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Peer-reviewed

Research Article

The Effectiveness of Mobile-Health Technologies to Improve Health Care Service Delivery Processes: A Systematic Review and Meta-Analysis

* E-mail: [email protected]

Affiliation Clinical Trials Unit, London School of Hygiene & Tropical Medicine, London, United Kingdom

Affiliation Department of Health Services Research and Policy, London School of Hygiene & Tropical Medicine, London, United Kingdom

Affiliation Department of Population Health, London School of Hygiene & Tropical Medicine, London, United Kingdom

Affiliation Warwick University, Coventry, United Kingdom

Affiliation Department of Primary Care and Public Health, Imperial College, London, United Kingdom

• Caroline Free, 
• Gemma Phillips, 
• Louise Watson, 
• Leandro Galli, 
• Lambert Felix, 
• Phil Edwards, 
• Vikram Patel, 
• Andy Haines

• Published: January 15, 2013
• https://doi.org/10.1371/journal.pmed.1001363
• Reader Comments

Mobile health interventions could have beneficial effects on health care delivery processes. We aimed to conduct a systematic review of controlled trials of mobile technology interventions to improve health care delivery processes.

Methods and Findings

We searched for all controlled trials of mobile technology based health interventions using MEDLINE, EMBASE, PsycINFO, Global Health, Web of Science, Cochrane Library, UK NHS HTA (Jan 1990–Sept 2010). Two authors independently extracted data on allocation concealment, allocation sequence, blinding, completeness of follow-up, and measures of effect. We calculated effect estimates and we used random effects meta-analysis to give pooled estimates.

We identified 42 trials. None of the trials had low risk of bias. Seven trials of health care provider support reported 25 outcomes regarding appropriate disease management, of which 11 showed statistically significant benefits. One trial reported a statistically significant improvement in nurse/surgeon communication using mobile phones. Two trials reported statistically significant reductions in correct diagnoses using mobile technology photos compared to gold standard. The pooled effect on appointment attendance using text message (short message service or SMS) reminders versus no reminder was increased, with a relative risk (RR) of 1.06 (95% CI 1.05–1.07, I 2  = 6%). The pooled effects on the number of cancelled appointments was not significantly increased RR 1.08 (95% CI 0.89–1.30). There was no difference in attendance using SMS reminders versus other reminders (RR 0.98, 95% CI 0.94–1.02, respectively). To address the limitation of the older search, we also reviewed more recent literature.

Conclusions

The results for health care provider support interventions on diagnosis and management outcomes are generally consistent with modest benefits. Trials using mobile technology-based photos reported reductions in correct diagnoses when compared to the gold standard. SMS appointment reminders have modest benefits and may be appropriate for implementation. High quality trials measuring clinical outcomes are needed.

Please see later in the article for the Editors' Summary

Editors’ Summary

Over the past few decades, computing and communication technologies have changed dramatically. Bulky, slow computers have been replaced by portable devices that can complete increasingly complex tasks in less and less time. Similarly, landlines have been replaced by mobile phones and other mobile communication technologies that can connect people anytime and anywhere, and that can transmit text messages (short message service; SMS), photographs, and data at the touch of a button. These advances have led to the development of mobile-health (mHealth)—the use of mobile computing and communication technologies in health care and public health. mHealth has many applications. It can be used to facilitate data collection and to encourage health-care consumers to adopt healthy lifestyles or to self-manage chronic conditions. It can also be used to improve health-care service delivery processes by targeting health-care providers or communication between these providers and their patients. So, for example, mobile technologies can be used to provide clinical management support in settings where there are no specialist clinicians, and they can be used to send patients test results and timely reminders of appointments.

Why Was This Study Done?

Many experts believe that mHealth interventions could greatly improve health-care delivery processes, particularly in resource-poor settings. The results of several controlled trials (studies that compare the outcomes of people who do or do not receive an intervention) of mHealth interventions designed to improve health-care delivery processes have been published. However, these data have not been comprehensively reviewed, and the effectiveness of this type of mHealth intervention has not been quantified. Here, the researchers rectify this situation by undertaking a systematic review and meta-analysis of controlled trials of mobile technology-based interventions designed to improve health-care service delivery processes. A systematic review is a study that uses predefined criteria to identify all the research on a given topic; a meta-analysis is a statistical approach that is used to pool the results of several independent studies.

What Did the Researchers Do and Find?

The researchers identified 42 controlled trials that investigated mobile technology-based interventions designed to improve health-care service delivery processes. None of the trials were of high quality—many had methodological problems likely to affect the accuracy of their findings—and nearly all were undertaken in high-income countries. Thirty-two of the trials tested interventions directed at health-care providers. Of these trials, seven investigated interventions providing health-care provider education, 18 investigated interventions supporting clinical diagnosis and treatment, and seven investigated interventions to facilitate communication between health-care providers. Several of the trials reported that the tested intervention led to statistically significant improvements (improvements unlikely to have happened by chance) in outcomes related to disease management. However, two trials that used mobile phones to transmit photos to off-site clinicians for diagnosis reported significant reductions in correct diagnoses compared to diagnosis by an on-site specialist. Ten of the 42 trials investigated interventions targeting communication between health-care providers and patients. Eight of these trials investigated SMS-based appointment reminders. Meta-analyses of the results of these trials indicated that using SMS appointment reminders significantly but modestly increased patient attendance compared to no reminders. However, SMS reminders were no more effective than postal or phone call reminders, and texting reminders to patients who persistently missed appointments did not significantly change the number of cancelled appointments.

What Do These Findings Mean?

These findings indicate that some mHealth interventions designed to improve health-care service delivery processes are modestly effective, but they also highlight the need for more trials of these interventions. Specifically, these findings show that although some interventions designed to provide support for health-care providers modestly improved some aspects of clinical diagnosis and management, other interventions had deleterious effects—most notably, the use of mobile technology–based photos for diagnosis. In terms of mHealth interventions targeting communication between health-care providers and patients, the finding that SMS appointment reminders have modest benefits suggests that implementation of this intervention should be considered, at least in high-income settings. However, the researchers stress that more trials are needed to robustly establish the ability of mobile technology-based interventions to improve health-care delivery processes. These trials need to be of high quality, they should be undertaken in resource-limited settings as well as in high-income countries, and, ideally, they should consider interventions that combine mHealth and conventional approaches.

Additional Information

Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001363 .

• A related PLOS Medicine Research Article by Free et al. investigates the effectiveness of mHealth technology-based health behavior change and disease management interventions for health-care consumers
• Wikipedia has a page on mHealth (note: Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
• mHealth: New horizons for health through mobile technologies is a global survey of mHealth prepared by the World Health Organization’s Global Observatory for eHealth (eHealth is health-care practice supported by electronic processes and communication)
• The mHealth in Low-Resource Settings website, which is maintained by the Netherlands Royal Tropical Institute, provides information on the current use, potential, and limitations of mHealth in low-resource settings
• The US National Institutes of Health Fogarty International Center provides links to resources and information about mHealth

Citation: Free C, Phillips G, Watson L, Galli L, Felix L, Edwards P, et al. (2013) The Effectiveness of Mobile-Health Technologies to Improve Health Care Service Delivery Processes: A Systematic Review and Meta-Analysis. PLoS Med 10(1): e1001363. https://doi.org/10.1371/journal.pmed.1001363

Academic Editor: Tony Cornford, London School of Economics, United Kingdom

Received: March 5, 2012; Accepted: November 16, 2012; Published: January 15, 2013

Copyright: © 2013 Free 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.

Funding: We gratefully acknowledge funding from the UK Department of Health, Global Health Division. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: VP is a member of the Editorial Board of PLOS Medicine . The authors have declared that no other competing interests exist.

Abbreviations: ECG, electrocardiogram; m-Health, mobile-health; MMS, multimedia message; PDA, personal digital assistant; RR, relative risk; SMS, short message service

Introduction

Mobile health, the use of mobile computing and communication technologies in health care and public health, is a rapidly expanding area within e-health. There is considerable enthusiasm for mobile-health interventions and it has been argued that there is huge potential for mobile-health interventions to have beneficial effects on health and health service delivery processes, especially in resource-poor settings [1] .

Mobile technologies include mobile phones; personal digital assistants (PDA) and PDA phones (e.g., BlackBerry, Palm Pilot); Smartphones (e.g., iphone); enterprise digital assistants (EDA); portable media players (i.e., MP3-players and MP4-players, e.g., ipod); handheld video-game consoles (e.g., Playstation Portable (PSP), Nintendo DS); and handheld and ultra-portable computers such as tablet PCs (e.g., ipad and Smartbooks).

These devices have a range of functions from mobile cellular communication using text messages (SMS), photos and video (MMS), telephone, and World Wide Web access, to multimedia playback and software application support. Technological advances and improved computer processing power mean that single mobile devices such as smart phones and PDA phones are increasingly capable of high level performance in many or all of these functions.

Mobile health interventions designed to improve health care service delivery processes have been used to provide support and services to health care providers (such as education, support in diagnosis or patient management) or target communication between health care services and consumers (such as appointment reminders and test result notification).

The features of mobile technologies that may make them particularly appropriate for improving health care service delivery processes relate to their popularity, their mobility, and their technological capabilities. The popularity of mobile technologies has led to high and increasing ownership of mobile technologies, which means interventions can be delivered to large numbers of people. In 2009, more than two-thirds of the world's population owned a mobile phone and 4.2 trillion text messages were sent [2] . In many high-income countries, the number of mobile phone subscriptions outstrips the population [3] . In low-income countries, mobile communication technology is the fastest growing sector of the communications industry and geographical coverage is high [4] – [7] .

The mobility and popularity of mobile technologies means that many people carry their mobile phone with them wherever they go. This allows temporal synchronisation of the intervention delivery and allows the intervention to claim people's attention when it is most relevant. For example, health care consumers can be sent appointment reminders that arrive the day before and/or morning of their appointment. Real-time (synchronous) communication also allows interventions to be accessed or delivered within the relevant context, i.e., the intervention can be delivered and accessed at any time and wherever it is needed. For example. at the time health care providers see a patient, they can access a management support system providing information and protocols for management decisions to whomever requires them. This application could be particularly relevant for providing clinical management support in settings where there is no senior or specialist health care provider support or where there is no such support at night or at weekends. As mobile technologies can be transported wherever one goes, interventions are convenient and easy to access.

The technological capabilities of mobile technologies are continuing to advance at a high pace. Current technological capabilities allow low cost interventions. There are potential economies of scale as it is technically easy to deliver interventions to large populations (for example, mobile technology applications can easily be downloaded and automated systems can deliver text messages to large numbers of people at low cost). The technological features that have been used for health interventions include text messages (SMS), software applications, and multiple media (SMS, photos) interventions. The technology allows interventions to be personalised and interactive.

In this rapidly changing field, existing systematic reviews of mobile-health (M-health) interventions to improve health care service processes require updating [8] . Existing reviews have focussed on specific topics. A review of randomised controlled trials of text message reminders for appointments found small benefits and a review of the effect of test notification by text message found insufficient evidence to determine if there were benefits [9] , [10] . Rapid advances in technology mean that it is now less relevant to conduct reviews focussing on specific devices (e.g., PDAs or hand-held computers [11] ). Specific devices become outdated but their functions (e.g., application software) are now available on newer devices (e.g., SMART phones).

A current overview of the evidence for all mobile technology interventions evaluated in controlled trials to improve health care processes is lacking.

This systematic review aimed to quantify the effectiveness of mobile technology based interventions delivered to health care providers or to support health care services, on any health or health care service outcome.

We adhered to our published plan of investigation as outlined in the study protocol [12] .

Participants were men and women of any age. We included all controlled trials using any mobile technology interventions (mobile phones; PDAs and PDA phones [e.g., BlackBerry, Palm Pilot]; Smartphones [e.g., the iphone]; enterprise digital assistants [EDA]; portable media players [i.e., MP3-players and MP4-players, e.g., ipod]; handheld video-game consoles [e.g., Playstation Portable (PSP), Nintendo DS]; and handheld and ultra-portable computers such as tablet PCs [e.g., the ipad] and Smartbooks) to improve or promote health or health service use or quality. Trials were included regardless of publication status or language.

We only included studies in which the mobile electronic device is the stated intervention under evaluation, i.e., we excluded studies evaluating mixed mobile electronic device and non-mobile electronic device interventions such as an intervention involving face-to-face educational sessions with a software application educational intervention compared to a control group receiving paper-based information only. We excluded general videos, unless authors stated they were specifically designed to be viewed on mobile technologies. Internet interventions that were not specifically designed for mobile technologies were outside the scope of this review.

The interventions in trials meeting the inclusion criteria and aiming to improve health care delivery process are reported herein. Other trials identified are reported elsewhere [13] . No trial was excluded from the review based on the type of health or health care service targeted, but trials not directed at health care service delivery were included in one of two papers reported elsewhere, one covering behaviour change interventions and self-management of diseases for health consumers and the second, data collection [13] . Trials involving appointment reminders are included in this paper but those involving broader behavioural support are reported elsewhere [13] .

The trials with interventions aiming to improve health care delivery processes were then categorised into two groups: those directed to health care providers or those involving communication between health care services and health care consumers (e.g., appointment reminders, test result notification). Interventions for health care providers were then subcategorized according to their purpose: education, diagnosis and management, and communication between health care providers. Interventions involving health care service communication to consumers were subcategorized according to their purpose: appointment reminders and test result notification. Primary outcomes were defined as any objective measure of health, health service delivery, or use. Secondary outcomes were defined as self-reported outcomes related to health behaviours, disease management, health service delivery or use, and cognitive outcomes. Outcomes reported for any length of follow-up were included.

We searched the following electronic bibliographic databases MEDLINE, EMBASE, PsycINFO, Global Health, The Cochrane Library (Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials [CENTRAL], Cochrane Methodology Register), NHS Health Technology Assessment Database, and Web of Science (science and social science citation index) from 1990 to Sept 2010 and the reference lists of included trials. The list of subheadings (MeSH) and textwords used in the search strategy can be found in Text S1 . All of these terms were combined with the Cochrane Library MEDLINE filter for controlled trials of interventions.

Two reviewers independently scanned the electronic records to identify potentially eligible trials.

Two reviewers independently extracted data on number of randomised participants, intervention, intervention components, sequence generation, allocation concealment, blinding of outcome assessors, completeness of follow-up, evidence of selective outcome reporting, and any other potential sources of bias on measures of effect using a standardised data extraction form. The authors were not blinded to authorship, journal of publication, or the trial results. All discrepancies were agreed upon by discussion with a third reviewer. Risk of bias was assessed according to the criteria outlined by the International Cochrane Collaboration [14] . We assessed blinding of outcome assessors and data analysts and we used a cut off of 90% complete follow-up for low risk of bias for completeness of follow-up. We contacted study authors for additional information about the included studies, or for clarification of the study methods as required.

All analyses were conducted in STATA v 11. We calculated risk ratios and standard mean differences. We used random effects meta-analysis to give pooled estimates. We examined heterogeneity visually by examining the forest plots and statistically using both the χ 2 test and the I 2 statistic. We assessed evidence of publication bias using Funnel plots.

The combined search strategies identified 26,221 electronic records; these were screened for eligibility, and the full texts of 334 potentially eligible reports were obtained for further assessment ( Figure 1 ). Out of the 334 potentially eligible reports, 42 met the study inclusion criteria and were directed at improving health care service delivery. There were 32 trials of interventions designed to support health care providers and ten trials of interventions targeting communication between health services and health care consumers.

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PRISMA 2009 flow diagram.

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Characteristics of Studies

Heath care provider support..

The 32 trials included 5,323 participants. Samples ranged from 14 to 1,874 participants. There were 15 randomised controlled trials with parallel groups, six randomised cross over trials, three cluster randomised controlled trials, and eight non-randomised controlled trials. Seven were trials of health care provider education [15] – [21] ( Table 1 ), 18 were trials of interventions supporting clinical diagnosis and management [22] – [39] (guidelines, protocols, decision support systems; Table 2 ), and seven were trials of interventions designed to facilitate verbal or data communication between health care providers for clinical/patient management [40] – [46] ( Table 3 ). Of these one used mobile technologies for verbal communication and seven communicated images. All the trials were conducted in high-income countries.

https://doi.org/10.1371/journal.pmed.1001363.t001

https://doi.org/10.1371/journal.pmed.1001363.t002

https://doi.org/10.1371/journal.pmed.1001363.t003

Communication between health care services and health care consumers.

The ten trials recruited 4,473 participants with sample sizes ranging from 31 to 1,859 participants. Seven were randomised controlled trials with parallel groups and three were non-randomised parallel group trials. Of the ten trials of health services support, eight were trials of short messaging service (SMS, text message)-based appointment reminders [47] – [54] ( Table 4 ) and two were trials of SMS-based patient notification of results [55] , [56] ( Table 5 ). Four appointment reminder trials were conducted in high-income countries and three were conducted in middle-income countries. Both the trials of patient notification of test results were conducted in high-income countries.

https://doi.org/10.1371/journal.pmed.1001363.t004

https://doi.org/10.1371/journal.pmed.1001363.t005

Interventions

The interventions are described according to the authors' descriptions in Tables 1 – 5 . Below we describe the interventions according to the functions employed (SMS messaging, photos, video, application software, telephone, and multimedia messages [MMS]) and devices employed (e.g., PDA, mobile phone, hand-held computer, and portable media player).

Health care provider support.

For the medical education interventions, six used application software delivered via personal digital assistants [15] – [20] . One trial employed a MP4/video technology using a portable media player [21] .

For interventions supporting clinical diagnosis and management, 14 trials used customised application software (12 on personal digital assistants, one on a tablet PC, one on a handheld computer). Four trials used photographs and video capabilities using mobile phones.

For interventions using mobile technologies to communicate between health care providers for clinical/patient management, three trials [40] , [43] , [46] relied on the use of MMS for sending images by mobile phone, and one trial used the telephone function of the mobile phone [44] . One trial used MMS on a PDA. One trial made use of MP4/video technology and the other made use of installed customised software, using hand-held computers.

Communication between health services and consumers.

All the interventions used SMS messages delivered by mobile phone. One appointment reminder trial also used voice messages. Details of the control groups are provided in Tables 1 – 5 . In the medical education trials, the control groups were medical education delivered via a range of standard traditional media. For the diagnosis and clinical management trials and health, verbal, and data communication trials, the control groups were standard care or standard methods. In six appointment reminder trials, the control group was no reminder; in two reminder trials, the comparison group was a telephone reminder; and in one the comparison group was a letter.

The trials reported between one and 15 outcomes. Nineteen of the 28 trials provided sufficient data to calculate effect estimates.

For primary outcomes, there were no objective measures of health or health service delivery reported.

In terms of secondary outcomes, for medical education interventions, one trial reported two outcomes regarding documentation of health care problems [19] and four trials reported nine knowledge outcomes [16] , [18] , [19] , [21] . For clinical diagnosis and management interventions, seven trials [28] , [30] , [33] – [37] reported 25 outcomes relating to appropriate management (3 outcomes), testing (3) [28] , referrals (1) [28] , screening (4) [33] , diagnosis (2) [34] , [35] , treatment (2) [35] , [36] , and triage (10) [37] . Six trials [26] , [34] , [36] , [38] , [39] reported 17 medical process outcomes: perceived difficulty in performing a task (1 outcome) [39] , use of tool (1) [36] , errors in report (2), errors in score calculation (2) [34] , completeness of reports (2) [38] , time to complete a report (2) [38] , time to record vital signs (1) [34] , time to diagnosis (3) [26] , and time to treatment (3) [26] . For interventions using mobile technologies to communicate between health care providers for clinical/patient management outcomes, six trials [40] , [42] – [44] , [46] , [57] reported 19 outcomes relating to the quality of nurse surgeon communication (6 outcomes) [44] , correct clinical assessment or diagnosis (4) [40] , [43] , [46] , test score (1) [42] and electrocardiogram (ECG) transmission (8) [57] , feasibility of delivery (1), time taken (4), and quality (3) [57] ).

The trials reported between one and three outcomes. Nine of the ten trials provided sufficient data to calculate effect estimates.

For primary outcomes, eight trials reported appointment attendance as an outcome [47] – [54] and two trials reported cancelled appointments as an outcome [47] .

In terms of secondary outcomes, for patient notification of test results, outcomes were the following: time to diagnosis (1 outcome), time from first contact to treatment (1) and time from test to treatment (1), and anxiety scores (2) [55] , [56] .

Study Quality

The assessment of s tudy quality is reported in Table 6 and the Cochrane risk of bias summary is reported in Figure 2 .

Cochrane summary risk of bias for trials of health care provider support interventions (n = 32).

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https://doi.org/10.1371/journal.pmed.1001363.t006

The assessment of s tudy quality is reported in Table 7 and the Cochrane risk of bias summary is reported in Figure 3 .

Cochrane summary risk of bias for trials of health services support (n = 10).

https://doi.org/10.1371/journal.pmed.1001363.g003

https://doi.org/10.1371/journal.pmed.1001363.t007

None of the trials were at low risk of bias for all quality criteria. There was no evidence of publication bias on visual and statistical examination of funnel plots.

We report the effect estimates for primary outcomes and a summary of the effect estimates for secondary outcomes (see Tables 8 – 12 for the secondary outcome effect estimates).

https://doi.org/10.1371/journal.pmed.1001363.t008

https://doi.org/10.1371/journal.pmed.1001363.t009

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Health care provider support

No studies reported our primary outcomes.

The following secondary outcomes were reported.

Medical education interventions: Of the nine knowledge outcomes reported, eight showed no statistically significant effects and one showed a statistically significant increase in knowledge ( Table 8 ). There were no statistically significant effects on the two reported outcomes regarding documentation.

Clinical diagnosis and management support interventions: Seven trials [28] , [30] , [33] – [37] using application software to deliver support reported 25 outcomes relating to appropriate management, testing, referrals screening, diagnosis, treatment, and triage; of these, 19 outcomes showed benefits of which 11 were statistically significant ( Table 9 ). The other six outcomes showed no clinically important or statistically significant direction of effect ( Table 9 ). For medical process measures (time for procedures, completeness of or errors in data/reports, perceived difficulty of procedures, diagnostic confidence) five trials [26] , [34] , [36] , [38] , [39] reported 17 outcomes; of these, five showed statistically significant benefits ( Table 10 ). Six outcomes showed negative effects in increasing time for processes or errors in data, of which three were statistically significant. One outcome had no clear direction of effect.

Interventions to facilitate verbal or data communication between health care providers: The effect estimates are provided in Table 6 . One trial [44] using a mobile phone to facilitate communication between nurses and surgeons reported six outcomes; one showed statistically significant benefit. Two trials [40] , [43] using photos transmitted via mobile phones reported three outcomes showing negative effects of the interventions, with statistically significant reductions in fracture detection when compared to standard radiographic pictures, reductions in correct assessment of potential to perform re-implantation, and correct recognition of skin ecchymoses when compared to a gold standard assessment by a specialist evaluating ecchymoses in person. One trial [42] reported a nonsignificant reduction in the ability of doctors to interpret endoscopy videos when viewed on a hand-held computer compared to a standard monitor. One trial [57] compared an ECG transmitted via mobile phone to an ECG transmitted by fax and reported statistically significant reductions for one of three outcomes regarding ECG quality. The authors report there were no effects of this difference in quality on ECG interpretation but do not provided data on this. Of four reported outcomes regarding the time taken to transmit the ECG, none were statistically significant.

Primary outcomes were reported in eight trials [47] – [54] that evaluated the effect of attendance reminders using SMS reminders versus no reminder and showed a statistically significant increase in attendance (pooled relative risk [RR] 1.06 [95% CI 1.05–1.07], I 2 squared 86%). The pooled effect for trials evaluating the effect of attendance reminder using text message against reminders that used other modes, such as postal reminder and phone calls, showed no significant change (RR 0.98; 95% CI 0.94–1.02, I 2  = 1.2%). Two trials [47] , [50] that evaluated the effects on cancellations of texting appointment reminders to patients who persistently fail to attend appointments showed no statistically significant change (pooled RR of 1.08; 95% CI 0.89–1.30, I 2  = 0%) ( Figure 4 ). Another trial [47] reported the effects on appointment cancellation of mobile phone reminders compared to postal mail (RR 2.67; 95% CI 0.92–7.71) and phone call reminders (RR 2.31; 95% CI 0.91–5.95) ( Table 11 ); both showed increases that were not statistically significant. One trial [52] evaluated the effect of appointment reminder by mobile phone call compared with a control group that received no reminder and showed a statistically significant increase in attendance (RR 1.24; 95% CI 1.07–1.43) ( Table 11 ).

Forest plots of the effect of SMS reminders on appointments.

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Secondary outcomes were as follows: One trial [56] reported statistically significant reductions in mean time to communicating the diagnosis to the patient and the mean time from test to treatment, but no effects on mean time from first contact to treatment ( Table 12 ).

Key Findings

We identified 42 controlled trials that investigated mobile technology-based interventions designed to improve health care service delivery processes. None of the trials were of high quality and nearly all were undertaken in high-income countries. Thirty-two of the trials tested interventions directed at health care providers. Of these trials, seven investigated interventions providing health care provider education, 18 investigated interventions supporting clinical diagnosis and treatment, and seven investigated interventions to facilitate communication between health care providers. None of the trials reported any objective clinical outcome, and the reported results for health care provider support interventions are mixed. There may be modest benefits in outcomes regarding correct clinical diagnosis and management delivered via application software, but there were mixed results for medical process outcomes regarding the time taken and completeness of or errors in reports or warning scores. For educational interventions for health care providers, there was no clear evidence of benefit. For interventions aiming to enhance communication between health care providers, one trial showed benefits in using the telephone functions of a mobile phone to enhance verbal communication between surgeons and nurses. Two trials showed reductions in the quality of clinical assessment using mobile technology based photos when compared to a gold standard and one trial reported a reduction in quality of ECG print outs delivered via mobile phones.

For the category of communication between health services and consumers, SMS reminders have modest benefits in increasing clinic attendance and appear similar in their effects to other forms of reminder. There is no evidence that SMS reminders influence appointment cancellations, but the 95% CIs for the pooled effect were wide. One trial [56] reported mixed results relating to time to treatment using SMS to notify patients of their test results.

Strengths and Limitations of the Review

To our knowledge, this is the first comprehensive systematic review of trials of all mobile technology interventions delivered to health care providers and for health services support to improve health or health services. The review expands and updates the findings of earlier systematic reviews that focussed on specific devices, and/or specific functions, and/or specific health topics [8] , [11] , [58] . We identified more than twice the number of trials of educational interventions and trials of PDA applications identified in previous reviews [11] , [58] . Our review findings are consistent with those of Krishna et al. and Car that text messages can reduce missed appointments [8] , [59] .

Our systematic review was broad in its scope. We only pooled outcomes where the intervention function (e.g., SMS messages), trial aim, and outcomes used in trials were the same. Here, findings in relation to clinical diagnosis and management and educational interventions are summarised, the individual trial results are reported in Tables 1 – 12 . It was not appropriate to pool these results as the interventions targeted different diseases and outcomes. Further, there are likely to be important differences in the intervention content of these interventions (such as the behaviour change techniques used), even in those using the same mobile technology functions (such as application software). It was not possible to explore how different intervention components influenced outcomes as the intervention components were not described consistently or in detail in the authors' papers. It was not possible to explore how the intervention components targeting the disease and outcomes influenced the results.

It was beyond the scope of our review to review internet or video-based interventions not specifically designed for mobile technologies. We also excluded interventions combining mobile technologies with other interventions such as face-to-face counselling, which should be subject to a separate systematic review. Thirteen trials (31%) did not provide sufficient data to calculate effect estimates and authors did not respond to requests for data, which could have resulted in bias in the systematic review findings. Factors influencing heterogeneity of effect estimates include low trial quality, in particular inadequate allocation concealment [60] , participant factors such as demographics or disease status, the setting (hospital, primary care), the intervention features (components, intensity, timing), the type of mobile technology device (e.g., PDA or mobile phone) or function (e.g., SMS, application software), and the nature of the control group (e.g., standard care in a high-income country or in a low-income country). We were unable to statistically explore factors influencing heterogeneity because there were few trials of similar interventions reporting the same outcomes, resulting in limited power for such analyses. It was not possible to statistically explore the mechanism of action of the interventions because there were too few similar interventions reporting the same outcomes. In addition, authors' descriptions of interventions were insufficiently detailed to allow mechanisms of action to be explored. It was outside the scope of this review to explore the cost-effectiveness of interventions with modest benefits such as appointment reminders.

At the request of the editors we re-ran our search on 1 November 2012 to any identify other trials eligible for this review published since our last search, and we identified eight trials. One high quality trial demonstrated that text message reminders increased Kenyan health workers' adherence to malaria treatment guidelines with improvements in artemether-lumefantrine management of 23.7 percentage points (95% CI 7.6–40.0) and immediate intervention of 24.5 percentage points (95% CI 8.1–41.0) and 6 mo [61] . Three trials reported statistically significant increases in clinic attendance with text message reminders (OR 1.61 [95% CI 1.03–2.51], respectively) [62] – [64] . These findings are similar to those reported in trials already included in the review [47] – [54] . One trial reported statistically significantly increased attendance with voice reminders compared to text message reminders [65] . One trial showed no effect on HIV viral load of a mobile phone-based AIDS care support intervention for community-based peer health workers [66] . One trial reported better performance in a cardiac arrest simulation for health care providers allocated to receiving a mobile phone application regarding advanced life support [67] . One trial reported more errors in interpreting ECGs delivered by MMS compared to paper print-outs [68] .

Meaning of the Study, Mechanisms of Action, Implications for Health Care Providers, or Policy

Trials of heath care provider support show some promising results for clinical management, appropriate testing, referral, screening, diagnosis, treatment, and triage. However, trials included in our review were subject to high or unclear risk of bias. In particular, only one of the 17 trials clearly reported that allocation was concealed and where there is no allocation concealment, the reported results may be an over-estimate of effects. To date no trials have reported effects of mobile technology-based clinical diagnosis and management support on objective health outcomes. Most of the trials supporting health care providers in clinical diagnosis and management employed PDA devices and customised application software functions. While PDA devices are no longer widely used, customised application software functions are now deliverable on smart phones or tablets. Mobile technology-based interventions may not be suitable for some clinical processes.

The data available for making clinical diagnoses or calculating early warning scores may be reduced and the time taken for medical processes may be increased. There was no clear evidence of benefit of mobile technology-based educational interventions for health care professionals. For interventions using mobile technologies to communicate visual data, there were increases in time to diagnosis or ECG transmission or diagnostic errors. Two trials using photos taken by mobile phone reduced diagnostic accuracy of fractures, skin ecchymoses, and potential to perform re-implantation when compared to a gold standard. However, the use of such technologies may be more relevant for settings where the gold standard is not available. Furthermore, the quality of photos on mobile phones has improved since these studies were completed.

Mobile technology-based diagnosis and management support may be most relevant to health care providers in developing countries where mobile phones potentially allow clinical support and evidence-based guidance to be delivered to health care professionals working remotely and in circumstances where senior health care professional support or other infrastructure is lacking [69] . One high quality trial has reported increased adherence to malaria treatment guidelines by health care workers in Kenya [61] ; however, the evidence from controlled trials to date is mostly from high-income countries where the control group “standard care” may be very different to “standard care” available in low- or middle-income countries.

SMS messages are modestly effective as appointment reminders. Their effects appear similar to other forms of reminder. Health care providers should consider implementing SMS appointment reminders because the cost of missed appointments in health services is high, the cost of providing SMS appointment reminders is low, and SMS reminders are cheaper than other forms of reminder (e.g., a letter with stamp).

Unanswered Questions and Future Research

High quality trials should be conducted to establish the effects of clinical diagnosis and management support (such as protocols/decision support systems) on clinical outcomes using customised application software functions on mobile phones. The effects of such support on the management of different diseases and on objective disease outcomes should be evaluated. It is imperative that future trials of clinical decision support, guidance, and protocol delivered via mobile technologies take place in low- and middle-income countries. Many of the interventions evaluated to date are single component interventions of low intensity. The effects of higher intensity multi-component mobile technology interventions should be evaluated. Authors must describe the components of future interventions in detail so that mechanisms of action and the impact of different components on outcome can be explored.

Trials should evaluate the effects of the use of photographic or video functions to support health care providers compared to standard care (where gold standard options are not available). As the technological capabilities of mobile phones improve, such as in photographic quality, further trials of the effects of using photos taken on mobile technologies on diagnostic accuracy may be a warranted. Further research should evaluate the effects and cost-effectiveness of mobile technologies to increase the speed of communication between clinicians and patients, such as test results.

Interventions combining elements delivered by mobile technology with other treatments such as clinics based counselling combined with text messages should be systematically reviewed.

The reported effects of health care provider support interventions are mixed. Trials report modest benefits for clinical diagnosis and management support outcomes. For interventions for health services, SMS reminders have modest benefits on attendance. Service providers should consider implementing SMS appointment reminders. One high quality trial published since our literature search was completed shows benefits in adherence to malaria treatment guidelines [61] . In other areas, high quality trials are needed to robustly establish the effects of optimised mobile health care provider interventions on clinically important outcomes in the long term.

Supporting Information

Checklist s1..

PRISMA checklist.

https://doi.org/10.1371/journal.pmed.1001363.s001

Excluded studies.

https://doi.org/10.1371/journal.pmed.1001363.s002

Search strategy.

https://doi.org/10.1371/journal.pmed.1001363.s003

Systematic review protocol.

https://doi.org/10.1371/journal.pmed.1001363.s004

Author Contributions

Conceived and designed the experiments: CF AH PE GP VP. Performed the experiments: GP LW LG LF CF PE. Analyzed the data: LW. Contributed reagents/materials/analysis tools: GP. Wrote the first draft of the manuscript: CF. Contributed to the writing of the manuscript: CF AH PE GP LW LG LF VP. ICMJE criteria for authorship read and met: CF AH PE GP LW LG LF VP. Agree with manuscript results and conclusions: CF AH PE GP LW LG LF VP.

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• Published: 14 May 2021

Mobile Health: making the leap to research and clinics

• Joy P. Ku   ORCID: orcid.org/0000-0003-4785-6044 1 &
• Ida Sim   ORCID: orcid.org/0000-0002-1045-8459 2  

npj Digital Medicine volume  4 , Article number:  83 ( 2021 ) Cite this article

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Health applications for mobile and wearable devices continue to experience tremendous growth both in the commercial and research sectors, but their impact on healthcare has yet to be fully realized. This commentary introduces three articles in a special issue that provides guidance on how to successfully address translational barriers to bringing mobile health technologies into clinical research and care. We also discuss how the cross-organizational sharing of data, software, and other digital resources can lower such barriers and accelerate progress across mobile health.

Introduction

Mobile devices have been a disruptive technology in many industries, but their impact on healthcare has yet to be fully realized. This is not due to a lack of interest. There are ~85,000 health apps 1 , 2 available for download, and over $8 billion was invested in “digital health” in 2018 3 . Novel miniaturized sensors are being developed to continuously detect biomarkers (e.g., from sweat 4 , tear fluid 5 ) that have traditionally been measured within a clinic. These developments are creating new possibilities for a vision of medicine that is more data-driven and personalized (e.g., ref. 6 ). In this article, we will refer to the use of such sensors and apps to collect personalized data for health in a ubiquitous manner as mobile health (mHealth).

As has been noted elsewhere 7 , 8 , data collection is only the first step in developing mHealth solutions that improve health outcomes. Clinicians and other stakeholders need to be convinced of the benefits of mHealth, and to-date it has been challenging to draw clear conclusions about the efficacy of these solutions, given the conflicting outcomes and heterogeneity in the implementation of mHealth interventions. This holds true whether assessing the impact of mHealth on hospital admission rates among patients with heart failure, adherence to prescribed rehabilitation exercises or lifestyle changes, or health outcomes like weight and blood pressure 9 , 10 . Myriad other factors, such as integration into the clinical or research study workflow, cost of implementation, usability of the device, and adequacy of privacy protections, also affect the likelihood of a solution transitioning from the prototype stage to routine use within research and clinics. Coming out of a multi-disciplinary workshop called mHealth Connect, three articles in this issue explicate some of these factors and provide guidance on how to successfully address translational barriers for different use cases. Specifically, the articles describe considerations when (1) selecting a suitable wearable sensor for a given application; (2) analyzing observational health behavior data generated by mHealth apps and devices; and (3) integrating these technologies into the clinical environment.

Their recommendations demonstrate the critical role data has in this new paradigm, so in addition to introducing the three articles, this paper calls for cross-organizational sharing of digital resources to accelerate progress within mHealth. Drawing on examples from other biomedical domains, we describe the positive impacts of sharing for three different types of resources and identify early efforts to encourage this behavior within mHealth. Thus, the insights offered through this and the other three articles in this issue can catalyze diverse activities to bring mHealth capabilities into clinical research and care.

mHealth Connect workshop

Despite the growing body of literature on consumer-oriented mHealth devices, there is a paucity of strong evidence for their benefit 9 . Few applications have made the leap from prototype to routine use for research or clinical purposes. mHealth Connect ( http://mobilize.stanford.edu/mhealthconnect/ ) was a workshop that brought together key stakeholder leaders across industry, clinical systems, and academia to collaboratively identify and overcome barriers to this translation. The workshop was launched in 2016 by two of the National Institutes of Health’s (NIH) Big Data to Knowledge (BD2K) Centers of Excellence–the Mobilize Center 11 and the Mobile-Sensor-to-Knowledge Center (MD2K) 12 —in response to concerns voiced by many BD2K researchers that many commercial mobile devices and apps on the market are poorly validated, without compelling clinical use cases, and are opaque and restrictive about data sharing. mHealth Connect enabled discussions around these and other critical issues to take place with a balance of stakeholders at the table and seeded collaborations to advance the field. The three mHealth papers in this issue arise from those discussions and the needs identified during them.

Scope of mHealth covered

While mHealth comprises a broad range of topics, as an outgrowth of two NIH Big Data to Knowledge Centers, mHealth Connect’s focus is on accelerating the use of data collected from mobile and wireless devices, such as wearable sensors, in clinical research and care. Because of the personal ubiquitous nature of mobile devices, the greatest new opportunity is in using mHealth to directly measure and improve patient health and health states outside the traditional confines of the hospital and clinic. The scope for this and the accompanying three papers thus excludes the following topics: (1) sensors and devices designed exclusively for the hospital or clinic setting and are intended solely to inform clinical decision-making (e.g., a Holter monitor, which would be excluded, versus AliveCor’s KardiaMobile device, which would be included), (2) strictly educational apps that are one-way channels for fixed media, (3) electronic health records (EHR) apps, and (4) apps for navigating the healthcare system (e.g., finding doctors, scheduling appointments) rather than for managing health or disease.

What these three papers do focus on are mobile apps and sensors used by patients in their daily lives to manage their health, with or without co-management by clinical team members or friends and family. These include devices measuring novel biomarkers, as well as consumer versions of traditional clinical equipment, such as blood pressure cuffs and spirometers, which an individual can use to collect measurements whenever and wherever they desire independent of clinical indications. The devices may be integrated into a clinical healthcare workflow, but they are not designed exclusively or primarily for that environment. The emphasis is on the availability of dynamic personalized data captured either passively or through active self-report, and the consequent value of this data for informing patient and clinician action to improve health and manage acute or chronic disease.

Guidelines for developing and deploying mHealth solutions

Recent years have seen a rise in resources providing guidelines to evaluate mHealth solutions, including from the U.S. Food and Drug Administration (FDA) 13 , 14 , 15 , 16 . Evaluation criteria assess a broad range of factors, including adherence to privacy laws, data security, interoperability with existing infrastructure and workflows, cost, usability, and validity of the content or intervention. Nascent efforts, such as Express Scripts’ planned digital health formulary, a list of approved digital health technologies to guide consumers and payers, are emerging to reinforce these guidelines 17 . While efforts to increase rigor in the evaluation of mHealth solutions are still taking shape, many questions remain on best practices and frameworks for mHealth development upstream of final regulatory or formulary approval.

The Clinical Trials Transformation Initiative (CTTI) provides one of the more comprehensive sets of guidelines for developing a mobile device-based solution, including the development of novel endpoints from mobile device data and the design of protocols that use mobile devices for data capture 18 . CTTI’s guidelines are intended for the relatively controlled conditions and limited durations of clinical trials, and therefore, necessarily exclude considerations for broad-scale clinical deployment. Nonetheless, they provide a useful path for individuals launching mHealth endeavors in general. Below we introduce a collection of articles based on our series of mHealth Connect meetings that augment existing guidelines provided by CTTI and others 18 , 19 , 20 .

Device selection for wellness, healthcare, and research applications

Regardless of the application, defining the target use case is critical for success. This definition is a fundamental tenet of many mHealth guidelines 18 , 19 , 20 , and it requires a process of user-centered design incorporating clinical, engineering, behavioral science, ethical, and disparities considerations (e.g., language, numeracy, literacy, and disabilities). All mHealth projects, even noncommercial ones, should have a clear business case detailing how continued use of the solution will be financially and logistically sustainable. The paper by Caulfield, et al. presents a framework for optimizing the match between sensors and classes of use cases, for refining the use case requirements, and then evaluating available devices against those requirements 21 .

Analysis of digital biomarkers for predictive models and unique insights

Digital biomarkers are clinically meaningful measures derived from mobile and wearable devices that correlate with or predict disease states. They can be analogues of traditional clinical quantities, such as heart rate, or novel indicators of health states. The full impact of mHealth comes from simulation or predictive models that combine digital biomarkers potentially with other data sources. An example is the cStress model, which blends real-time data streams on heart rate, heart rate variability, and interbeat interval data to derive a probability of stress in a given 1-min time window 22 . Developed using MD2K’s Cerebral Cortex, a cloud infrastructure for big data analysis of high-volume high-frequency data streams 12 , cStress utilized a prospective approach and actively recruited participants to collect data for its development.

Data analysis and model building can also be done retrospectively on observational datasets to gain insights that are challenging to obtain through traditional studies. In some cases, these datasets contain upwards of hundreds of thousands of individuals, enabling analysis about health and behavior on an unprecedented scale 23 , 24 . While such datasets can be a windfall, they present their own set of unique challenges for obtaining reliable results. The paper by Hicks, et al. presents a set of best practices for analyzing these large-scale, observational digital biomarker datasets from commercial personal technologies 25 .

Deploying mHealth solutions within clinical care

The necessity of a well-defined use case and business case becomes especially evident when it comes to the adoption and scaling of a mHealth solution. Is the mobile technology to be used by people with or without their clinicians? Is the intent to deploy locally in one care setting or to scale to global use? Particularly where clinician use is envisioned, integration into the clinical workflow is a prerequisite for adoption. To help guide expansion of mHealth technology into clinical care delivery, the paper by Smuck, et al. presents common factors driving successful utilization of wearables in the clinical care environment, as shown by two examples 26 .

Resource sharing to accelerate mHealth adoption

These papers aim to increase the likelihood of mHealth projects to achieve their aims, whether that is integrating mHealth technologies into the clinical workflow or developing a model to accurately predict health outcomes from mHealth data. The recommendations are intended to advance the work of individual groups, but they also point to opportunities for collective efforts that would advance activities across the entire community. In particular, we highlight the impact of sharing digital resources. Echoing Hicks, et al., we encourage “sharing models, software, datasets, and other digital resources whenever possible” 25 . Below we describe three categories of shared resources that can accelerate mHealth’s leap to research and the clinics: raw and processed data from devices; software and models used to analyze and interpret data; and evaluation results. We call attention to the positive impact the sharing of such resources has had in other biomedical domains and highlight initial efforts to bring these practices to mHealth.

The benefits of sharing experimental data, software, and models are well-delineated: enhanced transparency, the ability to more rapidly and easily extend existing efforts, and decreased duplication of efforts 27 , 28 . Large biomedical datasets that have been established specifically as research resources, such as the UK Biobank and the Osteoarthritis Initiative, have demonstrated the value of sharing, having supported hundreds of published research studies 29 , 30 . Smaller datasets from independent research labs can also positively impact a field. Previously shared data have served as benchmarks for comparing algorithms and aided in the validation of new models 31 , 32 , 33 . And pooling these datasets, such as in the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) project, has increased the statistical power of analyses and led to discoveries that would not have been possible from a single dataset alone 34 .

Such capabilities are critical for advancing mHealth. Having readily accessible datasets, both large and small, will facilitate the development of new digital biomarkers or robust mHealth-based predictive models, such as those described by Hicks, et al. We are starting to see some organized efforts to promote data sharing among the mHealth community. The International Children’s Accelerometry Database (ICAD) has created a pooled dataset, similar to ENIGMA 35 . Vivli, a platform for sharing data from clinical trials including trials with digital biomarkers, was launched in 2018 36 , and SimTK, a repository for the biocomputation and movement communities, recently added support for the sharing of mobile and other experimental data 37 .

Although invaluable, shared data alone will not propel mHealth applications into routine research and clinical use. Software methods and models are needed to glean insights from the data and thus, it is just as important that they also be shared, ideally with an open-source license to encourage modification and reuse for new applications. The programming language Python is a testament to the power of open-source with over 100,000 community-developed extensions 38 that make it a popular tool within bioinformatics and scientific computing. In biomechanics, researchers are sharing software for analyzing movement data within the open-source OpenSim simulation platform, extending the community’s ability to derive new insights 39 . While some mHealth software is being shared 40 , 41 , large, active communities have yet to develop around them. Initiatives, such as Open mHealth 42 as well as Shimmer and Nextbridge Exchange’s industry-based open-source effort to share analysis tools for wearable sensor data 43 , may help change the culture.

Similar initiatives would be useful throughout the mHealth development process, including the sharing of evaluation results, for example when evaluating devices during study design, as described by Caulfield, et al. 21 , and also when developing a reimbursement model to implement wearable technology into patient care, as mentioned by Smuck, et al. 26 . If individuals made their evaluation results available for others to leverage, we could appreciably streamline these processes. The CTTI Feasibility Studies Database 44 is a step towards this. The database compiles a list of devices, along with relevant evaluation criteria such as outcome measures and sample size, from publications examining the feasibility of mHealth in clinical trials. In a similar vein, the Digital Medicine Society provides a crowdsourced library of digital endpoints being used in industry-sponsored studies 45 . While there are some concerns about resource sharing—for example, potential misuse of shared resources and privacy breaches—technological and policy solutions can be implemented to mitigate them 46 , 47 . Compiling mHealth knowledge, data, and methods with such safeguards will accelerate the widespread adoption of mHealth for research and clinical care, and we urge individuals to contribute to such efforts.

It has been 13 years since the first iPhone was released, and 11 years since the first FitBit. In the intervening years, smartphone adoption has skyrocketed, fitness bands and smartwatches are commonplace, and “mobile health” NIH grants have grown from tens per year to over 610 in 2019. It has been said that digital health is now at “the end of the beginning” 48 . The mHealth Connect events have highlighted ways to go beyond the beginning: develop cross-disciplinary collaborations, pay attention to purpose, and consider factors beyond the technology itself. The papers in this series are intended as a guide for mHealth’s journey ahead and highlight ways in which we can collectively accelerate our progress along the path to clinical research and care.

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This work was supported by U54EB020404 and U54EB020405.

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J.K. led the drafting, and J.K. and I.S. contributed equally to the conception, writing, and review of this paper.

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Mobile learning: research context, methodologies and future works towards middle-aged adults – a systematic literature review

• Track 5: Multimedia and Education
• Published: 20 August 2022

Cite this article

• Syahida Mohtar   ORCID: orcid.org/0000-0002-4462-8890 1 ,
• Nazean Jomhari 1 ,
• Mumtaz Begum Mustafa 1 &
• Zulkifli Mohd Yusoff 2  

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Over the past several years, mobile learning concepts have changed the way people perceived on mobile devices and technology in the learning environment. In earlier days, mobile devices were used mainly for communication purposes. Later, with many new advanced features of mobile devices, they have opened the opportunity for individuals to use them as mediated technology in learning. The traditional way of teaching and learning has shifted into a new learning dimension, where an individual can execute learning and teaching everywhere and anytime. Mobile learning has encouraged lifelong learning, in which everyone can have the opportunity to use mobile learning applications to gain knowledge. However, many of the previous studies on mobile learning have focused on the young and older adults, and less intention on middle-aged adults. In this research, it is targeted for the middle-aged adults which are described as those who are between the ages of 40 to 60. Middle-aged adults typically lead very active lives while at the same time are also very engaged in self-development programs aimed at enhancing their spiritual, emotional, and physical well-being. In this paper, we investigate the methodology used by researchers based on the research context namely, acceptance, adoption, effectiveness, impact, intention of use, readiness, and usability of mobile learning. The research context was coded to the identified methodologies found in the literature. This will help one to understand how mobile learning can be effectively implemented for middle-aged adults in future work. A systematic review was performed using EBSCO Discovery Service, Science Direct, Google Scholar, Scopus, IEEE and ACM databases to identify articles related to mobile learning adoption. A total of 65 journal articles were selected from the years 2016 to 2021 based on Kitchenham systematic review methodology. The result shows there is a need to strengthen research in the field of mobile learning with middle-aged adults.

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1 Introduction

Adulthood can be categorized into early, middle and late adulthood. Middle-aged adults come between the ages of 40 to 60, in other words is when one is in between the younger and older generations [ 42 , 62 ]. This stage of age, notably is the age period that Hall [ 31 ] referred to as aging, where the signs of cognitive and physical ageing start to be noticeable, from the age of 40 and rapidly increase after the age of 65 [ 6 , 54 ]. According to Yaffe and Stewart [ 94 ], a large part of adult life is made up of the mid-life period. This has been associated with many descriptive terms: mid-life syndrome, mid-life crisis, middlescence, empty nest syndrome, second adolescence, second honeymoon, age of fulfillment, and menopause. Aging population contributes to healthcare issues, not only amongst the older adults but towards middle-aged adults too. As mentioned earlier, the healthcare issues amongst the middle-aged adults are related to the decline in physical abilities, relational, and psychological capacities. For example, women in their middle age experience menopause and perceived personality change, which lead to severe depression, physical, and emotional problems [ 80 ]. According to Yaffe and Stewart [ 94 ], the most frequently identified events or concerns among middle-aged adults were: increased personal concern for health, death of a friend or relative, change in wage/salary, and concern for change in physical appearance.

When middle-aged adults enter their 60s, their reaction time starts to slow down further, and they experience a significant declination in their performance. The brain may also no longer function at its optimal level, leading to problems like memory loss, dementia, and may have issues with other cognitive functions such as language, attention, and visuospatial abilities [ 35 , 61 ]. It has been widely assumed that the midlife period is a critical period, thinking about death and mortality, as well as experiencing decline in physical abilities, relational, and psychological capacities [ 80 ]. Therefore, early prevention should therefore be looked upon at the middle age stage to help with memory impairment, as well as emotional control.

Middle-aged adults typically lead very active lives while also engaging in self-development programs aimed at enhancing their spiritual, emotional, and physical well-being. Muslim adult, for instance, will prefer to go to the mosque, surau, or Islamic center to seek for Islamic education [ 42 ] to enrich their knowledge and gain serenity through the command of Islam. This indicates that an individual in the middle-aged is inclined to reflect and improve the quality of one’s daily practice. Unfortunately, during the Covid-19 pandemic outbreak, many lectures at the mosques and other institutions could not be held, resulting in many people having to work from the home. As a result, many have taken the initiative to hold religious lectures online through video conferences such as via Zoom, WebEx, Jitsi Meet, Google Meet applications, and many more [ 1 ]. There are also those who watch religious lectures that have been prerecorded on certain channels, such as YouTube or podcasts. However, the enthusiasm and motivation for online and prerecorded learning is not the same and less encouraging as compared to face-to-face lectures.

Health management apps have shown to be useful for treating a variety of illnesses such as chronic illnesses caused by obesity, high blood pressure, diabetes, and so on [ 32 ]. As middle-aged adults are smartphone and tablet active users, they can use these portable devices to track their healthy lifestyle habits, maintain social communication, prevent accidents, and seek information [ 91 ]. In addition to chronic illness management using mobile applications, there is also a concern on how middle-aged adults can utilize mobile technology in fulfilling their spiritual journey towards a quality lifestyle. For example, they can learn how to acquire a literal understanding of the Quran through a spiritual mobile application. This will help a Muslim to elevate their understanding, motivation, and devotion towards Islam, which eventually leads them to become a better person emotionally and psychologically. All of these exhibit many important experiences associated with middle-age adults, most involving work and family, and self-development [ 53 ].

Mobile devices such as smartphones have gained popularity because they allow people to stay in touch and provide easy access to information anywhere and anytime [ 89 ]. Therefore, investigating the acceptance and adoption of mobile learning by the middle-aged adults through a systematic literature is important in highlighting the gap for any future work.

This review paper presents the fundamentals of mobile learning and the utilization of mobile technology in the learning environments. Mobile learning theories are also highlighted to show the significance of mobile learning towards middle-aged adults. Based on the research context found in the selected literature, the researchers here provide a systematic mapping of the employed methodologies in the area of mobile learning research. The purpose of the systematic mapping is to determine the most appropriate methodology for future research on middle-aged adults in areas of mobile learning.

2 Mobile learning

M-learning is a subset of ‘e-learning’ while ‘e-learning’ is the subset of distance learning that focuses on learning across context and learning with mobile devices, which can take place anytime, anywhere [ 43 , 62 ]. For example, learning may happen at the workplace, at home, and at places of leisure. The learning may be related to work demands, self-improvement, or leisure; and it is mobile with respect to time where it happens at different times during the day, on working days, or on weekends [ 68 ].

According to Ozdamli and Cavus [ 70 ], learners, teacher, environment, content, and assessment are the basic elements of mobile learning. The core characteristics of mobile learning are ubiquitous, portable size of mobile tools, blended, private, interactive, collaborative, and instant information. They enable learners to be in the right place at the right time, that is, to be where they can experience the authentic joy of learning.

Since learning can be performed anywhere and anytime using electronic devices, Traxler [ 85 ] defines that mobile learning is a learning process that is delivered through the support of mobile devices such as personal digital assistants, smartphones, wireless laptops, and tablets. This understanding is supported by Keegan [ 45 ] who suggested that m-learning should be restricted to learning on small and portable devices as mobile devices that could be carried everywhere.

According to Nordin, et al. [ 69 ], the requirements for mobile learning environment include technology, that is, (1) highly portable (to support learning whenever and wherever), (2) individual(the design should be able to support individual learning, cater for individual learning styles and be adaptable to learners’ abilities), (3) unobtrusive(where learners should be able to retrieve knowledge without the technology becoming a deterrent), (4) available(enabling communication with friends, experts and/or teachers), (5) adaptable(the context of learning should be adaptable to situations and the individual’s skills and knowledge development), (6) persistent( able to manage the learner’s learning despite the changes in the technology itself), (7) useful(useful to learners for everyday chores), and (8) user-friendly(easy for people to use and must not create technophobia among new users).

3 Mobile technology

Today, it is fortunate that mobile technology’s on-demand capability puts learning back into the learner’s hands by allowing users to take the initiative in diagnosing their learning needs, formulating learning goals, identifying human, and material resources for learning, choosing and implementing appropriate learning strategies, and evaluating those learning outcomes [ 50 ].

Mobile technology covers a wide range of mobile devices such as portable electronic devices used to perform a wide variety of communication, business, productivity, and lifestyle tasks such as parenting [ 26 , 66 ]. It is also connected through a cellular communication network or a wireless connection. The common mobile technologies that allow these tasks are cellular phones, PDAs, handheld computers, tablets, laptops, and wearable devices. A standard mobile technology device, such as a cellular phone, may have one or more features such as a GPS, a web browser, an instant messenger system, an audio recorder, an audio player, a video recorder, and gaming systems [ 4 ].

In the area of healthcare, numerous studies have been conducted on the use of mobile devices with wearable devices [ 21 , 39 , 87 ] to monitor the health of the elderly and individuals with disabilities. By using mobile apps, the health of the elderly and young adults can also be tracked [ 12 , 20 , 40 ] and diagnosed using mobile game-based screening tools [ 34 ], especially when facing challenges and stressful time during the Covid-19 pandemic [ 79 ] , or during post-college life transition [ 27 ]. Not all older people are proficient in using mobile devices. Therefore, there are researchers who make studies related to how older and young adults (university students) manages their mobile device security and privacy settings of their mobile devices in the context of social interaction and motivation [ 64 , 67 , 90 ].

Besides the usage of mobile devices in the healthcare area, the growth of mobile devices is significant and impactful in the education area such as in teaching history using 3D [ 57 ] and safety education [ 13 ], personal learning and workplace learning [ 29 ]. The use of mobile devices such as smartphones and tablets has become truly ubiquitous and has a potential for improving student learning, which can happen in collaborative, authentic settings, i.e., real life contexts and use active learning approaches [ 18 ]. As smartphones have become popular devices among youth nowadays [ 36 , 65 ], these devices can be utilized and embraced in the classroom teaching environment. By having a smartphone with wi-fi connectivity, Bluetooth, camera, color display, audio/video recording capability, it is already suitable for a person to adopt m-learning [ 36 ]. Majority of students spend most of their time (6 to 24 hours) on the Internet using their smartphones [ 8 ].

Smartphones also have become essential communication tools for older adults to stay connected with their family and peers [ 93 ]. Compared with younger adults, older adults tend to be more likely to use mobile phones for their original design purpose—that is, making calls for instrumental reasons such as arranging plans and other instrumental activities rather than playing games, surfing the internet, or using auxiliary applications [ 91 ]. The intervention of mobile technology in older adults’ lifestyles can improve their well-being and keep their mind and body active as well as prevent or slow down cognitive decline. For instance, mobile games can be used to capture cognitive learning outcomes and the process of knowledge acquisition [ 92 ]. Through activities such as interacting with easy games [ 71 ], taking and managing photographs, sending messages via SMS, video or audio calls, and reading newspapers via webpages may help cognitive and noncognitive stimulation of older adults.

Mobile computing devices become more situated, personal, collaborative, and lifelong and these innovations will become embedded, ubiquitous, and equipped with enhanced features for rich social interaction, contextual awareness, and access to the Internet. Hence, extending learning outside the classroom and into the learner's environment, mobile learning can have a significant impact on middle-aged adults. However, based on the research context in the areas of mobile learning, existing studies have concentrated exclusively on aspects of the mobile device use, such as accessibility, usability, and adoption, among young and older adults, while middle-aged adults have received less attention. Thus, the use of mobile devices among middle-aged adults should be further investigated to determine how mobile devices can assist them in acquiring knowledge and developing themselves while leading hectic lifestyles and having to deal with the Covid-19 pandemic, towards their long-life wellbeing.

4 Multimedia in Mobile learning application

Using mobile device as a learning tool is a new way for learners to learn as they like, anywhere and anytime. Moreover, an application that contains multimedia elements such as text, animation, graphic and video will engage and attract the attention of the student. Mobile learning application used in mobile learning environments varies, such as Learning Management System (LMS), Short Messaging services (SMS), Podcasting, Social Networking, Instant Messaging, Blogging, Facebook, Microblogging, Wiki, QR, 3D and Augmented Reality [ 81 ].

SMS and videos have long been used as language learning tool through the use of mobile phones and personal digital assistants (PDAs) [ 68 ], and today, many have benefits from using WhatsApp, flashcards and mind maps, on-line videos, and social networks in learning. Recently, Duolingo is said to be a popular application for new language learning where learners can interact with intelligent chatbots that give corrective feedback and awards at the same time [ 49 ].

In the fast-aging population countries like China, senior users have become a significant new growth point that cannot be ignored in social network sites to keep continuous competitiveness. In China, WeChat is the most popular social software for senior citizens. This is due to the good user experience and operability, where some senior users manage to operate the application although they have no computer skills or they know little about the network [ 11 ].

On the other hand, instant messaging apps such as WhatsApp and Line have become a popular mobile app amongst students. In a classroom environment, the student may use these apps to interact with teachers outside the class and using smartphones to manage their group assignments. The use of instant messaging applications promotes collaborative learning [ 7 ] and flexible learning, improves student participation, increase communication and interaction between lecturers and students, as well as improve the performance of teaching and learning [ 10 ].

Text editors such as the Mobile MS office, content management systems such as Learning Management Systems (LMSs), and audio-video recording of lectures did not get much attention by the students in terms of its usage via the smartphone. The reason for the low usage of these functions and features could be due to the limited screens space, which makes it difficult to read large documents, and the small sized keypad makes data entry cumbersome [ 36 ]. To make mobile learning more interesting, game-based elements have been used to improve the students’ engagement and enjoyment in learning. For instance, Kahoot is a game-based technological platform that can be accessed from, for instance, smart devices or a laptop. The game-based learning application (app) can benefit working adults who are adult learners with diverse learning abilities. Chunking method was used to break down complex concepts into smaller parts in the form of multiple-choice questions. The students’ learning process is tested and corrected, in real time, through the statistics which are generated from this chunking process. Kahoot creates a safe environment for students to make mistakes through multiple choice questions, and yet relearn it without being judged by their peers. However, the drawback of Kahoot is, it does not adequately support the learning experience of adult learners [ 74 ].

To achieve a successful ageing life, positive spirituality indeed has a close relationship with physical and mental abilities. There have been studies that develop an Empathic-Virtual Coach (VC) to involve senior users in enjoying a healthy lifestyle with respect to diet, physical activity, and social interactions, while in turn supporting their carers [ 41 ]. Furthermore, in addition to physical support, adults also require emotional and spiritual help for a balanced lifestyle. For example, Sevkli, et al. [ 75 ] in their study had designed and developed mobile Hadith Learning Systems (HLS) that were able to encourage and promote hadith learning for young and middle-aged Muslims. Hence, mobile apps appear to be one of the tools that can be used to promote a balanced well-being lifestyle for the older people such as their social status, independence in their everyday activities, health status, standard of living, or leisure activities of the aging population.

5 Mobile learning theory

According to Lee, et al. [ 56 ], there is an increasing number of adult learners entering or returning to university. Despite the growing number of nontraditional adult students in online higher education, little is known about the dynamic processes of adult distance learning, through which adult students struggle to develop their learning ability to balance their life and study, and to become self-regulated learners, and ultimately as competent selves and lifelong learners. The implementations of mobile learning are supported and guided by theories such as Behaviorism, Cognitivism, Constructivism, Situated Learning, Problem-Based Learning, Context Awareness Learning, Socio-Cultural Theory, Collaborative Learning, Conversational Learning, Lifelong Learning, Informal Learning as well as Activity Theory, Connectivism, Navigationism, Location-based learning [ 46 , 68 ]. The classification of activities around the main theories and areas of learning relevant to learning with mobile technologies are shown in Table 1 .

Lifelong learning happens not only in learning institutions such as community colleges or higher learning institutions, but can also happen anytime and anywhere according to the needs of the individual [ 69 ]. Informal and lifelong learning are often referred to adult education or continuing education, which means a learning process that occurs as blended learning with everyday life unobtrusively and seamlessly [ 73 ]. The unique characteristic of lifelong learning is the fact that it is centered on the learner. Because of that, the use of technology in offering a flexible learning framework is often favored by adult learners [ 69 ]. In addition, when compared to conventional methods such as textbooks, mobile learning tools, especially learning through mobile apps, are intrinsically inspiring, provide greater satisfaction, increase student well-being, and have positive implications for long-term student persistence [ 78 ].

Lifelong adult learners are different from young learners (school or university students) who may devote significant amounts of time to study each day, as their learning time is scattered due to family responsibilities, work obligations, and other social obligations [ 44 ]. However, the keys to unlocking the secrets to successful adult learning online are embedded in the basic principles that guide adult learners. The subsequent six principles upon which Knowles [ 51 ] constructed his formal and andragogical concept are shown in Table 2 .

6 Methodology

This study carried out an extensive literature review to identify the research gap, focusing on the related literature published within the period of 2016 to 2021. The aim of this systematic review is to investigate the trend of previous research on the acceptance and adoption of mobile learning by middle-aged adults. In order to justify the research gap based on the previous studies, this article will also provide views on the existing mobile learning usage targeted at solving user’s adoption of mobile learning towards young and older adults.

To conduct the systematic review, the researchers followed the procedure defined by Kitchenham [ 48 ], which is one of the most complete and suitable methods for reviewing studies in computer science. We carried out this review in three main phases: 1) planning of systematic mapping; 2) conducting the review; and 3) reporting the review. The phases of this systematic review and the related activities are shown in Fig. 1 .

Phases of conducting this systematic review

Planning of the Systematic Mapping

Activities involved in this stage were aimed to identify the objectives of the review. These activities are as follows:

Discovering the gap of the existing systematic reviews

In this step, a comprehensive search was performed in the cyberspace to locate the related review studies in mobile learning. Some of the bibliographic databases accessed included EBSCO Discovery Service, Science Direct, Google Scholar, Scopus, and IEEE.

Specifying the research questions

The research questions we have formulated for this review attempt to acquire the understanding and to determine the research gap on mobile learning usage in assisting lifelong learning in the context of spiritual among middle-aged adults. These questions are related to the acceptance and adoption of mobile learning towards middle-aged adults. The research questions are:

What are the fundamentals and background of mobile learning in the learning environment, including its adoption, acceptance, and available applications?

What are the research methodologies employed in the current studies carried out in mobile learning field?

What are the core research gaps should be further investigated by researchers in mobile learning towards middle-aged adults?

Identifying the relevant bibliographic databases

To answer the research questions and find relevant studies, bibliographic databases that cover majority of journals and conference papers associated with the field of human-computer interaction and mobile learning were selected. Related literatures published within the period of 2016 to 2021 were chosen in this research and the relevant bibliographic databases are ACM ( https://www.acm.org/ ), Emerald ( https://www.emerald.com/insight/ ), EBSCO Discovery Service ( http://search.ebscohost.com ), Science Direct ( http://sciencedirect.com ), Google Scholar ( http://scholar.google.com ), Scopus ( http://scopus.com ), and IEEE ( http://ieee.com ).

Conducting the Review

Activities involved in this stage were aimed to selecting related studies. These activities are as follows:

Identifying the Relevant Studies

In identifying the relevant studies, a search using key words such as “human-computer interaction”, “mobile learning”, “middle-aged adults”, “us- ability” was conducted. Accordingly, Boolean OR was used for alternative spellings, synonyms, or alternative terms, and Boolean AND was applied to connect the main terms. The complete list of search keywords of the review is provided in Table 3 .

Two additional search strategies were applied to retrieve the maximum number of relevant papers. The first strategy was reviewing the reference list of selected papers to find more related papers. The second strategy was googling the authors of selected studies to find potential related research.

Defining Selection Criteria

For selecting the primary papers, the following criteria based on the purpose of this study are defined.

Inclusion Criteria:

Studies containing mobile learning, acceptance, and adoption among mobile devices users.

Studies dealing with factors that contribute to the adoption and acceptance of mobile learning in the educational environments or working environments.

Studies utilizing mobile learning applications related to education, health care, data collection, and engineering that motivate users to use mobile learning.

Studied involving mobile learning users in category young adults, middle-aged adults, and older adults.

Exclusion Criteria:

Studies in learning environments that do not relate to the mobile learning context.

Studies of mobile learning that involve children such as kindergarten students or users with special needs.

Studies that are reluctant to serious mobile learning.

Papers that are only available in the form of abstracts or PowerPoint presentations.

Papers that are not written in English.

Selecting Primary Studies

The titles and abstracts of searched papers were reviewed based on the inclusion and exclusion criteria. Every paper that met at least one of the criteria and without any of the exclusion criteria was included in the review. For papers that could not be excluded based on reading of the titles and abstracts, the full texts of the papers were reviewed. Through this process, 65 articles were selected from the 531 papers initially found. 292 papers were excluded only by reading the topics, 105 papers by reading the abstracts, and 65 papers by reading the full text.

Validation control of the Primary Studies

In order to maintain the quality of the selected studies, the primary studies chosen by the first reviewer were double-checked by a second author. The evaluation of the selected paper was based on the evaluation questions as follows:

Whether a proposed mobile learning solution is implemented in the research context?

Whether the methodology of mobile learning solution is suitable for middle-aged adult?

To what extent the proposed solution effects the middle-aged adult in mobile learning?

The procedure of selecting the primary papers is illustrated in Fig.  2 .

Selecting the primary papers

Data Extraction and Synthesis

In order to extract and synthesize the data to answer the research questions, the selected studies are classified into five categories as follows:

Mobile learning and their research context: This categorization answer the first research question and helps to find the fundamentals and background in mobile research based on research context such as acceptance, adoption, effectiveness, impact, intention of use, usability, and readiness.

Methodology in the mobile learning research area : In order to answer the second research question and find the methodologies employed in the related context, the research context with the methods employed by the researchers was mapped as shown in Table 7 . Based on this mapping, the instruments that have been used in mobile learning research involving middle-aged adults can be identified.

Instruments used in Mobile learning research context: This category answers the second research question. From the systematic mapping done, it was found that the common research instruments used were Questionnaire, Interview, Experiments and Task Analysis. Here, the most preferable instruments used in mobile learning research were highlighted.

Mobile learning solutions in general: This category answers the third research question in order to find the gap in mobile learning research. Articles found in this study include mobile learning articles for young and older adults to show the trend of research towards adulthood. Since the focus of this systematic mapping is on identifying mobile learning technology applied to the middle-aged adults, thus those works focusing on the application of mobile learning not on adult learners or studies on users with special needs were excluded.

Solution for middle-aged adult in mobile learning: This category also answers the third research question in presenting the future works related to mobile learning involving middle-aged adults. This article begins by explaining the use of mobile technology in a learning environment, and the mobile learning theories that form the basis for the comparison of the existing mobile learning solutions for middle-aged adults.

Effects of mobile learning on middle-aged adult: This category answer the importance of the mobile learning towards middle-aged adults for a healthy well-being by assessing the number of studies related to middle-aged adults.

Reporting the Review

In the following section, the outcomes of reviewing the selected studies were reported and the results were discussed in detail, to respond to the defined research questions.

7 Results of the systematic mapping

From the search procedure and criteria, a total number of 65 articles are extracted. The distribution of the primary studies according to the publishing year is shown in Table 4 and Fig. 3 . The articles searched for this systematic review study are from 2016 to 2021. The reason is that this study aims to identify the latest research trends in the field of mobile learning with middle-aged adults. Finding shows that there are several studies from 2016 to 2018 that focus on this topic. The number of articles on mobile learning increased significantly from 2019 to 2020, which may be due to the outbreak of the Covid 19 pandemic. In education, for example, many institutions and organizations have drastically shifted from the traditional teaching and learning approach to online platforms. As a result, there is a considerable amount of research on mobile learning focusing on students in schools, universities, and academic staff. Meanwhile, a lot of study has been done in the field of healthcare with the elderly and middle-aged individuals, because their health begins to decline at this age.

Distribution of reviewed studies by year

It would also be interesting to find out the distribution of studies by countries, as shown in Table 5 . This shows that China had contributed the most research articles in this area of mobile learning. In article [ 13 , 21 , 72 , 77 , 81 , 88 ], the country where the study was conducted was not specified.

8 Participants

The categories of participants in the selected studies consist of young adults, middle-aged adults, and older adults. The number of studies based on age category is illustrated in Table 6 and Fig. 4 . It is found that the number of studies involving young adults is higher compared to studies involving older adults and middle-aged adults. This is due to the fact that young adults are frequent users of smartphones and are more adept at using mobile apps. Furthermore, since they are unable to attend college or universities due to the Covid-19 outbreak, many students are required to study online from home using mobile devices.

Number of studies based on participants’ age category

The details of the reference pertaining to the articles based on participants’ categories (older adults (OA), middle-aged adults (MA), and young adults (YA)) are listed in Table 8 .

9 Research context in Mobile learning

The articles obtained for this study were categorized by research area, as shown in Table 7 . Based on the results, mobile learning was studied in the following areas: Education, Healthcare, Usability, Transactional Services, and Social and Communication. Figure 5 illustrates the number of articles published on each research topic. The finding shows that many researchers prefer to conduct research in the field of education. This is because computers and mobile devices are widely used in educational institutions among young adults. On the other hand, studies that focus on middle-aged and older adults are usually concerned with language or vocabulary learning. The healthcare field is also receiving a lot of attention from researchers, and studies on mobile learning in this field are usually related to elderly and middle-aged people because older people and middle-aged people tend to be more vulnerable to health problems. The number of articles from other fields is low because studies on middle-aged adults and mobile learning did not match the scope and range of years defined for this study.

Number of articles in the research domain

Because the study related to mobile learning is very broad, therefore the article obtained has been classified into research context. Research context was determined based on the previous and current research in the field of mobile learning. It was found that many researchers in the field of mobile learning have studied the acceptance, adoption, effectiveness, impact, intention of use, readiness, and usability of mobile learning. The categorized articles are listed in Table 9 in section 11, with additional information on the methodology used in each study. Figure 6 shows the number of articles obtained by research context.

Number of papers by research context

10 Mobile learning towards the middle-aged adults

From these articles, not many researchers have examined the adoption of mobile learning by middle-aged adults. As mentioned earlier, a person in his or her forties is already inclined to focus on and enhance the standard of daily practice while also finding serenity. At this stage, many people have developed an inclination and willingness to gain more religious knowledge. Adult Muslims who work during the day, would rather choose to visit a mosque or surau to learn about Islam through religious lectures in the evening or at night. During the Covid-19 pandemic outbreak, many people were forced to work from home, and many lectures at the mosque were cancelled. As a result, many have taken the initiative to hold religious lectures via video conferences over the internet (e.g.: Zoom, WebEx). Others tend to watch religious lectures that have been posted on YouTube or other related platforms. However, as opposed to face-to-face seminars, the excitement and encouragement to attend online and prerecorded learning is lacking. Midlife brings with it a multitude of significant life experiences, the majority of which revolve around work, family, especially parenting, and self-development. Tablets are being used more commonly by middle-aged adults to monitor healthy lifestyle behaviors, maintain social contact, avoid injuries, and search information.

Many middle-aged and older adults are using the Internet to obtain information about health conditions and treatments, to get social support and advice from others with similar health-related experiences, and to access apps to help them manage their health [ 28 ]. For instance, Huang, et al. [ 32 ], studied on the attitude of middle-aged adults towards health app usage. From the study, they discovered that middle-aged adults who have no habits in health management tend to consider health applications as valuable tools and have a positive impact on them, while those who already have the habit, do not tend to consider health applications as valuable tool to be used in their daily routines. There are also some middle-aged adults who decide not to use health apps due to some sentimental reasons and the confidence of middle-aged adults in using a smartphone influences their cognitive assessment of health apps.

Table 8 shows the list of studies that are related to middle-aged adults. The age range of the middle-aged adults by each researcher varies. In this study, the age range of the adult is between 40 to 60 years old, which means the selected articles involve participants in this age range. A total of 22 articles were selected that involved middle-aged adults. In the field of language learning, two papers were identified. From these articles, it is found that the study of mobile learning with middle-aged adults is widely conducted in education area. The use of mobile apps in healthcare is also considered important, as this area is also the focus of researchers. The remaining articles are related to the study of user requirements, usability, and the design and development of mobile apps for middle-aged adults.

11 Research methodology

Research methodology is the main key to perform academic research and the strength of a research. The research methodology found used in the selected articles are Questionnaire, Interview, Systematic Literature Review, Literature Review, Reporting, Task Analysis and Experiment. Figure 7 shows the most popular research method used by a researcher in the field of mobile learning is questionnaire (n=24). This methodology has been used in studies that require a large amount of data from many respondents. The second most popular research method used in mobile learning research area is the Interview (n=9). There are also studies that require the use of multiple research methods to answer research questions.

Number of articles by research methodology

Table 9 shows the methodologies employed in the selected articles. However, articles [ 23 , 49 , 63 , 72 , 81 , 83 , 88 ], and [ 77 ] are not included because these articles are review articles.

In the literature, the questionnaire was found to be the most common method used by researchers for data collection involving many participants among young adults and middle-aged adults. On the other hand, the interview method only involved small groups of participants and was carried out in a short time period. Task analysis with interview method was used in three research studies to evaluate the usability, acceptance, and adoption. The studies were done towards young adults and older adults.

In the quantitative research method, the questionnaire instrument was used by the researchers to understand users’ motivation to use e-learning as a medium of learning [ 60 ]; the use of mobile technology and means of internet access [ 24 ]; awareness in using mobile devices towards mobile learning [ 14 ]; investigate the perception of students related to educational use of mobile phones [ 36 , 55 , 76 ]; investigate students’ behavioral intentions [ 3 ] and knowledge transfer among adult workers [ 52 ]; identify factors that affect the intention to use m-learning by learning the experience of the m-learning system by the participants [ 84 ], measure usability [ 19 , 75 ]; use and engagement with m-learning [ 2 ]; collaborative learning experience in social media environment [ 7 ], students’ immersion in the game and their perceived learning outcomes [ 33 ], and the use of mobile application [ 11 , 65 ].

Almost all researchers have formally collected demographic data such as gender, age, degree program, year of study, and race of the participants. There is only one study that collects data on working background because the participants in the study involved working adults. Amongst the selected articles , Al-Adwan, et al. [ 3 ] and Lazar, et al. [ 55 ], validated the content of the survey using experts before the questionnaire was distributed to participants. Dhanapal, et al. [ 19 ] and Huizenga, et al. [ 33 ] carried out a pilot test to identify the flaws and improves the questionnaire. All but one of the researchers used Point Likert scale, while MICAN [ 65 ] uses short answer questions, multiple choices with 1 or n answers, single or two-dimensional questions. The duration of data collection was less than 40 weeks depending on the targeted number of participants.

For the qualitative research method, data was collected via task analysis and interviews. Data were captured through multiple channels including video data analysis and interview content analysis. From the selected articles, it is found that task analysis and interview method were employed in the mobile learning domain to understand participants’ actions, performance, and usability towards mobile apps. The task activities that have been examined by researchers are navigation tasks (with task activity duration of 1.5 hours for older adults to complete searching and navigating using several mobile applications) [ 58 ], quiz activities using Kahoot application (held within 13 weeks for working adult and the task activities were perform in a classroom environment) [ 74 ], mobile devices usage training ( duration of 9 months of training intervention involving older people), and the task activities (e.g.: sending messages, video and audio calls ) was performed in a hospital [ 15 ]; Vocabulary learning [ 86 , 97 ]; games application with task duration of 5 to 20 minutes [ 71 ]; and usability testing [ 30 ]. Open-ended questions were used in the interview sessions [ 71 ] and all the audio recordings of the interviews were transcribed verbatim for analysis purposes [ 58 ].

In the experimental research design, two groups were created with specific condition applied. The treatment group and the control group involved in the experiment and questionnaire research approach can be seen in articles [ 9 , 16 , 38 , 92 ] as listed in Table 9 . For instance, in Bensalem [ 9 ], aims at investigating students' perceptions about the use of WhatsApp in learning vocabulary and in the study, twenty-one participants were randomly assigned to the experimental group. Participants from the experimental group are required to complete and submit their vocabulary assignments via WhatsApp. In the assignment, students are required to search the meaning of new words in a dictionary and build sentences using each word. On the other hand, participants from a control group need to submit the same homework assignment using the traditional paper and pencil method. Later, a questionnaire was distributed to the participants and the collected data was used to measure the participants’ perception of the use of WhatsApp in vocabulary learning.

12 Discussion

In this article, a systematic review was conducted to provide a thorough analysis on the methodologies adopted by researchers in mobile learning. The number of research papers in the year 2020 exceeds the number of research papers in the previous year. This could be due to the outbreak of the Covid-19 pandemic that triggered higher number of papers. During the pandemic, everyone had to work from home, and many organizations, including public and private higher learning institutions, were unable to carry out traditional teaching and learning activities. As a result, many studies or meetings were required to be conducted online.

The country with the highest number of research papers in the field of mobile learning is China with 11 articles. There is a lack of study in mobile learning that focuses on middle-aged adults. Out of 65 research papers, a total of 22 research papers are related to middle-aged adults whereby the distribution of research can be seen in countries such as in Czech Republic (n=1), United States (n=5), China (n=3), Germany (n=1). Singapore (n=2), Turkey (n=1), Brazil (n=1), Poland (n=1), Bangladesh (n=1), United Kingdom (n=2) and 2 articles did not mention the country in which the research was carried out. Studies related to middle-aged adults in Malaysia are not very encouraging, therefore the study of middle-aged adults in the field of mobile learning needs to be given more attention.

The articles selected in this systematic review were classified by research context to identify the focus of previous researchers on the use of mobile learning by middle-aged individuals. Overall, it was found that studies related to the adoption of mobile learning, mobile applications and mobile devices have gained significant attention among researchers, followed by studies related to the acceptance and mobile learning usage. However, studies on examining the adoption and effectiveness of mobile learning usage towards middle-aged adults are still lacking. Examining the effectiveness of mobile learning usage is crucial to provide guidance towards decision making and development work in the future.

The field of education is a popular field for researchers as it involves teachers and young adults who are mainly engaged in the learning environments. Research on middle-aged adults in the educational field is found in seven articles, where two of the articles focused on vocabulary learning. One study on Hadith learning for middle-aged adults, which has been classified as a study on spiritual learning under the educational research domain was also identified. The remaining four articles are respectively related to the use of game applications in teaching adults, the use of mobile devices in sharing information among adult workers, and the readiness of the teachers in adopting mobile learning in a classroom. Besides that, there is also a lack of research towards middle-aged adults in the area of mobile usability and user requirements. Research in the healthcare domain mostly involves older adults where most researchers extensively investigate the use of mobile devices and mobile applications towards healthy ageing and wellbeing.

The coding of the research methods was based on the methods reported by the researchers in their methodology section. Questionnaire is a popular instrument used across quantitative and mixed research approaches for data collection. The questionnaire developed by the researcher will be validated by the experts and tested before it was distributed accordingly to the targeted participants. Task analysis and interview approach can be used to observe the behavior of the users and to evaluate users’ feedback in the learning environment. Even though the method was not extensively used by the researchers from the selected literature focusing on middle-aged adults, this method to be employed in the mobile learning research to gain more insight on the effectiveness of mobile technologies in the learning environment of middle-aged adults was suggested.

Nowadays, almost everyone owns a smartphone, as smartphone prices have dropped significantly, making them affordable for more users. All smartphone users are capable to use most of the basic features of the mobile device, such as downloading applications from the Apple Store or Google Play. Given that middle-aged individuals are heavy smartphone users, it is critical to understand how users utilize mobile technology such as smartphones not just for work, leisure, and entertainment, but also for knowledge acquisition.

Middle-aged adults are self-directed, able to take responsibility for their learning, have a variety of experiences and backgrounds, and are motivated and willing to learn while effectively managing real-world situations. Hence, middle-aged adults can benefit from webinars and short courses delivered online. Therefore, more research should be conducted on mobile learning for middle-aged adults.

13 Conclusion and future work

The novelty of this study is that it contributes to the understanding of the research trends based on research context and methods used in research related to middle-aged adults in mobile learning. It is noted that there are still few studies that address the adoption and effectiveness of mobile apps in the area of religious orientation, especially among middle-aged adults. For instance, before the Covid-19 outbreak, middle-aged Muslims in Malaysia preferred to attend religious courses and trainings to improve their spiritual and religious orientation [ 96 ] based on face-to-face with teachers in a classroom. Therefore, it is critical to determine whether middle-aged adults intend and consent to religious and spiritual learning, such as learning the Quran to be conducted via mobile devices. It is hoped that the use of mobile learning will enable adults' lifelong learning to be improved and done continuously under any situation in the future. This study suggests further studies on middle-aged in the field of mobile learning as follows:

Skills and Knowledge Development

The use of mobile learning among middle-aged adults begins with an awareness and intention to use mobile devices. Generally, middle-aged adults who own smartphones, they already have skills to download apps from the Google Store or App Store and set security preferences. Hence, they must intend to use mobile learning to develop their skills and knowledge. This is because between the ages of 40 and 60, they are usually busy with their work while facing problems such as increasing concerns about health, death of a friend or relative, changes in wages/salaries, and concerns about changes in physical appearance. Therefore, middle-aged adults need to seek knowledge that will make them be satisfied and enable them to lead a better and healthier lifestyle. For example, middle-aged Muslims can learn to understand the Quran through mobile learning to achieve a better quality of life because the Quran is the final revelation and book from Allah s.w.t to humankind as guidance and direction to the right path.

Mobile Learning Application with Multimedia

Mobile learning Application with multimedia plays a great role in motivating learners in learning via digital devices such as smartphones. It is crucial to design and develop mobile learning apps with appropriate multimedia elements such as texts, images, icons, and animations that meet the needs of middle-aged adult learners. In addition, middle-aged adults need to be helped to increase their motivation to learn and improve their memory performance in vocabulary memorization. Therefore, for future work, mobile app development needs to be carefully developed based on user needs especially for the multimedia elements such as the text, graphic, video and animation.

Mobile Learning Application and Quick Assessment

Assessment is a critical component of learning since it demonstrates progress. Because most of the learning occurs online and involves many students, a teacher develops easy assessment tools and procedures that enable them to rapidly assess their students’ learning progress. Numerous game-based apps have aided in the facilitation of teaching and may be used to measure a student learning progress. Additionally, to make mobile learning more interesting, game-based elements have been used to improve the students’ engagement and enjoyment in learning. For instance, Kahoot is a game-based technological platform that can be accessed using, for instance, a smart device or a laptop. The game-based learning application (app) can benefit working adults who are adult learners with diverse learning abilities. Chunking method was used to break down complex concepts into smaller parts in the form of multiple-choice questions. The students’ learning process is tested and corrected, in real time, through the statistics which are generated from this chunking process. Kahoot creates a safe environment for students to make mistakes through multiple choice questions, and yet relearn it without being judged by their peers. However, the drawback of Kahoot is, it does not adequately support the learning experience of adult learners Seah [ 74 ]. Therefore, in the future, the development of mobile learning apps for middle-aged adults might include a gamification aspect that allows easy assessment for self-monitoring of learning progress.

Research Methodology

The finding of this study shows that questionnaire is a popular instrument used across quantitative and mixed research approaches for data collection. The questionnaire developed by the researcher will be validated by the experts and tested before it was distributed accordingly to the targeted participants. However, based on the research context and methodologies found in the literature, the study on middle-aged adults was not getting the enough intention among researchers. Furthermore, as Covid-19 pandemic has impacted people’s life, many are reluctant to participate in answering questionnaires as they may be unmotivated due to job loss, adaptation to new norms or due to the death of their family members. Therefore, in the future, it is hereby recommended that a contribution back to society such as given some tokens to the participants [ 66 , 90 ] can be practiced in the research methodology. Besides that, a researcher also can conduct a free intensive course of related field to a group of respondents to upgrade the lifestyle and well-being among respondents. Hence, this can increase public participation in research, especially when involving busy and elderly respondents and at the same time the respondents can learn new knowledge while also contributing to the research study.

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Mohtar, S., Jomhari, N., Mustafa, M.B. et al. Mobile learning: research context, methodologies and future works towards middle-aged adults – a systematic literature review. Multimed Tools Appl (2022). https://doi.org/10.1007/s11042-022-13698-y

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What is a smart device? - a conceptualisation within the paradigm of the internet of things

• Manuel Silverio-Fernández   ORCID: orcid.org/0000-0002-9327-1218 1 ,
• Suresh Renukappa 1 &
• Subashini Suresh 1  

Visualization in Engineering volume  6 , Article number:  3 ( 2018 ) Cite this article

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The Internet of Things (IoT) is an interconnected network of objects which range from simple sensors to smartphones and tablets; it is a relatively novel paradigm that has been rapidly gaining ground in the scenario of modern wireless telecommunications with an expected growth of 25 to 50 billion of connected devices for 2020 Due to the recent rise of this paradigm, authors across the literature use inconsistent terms to address the devices present in the IoT, such as mobile device, smart device, mobile technologies or mobile smart device. Based on the existing literature, this paper chooses the term smart device as a starting point towards the development of an appropriate definition for the devices present in the IoT. This investigation aims at exploring the concept and main features of smart devices as well as their role in the IoT. This paper follows a systematic approach for reviewing compendium of literature to explore the current research in this field. It has been identified smart devices as the primary objects interconnected in the network of IoT, having an essential role in this paradigm. The developed concept for defining smart device is based on three main features, namely context-awareness, autonomy and device connectivity. Other features such as mobility and user-interaction were highly mentioned in the literature, but were not considered because of the nature of the IoT as a network mainly oriented to device-to-device connectivity whether they are mobile or not and whether they interact with people or not. What emerges from this paper is a concept which can be used to homogenise the terminology used on further research in the Field of digitalisation and smart technologies.

Introduction

In 2011 Cisco predicted that 50 billion of Things would be connected to the Internet by 2020 (Evans, 2011 ). On the other hand, more recent investigations show that 25 billion devices will be connected to the internet by 2020 and those connections aim at facilitating the process of autonomous intelligent decision making (Gartner, 2014 ). No matter which prediction is right the main highlight is that smart things will be several times more than the estimated world population.

The IoT is proliferating across all sectors, creating opportunities and becoming a competitive marketplace weapon as the focus of primary benefits, shifts from both internal and external improvements of the worldwide industries (Gartner, 2016 ). Sectors benefitted from the IoT are: transportation, smart city, smart domotics, smart health, e-governance, assisted living, e-education, retail, logistics, agriculture, automation, industrial manufacturing, process management, among others (Gubbi, Buyya, Marusic, & Palaniswami, 2013 ) and (Miorandi, Sicari, De Pellegrini, & Chlamtac, 2012 ).

There are many ways to define the IoT, some popular definitions are:

“a dynamic global network infrastructure with self- configuring capabilities based on standard and interoperable communication protocols where physical and virtual ‘Things’ have identities, physical attributes, and virtual personalities and use intelligent interfaces, and are seamlessly integrated into the information network” (Van Kranenburg, 2008 ).

“Things having identities and virtual personalities operating in smart spaces using intelligent interfaces to connect and communicate within social, environmental, and user contexts” (INFSO, 2008 ).

(Lopez, Rios, Bao, & Wang, 2017 ) established three main components required for the IoT namely Smart things, network infrastructure and backend servers (see Fig.  1 ). This simplified architecture describes the essence behind the paradigm of the IoT. The Smart devices seen Fig. 1 are designed to interact both with users and other devices connected to the network, some of these devices might not even require interacting directly with users.

Simplified structure of the IoT

There is a broad range for the objects or “things” in the IoT, some of these objects can get different names in the literature, such as smart devices, mobile devices, smart things or smart objects. Smart devices are considered objects capable of communication and computation which range from simple sensor nodes to home appliances and smartphones (Stojkoska & Trivodaliev, 2017 ). This author also considers smart devices as the objects present in the network of the IoT.

The devices in the IoT should have the capability to dynamically adapt to the changing contexts and take actions based on their operating conditions; they should be self-configuring and interoperable, having unique identities and being able to communicate and exchange data with other devices and systems (Ray, 2016 ). Therefore, smart device should be context-aware and have network connectivity.

Currently, in the literature, different terms are found for what this paper calls smart devices. Lo, Yu and Tseng ( 2014 ) used the term smart device, whereas İlhan, Yıldız, & Kayrak ( 2016 ) used the term smart mobile device. The term mobile devices is also used by some authors, such as Lau , et al. ( 2017 ), Khan & Khan ( 2017 ) and Furthmüller & Waldhorst ( 2012 ). Azhar & Cox ( 2015 ) uses the terms “mobile tools”, “mobile technologies” and “mobile devices” for devices that allow workers to get instant access to project documents, plans and specifications. Azhar & Cox ( 2015 ) addresses tablets, cloud technologies, Radio Frequency Identification Tag and wearable devices as mobile technologies when tablets, smartphones and wearables are devices that implement various mobile technologies. This misconception is led by the lack of a clear concept of smart device.

This paper intends the define a clear and scalable concept of Smart device, which researchers around the globe can use for further research. The review methodology is described in Section 3, findings are explained in section 4, and the conclusions are discussed in section 5.

Justification

Nowadays the paradigm of Industry 4.0 aims at introducing a new level of organisation and control within the current industry, thus taking the last industrial revolution to a new level of efficiency. Figure  2 shows the four industrial revolutions and locates the Industry 4.0 within a chronological context. Each industrial revolution was separated by a hundred years. Differently, the industry 4.0 comes after only half a century. The term Industry 4.0 is regarded as a fourth industrial revolution which defines a new level of organisation and control over the entire value chain of the life cycle of products Rüßmann et al. ( 2015 ). The central objective of Industry 4.0 is fulfilling individual customer needs which affect areas such as management, research and development, manufacturing, utilisation and recycling of products.

Four industrial revolutions

One of the key players in this revolution is the IoT, which attempts to collect and analyse data and be part of the core process of all industries. According to Lee, Kao & Yang ( 2014 ) The industry 4.0 relies on the IoT for converting regular machines to self-aware and self-learning machines, hence improving their overall performance and maintenance management with the surrounding interaction.

Industry 4.0 proposes a significant change in how things work in the Built environment, but there is also the natural environment which has no or little human intervention (for example, the rural sites along train tracks which interconnect cities). The IoT can also be a valuable tool for gathering relevant data for different industries in the Built and natural environment.

In order to help with this embedment of the IoT into the different industries on a worldwide level, several investigations have already contributed with frameworks and toolkits for development of Smart cities through the implementation of the IoT. Some examples are:

An information framework for creating smart city through the implementation the Internet of Things (Jin et al., 2014 ).

Building a Framework for Internet of Things and Cloud Computing (Anon et al., 2014 ).

Stojkoska & Trivodaliev ( 2017 ) highlights Smart devices as the core devices present in the IoT hence when developing any research or business project related to the IoT the following question arises: What is a smart device?

At the moment of performing this study such question was not answered. The industry 4.0 and any project related to the IoT need to define a dynamic list with a finite number of devices which can be considered as smart. This study will offer clarity and transparency between technology consultants, researchers and companies from all industries which intend to incorporate the paradigm of the IoT.

Review methodology

This study is aimed at exploring the key features that are directly or indirectly related to smart devices across the literature for subsequently developing a scalable concept of Smart device. This section presents the methodology used to select the most appropriate research publications covering the topic of smart devices.

This paper follows a systematic approach for reviewing compendium of literature to explore the current research in this field. The search for peer-reviewed journal articles has been done via databases, subsequently, this allowed to perform a literature review. A literature review is a systematic and reproducible method for identifying and synthesising the existing body of recorded work generated by researchers or scholars (Fink, 2013 ). It provides a summary of themes and issues in a specific research field.

The literature was searched using the online service Google Scholar and Science Direct. The main advantages of these services are ease of use and broader universe of cited and citing items (Franceschet, 2010 ).

Selection criteria

Stojkoska and Trivodaliev ( 2017 ) highlights Smart devices as the core devices present in the IoT. On the other hand, Lanotte and Merro ( 2018 ) mention both smart devices and mobile devices. Bisio et al. ( 2018 ) mentions only mobile devices as the devices present in the IoT. Although there is a lack of consensus between which term is the right one to be used when referring to the IoT, the etymologic meaning of these term associates the term “mobile devices” to devices with a high degree of mobility, whereas the term “smart device” implies certain level of embedded cleverness in the device. Based on the inherent characteristics of these terms this paper chooses the term Smart device as the name for the objects present in the IoT, thus agreeing with Stojkoska and Trivodaliev ( 2017 ). Nevertheless, according to the Google’s web search trends presented in Fig.  4 , the term “mobile device” shows a higher popularity for when compared with the term “smart device”, this encourages the utilisation of this keyword in the filters of the inclusion criteria implemented in this research. Subsequently, both keywords “smart device” and “mobile device” were selected and independent searches for peer-reviewed journal articles has been done via databases.

The Google trend data presented in Figs. 3 and 4 is adjusted and proportionate to the time and location of the query. Each data point is divided by the total searches of the geography and time range it represents, to compare relative popularity. Otherwise places with the most search volume always be ranked highest. The resulting numbers are then scaled on the Y-axis from 0 to 100 based on a topic’s proportion to all searches on all topics.

Interest over time according to Google trends since 2010 for terms Smart device and Internet of Things

Interest over time according to Google trends since 2010 for terms Smart device and Mobile device

As can be seen in Fig. 3 , from the year 2012 there was an relevant growth in the trends of web search popularity for the term “Internet of Things”, consequently, this study adjusted the selection criteria to survey journal papers from the year 2012. In terms of selecting research publications prior 2012, although there are publications, this does not mean adding them to the data collection would enhance the quality of the output of this research. The concept of the IoT has been quickly evolving in latest years in it is the intend of this study to capture the current features perceived in smart devices in the current research community.

In summary, the selection criteria implemented in this study was fundamentally based on Journal papers published from 2012 to June 2017. The inclusion criteria of papers in this time range is the appearance of any of the selected keywords in the title of the paper. The selected keywords were: “Internet of Things”, “Smart device” and “Mobile device”.

Data analysis

Thirty publications were selected from the searches based on the keyword “mobile devices” and Twenty from the keyword “smart devices”.

Following the guidelines of White & Marsh ( 2006 ) a systematic content analysis was implemented to create themes for the capabilities of smart devices and mobile devices perceived in the analysed papers as well as other keywords utilised by authors for referring to smart devices. Once these themes were identified they were analysed and utilised for the elaboration of a new concept for the term “smart device”.

Findings and discussion

Terminology used in the literature.

The literature review showed an inconsistent terminology, many authors used the term “mobile device” for addressing smartphones, tablets and wearables. Other authors use the term “smart device” to referring to the same devices. Table  1 shows the terms used in the literature for smart devices based on the keyword used for searching the databases.

Key features of smart devices

A systematic content analysis revealed distinct themes which describe the key capabilities of the devices addressed by the reviewed papers. Tables 2 and 3 show the selected peer-reviewed journal articles selected in the review and their mention of each of the key features exposed by the content analysis. The number of mentions of each feature is used as a quantitative parameter for measuring the relevance of the features discovered in the literatures.

The key features that authors in the literature allocate to smart devices were grouped in the following terms: Autonomy, connectivity, context-awareness, User-interaction, mobility and data storage. Based on the search results obtained from the keywords “smart device” and “mobile device” the key features shown in Tables 2 and 3 can be sorted by the amount of mentions in the literature. Tables 4 and 5 shows the key features of the keywords “smart device” and “mobile device” respectively. As can be observed in both cases, connectivity and user-interaction both have more than a 50% of mentions.

The feature “mobility” comes mainly from the search performed with the keyword “mobile device”, authors particularly assume there is mobility or portability when using the term mobile devices, which is not always the case for smart device. Mobility and user-interaction are not considered key features as in doing so we would contradict some of the key principles of the IoT, which establish that any “thing” can be connected, whether it is mobile or not and whether it interacts with people or not (Miller, 2015 ). In fact, one of the core ideas of the IoT is the that devices interact with other devices, not necessarily people; hence the name “Internet of Things”, an Internet designed for things, not people. Regarding data storage, although it represents an important capability, it is embedded within other functionalities such as autonomy, context-awareness and connectivity. Therefore, it is not considered as one of the key features for a device to become “smart”; instead this paper considers data-storage as an imbedded attribute inherently required by a device in order to adopt a higher set of features.

Ultimately, the key features considered by this study for a device to become “Smart” are connectivity, autonomy and context-awareness. Being connectivity the most relevant based on the amount of mentions obtained from the literature. The following sections describe in more detail the key features found in the literature.

The main idea behind autonomy consists of devices performing tasks autonomously without the direct command of the user. From the analysis obtained from the keyword “Smart device” several references to smart devices were denoting autonomous performance of tasks. For example, Zhang , et al. ( 2013 ) explored the factors that play important roles in multitasking scenarios, this requires from smart phones to have certain processing capacity and to perform tasks on the background. In addition, Gans, Alberini, & Longo ( 2013 ) and Schleich, Faure, & Klobasa ( 2017 ) intended to use smart devices as “smart” meters or advanced meters to measure information through sensors and send it through a network autonomously. The term Smart device is also used by Najjar & Amer ( 2016 ) for a control system utilised in engine cars this if founded on the idea of autonomous performance of tasks.

From the analysis obtained from the keyword “Mobile device” various publications refer to mobile devices as tools that can process information autonomously. Vazquez-Fernandez & Gonzalez-Jimenez ( 2016 ) discussed autonomous biometric data processing within mobile devices for face recognition systems. Also, Sung, Chang & Yang ( 2015 ) mentions the utilisation of mobile devices for asynchronous tasks.

Connectivity

The concept of connectivity in smart devices refers to establishing a connection to a network of any size; Sometimes the main purpose might be gaining internet access, other times it might be sharing information with other devices on the network. The key factor for identifying that an author considers that a smart device has internet access is either when network connectivity is explicitly mentioned or when an activity that requires network connectivity is addressed. For example, Harwood, Dooley, Scott, & Joiner ( 2014 ) states that high internet use is something common on smart devices, this is a direct statement about the utilisation of smart devices for internet access which requires network connectivity. On the other hand, Khan, Shrestha, Wahid, & Babyn ( 2015 ) mentions direct wireless interfacing and full-duplex communication between devices, this statement is a bit more indirect but at the same time assumes that smart devices have network connectivity.

One of the most explicit reference to connectivity was obtained from Cheng & Mitomo ( 2017 ) which explains that what makes these devices “smart” is their wireless communication capability, which enables them to connect to the internet.

Context-awareness

The main idea behind context-awareness is the ability of smart devices to perceive information from the environment through sensors such as camera, accelerometer, microphone and Global Positioning System (GPS). The information gathered through sensors can then be utilised to make autonomous decisions or to provide direct assistance to the user.

The analysis was oriented to detect any mention of the utilisation of sensors with either “smart devices” or “mobile devices” keywords. Godwin , et al., ( 2013 ) and Zhang et al., ( 2013 ) mentioned the utilisation of smart devices for photography or video recording, whereas Husnjak, Perakovic & Jovovic ( 2014 ) addressed the implementation of smart devices for human voice recognition.

The literature obtained from the keyword “mobile device” mentions the utilisation of GPS, accelerometer, microphone and camera. Furthmüller & Waldhorst ( 2012 ) explained that mobile devices ffer a set of resources in which we find sensors like GPS and accelerometer. Maryn, Ysenbaert, Zarowski, & Vanspauwen ( 2017 ) mentioned various built-in sensors carried by mobile devices such as a microphone, camera, GPS, accelerometer and light sensor.

User-interaction

The literature suggest that Smart devices are designed to interact with users, whether it is a smartphone or smart bracelet, there is certain level of interaction with a user in which the device either collects or provide data to the user. In this study the main criteria for the identification of interaction with users is the mention of consumer, user, or any activity which requires a person. For example, Harwood, Dooley, Scott, & Joiner ( 2014 ) explained that a smart device allows users to ubiquitously conduct activities such as gaming, internet-browsing, texting, emailing, social networking and phone calls, all these activities are specifically designed for a user.

Despite the inclination of Smart devices being used by users, Stojkoska & Trivodaliev ( 2017 ) states that smart devices are the objects presents in the IoT. In addition, Miller ( 2015 ) establishes that the IoT is all about the interconnection of devices, to the point where some devices might never interact directly with users, whereas instead they interact with other devices. Considering the theory behind the IoT this study does not consider user-interaction as a key feature for a device to become “Smart”.

Mobility – Portability

The aspect of mobility and portability was found specifically for the keyword “mobile device”. Some authors refer to portability or mobility as one of the main advantages of mobile devices. As per Moreira, Ferreira, Santos, & Duro ( 2016 ), portability is a key aspect of interest for practitioners in the field of education for mobile learning applications. Also, Sattineni & Schmidt ( 2015 ) mentioned how big companies like Apple and Microsoft have designed tablets to handle and process everything a normal full-size computer can along with the bonus of mobility.

Although mobility is very characteristic feature of Smartphones, tablets and smart watches, this feature does not apply to every smart device. One example is a Smart board, a board which is not mobile but is can be considered a smart device. Malkawi ( 2017 ) presents a smart board as an electronic white board connected to a computer and data show which can be used for distinct users as a typical white board as well as to open applications, navigate the web, use drawing tools, visualising text, images, augio, video and creating virtual forms or shapes.

The definition of Smart devices goes beyond Smartphones and tablets, the paradigm of IoT says that anything can be connected and smart which means that objects with low mobility should also be included into this group. For this reason, mobility is not considered as a key feature for a device to become Smart.

What is a smart device?

The key features found in smart devices through the review of the literature have been grouped into three main categories namely, context-awareness, device connectivity and autonomy. The literature also suggests “user interaction” and “mobility-portability” as key features to consider, but at the same time the theory behind the IoT establishes that this paradigm is about things interacting with other things.

Most of the authors when referring to “mobile devices” envision smartphones, tablets and wearables, by doing so they are addressing the same devices that other authors call “smart devices”. This paper chooses not to include the feature of mobility to the concept of smart device hence it would then discard all devices which comply with the main categories but are not mobile. Instead, the term mobile smart device can be used.

Considering the main features of smart devices, this paper proposes the following definition:

A smart device is a context-aware electronic device capable of performing autonomous computing and connecting to other devices wire or wirelessly for data exchange.

This concept encompasses a dynamic but finite number of devices which can integrate in a network and participate in the paradigm of the IoT. section 4.1 describes other terms which have been added to the term “smart device” to describe specific features of these devices. Such terms can be: metering, wearable, hand-held, etc. Consequently, we can refer to smart devices which interact with users by using the term “smart wearable device” or “smart hand-held device”. Nevertheless, Smart device is proposed by this study as the core term to be used for the devices present in the IoT.

Conclusions

This paper addresses the concept of smart device within the paradigm of the IoT. This concept has been under development for the last decade, and due to the growing complexity of these devices and the fast changing and evolving research community, there was a need for a clear definition conceptualisation of this term. The concept developed in this paper is modular and scalable, this means that new key features might be added depending on the changing features of the global market and state of technology.

This study proposes three pillars or key features that make a device or object “smart”, namely Autonomy, context-awareness and connectivity. It can be inferred that almost any device or object can become smart by adding these features. For example, if a chair gets a sensor (context-awareness) for detecting when, then it processes that information (autonomous computing) and sends it through a network (device connectivity), in that moment we can call that chair “Smart”. Moreover, by using a similar approach with other devices, we can easily implement the paradigm of IoT in the industry and homes.

User-interaction was one of the features with higher appearance (see Tables 2 and 3 ) in the review, as authors consider smart or mobile devices as devices designed to interact with users. This generates a contradiction between the core theory of the IoT. According to Stojkoska & Trivodaliev ( 2017 ) and Miller ( 2015 ) the IoT is designed for objects to interact between them, and although humans play a role in this network, some devices might only interact with other devices.

Miller ( 2015 , page 9) states:

“Most of the things connected to the IoT are actually simple devices that are often referred as smart devices. The devices themselves aren’t necessarily smart in and of themselves, but become smart when joined together with other connected devices”.

This sentence highlights smart devices as the key element of the IoT which need to interact with other devices in order to become “smart”. Therefore, a device in isolation is not smart, it needs to interact with other devices. Although the ultimate purpose of the IoT might be to provide services to final users the emphasis in the interaction of a smart device is on the interaction with other devices not people.

This study will offer clarity and transparency between technology consultants, researchers and companies from all industries which intend to incorporate the paradigm of the IoT. The concept offered in this research will help to build a set of finite smart devices which companies can use based on their company size, company type and project type. As mentioned by Lee, Kao & Yang ( 2014 ) the industry 4.0 relies on the IoT for improving its overall performance and maintenance management with the surrounding interaction, consequently a clear definition of smart device serves as a tool to ease the future development of frameworks for executing the IoT and the industry 4.0.

Abbreviations

Global Positioning System

Internet of Things

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Silverio-Fernández, M., Renukappa, S. & Suresh, S. What is a smart device? - a conceptualisation within the paradigm of the internet of things. Vis. in Eng. 6 , 3 (2018). https://doi.org/10.1186/s40327-018-0063-8

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Smartphone usage and increased risk of mobile phone addiction: A concurrent study

Subramani parasuraman.

Unit of Pharmacology, AIMST University, Kedah, Malaysia

Aaseer Thamby Sam

1 Unit of Pharmacy Practice, Faculty of Pharmacy, AIMST University, Kedah, Malaysia

Stephanie Wong Kah Yee

Bobby lau chik chuon.

This study aimed to study the mobile phone addiction behavior and awareness on electromagnetic radiation (EMR) among a sample of Malaysian population.

This online study was conducted between December 2015 and 2016. The study instrument comprised eight segments, namely, informed consent form, demographic details, habituation, mobile phone fact and EMR details, mobile phone awareness education, psychomotor (anxious behavior) analysis, and health issues. Frequency of the data was calculated and summarized in the results.

Totally, 409 respondents participated in the study. The mean age of the study participants was 22.88 (standard error = 0.24) years. Most of the study participants developed dependency with smartphone usage and had awareness (level 6) on EMR. No significant changes were found on mobile phone addiction behavior between the participants having accommodation on home and hostel.

Conclusion:

The study participants were aware about mobile phone/radiation hazards and many of them were extremely dependent on smartphones. One-fourth of the study population were found having feeling of wrist and hand pain because of smartphone use which may lead to further physiological and physiological complication.

INTRODUCTION

Mobile/hand phones are powerful communication devices, first demonstrated by Motorola in 1973, and made commercially available from 1984.[ 1 ] In the last few years, hand phones have become an integral part of our lives. The number of mobile cellular subscriptions is constantly increasing every year. In 2016, there were more than seven billion users worldwide. The percentage of internet usage also increased globally 7-fold from 6.5% to 43% between 2000 and 2015. The percentage of households with internet access also increased from 18% in 2005 to 46% in 2015.[ 2 ] Parlay, the addiction behavior to mobile phone is also increasing. In 2012, new Time Mobility Poll reported that 84% people “couldn't go a single day without their mobile devices.”[ 3 ] Around 206 published survey reports suggest that 50% of teens and 27% of parents feel that they are addicted to mobiles.[ 4 ] The recent studies also reported the increase of mobile phone dependence, and this could increase internet addiction.[ 5 ] Overusage of mobile phones may cause psychological illness such as dry eyes, computer vision syndrome, weakness of thumb and wrist, neck pain and rigidity, increased frequency of De Quervain's tenosynovitis, tactile hallucinations, nomophobia, insecurity, delusions, auditory sleep disturbances, insomnia, hallucinations, lower self-confidence, and mobile phone addiction disorders.[ 6 ] In animals, chronic exposure to Wi-Fi radiation caused behavioral alterations, liver enzyme impairment, pyknotic nucleus, and apoptosis in brain cortex.[ 7 ] Kesari et al . concluded that the mobile phone radiation may increase the reactive oxygen species, which plays an important role in the development of metabolic and neurodegenerative diseases.[ 8 ]

In recent years, most of the global populations (especially college and university students), use smartphones, due to its wide range of applications. While beneficial in numerous ways, smartphones have disadvantages such as reduction in work efficacy, personal attention social nuisance, and psychological addiction. Currently, the addiction to smartphones among students is 24.8%–27.8%, and it is progressively increasing every year.[ 9 ] Mobile phone is becoming an integral part to students with regard to managing critical situations and maintaining social relationships.[ 10 ] This behavior may reduce thinking capabilities, affect cognitive functions, and induce dependency. The signs of smartphone addiction are constantly checking the phone for no reason, feeling anxious or restless without the phone, waking up in the middle of night to check the mobile and communication updates, delay in professional performance as a result of prolonged phone activities, and distracted with smartphone applications.[ 11 ]

Mobile phone is the most dominant portal of information and communication technology. A mental impairment resulting from modern technology has come to the attention of sociologists, psychologists, and scholars of education on mobile addiction.[ 12 ] Mobile phone addiction and withdrawal from mobile network may increase anger, tension, depression, irritability, and restlessness which may alter the physiological behavior and reduce work efficacy. Hence, the present study was planned to study the addiction behavior of mobile phone usage using an online survey.

This study was approved by Human and Animal Ethics Committee of AIMST University (AUHAEC/FOP/2016/05) and conducted according to the Declaration of Helsinki. The study was conducted among a sample of Malaysian adults. The study participants were invited through personal communications to fill the online survey form. The study was conducted between December 2015 and 2016. The study instrument comprised eight segments, namely, informed consent information, consent acceptance page, demographic details, habituation, mobile phone fact and electromagnetic radiation (EMR) details, mobile phone awareness education, psychomotor (anxious behavior) analysis, and health issues. If any of the participants were not willing to continue in the study, they could decline as per their discretion.

Totally, 450 participants were informed about the study and 409 participated in the study. The demographic details of the study participants are summarized in Table 1 . The incomplete forms were excluded from the study. The participants' details were maintained confidentially.

Demographic details of the study participants

Statistical analysis

Frequency of the data was calculated and the data were analyzed using two-sided Chi-square test with Yate's continuity correction.

Totally, 409 individuals participated in the study, of which 42.3% were males and 57.7% were females, between the age group of 18 and 55 years. Nearly 75.6% of the respondents were between the age group of 21 and 25 years. The mean age of the study participants was 22.88 (standard error = 0.24) years. The study participants' demographic details are summarized in Table 1 .

About 95% of the study participants were using smart phones, with 81.7% of them having at least one mobile phone. Most of the study participants used mobile phone for more than 5 years. Around 64.3% of the study participants use mobile phone for an hour (approximately) and remaining use it for more than an hour. Nearly 36.7% of the study participants have the habit of checking mobile phones in between sleep, while 27.1% felt inconvenience with mobile phone use. Majority of the respondents were using mobile phone for communication purposes (87.8%), photo shooting (59.7%), entertainment (58.2%), and educational/academic purposes (43.8%). Habits of mobile phone usage among the study participants are summarized in Table 2 .

Habituation analysis of mobile phone usage

The study results indicate that 86.8% of the participants are aware about EMR and 82.6% of the study participants are aware about the dangers of EMR. The prolonged use/exposure to EMR may cause De Quervain's syndrome, pain on wrist and hand, and ear discomfort. Among the study participants, 46.2% were having awareness on De Quervain's syndrome, 53.8% were feeling ear discomfort, and 25.9% were having mild-to-moderate wrist/hand pain. Almost 34.5% of the study participants felt pain in the wrist or at the back of the neck while utilizing smartphones [ Table 3a ]. Many of the study participants also agreed that mobile phone usage causes fatigue (12% agreed; 67.5% strongly agreed), sleep disturbance (16.9% agreed; 57.7% strongly agreed), and psychological disturbance (10.8% agreed; 54.8% strongly agreed) [ Table 3b ]. The study participants were having level 6 of awareness on mobile phone usage and EMR.

Analysis of awareness of mobile phone hazards

The behavioral analysis of the smartphone usage revealed that 70.4% of the study participants use smartphone longer than intended and 66.5% of the study participants are engaged for longer duration with smartphone. Nearly 57.7% of the study participants exercise control using their phones only for specific important functions. More number of study participants (58.2%) felt uncomfortable without mobile and were not able to withstand not having a smartphone, feeling discomfort with running out of battery (73.8%), felt anxious if not browsing through their favorite smartphone application (41.1%), and 50.4% of the study participants declared that they would never quit using smartphones even though their daily lifestyles were being affected by it. The study also revealed another important finding that 74.3% of smartphone users are feeling dependency on the use of smartphone. The addiction behavior analysis data of mobile phone are summarized in Table 4 .

Addiction behavior analysis of mobile phone

The study results also suggest that female participants were having more awareness than male participants ( P < 0.001) [ Table 5a ] and were more dependent on smartphones than male participants ( P < 0.05) [ Table 5b ]. Female participants were ready to quit using smartphones, if it affected daily lifestyle compared with male participants ( P < 0.05) [ Table 5b ]. Habituation of mobile phone use and addiction behavior were compared between both genders of the study participants and are summarized in Table 5a and ​ andb, b , respectively.

Comparison of habituation of mobile phone usage between genders

Comparison of addiction behavior between genders

A total of 297 participants were having accommodation in hostel, among them 39.6% of the study participants checked their mobile phone on an average of 21–30 times, a day, and 11.7% of the study participants checked their mobile phone more than 30 times a day. A total of 112 participants have accommodation in home, among them 28.6% of the study participants checked their mobile phone 21–30 times a day, and 13.4% of the study participants checked their mobile phone more than 30 times a day.

A total of 66.1% of participants having accommodation in home use their phones longer than intended, whereas 71.8% of participants having accommodation in hostel are using phone longer than intended. Forty-one (36.6%) and 109 (36.6%) participants from home and hotel checked mobile phone in-between sleep, respectively. About 67.9% of participants having accommodation in home felt dependent on mobile and it was the same for participants having accommodation in hostel (76.5%).

The study results suggest that a significant number of the participants had addiction to mobile phone usage, but were not aware on it, as mobile phones have become an integral part of life. No significant differences were found on addiction behavior between the participants residing in hostel and homes. Mobile phone abuse is rising as an important issue among the world population including physical problems such as eye problems, muscular pain, and psychological problem such as tactile and auditory delusions.[ 13 ] Along with mobile phone, availability of Wi-Fi facility in residence place and work premises also increases mobile phone dependence. The continuous and constant usage of mobile phone reduces intellectual capabilities and work efficacy. A study conducted in Chinese population (160 million out of the total 1.3 billion people) showed that people affected by mobile phone dependence have difficulty in focusing on work and are unsociable, eccentric, and use phones in spite of facing hazards or having knowledge of harmful effects of this form of electromagnetic pollution.[ 14 ]

The statement “I will never quit using my smartphone even though my daily lifestyles are affected by it” was statistically significant ( P = 0.0229). This points to a trend of mobile phone addiction among the respondents. This finding was discussed by Salehan and Negahban. They stated that this trend is due to the fast growth in the use of online social networking services (SNS). Extensive use of technology can lead to addiction. The use of SNS mobile applications is a significant predictor of mobile addiction. Their result showed that the use of SNS mobile applications is affected by both SNS network size and SNS intensity of the user. It has implications for academia as well as governmental and non-for-profit organizations regarding the effect of mobile phones on individual's and public health.[ 15 ] The health risks associated with mobile phones include increased chances of low self-esteem, anxiety or depression, bullying, eye strain and “digital or mobile phone thumb,” motor vehicle accidents, nosocomial infections, lack of sleep, brain tumors and low sperm counts, headache, hearing loss, expense, and dishonesty. The prevalence of cell phone dependence is unknown, but it is prevalent in all cultures and societies and is rapidly rising.[ 16 ] Relapse rate with mobile phone addiction is also high, which may also increase the health risk and affect cognitive function. Sahin et al . studied mobile phone addiction level and sleep quality in 576 university students and found that sleep quality worsens with increasing addiction level.[ 17 ]

The statement “Feeling dependent on the use of smartphone” was also statistically significant ( P = 0.0373). This was also explored by Richard et al . among 404 university students regarding their addiction to smartphones. Half of the respondents were overtly addicted to their phones, while one in five rated themselves totally dependent on their smartphones. Interestingly, higher number of participants felt more secure with their phones than without. Using their phones as an escapism was reported by more than half of the respondents. This study revealed an important fact that people are not actually addicted to their smartphones per se ; however, it is to the entertainment, information, and personal connections that majority of the respondents were addicted to.[ 18 ]

The 2015 statistical report from the British Chiropractic Association concluded that 45% of young people aged 16–24 years suffered with back pain. Long-term usage of smart phone may also cause incurable occipital neuralgia, anxiety and depression, nomophobia, stress, eyesight problem, hearing problems, and many other health issues.[ 19 ]

A study conducted among university students of Shahrekord, Iran, revealed that 21.49% of the participants were addicted to mobile phones, 17.30% participants had depressive disorder, 14.20% participants had obsessive-compulsive disorder, and 13.80% had interpersonal sensitivity.[ 12 ] Nearly 72% of South Korean children aged 11–12 years spend 5.4 h a day on mobile phones, 25% of those children were considered addicts to smartphones.[ 20 ] Thomée et al . collected data from 4156 adults aged between 20 and 24 years and observed no clear association between availability demands or being awakened at night and the mental health outcomes.[ 21 ] Overuse of mobile phone can lead to reduced quality of interpersonal relationships and lack of productivity in daily life. The study outcome from different studies showed variable results on addictive behavior on mobile phone usage. The fact is over-/long-time usage of mobile phone may cause behavioral alteration and induce addictive behavior.

This study suggests that most of the study participants are aware about mobile phone/radiation hazards and many of them developed dependent behavior with smartphone. No significant changes were found on mobile phone dependency behavior between participants having accommodation in house and hostel. One-fourth of the study population is having a feeling of wrist and hand because of smartphone usage which may lead to further physiological and physiological complications.

Limitations

• Cluster sampling from a wider population base could have provided a more clear idea regarding the topic of interest
• Increasing the time frame and number of study phases was not possible due to logistical issues
• Impact of smartphone addiction on sleep pattern could have been studied in-depth.

Financial support and sponsorship

Conflicts of interest.

There are no conflflicts of interest.

• Open access
• Published: 16 February 2024

Barriers and facilitators to health technology adoption by older adults with chronic diseases: an integrative systematic review

• Alessia Bertolazzi 1 ,
• Valeria Quaglia 1 &
• Ramona Bongelli 1  

BMC Public Health volume  24 , Article number:  506 ( 2024 ) Cite this article

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In recent years, healthcare systems have progressively adopted several technologies enhancing access to healthcare for older adults and support the delivery of efficient and effective care for this specific population. These technologies include both assistive technologies designed to maintain or improve the independence, social participation and functionality of older people at home, as well as health information technology developed to manage long-term conditions. Examples of such technologies include telehealth, wearable devices and mobile health. However, despite the great promise that health technology holds for promoting independent living among older people, its actual implementation remains challenging.

This study aimed to conduct an integrative systematic review of the research evidence on the factors that facilitate or hinder the adoption of different types of technology by older individuals with chronic diseases. For this purpose, four electronic databases (PsycArticles, Scopus, Web of Science and PubMed) were queried to search for indexed published studies. The methodological quality of the selected papers has been assessed using the Mixed Methods Appraisal Tool (MMAT).

Twenty-nine articles were selected, including 6.213 adults aged 60 or older. The studies have been synthesised considering the types of technological interventions and chronic diseases, as well as the main barriers and facilitators in technology acceptance. The results revealed that the majority of the selected articles focused on comorbid conditions and the utilisation of telemedicine tools. With regard to hindering and facilitating factors, five main domains were identified: demographic and socioeconomic, health-related, dispositional, technology-related and social factors.

The study results have practical implications not only for technology developers but also for all the social actors involved in the design and implementation of healthcare technologies, including formal and informal caregivers and policy stakeholders. These actors could use this work to enhance their understanding of the utilisation of technology by the ageing population. This review emphasises the factors that facilitate technology adoption and identifies barriers that impede it, with the ultimate goal of promoting health and independent living.

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Over the last few decades, the elderly population has grown significantly, and it is projected that the proportion of people aged 65 and above will continue to increase from 10% in 2022 to 16% in 2050 [ 1 ]. This demographic shift has led to increased pressure on healthcare systems’ ability to plan and provide effective healthcare services for older adults. In fact, the ageing of the population has resulted in an increase in long-term diseases such as diabetes, chronic respiratory diseases (e.g., chronic obstructive pulmonary disease and asthma), neurological disorders (e.g., dementia, Alzheimer’s disease and Parkinson’s disease) and cardiovascular disease (e.g., ischaemic heart disease, cerebrovascular disease and hypertensive heart disease) [ 2 ]. Along Europe, 72.5% of people aged 85 years or older reported the presence of at least one health problem [ 3 ]. In the U.S., 23.9% of the population aged 65 or older has one chronic condition, while 63.7% has two or more [ 4 ]. The growing prevalence of multimorbidity is associated with increased utilisation and cost of healthcare services [ 5 ]. The growth of the elderly population with multiple long-term diseases has implications not only at the societal level but also at the individual level. Older people have specific health needs that need to be met in a timely manner, as complications and limitations related to illness can impact their independence, autonomy and overall well-being [ 6 ].

However, this social group faces specific difficulties in accessing healthcare services. Several studies have investigated the factors influencing access to healthcare. These studies suggest that sociodemographic determinants (e.g., female gender, older age, etc.) [ 7 ], age-related factors (e.g., limited mobility, sensory impairments, and disability) [ 8 ], socioeconomic variables (such as lower income, lack of complementary insurance, cost, and transportation) [ 7 , 9 ], as well as organisational features of healthcare systems (e.g., extended waiting periods for medical examinations) [ 10 ], play significant roles in influencing access to healthcare.

To overcome these barriers, healthcare systems have progressively implemented various types of digital health technologies aimed at enhancing elderly care. The literature presents various terminologies in this regard. The World Health Organization defined digital health as ‘the field of knowledge and practice associated with the development and use of digital technologies to improve health’ [ 11 , 12 ]. Particularly, digital technology refers to both the software, which includes computer coding programmes that provide instructions for computer operations, and the hardware, which consists of physical computer devices. These components work together using digital coding, also known as binary coding. Additionally, digital technology encompasses the infrastructure that supports these software and hardware components [ 13 ].

Furthermore, specific terms have been coined to describe digital health technologies for older adults. For instance, the umbrella term ‘gerontechnology’ has emerged to define the set of technologies intended to promote the independence of older adults, facilitate ageing in place and accommodate age-related declines and impairments [ 14 ]. These technologies include both assistive technologies designed to maintain or improve the independence, social participation and functionality of older people at home, as well as health information technology for managing long-term conditions. Examples of these technologies include telehealth, wearable devices, and mobile health [ 15 , 16 ]. In the Nordic European welfare state systems, the term ‘welfare technology’ has been coined to emphasise the public and universalistic nature that these devices should have. This term refers to a range of digital tools with integrated platforms that are adopted by public care services to promote welfare among individuals [ 17 ].

In this paper, the expression ‘health technologies’ will be employed to encompass a range of digital technologies designed for the management of health conditions. These include the electronic health record, mobile health apps, wearable devices, telehealth and telemedicine [ 11 , 12 ].

Health technologies can be applied in multiple areas, such as self-management of chronic diseases and the sharing and transfer of clinical data. These applications can improve adherence to therapeutic regimens, facilitate communication with healthcare professionals and enable timely interventions. Previous research on telemedicine has shown that it can reduce travel time and costs, making it an important resource for older patients living in underserved areas. It can also diminish patients’ waiting times for medical encounters, thus shortening the time for diagnosis. Lastly, especially during pandemic times, it can also reduce the risk of contagion and infections [ 18 , 19 ]. Moreover, health technologies can promote aging in place, enabling older people to safely remain in their homes (or in appropriate housing, depending on their health conditions), thereby reducing hospitalization and avoiding institutionalization. A recent systematic review revealed that smart home technologies used for the management of chronic diseases in older adults can improve several health outcomes [ 20 ]. The monitoring of daily living activities, such as mobility, posture, falls or sleeping disorders can facilitate personalized and timely interventions. Furthermore, it promotes physical activity, enhances the quality of life and fosters a sense of security and well-being in older people. Other instruments, such as external memory aids and telemedicine, can support chronic patients in managing medication, or enhance the control of vital signs [ 20 ].

However, despite the great promise of health technology in enabling older people to maintain their independence for longer, its actual implementation remains challenging. The literature highlights several factors that may have a negative impact on the adoption of health technologies in the elderly population.

One line of research has focused on socio-structural characteristics that may increase inequalities in technology use. Previous studies have found that older individuals with lower income and education levels have limited access to broadband, lower health literacy and lower digital competencies, which in turn leads to limited technology adoption [ 21 , 22 , 23 , 24 ].

The second research line has concentrated on individual factors that may influence the acceptance of technology, such as age or health conditions. Cognitive deficits, as well as physical impairments (e.g., vision and hearing loss and mobility limitations), can pose significant challenges in the use of technology [ 15 , 25 ]. In addition, other studies have examined the attitudes of the elderly towards health technologies. For instance, some of them have focused on the strategies employed by individuals to resist stereotypes associated with old age. The rejection of technology may indeed be associated with negative (self)perceptions related to the loss of dignity and autonomy, as well as the fear of being stigmatised as someone no longer able to take care of oneself [ 26 , 27 , 28 ]. Furthermore, perceptions of the usefulness and ease of use of technology, as well as beliefs about privacy, may influence its adoption. While privacy concerns have not been observed for some technologies, such as telemedicine [ 26 ], they could be a barrier for other devices, including fall detection or bed occupancy sensors [ 29 , 30 ].

The third line of study focused on aspects related to the technology itself, including the role of users in the technology design process and the socio-material characteristics of the technology, as well as users’ adaptation strategies. Regarding the first aspect, while participatory technology design methods are becoming more widespread, there are difficulties in implementing these methods in practice. Users are often still perceived as passive consumers, and their needs may not be fully addressed [ 31 , 32 ]. Concerning the second aspect, research has emphasised that in order to understand the strategies for the adoption or refusal of technologies, they should be considered within the context of the usual practices of the elderly population [ 33 ]. In fact, research has shown that older people tend to adapt technologies to suit their needs through ‘bricolage’ arrangements, using devices in ways that were not originally intended [ 33 , 34 , 35 ].

Across these strands of research, however, tension emerges between two different perspectives regarding the impact that health technologies would have on social actors. On one side, there is the self-surveillance effect of these technologies, while on the other side, there is the empowerment effect that would be embedded in health technologies [ 36 , 37 , 38 ]. The first perspective emphasises the disciplinary effects of health technologies, which could engender behavioural changes through continuous data generation and transmission [ 39 , 40 , 41 , 42 ]. This ultimately would promote both an expansion of the ‘medical gaze’ into the everyday lives of self-tracked patients [ 37 ] and an individualistic dimension of health, shifting the responsibility from healthcare systems to individuals [ 43 , 44 ].

The second point of view presents the individualisation of responsibility for one’s health condition in a positive manner and highlights the empowering potential of technologies. Health technologies empower patients by instilling in them a greater awareness of their health status. This awareness would thus lead to a greater sense of responsibility for one’s own health and trigger a virtuous cycle [ 45 – 46 ].

Therefore, although much research has been conducted, a variety of factors is at play in the acceptance of technology by older adults. Additionally, there are ambivalent points of view about the impact of health technologies on people. Upon examining recent reviews, it appears that studies are narrow in focus, as they only consider one set of factors at a time or a single technology, or they are not systematic and are based on a scoping review [ 47 ]. Particularly, the research appears to be limited to specific technologies, such as falls-prevention interventions [ 48 ], m-Health technology [ 49 ], telemedicine [ 18 , 50 ] and electronic personal health records [ 51 ]. Moreover, it solely examines hurdles and barriers without considering facilitating factors [ 51 ]. It also fails to indicate the specific medical condition or set of conditions that the technology is targeted at [ 47 , 52 , 53 ].

To shed light on this topic, the present integrative systematic review aims to identify barriers and facilitators that impact the adoption of different health technologies by older individuals with chronic diseases. Considering the multitude of diseases that could be included in the review and the wide range of technologies addressed in the health management of elderly individuals with chronic conditions, we have chosen to adopt a broad conceptualisation of ‘facilitators’ and ‘barriers’. With the first term, we refer to factors that support the adoption of technology and provide an incentive to continue using it. Additionally, we consider factors that have been identified in the literature as not hindering the utilisation of technology. Barriers, on the other hand, consist of all the elements that hinder the adoption of technology or discourage its use.

Thus, the research questions that guided the review were as follows:

What are the main factors hindering the adoption of technology by older adults with chronic diseases?

What are the main factors facilitating the adoption of technology by older adults with chronic diseases?

An integrative systematic review was conducted by implementing a search strategy that allowed for a comprehensive examination of the barriers and facilitators to the adoption of chronic disease-related technology in the elderly population. This method can have direct applicability for practical implementation and policymaking, and ‘allows for the inclusion of diverse methodologies (e.g., experimental and non-experimental research)’ [ 54 ]. Therefore, our analysis includes qualitative research, randomised and non-randomised quantitative studies and mixed method studies. It also comprises papers focused on different technologies and different types of chronic diseases.

Inclusion and exclusion criteria

In order to identify eligible articles, inclusion and exclusion criteria were established before starting the literature search. These criteria were based on the exploratory research questions in the review. The pre-defined inclusion criteria for study selection were as follows: (a) the sample must include participants aged 60 or older; (b) the sample must include participants affected by chronic disease; (c) the studies must focus on facilitators or barriers to the adoption of technologies related to chronic disease management; (d) the studies must be empirical and use qualitative, quantitative or mixed methods; (e) the studies must be published in English; (f) the studies must focus on technology targeting older people. The exclusion criteria adopted were as follows: (a) mixed sample population with participants above and below 60 years of age; (b) publications such as theoretical contributions, letters to the editor, systematic or scoping reviews, dissertations, conference proceedings, or those adopting non-standardised techniques and lacking sufficient analytical rigour (e.g., narrative reviews); (c) studies that evaluated a healthcare service instead of digital technology tools enabling the service (e.g., studies that evaluated the general telemedicine service without focusing on the platform enabling telemedicine services).

Search strategy

Four electronic databases were queried to search for published studies: PsycArticles, Scopus, Web of Science and PubMed. Records published between January 2012 and April 2022 were considered, operating with the following PICo framework [ 55 ] and using a Boolean search strategy through keywords and Medical Subject Headings (MeSH) terms (see Table  1 ). The full search strategy can be found in Additional file 1 .

The timeframe was chosen based on the findings from previous reviews [47; 53] and the evolution of the digital health tools under consideration. Publications reporting was guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Statement (PRISMA) flow diagram [ 56 ].

PRISMA flow diagram

As shown in Fig.  1 , a total of 9.906 publications have been retrieved from the databases. After removing the duplicates, 9.370 have been screened. All the authors reviewed the titles and abstracts of the records to identify relevant studies that met the inclusion/exclusion criteria. Disagreements were resolved through discussions until a consensus was reached.

Data extraction and analysis

As a result of the first screening, fifty-four publications were identified for possible inclusion. The methodological quality of the selected records has been assessed using the Mixed Methods Appraisal Tool (MMAT) [ 57 ]. All authors independently undertook the quality assessment process by performing blind MMAT evaluations of the same articles and comparing the results.

Regarding data extraction, the full texts of the included articles were obtained to extract pertinent details such as the authors’ names, year of publication, study objectives, study design, country of study, study setting (e.g., home or hospital), type of chronic disease and type of technology used. Each author independently identified facilitators and barriers through the content analysis of the included articles. One author independently (A.B.) grouped together homogeneous factors and redefined similar factors with different names. The final list of factors was discussed among authors until reaching a consensus. To synthesise and organise the results more effectively, these factors were further grouped into macro-categories, which are referred to as domains. The five identified domains emerged through a combination of inductive and deductive approaches. Some domains were deduced from previous systematic reviews [ 47 , 51 ], including technology-related factors and social factors. Some factors emerged inductively from the data itself, such as socioeconomic factors, health-related factors and dispositional factors.

During the quality assessment process, the authors agreed to exclude twenty-five articles for various reasons. These reasons included poor methodological quality, inadequate focus on barriers and facilitating factors (such as factors that were mere inferences of the authors) or articles that solely presented protocols for developing technology. Twenty-nine articles were selected, including 6.213 adults aged 60 or older. The characteristics of the screened studies are shown in Additional File 2 . As for the nomenclature of technological interventions, the original designations used within each article have been preserved to avoid inappropriate simplifications that could have resulted from their reclassification. Regarding the research design of the included studies, fifteen were based on research trials, presenting different lengths of the evaluation period: two weeks [ 58 ], five weeks [ 59 ], two months [ 60 , 61 , 62 ], three months [ 63 , 64 ], nine months [ 65 ], one year [ 66 , 67 ], and fifteen months [ 68 ]. However, one trial was conducted in a laboratory [ 69 ], so no evaluation period was planned. Additionally, three studies relied on data derived from prior trials, with subsequent secondary analyses performed [ 70 , 71 , 72 ]. The remaining articles had adopted different methodologies, encompassing qualitative approaches– such as interviews [ 73 , 74 , 75 , 76 ], focus groups [ 77 , 78 , 79 ], or both [ 80 ]–, quantitative ones through cross-sectional studies [ 81 , 82 , 83 , 84 ], or mixed methods [ 85 ].

The analysis of the included articles focused on (a) types of technological interventions, (b) types of chronic diseases and (c) the main barriers and facilitators in technology acceptance.

Concerning the types of technological interventions for managing chronic diseases, eight studies have examined telemedicine, which includes telecare, telemonitoring, telehealth, and telerehabilitation programmes [ 60 , 61 , 62 , 63 , 65 , 67 , 71 , 86 ]. Seven studies have focused on digital health platforms, such as web portals and video conferencing for home-based education [ 59 , 64 , 66 , 78 , 79 , 83 , 85 ]. Five studies have explored wearable technology, including the use of pedometers and self-tracking technology [ 58 , 72 , 73 , 75 , 77 ]; instead, m-health services have been examined by three studies [ 70 , 74 , 84 ]. Three studies have investigated information and communication technology (ICT) [ 80 , 81 , 82 ], that is services obtained through ICT (e.g., messaging services, using email to communicate with doctors, medication services and reminders, online tools, etc.); instead, one article focused on the Internet for health information seeking [ 71 ], as it specifically refers to the Internet exclusively for searching for health information online. Lastly, two articles have examined home assistive technologies (smart home) [ 68 , 69 ], two studies have focused on assistive robots [ 69 , 76 ], and one on active video games [ 75 ].

In terms of the specific chronic diseases targeted by technological interventions, 16 articles included aged people with multiple chronic conditions for technological intervention [ 59 , 60 , 61 , 63 , 65 , 66 , 67 , 69 , 73 , 78 , 79 , 80 , 82 , 83 , 85 , 86 ]. While, other articles focused on a specific chronic disease: COPD [ 71 , 72 , 75 ], cognitive impairments [ 68 , 70 , 76 ], heart failure [ 74 , 84 ], Parkinson’s disease [ 77 , 81 ], hypertension [ 58 ], vestibular dysfunction [ 64 ], and diabetes [ 62 ].

The identified barriers and facilitators are shown in Table  2 .

The identified factors were grouped into five domains, which have been used to organise the results: demographic and socioeconomic factors, health-related factors, dispositional factors, technology-related factors and social factors. Evidently, some of the factors placed in one of the five identified domains could simultaneously fall into another domain or present aspects of continuity with other domains. Therefore, the classification made is not rigid, but it is intended to make the results as intelligible as possible.

Demographic and socioeconomic factors

The impact of age on the adoption of technologies appears to be conflicting, considering demographic and socioeconomic patients’ characteristics. Several studies indicate that older age is a barrier to the utilisation of technology [ 81 , 82 , 85 ], mainly when it comes to using ICT tools for communication with healthcare providers or health education. However, other research did not find that increasing age is a limiting factor for technological interventions, including devices such as a pedometer [ 73 ], mobile applications for reporting health outcomes [ 84 ] and a telemedicine service [ 62 ].

In contrast, the impact of educational level on technology use appears to be more consistently supported by the literature [ 7 , 8 , 71 , 73 , 82 ]. Well-educated older adults seem to have an advantage in adopting various technologies [ 73 , 82 ], whereas poor education limits their use and acceptance [ 71 ]. An additional factor that has hindered the adoption of technologies by the elderly is related to the economic aspect of technology use. Several investigations consider the cost of technology as a barrier [ 58 , 69 , 85 ], as well as having a low income or not having an adequate socioeconomic status [ 74 ]. In the same vein, another study highlights the repercussions of low socioeconomic status, such as the absence of an Internet connection or insufficient space in the household for technological devices [ 58 ]. On the other hand, home-based technologies offer the advantage of being cost-effective, as they allow people to save time and money on transportation to medical examinations [ 59 , 65 , 74 , 79 ].

Health-related factors

Studies examining factors associated with the health status of the elderly converge, highlighting that age-related physical limitations can be a significant barrier to the adoption of technological interventions. More specifically, the analysis has revealed a high prevalence of sensory impairments, such as poor vision [ 71 ] or hearing loss [ 76 ], motor deficits [ 73 ] and cognitive disorders, including poor memory [ 58 , 71 ], limited learning skills [ 71 ] and general cognitive degeneration [ 76 ].

Furthermore, the lived experience of various health conditions plays a significant role in determining older adults’ acceptance of technology, both positively and negatively. On the one hand, technology seems to be more likely to be accepted if it has been adopted in the early stages of the disease [ 65 ]. On the other hand, research has underlined the detrimental effect of comorbidities or complex health conditions on the adoption of technology [ 60 , 66 , 86 ]. Different types of long-term diseases can also affect technology acceptance. For example, Rodríguez-Fernández et al. [ 86 ] found that a history of cancer, arthritis and hypertension was positively associated with the effective utilisation of telemedicine. On the contrary, depressive symptoms were negatively associated with it, as well as technical difficulties in using telemedicine were associated with a history of diabetes, heart disease and anxiety symptoms. States of severe anxiety about their illness appear to hinder the use of technology, as observed in the study conducted by Middlemass et al. [ 65 ]. Whereas a complex disease experience may lead to perceiving the activity of controlling one’s condition as ‘work’ [ 73 ] or an ‘extra burden’ [ 66 ], other studies have indicated that self-monitoring technologies appear to be capable of triggering a virtuous circle. In fact, older patients reported greater awareness and understanding of their condition [ 65 , 71 ], as well as an improvement in health and quality of life through the support they received [ 66 , 69 ]. Continuous monitoring enables both patients and healthcare providers to gain insights and learn about the medical condition, as well as to make timely adjustments (e.g., modifying diet or medications) [ 55 , 66 , 69 , 77 ].

Dispositional factors

Further aspects are investigated in the literature pertaining to dispositional factors, specifically the beliefs, attitudes and behaviours exhibited by the ageing population towards technologies. A conservative mindset, as well as a strong attachment to daily routines, have been identified as obstacles to the adoption of various technologies [ 71 , 76 , 79 ]. Similarly, a lack of motivation and interest in learning new ways to manage their conditions is an important barrier [ 60 , 64 , 65 , 71 , 76 , 79 ]. Resistance to the adoption of technology may also be the result of a general fear of using new services that could potentially alter their lives [ 80 ]. Additionally, specific fears, such as the fear of online fraud [ 71 ], the fear of sudden device malfunction [ 58 , 80 ] and the fear of making mistakes when using healthcare devices, can contribute to this resistance [ 75 ].

In addition, privacy seems to be a significant factor in hindering access to technology, especially when it comes to the use of the Internet for health information seeking [ 71 ] and patient web portals [ 78 , 83 ]. Since the ageing population tends to be unfamiliar with new technologies, training is a crucial factor in promoting and enhancing access to eHealth. The knowledge necessary to use technology can be gained through targeted training [ 58 ] or acquired through previous experience [ 74 , 80 ]. However, reluctance to learn how to operate technology, as well as perceived difficulties in the learning process, can make that process challenging.

Technical factors

As for technology-related factors, the analysed publications consistently emphasise the importance of the perceived ease of use of the various technologies discussed [ 59 , 74 , 82 , 80 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 61 ]. Conversely, the perception of difficulties in using devices or programmes may hinder the acceptance of such technologies [ 60 , 75 ]. The reviewed studies highlight several technical factors that patients report as problematic. These include connection problems [ 59 , 60 , 65 ], which are particularly evident for patients living in rural or isolated areas. Additionally, interface design issues, such as low-quality graphics [ 85 ] and unclear navigation buttons in portals [ 79 , 85 ], are also mentioned.

Therefore, a simple design [ 80 ] and clear presentation and organisation of information are features that facilitate the use of technology [ 85 ]. Besides, the technical factors that promote or hinder access to health technologies vary depending on their specificities and their different uses. For instance, in the case of wearable devices, it is important for older people that the technologies are non-invasive and that users perceive them as comfortable [ 77 ].

Similarly, in the case of assistive robots, the literature highlights the significance of the robot’s appearance as a relevant factor [ 76 ], particularly in terms of how the robot is perceived as trustworthy and likeable [ 69 ]. The robot’s speech interaction capabilities have also emerged as particularly important, especially the presence of a speech recognition system and the implementation of robot eye contact and validated gestures to accompany the speech [ 69 ].

Interestingly, a technical aspect that seems to encourage the use of both wearable devices and portals concerns the self-tracking functions. These functions include the ability of the tools to set measurable goals for physical activity, quantify users’ health status and activities, receive reminders, allow users to see long-term improvements and share data with healthcare providers [ 60 , 73 , 78 , 83 ].

Moreover, another important feature concerns the ability to communicate effectively with healthcare providers, specifically in the case of telehealth. Since telehealth is considered useful in managing medical conditions [ 58 ], facilitating factors include the device’s ability to function correctly, transmit accurate and reliable information and provide prompt intervention [ 58 ].

Social factors

Lastly, the analysis has revealed several social factors that are considered relevant based on the literature. First, an important facilitating factor is the perception that health technologies help enhance social ties with nurses and clinicians [ 63 , 66 ]. Moreover, in the case of telehealth, there is a perception of receiving social support and an improvement in communication between patients and healthcare providers [ 67 ].

Research has highlighted ambivalent perspectives on the role played by social networks in the lives of the elderly. On the one hand, having offspring or partners to rely on is considered a facilitating factor for using technology [ 65 , 71 ], especially when they actively encourage the elderly person to use these devices [ 65 ]. In addition, certain technologies seem to enhance connectedness with others; for example, active video games allow people to play with others [ 75 ] and videoconferencing platforms are used for home-based education [ 59 ]. The latter helped older adults who lived alone to meet new people, and being part of a group allowed them to share information and knowledge with others who had the same condition. Additionally, individuals who suffered from anxiety or depression found it less challenging to participate in online groups rather than to interact with people in person [ 59 ].

On the other hand, having more than one person in the house is instead seen as a barrier to technology use because it alters the data registered by sensors [ 76 ]. Furthermore, another hindering factor in the use of health technologies is the dependence on others for their use. This is evident in the cases of telehealth [ 65 ], ICT [ 80 ] or video games for physical activity that require other individuals to participate. The concern that technologies could replace in-person contact/visits by both clinicians and relatives constitutes a barrier, particularly in the case of ICT [ 80 ] and telehealth [ 65 ].

This study contributes to the understanding of the key factors that influence the acceptance or rejection of health technologies among elderly individuals with chronic diseases or conditions. This is achieved through an analysis of recent empirical literature. Selected studies examine a variety of chronic conditions (i.e. Parkinson’s disease, heart failure, COPD, cognitive impairment, etc.), investigating older adults’ acceptance or rejection of different technologies (i.e. wearable and mobile technologies, telemedicine, assistive robots, etc.). Our review suggests that the technology acceptance or refusal by aged people depends on a wide range of factors, grouped as follows: demographic-socioeconomic, health-related, dispositional, technology-related and social factors.

Regarding demographic and socioeconomic factors, a low educational level [ 71 ] and low income [ 58 , 69 , 85 ] have emerged as the main obstacles, which is consistent with previous findings [ 7 , 9 ]. Instead, the results concerning the impact of age appear more controversial, as some studies included in the review emphasize the negative effect of older age on the adoption of technologies [ 81 , 82 ], while others did not demonstrate statistically significant differences in technology adoption with advancing age [ 62 , 72 , 84 ]. This inconsistency can be explained by the fact that the samples considered in the examined studies include a target group– individuals over 60 years old– with heterogeneous characteristics. Differences may exist between age groups (for example, between those under 75 and those over 75), as well as within the same age group, given the variation in the aging process and the progression of chronic diseases from person to person.

Therefore, policymakers and technology developers should consider the needs of the most vulnerable and underprivileged social groups, particularly those with low household income and limited educational achievement [ 22 , 23 , 24 ]. To enhance access to health technologies, costs should be minimized or even eliminated, for example, by providing support to low-income individuals to access broadband [ 22 , 23 ]. Specific interventions should be aimed at improving technological competencies and digital health literacy among the elderly. For example, short e-learning courses have been found to be useful for enhancing their technological skills [ 87 ] and could be a suitable solution to bridge the digital divide.

Our review highlighted several aspects of older people’s health status, one of the individual-level factors that can negatively impact healthcare technology access. These factors include poor vision [ 71 ], hearing loss [ 76 ], motor deficits [ 73 ], cognitive issues such as poor memory [ 58 , 71 ] and limited learning skills [ 71 ], general cognitive degeneration [ 76 ] and the presence of comorbidities or complex health conditions [ 60 , 66 , 86 ]. However, considering the facilitating factors outlined in the review, it is possible to identify some strategies to mitigate these barriers. Firstly, healthcare professionals should recommend the early adoption of health technologies to ensure that older people have the opportunity to learn how to use them before the progression of the disease(s) [ 65 ]. Secondly, the developers should provide devices with a design that is as accessible as possible. For example, they could increase screen contrast and use an adequate font size to allow individuals with poor vision to read [ 63 , 65 , 79 , 83 , 84 ]. Additionally, robots and devices should incorporate sound alerts and a language that can be heard and understood by the elderly with hearing loss and cognitive impairment [ 58 , 59 , 69 , 76 , 85 ]. Findings also showed several dispositional factors that influence the acceptance or refusal to adopt technologies. For those individuals from older generations who are less familiar with technology, there is a higher likelihood of encountering resistance when it comes to using digital health tools. In fact, a conservative mindset, and a lack of interest in learning new methods of managing their conditions have been identified as significant barriers to the adoption of technologies among the elderly [ 65 , 71 , 79 ]. These findings are consistent with previous studies, which indicate that older adults are often hesitant to embrace new technologies [ 50 , 51 , 75 ]. This reluctance may stem from their familiarity with alternative methods of managing their diseases and their perceived lack of necessity for these devices [ 88 , 89 ]. Thus, healthcare providers should consider introducing digital health devices to the elderly through personalised and easy-to-understand training [ 65 , 66 , 79 , 83 ]. They should also reassure prospective users about privacy issues and provide constant and timely support in case of doubts or device malfunctions [ 58 , 65 , 66 , 78 ].

Technical factors emerged as crucial in either promoting or hindering older people’s access to technology. Literature has shown that, in order to be accepted and used over time, devices should be non-invasive and perceived as comfortable by users [ 77 ]. They also have functions to measure and quantify body functions and health status, set measurable goals for physical activity, send reminders to users, allow users to track long-term improvements and share data with physicians [ 75 ]. As mentioned earlier, technologies should have a simple design [ 80 ], and the organisation of the information should be as clear as possible [ 85 ]. Technologies for elderly healthcare have to accommodate the needs of individuals with different dis/abilities and physical/cognitive limitations. Furthermore, developers should consider the possibility of involving end users in the design and development process of digital health devices. Because of egocentric bias, younger designers might indeed face challenges in envisioning the product’s usage from the standpoint of an elderly adult [ 90 ]. Patients involved in the technology development process are more satisfied and inclined to adopt the technology [ 58 ]. In particular, if wearable devices are designed in collaboration with patients, it is easier to avoid issues regarding comfort, size, and ease of fitting, which often pose a barrier to adoption [ 77 ]. The involvement of patients in the early stages of technology development can enable the design of more user-centred technologies, and contribute to the early identification of potential issues, thus avoiding the addition of features that patients do not need [ 31 , 58 , 66 ].

The current integrative systematic review has highlighted a domain that is often overlooked but could actually play a crucial role in facilitating technology adoption– the social factors domain. The factors that we have classified in this domain indicate that health technologies can enhance connectivity with others, including nurses, physicians and relatives or patients with a similar health condition. In other words, besides helping to manage the disease, certain technologies can unintentionally have a positive effect by improving the social relationships of older people and encouraging them to embrace technology. Telecare technologies are perceived as useful for receiving social support and improving communication between patients and healthcare providers [ 67 ]. Active video games allow people to play with others [ 75 ]. Videoconferencing platforms used for home-based education help older adults, especially those who live alone, meet new people and become part of a group [ 59 , 71 , 75 , 80 ]. This allows them to share information and knowledge with other people who have the same condition [ 59 ]. Additionally, individuals who have experienced anxiety or depression found it less challenging to participate in online groups than to interact with others in person [ 59 ]. However, studies have reported that patients fear technologies could replace in-person visits from both clinicians and relatives [ 65 , 80 ]. Therefore, formal and informal caregivers should receive proper training in the use of healthcare technologies and should be encouraged to alternate between remote and in-person consultations, as this is in the best interest of the older person.

Lastly, our results contribute to a deeper understanding of the ongoing debate surrounding the impact of health technology use, specifically the ‘self-surveillance/empowerment dichotomy’.

On the one hand, the perspective of empowerment positively frames the individual responsibility in managing the disease [ 45 – 46 ]. The ‘empowered patient’ gains power through a better understanding of their illness, which in turn produces a greater sense of responsibility towards self-management of the disease. Patient empowerment thus enhances motivation and adherence to the use of health technologies. As demonstrated in this review, research on platforms for home-based telerehabilitation and health education programs, as well as on wearable activity trackers and active video games, has indicated that motivation can be fostered by various factors.

First, several studies included in the current review have shown that older adults appreciate the self-tracking functions enabled by various types of technology [ 60 , 67 , 73 , 75 , 78 ]. Health information technologies for self-tracking stimulate individuals to engage in physical or monitoring activities and evaluate their progress towards a goal [ 60 , 73 , 75 , 78 ]. Reminders for goal setting have been shown to yield motivational benefits for older adults [ 60 , 66 , 74 , 75 ], as well as to receive positive feedback on the accomplishment of personal objectives [ 69 ].

Second, m-health devices and telemonitoring platforms also provide patients with the ability to learn more about their disease, and access information and data [ 60 , 73 , 75 , 78 ]. Patients perceive greater control over the self-management of their disease and a better understanding of their condition [ 67 ]. An increased self-understanding of one’s body and illness can trigger in individuals an attitude of heightened awareness and self-efficacy, which could increase motivation, enable positive coping actions towards self-care, and improve health behaviours. Specifically, seniors show greater motivation to engage with technology when they perceive a clear connection between improved health behaviours and better health outcomes. They can recognize the additional health benefits it offers, such as enhanced autonomy and an improved quality of life [ 58 , 66 , 67 , 75 ].

Third, the motivating factors can be socially focused, as an increased sense of connectedness can contribute to generating motivation to adopt technology. Concerning telehealth platforms, the external monitoring of patients by healthcare providers (nurses and physicians) produces a perception of social support and, consequently, can motivate the usage of technology [ 67 ]. In the trial conducted by Doyle et al. on a digital health platform [ 66 ], the triage service implemented by nurses provided reassurance to participants, as they were monitored ‘behind the scenes’ by healthcare providers who could oversee their parameters and suggest interventions. Additionally, participants expressed appreciation for the social interactions established between them and the nurses [ 66 ]. Similarly, digital health platforms, especially those based on telerehabilitation trainings with other participants, can contribute to establishing social interactions with other patients who have the same disease [ 59 , 60 , 66 , 75 ]. The participants in the study by Simmich et al. [ 75 ] considered the enjoyment derived from playing games with others, specifically through active video games, as a significant motivating factor.

What Petrakaki et al. have referred to as ‘technological self-care’ is the unintended consequence of health technologies that strengthen an individual’s ability to take care of their own health [ 38 ]. This includes both personal self-discipline in meeting systemic expectations and the collective encouragement of sharing health knowledge from medical authorities with patients and then disseminating that knowledge to the broader community. This has wider ramifications for the community as a whole [ 38 ].

On the other hand, the ‘surveillance effect’, which involves the continuous monitoring of medical data, can remind patients of the negative aspects of their disease. Some may perceive the effort required by these devices as excessive, negatively affecting their motivation to use the devices [ 66 , 73 ]. Considering the pilot studies, technology appeared to older adults as an added burden to their complicated condition and some participants abandoned the trial due to the onset of health complications [ 58 , 66 , 68 ] or hospitalization [ 63 ].

More generally, program completion and adherence are challenging [ 60 , 64 , 73 ], which is particularly evident in long-term trials (one year or more) focused on activity trackers or platforms for rehabilitation training [ 66 , 68 ]. Adherence seemed high at the beginning of the trial, but over time, there was a decline in compliance for using technology for assisted home exercises [ 60 , 64 ]. Early withdrawal from the trial can be attributed to various factors, including technical difficulties arising from both structural barriers such as poor connectivity [ 59 , 68 ] and false alarms generated by the devices [ 68 ]. Likewise, frustration stemming from a negative experience with the technology, perceived as too complicated to use, contributes to participant dropout [ 58 , 60 , 66 ].

Consistently, a recent investigation into the reasons for the abandonment of wearable activity trackers has identified six factors [ 91 ]. Among these factors, the loss of motivation, which is linked to lower technology acceptance and a negative perception of personal quantification, is one of the most influential [ 91 ]. The positive or negative ‘emotional investment’ [ 92 ] that people activate when using technologies should be considered for successful adoption.

Some trials examined in the present review employed specific motivational techniques to enhance patients’ adherence to the intervention. These techniques included the use of motivational interviews [ 60 ], setting goals and action plans [ 60 , 72 ], employing reminders and motivational messages to enhance disease self-management and promote self-efficacy [ 60 , 66 , 72 , 74 , 75 ], as well as providing information on health consequences and assessing outcome goals [ 60 ]. In addition, some studies have emphasized the role of caregivers. Their involvement during the trial and the support they provided to the participants ensured better adherence [ 58 , 68 ]. However, motivational strategies and behaviour change techniques may not be fully effective if they are not accompanied by increased self-reflexivity and self-knowledge of one’s own self and their illness [ 93 ].

Moreover, the integration of self-management programs in primary care is often motivated by the need to contain financial pressure and costs associated with managing chronic diseases within healthcare systems [ 43 ]. Nevertheless, the implicit transfer of medical responsibilities from healthcare systems to individuals could result in the exclusion of patients who lack access to health technologies or choose not to adopt them [ 44 ]. This could potentially worsen inequalities in healthcare access.

Despite the results achieved, this study has some limitations. First, this integrative systematic review excluded certain publications (such as dissertations, conference proceedings, etc.) due to the adopted search strategy. Yet, we believe that the systematic review process adopted, which involved medical, psychological, and sociological databases, as well as three independent researchers who conducted screening and data extraction, ensured a rigorous approach to identifying papers containing consolidated results and relevant information. Second, the analysis focuses on publications written in English, and this may exclude other empirical evidence. Thirdly, an assessment of inter-rater agreement among the authors who reviewed the records has not been conducted. Fourth, this review could not include all studies published before 2012. It is challenging to determine the exact historical moment when certain digital health technologies began to spread, as their implementation depends on various factors (economic, social, cultural, etc.) and can vary in different world regions [ 94 , 95 ]. Moreover, this review encompasses technologies that have experienced different stages of development. Regardless, to achieve results suitable for current technological developments, it has been decided to establish a timeframe.

Despite these limitations, the selected studies allowed us to conduct an updated analysis of recent literature and identify factors that influence the use of a wide range of technologies by older people with chronic diseases.

The purpose of the present study was to systematically review the recent literature that addresses the factors related to the adoption or refusal of health technology by elderly individuals with chronic diseases. Moreover, the review provides an overview of the current state of health technologies for elderly individuals with chronic diseases, as well as the specific types of chronic diseases that have been targeted. The findings of the study might help to improve healthcare delivery for this specific population as well as delay disease progression and prevent complications. Besides the positive effects at the individual level, considering the barriers and facilitators that promote the use of health technologies for older individuals leads to a significant decrease in public health expenditure.

Future research aiming to promote technology adoption should therefore consider these factors at different levels: the level of the users, the level of the caregivers and the societal level. In addition, the research will need to delve into the actual effect of the Covid-19 pandemic on the use of technologies. Indeed, on one hand, the pandemic could have acted as a catalyst for the accelerated adoption of technology, enhancing both the implementation and utilization of Internet-based services, such as telemedicine. On the other hand, the pandemic did not address the gap in terms of digital skills, health literacy, and technological competencies among the most vulnerable people, as indicated in a recent systematic review by Elbaz et al. [ 18 ].

The study results have practical implications not only for technology developers but also for all the social actors involved in the design and implementation of healthcare technologies, including formal and informal caregivers and policy stakeholders. These actors could use this systematic review to enhance their understanding of the utilisation of technology by the ageing population. This review emphasises the factors that facilitate technology adoption and identifies barriers that impede it, with the ultimate goal of promoting health and independent living.

Data availability

The authors declare that all datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Chronic Obstructive Pulmonary Disease

Information and Communication Technology

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Acknowledgements

The Authors thank the chairs and the session participants for their valuable suggestions received during the ESPAnet Conference “Social Policy Change between Path Dependency and Innovation”, held in Vienna in 2022, where the preliminary results of the research have been presented. Moreover, the authors acknowledge Dr Giovanni Lamura, of the Centre for Socio-Economic Research on Ageing (IRCCS-INRCA Institute), for his helpful suggestions. VQ acknowledges her research contract co-funded by the European Union - PON Research and Innovation 2014–2020, in accordance with Article 24, paragraph 3, lett. a), of Law No. 240 of December 30, 2010, as amended, and Ministerial Decree No. 1062 of August 10, 2021.

This work has been funded by the European Union - NextGenerationEU under the Italian Ministry of University and Research (MUR) National Innovation Ecosystem [grant ECS00000041 - VITALITY - CUP E13C22001060006].

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Bertolazzi, A., Quaglia, V. & Bongelli, R. Barriers and facilitators to health technology adoption by older adults with chronic diseases: an integrative systematic review. BMC Public Health 24 , 506 (2024). https://doi.org/10.1186/s12889-024-18036-5

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The state of AI in early 2024: Gen AI adoption spikes and starts to generate value

If 2023 was the year the world discovered generative AI (gen AI) , 2024 is the year organizations truly began using—and deriving business value from—this new technology. In the latest McKinsey Global Survey  on AI, 65 percent of respondents report that their organizations are regularly using gen AI, nearly double the percentage from our previous survey just ten months ago. Respondents’ expectations for gen AI’s impact remain as high as they were last year , with three-quarters predicting that gen AI will lead to significant or disruptive change in their industries in the years ahead.

About the authors

This article is a collaborative effort by Alex Singla , Alexander Sukharevsky , Lareina Yee , and Michael Chui , with Bryce Hall , representing views from QuantumBlack, AI by McKinsey, and McKinsey Digital.

Organizations are already seeing material benefits from gen AI use, reporting both cost decreases and revenue jumps in the business units deploying the technology. The survey also provides insights into the kinds of risks presented by gen AI—most notably, inaccuracy—as well as the emerging practices of top performers to mitigate those challenges and capture value.

AI adoption surges

Interest in generative AI has also brightened the spotlight on a broader set of AI capabilities. For the past six years, AI adoption by respondents’ organizations has hovered at about 50 percent. This year, the survey finds that adoption has jumped to 72 percent (Exhibit 1). And the interest is truly global in scope. Our 2023 survey found that AI adoption did not reach 66 percent in any region; however, this year more than two-thirds of respondents in nearly every region say their organizations are using AI. 1 Organizations based in Central and South America are the exception, with 58 percent of respondents working for organizations based in Central and South America reporting AI adoption. Looking by industry, the biggest increase in adoption can be found in professional services. 2 Includes respondents working for organizations focused on human resources, legal services, management consulting, market research, R&D, tax preparation, and training.

Also, responses suggest that companies are now using AI in more parts of the business. Half of respondents say their organizations have adopted AI in two or more business functions, up from less than a third of respondents in 2023 (Exhibit 2).

Gen AI adoption is most common in the functions where it can create the most value

Most respondents now report that their organizations—and they as individuals—are using gen AI. Sixty-five percent of respondents say their organizations are regularly using gen AI in at least one business function, up from one-third last year. The average organization using gen AI is doing so in two functions, most often in marketing and sales and in product and service development—two functions in which previous research  determined that gen AI adoption could generate the most value 3 “ The economic potential of generative AI: The next productivity frontier ,” McKinsey, June 14, 2023. —as well as in IT (Exhibit 3). The biggest increase from 2023 is found in marketing and sales, where reported adoption has more than doubled. Yet across functions, only two use cases, both within marketing and sales, are reported by 15 percent or more of respondents.

Gen AI also is weaving its way into respondents’ personal lives. Compared with 2023, respondents are much more likely to be using gen AI at work and even more likely to be using gen AI both at work and in their personal lives (Exhibit 4). The survey finds upticks in gen AI use across all regions, with the largest increases in Asia–Pacific and Greater China. Respondents at the highest seniority levels, meanwhile, show larger jumps in the use of gen Al tools for work and outside of work compared with their midlevel-management peers. Looking at specific industries, respondents working in energy and materials and in professional services report the largest increase in gen AI use.

Investments in gen AI and analytical AI are beginning to create value

The latest survey also shows how different industries are budgeting for gen AI. Responses suggest that, in many industries, organizations are about equally as likely to be investing more than 5 percent of their digital budgets in gen AI as they are in nongenerative, analytical-AI solutions (Exhibit 5). Yet in most industries, larger shares of respondents report that their organizations spend more than 20 percent on analytical AI than on gen AI. Looking ahead, most respondents—67 percent—expect their organizations to invest more in AI over the next three years.

Where are those investments paying off? For the first time, our latest survey explored the value created by gen AI use by business function. The function in which the largest share of respondents report seeing cost decreases is human resources. Respondents most commonly report meaningful revenue increases (of more than 5 percent) in supply chain and inventory management (Exhibit 6). For analytical AI, respondents most often report seeing cost benefits in service operations—in line with what we found last year —as well as meaningful revenue increases from AI use in marketing and sales.

Inaccuracy: The most recognized and experienced risk of gen AI use

As businesses begin to see the benefits of gen AI, they’re also recognizing the diverse risks associated with the technology. These can range from data management risks such as data privacy, bias, or intellectual property (IP) infringement to model management risks, which tend to focus on inaccurate output or lack of explainability. A third big risk category is security and incorrect use.

Respondents to the latest survey are more likely than they were last year to say their organizations consider inaccuracy and IP infringement to be relevant to their use of gen AI, and about half continue to view cybersecurity as a risk (Exhibit 7).

Conversely, respondents are less likely than they were last year to say their organizations consider workforce and labor displacement to be relevant risks and are not increasing efforts to mitigate them.

In fact, inaccuracy— which can affect use cases across the gen AI value chain , ranging from customer journeys and summarization to coding and creative content—is the only risk that respondents are significantly more likely than last year to say their organizations are actively working to mitigate.

Some organizations have already experienced negative consequences from the use of gen AI, with 44 percent of respondents saying their organizations have experienced at least one consequence (Exhibit 8). Respondents most often report inaccuracy as a risk that has affected their organizations, followed by cybersecurity and explainability.

Our previous research has found that there are several elements of governance that can help in scaling gen AI use responsibly, yet few respondents report having these risk-related practices in place. 4 “ Implementing generative AI with speed and safety ,” McKinsey Quarterly , March 13, 2024. For example, just 18 percent say their organizations have an enterprise-wide council or board with the authority to make decisions involving responsible AI governance, and only one-third say gen AI risk awareness and risk mitigation controls are required skill sets for technical talent.

Bringing gen AI capabilities to bear

The latest survey also sought to understand how, and how quickly, organizations are deploying these new gen AI tools. We have found three archetypes for implementing gen AI solutions : takers use off-the-shelf, publicly available solutions; shapers customize those tools with proprietary data and systems; and makers develop their own foundation models from scratch. 5 “ Technology’s generational moment with generative AI: A CIO and CTO guide ,” McKinsey, July 11, 2023. Across most industries, the survey results suggest that organizations are finding off-the-shelf offerings applicable to their business needs—though many are pursuing opportunities to customize models or even develop their own (Exhibit 9). About half of reported gen AI uses within respondents’ business functions are utilizing off-the-shelf, publicly available models or tools, with little or no customization. Respondents in energy and materials, technology, and media and telecommunications are more likely to report significant customization or tuning of publicly available models or developing their own proprietary models to address specific business needs.

Respondents most often report that their organizations required one to four months from the start of a project to put gen AI into production, though the time it takes varies by business function (Exhibit 10). It also depends upon the approach for acquiring those capabilities. Not surprisingly, reported uses of highly customized or proprietary models are 1.5 times more likely than off-the-shelf, publicly available models to take five months or more to implement.

Gen AI high performers are excelling despite facing challenges

Gen AI is a new technology, and organizations are still early in the journey of pursuing its opportunities and scaling it across functions. So it’s little surprise that only a small subset of respondents (46 out of 876) report that a meaningful share of their organizations’ EBIT can be attributed to their deployment of gen AI. Still, these gen AI leaders are worth examining closely. These, after all, are the early movers, who already attribute more than 10 percent of their organizations’ EBIT to their use of gen AI. Forty-two percent of these high performers say more than 20 percent of their EBIT is attributable to their use of nongenerative, analytical AI, and they span industries and regions—though most are at organizations with less than $1 billion in annual revenue. The AI-related practices at these organizations can offer guidance to those looking to create value from gen AI adoption at their own organizations.

To start, gen AI high performers are using gen AI in more business functions—an average of three functions, while others average two. They, like other organizations, are most likely to use gen AI in marketing and sales and product or service development, but they’re much more likely than others to use gen AI solutions in risk, legal, and compliance; in strategy and corporate finance; and in supply chain and inventory management. They’re more than three times as likely as others to be using gen AI in activities ranging from processing of accounting documents and risk assessment to R&D testing and pricing and promotions. While, overall, about half of reported gen AI applications within business functions are utilizing publicly available models or tools, gen AI high performers are less likely to use those off-the-shelf options than to either implement significantly customized versions of those tools or to develop their own proprietary foundation models.

What else are these high performers doing differently? For one thing, they are paying more attention to gen-AI-related risks. Perhaps because they are further along on their journeys, they are more likely than others to say their organizations have experienced every negative consequence from gen AI we asked about, from cybersecurity and personal privacy to explainability and IP infringement. Given that, they are more likely than others to report that their organizations consider those risks, as well as regulatory compliance, environmental impacts, and political stability, to be relevant to their gen AI use, and they say they take steps to mitigate more risks than others do.

Gen AI high performers are also much more likely to say their organizations follow a set of risk-related best practices (Exhibit 11). For example, they are nearly twice as likely as others to involve the legal function and embed risk reviews early on in the development of gen AI solutions—that is, to “ shift left .” They’re also much more likely than others to employ a wide range of other best practices, from strategy-related practices to those related to scaling.

In addition to experiencing the risks of gen AI adoption, high performers have encountered other challenges that can serve as warnings to others (Exhibit 12). Seventy percent say they have experienced difficulties with data, including defining processes for data governance, developing the ability to quickly integrate data into AI models, and an insufficient amount of training data, highlighting the essential role that data play in capturing value. High performers are also more likely than others to report experiencing challenges with their operating models, such as implementing agile ways of working and effective sprint performance management.

About the research

The online survey was in the field from February 22 to March 5, 2024, and garnered responses from 1,363 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, 981 said their organizations had adopted AI in at least one business function, and 878 said their organizations were regularly using gen AI in at least one function. To adjust for differences in response rates, the data are weighted by the contribution of each respondent’s nation to global GDP.

Alex Singla and Alexander Sukharevsky  are global coleaders of QuantumBlack, AI by McKinsey, and senior partners in McKinsey’s Chicago and London offices, respectively; Lareina Yee  is a senior partner in the Bay Area office, where Michael Chui , a McKinsey Global Institute partner, is a partner; and Bryce Hall  is an associate partner in the Washington, DC, office.

They wish to thank Kaitlin Noe, Larry Kanter, Mallika Jhamb, and Shinjini Srivastava for their contributions to this work.

This article was edited by Heather Hanselman, a senior editor in McKinsey’s Atlanta office.

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