayan language, but otherwise we found no significant baseline sociodemographic or clinical differences, including for self-identified Maya ethnicity. A full comparison of baseline characteristics between participants lost to follow-up and those retained and additional health service data characteristics are available elsewhere (Appendixes 6–7 [16]).

Qualitative. In the investigation into reasons for the low levels of enrollment among men, a common theme was that men often leave for work early in the morning and return late, after clinics close. In addition, “ machismo ” (an exaggerated sense of masculinity) negatively affected men’s self-care and their willingness to participate. As one educator reported,

I think it is because of the lack of time men have. The men are the ones who go out to work; they have to go out to the fields to work which doesn’t give them time . . . and I think they are closed in, they don’t like to get checkups often . . . they are embarrassed to say that they have some illness.

Interviewees also reported that many men did not view health education to be of material benefit. One participant shared criticisms of the intervention that he had heard from another person with diabetes:

There is a man who also has diabetes that doesn’t agree with just talks. He said they are only bringing knowledge and lectures. They are not giving medicine or economic support. He said it’s better to invest his time working than talking, then he can buy his own medicine.

Effectiveness

Quantitative. In adjusted multilevel models, mean change in HbA 1c was −0.4% (95% CI, −0.6% to −0.3%; P < .001), mean change in systolic blood pressure was −5.0 mm Hg (95% CI, −6.4 to −3.7 mm Hg; P < .001), mean change in diastolic blood pressure was −2.6 mm Hg (95% CI, −3.4 to −1.9 mm Hg; P < .001), and mean change in BMI was 0.5 (95% CI, 0.3 to 0.6; P < .001) ( Table 2 ). Mean change in diabetes knowledge assessed using the DKQ-24 was 3.9 (95% CI, 3.6 to 4.1; P < .001) and mean change in diabetes distress using the DDS was −0.4 (95% CI, −0.4 to −0.3; P < .001). We also found significant improvements in most self-care activities.

The results from the sensitivity analysis investigating the impact of missing data through multiple imputation were similar to the results of the primary analysis; details are available elsewhere (Appendix 8 [16]). All missing data for this exercise were imputed, except for binary SDSCA outcomes because of multicollinearity. In the second sensitivity analysis, which assessed the impact of conducting the intervention virtually during the COVID-19 pandemic, improvements in knowledge, distress, and diet outcomes were similar before and during the pandemic; details are available elsewhere (Appendix 9 [16]). However, we did not observe improvements in physical activity outcomes during the pandemic.

Qualitative. The primary mechanism affecting effectiveness was the personalized nature of visits, which addressed participants’ specific needs while building trust. Themes mentioned included 1) the tailoring of educational content to each participant, 2) the supportiveness of educators, 3) the favorability of home-based visits, and 4) the patients’ ability to choose a preferred language. As 1 study educator explained,

If the patient preferred to speak in Kaqchikel, I would speak to them in Kaqchikel; if they wanted to speak in Spanish, then I would speak Spanish. I think it is very important that participants receive the education in their preferred language. This gives them more confidence . . . it is much better to have personalized education because the participant can express their doubts and not be embarrassed or worried about what their peers hear . . . in our program we go step by step, theme by theme, personalized to the participant.

Another educator commented on the role of family and community support:

When the family participated it had a great influence on the participant. When the family attended the education visits and understood it, they could support each other at home and throughout the following days. When there was family support, there was more positive changes in the participants.

Quantitative. Of the 612 participants, 386 (63.1%) were referred from health centers; 31.4% (192 of 612) from Maya Health Alliance programs; 4.2% (26 of 612) from private clinics; 1.0% (6 of 612) from the regional public hospital; and 0.3% (2 of 612) through door-to-door promotion.

All 44 health facilities approached agreed to participate. Of 10 public health facilities, 8 were health centers and 2 were hospitals. Seven of 8 health centers and 1 of 2 public hospitals referred patients. Of 24 private clinics, only 3 referred patients. None of the 8 pharmacies who agreed to participate referred participants.

Qualitative. Interviews highlighted partial adoption by participating health centers. Health center staff allowed recruitment of participants attending diabetes peer groups but were otherwise not active. One study educator acknowledged this lack of integration: “All we did was present the project to the directors to get approval. They gave us 10 to 15 minutes to present our project at the diabetes club meetings and that was it.”

One identified barrier that prevented adoption was the lack of a training program for health center staff. One study educator noted this could be improved: “In the future we could coordinate with the health centers to do trainings with the staff working in diabetes. We could train the staff and also the health center directors before the intervention so that they are more involved.”

Implementation

Quantitative. The median time between baseline and end-point data collection was 268 (interquartile range, 225–343) days. The median time between first and last education visits was 155 (interquartile range, 144–182) days. During the intervention (pre– and post–COVID-19), 83.7% (525 of 627) of participants completed all 6 education visits. The mean (SD) duration of home visits was 70.9 (15.4) minutes. The mean (SD) duration of telephone visits was 41.4 (13.2) minutes.

Direct intervention costs were US$90.19 per participant (Appendix 10 [16]). In comparison, government health expenditure per capita is US$94.49, and total current health expenditure per capita is US$259.62, with 57.5% of costs being out of pocket (28).

The median of suggested curriculum elements that were completed for all visits was 94.3% (interquartile range, 91.8%–96.5%).

Qualitative. The main intervention modifications were caused by the COVID-19 pandemic. Implementing the intervention was more difficult after the transition to virtual visits. One positive aspect was the ability to schedule visits during nonworking hours. One study educator summarized:

One challenge was that cellular reception was very bad . . . I had to call 3 to 4 times to finish a study visit. Also, the phone numbers we had were often not the participants’. . . . When we called, the participant would not be home, and it was uncomfortable for the family member or neighbor. This gave us less time for the visit. Phone visits had fewer questions than home visits because we could not show them pictures, which helped generate a lot of questions. . . . We did cover all the topics, but the patients were a little more closed.

Although home visits were preferred, participants were generally satisfied with the quality of telephone visits: “Phone visits are fine. It is not that I don’t like them, they were fine and logical, but if it is possible home visits are better.”

Maintenance

Qualitative. All interview participants desired to continue practicing what they had learned during the intervention. A common theme was that changes were difficult but became easier over time: “[T]he beginning was the most difficult because humans are used to eating what they want to. You have no diet, you eat everything. But later you start adapting to the diet and eventually you are used to it and it is easier.”

At the organizational level, all health center staff expressed support. The main barrier to continuing the intervention was lack of time: “I think that our availability, our time would be the biggest challenge. I don’t think that the intervention would be difficult for us to do, but the time we have is what would be difficult.”

Mixed methods

We summarized the quantitative and qualitative findings and meta-inferences globally because we found no significant differences between the purposefully sampled groups ( Table 3 ). First, DSMES interventions struggle to enroll men when they lack strategies that accommodate work schedules and address cultural barriers to self-care and education. Second, although all types of health facilities were eager to participate in identifying and referring participants, their day-to-day participation was limited. Better integration is critical in scaling up DSMES interventions in the public health system. Third, although the public health center system has interest in “personalized” DSMES, achieving sustainability requires addressing budgetary and time constraints. A key sustainability limitation of the public–private approach used here was that the public sector’s capacity to adequately support the cost of DSMES staff was unclear.

This study reports outcomes of a culturally tailored DSMES intervention scaled up within the public health system in rural Guatemala. The intervention led to substantial improvements in clinical and psychometric outcomes despite challenges posed by the COVID-19 pandemic.

A recent systematic review of DSMES interventions in low- and middle-income countries concluded that evidence is limited by study heterogeneity and that randomized controlled trials are needed (29). However, it is unlikely that randomized controlled trials will be completed in most settings where diabetes is a pressing concern, not just because of cost but also because of 1) the face validity of DSMES principles, and 2) their recognition as standard of care in high-income settings (9,30). Therefore, analyses of nonexperimental interventions using detailed implementation assessment frameworks can provide data to assist policy makers.

Our study is the largest observational study on type 2 diabetes in Guatemala, and several findings are worth highlighting. First, access to medical care and medications at baseline was higher than previously reported during the last decade (Table 2, Appendix 7 [16]), likely a result of efforts by the Ministry of Health’s Chronic Disease Commission to strengthen chronic disease care in rural centers (9). On the other hand, baseline self-care indicators for diet and exercise were low, suggesting that education has not kept pace. A recent pooled analysis emphasizes that lifestyle education for diabetes is a major unmet need globally (30).

Our analysis of barriers and facilitators to implementation highlights several important challenges to scaling DSMES in low-resource settings. First, although men commonly are underreached by these initiatives (30), limited data on disease prevalence has made precise assessments difficult. We found that women were overrepresented by nearly 40 percentage points. Second, participant interviews highlighted that the high degree of personalization of the intervention was essential for their engagement. This personalization likely fosters effectiveness by building a trusted supportive relationship. This is an important point for future scalability, as health center staff members felt that time and budgetary constraints would make precisely this degree of personalization infeasible. Solving these staffing constraints on tailored interactions with patients is critical to scaling lifestyle interventions in low-resource settings (12). In particular, it is likely that dedicated DSMES providers — analogous to certified diabetes educators — are essential to effective relationship building, and future work will need to explore this as an alternative to the more typical “generalist” frontline worker model common in Guatemala and the region.

Our study has several limitations and strengths. First, our study focuses on DSMES in one geographic area of Guatemala, although this is balanced by strengths such as the pragmatic design, the large sample size, and the comparison to a representative population sample permitting assessment of reach. Second, we used a pretest–posttest study design, which limits our ability to report causality, although we did adjust the analysis for important prespecified covariates. Third, the intervention was limited to a duration of 6 months. Planned 12-month assessments were abandoned because of the COVID-19 pandemic. Fourth, because of COVID-19, we modified our intervention and evaluation plan, resulting in missing outcome data. We addressed these issues through sensitivity analyses, including multiple imputation (Appendixes 8–9 [16]). The findings from these sensitivity analyses support our primary conclusions.

We found that in a rural population of individuals with type 2 diabetes in Guatemala, a community health worker–led DSMES intervention within the public health system led to improvements in HbA 1c , blood pressure, diabetes knowledge, disease-related stress, and diet and physical activity. Our mixed-methods implementation research shows that scaling up DSMES in low-resource health systems requires careful consideration of implementation barriers and facilitators. Long-term staffing and cost of the intervention are also important concerns.

Scott Tschida and David Flood contributed equally to this publication. Funding for this study was provided by the World Diabetes Foundation (WDF 14-909). This study was approved by the institutional review boards of Maya Health Alliance and the Institute of Nutrition of Central America and Panama. No copyrighted materials were used in this article.

Corresponding Author: Scott Tschida, Wuqu’ Kawoq, 2da Avenida 3-48 Zona 3, Barrio Patacabaj, Tecpán, Chimaltenango, Guatemala. Telephone: 502-7840-3112. Email: [email protected] .

Author Affiliations: 1 Center for Research in Indigenous Health, Wuqu’ Kawoq, Tecpán, Chimaltenango, Guatemala. 2 Department of Internal Medicine, National Clinician Scholars Program, University of Michigan, Ann Arbor, Michigan. 3 Instituto de Salud Incluyente, San Lucas Sacatepéquez, Sacatepéquez, Guatemala. 4 Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colorado. 5 Department of Family Medicine, University of Michigan, Ann Arbor, Michigan. 6 Centro de Investigación para la Prevención de las Enfermedades Crónicas, Instituto de Nutrición de Centro América y Panamá, Guatemala City, Guatemala. 7 Department of Global Health and Social Medicine, Harvard Medical School, Boston, Massachusetts. 8 Division of Global Health Equity, Brigham and Women’s Hospital, Boston, Massachusetts.

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Abbreviations: IQR, interquartile range; NA, not available. a Individuals with diabetes from a unique population-based survey conducted in the study area during 2018 and 2019 (22). b Student t test for continuous data and proportion test for categorical data. c Education was treated as a continuous variable in our regression models but is presented in categories here, because only categorical data on education were available in the population survey.

Abbreviation: IQR, interquartile range. a All models were hierarchical mixed-effect models that included a random-intercepts effect for study participant. Adjusted models included fixed effects for the intervention time, age, sex, ethnicity, education level, time since diagnosis, difficulty paying for medications, and baseline value. Primary and secondary outcomes were linear regression models. Days per week in self-care activities were assessed in ordinal regression models, and yes/no questions in logistic regression models, where 0 = no and 1 = yes. b Determined by linear mixed-effects model. c Scores range from 0 to 24, with higher scores indicating more knowledge (23). d Scores range from 1 to 6, with higher scores indicating more distress (24). e Self-management was measured by selected questions from the Summary of Diabetes Self-Care Activities instrument (25). f Values are marginal effect (95% CI).

Abbreviations: DSMES, diabetes self-management education and support; HbA 1c , glycated hemoglobin A 1c ; RE-AIM, reach, effectiveness, adoption, implementation, and maintenance.

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