Relaxation
4.1. unobtrusive stress detection system with smart bands.
Our stress detection system developed in [ 32 ] allows users to be aware of their stress levels during their daily activities without creating any interruption or restriction. The only requirement to use this system is the need to wear a smart band. Participants in this study wore the Empatica E4 smart band on their non-dominant hand. The smart band provides Blood Volume Pressure, ST, EDA, IBI (Interbeat Interval) and 3D Acceleration. The data are stored in the memory of the device. Then, the artifacts of physiological signals were detected and handled. The features were extracted from the sensory signals and fed to the machine learning algorithm for prediction. In order to use this system, pre-trained machine learning models are required. For training the models, feature vectors and collected class labels were used.
The body sweats when emotional arousal and stress are experienced and, therefore, skin conductance increases [ 40 ]. This makes EDA a promising candidate for stress level detection. Intense physical activity and temperature changes contaminate the SC (Skin Conductance) signal. Therefore, affected segments (artifacts) should be filtered out from the original signal. In order to detect the artifacts in the SC signal, we used an EDA toolkit [ 41 ] which is 95% accurate on the detection of the artifacts. While developing this tool, technicians labeled the artifacts manually. They trained a machine learning model by using the labels. In addition to the SC signal, 3D acceleration and ST signals were also used for artifact detection. We removed the parts that this tool detected as artifacts from our signals. We further added batch processing and segmentation to this tool by using custom software built-in Python 2.7.
After the artifact removal phase, features were extracted from the EDA signal. This signal has two components phasic and tonic; features from both components were extracted (see Table 2 ). The cvxEDA tool [ 42 ] was used for the decomposition of the signal into these components. This tool uses convex optimization to estimate the Autonomic Nervous System (ANS) activity that is based on Bayesian statistics.
EDA features and their definitions.
Feature | Description |
---|---|
Quartdev Tonic | Quartile deviation (75 percentile–25 percentile) of the phasic component |
Strong Peaks Phasic | The number of strong peak per 100 s |
Peaks Phasic | The number of peaks per 100 s |
Perc20 | 20th percentile of the phasic component |
Perc80 Tonic | 80th percentile of the phasic component |
Mean Tonic | Mean of the phasic component |
SD Tonic | Standard deviation of phasic component |
The tonic component in the EDA signal represents the long-term slow changes. This component is also known as the skin conductance level. It could be regarded as the indicator of general psychophysiological activation [ 43 ].
The phasic component represents faster (event-related ) differences in the SC signal. The Peaks of phasic SC component as a reaction to a stimulus is also called Skin Conductance Response [ 43 ]. After we decompose the phasic component from the EDA signal, peak related features were extracted.
Heart activity (or, more specifically, HRV) reacts to changes in the autonomic nervous system (ANS) caused by stress [ 44 ] and it is, therefore, one of the most commonly used physiological signal for stress detection [ 40 ]. However, vigorous movement of subjects and improperly worn devices may contaminate the HRV signal collected from smartwatches and smart bands. In order to address this issue, we developed an artifact handling tool in MATLAB programming language [ 45 ] that has batch processing capability. First, the data were divided into 2 min long segments with 50% overlapping. Two-minute segments were selected because it is reported that the time interval for stress stimulation and recovery processes is around a few minutes [ 46 ]. The artifact detection percentage rule (also employed in Kubios [ 47 ]) was applied after the segmentation phase. In this rule, each data point was compared with the local average around it. When the difference was more than a predetermined threshold percentage, (20% is commonly selected in the literature [ 48 ]), the data point was labeled as an artifact. In our system, we deleted the inter-beat intervals detected as the artifacts and interpolated these points with the cubic spline interpolation technique which was used in the Kubios software [ 47 ]. The time-domain features of HRV are calculated. In order to calculate the frequency domain features, we interpolated the RR intervals to 4 Hz. Then, we applied the Fast Fourier Transform (FFT). These time and frequency domain features (see Table 3 ) were selected because these are the most discriminative ones in the literature [ 30 , 49 , 50 ].
HRV features and their definitions [ 32 ].
Feature | Description |
---|---|
Mean RR | Mean value of the inter-beat (RR) intervals |
STD RR | Standard deviation of the inter-beat interval |
pNN50 | Percentage of the number of successive RR intervals varying more than 50 ms from the previous interval |
RMSSD | Root mean square of successive difference of the RR intervals |
SDSD | Related standard deviation of successive RR interval differences |
HRV triangular index | Total number of RR intervals divided by the height of the histogram of all RR intervals measured on a scale with bins of 1/128 s |
TINN | Triangular interpolation of RR interval histogram |
LF | Power in low-frequency band (0.04–0.15 Hz) |
HF | Power in high-frequency band (0.15–0.4 Hz) |
pLF | Prevalent low-frequency oscillation of heart rate |
pHF | Prevalent high-frequency oscillation of heart rate |
VLF | Power in very low-frequency band (0.00–0.04 Hz) |
LF/HF | Ratio of LF-to-HF |
Research has shown that movements of the human body and postures can indeed be employed as a means to detect signs of different emotional states. The dynamics of body movement were investigated by Castellano et al. who used multimodal data to identify human affective behaviors. Specific movement metrics, such as the amount of movement, intensity and fluidity, were used to help deduct emotions, and it was found that the amount of movement was a major factor in distinguishing different types of emotions [ 51 ]. Melzer et al. investigated whether movements comprised of collections of Laban movement components could be recognized as expressing basic emotions [ 52 ]. The results of their study confirm that, even when the subject has no intention of expressing emotions, particular movements can assist in the perception of bodily expressions of emotions. Accelerometer sensors may be used to detect these movements and different types of affect. The accelerometer sensor data are used for two different purposes in our system. Firstly, we extracted features from the accelerometer sensor, for detecting stress levels. We also selected the features to be used as described in Table 4 [ 53 ] and, as mentioned above, this sensor was also employed to clean the EDA signal in the EDAExplorer Tool [ 41 ].
ACC features and their definitions.
Feature | Description |
---|---|
Mean X | Mean acceleration over axis |
Mean Y | Mean acceleration over axis |
Mean Z | Mean acceleration over axis |
MeanAccMag | Mean acceleration over acceleration magnitude |
Energy | FFT energy over mean acceleration magnitude |
A skin temperature signal is used for the artifact detection phase of the EDA signal in the EDAExplorer Tool [ 41 ]. After we divide our data into segments, different modalities were merged into one feature vector. The heart activity signal started with a delay (to calculate heartbeats per minute at the start) and all signals were then synchronized. We included start and end timestamps for each segment, and each modality was merged with a custom Python script.
The Weka machine learning toolkit [ 54 ] is used for identifying stress levels. The Weka toolkit has several preprocessing features before classification. Our data set was not balanced when the number of instances belonging to each class was considered. We solved this issue by removing samples from the majority class. We selected random undersampling because it is the most commonly applied method [ 55 ]. In this way, we prevented classifiers from biasing towards the class with more instances. In this study, we employed five different machine learning classification algorithms to recognize different stress levels: MultiLayer Perceptron (MLP), Random Forest (RF) (with 100 trees), K-nearest neighbors (kNN) ( n = 1–4), Linear discriminant analysis (LDA), Principal component analysis (PCA) and support vector machine (SVM) with a radial basis function. These algorithms were selected because they were the most commonly applied and successful classifiers for detecting stress levels [ 30 , 48 ]. In addition, 10-fold stratified cross-validation was then applied and hyperparameters of the machine learning algorithms were fine-tuned with grid search. The best performing models have been reported.
We applied correlation-based feature selection (CBFS) technique which is available in the Weka machine learning package for combined signal [ 56 ]. The CBFS method removes the features that are less correlated with the output class. For every model, we selected the ten most important features. This method is applied for MLP, RF, kNN and LDA. In order to create an SVM based model, we applied PCA based dimensionality reduction where the covered variance is selected as 0.95 (the default setting).
The CBFS method computes the correlation of features with the ground truth label of the stress level. Insights about the contribution of the features to the stress detection performance can be obtained from Figure 1 and Figure 2 . Three of the best features (over 0.15 correlation) are frequency domain features. These features are high, low and very-low frequency components of the HRV signal (see Figure 1 ). When we examine the EDA features, peaks per 100 s feature are the most important and distinctive feature by far. Since the EDA signal is distorted under the influence of the stimuli, the number of peaks and valleys increases. Lastly, when the acceleration signal is investigated, the most discriminative feature is mean acceleration in the z -axis (see Figure 2 b). This could be due to the nature of hand and body gestures which are caused by stressed situations.
Top-ranking features selected for the HRV signal.
Top-ranking features selected for the EDA and ACC signals.
Context is a broad term that could contain different types of information such as calendars, activity type, location and activity intensity. Physical activity intensity could be used to infer contextual information. In more restricted environments such as office, classrooms, public transportation and physical activity intensity could be low, whereas, in outdoor environments, physical activity intensity could increase. Therefore, an appropriate relaxation method will change according to the context of individuals.
For calculating physical activity intensity, we used the EDAExplorer tool [ 41 ]. The stillness metric is used for this purpose. It is the percentage of periods in which the person is still or motionless. Total acceleration must be less than a threshold (default is 0.1 [ 41 ]) for 95 percent of a minute in order for this minute to count as still [ 41 ]. Then, the ratio of still minutes in a session can be calculated. For the ratio of still minutes in a session, we labeled sessions below 20% as still, above 20% as active and suggested relaxation method accordingly (see Figure 3 ).
The whole system diagram is depicted. When a high stress level is experienced, by analyzing the physical activity based context, the system suggests the most appropriate reduction method.
The proposed stress level monitoring mechanism, for real-life settings, was evaluated during an eight day Marie Skłodowska-Curie Innovative Training Network (ITN) training event in Istanbul, Turkey, for the AffecTech project. AffecTech is a program funded by Horizon 2020 (H2020) framework established by the European Commission. The AffecTech project is an international collaborative research network involving 15 PhD students (early stage researchers (ESR)) with the aim of developing low-cost effective wearable technologies for individuals who experience affective disorders (for example, depression, anxiety and bipolar disorder).
The eight-day training event included workshops, lectures and training with clearly defined tasks and activities to ensure that the ESR had developed the required skills, knowledge and values outline prior to the training event. At the end of the eight-day training, ESRs were required to deliver a presentation about their PhD work to two evaluators from the European Union where they received feedback about their progress (see Figure 4 for raw physiological signals at the start of the presentation). For studying the effects of emotion regulation on stress, yoga, guided mindfulness and mobile-based mindfulness, sessions were held by a certified instructor.
Sample data belong to a presentation session. The increase in EDA, ST and IBI could be observed when the subject started the presentation.
During the training, physiological and questionnaire data were collected from the 16 ESR participants (9 men, mean age 28); 15 ESRs and one of the AffecTech project academics, all of whom gave informed consent to participate in the study. Participants were from different countries with diverse nationalities (two from Iran, two from Spain, two from Italy, one from Argentina, one from Pakistan, one from China, one from Switzerland, one from Belarus, one from France, one from England, one from Barbados, one from Turkey and one from Bulgaria). Due to the fault of one of the Empatica E4 devices, it was not possible to include data from one participant. The remaining 15 participants completed all stages of the study successfully.
During the eight days of training and presentations, psychophysiological data were collected from 16 participants during the training event from Empatica E4 smart band while they are awake. For studying the effects of emotion regulation on stress, yoga, guided mindfulness and mobile-based mindfulness sessions were held by a certified instructor. The timeline of the event is shown in Figure 5 .
Time-line depicting eight days of the training event. Presentations, relaxations and lectures are highlighted.
The psychophysiological signal data were collected using the Empatica E4 smart band whilst participants were awake throughout the eight days of the AffecTech training. Physiological data included IBI, EDA, ACC (Accelerometer) and ST and stored in different csv files. In addition, 27.39% of the data are obtained from free times (free day and after training until subjects slept 5:00 p.m.–10:00 p.m.), 43.83% of the data comes from lectures in the training, 11.41% is the presentation session and relax sessions consist of 17.35% of the data. As mentioned previously, we randomly undersampled (most commonly applied method [ 55 ] ) the data to overcome the class imbalance problem. The participants’ blood pressure (BP) was also recorded using CE(0123) Harvard Medical Devices Ltd. automated sphygmomanometer prior to and after each stress reduction event (yoga and mindfulness), in order to demonstrate whether the participants stress levels were modified. On each occasion that the participants’ BP was recorded, the mean of three recordings was used as the final BP. A reduction in the participants’ blood pressure and/or pulse rate may be seen, which demonstrates a reduction in stress level.
The procedure used in this study was approved by the Institutional Review Board for Research with Human Subjects of Boğaziçi University with the approval number 2018/16. Prior to data acquisition, each participant received a consent form describing the experimental procedure and its benefits and implications to both the society and the subject. The procedure was also explained verbally to the subject. All of the data are stored anonymously.
A session-based self-report questionnaire comprised of six questions based on the Nasa Task Load Index (NASA-TLX) [ 57 ]. The frustration scale was specifically used to measure perceived stress levels [ 32 ]. We asked the following question to the participants for each session:
How irritated, stressed and annoyed versus content, relaxed and complacent did you feel during the task?
Questionnaires were completed daily (at the end of the day) and, after each presentation, lecture and stress reduction event (such as yoga and mindfulness).
During the eight day training, it is assumed that the participants’ stress levels are likely to have increased day by day because they were required to give a presentation (perceived as a stressful event) reporting their PhD progress to the EU project evaluators at the end of the training.
Underpinned by James Gross’s Emotion Regulation model (see Figure 6 ) [ 4 ], we modified the situation to help the participants to reduce their thoughts of the end of the training presentation. To help participants manage their stress levels, we applied Yoga and mindfulness sessions on two separate days (day three and day four, respectively). These sessions lasted approximately 1 h and, throughout the sessions, participants wore an Empatica E4 smartband. In addition to the physiological signals coming from the Smartbands, participants’ blood pressure values were also recorded before and after the yoga and mindfulness sessions.
Application of James Gross’s Emotion Regulation model [ 4 ] in the context of stress management.
5.1. statistical data analysis, 5.1.1. validation of different perceived stress levels by using the self-reports.
In order to validate that the participants experienced different perceived stress levels in different contexts (lecture, relaxation, presentation), we used the Frustration item (see Section 4.5) from the NASA-TLX [ 57 ]. The distribution of answers is demonstrated in Figure 7 . Our aim is to show that the perceived stress levels (obtained from self-report answers) differ in relaxation sessions considerably when compared to the presentation session (high stress). To this end, we applied the t -test (in R programming language) to the perceived stress self-report answers of yoga versus presentation, mindfulness versus presentation and pause (mobile mindfulness) versus presentation session pairs. The paired t -test is used to evaluate the separability of each session. The degree of freedom is 15. We applied the variance test to each session tuple; we could not identify equal variance in any of the session tuples. Thus, we selected the variance as unequal. We used 99.5% confidence intervals. The t -test results’ ( p -values and test statistics) are provided in Table 5 . For all tuples, the null hypothesis stating that the perceived stress of the relaxation method is not less than the presentation session is rejected. The perceived stress levels of participants for all meditation sessions are observed to be significantly lower than the presentation session (high stress).
Visual representation of the frustration scores collected in different types of sessions.
T -test results for session tuple comparison of perceived stress levels using self-reports.
Session Tuple | -Test Statistic | -Value |
---|---|---|
Yoga—Presentation | −4.0027 | < 0.005 |
Guided Mindfulness—Presentation | −5.4905 | < 0.005 |
Mobile Mindfulness—Presentation | −4.2677 | < 0.005 |
In this section, we compared the effect of stress management tools such as yoga and mindfulness on blood pressure. It is expected that blood pressure sensors will be part of unobtrusive wrist-worn wearable sensors soon. We plan to integrate a blood pressure (BP) module to our system when they are available. Therefore, by using the measurements of a medical-grade blood pressure monitor, we provided insights about how stress reaction affects BP. We further applied and tested the prominent emotion regulation model of James Gross by analyzing these measurements in the context of stress management. We measured the diastolic and systolic BP and pulse using a medical-grade blood pressure monitor before and after the yoga and mindfulness sessions. In order to ensure that the participants were relaxed and that an accurate BP was recorded, BP was measured three times with the mean as the recorded result. A one-sample t -test was applied to the difference between mean values. The results are shown in Table 6 .
The difference between the mean diastolic blood pressure, the mean systolic blood pressure and the mean pulse, before and after sessions of guided mindfulness and guided yoga. (* p < 0.05).
Activity | Systolic | Diastolic | Pulse |
---|---|---|---|
Guided Mindfulness | −1.31% | 1.75% * | −5.75% * |
Guided Yoga | −5.81% * | −1.93% | 8.06% * |
Mindfulness decreased the systolic BP, –1.13% (ns), increased diastolic BP, +1.75% ( p < 0.05) and decreased the pulse –5.75% ( p < 0.05). Medicine knows that systolic blood pressure (the top number or highest blood pressure when the heart is squeezing and pushing the blood around the body) is more important than diastolic blood pressure (the bottom number or lowest blood pressure between heartbeats) because it gives the best idea of the risk of having a stroke or heart attack. In this view, the significant reduction of systolic BP after mindfulness is an important result.
Moreover, the difference between systolic and diastolic BP is called pulse pressure. For example, 120 systolic minus 60 diastolic equals a pulse pressure of 60. It is also known that a pulse pressure greater than 60 can be a predictor of heart attacks or other cardiovascular diseases, while a low pulse pressure (less than 40) may indicate poor heart function. In our study, pulse pressure was lower after mindfulness (we had both a significant reduction in systolic BP and an increase in diastolic BP), but its value was higher than 40 (42.69 mean difference before the mindfulness and 40.48 mean difference after the mindfulness), suggesting that this result can also be considered clinically positive.
During yoga, there was a decrease in systolic BP by −5.81% ( p < 0.05), diastolic BP by −1.93% (ns) and increase in pulse +8.06% ( p < 0.05). Yoga appears to be more effective than mindfulness at decreasing systolic and diastolic blood pressure, although mindfulness seems to be more effective than yoga for decreasing the pulse due to the activity involved in yoga.
We tested our system by using the known context labels of sessions as the class label. We used Lecture (mild stress), Yoga and Mindfulness (relax) and Presentation in front of the board of juries (high stress) as class labels by examining perceived stress self-report answers in Figure 6 . We investigated the success of relaxation methods, different modalities and finding the presenter.
We evaluated the effect of using the interbeat-interval, the skin conductance and the accelerometer signals separately and in a combined manner on two and three class classification performance. These classes are mild stress, high stress and relax states from mindfulness and yoga sessions. The results are shown in Table 7 , Table 8 and Table 9 . For the three-class classification problem, we achieved a maximum accuracy of 72% by using MLP on only HRV features and 86.61% with only accelerometer features using the Random Forest classifier and 85.36% accuracy combination of all features with LDA classifier (see Table 7 ). The difficulty in this classification task is a similar physiological reaction to relax and mild stress situations. However, since the main focus of our study is to discriminate high stress from other classes to offer relaxation techniques in this state, it did not affect our system performance. We also investigated high-mild stress and high stress-relax 2-class classification performance. For the discrimination of high and mild stress, HRV outperformed other signals with 98% accuracy using MLP (see Table 8 ). In the high stress-relax 2-class problem, only HRV features with RF achieved a maximum accuracy of 86%, whereas ACC features with MLP achieved a maximum of 94% accuracy. In this problem, the combination of all signals with RF achieved 92% accuracy which is the best among all classifiers (see Table 9 ). For all models, EDA did not perform well. This might be caused by the loose contact with EDA electrodes in the strap due to loosely worn smartbands.
Effect of different modalities and their combination on the system performance. Note that the number of classes is fixed at 3 (high stress, mild stress and relax).
Algorithm | Accuracy, % | |||
---|---|---|---|---|
HRV | EDA | ACC | Combined | |
MLP | 72.14 | 36.61 | 74.29 | 82.68 |
RF | 67.86 | 36.96 | 86.61 | 85.18 |
kNN | 65.00 | 29.82 | 70.89 | 78.39 |
LDA | 69.82 | 31.96 | 73.39 | 85.36 |
SVM | 47.14 | 30.54 | 58.57 | 46.96 |
Effect of different modalities and their combination on the system performance. Note that the number of classes is fixed at 2 (high stress and mild stress).
Algorithm | Accuracy, % | |||
---|---|---|---|---|
HRV | EDA | ACC | Combined | |
MLP | 98.00 | 60.00 | 64.00 | 98.00 |
RF | 98.00 | 42.00 | 72.00 | 98.00 |
kNN | 94.00 | 44.00 | 58.00 | 94.00 |
LDA | 94.00 | 40.00 | 54.00 | 94.00 |
SVM | 66.00 | 54.00 | 54.00 | 66.00 |
Effect of different modalities and their combination on the system performance. Note that the number of classes is fixed at 2 (high stress and relax).
Algorithm | Accuracy, % | |||
---|---|---|---|---|
HRV | EDA | ACC | Combined | |
MLP | 82.00 | 66.00 | 96.00 | 90.00 |
RF | 86.00 | 60.00 | 94.00 | 92.00 |
kNN | 82.00 | 66.00 | 88.00 | 90.00 |
LDA | 78.00 | 64.00 | 92.00 | 88.00 |
SVM | 78.00 | 62.00 | 52.00 | 74.00 |
We applied three different relaxation methods to manage stress levels of individuals. In order to measure the effectiveness of each method, we examined how easily these physiological signals in the relaxation sessions can be separated from high stress presentations. If it can be separated from high stress levels with higher classification performance, it could be inferred that they are more successful at reducing stress. As seen in Table 10 and Table 11 , mobile mindfulness has lower success in reducing stress levels. Yoga has the highest classification performance with both HR and EDA signals.
The classification accuracy of the relaxation sessions using stress management methods and stressful sessions using EDA.
Algorithm | Accuracy, % | ||
---|---|---|---|
Guided Mindfulness | Yoga | Mobile Mindfulness | |
MLP | 65.71 | 78.57 | 75.00 |
RF | 67.14 | 87.14 | 67.64 |
kNN | 64.29 | 82.86 | 77.94 |
LDA | 65.71 | 80.00 | 51.47 |
SVM | 70.00 | 72.86 | 58.82 |
The classification accuracy of the relaxation sessions using stress management methods and stressful sessions using HRV.
Algorithm | Accuracy, % | ||
---|---|---|---|
Guided Mindfulness | Yoga | Mobile Mindfulness | |
MLP | 90.00 | 97.50 | 93.94 |
RF | 97.50 | 95.00 | 87.89 |
kNN | 90.00 | 90.00 | 93.93 |
LDA | 87.50 | 87.50 | 75.75 |
SVM | 85.00 | 80.00 | 81.82 |
In this study, by using our automatic stress detection system with the use of Empatica-E4 smart-bands, we detected stress levels and suggested appropriate relaxation methods (i.e., traditional or mobile) when high stress levels are experienced. Our stress detection framework is unobtrusive, comfortable and suitable for use in daily life and our relaxation method suggestion system makes its decisions based on the physical activity-related context of a user. To test our system, we collected eight days of data from 16 individuals participating in an EU research project training event. Individuals were exposed to varied stressful and relaxation events (1) training and lectures (mild stress), (2) yoga, mindfulness and mobile mindfulness (PAUSE) (relax) and (3) were required to give a moderated presentation (high stress). The participants were from different countries with diverse cultures.
In addition, 1440 h of mobile data (12 h in a day) were collected during this eight-day event from each participant measuring their stress levels. Data were collected during the training sessions, relaxation events and the moderated presentation and during their free time for 12 h in a day, demonstrating that our study monitored daily life stress. EDA and HR signals were collected to detect physiological stress and a combination of different modalities increased stress detection, performance and provided the most discriminative features. We first applied James Gross ER model in the context of stress management and measured the blood pressure during the ER cycle. When the known context was used as the label for stress level detection system, we achieved 98% accuracy for 2-class and 85% accuracy for 3-class. Most of the studies in the literature only detect stress levels of individuals. The participants’ stress levels were managed with yoga, mindfulness and a mobile mindfulness application while monitoring their stress levels. We investigated the success of each stress management technique by the separability of physiological signals from high-stress sessions. We demonstrated that yoga and traditional mindfulness performed slightly better than the mobile mindfulness application. Furthermore, this study is not without limitations. In order to generalize the conclusions, more experiments based on larger sample groups should be conducted. As future work, we plan to develop personalized perceived stress models by using self-reports and test our system in the wild. Furthermore, attitudes in the psychological field constitute a topic of utmost relevance, which always play an instrumental role in the determination of human behavior [ 58 ]. We plan to design a new experiment which accounts for the attitudes of participants towards relaxation methods and their effects on the performance of stress recognition systems.
We would like to show our gratitude to the Affectech Project for providing us the opportunity for the data collection in the training event and funding the research.
Y.S.C. is the main editor of this work and made major contributions in data collection, analysis and manuscript writing. H.I.-S. made valuable contributions in both data collection and manuscript writing. She was the yoga and mindfulness instructor in the event and contributed the related sections regarding traditional and mobile methods. She also led the blood pressure measurement efforts before and after relaxation methods. D.E. and N.C. contributed equally to this work in design, implementation, data analysis and writing the manuscript. J.F.-Á., C.R. and G.R. contributed the experiment design and provided valuable insights into both emotion regulation theory. They also contributed to the related sections in the manuscript. C.E. provided invaluable feedback and technical guidance to interpret the design and the detail of the field study. He also performed comprehensive critical editing to increase the overall quality of the manuscript. All authors have read and agreed to the published version of the manuscript.
This work has been supported by AffecTech: Personal Technologies for Affective Health, Innovative Training Network funded by the H2020 People Programme under Marie Skłodowska-Curie Grant Agreement No. 722022. This work is supported by the Turkish Directorate of Strategy and Budget under the TAM Project number DPT2007K120610.
The authors declare no conflict of interest.
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Many of us are facing challenges that can be stressful and overwhelming. Learning to cope with stress in a healthy way will help you, the people you care about, and those around you become more resilient.
Stress can cause the following:
Here are some ways you can manage stress, anxiety, grief, or worry:
For Everyone
For Families and Children
How to de-stress when you're feeling overwhelmed
No doubt about it, school is stressful. Academic deadlines, worrying about grades, pressure from parents and teachers, and juggling these challenges with your other responsibilities can leave you feeling frazzled and anxious.
The bad news is that all this stress can take a toll on your health, happiness, relationships, and grades. The good news is that learning to utilize some effective stress management strategies for students can help you tame these anxieties and keep your stress at manageable levels—even during midterms and finals.
Most students experience significant amounts of stress. This can significantly affect your academic performance, social life, and well-being. Learning stress management techniques can help you avoid negative effects in these areas. Strategies that can help include:
Let's take a closer look at why stress management is so important for students and what you can do to get a handle on the stress in your life.
A study by the American Psychological Association (APA) found that teens report stress levels similar to adults. This means teens are experiencing significant levels of chronic stress and feel their stress levels generally exceed their ability to cope effectively .
Roughly 30% of the teens reported feeling overwhelmed, depressed, or sad because of their stress. According to one Pew Research Center report, 70% of teens view anxiety and depression as major problems for people their age.
Stress can also affect health-related behaviors. Stressed students are more likely to have problems with disrupted sleep, poor diet, and lack of exercise. This is understandable given that nearly half of APA survey respondents reported completing three hours of homework per night in addition to their full day of school work and extracurriculars.
Why are students today so stressed? According to the APA 2023 Stress in America report, Gen Z and younger millennials are overwhelmed by stress. The causes of this stress come from many areas. Financial worries , loneliness and isolation, climate concerns, political strife, the collective trauma linked to the pandemic, and other world events are all factors that play a part in the pressure on young people today.
Another study found that much of high school students' stress originates from school and activities, and that this chronic stress can persist into college years and lead to academic disengagement and mental health problems.
Common sources of student stress include:
High school students face the intense competitiveness of taking challenging courses, amassing impressive extracurriculars, studying and acing college placement tests, and deciding on important and life-changing plans for their future. At the same time, they have to navigate the social challenges inherent to the high school experience.
This stress continues if students decide to attend college. Stress is an unavoidable part of life, but research has found that increased daily stressors put college-aged young adults at a higher risk for stress than other age groups.
Making new friends, handling a more challenging workload, feeling pressured to succeed, being without parental support, and navigating the stresses of more independent living are all added challenges that make this transition more difficult. Romantic relationships always add an extra layer of potential stress.
Students often recognize that they need to relieve stress . However, all the activities and responsibilities that fill a student’s schedule sometimes make it difficult to find the time to try new stress relievers to help dissipate that stress.
Here you will learn 10 stress management techniques for students. These options are relatively easy, quick, and relevant to a student’s life and types of stress .
Blend Images - Hill Street Studios / Brand X Pictures / Getty Images
Students, with their packed schedules, are notorious for missing sleep. Unfortunately, operating in a sleep-deprived state puts you at a distinct disadvantage. You’re less productive, may find it more difficult to learn, and may even be a hazard behind the wheel.
Research suggests that sleep deprivation and daytime sleepiness are also linked to impaired mood, higher risk for car accidents, lower grade point averages, worse learning, and a higher risk of academic failure.
Don't neglect your sleep schedule. Aim to get at least 8 hours a night and take power naps when needed.
David Malan / Getty Images
Guided imagery can also be a useful and effective tool to help stressed students cope with academic, social, and other stressors. Visualizations can help you calm down, detach from what’s stressing you, and reduce your body’s stress response.
You can use guided imagery to relax your body by sitting in a quiet, comfortable place, closing your eyes, and imagining a peaceful scene. Spend several minutes relaxing as you enjoy mentally basking in your restful image.
Consider trying a guided imagery app if you need extra help visualizing a scene and inducting a relaxation response. Research suggests that such tools might be an affordable and convenient way to reduce stress.
One of the healthiest ways to blow off steam is to get regular exercise . Evidence indicates that students who participate in regular physical activity report lower levels of perceived stress.
While these students still grapple with the same social, academic, and life pressures as their less-active peers, these challenges feel less stressful and are easier to manage.
Finding time for exercise might be a challenge, but there are strategies that you can use to add more physical activity to your day. Some ideas that you might try include:
Exercise can help buffer against the negative effects of student stress. Starting now and keeping a regular exercise practice throughout your lifetime can help you live longer and enjoy your life more.
When your body is experiencing a stress response, you’re often not thinking as clearly as you could be. You are also likely not breathing properly. You might be taking short, shallow breaths. When you breathe improperly, it upsets the exchange of oxygen and carbon dioxide in your body.
Studies suggest this imbalance can contribute to various physical symptoms, including increased anxiety, fatigue, stress, emotional problems, and panic attacks.
A quick way to calm down is to practice breathing exercises . These can be done virtually anywhere to relieve stress in minutes.
Because they are fast-acting, breathing exercises are a great way to cope with moments of acute stress , such as right before an exam or presentation. But they can also help manage longer-lasting stress such as dealing with relationships, work, or financial problems.
Another great stress management technique for students that can be used during tests, before bed, or at other times when stress has you physically wound up is progressive muscle relaxation ( PMR ).
This technique involves tensing and relaxing all muscles until the body is completely relaxed. With practice, you can learn to release stress from your body in seconds. This can be particularly helpful for students because it can be adapted to help relaxation efforts before sleep for a deeper sleep.
Once a person learns how to use PMR effectively, it can be a quick and handy way to induce relaxation in any stressful situation, such as bouts of momentary panic before a speech or exam, dealing with a disagreement with your roommate, or preparing to discuss a problem with your academic advisor.
As convenient stress reliever that has also shown many cognitive benefits, music can help relieve stress and calm you down or stimulate your mind depending on what you need in the moment.
Research has found that playing upbeat music can improve processing speed and memory. Stressed students may find that listening to relaxing music can help calm the body and mind. One study found that students who listened to the sounds of relaxing music were able to recover more quickly after a stressful situation.
Students can harness the benefits of music by playing classical music while studying, playing upbeat music to "wake up" mentally, or relaxing with the help of their favorite slow melodies.
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Having emotional support can help create a protective buffer against stress. Unfortunately, interpersonal relationships can also sometimes be a source of anxiety for students. Changes in friendships, romantic breakups, and life transitions such as moving away for college can create significant upheaval and stress for students.
One way to combat feelings of loneliness and make sure that you have people to lean on in times of need is to expand your support network and nurture your relationships.
Look for opportunities to meet new people, whether it involves joining study groups or participating in other academic, social, and leisure activities.
Remember that different types of relationships offer differing types of support . Your relationships with teachers, counselors, and mentors can be a great source of information and resources that may help you academically. Relationships with friends can provide emotional and practical support.
Widening your social circle can combat student stress on various fronts and ensure you have what you need to succeed.
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You may not realize it, but your diet can either boost your brainpower or sap you of mental energy. It can also make you more reactive to the stress in your life. As a result, you might find yourself turning to high-sugar, high-fat snacks to provide a temporary sense of relief.
A healthy diet can help combat stress in several ways. Improving your diet can keep you from experiencing diet-related mood swings, light-headedness, and more.
Unfortunately, students are often prone to poor dietary habits. Feelings of stress can make it harder to stick to a consistently healthy diet, but other concerns such as finances, access to cooking facilities, and time to prepare healthy meals can make it more challenging for students.
Some tactics that can help students make healthy choices include:
One way to improve your ability to manage student stress is to look for ways you cut stress out of your life altogether. Evaluate the things that are bringing stress or anxiety into your life. Are they necessary? Are they providing more benefits than the toll they take on your mental health? If the answer is no, sometimes the best option is just to ditch them altogether.
This might mean cutting some extracurricular activities out of your schedule. It might mean limiting your use of social media. Or it might mean learning to say no to requests for your time, energy, and resources.
While it might be challenging at first, learning how to prioritize yourself and your mental well-being is an important step toward reducing your stress.
When you find yourself dealing with stress—whether it's due to academics, relationships, financial pressures, or social challenges—becoming more aware of how you feel in the moment may help you respond more effectively.
Mindfulness involves becoming more aware of the present moment. Rather than judging, reacting, or avoiding problems, the goal is to focus on the present, become more aware of how you are feeling, observe your reactions, and accept these feelings without passing judgment on them.
Research suggests that mindfulness-based stress management practices can be a useful tool for reducing student stress. Such strategies may also help reduce feelings of anxiety and depression.
It is important to remember that stress isn't the same for everyone. Figuring out what works for you may take some trial and error. A good start is to ensure that you are taking care of yourself physically and emotionally and to experiment with different stress relief strategies to figure out what works best to help you feel less stressed.
If stress and anxiety are causing distress or making it difficult to function in your daily life, it is important to seek help. Many schools offer resources that can help, including face-to-face and online mental health services. You might start by talking to your school counselor or student advisor about the stress you are coping with. You can also talk to a parent, another trusted adult, or your doctor.
If you or a loved one are struggling with anxiety, contact the Substance Abuse and Mental Health Services Administration (SAMHSA) National Helpline at 1-800-662-4357 for information on support and treatment facilities in your area.
For more mental health resources, see our National Helpline Database .
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By Elizabeth Scott, PhD Elizabeth Scott, PhD is an author, workshop leader, educator, and award-winning blogger on stress management, positive psychology, relationships, and emotional wellbeing.
Stress is an inseparable part of any human experience, which is why its effects on the body need to be examined further. Although efforts must be applied to reduce the extent of stress and the exposure to it, eradicating the specified phenomenon from an individual’s life is presently impossible and barely productive (American Psychiatric Association, 2013). Instead, the effects of stress and their mitigation need to be learned closer to reduce possible health outcomes. Especially after the pandemic of COVID-19 has made the levels of stress in people worldwide skyrocket, the significance of studying the levels of stress on the human body has grown tremendously (Schönrich et al. 3). This paper will examine the effects of stress on different systems within a human body, further recommending the strategies that can be used to alleviate the adverse outcomes.
Before proceeding with listing the multiple outcomes of stress affecting the human body and its multiple systems, one might want to define the subject matter first. The notion of stress might seem simple enough, meaning mostly feeling of unease caused by negative emotions. However, the concept of stress is far more complex due to the presence of multiple factors determining its development, as well as the numerous ways in which it can manifest itself (Schönrich et al. 2). Therefore, to define stress, one may need to consider several perspectives.
As a separate health issue, stress does not occur in the Diagnostic and Statistical Manual of Mental Disorders (DSM–5). Instead, DSM-5 offers definitions for posttraumatic stress disorder (PTSD), acute stress disorder (ASD), anxiety, and related conditions (American Psychiatric Association 265). The described approach is quite reasonable since the very notion of stress is quite broad. Indeed, examining the subject matter, one will recognize the presence of a twofold nature of it. Namely, stress encompasses both the state of anxiety and emotional unease, while also implying the range of external factors affecting an individual. Collier et al. suggest that stress should be defined as “the environment that places a strain on a biological system” (10367). As shown in the described definition, the notion of stress is seen as a combination of the components that elicit negative emotions and confusion.
To examine the effects of stress on the human body, a basic understanding of how the human body functions are needed. To simplify the exploration of the complex neurological pathways that the stress response suggests, one may need to isolate eleven primary systems within the human body. These are the musculoskeletal, respiratory, cardiovascular, endocrine, gastrointestinal, nervous, reproductive, digestive, immune, urinary, and exocrine (Rathus and Nevid 17). Since changes occur within every system and are intertwined closely within the human body, it is crucial to consider each with the described connection in mind.
As an immediate and instinctive response to stress, the muscles in the human body become tense. The specified reaction causes muscles to become the shield against a possible injury, also allowing one either to fight effectively or to run (Rathus and Nevid 121). The increase in muscle tension is spurred by the rise in the levels of cortisol, which is a steroid hormone produced by the adrenal cortex located in the adrenal gland (Rathus and Nevid 121). In turn, chronic stress causes muscles to be overly tense constantly, which may lead to long-term effects such as muscle cramps (Rathus and Nevid 122). Prolonged stress also affects the exocrine system in the long term, causing hair loss and brittle nails.
In the event of a sudden introduction of stress factors, the respiratory system responds in increased activity. Namely, the number of breaths per minute increases due to the rise in the need to supply oxygen to muscles and the brain (Hales and Hales 22). The described outcome is linked directly to the aforementioned “fight or flight” instinct, which enables the body to increase the speed and precision of its reactions to external factors. Furthermore, due to the constriction of the air pathways, breaths become shorter and faster (Rathus and Nevid 124). Thus, the respiratory system becomes overloaded in the event of acute stress; in fact, studies show that an asthma attack may occur as a result (Rathus and Nevid 124).
Due to the need to supply an increased amount of oxygen to lungs and muscles, the rise in breaths per minute causes the cardiovascular system to function at a faster pace as well, raising the heartbeat significantly. The observed phenomenon is explained by stronger heart contractions caused by the increase in the levels of cortisol, as well as adrenaline and noradrenaline (Hales and Hales 22). Furthermore, due to the need for a larger oxygen intake for the body, the amount of blood pumped through the blood vessels and the heart increases substantially, causing a faster heart rate and an increased workload for the cardiovascular system.
Being under the influence of stress-inducing factors, the nervous system also produces an immediate response. However, before assessing the effects of stress on it, one should mention that the nervous system is typically split into two main parts, namely, the autonomic and somatic ones (Hales and Hales 24). The former, in turn, is subdivided into the sympathetic (SNS) and parasympathetic (PNS) nervous systems (Hales and Hales 24). The latter plays a direct role in activating the aforementioned “fight or flight” response as it sends signals to the adrenal medulla and the pituitary gland (Hales and Hales 22). As a result, the glands releasing cortisol, adrenalin, and noradrenalin are activated, causing immediate changes in the rest of the systems, particularly, the endocrine and the respiratory ones. Thus, the chain of immediate responses toward the emerging risk is launched. When affected by stress in the long term, the nervous system continues to respond, causing further deterioration of the body.
As emphasized above, stress factors cause an immediate release of the hormones that activate the rest of the systems. Therefore, what is known as the hypothalamic-pituitary-adrenal (HPA) axis within the endocrine system is activated once stress factors emerge. As a result, stress-related hormones, primarily, cortisol, adrenalin, and noradrenalin, are produced. Cortisol, in turn, supplies the energy needed to address a stress-related situation.
The gastrointestinal system also responds to stress quite promptly due to the immense number of neurons in it. However, due to the disruption of the standard functioning of the gastrointestinal cells, stress can result in muscle spasms within the gastrointestinal system. The described phenomenon may entail a variety of effects ranging from diarrhea to constipation.
Examining the effects of stress on the human reproductive system, one should consider the differences between the male and female ones. In the male system, due to the rise in the levels of testosterone, which is activated through the parasympathetic path, the phenomenon of arousal is often observed as a response to immediate threat and stress (Hales and Hales 23). In the female reproductive system, long-term effects such as the disruption of the menstrual cycle and the inability to conceive can be seen as the key outcomes.
In an overactive bladder, the increased level of stress may lead to more rapid functioning and the need to urinate more frequently, leading to incontinence. In the long term, the specified effects may cause additional health conditions, such as bladder inflammation. Similarly, the excretory system’s functioning is disrupted to a considerable degree under the influence of both short- and long-term stress. The specified effects are likely to aggravate until the stress factors are removed from an individual’s environment, which is why the threat of kidney damage must be considered for those experiencing constant emotional distress.
As a rule, a significant drop in the functioning of the immune system is observed after individual experiences severe stress. When considering short-term stress, the immune system of an individual remains unaffected for the most part; however, in the long term, the immune system suffers significantly. Due to the focus on managing a specific set of stress factors, the human body loses the ability to produce antibodies as effectively as it used to do. Consequently, one’s ability to withstand the impact of multiple health threats is diminished to a large extent, causing one to become more susceptible to infectious diseases and, overall, more vulnerable to health threats. The described outcomes suggest that the immune system must remain one of the priorities when addressing stress as a health concern.
Finally, the effects that stress produces on the lymphatic system of an individual need to be touched upon. The lymphatic system is also affected once an individual is exposed to stress, causing the neural-inflammatory signaling to be reduced significantly. Long-term exposure to stress may cause the development of cancerous cells in lymph nodes, as a recent study explains (Le and Sloan 3). Therefore, addressing the problem of stress promptly is essential to prevent oncological issues from developing.
Although stress is often taken for granted and believed to have mostly superficial effects solely on the nervous system, it affects profoundly the entirety of the human body. Even in the instances when stress occurs for a short amount of time, the changes taking place in one’s body are very noticeable, causing a string of adverse effects. In the long term, the effects of stress on one’s health are detrimental since stress affects every single system. Thus, creating strategies for managing stress as a tangible threat to one’s well-being is instrumental. Moreover, promoting patient education concerning the strategies for managing stress and preventing it from taking place needs to be designed.
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Le, Caroline P., and Erica K. Sloan. “Stress-Driven Lymphatic Dissemination: An Unanticipated Consequence of Communication between the Sympathetic Nervous System and Lymphatic Vasculature.” Molecular & Cellular Oncology , vol. 3, no. 4, 2016, pp. 1-8.
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Schönrich, Günther, Martin J. Raftery, and Yvonne Samstag. “Devilishly Radical NETwork in COVID-19: Oxidative Stress, Neutrophil Extracellular Traps (NETs), and T Cell Suppression.” Advances in Biological Regulation , vol. 77, 2020, pp. 1-12.
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