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Practice of stress management behaviors and associated factors among undergraduate students of Mekelle University, Ethiopia: a cross-sectional study

  • Gebrezabher Niguse Hailu 1  

BMC Psychiatry volume  20 , Article number:  162 ( 2020 ) Cite this article

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Stress is one of the top five threats to academic performance among college students globally. Consequently, students decrease in academic performance, learning ability and retention. However, no study has assessed the practice of stress management behaviors and associated factors among college students in Ethiopia. So the purpose of this study was to assess the practice of stress management behaviors and associated factors among undergraduate university students at Mekelle University, Tigray, Ethiopia, 2019.

A cross-sectional study was conducted on 633 study participants at Mekelle University from November 2018 to July 2019. Bivariate analysis was used to determine the association between the independent variable and the outcome variable at p  < 0.25 significance level. Significant variables were selected for multivariate analysis.

The study found that the practice of stress management behaviors among undergraduate Mekelle university students was found as 367(58%) poor and 266(42%) good. The study also indicated that sex, year of education, monthly income, self-efficacy status, and social support status were significant predictors of stress management behaviors of college students.

This study found that the majority of the students had poor practice of stress management behaviors.

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Stress is the physical and emotional adaptive response to an external situation that results in physical, psychological and behavioral deviations [ 1 ]. Stress can be roughly subdivided into the effects and mechanisms of chronic and acute stress [ 2 ]. Chronic psychological stress in early life and adulthood has been demonstrated to result in maladaptive changes in both the HPA-axis and the sympathetic nervous system. Acute and time-limited stressors seem to result in adaptive redistribution of all major leukocyte subpopulations [ 2 ].

Stress management behaviors are defined as behaviors people often use in the face of stress /or trauma to help manage painful or difficult emotions [ 3 ]. Stress management behaviors include sleeping 6–8 h each night, Make an effort to monitor emotional changes, Use adequate responses to unreasonable issues, Make schedules and set priorities, Make an effort to determine the source of each stress that occurs, Make an effort to spend time daily for muscle relaxation, Concentrate on pleasant thoughts at bedtime, Feel content and peace with yourself [ 4 ]. Practicing those behaviors are very important in helping people adjust to stressful events while helping them maintain their emotional wellbeing [ 3 ].

University students are a special group of people that are enduring a critical transitory period in which they are going from adolescence to adulthood and can be one of the most stressful times in a person’s life [ 5 ]. According to the American College Health Association’s National College Health Assessment, stress is one of the top five threats to academic performance among college students [ 6 ]. For instance, stress is a serious problem in college student populations across the United States [ 7 ].

I have searched literatures regarding stress among college students worldwide. For instance, among Malaysian university students, stress was observed among 36% of the respondents [ 8 ]. Another study reported that 43% of Hong Kong students were suffered from academic stress [ 9 ]. In western countries and other Middle Eastern countries, including 70% in Jordan [ 10 ], 83.9% in Australia [ 11 ]. Furthermore, based on a large nationally representative study the prevalence of stress among college students in Ethiopia was 40.9% [ 12 ].

Several studies have shown that socio-demographic characteristics and psychosocial factors like social support, health value and perceived self-efficacy were known to predict stress management behaviors [ 13 , 14 , 15 , 16 , 17 ].

Although the prevalence of stress among college students is studied in many countries including Ethiopia, the practice of stress management behaviors which is very important in promoting the health of college students is not studied in Ethiopia. Therefore this study aimed to assess the practice of stress management behaviors and associated factors among undergraduate students at Mekelle University.

The study was conducted at Mekelle university colleges from November 2018 to July 2019 in Mekelle city, Tigray, Ethiopia. Mekelle University is a higher education and training public institution located in Mekelle city, Tigray at a distance of 783 Kilometers from the Ethiopian capital ( http://www.mu.edu.et/ ).

A cross-sectional study was conducted on 633 study participants. Students who were ill (unable to attend class due to illness), infield work and withdrawal were not included in the study.

The actual sample size (n) was computed by single population proportion formula [n = [(Za/2)2*P (1 − P)]/d2] by assuming 95% confidence level of Za/2 = 1.96, margin of error 5%, proportion (p) of 50% and the final sample size was estimated to be 633. A 1.5 design effect was used by considering the multistage sampling technique and assuming that there was no as such big variations among the students included in the study.

Multi-stage random sampling was used. Three colleges (College of health science, college of business and Economics and College of Natural and Computational Science) were selected from a total of the seven Colleges from Mekelle University using a simple random sampling technique in which proportional sample allocation was considered from each college.

Data were collected using a self-administered questionnaire by trained research assistants at the classes.

The questionnaire has three sections. The first section contained questions on demographic characteristics of the study participants. The second section contained questions to assess the practice of stress management of the students. The tool to assess the practice of stress management behaviors for college students was developed by Walker, Sechrist, and Pender [ 4 ]. The third section consisted of questions for factors associated with stress management of the students divided into four sub-domains, including health value used to assess the value participants place on their health [ 18 ]. The second subdomain is self-efficacy designed to assess optimistic self-beliefs to cope with a variety of difficult demands in life [ 19 ] and was adapted by Yesilay et al. [ 20 ]. The third subdomain is perceived social support measures three sources of support: family, friends, and significant others [ 21 ] and was adapted by Eker et al. [ 22 ]. The fourth subscale is perceived stress measures respondents’ evaluation of the stressfulness of situations in the past month of their lives [ 23 ] and was adapted by Örücü and Demir [ 24 ].

The entered data were edited, checked visually for its completeness and the response was coded and entered by Epi-data manager version 4.2 for windows and exported to SPSS version 21.0 for statistical analysis.

Bivariate analysis was used to determine the association between the independent variable and the outcome variable. Variables that were significant at p  < 0.25 with the outcome variable were selected for multivariable analysis. And odds ratio with 95% confidence level was computed and p -value <= 0.05 was described as a significant association.

Operational definition

Good stress management behavior:.

Students score above or equal to the mean score.

Poor stress management behavior:

Students score below the mean score [ 4 ].

Seciodemographic characteristics

Among the total 633 study participants, 389(61.5%) were males, of those 204(32.2%) had poor stress management behavior. The Median age of the respondents was 20.00 (IQR = ±3). More ever, this result showed that 320(50.6%) of the students came from rural areas, 215(34%) of them had poor stress management behavior.

The result revealed that 363(57.35%) of the study participants were 2nd and 3rd year students, of them 195 (30.8%) had poor stress management.

This result indicated that 502 (79.3%) of the participants were in the monthly support category of > = 300 ETB with a median income of 300.00 ETB (IQR = ±500), from those, 273(43.1%) students had poor stress management behavior (Table  1 ).

figure 1

Status of practice of stress management behaviors of under graduate students at Mekelle University, Ethiopia

Psychosocial factors

This result indicated that 352 (55.6%) of the students had a high health value status of them 215 (34%) had good stress management behavior. It also showed that 162 (25.6%) of the students had poor perceived self-efficacy, from those 31(4.9%) had a good practice of stress management behavior. Moreover, the result showed that 432(68.2%) of the study participants had poor social support status of them 116(18.3%) had a good practice of stress management behavior (Table  1 ).

Practice of stress management behaviors

The result showed that the majority (49.8%) of the students were sometimes made an effort to spend time daily for muscle relaxation. Whereas only 28(4.4%) students were routinely concentrated on pleasant thoughts at bedtime.

According to this result, only 169(26.7%) of the students were often made an effort to determine the source of stress that occurs. It also revealed that the majority (40.1%) of the students were never made an effort to monitor their emotional changes. Similarly, the result indicated that the majority (42.5%) of the students were never made schedules and set priorities.

The result revealed that only 68(10.7%) of the students routinely slept 6–8 h each night. More ever, the result showed that the majority (34.4%) of the students were sometimes used adequate responses to unreasonable issues (Table  2 ).

Status of the practice of stress management behaviors

The result revealed that the practice of stress management behaviors among regular undergraduate Mekelle university students was found as 367(58%) poor and 266(42%) good. (Fig  1 )

Factors associated with stress management behaviors

In the bivariate analysis sex, college, year of education, student’s monthly income’, perceived-self efficacy, perceived social support and perceived stress were significantly associated with stress management behavior at p < =0.25. Whereas in the multivariate analysis sex, year of education, student’s monthly income’, perceived-self efficacy and perceived social support were significantly associated with stress management behavior at p < =0.05.

Male students were 3.244 times more likely to have good practice stress management behaviors than female students (AOR: 3.244, CI: [1.934–5.439]). Students who were in the age category of less than 20 years were 70% less to have a good practice of stress management behaviors than students with the age of greater or equal to 20 year (AOR: 0.300, CI:[0.146–0.618]).

Students who had monthly income less than300 ETB were 64.4% less to have a good practice of stress management behaviors than students with monthly income greater or equal to 300 ETB (AOR: 0.356, CI:[0.187–0.678]).

Students who had poor self- efficacy status were 70.3% less to have a good practice of stress management behaviors than students with good self-efficacy status (AOR: 0.297, CI:[0.159–0.554]). Students who had poor social support were 70.5% less to have a good practice of stress management behaviors than students with good social support status (AOR: 0.295[0.155–0.560]) (Table  3 ).

The present study showed that the practice of stress management behaviors among regular undergraduate students was 367(58%) poor and 266(42%) good. The study indicated that sex, year of education, student’s monthly income, social support status, and perceived-self efficacy status were significant predictors of stress management behaviors of students.

The current study revealed that male students were more likely to have good practice of stress management behaviors than female students. This finding is contradictory with previous studies conducted in the USA [ 13 , 25 ], where female students were showed better practice of stress management behaviors than male students. This difference might be due to socioeconomic and measurement tool differences.

The current study indicated that students with monthly income less than 300 ETB were less likely to have good practice of stress management behaviors than students with monthly income greater than or equal to 300 ETB. This is congruent with the recently published book which argues a better understanding of our relationship with money (income). The book said “the people with more money are, on average, happier than the people with less money. They have less to worry about because they are not worried about where they are going to get food or money for their accommodation or whatever the following week, and this has a positive effect on their health” [ 26 ].

The present study found that first-year students were less likely to have good practice of stress management behaviors than senior students. This finding is similar to previous findings from Japan [ 27 ], China [ 28 ] and Ghana [ 29 ]. This might be because freshman students may encounter a multitude of stressors, some of which they may have dealt with in high school and others that may be a new experience for them. With so many new experiences, responsibilities, social settings, and demands on their time. As a first-time, incoming college freshman, experiencing life as an adult and acclimating to the numerous and varied types of demands placed on them can be a truly overwhelming experience. It can also lead to unhealthy amounts of stress. A report by the Anxiety and Depression Association of America found that 80% of freshman students frequently or sometimes experience daily stress [ 30 ].

The current study showed that students with poor self-efficacy status were less likely to have good practice of stress management behaviors. This is congruent with the previous study that has demonstrated quite convincingly that possessing high levels of self-efficacy acts to decrease people’s potential for experiencing negative stress feelings by increasing their sense of being in control of the situations they encounter [ 14 ]. More ever this study found that students with poor social support were less likely to have a good practice of stress management behaviors. This finding is similar to previous studies that found good social support, whether from a trusted group or valued individual, has shown to reduce the psychological and physiological consequences of stress, and may enhance immune function [ 15 , 16 , 17 ].

Ethics approval and consent to participate

Ethical clearance and approval obtained from the institutional review board of Mekelle University. Moreover, before conducting the study, the purpose and objective of the study were described to the study participants and written informed consent was obtained. The study participants were informed as they have full right to discontinue during the interview. Subject confidentiality and any special data security requirements were maintained and assured by not exposing the patient’s name and information.

Limitation of the study

There is limited literature regarding stress management behaviors and associated factors. There is no similar study done in Ethiopia previously. More ever, using a self-administered questionnaire, the respondents might not pay full attention to it/read it properly.

This study found that the majority of the students had poor practice of stress management behaviors. The study also found that sex, year of education, student’s monthly income, social support status, and perceived-self efficacy status were significant predictors of stress management behaviors of the students.

Availability of data and materials

The datasets used during the current study is available from the corresponding author on reasonable request.

Abbreviations

Adjusted Odd Ratio

College of Business& Economics

College of health sciences

Confidence interval

College of natural and computational sciences

Crud odds ratio

Ethiopian birr

Master of Sciences

United States of America

United kingdom

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Hailu, G.N. Practice of stress management behaviors and associated factors among undergraduate students of Mekelle University, Ethiopia: a cross-sectional study. BMC Psychiatry 20 , 162 (2020). https://doi.org/10.1186/s12888-020-02574-4

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Learning and memory under stress: implications for the classroom

  • Susanne Vogel 1 &
  • Lars Schwabe 1  

npj Science of Learning volume  1 , Article number:  16011 ( 2016 ) Cite this article

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  • Hippocampus
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  • Stress and resilience

Exams, tight deadlines and interpersonal conflicts are just a few examples of the many events that may result in high levels of stress in both students and teachers. Research over the past two decades identified stress and the hormones and neurotransmitters released during and after a stressful event as major modulators of human learning and memory processes, with critical implications for educational contexts. While stress around the time of learning is thought to enhance memory formation, thus leading to robust memories, stress markedly impairs memory retrieval, bearing, for instance, the risk of underachieving at exams. Recent evidence further indicates that stress may hamper the updating of memories in the light of new information and induce a shift from a flexible, ‘cognitive’ form of learning towards rather rigid, ‘habit’-like behaviour. Together, these stress-induced changes may explain some of the difficulties of learning and remembering under stress in the classroom. Taking these insights from psychology and neuroscience into account could bear the potential to facilitate processes of education for both students and teachers.

Stressful events are very common in educational settings, both for students and for teachers. A multitude of exams, evaluations and deadlines creates an enormous pressure to perform. This stress, however, can have a critical impact on learning and memory processes, 1 , 2 which are at the heart of our educational system. Beyond their relevance in educational contexts, stress-induced alterations in learning and memory are also thought to contribute to stress-related mental disorders, such as major depressive disorder or post-traumatic stress disorder. 3 Therefore, a large number of studies has been conducted to better understand how stress affects learning and memory. The effects of stress were found to be complex, though, with stress having both enhancing and impairing effects on memory, depending on the specific memory process or stage that is affected by stress 1 , 4 and the activity profile of major physiological stress response systems.

This review summarises the current state of knowledge on the impact of (acute) stress on memory and derives implications for educational settings from these laboratory findings. Because our focus is on memory processes most relevant in the classroom, we will concentrate mainly on the effects of (moderate) stress (induced in laboratory settings) on episodic and semantic memory, as well as the engagement of multiple memory systems in healthy humans (for reviews on the influence of stress on other forms of memory or other cognitive processes, see e.g. Arnsten 5 and Sandi 6 ). As the influence of stress on learning and memory is intimately linked to the physiological and endocrine changes initiated on a stressful encounter, we will cover these changes first. Next, we will provide a concise overview of how stress, through the action of major stress mediators, induces time-dependent changes in how much information is learned, consolidated and retrieved (i.e., memory quantity). In the third part of this review, we will discuss recent findings on how stress may change the dynamics of memories, their updating in the face of novel information, and the integration of new knowledge into existing memories, all key processes in educational settings. We will then highlight the impact of stress on the engagement of different memory systems, arguing that stress effects are not limited to how much we learn or remember but that stress also changes the nature (or quality) of memories, for instance, the strategies that are used during learning. Based on these empirical findings, we will finally discuss the implications of stress effects on learning and memory processes for the classroom.

The well-coordinated physiological response to stressors

Difficult situations in the classroom such as exams or interpersonal conflicts can challenge or exceed the coping strategies or resources available and thus threaten our homoeostasis, our inner balance, leading us to feel ‘stressed’. 7 The individual appraisal of the situation is critical as it determines the response that follows. 8 , 9 If a situation is appraised as stressful, a well-described cascade of physiological and endocrine changes is set in motion in order to re-establish homoeostasis and to promote long-term well-being. 10 Although this stress response is very complex with numerous mediators involved, two major stress systems appear to be critical for the modulation of learning and memory processes, the rapid autonomic nervous system (ANS) and the slower hypothalamus–pituitary–adrenal axis ( Figure 1 ). Within seconds, the ANS is activated, leading to the release of catecholamines such as noradrenaline (NA), both from the adrenal medulla and the locus coeruleus in the brain. 10 Catecholamines prepare the body for ‘fight-or-flight’ responses and rapidly affect neural functioning in several brain regions critical for learning and memory, such as the hippocampus, amygdala and prefrontal cortex (PFC). 5 , 11 Somewhat slower, a second system is activated in response to stress, the hypothalamus–pituitary–adrenal axis, resulting in the release of corticosteroids (in humans mainly cortisol) from the adrenal cortex. Cortisol reaches peak level concentrations ~20–30 min after stressor onset, 10 readily enters the brain and binds to two different receptors to induce its effects on cognition: The glucocorticoid receptor (GR) is expressed ubiquitously throughout the brain, whereas the mineralocorticoid receptor (MR) is mainly expressed in brain regions related to memory and emotion, for instance, the hippocampus, amygdala and PFC. 12 , 13 On binding to these receptors, cortisol operates via two different modes of action, a non-genomic, often MR-mediated mode develops rapidly 14 and enhances neural excitability in the amygdala and hippocampus, 15 , 16 presumably supporting memory formation. This rapid mode is followed by a slower, often GR-dependent mode that is assumed to develop ~60–90 min after stressor onset and to involve longer-lasting changes to DNA translation and transcription. 17 The slow genomic mode is assumed to revert the acute effects of stress and to re-establish homoeostasis by decreasing neural excitability in the amygdala and hippocampus long after stress. 4

figure 1

Systems activated in response to stressful events. On a stressful encounter, the autonomic nervous system (left) is activated within seconds to release catecholamines (e.g., noradrenaline) from the adrenal medulla and the locus coeruleus in the brain stem. Catecholamines are implicated in the ‘fight-or-flight’ response, but they also have profound effects on attention, working memory and long-term memory. Somewhat slower, the hypothalamus–pituitary–adrenal axis is activated, releasing corticotropin-releasing hormone (CRH) from the hypothalamus which stimulates the anterior pituitary to secrete adrenocorticotropic hormone (ACTH). ACTH in turn causes the adrenal cortex to produce cortisol and release it into the blood stream. Cortisol reaches peak level concentrations ~20–30 min after stress onset and readily enters the brain to affect cognition and behaviour. Cortisol feedback to the pituitary, hypothalamus and other brain areas (e.g., the hippocampus) prevents the system from overshooting.

This striking temporal profile of the stress response leads to differential effects of stress on learning and memory, depending on the temporal proximity between the stressful event and the memory process investigated. For instance, stress experienced just before memory retrieval, when catecholamine levels are still high and cortisol levels are not elevated yet, may have very different effects than stress experienced 90 min before retrieval, when catecholamine levels returned to baseline and genomic cortisol actions are at work. 18 , 19 Moreover, distinct memory stages, i.e., encoding, consolidation or retrieval may be differently affected by these time-dependent physiological changes after a stressful encounter. In the next section, we will portray the time-dependent effects of stress on learning and memory, taking into account both the specific memory stage affected and the temporal proximity between the stressful event and the memory formation or retrieval process ( Figure 2 ).

figure 2

The effects of stress on memory depend on the specific memory process investigated and the temporal proximity between the stressful event and this memory process. While stress (indicated as red flash) long before encoding impairs memory formation, stress shortly before or after the presentation of new information generally enhances subsequent memory performance. In sharp contrast, stress before memory retrieval impairs the recall of information learned previously which may directly affect performance at exams. In education, knowledge needs to be frequently updated by new facts or concepts relating to prior knowledge. In addition to its effects on memory encoding and retrieval, stress appears to impair this integration of new information into existing knowledge structures.

Time-dependent effects of stress on memory quantity

Emotionally arousing events are typically very well-remembered. Likewise, individuals who experienced extremely stressful (traumatic) events may suffer from very vivid memories of these events, suggesting that severe stress during or just before encoding may boost memory formation. In line with these observations, studies showed that also lower levels of stress (as they may occur more frequently in schools) during or just before learning may strengthen human memory. 20 – 23 This effect of stress on encoding was often stronger for emotional compared with neutral learning material. 24 Another factor moderating the influence of stress on learning is the correspondence between the stressful context and the learning material. For example, stress during learning specifically enhanced memory for material that was related to the context of the stressful task and thus putatively more relevant. 20 Material that is unrelated to an ongoing stressor, however, is typically not very well-remembered later on. 25 Despite many studies showing a stress-induced learning enhancement if stressor and learning coincide, some studies found the opposite effect. 26 , 27 This divergence might be due to other factors than just the timing of the stressful encounter, such as differences in the interval between study and retrieval or individual differences due to sex, genetics or the developmental background. 28 – 31 In sum, being moderately stressed can enhance memory formation for emotional material and information that is related to the stressful context, whereas stress may impair the encoding of stressor-unrelated material.

At the neural level, catecholamines such as NA appear to play a critical role in the enhancing effects of stress or emotional arousal on learning. Studies in rodents demonstrated that NA exposure strengthened synaptic contacts in the hippocampus 11 and that the concentration of NA in the amygdala after encoding predicted memory strength. 32 Corticosteroids, however, appear to play an important role as well. For instance, MR-activation rapidly enhanced neural excitability in the amygdala and hippocampus which may further aid successful memory encoding. 15 , 16 Additional evidence for a role of corticosteroids came from human pharmacological studies, demonstrating that the administration of 20 mg cortisol prior to learning boosted later memory, especially for emotionally arousing pictures. 33 Notably, this memory advantage for emotional material depends on NA, as it can be blocked by the beta-blocker propranolol. 34 Human neuroimaging studies then set out to elucidate the neural mechanism underlying the stress-induced learning enhancement. The immediate release of NA under stress activated a network of brain regions known as the salience network encompassing the amygdala, anterior cingulate cortex and anterior insula. 35 , 36 This rapid upregulation of the salience network allowed enhanced vigilance and better processing of threat-related information which may improve memory encoding in stressful situations. Some minutes later, the release of cortisol reduced global signal in the electroencephalogram (EEG), which was interpreted as a reduction in background processing in order to allow efficient processing of relevant information by enhancing the signal-to-noise ratio. 37 In line with an enhanced processing of important information, the stress-induced increase in processing and encoding of study items in the brain was related to better memory performance for these items at test. 38 , 39 Several studies also investigated the interplay of NA and cortisol in memory encoding. Supporting evidence for such an interaction came, for instance, from a study showing that emotional learning material activated the amygdala, an effect that depended on NA availability as it was abolished by propranolol. 40 Importantly, this amygdala response to emotional stimuli was particularly prominent in those individuals with higher cortisol levels during encoding. 41 Moreover, the combined administration of cortisol and yohimbine, a drug increasing NA stimulation, switched neural activity towards a strong deactivation of prefrontal areas, 42 potentially releasing the amygdala from inhibitory top-down control and improving memory encoding.

While stress around the time of learning enhances memory, stress (or cortisol administration of 25 mg) long before learning or in a distinctly different context does not promote new learning 43 and can even hinder successful encoding of new information. 21 For example, while stress directly before learning enhanced later recognition memory, memory was impaired if stress was experienced 30 min before learning. 21 This memory impairing effect of stress long before learning has been associated with a decrease in neural excitability in the hippocampus long after cortisol administration, 44 which might suggest that genomic actions of cortisol protect the consolidation of information learned during the stressful encounter. 2 In line with this finding of decreased hippocampal excitability, cortisol administered more than 1 h before MRI measurements reduced hippocampal and amygdala activity in humans, 45 , 46 possibly impairing the formation of new memories. In the same time period, the activity of the salience network decreased again to pre-stress levels while activity in the executive control network increased, 35 allowing the individual to recover from the stressful situation and to re-approach homoeostasis. However, there is evidence that this reversal of heightened salience network activity, which is important for higher cognitive control functions to improve coping in the aftermath of stress, does not occur when the participants remain in the stressful context. For instance, the coupling between the amygdala and the salience network remained enhanced after 1 h if the participants were still in the context of the stress induction procedure, 47 again highlighting the role of context as a moderator of stress effects on learning.

When stress is experienced before or during a learning episode, its effects on memory encoding can hardly be dissociated from those on memory consolidation. Also in educational settings, influences of stress on memory encoding can often not be separated from those on memory storage. However, by administering stress or stress mediators shortly after learning, thus excluding an influence on memory encoding, experimental studies were able to isolate stress effects on memory consolidation. Several studies in humans showed that stress or adrenaline injections shortly after learning improved memory consolidation, an effect which was more pronounced for emotionally arousing material, 26 , 48 , 49 , 50 highlighting the importance of the emotionality of the study material. Studies in rodents also demonstrated that the administration of NA or corticosteroids just after learning improved consolidation, 51 and that this enhancing effect (at least on hippocampal memory) required the interaction between NA and GR-mediated cortisol effects in the amygdala. 52 – 55

The effects of stress on memory are, however, not limited to the formation of memories (i.e., memory encoding and consolidation) but extend also to memory retrieval. Given that exams and tests can easily cause stress in students and students are evaluated based on their performance in these tests, it is particularly relevant to understand how stress affects memory recall. In line with seminal findings in rodents, 56 many studies in humans demonstrated that acute stress impaired memory retrieval after a stressful encounter (refs 18 , 19 , 57 , 58 , 59 but see refs 60 , 61 ). Retrieval in the stressful situation itself seemed not to be affected or even enhanced, 18 , 19 particularly when retrieval performance was directly relevant to the stressful encounter. Retrieval more than 20 min after stress, however, when cortisol levels were already elevated, was impaired by the cortisol response to stress 18 , 19 , 58 ( Figure 3 ) and the impairment appeared to be even stronger at a time point when genomic cortisol actions had developed, 18 suggesting that the impairing effects of stress can last much longer than previously known. This retrieval deficit after stress was not only found in adults but was also observed in 8–10-year-old children, highlighting the relevance of these findings for educational settings. 59 The disrupting effect of stress on retrieval was stronger for emotional material 26 , 62 and also the context appeared to play a moderating role on the effects of stress on retrieval. For instance, if the retrieval test was relevant for the stressful situation or if both learning and test took place in the same context, so that the context served as a retrieval cue, recall was spared from the impairing effects of stress. 19 , 63

figure 3

Stress impairs memory retrieval. Participants learned a two-dimensional object location task similar to the game ‘concentration’ (note that for illustrative purposes encoding is depicted by a book, similar to studying in class). One day later, participants either underwent a mild stress induction procedure (indicated by the red flash) or a non-stressful control procedure before recalling the card pair locations learned on day 1. Participants in the stress group recalled significantly fewer card pair locations on day 2 than participants in the control group (relative to their performance on day 1), indicating that stress before retention testing reduced memory performance. Adjusted, with permission, from ref. 63 .

The negative effect of stress on retrieval could be mimicked by administering a GR agonist and blocked by the cortisol synthesis inhibitor metyrapone in rodents, which suggests a GR-dependent pathway 43 , 56 , 64 , 65 reducing blood flow in the medial temporal lobe. 66 However, the interaction with NA appears to be crucial as the impairing effects of cortisol depended on noradrenergic activation of the amygdala. 52 For instance, blocking the action of NA pharmacologically with propranolol abolished the impairing effect of cortisol on emotional memory retrieval. 67 Thus, similar to memory consolidation, the interaction between GR-mediated cortisol action and NA appears to be crucial for stress-induced effects on memory retrieval. 67

To summarise, stress affects memory in a time-dependent manner, often enhancing memory formation around the time of the stressful encounter but impairing memory retrieval and the acquisition of information encoded long after the stressful event. These effects depend on interactions between NA and cortisol in the amygdala and are thus often stronger for emotional than for neutral learning material. In the next paragraph, we will move beyond stress-induced changes in memory performance and describe how stress may also affect the integration of new information into existing memories, i.e., knowledge updating.

Stress and the dynamics of memory

Very often, students are not only required to recall study material, but to integrate new information into existing knowledge structures. In fact, integrating new information into existing memories is a key process in education (as well as in life in general where we are constantly required to update our knowledge). Such updating implicates that memories remain malleable even long time after they have been formed initially and research over the past 15 years shows that this is indeed the case (for review, see ref. 68 ). There is compelling evidence that consolidated, seemingly stable memories return to a labile state when they are reactivated, 68 – 71 which requires the re-stabilization of those memories in a process called reconsolidation. During reconsolidation, the reactivated memory can be weakened, strengthened or altered. 69 , 71 In other words, reconsolidation most likely represents the mechanism underlying memory updating processes. 72 As reconsolidation involves the hippocampus 71 and the PFC, 73 areas that are main targets of stress modulators, it seems reasonable to assume that stress would also affect reconsolidation. First evidence for such stress effects on reconsolidation came from rodent studies showing that stress or cortisol injections after memory reactivation impaired subsequent memory expression, suggesting that stress impaired reconsolidation. 74 , 75 For instance, stress after reactivation of a memory trace interfered with performance at a later memory test, an effect which depended on GR-mediated cortisol activity in the amygdala. 75 Several studies in humans support the hypothesis that stress can affect memory reconsolidation and thus memory updating, yet the specific conditions leading to impairing or enhancing effects of stress on reconsolidation are still under investigation. 76 – 78

Further evidence for a critical role of stress in the updating of memories comes from studies on the so-called misinformation effect. This effect describes the incorporation of misleading information presented after encoding the original event into the memory for this event. 79 Although this effect mainly concerns the biasing influence of misinformation on memory, it provides important insights into memory updating in general and studying how stress affects the misinformation effect may allow a deeper understanding of how stress affects the updating of memories. For instance, it was shown that if highly arousing information is learned during stress, this resulted in more robust memories that were less vulnerable to being ‘updated’ by subsequent (mis)information. 80 Similarly, misinformation was less often incorporated into existing memories if the participants were stressed before the presentation of misinformation, thus indicating that stress interferes with the updating of the existing memory trace 81 ( Figure 4 ). As the mechanism underlying the misinformation effect is assumed to be reconsolidation, 72 this finding is in line with reports showing an impairing effect of stress on memory reconsolidation. 74 , 75 , 78 In sum, there is accumulating evidence that stress may interfere with the updating of memories, which may have negative implications for education where new information often has to be incorporated into existing knowledge.

figure 4

Stress reduces the integration of new information into existing memories. On day 1, participants were instructed to memorise different stories presented in short movie clips (note that encoding is illustrated by a book for illustrative purposes). On day two, participants either underwent a mild stress induction procedure (indicated by the red flash) or a non-stressful control procedure before they were presented with a questionnaire regarding the study material from day 1. Importantly, some items of this questionnaire included wrong information about the study material (misinformation, shown in orange). On day 3, forced choice questions were used to test whether the misinformation had been integrated into the memory trace of the study material. In the memory test, possible answers were the correct original information, the misinformation presented the day before and other incorrect answers (lures) that were not referred to on day 2. Overall, participants endorsed misinformation more often than lures, thus demonstrating a misinformation effect. Critically, stressed participants endorsed fewer misinformation items than participants of the control group, suggesting that stress reduced the modification of the original memory on day 2. Adjusted, with permission, from ref. 81 . * P <0.005

Stress alters the way we learn: effects on memory quality

Most studies investigating the effects of stress on memory encoding, retrieval or updating focused on memories encoded by the hippocampus. However, experiences can be encoded by multiple memory systems operating in parallel, differing in their neural substrate and in the information processed. 82 , 83 Several studies demonstrated that stress has a critical impact on which of these memory systems is used to form and retrieve memories, implicating that stress changes the nature or quality of memories 84 , 85 (see Figure 5 ). Early studies in rodents demonstrated that stress or amygdala activation through anxiogenic drugs at encoding induced a shift from a flexible ‘cognitive’ memory system depending on the hippocampus towards a more rigid, ‘habit’-like memory system based on the dorsal striatum. 82 , 86 , 87 Thus, under stress, more rigid stimulus–response associations are learned rather than complex representations of our environment including the relationship between stimuli or task requirements. This shift in the system that controls memory could be blocked by an MR-antagonist, suggesting that the shift is due to MR-mediated cortisol action. 88 , 89 Importantly, stress itself did not disrupt learning, but blocking the shift towards habit memories markedly impaired performance, 88 suggesting that the shift towards the striatum-based habit system is adaptive and beneficial for performance under stress. So far, only one study investigated whether this stress-induced shift also affects memory retrieval, and indeed anxiogenic drugs injected into the amygdala before retrieval biased rats towards an increased use of their dorsal striatum at the expense of the hippocampal memory system. 90 To summarise, these studies in rodents suggest that stress induces a qualitative shift in the systems guiding learning (and, most likely, retrieval), from a cognitive, hippocampus-dependent memory system towards a habit-like memory system based on the striatum.

figure 5

Stress shifts the balance between multiple systems underlying learning and memory. At rest, this balance is tilted towards the ‘cognitive’ memory system depending on the hippocampus, allowing for the formation and recall of flexible memories. Stress, however, is thought to alter the system domination learning and memory. Under stress (indicated by a red flash), the balance tips towards more rigid ‘habit’ memories encoded by the dorsal striatum. Thus, stress affects not only how much is learned (memory quantity) but also what is encoded and how memories are built (memory quality).

In line with these rodent findings, stress shifts the systems dominating memory encoding also in humans towards an increased use of striatal habit-like memory, at the expense of hippocampal memory. 91 – 93 For example, stressed participants often used a habit-like striatal learning strategy instead of a hippocampal strategy to solve a learning task. 93 Similar to the findings in rodents, stress did not affect learning performance per se if participants switched to the striatal memory system, 91 yet performance was impaired when participants tried to recruit the hippocampal memory system despite stress. 93 Accordingly, task performance was positively correlated with hippocampal activity in non-stressed control participants, whereas performance correlated positively with striatal activity and even negatively with hippocampal activity in stressed participants. 93 The amygdala appeared to orchestrate this stress-induced shift by rapidly increasing functional connectivity with the dorsal striatum and decreasing its coupling with the hippocampus. 94 , 95 Importantly, an MR-antagonist blocked the stress-induced shift both at the behavioural and neural level, 94 , 95 demonstrating that the stress-induced shift appears to depend on cortisol acting via the MR. 89

In addition to the shift from hippocampal to striatal memory, stress affects the balance between memory systems underlying instrumental behaviour, i.e., behaviour aimed at obtaining rewards or avoiding punishments. Learning and performing these actions can be controlled by a ‘habitual’ system relying on the dorsolateral striatum which acts largely independently of the current value of the action-outcome, or a ‘goal-directed’ system depending on the PFC, dorsomedial striatum, and dorsomedial thalamus which is sensitive to changes in outcome value. 96 Under stress, human and rodent behaviour is rendered more habitual and based on stimulus–response associations rather than action-outcome associations which underlie goal-directed actions. 97 – 101 Moreover, the behaviour of stressed individuals was more resistant against extinction procedures, 92 further highlighting the rigid, rather habitual behaviour of stressed individuals. For example, stressed infants continued to use habit actions even though the behaviour was not reinforced anymore, whereas non-stressed infants stopped showing the behaviour when the reinforcement ended. 100 The stress-induced modulation of instrumental behaviour can be abolished by beta blockers, suggesting that NA plays a crucial role in this shift towards habit behaviour. 98 Again, NA appears to interact with the effects of cortisol as the stress-induced shift towards habits can be mimicked by the combined administration of cortisol and yohimbine, 97 and beta-adrenergic blockade by propranolol prevents the stress-induced bias towards habits. 98 In the brain, this shift has been associated with a reduced sensitivity of the orbitofrontal and medial PFC to changes in outcome value, whereas brain regions implicated in habit learning were not affected. 99

To summarise, stress cannot only affect how much information we learn and remember, but stress also flips the balance between the systems dominating learning and memory, which has considerable consequences for the nature and flexibility of memories and the goal-directedness of behaviour.

Stress and memory in the classroom

School children often encounter stressful events inside and outside of their school environment 102 and nearly 70% of primary school children report symptoms of stress such as worries, anxiety or sadness. 103 In the preceding chapters, we argued that situations appraised as stressful have strong and diverse effects on human memory. While learning during or immediately after stress is often enhanced, stress disrupts memory retrieval and updating, and these effects are most pronounced for emotionally arousing material. Finally, we argued that stress shifts the balance between multiple systems underlying memories and instrumental behaviours towards the formation and recall of rather rigid habit-like memories. Together, these findings highlight that stress may critically shape our memories, which is of utmost importance in all educational contexts.

In the classroom, these stress effects on memory may have far-reaching consequences for students. For instance, emotions or light to moderate forms of stress (i.e., cognitive challenges without excessive demands or moderate emotional arousal that results, e.g., from hearing something that is unexpected) may increase memory formation, which may have positive effects on memories for study material. Yet, these effects likely follow an inverted u-shape and can revert with too high levels of stress. 28 , 104 Moreover, stress may lead to stronger memories for negative events happening in the classroom, such as failed exams, embarrassing experiences or interpersonal conflicts (e.g., bullying) and these strong negative memories may induce long-lasting frustration and a negative attitude towards school and the individual’s abilities. These negative consequences of stress on students may be intensified by the deleterious effects of stress on memory retrieval. Moderate or high levels of stress before exams will most likely hinder memory retrieval and lead to an underestimation of the students’ knowledge, putatively resulting in bad grades. Furthermore, stress may hinder the integration of new information into existing knowledge structures, which may prevent the updating of knowledge by new facts or a deep multidisciplinary understanding of concepts which is often required in education. Finally, by altering the balance between memory systems, stress may lead to strong, rigid memories and the retrieval of habits rather than creative and complex solutions to new problems, which may again lead to an underestimation of the students’ abilities.

Although the effects of stress on memory are highly relevant to students, also teachers frequently encounter stressful events and >40% report high levels of work stress. 105 Also for teachers, appraising events as stressful may lead to strong negative memories of unpleasant situations in the classroom with implications for their work attitude and potentially their mental health. Moreover, stress may impair the quality of teaching if the teacher’s flexibility is decreased, which might hamper adaptive responding to the individual needs and resources of students. Instead, habitual procedures may be supported by stress, leading to a more repetitive teaching style, which may in turn lead to more problems in class.

Considering this wide range of possible stress effects in educational settings, strategies to deal with stress and its consequences are needed. First and foremost, teachers should be aware of the impact stress may have on memory formation, retrieval and updating. Moreover, students should be educated about the influence of stress on memory to raise awareness for the powerful effects stress may exert and the need for efficient coping strategies. It is important to note that potentially stressful events do not necessarily lead to a stress response, but that the individual appraisal of the situation and the available coping strategies determine whether a situation results in the activation of stress systems or not. This dependence on appraisal and coping can explain why some individuals suffer much less from potentially stressful circumstances than others. Thus, next to changing potentially stressful situations, students should be educated about effective coping strategies. 8 , 106

Furthermore, based on findings demonstrating that emotional material is typically better remembered than neutral material, an emotional component (mainly positive) may be added while students learn new information to enhance later memory. 21 , 23 , 24 , 33 , 49 , 107 , 108 For example, this could be achieved by explicit positive verbal reinforcement of students during learning in class. Furthermore, movie clips might be used which do not only focus on the learning material itself, but place it into an emotional context, e.g., by making the links to the student and his or her everyday life explicit.

To counteract the strong negative effects of stress on memory retrieval and updating, strong stressors before exams or before new information is presented to update students’ knowledge should be avoided as far as possible. To reduce stress, practice exams may familiarise the students with the exam situation and trainings in stress reduction techniques or other coping strategies might help students to alleviate stress symptoms. Teachers should also be aware that different forms of retrieval may be differentially affected by stress. Free recall seems to be disrupted more easily by stress than cued recall, 62 suggesting that recall cues may enhance the chance that students can actually retrieve the information they have learned. It is important to note that the impairing effects of stress on retrieval are quite long-lasting, such that stressors long before the exam (e.g., at home) may still affect performance in the test situation. Therefore, children with trouble at home or frequent stressful life events may need special attention before exams to reduce the effects of stress.

Stress does not only induce a deficit in memory retrieval and memory updating, it also changes the way information is stored and retrieved by multiple memory systems. Stress before learning may bias students towards rigid forms of learning, which may hinder the successful transfer of knowledge and reduce cognitive flexibility in problem solving. However, the negative effects of stress on memory retrieval may be counteracted to some extent by thoroughly and repeatedly practicing useful routines which can be recalled rather automatically. This may be especially relevant for the training of correct actions during emergency situations. For instance, given that flexible memory recall and knowledge application is hindered under stress, pilots or physicians should be trained extensively in the correct routines they should apply in stressful emergency situations. If these procedures are automatised, it is much more likely that they can actually be retrieved and translated to behaviour.

Last, students and teachers should be aware of the powerful effects of context. It has been shown repeatedly that memory is enhanced when learning and recall take place in the same context as the context serves as a strong retrieval cue. 109 Moreover, although stress often impairs retrieval, this effect seems to be alleviated if learning and retrieval context match, indicating that the effect of context might counteract stress-induced memory impairments. 63

Conclusion and outlook

Stress has far-reaching consequences on our ability to learn and remember, with major implications for educational settings. Considering that stress is ubiquitous in education and even primary school children often report stress symptoms, understanding the effects of stress on memory is very important. For one, an optimised education is of utmost importance for the individual, laying the foundation of later career success and socioeconomic status. In addition, our educational system is highly relevant for society as a whole by building and instructing the next generation.

Despite the striking advances the field has seen in our understanding of how stress changes learning and memory processes, several questions remain to be answered, e.g., concerning interindividual differences in the effects of stress on memory. While some studies suggested that differences in personality, gender or stress system reactivity may moderate how stress affects learning, 28 the findings are not conclusive yet and the involved mechanisms are not understood sufficiently well to derive recommendations for teachers. Understanding these interindividual differences is a key to personalised approaches or training programmes directed at preventing stress-induced impairments. In addition, more research is necessary to understand the precise development of stress effects on memories over time as it is currently unclear when exactly the enhancing and impairing effects of stress on memory formation arise and how long they last. Likewise, it is currently not well-understood whether different types 110 or intensities 104 of stressors have different effects on memory. Furthermore, most studies did not explicitly distinguish between stress effects on different types of declarative memory, i.e., semantic and declarative memories. Future studies are required to assess whether stress has differential effects on these memory systems, which would provide important insights into how stress changes different forms of learning and memory. Finally, the exposure to prolonged or repeated stress, as well as stress during critical periods of brain development may also have strong effects on learning and memory in children which need to be better understood to counteract the impairments they may cause. 111 Thus, different intensities of stress at different time points during development may induce different effects which remain to be further investigated. Future research on the effects of stress on learning and memory will hopefully answer these and related questions and thus further deepen our understanding of how stress affects memory and why individuals differ in response to stress. Answering these questions may help to personalise learning settings to the specific needs of the individual, to make optimal use of the beneficial effects of emotions on memory, and to alleviate the cognitive impairments stress and strong emotional responses may cause.

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This work was supported by the University of Hamburg. Both authors are supported by the University of Hamburg.

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stress management of students research paper

Stress Management in University Students Research Paper

This report and the information presented in it is structurally a systematic review. Researchers note a systematic review “uses systematic and reproducible methods to identify, select and critically appraise all relevant research, and to collect and analyze data from the studies that are included in the review” ( Systematic reviews in the health sciences , 2020, para. 1). This systematic review will observe articles on the topic of “of stress management in university students.”

Introduction

Students turn to various stress management tactics such as coping, psychotherapy, exercise, and pet therapy to minimize stress and anxiety patterns, and depression symptoms, which are due to many stress factors in the educational process.

It is possible to note that “at the most basic level, stress is our body’s response to pressures from a situation or life event” ( Stress , 2020, para. 2). Researchers state that “stress has a way of becoming chronic as the worries of everyday living weigh us down” ( What Is Stress Management ? 2018, para. 5). Researchers are exploring the interconnectedness of stress management methods and stress levels of university students to identify the most effective strategy for improving the psychological state. The purpose of this systematic review is to investigate how stress management research techniques have changed in the PICOS framework and tendencies in stress levels and stress factors in the period of the last ten years.

Methodology

Eligibility criteria.

The eligibility criteria of selected studies of the systematic review are the PICOS (Population, Intervention, Control/Comparator, Outcome, and Study Design) framework. It is because “without a well-focused question, it can be very difficult and time-consuming to identify appropriate resources and search for relevant evidence” ( PICO Framework , 2019, para. 1). The PICOS framework allows formulating the research question and selects the competent data sources and highlights information that interests the researcher. Studies should include university students as a population, socio-psychological surveys, and physical activities as interventions.

Pretest and posttest data and time frames as comparators and control should also be represented. Changes in stress, anxiety, and depression levels, found stress factors, and the difference between them serve as the outcome. The eligibility criteria for the systematic review are 2015-2020 and 2005-2010 years frames for studies. Such structures allow comparing research methods in the framework of the PICOS framework and the trends of stress management in university students. The publication status of this report is an academic paper, and the language is English, which is due to the condition of the international language of communication.

Information Sources

The information sources for this systematic review were the four largest databases, which are Google Scholar, Frontiers in Physiology, Taylor & Francis Online, and Z-Library. It is possible to note that “Google Scholar is a web search engine that specifically searches scholarly literature and academic resources” ( What is Google Scholar and how do I use it? 2019, para. 1). According to official information, “Frontiers in Physiology is a leading journal in its field, publishing rigorously peer-reviewed research on the physiology of living systems” ( Scope & mission , 2020, para. 1).

Authors note “Taylor & Francis partners with world-class authors, from leading scientists and researchers to scholars and professionals operating at the top of their fields” ( About Taylor & Francis Group , 2020, para. 1). It is also possible to state that “Z-Library is one of the largest online libraries in the world that contains over 4,960,000 books and 77,100,000 articles” ( Z-Library articles , 2020, para. 1). All the databases described above provide a massive layer of scientific information and use convenient and fast search engines, which the author of this systematic review last used on March 30, 2020.

Electronic search strategy for Google Scholar:

  • Enter keywords such as “stress, anxiety, depression, levels, management, university students” in the search bar;
  • Click on “from 2016” or “select dates” and enter a specific time interval;
  • Click on the title of the found study or the PDF version link on the right.

Electronic search strategy for Frontiers in Physiology:

  • Select the “article” category and click on it;
  • Click on the title of the found study;
  • Download PDF version of the found article.

Electronic search strategy for Taylor & Francis Online:

  • Select “Only show Open Access” point;
  • Electronic search strategy for Z-Library.

Electronic search strategy for Z-Library:

  • Select the “article” category and click on the title of the found study.

Study Selection

The method for selecting studies is PRISMA, which stands for Preferred Reporting Items for Systematic Reviews and Meta-Analyses. According to official information, “PRISMA is an evidence-based minimum set of items for reporting in systematic reviews and meta-analyses” ( Welcome to the preferred reporting items for systematic reviews and meta-analyses (PRISMA) website, 2015, para. 1). This methodology includes processes of screening (n = 30), eligibility (n = 30), exclusion from review (n = 15) and inclusion in review (n = 15).

Data Collection Process

Risk of bias in individual studies.

CASP (Critical Appraisal Skills Program) is a risk of bias assessment methodology in this systematic review. According to official information, “these checklists were designed to be used as educational pedagogic tools, as part of a workshop setting” ( CASP Critical Appraisal Skills Programme , 2018, para. 4). It is essential to mention that the CASP Checklist for qualitative researches was selected among all tools to assess examined studies.

Flow Diagram

Flow Diagram

2015-2020 studies excluded from the review:

  • Ahmed, Z. and Julius, S. H. (2015). ‘Academic performance, resilience, depression, anxiety and stress among women college students’;
  • Binfet, J. T. et al. (2018). ‘Reducing university students’ stress through a drop-in canine-therapy program’;
  • Kaya, C. et al. (2015). ‘Stress and life satisfaction of Turkish college students’;
  • Kim, S. H. and Choi, Y. N. (2017). ‘Correlation between stress and smartphone addiction in healthcare-related university students’;
  • Kuang-Tsan, C. and Fu-Yuan, H. (2017). ‘Study on the relationship among university students’ life stress, smart mobile phone addiction, and life satisfaction’;
  • Pengpid, S. and Peltzer, K. (2018). ‘Vigorous physical activity, perceived stress, sleep and mental health among university students from 23 low-and middle-income countries’;
  • Song, Y. and Lindquist, R. (2015). ‘Effects of mindfulness-based stress reduction on depression, anxiety, stress and mindfulness in Korean nursing students’;
  • Spadaro, K. C. and Hunker, D. F. (2016). ‘Exploring the effects of an online asynchronous mindfulness meditation intervention with nursing students on stress, mood, and cognition: a descriptive study’;
  • Van der Riet, P. et al. (2015). ‘Piloting a stress management and mindfulness program for undergraduate nursing students: student feedback and lessons learned’;
  • Zhao F. F. et al. (2015). ‘The study of perceived stress, coping strategy and self‐efficacy of Chinese undergraduate nursing students in clinical practice’.

2005-2010 studies excluded from the review:

  • Deniz, M. (2006). ‘The relationships among coping with stress, life satisfaction, decision-making styles and decision self-esteem: an investigation with Turkish university students’;
  • Dixon, S. K. and Kurpius, S. E. R. (2008). ‘Depression and college stress among university undergraduates: Do mattering and self-esteem make a difference?’ ;
  • Dusselier, L. et al . (2005). ‘Personal, health, academic, and environmental predictors of stress for residence hall students’;
  • Mercer, A., Warson, E. and Zhao, J. (2010). ’Visual journaling: An intervention to influence stress, anxiety and affect levels in medical students’;
  • Segrin, C. et al . (2007). ‘Social skills, psychological well-being, and the mediating role of perceived stress’.

2015-2020 Study Data Summary

Beiter et al. (2015) examined Franciscan University undergraduate students (n = 374, 18-24 y/o) to verify the presence of stress, anxiety, and depression symptoms rates. The research methodology consists of a 21-question version of the Depression Anxiety Stress Scale (DASS21), everyday life concern factors rating, and demographic questions (Beiter et al ., 2015, p. 90). The gender distribution of the population is females (63%) and males (37%). Surveys showed normal (62%, 60%, 67%), mild (12%, 15%, 10%), moderate (15%, 7%, 12%), severe (8%, 7%, 6%), or extremely severe (3%, 8%, 5%) presence of stress, anxiety and depression rates. The overall positive correlation among all categories is significant (P <.05). Prevailing stress groups are transfer (P <.01), upperclassmen (P <.05), and living off-campus students (P <.05).

Daltry (2015) explored acceptance and commitment therapy (ACT) to improve the anxiety management of university students (n = 6). Some participants (n = 2) chose individual counseling, other Caucasian female undergraduate students (n = 4, 18-20 y/o) participated in group counseling. The research methodology includes the dependent sample t-test. During the nine-session therapy, students completed the Burns Anxiety Inventory, the Acceptance and Action Questionnaire-2, the Distress Tolerance Scale (Daltry, 2015, p. 39). Results show the effectiveness of the therapy and significant difference (p =.005) between anxiety levels before (mean = 54.00, SD = 11.17) and after (mean = 24.00, SD = 14.07) the ACT.

Garett, Liu, and Young, (2017) examine freshmen students’ (n = 197) stress levels and their fluctuations depending on exams during the semester (Oct.-Dec.). The research methodology consists of General Linear Mixed-Model (Garett, Liu, and Young, 2017, p. 1). During the study (10 weeks), students were filling out personal stress reports. Analysis of the data showed that the stress level of first-year students varies during the average period of study (mean = 3.4, SD = 0.99), mid-term (mean = 3.57), and final examinations (mean = 3.95). Women are more susceptible to stress (mean + 0.2), and the relationship between stress indicators and the female gender is significant (p = 0.03). The most effective stress management tactic is exercise (p <.01).

Geng and Midford (2015) investigated the levels of stress among first-year university students (n = 139) undertaking teaching practicums, other years’ university students (n = 143), and factors contributing to stress disorders development. Researchers use the PSS-10 scale and online questionnaire methods (Geng and Midford, 2015, p. 1). First-year students experience (mean = 22.50, SD = 6.14) more severe stress rates than other years students (mean = 20.31, SD = 5.91). A significant difference (p <.01) is present among the stress rates of different years’ college students. The survey showed such basic predictors of stress as unawareness of mentors’ support (62.1%), university work commitment (51.7%), paid work outside university (35.5%), and performance assessment (14.3%).

Jafar, et al . (2016) conducted a study of students (n = 30) of the Islamic Azad University dividing them into experimental (n = 15) and control (n = 15) groups to determine the levels of anxiety. During the quasi-experimental intervention, students took part in Anxiety Inventory testing and then in stress management training (Jafar, et al ., 2016, p. 47). Experimental group showed pretest (mean = 11.40, SD = 77.7), posttest (mean = 7.08, SD = 4.38), follow-up (8.61, SD = 4.5) anxiety levels. Control group showed pre-test (mean = 10.88, SD = 7.61), post-test (mean = 10.04, SD = 5.56), follow-up (10.08, SD = 8.32) anxiety levels. There are pretest-posttest (P < 0.01) and posttest-follow-up (P < 0.01) differences in anxiety rates in both groups.

Salam et al . (2015) examined the degree of stress and the common stress management tactics among medical students (n = 234) of the University Kebangsaan Malaysia. The main methods that the authors used in the study are observational. Students completed a standardized questionnaire related to subjective experiences and stress. The study shows the presence of stress (49%) among students.

The most susceptible to stress were third-year (mean = 6.41, SD = 3.69), female (mean = 7.00, SD = 4.12), and Malay (mean = 7.43, SD = 2.14) groups of students (Salam, A. et al ., 2015, p. 171). The most frequent stress management tactic among third-year female (mean = 60.47, SD = 9.97) and Malay (mean = 59.83, SD = 10.45) groups is task-oriented. The least frequent stress management tactic among third-year female (mean = 42.61, SD = 9.20) and Malay (mean = 59.83, SD = 10.45) groups is emotion-oriented.

Saleh, Camart, and Romo (2017) investigated mental factors predictive of stress among college students (n = 483) in France. The age of the student population is ranked between 18-24 years (mean = 20.23, SD = 1.99). The primary survey method for research is online data collection based on Google Docs questionnaires and regression analyses (Saleh, Camart, and Romo, 2017, p.19). Students noted the presence of anxiety feelings (86.3%), depressive symptoms (79.3%), and psychological distress (72.9%). The main factors influencing the occurrence of stress disorders are the low sense of self-efficacy (62.7%), low self-esteem (57.6%), and little optimism (56.7%).

Samaha and Hawi (2016) studied the perceived stress and smartphone addiction interconnections among university students (n = 249). The population of students is partly male (54.2%) with the age range of 17-26 y/o (mean = 20.96, SD = 1.93). The methodology of the authors’ survey is Pearson correlations. Students completed Addiction Scale – Short Version, the Perceived Stress Scale, and the Satisfaction with Life Scale testing (Samaha, and Hawi, 2016, p. 321).

There are percentages of students with low risk (49.1%) of smartphone addiction and high risk (44.6%) of smartphone addiction. There are percentages of students with low perceived stress levels (53.4%) and high perceived stress levels (46.6%). Results show a non-significant positive correlation (p <.002) between perceived stress and smartphone addiction.

Wood et al . (2018) conducted a pet study of the effects of pet therapy involving dogs on the anxiety levels of university students (n = 131). Age of participants ranged from 18 to 35 years (mean = 19.92, SD = 2.60). The population of participants is composed of male (n = 35, 26.7%) and female (n = 96, mean = 73.3) representatives. Participants were tested before and after pet therapy intervention (15 min). The rates of anxiety before (mean = 43.16, SD = 10.56) and after (mean = 29.94, SD =9.94) dog therapy vary significantly (p < 0.001). Pet therapy involving dogs has a positive effect on reducing anxiety among Saint-Joseph University students.

Younes et al . (2016) examined the correlation and relationships between depressive syndromes, stress, and anxiety rates and Internet addiction among university students (n = 600). The participants from the university were only students of healthcare-related faculties. Researchers used methods such as DASS 21, the Rosenberg Self Esteem Scale (RSES), the Young Internet Addiction Test (YIAT), and the Insomnia Severity Index (Younes et al ., 2016, p. 1). The cross-sectional questionnaire-based survey revealed average indicators of existing (mean = 30, SD = 18.474) and potential (16.8%) prevalence of Internet addiction. Potential Internet addiction is more often seen in men (23.6%) than in women (13.9%), therefore, varies greatly (p = 0.003). Potential internet addiction indicators have high correlation (p < 0.001) with stress disorders.

2015-2020 Studies Risk of Bias Assessment

2005-2010 study data summary.

Abdulghani (2008) explored the correlation between stress levels of King Saud University students (n = 600) and academic indicators such as academic years of education process and grades. The researcher chooses a voluntary questionnaire as research (Abdulghani, 2008, p. 569). Responding participants (n = 494, the mean age = 21.4, the SD age = 1.9) completed the Kessler10 stress inventory. The results show some students (57%) experience stress mild (21.5%), moderate (15.8%), and severe (19.6%) stress levels, others do not (43.1%). The association between stress levels and the first years of study is significant (p < 0.0001). %). The association between stress rates and academic grades is not significant (p = 0.46). The most frequent major stressors are the educational process (60.3%) and the environment (2.8%).

Bayram and Bilgel (2008) investigated the psychological state of university students (n = 1,617) in Turkey regarding anxiety, stress and depressive symptoms rates. There are male (44.4%) and female (55.6%) representatives of the mean ages of 20.7 (SD = 1.7) and 20.3 (S = 1.6) years. The primary research methodology is DASS-42 (Bayram and Bilgel, 2008, p. 667). Interviewed university students experience levels of stress (mean = 14.92, SD = 6.71), anxiety (mean = 9.83, SD = 5.94), and depression (mean = 10.03, SD = 6.88) levels. Women are much more likely to experience stress (p = 0.001), anxiety (p = 0.005). Freshmen also have elevated rates of stress (p = 0.044), anxiety (p = 0.000), and depressive syndromes (p = 0.003).

Dahlin, Joneborg, and Runeson (2005) examined the presence of depression and related stressors in Swedish students (n = 342) of the Karolinska Institute Medical University. During the study, responders (90.4%) completed the Higher Education Stress Inventory (HESI), the Major Depression Inventory (MDI), and Meehan’s suicidal ideation questions (Dahlin, Joneborg, and Runeson, 2005, p. 594). Students report depressive symptoms (12.9%) and suicide attempts (2.7%). Depressive symptoms prevail in the female group (16.1%) more than in the male (8.1%). The study showed that the most common stress factor is a lack of feedback.

Oman et al . (2008) studied the effect of moderated physical activity on stress levels among randomly selected university students. The research methodology consists of the pretest, moderate physical activity intervention (8 weeks), and posttest. Randomly selected groups of students took part in Mindfulness-Based Stress Reduction (n = 15), Easwaran’s Eight-Point Program (n = 14), and Wait-List Control (n = 15) (Oman et al ., 2008, p. 569). Data analysis showed perceived stress levels (mean = 18.11, SD = 6.19) (mean = 18.11 – 2.41) decreased after moderation physical activity intervention with a significant difference (p =.099).

Shah et al . (2010) conducted a study (3 months) examined perceived stress levels of Pakistani university students (n = 200) of Medicine in CMH Lahore Medical College. Research methods are a cross-sectional, questionnaire-based survey (PSS-14) and 33-item questionnaire. Results show respondents (n = 161, 80.5%) of both male (n = 53, 32.92%) and female (n = 108, 67.08%) groups of 17 – 25 years (mean = 20.35, SD = 1.09) experience stress (mean = 30.84, SD = 7.01). The female group (mean = 31.94, SD = 6.28) of respondents is more susceptible to stress than the male group (mean = 28.60, SD = 7.92). A significant difference between the two indicators is present (p < 0.05). The level of stress insignificantly negatively correlates with academic performance (p > 0.05).

2005-2010 Studies Risk of Bias

Conclusions and summary of evidence.

This systematic review examines ten scholarly articles over the past five scholarly years, five articles over the 2005-2010 years, and compares them from the perspectives of PICOS and stress management. An overview of all studies shows that the population in terms of social status, age, and gender groups has not changed over ten years. A comparison of the studies of Abdulghani, Bayram and Bilge, and Younes et al ., Samaha, and Hawi shows an increase in intervention methods. The types of comparators and controls have also not changed, which is noticeable in the examples of Wood et al . and Oman et al . studies. A study of the findings of all studies shows that researchers use general principles for interpreting outcomes.

A comparison of the studies of Salam et al. and Shah et al . speak of a continuing trend of increased stress among women over the past ten years. In addition to the significant primary stressors associated with educational processes, studies by Younes et al ., and Samaha and Hawi show the emergence of new factors, namely smartphones and the Internet. The works of Geng and Midford and Abdulghani mark the continued trend of a high level of stress among first-year students. Future research should be aimed at studying the reform of stress management methods to develop techniques for female students and first-year students.

Limitations

The fundamental limitations of a systematic review are the themes of stress management and university students. Another principal limitation of the systematic review is the periods of 2015-2020 and 2005-2010 years. The publication format of the reviewed works, namely, scientific articles also a limitation. Electronic databases as sources of information also represent the confines of the report. PICOS framework, PRISMA process, CASP risk of the bias assessment tool is also necessary to research limitations.

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ORIGINAL RESEARCH article

Perceived academic stress and depression: the mediation role of mobile phone addiction and sleep quality.

\nXin Zhang&#x;

  • 1 Department of Social Medicine, School of Public Health, Health Management College, Harbin Medical University, Harbin, China
  • 2 Institute of Food Safety and School Health, Heilongjiang Center for Disease Control and Prevention, Harbin, China
  • 3 Department of Educational Administration, Ningbo College of Health Sciences, Ningbo, China
  • 4 Department of Elderly Healthcare and Management, School of Health Services and Management, Ningbo College of Health Sciences, Ningbo, China

Background: Although academic stress is a well-known risk factor for students' depression, little is known about the possible psychological mechanisms underlying this association. In this study, we investigated the prevalence of depression and sleep disturbance among Chinese students, examined the relationship between perceived academic stress and depression, considered if mobile phone addiction and sleep quality is a mediator of this relationship, and tested if mobile phone addiction and sleep quality together play a serial mediating role in the influence of perceived academic stress on depression.

Method: A cross-sectional survey was conducted among students from September to December 2018 in Heilongjiang Province, China. The final analysis included 5,109 students. Mobile phone addiction, sleep quality, and depressive symptoms were assessed using the Mobile Phone Addiction Index, Pittsburgh Sleep Quality Index, and Center for Epidemiologic Studies-Depression scales, respectively. The serial mediation model was used to analyse the relationship between perceived academic stress, mobile phone addiction, sleep quality, and depression.

Results: Among all participants, the prevalence of depressive symptoms and sleep disturbance was 28.69 and 27.95%, respectively. High school students showed the highest scores of perceived academic stress (2.68 ± 1.06), and the highest prevalence of depressive symptoms (33.14%) and sleep disturbance (36.47%). The serial mediation model indicated that perceived academic stress was a significant predictor of depression (B = 0.10, SE = 0.02, 95% CI = 0.06 – 0.13). Additionally, mobile phone addiction (B = 0.08, 95% boot CI = 0.06–0.11) and sleep quality (B = 0.27, 95% boot CI = 0.22–0.33) played a mediating role between perceived academic stress and depression. Mobile phone addiction and sleep quality together played a serial mediating role in the influence of perceived academic stress on depression (B = 0.11, 95% boot CI = 0.08–0.14). Furthermore, the indirect effect (i.e., the mediating effect of mobile phone addiction and sleep quality) was significant and accounted for 64.01% of the total effect.

Conclusions: Our research results underscore the need for stakeholders—including family members, educators, and policy makers—to take preventative intervention measures to address depression among Chinese students, especially high school students.

- Perceived academic stress significantly predicts depression.

- Sleep quality mediates perceived academic stress and depression.

- Mobile phone addiction mediates perceived academic stress and depression.

- Mobile phone addiction and sleep quality together play a serially mediating role in the influence of PAS on depression.

Introduction

Depression (major depressive disorder) is a widespread chronic medical illness that can influence mood, thoughts, and physical health ( 1 ), and is a severe problem faced by students worldwide. A meta-analysis that included 183 studies from 43 countries shows that the overall pooled crude prevalence of depression was 27.2% among medical students ( 2 ). Previous studies demonstrated that the prevalence of depression was 51.3, 38.3, 28.4, and 30.6% among Indian students ( 3 ), Japanese adolescents ( 4 ), Chinese university students ( 5 ), and Cameroon medical students ( 6 ), respectively. It is important to evaluate the prevalence of depressive symptoms and explore the effect mechanism of depressive symptoms to protect students from the harmful effects of depression. Studies related to students' depressive symptoms often focus on a particular group of students, such as medical ( 2 ), college ( 7 ), and university students ( 8 ), and scant research exists about depressive symptoms among students at different levels of education. Many risk factors have been associated with depression, including being female ( 9 , 10 ), life stressors ( 9 , 10 ), physical and mental factors, social media addiction ( 11 ), and parental factors, including parental psychopathology and parenting attachment ( 12 ). Stress has been shown to be one of the most important risk factors of depression, and numerous studies have demonstrated that stress plays an important role in the emergence of depression ( 13 – 15 ). For example, Torres-Berrío et al. supposed that depression is caused by a combination of genetic predisposition and life events ( 16 ). Stress often leads to adverse consequences—such as depression and anxiety ( 17 – 19 ), mobile phone addiction (MPA) ( 20 , 21 ), poor sleep quality (PSQ) ( 22 , 23 ), changes in legal drug consumption ( 24 ), cardiovascular disease ( 25 ), and worsens the outcomes of many medical illnesses ( 26 ), potentially even leading to suicide ( 27 , 28 ). Additionally, various physical and mental factors influence the prevalence of depressive symptoms, such as PSQ ( 29 ), bodily pain ( 30 ), and poor cognitive and physical functioning ( 31 ). Scholars have noted that there is a remarkable association between alterations in sleep patterns and depression ( 32 ). Furthermore, in the internet age, studies show that individuals who experience depressive symptoms often suffer from social media addictions, such as Facebook ( 33 , 34 ), mobile phone ( 35 ), and internet addictions ( 8 ). For instance, Ivanova found that MPA was positively related to both depression and loneliness in Ukrainian students ( 36 ).

In China, the school environment and parental practices contribute to the extraordinarily high expectations of students' academic performance ( 37 ). Chinese students experience high levels of academic stress throughout their academic careers, including numerous, intense examinations—such as end-of-term tests and the standardized senior high school and university entrance examinations—and a heavy homework burden ( 37 ). Scholars have demonstrated that Chinese students experience sleep deprivation owing to this culture of academic achievement. A study of 9,392 Chinese students in primary education through university levels showed that 35.6% of participants slept <7 h a day ( 38 ). In addition to the threat of academic stress and sleep deprivation, MPA is a risk factor affecting Chinese students' physical and mental health. Mobile phones have become an integral part of students' quotidian lives—Meng's survey from December 2016 to January 2017 found that 100% of the college students had mobile phones ( 39 )—and the prevalence of problematic mobile phone use has been found to be 28.2% among Chinese college students ( 40 ). Our study explored the correlations between perceived academic stress (PAS), MPA, sleep quality, and depression among Chinese students in middle school through college levels. Based on previous literature, our study proposed research hypotheses, and tested hypothesis by using survey data on Chinese students. To our knowledge, this was the first study to investigate relations between these variables among Chinese students by using the serial mediation model.

Literature Review and Research Hypotheses

Academic stress.

Academic concerns are the most important sources of chronic and sporadic stress for young people in both Western and Asian countries ( 41 ). Academic stress is defined as a student's psychological state resulting from continuous social and self-imposed pressure in a school environment that depletes the student's psychological reserves ( 42 , 43 ). Students experience academic stress throughout their secondary school ( 41 ), high school ( 44 ), and university ( 45 , 46 ), educational careers. Studies have shown that academic stress has been positively associated with depression ( 41 ), PSQ ( 24 , 47 ), and MPA ( 48 ) among students. Jayanthi observed that, compared to adolescents who do not experience academic stress, adolescents who experienced academic stress were 2.4 times more likely to have depressive symptoms ( 41 ). Other studies have found that there is a relationship between high academic stress and PSQ ( 47 , 49 ). However, scholars have not adequately addressed the adverse consequences (e.g., depression, PSQ, and MPA) of Chinese students' academic stress. Hence, we propose the following hypotheses:

H1 : PAS is positively associated with depression.

H2 : PAS is positively associated with MPA.

H3 : PAS is positively associated with PSQ.

MPA is one of the most common behavioral (i.e., non-drug) addictions ( 48 ), and is accompanied by negative effects, such as PSQ ( 50 ), depression ( 35 ), and impaired academic performance ( 51 ). The positive relationship between MPA and PSQ has been proved in previous studies, including a longitudinal study conducted among Korean adolescents ( 52 ) and a one-year prospective study among Chinese college students ( 50 ). Zhang found that among Chinese university students, there is a significant positive relationship between smartphone addiction and bedtime procrastination, which is one of the indicators of PSQ ( 53 ). Hence, we propose the following hypothesis:

H4 : MPA is positively associated with PSQ.

Similarly, the positive relationship between MPA and depression has been proved in previous studies, including a cross-sectional study conducted among Saudi university students ( 35 ), a cross-sectional study among Ukrainian college students ( 36 ), and a systematic review of relations between problematic smartphone use, anxiety and depression psychopathology ( 54 ). Furthermore, another study based on three cohorts of Korean children and adolescents confirmed the bidirectional relationship between MPA and depression ( 55 ). Hence, we propose the following hypothesis:

H5 : MPA is positively associated with depression.

Researchers have documented that stress is associated with MPA, and that MPA is associated with depression. For example, according to Wan et al., smartphone addictions are significantly positively associated with both depression and stress among Malaysian public university students ( 56 ). However, it is unclear if MPA mediates the relationship between PAS and depression. Hence, we propose the following hypothesis:

H6 : MPA mediates the relationship between PAS and depression.

Sleep Quality

Sleep disturbance has complex associations with depression (major depressive disorder) ( 31 ), and is a common physical symptom of depression. Numerous studies have confirmed the remarkable association between PSQ and depression ( 29 , 57 , 58 ). For example, Okun et al. found that PSQ is positively related to depression symptoms in postpartum women ( 29 ). Hence, we propose the following hypothesis:

H7 : PSQ is positively associated with depression.

Scholars have also demonstrated that there are relationships between stress, PSQ, and depression. A prospective birth cohort study showed that PSQ is associated with stress and depression symptoms among Chinese pregnant women ( 58 ). Zhang et al. found that perceived stress is associated with sleep quality and depressive symptoms among Chinese nursing students ( 59 ). However, it has not been documented if sleep quality mediates the relationship between PAS and depression among Chinese students. Hence, we propose the following hypothesis:

H8: Sleep quality mediates the relationship between PAS and depression.

Mobile Phone Addiction and Sleep Quality and the Relationship Between Perceived Academic Stress and Depression

Scholars have posited that there are significant associations between MPA, depression levels, and sleep quality. Demirci found that there were positive correlations between MPA, depression levels, and sleep quality ( 60 ). The results of Kaya's multivariate regression analysis showed a relationship between smartphone usage, PSQ, and depression in university students ( 57 ). A recent meta-analysis also found that there are positive correlations between MPA, depression, and sleep quality ( 61 ). Another literature review and case study found that depressive symptoms are associated with screen time-induced poor sleep, digital device night use, and mobile phone dependency ( 62 ). Although these studies explored the correlations between MPA, sleep quality, and depression among students, several scholars have added academic stress into the relationship—for example, a review found that sleep disturbance, anxiety, stress, and depression have been associated with problematic mobile phone use ( 63 ). There still exist gaps in the literature on how PAS influences depression. First, few scholars have focused on PAS, MPA, sleep quality, and depression among Chinese students. Second, the underlying mediating mechanisms that account for this association have been disregarded partly. Based on H6 and H8, it remains unclear if MPA and sleep quality serially mediate the relationship between PAS and depression. Therefore, we propose the following hypothesis:

H9: MPA and sleep quality serially mediate the relationship between PAS and depression.

Study Objectives

In this study, our primary aim was to investigate the prevalence of depression and sleep disturbance among Chinese students. Our secondary aim was to test if there were relationships between PAS, MPA, sleep quality, and depression. First, we tested if there was a relationship between PAS and depression among Chinese students (H1: PAS is positively associated with depression). Second, we tested if MPA was a mediator of the relationship between PAS and depression (H2: PAS is positively associated with MPA, H5: MPA is positively associated with depression, and H6: MPA mediates the relationship between PAS and depression). Third, we tested if sleep quality was a mediator of the relationship between PAS and MPA (H3: PAS is positively associated with PSQ, H7: PSQ is positively associated with depression, and H8: Sleep quality mediates the relationship between PAS and depression). Finally, we also tested if MPA and sleep quality together played a serial mediating role in the influence of PAS on depression (H4: MPA is positively associated with PSQ and H9: MPA and sleep quality serially mediate the relationship between PAS and depression).

Data were collected from a cross-sectional questionnaire survey that was conducted from September to December 2018 in Heilongjiang Province, China, by the Heilongjiang Center for Disease Control and Prevention. A multistage cluster sampling method was used. In the first stage, three cities of Heilongjiang province were randomly selected by economic characteristics. In the second stage, one urban district and one rural township were chosen at random. In the third stage, two middle schools were randomly selected in each urban district and rural township; Since nine-year compulsory education was implemented in China, high school education is not included in the nine-year compulsory education, high schools are more in urban districts than in rural townships, two high schools and one high school were randomly selected in urban district and rural township, respectively; Since vocational high schools and universities are scarce in rural townships, one vocational high school and one college were randomly selected from the urban district. In the fourth stage, two classes were randomly selected from each grade of middle school, high school, vocational high school, and from grades 1, 2, and 3 in college. Since senior students may have been looking for a job or working as an intern, some of them were not on campus, they were not been investigated. Finally, four middle schools, three high schools, one vocational high school, and one college were randomly selected within each city (Harbin, Jiamusi, and Jixi) of Heilongjiang Province. Data were collected through a self-administered questionnaire distributed in class. Students completed the survey within 1 h, while a well-trained member of the research group supervised. All the students were informed of the purpose of the study and assured that their identities would remain confidential. Students and their parents provided written informed assent to participate in the study.

Participants

Finally, we recruited 6,480 students in our investigation; 6,430 (99.23%) valid questionnaires were analyzed after excluding those with incomplete information. Participants were included in the sample if they had one constant internet-accessible mobile phone, which is similar with previous studies ( 64 – 68 ). A total of 5,109 (79.46%) participants reported having one constant internet-accessible mobile phone at the time of the survey. The final sample comprised 1,904 middle school students from grades 1, 2, and 3, respectively; 1,859 high school students from grades 1, 2, and 3, respectively; 660 vocational high school students from grades 1, 2, and 3, respectively; and 686 college students from grades 1, 2 and 3, respectively. Of these participants, there were 2,422 (47.41%) boys and 2,687 (52.59%) girls; on average, the mean age of participants was 15.53 years, with a standard deviation of 2.22, ranging from 11 to 25 years. Approval was obtained from the Medical Research Ethics Committee of Harbin Medical University and the principals of the participating schools.

Perceived Academic Stress

Consistent with previous studies ( 69 – 71 ), PAS was measured using one self-report item “How much academic stress did you feel in the study during the past month?” using a 5-point Likert scale where 1 = “No,” 2 = “relatively low,” 3 = “average/general,” 4 =“relatively high” and 5 = “extremely heavy,” with a higher score indicating more PAS.

Center for Epidemiologic Studies-Depression Scale. The 20-item CES-D developed by Radloff ( 72 ) is a self-report measure that has been widely used to assess depressive symptoms in different populations ( 73 ). The reliability and validity of the CES-D have been tested among Chinese populations ( 74 ). The CES-D, when used in Chinese adolescents and university students, has shown good reliability ( 75 – 78 ), as well as good validity ( 77 , 78 ). There are four components of CES-D, namely somatic and retarded activity, depressed affect, positive affect, and interpersonal relationships. Among the 20 items, four (items 4, 8, 12, and 16) are reversed scores. All items are evaluated on a 4-point Likert scale in relation to their incidence during the previous week, and are scored from 0 to 3 (0 = not at all, 1 = a little, 2 = some, 3 = a lot); total possible scores thus range from 0 to 60, with higher scores indicating greater number of symptoms ( 79 ).

For the original CES-D scale, a total score of 16 or greater is considered as indicative of subthreshold depression ( 72 ). Many studies have evaluated the diagnostic accuracy of the CES-D to detect depression among the general population and proposed a variety of cut-off scores, such as a cut-off score of 21 for Chinese patients with type 2 diabetes ( 80 ), and a cut-off score of 22 for the older Chinese population ( 81 ). However, the cut-off score of 16 has been widely used for Chinese adolescents and university students ( 7 , 76 , 82 – 84 ). Therefore, the same cut-off score has been used in our study too. Students with CES-D scores between 16 and 21 were defined as “mildly depressed,” between 21 and 24 as “moderately depressed,” and ≥ 25 as “severely depressed” ( 83 ). The CFA on the four-factor model showed a good model fit, with χ 2 = 16.54, df = 1, P < 0.000, RMSEA = 0.06, SRMR = 0.01, CFI = 0.99, TLI = 0.98. Additionally, the Cronbach's alpha coefficient was 0.84 for the total scale, all four dimensions had acceptable reliability with Cronbach's alpha coefficient of 0.70, 0.83, 0.78, and 0.62.

Mobile Phone Use Situation and Mobile Phone Addiction

Mobile phone use situation was assessed by three items. First, “How many hours do you use your mobile phone every day?” to which participants answered with one of four options: “less than a half hour,” “a half hour to one hour,” “one to two hours,” or “more than two hours.” Second, “How long have you had a mobile phone?” to which participants answered “ <1 year,” “1–2 years,” “2–3 years,” or “more than 3 years.” Third, “How much do you spend on mobile phone charges every month?” to which participants answered “less 30 yuan,” “30–50 yuan,” “50–100 yuan,” or “more than 100 yuan.”

The Mobile Phone Addiction Index (MPAI) was used in our study ( 85 ). Participants rated the 17 items on a 5-point Likert scale ranging from 1 (not at all) to 5 (always). Higher scores indicated greater addiction to mobile phones ( 86 ). There are four components of MPAI, namely inability to control craving, feeling of anxiety and being lost, withdrawal or escape, and productivity loss. The Confirmatory Factor Analysis (CFA) on the four-factor model showed a good model fit, with χ 2 = 6.44, df = 1, p < 0.05, RMSEA = 0.03, SRMR = 0.004, CFI = 0.99, TLI = 0.99. Additionally, the Cronbach's alpha coefficient was 0.90 for the total scale. All four dimensions had satisfactory reliability with Cronbach's alpha coefficient of 0.76, 0.81, 0.85, and 0.75.

The Pittsburgh Sleep Quality Index (PSQI) was used in our study ( 87 ). PSQI scale contains 19 items covering seven components: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleep medication, and daytime dysfunction. Each component was scored from 0 (no difficulty) to 3 (severe difficulty). The total score was calculated from the seven component scores, ranging from 0 to 21. A score of more than 5 implied poor sleep ( 87 ). The CFA on the seven-factor model showed a good model fit, with χ 2 = 79.49, df = 11, P < 0.000, RMSEA = 0.04, SRMR = 0.02, CFI = 0.99, TLI = 0.98. Additionally, Cronbach's alpha coefficient was 0.624 for the PSQI scale in our study.

Data Analyses

SPSS version 19.0. and Mplus 7.0 were used to analyse data in our study. Descriptive analyses were first conducted of participants' characteristics, participants' mobile phone use and the prevalence of sleep disturbance, MPA, and depression. We tested the reliability and validity of the MPAI scale, PSQI scale and CES-D scale by examining their Cronbach's alpha coefficient and performing a CFA. Spearman's correlation analysis was performed to examine the general relationships among the four variables—PAS, MPA, sleep quality, and depression. A structural equation model (SEM) was built to examine hypotheses. We tested the mediating role of MPA and sleep quality; the constructed serial mediation model included three latent variables (MPA, sleep quality and depression) and one manifest variable (PAS), PAS was the independent variable, depression was the dependent variable, and MPA and sleep quality were the mediating variables ( 88 ). The bootstrapping analyses used 5,000 samples at the 95% confidence interval (CI) to indicate significance.

To determine whether the model fits the data well, multiple indices were tested, including (1) the model χ 2 and its p value, in which non-significance is desirable for good fit. With increasing sample size and a fixed degree of freedom, the χ 2 value increases. It is difficult to get a nonsignificant chi-square (indicative of good fit) when sample sizes are over 200 ( 89 ). This can lead to a problem where plausible models might be rejected. Because this statistic is sensitive to the sample size, inspection of the other fit indices is recommended ( 90 ). (2) The root mean square error of approximation (RMSEA) in which values ranging from 0.05 to 0.08 represent adequate fit, and values <0.05 indicate good fit. (3) The standardized root mean square residual (SRMR) in which values are ≤0.08 indicate good fit. (4) The comparative fit index (CFI), in which values range from 0.90 to 0.95 indicate an adequate fit and values ≥0.95 indicate a good fit, and (5) the Tacker-Lewis index (TLI) in which values >0.90 indicate a good fit.

Descriptive Statistics

The mean scores of PAS were 2.61 ± 1.03, 2.68 ± 1.06, 2.13 ± 0.98, and 2.29 ±0.96 for middle school students, high school students, vocational high school students, and college school students, respectively.

Among the participants, 45.55% used their mobile phone more than 2 h daily; 39.5% of the participants had a mobile phone for more than 3 years; 53.24% of the participants spent more than or equal to 30 yuan on mobile phone charges every month ( Table 1 ). The mean MPAI score of all the participants was 30.62 ± 11.92.

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Table 1 . Participants' mobile phone use.

The prevalence of depressive symptoms was 28.69% ( n = 1,466) with a mean CES-D score of 12.52 ± 8.86. Prevalence of depression at a mild level (CES-D ≥ 16 and CES-D < 21), moderate level (CES-D ≥ 21 and CES-D < 25), and severe level (CES-D ≥ 25) was 12.62, 6.95, and 9.12%, respectively. The prevalence of depressive symptoms among high school students (33.14%) was the highest, while the prevalence of depressive symptoms among college students (21.43%) was the lowest ( Table 2 ).

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Table 2 . Depression classifications of participants.

The prevalence of sleep disturbance was 27.95% ( n = 1,428) with a mean global PSQI score of 4.29 ± 2.59. The prevalence of sleep disturbance among high school students (36.47%) was the highest, while the prevalence of sleep disturbance among middle school students (20.75%) was the lowest. The average sleep time and sleep latency were 7.40 ± 1.28 h and 15.81 ± 12.48 min, respectively. Among the participants, 14.50% reported that they had bad or very bad sleep quality; 36.29% reported that their sleep latency was more than 15 min; 50.89% reported that they slept ≤7 h a day; 12.62% reported that their sleep efficiency was ≤85%; 67.59% reported that they experienced sleep disturbances; 2.90% of them reported that they used sleep medication; and 78.80% reported that they had daytime dysfunction ( Table 3 ).

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Table 3 . Prevalence of sleep disturbance for participants at different education levels.

Means, Standard Deviation (SD) and correlations of the main variables in the mediation model are shown in Table 4 . The results, indicating that the variables were significantly and positively correlated, provide initial support for the hypotheses of this study, and act as a foundation of the serial mediation model.

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Table 4 . Means, SD, Pearson's correlation coefficient of variables.

Test for Serial Mediation Model

SEM was used to provide the fit indexes of the serial mediation model. A model was constructed with MPA (M1) as a mediator and sleep quality (M2) as another mediator. In this model, PAS was set as the predictor (X) and depression as the outcome (Y). Results of the serial mediation model indicated that the constructed model exhibited a satisfactory fit with the data: χ 2 = 1,196.50, df = 95, P < 0.000, SRMR = 0.04, RMSEA = 0.05, CFI = 0.95, and TLI = 0.94.

First, PAS was positively associated with depression (B = 0.10, SE = 0.02, 95% CI = 0.06–0.13). Higher levels of PAS were related to higher levels of depression, and thus H1 was supported. Second, PAS positively predicted MPA (B = 0.18, SE = 0.02, 95% CI = 0.15–0.21). Higher levels of PAS were related to higher levels of MPA, and thus H2 was supported. Third, PAS was positively associated with PSQ (B = 0.23, SE = 0.02, 95% CI = 0.19–0.26). Higher levels of PAS were related to poorer sleep quality, and thus H3 was supported. Fourth, MPA was positively associated with PSQ (B = 0.51, SE = 0.02, 95% CI = 0.47–0.54). Higher levels of MPA were related to poorer sleep quality, and thus H4 was supported. Fifth, MPA was positively associated with depression (B = 0.17, SE = 0.02, 95% CI = 0.13–0.22). Higher levels of MPA were related to higher levels of depression, and thus H5 was supported. Last, PSQ was positively associated with depression (B = 0.44, SE = 0.03, 95% CI = 0.39–0.49). PSQ was related to higher levels of depression, and thus H7 was supported.

Total, Direct, and Indirect Effects

Table 5 shows all possible indirect effects of the mediation model. First, the indirect effect of PAS on depression through MPA was significant (B = 0.08, 95% boot CI = 0.06–0.11), and thus H6 was supported. Second, the indirect effect of PAS on depression through sleep quality was significant (B = 0.27, 95% boot CI = 0.22–0.33), and thus H8 was supported. Third, the indirect effect of PAS on depression through MPA and sleep quality was also significant (B = 0.11, 95% boot CI = 0.08–0.14), and thus H9 was also supported. The total indirect effect was B = 0.46, 95% boot CI = 0.40–0.53, and the mediating effect of MPA and sleep quality were significant ( P < 0.001), accounting for 64.01% (total indirect effect/total effect) of the total effect. The indirect effect related to sleep quality accounted for 82.61% of the total indirect effect, that is, (indirect effect 2 + indirect effect 3)/total indirect effect ( Table 5 ).

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Table 5 . Total, direct, and indirect effects.

Although academic stress is a well-known risk factor for depression in students, little is known about the possible psychological mechanisms underlying this association, or how MPA and PSQ—which also are risk factors of depression—operate to have an impact on it. The main aim of our study was to test if there is a relationship between PAS and depression and if MPA and sleep quality together play a serial mediating role in the influence of PAS on depression among Chinese students. To the best of our knowledge, this was the first study to investigate the relationship between the variables using SEM. As expected, the serial mediation model showed that PAS was a significant predictor of depression. MPA and sleep quality played a mediating role between PAS and depression. Furthermore, MPA and sleep quality together played a serial mediating role in the influence of PAS on depression. In our study, the indirect effect (i.e., the mediating effect of MPA and sleep quality) was significant and accounted for 64.01% of the total effect. Thus, apart from the direct effect of PAS on depression, the indirect effect of PAS on depression should be emphasized. Our findings provide significant insights into the risk factors for depressive symptoms in students.

Depression Among Students

According to studies that have focused on depression among Chinese students, the prevalence of depression varies from 22.0 to 68.5% ( 5 , 91 – 95 ). In our study, the prevalence of depressive symptoms was 28.69%. The differences across these studies may have resulted from temporal or regional disparities or variations in depression definitions and assessment methods. Depressive symptoms are related to many negative consequences, such as increased suicide risk among students ( 96 ) and increased college withdrawal rates ( 97 ). Controlling depressive symptoms among students can both protect human capital value from the societal perspective and maintain students' physical and mental health from the individual perspective. In our study, the most stressed, depressed, and sleep-deprived students were high school students. Thus, Chinese high school students' physical and mental health requires attention. In China, high school students are admitted to colleges and universities based on gaokao , the standardized National College Entrance Examination ( 98 ). These admission decisions are extremely important, as they impact high school students' future educational opportunities, career paths, and life experiences. Our research results prove that Chinese students experience the most stressful and competitive academic environment of their academic careers when they are in high school.

Mediating Role of Mobile Phone Addiction

Chinese students spend considerable time on mobile phones−45.55% of the participants spent more than 2 h daily on their mobile devices. 39.5% of participants had had a mobile phone for more than 3 years, while the mean age of participants was 15.53 years. Using the mediation model, we illustrated the mediating role of MPA in line with our hypothesis. As H6 predicted, MPA played a role in the path from PAS to depression. MPA could partially explain the association between PAS and depression among Chinese students—hence, MPA was not only an outcome of PAS, but also a catalyst of depression. First, we found that high levels of PAS were associated with high levels of MPA. This finding is consistent with previous research results ( 48 ) and suggests that PAS may be a significant trigger for students' negative behaviors—such as MPA. Scholars have posited that young people's digital distraction activities—including playing computer games and online surfing—may be interpreted as a way to avoid problems, reality, and stress ( 99 , 100 ). High levels of PAS were associated with high levels of MPA, which may be due to students' use of mobile phones to escape from academic stress. Second, we found that high levels of MPA were associated with high levels of depression, which is in line with existing research results ( 35 , 36 , 101 ). Students who experience MPA may neglect real-world social engagement ( 102 ) resulting in academic underperformance ( 103 ), clinical health symptoms ( 68 ), which are related to negative emotions—such as depression. Our findings add to the existing research that suggests that when students are facing academic stress, they may be addicted to their mobile phones to escape from academic stress, and thus the negative consequences of MPA may lead to depression in students.

Mediating Role of Sleep Quality

As H8 predicted, sleep quality is not only an outcome of academic pressure—it is also a catalyst of depression. Moreover, the indirect effect related to sleep quality accounted for 82.61% of the total indirect effect. Thus, compared to MPA, sleep quality played a more important role in the path from PAS to depression. We found that higher levels of PAS were associated with poorer sleep quality. This finding is consistent with previous research findings ( 24 , 47 ). For example, Waqas et al. demonstrated that perceived stress is a significant predictor of PSQ ( 47 ). In China, students exist in a prolonged competitive learning environment and experience unrelenting academic stress. To achieve better academic performance and meet the extraordinarily high expectations of parents and educators, Chinese students have heavy homework burdens and learning burdens, resulting in sleep deprivation. Furthermore, academic stress decreases sleep quality. According to Almojali et al., students who are not suffering from academic stress are less likely to experience PSQ ( 104 ). Previous studies have proved that sleep deficiency and sleep health problems are common among Chinese students ( 105 ). Our research results may explain why higher levels of PAS were related to poorer sleep quality.

We also found that high levels of PSQ were associated with high levels of depression, which is consistent with prior research findings ( 31 , 50 , 57 ). Scholars have proved that PSQ is related to multiple negative consequences that may lead to depression—including daytime dysfunction, poor academic performance, and fatigue ( 106 , 107 ). Our findings add to the existing research that suggests that sleep quality is a mediator between PAS and depression among students, which means that higher levels of PAS were related to poorer sleep quality—such as sleep deficiency and daytime dysfunction—which was related to higher levels of depression.

Serial Mediating Effect of Mobile Phone Addiction and Sleep Quality

As per H9, MPA and sleep quality together play a serial mediating role in the influence of PAS on depression. The results of our study showed that higher levels of PAS were related to higher levels of MPA, which was associated with poorer sleep quality, which was associated with higher levels of depression. Numerous studies have documented that there is a positive relationship between MPA and PSQ ( 50 , 52 ). For example, Kang et al. found that there were bidirectional longitudinal relationships between MPA and PSQ ( 50 ). Scholars have posited that the more screen time young people use, the less sleep time they have ( 108 ). Moreover, young people often use their mobile phone in the bedroom—bedtime mobile phone use is related to higher insomnia scores and increased fatigue ( 109 ), and both insomnia and fatigue are related to depression ( 110 , 111 ). This may explain why MPA and PSQ together play a serial mediating role in the influence of PAS on depression. Our findings suggests that Chinese students are likely to distract themselves from PAS by using their mobile phones, and thus shortening their sleep duration, decreasing their sleep quality, leading to PSQ, and resulting in depressive symptoms.

Measures to Reduce Depressive Symptoms Among Chinese Students

To reduce depressive symptoms among students, their PAS should be managed. Given the multiple, negative consequences (MPA, PSQ, and depression) of PAS, stakeholders—family members, educators (including teachers, school administrators, and school health professionals), and policy makers—should take preventative measures to help students manage and relieve academic stress, such as provide counseling services ( 112 ), foster their psychological resilience ( 113 ), and increase social support ( 19 ) to improve their overall well-being. Second, students' sleep quality should be ensured to reduce depressive symptoms. Stakeholders should actively promote counseling and intervention for students experiencing sleep disturbances. Third, given that higher levels of MPA are associated with poorer sleep quality and higher levels of depression, stakeholders should develop mitigating strategies to manage mobile phone use to ensure students' sleep quality and to relieve their depressive symptoms. Rational and normative mobile phone use should be advocated and classroom management strategies enforced to ensure that students use their mobile phones at restricted times and places for positive purposes, such as online learning. Fourth, regular psychological assessment of depression, MPA, and PSQ will help stakeholders detect and manage students' health problems. Last, parents and family members, educators, and policy makers should encourage students to exercise more to alleviate MPA ( 114 ), improve sleep quality ( 115 ), and reduce depression ( 116 ).

Limitations

This study has several limitations. First, although we conducted our research based on previous studies, due to the cross-sectional design of our study, we could not confirm causal relationships among the study variables. Second, the study period was September to December 2018, which was more than 2 years ago. However, we believe that the results of our study are valuable for understanding the mechanisms of how PAS influences students' depression through MPA and sleep quality, and our study can provide a basis for future research. Third, the study measured the participants' perceived academic stress using a single item, which may not have captured various other relevant stressors, such as parental learning expectations. Future studies should use a multiple-item scale to assess the participants' perceived academic stress. Forth, this study was limited to middle school, high school, vocational high school, and college students. Future research on Chinese students at all education levels from primary school to postgraduate levels is necessary. Fifth, perceived academic stress can increase during stressful conditions ( 117 ), such as during exams or major change in life (e.g., from high-school student to freshman). While our study was conducted in 27 schools in three cities, it is impossible that we conducted the survey when the participants had no examinations or changes. Future studies can control for stressful academic conditions in the analyses to enhance their accuracy. Last, gender, age, and other factors are important influencing factors of PAS, MPA, sleep quality, and depression. Since the main aim of this study was to test if there was a relationship between PAS and depression and if MPA and sleep quality together play a serial mediating role in the influence of PAS on depression, the aforementioned factors were not considered in this study. Future studies should consider these factors and test the relationships between PAS, MPA, sleep quality, depression, and other health indicators.

Conclusions

Our study's results showed that Chinese students face the risk of depression and sleep disturbance, and the most stressed, depressed, and sleep-disturbed students are those in high school. Second, the results of the serial mediation model indicated that PAS predicted depression, and MPA and sleep quality played a mediating role between PAS and depression. Furthermore, MPA and sleep quality together play a serial mediating role in the influence of PAS on depression. Our study extends the understanding of how PAS is associated with depression among Chinese students. Considering the harmful effects of depression, stakeholders—including parents and family members, educators, and policy makers—should take preventative measures to alleviate Chinese students' depression and depressive symptoms.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. Requests to access these datasets should be directed to wuqunhong@163.com .

Author Contributions

XZ, FG, ZK, and QW: conceptualization. XZ, HZ, JW, and HL: formal analysis. FG, JZ, JL, JY, HZ, and BL: investigation. XZ, FG, ZK, and BL: data curation. XZ, FG, and ZK: writing—original draft preparation. QW and BL: writing—review and editing. All authors have read and agreed to the published version of the manuscript.

This research was funded by QW of The National Key Social Science Fund of China (Grant No.19AZD013).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

The authors would like to express our appreciation to all of the individuals for their involvement in the study, including each of students and teachers for their support during the data collection.

Abbreviations

PAS, Perceived Academic Stress; MPA, Mobile Phone Addiction; MPAI, Mobile Phone Addiction Index; CES-D, Center for Epidemiologic Studies-Depression; PSQ, Poor Sleep Quality; PSQI, Pittsburgh Sleep Quality Index; SEM, Structural Equation Model.

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Keywords: perceived academic stress, mobile phone addiction (MPA), sleep quality, depression, depressive symptoms, Chinese students

Citation: Zhang X, Gao F, Kang Z, Zhou H, Zhang J, Li J, Yan J, Wang J, Liu H, Wu Q and Liu B (2022) Perceived Academic Stress and Depression: The Mediation Role of Mobile Phone Addiction and Sleep Quality. Front. Public Health 10:760387. doi: 10.3389/fpubh.2022.760387

Received: 18 August 2021; Accepted: 07 January 2022; Published: 25 January 2022.

Reviewed by:

Copyright © 2022 Zhang, Gao, Kang, Zhou, Zhang, Li, Yan, Wang, Liu, Wu and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Qunhong Wu, wuqunhong@163.com ; Baohua Liu, liubaohuawoshi@163.com

† These authors have contributed equally to this work

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

This paper is in the following e-collection/theme issue:

Published on 20.2.2024 in Vol 26 (2024)

A Social Media–Based Mindfulness Psycho-Behavioral Intervention (MCARE) for Patients With Acute Coronary Syndrome: Randomized Controlled Trial

Authors of this article:

Author Orcid Image

Original Paper

  • Huijing Zou 1 , PhD   ; 
  • Sek Ying Chair 2 , PhD   ; 
  • Bilong Feng 3 , BSN   ; 
  • Qian Liu 1 , PhD   ; 
  • Yu Jia Liu 1 , BSN   ; 
  • Yu Xin Cheng 1 , BSN   ; 
  • Dan Luo 1 , PhD   ; 
  • Xiao Qin Wang 1 , PhD   ; 
  • Wei Chen 1 , BSN   ; 
  • Leiqing Huang 1 , BSN   ; 
  • Yunyan Xianyu 4 , PhD   ; 
  • Bing Xiang Yang 1 , PhD  

1 School of Nursing, Wuhan University, Wuhan, China

2 The Nethersole School of Nursing, The Chinese University of Hong Kong, Hong Kong, China (Hong Kong)

3 Zhongnan Hospital of Wuhan University, Wuhan, China

4 Renmin Hospital of Wuhan University, Wuhan, China

Corresponding Author:

Bing Xiang Yang, PhD

School of Nursing

Wuhan University

No. 115 Donghu Road, Wuchang District

Wuhan, 430071

Phone: 86 02768788685

Email: [email protected]

Background: Psychological distress is common among patients with acute coronary syndrome (ACS) and has considerable adverse impacts on disease progression and health outcomes. Mindfulness-based intervention is a promising complementary approach to address patients’ psychological needs and promote holistic well-being.

Objective: This study aims to examine the effects of a social media–based mindfulness psycho-behavioral intervention (MCARE) on psychological distress, psychological stress, health-related quality of life (HRQoL), and cardiovascular risk factors among patients with ACS.

Methods: This study was a 2-arm, parallel-group randomized controlled trial. We recruited 178 patients (mean age 58.7, SD 8.9 years; 122/178, 68.5% male) with ACS at 2 tertiary hospitals in Jinan, China. Participants were randomly assigned to the MCARE group (n=89) or control group (n=89). The 6-week intervention consisted of 1 face-to-face session (phase I) and 5 weekly WeChat (Tencent Holdings Ltd)–delivered sessions (phase II) on mindfulness training and health education and lifestyle modification. The primary outcomes were depression and anxiety. Secondary outcomes included psychological stress, HRQoL, and cardiovascular risk factors (ie, smoking status, physical activity, dietary behavior, BMI, blood pressure, blood lipids, and blood glucose). Outcomes were measured at baseline (T0), immediately after the intervention (T1), and 12 weeks after the commencement of the intervention (T2).

Results: The MCARE group showed significantly greater reductions in depression (T1: β=–2.016, 95% CI –2.584 to –1.449, Cohen d =–1.28, P <.001; T2: β=–2.089, 95% CI –2.777 to –1.402, Cohen d =–1.12, P <.001) and anxiety (T1: β=–1.024, 95% CI –1.551 to –0.497, Cohen d =–0.83, P <.001; T2: β=–0.932, 95% CI –1.519 to –0.346, Cohen d =–0.70, P =.002). Significantly greater improvements were also observed in psychological stress (β=–1.186, 95% CI –1.678 to –0.694, Cohen d =–1.41, P <.001), physical HRQoL (β=0.088, 95% CI 0.008-0.167, Cohen d =0.72, P =.03), emotional HRQoL (β=0.294, 95% CI 0.169-0.419, Cohen d =0.81, P <.001), and general HRQoL (β=0.147, 95% CI 0.070-0.224, Cohen d =1.07) at T1, as well as dietary behavior (β=0.069, 95% CI 0.003-0.136, Cohen d =0.75, P =.04), physical activity level (β=177.542, 95% CI –39.073 to 316.011, Cohen d =0.51, P =.01), and systolic blood pressure (β=–3.326, 95% CI –5.928 to –0.725, Cohen d =–1.32, P =.01) at T2. The overall completion rate of the intervention (completing ≥5 sessions) was 76% (68/89). Positive responses to the questions of the acceptability questionnaire ranged from 93% (76/82) to 100% (82/82).

Conclusions: The MCARE program generated favorable effects on psychological distress, psychological stress, HRQoL, and several aspects of cardiovascular risk factors in patients with ACS. This study provides clues for guiding clinical practice in the recognition and management of psychological distress and integrating the intervention into routine rehabilitation practice.

Trial Registration: Chinese Clinical Trial Registry ChiCTR2000033526; https://www.chictr.org.cn/showprojEN.html?proj=54693

Introduction

Acute coronary syndrome (ACS), an acute manifestation of ischemic heart disease, has become a major public health problem worldwide [ 1 ]. In China, ischemic heart disease affects approximately 11.4 million people [ 2 ], and there remains a rising trend in the morbidity and mortality of ACS [ 3 ], thus posing a huge challenge for the health care system.

Psychological distress, such as depression and anxiety, is highly prevalent in patients with ACS [ 4 ] and has considerable adverse impacts on disease progression and health outcomes. Clear evidence supports that psychological distress is associated with functional disability, reduced health-related quality of life (HRQoL), and increased risks of cardiac events [ 5 , 6 ]. Nonetheless, current health care practice has paid inadequate attention to the recognition and management of psychological distress. A growing consensus advocates that psychological distress is a crucial risk factor of ACS that should be addressed in disease management [ 5 , 7 , 8 ]. The American Heart Association has recommended mindfulness-based intervention as a promising complementary approach to promoting psychological health and well-being for patients with cardiovascular disease [ 8 ]. Emerging evidence has proven its benefits in improving a wide range of psychological and physical outcomes [ 9 , 10 ], including among patients with ischemic heart disease [ 11 ], indicating its potential as an additional supplement to conventional cardiac care.

Another practice gap in China is that optimal rehabilitation and effective control of cardiovascular risk factors are rarely achieved due to insufficient awareness and competency of health care professionals, inadequate workforce, and limited resources [ 12 ]. The health care system primarily focuses on in-hospital treatments of acute attacks of ACS and neglects the posthospital management of risk factors. An investigation of 991 hospitals in China showed that only 228 (23%) hospitals provided center-based cardiac rehabilitation services, which were mainly distributed in urban areas (89.1%) [ 2 ].

In recent years, mobile health or eHealth technologies are increasingly being used to improve the availability, feasibility, and affordability of posthospital care with inspiring results in promoting medication adherence, lifestyle changes, and health outcomes [ 13 , 14 ]. With the popularization and widespread coverage of mobile internet access, WeChat (Tencent Holdings Ltd), a free smartphone app, has become one of the most popular and widely used social media in China. It provides various services, including instant messaging, voice and video calls, microblogging and subscription services, and web-based banking. WeChat may provide an unprecedented approach to addressing the shortage of posthospital care, considering its wide population reach and powerful peripheral functions.

This study proposed a WeChat-based mindfulness psycho-behavioral intervention (MCARE), which integrated mindfulness training with health education and lifestyle modification to assist patients in managing risk factors. This randomized controlled trial (RCT) aimed to examine the effects of the MCARE program on psychological distress (primary outcomes), psychological stress, HRQoL, and cardiovascular risk factors (secondary outcomes) among patients with ACS.

Study Design and Participants

This study was a 2-arm, 1:1 parallel-group RCT. Participants were recruited using convenience sampling from June to September 2020 at the wards of the cardiology department of 2 public tertiary hospitals in Jinan, China. Inclusion criteria were as follows: (1) age 18 to 75 years; (2) clinical diagnosis of ACS (including unstable angina and acute myocardial infarction); (3) ability to read, understand, communicate, and complete questionnaires in Chinese; and (4) possession of an operational smartphone and an active WeChat account. Participants were excluded if they (1) were in the active state of myocardial infarction or receiving open-heart surgical treatment; (2) had a clinical diagnosis of serious physical comorbidities, for example, cancer and renal failure; (3) had psychiatric disorders; (4) had cognitive impairments as documented in the health records; or (5) were currently participating in other interventions.

The sample size was calculated following a power analysis approach using G*Power 3.1 (Universität Düsseldorf). Considering a significance level of 0.05; a statistical power of 80%; an effect size of Cohen d =0.50 for primary outcomes, namely depression and anxiety [ 15 ]; and a potential attrition rate of 20%, in all,160 participants (80 per group) were required.

Ethical Considerations

Ethics approvals were obtained from the Joint Chinese University of Hong Kong-New Territories East Cluster Clinical Research Ethics Committee (2019.323) and the Ethics Committee of the School of Nursing, Shandong University (2019-R-017). Participants gave informed consent to participate in the study before taking part. All information and data from participants were anonymous and confidentially was guaranteed by coding participants with unique identification numbers (eg, 001). All participants received a small red envelope sent via WeChat upon completion of each intervention session and after each data collection to acknowledge the time and effort they dedicated to participating in the study.

All procedures of the RCT were completed in the same manner in 2 research hospitals. Two research assistants (registered nurses) were hired and received training on participant recruitment; data collection; and the rationale, principles, and procedure of the study. The research assistants approached eligible participants, introduced the study details, and invited them to participate in this study. Then they obtained written informed consent and performed a baseline assessment.

Participants were randomly allocated into either the intervention or control groups at a 1:1 ratio using a sequence of block randomized numbers generated by an independent statistician using a computer procedure with a block size of 4. The group allocation was concealed by placing the random sequence in a sequentially numbered, opaque, and sealed envelope by an independent person. The research assistants were blinded to group allocation.

Interventions

The MCARE program was developed on the basis of the Common-Sense Model of Self-Regulation theory [ 16 ], clinical guideline recommendations [ 7 , 17 ], and experimental studies [ 8 , 10 , 11 ]. It aimed to promote psychological and physical well-being by targeting emotional and cognitive procedures for managing health threats. Figure 1 illustrates the conceptual framework underpinning the MCARE program.

stress management of students research paper

The MCARE program comprised 6 weekly sessions and each session focused on a thematic topic in mindfulness training and disease management (Table S1 in Multimedia Appendix 1 ). Mindfulness training topics included simple awareness, mindfulness of breath, mindfulness of the body, dealing with thoughts, dealing with difficulties, and maintenance. Disease management topics were basic disease information, healthy dietary behavior, physical activity, body weight control, smoking cessation, and management of metabolic risk factors. Additionally, participants were required to perform home-based mindfulness practice for 10 to 20 minutes per day for 6 days per week. Figure 2 summarizes the detailed delivery plan. The first session was delivered face-to-face during hospitalization (phase I) and the following 5 sessions were delivered using WeChat after discharge (phase II). Following the third and sixth sessions, participants received a voice call to monitor and review their performance, address barriers or problems, and provide support and encouragement. Participants also received an information handbook and audio and video guides on mindfulness practice.

stress management of students research paper

Participants in the control group received routine medical treatment and care before hospital discharge. To control the nonspecific effect of attention, the control group also received WeChat contacts at the same frequency as the intervention group. The contents of the contacts were limited to general information about the importance of managing stress and risk factors without providing specific advice and strategies.

The research assistants collected data at baseline (T0), immediately after the intervention (T1), and 12 weeks following the commencement of the intervention (T2). A self-designed data collection sheet was used to assess baseline sociodemographic and clinical characteristics through patient interviews and medical record reviews.

Primary outcomes were psychological distress, including depression and anxiety, which were assessed using the 9-item Patient Health Questionnaire (PHQ-9) [ 18 ] and the 7-item Generalized Anxiety Disorder (GAD-7) [ 19 ], respectively. Secondary outcomes were psychological stress, HRQoL, and cardiovascular risk factors. Psychological stress was assessed using the 10-item Perceived Stress Scale [ 20 ]. HRQoL was measured using the HeartQoL questionnaire [ 21 ], which comprises 14 items capturing disease-specific HRQoL in physical (10 items) and emotional (4 items) dimensions. Cardiovascular risk factors included (1) smoking status, as measured by self-reported 7-day smoking history [ 22 ]; (2) physical activity, as measured using the International Physical Activity Questionnaire-Short Form [ 23 ]; (3) dietary behavior, as assessed using the nutrition subscale of the Health-Promoting Lifestyle Profile-II [ 24 ]; (4) BMI, as calculated using the formula: BMI = body weight (in kilograms) / height (in meters) squared ; (5) blood pressure (BP); (6) blood lipid profiles; and (7) blood glucose. Body weight, height, and BP were obtained by anthropometric measures, and blood lipids and blood glucose were measured via laboratory tests of fasting blood samples.

The acceptability of the MCARE program among participants in the MCARE group was also measured at T1 using a self-developed dichotomous questionnaire (positive ratings ≥80% are considered acceptable). Additionally, the completion of the intervention, performance of home mindfulness practice, difficulties or problems encountered by participants, and adverse events were collected.

Baseline (T0) assessment was conducted at the wards, and follow-up assessments (T1 and T2) were completed via telephone interviews. After the first face-to-face session, the assessors would schedule the telephone interview for each participant. Participants were reminded to return to the research hospitals or go to a nearby accredited hospital to complete fasting blood tests at T2. If the participants did not answer the telephone call, they would contact the participant 3 times at different periods of a day within 1 week. If none of these telephone calls reached the participant, the participant would be considered lost to follow-up.

Statistical Analysis

Statistical analyses were performed using IBM SPSS (version 25.0). All statistical tests were 2-tailed tests and statistical significance was set at 0.05. Appropriate descriptive statistics were calculated to summarize the participant characteristics and outcomes. The intention-to-treat principle was applied in outcome analysis. The generalized estimating equation (GEE) analyses were performed to examine the differential changes of each outcome variable across 3 data collection time points between intervention and control groups. Baseline characteristics and outcomes between intervention and control groups were compared and no significant differences were observed, therefore only the crude GEE models were performed without adjustment of confounding variables. A dummy variable (group) was set to represent the MCARE group with the control group as the reference. To represent time differences, the baseline (T0) was set as the reference, and another 2 dummy variables, T1 and T2, were assigned to correspond to immediate postintervention and 12 weeks. The interaction terms of the group-by-time dummy variables, group×T1 and group×T2, were included in the GEE models to assess the overall differences in the outcomes between the 2 groups at T1 and T2. Effect sizes were estimated using Cohen d statistic for continuous outcomes and odds ratio for binary outcomes.

We calculated the percentage of missing data for each outcome (9.0% to 9.9%) and compared baseline sociodemographic and clinical characteristics between participants who had completed all observations and those who had at least 1 missing observation. Additionally, to examine the effects of the missing data on the estimation of intervention effects we conducted a sensitivity analysis using completed case analysis. In the completed case analysis, only participants who had completed all assessments at T0, T1, and T2 were included in the estimation of intervention effects. We also examined the correlation between participants’ completion of intervention and dosage of home mindfulness practice on changes in outcome variables between T1 and T0 by performing Pearson correlation analysis.

Participant Recruitment and Retention

Of the 275 patients with ACS screened for eligibility, 52 patients did not meet the eligibility criteria and 45 patients declined to participate. Finally, 178 participants were enrolled ( Figure 3 ). At follow-up, 157 (88.2%) participants completed T1 assessment, and 146 (82.0%) participants completed T2 assessment. Cardiovascular risk factors were measured only at T0 and T2 with a total of 356 observations, and 32 (9.0%) were missing. All the other outcome variables had a total of 534 observations across 3 study time points with 53 (9.9%) missing observations. There was no significant difference in baseline characteristics between participants who completed all observations (n=146) and who had at least 1 missing observation (n=32; Table S2 in Multimedia Appendix 1 ). No adverse events related to the intervention were reported.

stress management of students research paper

Baseline Characteristics

Table 1 presents the detailed baseline characteristics of participants and the intervention and control groups were well-matched. Mean age of the participants was 58.7 (SD 8.9) years, ranging from 28 to 75 years. The majority of them were male (122/178, 68.5%), married (172/178, 96.6%), received a secondary education or less (147/178, 82.6%), and had a New York Heart Association class of I or II (148/178, 83.1%). Over half of the participants were experiencing ACS for the first time (104/178, 58.4%) and did not receive percutaneous transluminal coronary intervention (99/178, 55.6%). The mean scores for PHQ-9 and GAD-7 were 5.66 (SD 3.30) and 5.38 (SD 2.97), respectively. Over half of the participants had depressive symptoms (PHQ-9 score ≥5; 107/178, 60.1%) and anxiety symptoms (GAD-7 score ≥5; 95/178, 53.4%), indicating psychological distress is highly prevalent.

a CNY ¥: Chinese yuan, US $1=CNY ¥6.90 at the time of the study (2020).

b ACS: acute coronary syndrome.

c NYHA: New York Heart Association.

d LVEF: left ventricular ejection fraction.

e PHQ-9: 9-item Patient Health Questionnaire.

f GAD-7: 7-item Generalized Anxiety Disorder.

g PSS-10: 10-item Perceived Stress Scale.

h HRQoL: health-related quality of life.

i IPAQ-SF: International Physical Activity Questionnaire-Short Form.

j HPLP-II: Health-Promoting Lifestyle Profile-II.

k BP: blood pressure.

l LDL-C: low-density lipoprotein cholesterol.

m HDL-C: high-density lipoprotein cholesterol.

n TG: triglyceride.

o TC: total cholesterol.

p FBG: fasting blood glucose.

Intervention Effects

There were significant time-by-group interaction effects on psychological distress with moderate to large effects at both T1 and T2 ( Table 2 ). Participants in the intervention group demonstrated significantly greater reductions in depression (T1: β=–2.016, 95% CI –2.584 to –1.449, Cohen d =–1.28, P <.001; T2: β=–2.089, 95% CI –2.777 to –1.402, Cohen d =–1.12, P <.001) and anxiety (T1: β=–1.024, 95% CI –1.551 to –0.497, Cohen d =–0.83, P <.001; T2: β=–0.932, 95% CI –1.519 to –0.346, Cohen d =–0.70, P =.002) than those in the control group.

a The control group (group =0) and the baseline measurement (time =0) were set as the reference categories in the generalized estimating equation model and its corresponding null variables.

b Group effect was defined as group differences at baseline between intervention and control groups.

c Time effect at T1 is defined as change of scores for the control group at T1 compared with T0; T2 is defined as change of scores for the control group at T2 compared with T0.

d Group×time effect at T1 defined as additional change of scores for the intervention group compared with the control group at T1; T2 defined as additional change of scores for the intervention group compared with the control group at T2. Effect sizes were estimated using Cohen d statistic for continuous outcomes and odds ratio for binary outcomes.

f Intervention and control group data are presented as mean (SD).

g N/A: not applicable.

h GAD-7: 7-item General Anxiety Disorder.

Compared with control group, the intervention group demonstrated significantly greater improvements in psychological stress (β=–1.186, 95% CI –1.678 to –0.694, Cohen d =–1.41, P <.001), physical HRQoL (β=0.088, 95% CI 0.008-0.167, Cohen d =0.72, P =.03), emotional HRQoL (β=0.294, 95% CI 0.169-0.419, Cohen d =0.81, P <.001), and general HRQoL (β=0.147, 95% CI 0.070-0.224, Cohen d =1.07, P <.001) at T1 ( Table 3 ). However, the significant effects were only sustained for psychological stress (β=–1.268, 95% CI –1.992 to –0.544, Cohen d =–1.17, P =.001) and emotional HRQoL ( β =0.249, 95% CI 0.102-0.395, Cohen d =0.62, P =.001) but not for physical HRQoL and general HRQoL at T2. For cardiovascular risk factors, the intervention group showed significantly greater improvements in dietary behavior (β=0.069, 95% CI 0.003-0.136, Cohen d =0.75, P =.04), physical activity level (β=177.542, 95% CI –39.073 to 316.011, Cohen d =0.51, P =.01), and systolic BP (β=–3.326, 95% CI –5.928 to –0.725, Cohen d =–1.32, P =.01) at T2 ( Table 4 ). No significant group-by-time interaction effects were observed for other outcomes (all P >.05). The sensitivity analysis showed consistent results in the directions of the GEE regression coefficients (Table S3 in Multimedia Appendix 1 ).

e PSS-10: 10-item Perceived Stress Scale.

e Intervention and control group data are presented as n (%).

f N/A: not applicable.

g IPAQ-SF: International Physical Activity Questionnaire-Short Form.

h Intervention and control group data are presented as mean (SD).

i HPLP-II: health-promoting lifestyle profile-II.

j BP: blood pressure.

k LDL-C: low-density lipoprotein cholesterol.

l HDL-C: high-density lipoprotein cholesterol.

m Intervention and control group data are presented as median (IQR).

Intervention Adherence

The overall attrition rate of this study was 18% (32/178; intervention group: 13/89, 15% and control group: 19/89, 21%). The completion rate for each intervention session ranged from 68.5% (61/89) to 100% (89/89) and the overall completion rate (defined as completing at least 5 of the 6 intervention sessions) was 76% (68/89). Further analysis showed the number of completed sessions was significantly and positively correlated with changes in depression ( r =0.324, P =.003), psychological stress ( r =0.224, P =.04), emotional HRQoL ( r =0.224, P =.04), and general HRQoL ( r =0.279, P =.01) at T1 (Table S4 in Multimedia Appendix 1 ), suggesting that adherence to the intervention may influence the intervention effects. The average frequency of home mindfulness practice ranged from 2.2 (SD 2.0) to 3.7 (SD 1.7) times per week and the total average amount was 19.0 (SD 8.9; range 1 to 38) times during the 6 weeks, which was much less than the designed dosage. In addition, the frequency of home mindfulness practice was significantly and positively correlated with changes in depression ( r =0.865, P <.001), anxiety ( r =0.626, P <.001), psychological stress ( r =0.353, P =.001), emotional HRQoL ( r =0.497 , P <.001), and general HRQoL ( r =0.399, P <.001) at T1 (Table S4 in Multimedia Appendix 1 ).

Acceptability of the Intervention

At T1, a total of 82 (92%) of 89 participants in the intervention group completed the acceptability questionnaire. Positive responses to the questions ranged from 93% (76/82) to 100% (82/82; Table S5 in Multimedia Appendix 1 ), indicating high satisfaction with the intervention. Furthermore, 13 (15%) participants reported difficulties or problems in applying intervention skills or strategies in daily life over the 6-week intervention, including lack of a suitable environment to apply the skills and strategies (n=6), lack of a quiet environment to concentrate for home mindfulness practice (n=5), lack of time for home mindfulness practice (n=3), and physical discomfort (n=3).

Principal Findings

This study provided evidence for the effects of a social media–based intervention for patients with ACS. The MCARE program significantly improved psychological distress in terms of depression and anxiety at immediate postintervention and 12-week follow-up. Furthermore, the MCARE program has significant effects on psychological stress, HRQoL, dietary behavior, physical activity, and systolic BP.

The findings were supported by previous reports that mindfulness-based interventions [ 11 , 25 ] and health education [ 26 ] had significant effects on reducing depression and anxiety for patients with cardiovascular disease. Mindfulness training together with health education mainly targeted promoting awareness of and response to the feelings, emotions, and bodily sensations caused by the physical and psychological distress and increasing disease management knowledge and skills. Therefore, the MCARE program was assumed to have meaningful effects on psychological distress for patients with ACS. Moreover, the benefits of the MCARE program on psychological distress were likely to be sustained for a short-term period from immediate postintervention to 12-week follow-up. This might be explained by the residual gains of mindfulness skills and disease management knowledge and skills from the MCARE program. The present-focused mindfulness practice may generate a short-term, sustainable, beneficial effect on improving emotional regulation skills to facilitate participants to cope with difficult situations and lead to reduced psychological distress.

The MCARE program also significantly improved psychological stress and HRQoL, which is consistent with previous systematic reviews of mindfulness-based interventions [ 27 ] and educational interventions [ 28 ] for patients with ischemic heart disease. The MCARE program could help participants cope with their condition and thus reduce mental and emotional distress, which in turn contributed to the improvement of HRQoL, particularly in the emotional dimension.

Previous research rarely reported the effects of mindfulness-based interventions on cardiovascular risk factors. This study demonstrated the promising effects of the MCARE program on dietary behavior, physical activity, and systolic BP for patients with ACS, providing a valuable reference for future research. Health education would help patients understand the etiology, development, duration, and prognosis of their illness and learn how to manage the risk factors [ 29 ], and mindfulness training would increase their awareness of managing health threats, which together, in turn, might have empowered them to eat healthier and be more physically active. The enhanced knowledge and awareness about the illness, together with regular BP surveillance and behavioral change might have contributed to improved BP control.

The findings suggested the MCARE program had nonsignificant effects on smoking status, BMI, blood lipid profiles, and blood glucose. This might be due to that the MCARE program only provided general education and support without more effective strategies such as targeted and direct medical cessation therapy for smokers [ 30 ] and targeted weight loss strategy for patients who are overweight or obese. Blood lipid and glucose control are important targets of ACS management, which may mostly rely on adherence to core cardioprotective medications that lower blood lipid and glucose. Complementary interventions via the promotion of a healthy lifestyle, providing education on disease knowledge, and regular monitoring may facilitate blood lipid and glucose control; however, it may take a long time to observe a significant improvement in the concentration of blood lipid and glucose.

Limitations

This study has several limitations. First, this study was carried out in the Chinese population and used a convenience sampling method, which may lead to selection bias, thus limiting the representativeness of the study population and the generalizability of findings. In order to improve the representativeness of the study population, multicenter studies conducted in various regions and diverse settings are warranted. Second, the measure of the majority of outcomes relied on participants’ self-reporting, although the instruments adopted in the study were reliable and validated. The subjective data may be subject to self-reporting and recall bias. Third, this study only evaluated the intervention effects at immediate postintervention and 12-week follow-up, which was unable to explore the sustainability of the intervention effects over the long term. Furthermore, we did not evaluate the consistent use of the intervention and analyze its association with health outcomes including cardiometabolic status. Thus, the patterns of use and the relationships between the use of intervention and health outcomes require further exploration in studies with a larger sample size and a longer follow-up period. Fourth, due to the scope of this study, we did not examine whether the MCARE program supported the conceptual framework or explore whether the key concepts related to the framework would explain the possible mechanism of the intervention effects. Last, this study did not assess all potential confounding factors that may impact the psychological distress and secondary outcomes, such as the baseline cardiovascular risk of participants by objective ergometry test, which may introduce bias in the estimation of intervention effects.

Conclusions

This study pioneered a social media–based intervention for patients with ACS. The findings demonstrated that the MCARE program was an effective approach to improving psychological distress, psychological stress, HRQoL, and several cardiovascular risk factors.

Acknowledgments

The authors thank the head nurses and doctors in the research hospitals for their assistance and coordination for data collection and the patients who participated in the study.

Data Availability

The data sets generated during or analyzed during this study are available from the corresponding author on reasonable request.

Conflicts of Interest

None declared.

Structure and content of the mindfulness psycho-behavioral intervention (MCARE) program and supplemental results.

CONSORT eHEALTH checklist (V 1.6.1)

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Abbreviations

Edited by A Mavragani; submitted 30.04.23; peer-reviewed by J Chen, D Fletcher; comments to author 24.10.23; revised version received 30.10.23; accepted 03.01.24; published 20.02.24.

©Huijing Zou, Sek Ying Chair, Bilong Feng, Qian Liu, Yu Jia Liu, Yu Xin Cheng, Dan Luo, Xiao Qin Wang, Wei Chen, Leiqing Huang, Yunyan Xianyu, Bing Xiang Yang. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 20.02.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

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Home > Topics > Mr. NODA Kazuma receives Excellent Student Paper Award at The 3rd International Conference on Smart Manufacturing, Industrial & Logistics Engineering (SMILE2023)

Mr. NODA Kazuma receives Excellent Student Paper Award at The 3rd International Conference on Smart Manufacturing, Industrial & Logistics Engineering (SMILE2023)

Category:Award|Publishing : February 20, 2024

Award winner

Mr. NODA Kazuma , Architecture, Civil Engineering and Industrial Management Engineering Program , Department of Engineering

( Sun Jing Laboratory )

The 3rd International Conference on Smart Manufacturing, Industrial & Logistics Engineering (SMILE2023) Excellent Student Paper Award

( The 3rd International Conference on Smart Manufacturing, Industrial & Logistics Engineering (SMILE2023) )

Award-winning research

"An Optimal Allocation Model for Smart Production System in Limited-Cycle Problem with Multiple Periods considering Quality and Due Time~ the Case of Different Worker Levels ~"

In recent years, quality irregularities in companies have been covered in the media, affecting sales and stock prices. Furthermore, due to the increase in the number of foreign workers and differences in the number of years workers have been with the company, workers' abilities also vary. In this study, we constructed a new mathematical model that takes quality into account in addition to the multi-period constraint cycle model of previous studies. We proposed the optimal worker assignment for a production line with different worker levels and analyzed its characteristics.

Award winner's comments

I am very honored to receive this award. I feel that this award was made possible by the cooperation of many people, including Assoc. Prof. Jing Sun and other members of my laboratory, and I would like to express my sincere gratitude. Encouraged by this award, I will continue to devote myself even more to my research activities and contribute to the further development of my research field.

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CBSE Board Exams: 8 Tips To Manage Time After Getting The Question Paper

Curated By : Education and Careers Desk

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Last Updated: February 19, 2024, 18:57 IST

Delhi, India

stress management of students research paper

Begin on a positive note by smiling and greeting the invigilator.

Upon receiving the question paper in the examination hall, students are typically provided a 15-minute reading period before writing.

Preparing for Class 10 and Class 12 exams can induce stress and anxiety across various educational boards such as CBSE, ICSE and state boards. As the exam dates draw nearer, students often experience restlessness and doubts about their capabilities. However, effective revision can alleviate stress and boost confidence during exams. Among the crucial skills to hone during exam preparation and writing is time management.

Time management plays a pivotal role in exam performance, aiding students in setting and achieving short-term goals efficiently. It ensures students avoid last-minute rushes, allowing ample time for revision. Moreover, it guides students in determining which exam section to tackle first and allocates appropriate time for answering questions.

Upon receiving the question paper in the examination hall, students are typically provided a 15-minute reading period before writing. This brief interval serves as a calming period, particularly for students travelling from distant areas, enabling them to review the questions and compose themselves.

Here are several practical tips for students to follow during the initial moments after receiving their question paper in a CBSE exam centre:

Ensure accurate recording of the roll number on both the answer sheet and question paper.

Carefully read the instructions on the question paper, verifying the number of pages and printing quality.

Review the questions and marks allotted to each, strategizing on which section to tackle first.

Start with questions carrying the highest marks and identify internal options for prioritisation.

Plan the sequence of tackling sections to optimise time management.

Utilise the final minutes of the reading period to focus on multiple-choice questions (MCQs).

If encountering any discrepancies or questions seemingly beyond the syllabus, inform the invigilator promptly.

By adhering to these strategies, students can effectively manage their time, enhance their exam performance and navigate potential challenges with confidence during their CBSE exams.

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How to Relax in Stressful Situations: A Smart Stress Reduction System

Yekta said can.

1 Computer Engineering Department, Bogazici University, 34342 Istanbul, Turkey; [email protected] (N.C.); [email protected] (D.E.); rt.ude.nuob@yosre (C.E.)

Heather Iles-Smith

2 Leeds Teaching Hospitals NHS Trust/University of Leeds, Leeds LS1 3EX, UK; [email protected]

Niaz Chalabianloo

Javier fernández-Álvarez.

3 General Psychology and Communication Psychology, Catholic University of Milan, 20123 Milan, Italy; [email protected] (J.F.-Á.); [email protected] (C.R.); [email protected] (G.R.)

Claudia Repetto

Giuseppe riva.

Stress is an inescapable element of the modern age. Instances of untreated stress may lead to a reduction in the individual’s health, well-being and socio-economic situation. Stress management application development for wearable smart devices is a growing market. The use of wearable smart devices and biofeedback for individualized real-life stress reduction interventions has received less attention. By using our unobtrusive automatic stress detection system for use with consumer-grade smart bands, we first detected stress levels. When a high stress level is detected, our system suggests the most appropriate relaxation method by analyzing the physical activity-based contextual information. In more restricted contexts, physical activity is lower and mobile relaxation methods might be more appropriate, whereas in free contexts traditional methods might be useful. We further compared traditional and mobile relaxation methods by using our stress level detection system during an eight day EU project training event involving 15 early stage researchers (mean age 28; gender 9 Male, 6 Female). Participants’ daily stress levels were monitored and a range of traditional and mobile stress management techniques was applied. On day eight, participants were exposed to a ‘stressful’ event by being required to give an oral presentation. Insights about the success of both traditional and mobile relaxation methods by using the physiological signals and collected self-reports were provided.

1. Introduction

Stress constitutes a complex process that is activated by a physical or mental threat to the individuals’ homeostasis, comprising a set of diverse psychological, physiological and behavioral responses [ 1 ]. Although it is usually considered a negative response, stress actually constitutes a key process for ensuring our survival. However, when a stress response is repeatedly triggered in the absence of a challenging stimulus, or if there is constant exposure to challenging situations, stress can become harmful. Evidence suggests that, in either of these two contexts, stress is a persistent factor for the development of psycho-pathological conditions [ 2 , 3 ].

When faced with stressful events, people make autonomic and controlled efforts to reduce the negative impact and maximize the positive impact that every specific situation may provoke. Generally, this process is denominated as emotion regulation, formally defined as the process by which individuals can influence what emotions they have, when they have them and how they experience and express those emotions [ 4 ]. It has been suggested that the term emotion regulation can be understood as a broad tag that comprises the regulation of all responses that are emotionally charged, from basic emotions to complex mood states as well as regulation of everyday life [ 5 ].

Failure to address triggers of stress has been shown to lead to chronic stress, anxiety and depression, and attributed to serious physical health conditions such as cardiovascular disease [ 6 ]. The World Health Organization concluded that psychological stress is one of the most significant health problems in the 21st-century and is a growing problem [ 7 ]. There are various interventions to minimize stress based on individual preferences and requirements. Stress management techniques including ancient practices such as Tai Chi [ 8 ] and yoga [ 9 ] as well as other physical activities [ 10 ] are often cited as being helpful in combating stress. Likewise traditional meditation, mindfulness [ 11 ] and cognitive behavioural therapy (CBT) [ 12 ] all have established benefits. These techniques are not applicable in office or social environments, or during most daily routines. Therefore, a smart device based stress management application may be of benefit. Recently, smartphone applications such as Calm, Pause, Heartmath and Sway have been developed for indoor environments. However, these applications are not individualized nor do they include biofeedback and studies that validate their effects are limited [ 13 ].

In this study, we used the stress level detection scheme using physiological signals and added a physical activity based context analyzer. When the user experiences a high stress level, the system suggests appropriate stress reduction methods (traditional or mobile). We further compare the effects of traditional and mobile stress alleviation methods on physiological data of 15 international Ph.D. students (participants) during eight days of training. In addition, 1440 h of physiological signals from Empatica E4 smart bands were collected in this training event. Stress management techniques based on the emotion regulation model of James Gross [ 4 ] were applied to reduce participant stress levels. To the best of our knowledge, this work is the first one suggesting appropriate stress reduction methods based on contextual information and comparing both traditional and mobile stress management interventions in the real-life environment using a commercial smart-band based automatic stress level detection system that eliminates motion artifacts. Using such a system is essential because these offline stress level detection algorithms could be used in real-time biofeedback apps.

Application of our stress level detection algorithm, in a real world context, could allow individuals to receive feedback regarding high stress levels along with recommendations for relaxation methods. Additional continued monitoring may also enable the individual to better understand the effectiveness of any stress reduction methods. However, for our stress detection algorithm to be applied in daily life, the smart device should be unobtrusive (i.e., should not be comprised of cables, electrodes, boards). Our system works on smart-bands which are perfect examples of this type of unobtrusive wearable device.

This paper describes emotion regulation in the context of stress management and how yoga and mindfulness can be used for regulating emotions ( Section 2 ). Methods of detecting stress and analyzing context based on physical activity are described ( Section 3 ) and data are presented related to our method for stress level detection with the use of smart-bands ( Section 4 ). Experimental results and discussion are also presented ( Section 5 ) and we present the conclusions and future works of the study ( Section 6 ).

The major research contributions of this study are the following:

  • Developing a physical activity based context analyzer and relaxation method suggestion system
  • Comparison of stress reduction methods (mobile mindfulness, traditional mindfulness and yoga) and their effectiveness in the context of stress management with the use of an unobtrusive smartwatch based stress level detection system
  • Application of James Gross’s prominent emotion regulation model in the context of stress management and measuring the physiological component with smart bands.

2. Background

2.1. emotion regulation in the context of stress management.

Stress is a normal part of daily life. However, its effects often vary across individuals and despite similar circumstances, some people do not feel under strain while others may be severely affected. Multiple reasons exist for these differences between individuals, including how people perceive reality and how they respond to the numerous stimuli to which they are exposed. When a person believes that a certain situation surpasses their available coping mechanisms, it is referred to as perceived stress. Thus, perceived stress varies from person to person depending on the value that an individual gives to a situation and their self-recognition of the resources to deal with it.

Numerous psychological scientists have investigated perceived stress. Individuals who display a mismatch between contextual demands and perceived resources constantly (rather than during a specific moment in time) are referred to as experiencing chronic stress. Chronic stress has not only been shown to be very relevant in people’s well-being and quality of life, but also important in the appearance and maintenance of several physical and mental diseases [ 14 ].

As a consequence, mounting research has focused on the mechanisms that people implement in order to alleviate the physical and cognitive burden associated with that perceived stress. Coping styles, stress management techniques, self-regulation, or emotion regulation techniques are different labels that define the way people implement certain behavioral, cognitive, or emotional strategies to maintain allosteric load [ 15 ]. In other words, every living organism needs to vary among plasticity and stability in order to survive. Human beings are not the exception to the rule and the complex system that applies to every single person and the necessity of reaching a constant level of regulation permits the individuals to pursue their goals.

Specifically, emotion regulation has been defined as the study of “the processes by which we influence which emotions we have when we have them, and how we experience and express them” [ 4 ]. A large body of evidence has shown that there are very different consequences depending on the effectiveness people achieve to regulate their emotions. Naturally, both at an implicit or explicit level, people regulate emotions in order to maintain those allosteric levels previously mentioned. Therefore, when there are specific stressors that demand a particular cognitive or physical response, the emotional reactivity may be stronger and the need for a proper regulation more relevant. Indeed, emotion regulation has shown to be a transdiagnostic factor that is present at a wide range of mental disorders. In other words, the way people initiate, implement and monitor their emotional processes, in order to reach more desirable states, has a significant impact on the stress levels. Some emotion regulation (ER) strategies have shown to be correlated with mental health issues. Among these strategies, cognitive reappraisal, problem-solving, or acceptance shall be mentioned as strategies that are negatively correlated with psychopathology, while rumination, experiential avoidance, or suppression are positively correlated with psychopathology [ 16 ]. In this regard, hinging on the different ER strategies deployed, ER can constitute a protective factor to face stress responses that all individuals experience after minor or major stressors [ 17 ]. Additionally, an adaptive regulation of emotions, by managing stress, may also be beneficial for clinical populations, such as people suffering from affective disorders [ 18 , 19 ].

Therefore, from whole psychotherapeutic treatments to single self-applied applications, studies in the literature have focused on how people can better regulate their emotions and manage their stress levels. Among many other techniques, cognitive behavioral therapy, autogenic training, biofeedback, breathing exercises, relaxation techniques, guided imagery, mindfulness, yoga, or Tai-Chi, are some of the stress management interventions that have received attention from researchers [ 20 , 21 ].

2.2. Yoga and Mindfulness: As Tools for Emotion Regulation

2.2.1. yoga.

Yoga is an ancient Eastern practice that developed more than 2000 years ago. Although its original creator and source are uncertain, the earliest written word ‘Yoga Sutra’ describes the philosophy of yoga focussing on growing spirituality, regulating emotions and thoughts. Initially, the focus was on awareness of breathing and breathing exercises ‘pranayama’ to calm the mind and body, ultimately reaching a higher state of consciousness.

As yoga evolved, physical movement in the form of postures was included and integrated with yogic breathing ‘prana’ and elements of relaxation. The underlying purpose is to create physical flexibility, reduce pain and unpleasant stimuli and reduce negative thoughts and emotions to calm the mind and body, thereby improving well-being. In the healthcare literature, the benefits are reported to be far-reaching both for mental and physical health conditions such as anxiety, depression, cardiovascular disease, cancer and respiratory symptoms. It is also reported to reduce muscular-skeletal problems and physical symptoms through increasing the awareness of the physical body.

Yoga has become a global phenomenon and is widely practiced in many different forms. Generally, all types of yoga include some elements of relaxation. Additionally, some forms include mainly pranayama and others are more physical in nature. One such practice is vinyasa flow which involves using the inhale and exhale of the breathing pattern to move through a variety of yoga postures; this leads to the movement becoming meditative. The practice often includes pranayama followed by standing postures linked together with a movement called vinyasa, (similar to a sun salutation) which helps to keep the body moving and increases fitness, flexibility and helps maintain linkage with the breath. The practice also often includes a range of seated postures, an inversion (such as headstand or shoulder stand) and final relaxation ‘savasana’.

2.2.2. Mindfulness

Mindfulness involves being more present at the moment by acknowledging the here and now, often referred to as ‘being present’ rather than focussing on the past or future [ 8 ]. Being present may include being aware of our surroundings and the environment, or of what we are eating and drinking and physical sensations such as the sun or wind on our skin.

Acknowledging the thoughts and body are also aspects of mindfulness. Each day humans experience thousands of thoughts, the majority being of no consequence. In some instances, these thoughts are repetitive and negative in nature which can lead to increased stress and the related unpleasant physical symptoms such as feeling anxious, nausea and tension headaches. Being mindful includes an awareness of our thinking and whether we are caught up with our thoughts rather than being aware of the moment. Additionally, on a daily basis, awareness of the physical body may be minimal; being mindful includes increasing this awareness through becoming more connected with the sensations in the body. This might include experiencing the legs moving when walking, or feeling the ground under the feet or the natural way of the body whilst standing.

Mindfulness has been shown to be of benefit to physical and mental health. It is currently recommended by the National Institute for Clinical Excellence [ 22 ] as adjunctive therapy to Cognitive Behavioural Therapy (CBT) for the prevention of relapse depression.

However, it may be challenging for some individuals to do this with a multitude of distractions around them and, therefore, they may choose to identify a particular time and place when and where they can sit in a comfortable position to start to become aware of their breathing and bodily sensations.

2.2.3. Mobile Mindfulness Inspired By Tai-Chi—Pause

Tai-Chi is an internal Chinese martial art practiced for both its defense training, its health benefits and meditation. There is good evidence of benefits for depression, cardiac and stroke rehabilitation and dementia [ 23 ]. The term Tai-Chi refers to a philosophy of the forces of yin and yang, related to the moves. An iPhone application Pause inspired by Tai-Chi is used for guided mindfulness which draws upon the principles of mindfulness meditation to trigger the body’s rest and digest response, quickly restoring attention [ 24 ].

3. Related Work

Researchers have created the ability to detect stress in laboratory environments with medical-grade devices [ 25 , 26 , 27 , 28 ]; smartwatches and smart bands started to be used for stress level detection studies [ 29 , 30 , 31 ]. These devices provide high comfort and rich functionality for the users, but their stress detection accuracies are lower than medical-grade devices due to low signal quality and difficulty obtaining data in intense physical activity. If data are collected for long periods, researchers have shown that their detection performance improves [ 32 ]. During movement periods, the signal can be lost (gap in the data) or artifacts might be generated. Stress level detection accuracies for 2-classes by using these devices are around 70% [ 29 , 30 , 33 , 34 ].

After detecting the stress level of individuals, researchers should recover from the stressed state to the baseline state. To the best of our knowledge, there are very few studies that combine automatic stress detection (using physiological data) with recommended appropriate stress management techniques. Ahani et al. [ 35 ] examined the physiological effect of mindfulness. They used the Biosemi device which acquires electroencephalogram (EEG) and respiration signals. They successfully distinguished control (non-meditative state) and meditation states with machine learning algorithms. Karydis et al. [ 36 ] identified the post-meditation perceptual states by using a wearable EEG measurement device (Muse headband). Mason et al. [ 37 ] examined the effect of yoga on physiological signals. They used PortaPres Digital Plethtsmograph for measuring blood pressure and respiration signals. They also showed the positive effect of yoga by using these signals. A further study validated the positive effect of yoga with physiological signals; researchers monitored breathing and heart rate pulse with a piezoelectric belt and a pulse sensor [ 21 ]. They demonstrated the effectiveness of different yogic breathing patterns to help participants relax. There are also several studies showing the effectiveness of mobile mindfulness apps by using physiological signals [ 20 , 38 , 39 ]. Svetlov et al. [ 20 ] monitored the heart rate variability (HRV), electrodermal activity (EDA), Salivary alpha-amylase (sAA) and EEG values. In other studies, EEG and respiration signals were also used for validating the effect of mobile mindfulness apps [ 38 , 39 ]. When the literature is examined, it could be observed that the effect of ancient relaxation methods and mobile mindfulness methods are examined separately in different studies. Ancient methods generally require out of office environments that are not suitable for most of the population, since, in the modern age, people started to spend more time in office-like environments. On the other hand, some smartphone applications such as Pause, HeartMath and Calm do not require extra hardware or equipment and be applicable in office environments. Hence, an ideal solution depends on the context of individuals. A system that monitors stress levels, analyzes the context of individuals and suggests an appropriate relaxation method in the case of high stress will benefit society. Furthermore, mobile methods along with the ancient techniques should be applied in stressful real-life events and their effectiveness should be compared by investigating physiological signals. When the literature is examined, there is not any study comparing the performance of these methods in real-life events (see Table 1 ). Another important finding is that these methods should be compared with unobtrusive wearable devices so that they could be used for a biofeedback system in daily lives. Individuals may be reluctant to use a system with cables, electrodes and boards in their daily life. Therefore, a comparison of different states with such systems could not be used in daily life. There is clearly a need for a suggestion and comparison of ancient and mobile meditation methods by using algorithms that could run on unobtrusive devices. An ideal system should detect high stress levels, suggest relaxation methods and control whether users are doing these exercises right or not with unobtrusive devices. Our algorithm is suitable to be embedded in such daily life applicable systems that use physiological signals such as skin temperature (ST), HRV, EDA and accelerometer (ACC). In this paper, we present the findings of our pilot study that tested the use of our algorithm during general daily activities, stress reduction activities and a stressful event.

Comparison of our work with the studies applying different types of meditation techniques for stress management in the literature.

4. Methodology

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.

4.1.1. EDA Preprocessing Artifact Detection and Removal Methods

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.

4.1.2. EDA Feature Extraction Methods

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.

Tonic Component Features

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 ].

Phasic Component Features

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.

4.1.3. Heart Activity Preprocessing (Artifact Detection and Removal) and Feature Extraction Methods

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 ].

4.1.4. Accelerometer Feature Extraction Methods

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.

4.1.5. Skin Temperature

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.

4.1.6. Machine Learning Classifier Algorithms

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.

4.1.7. Dimensionality Reduction

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).

4.1.8. Insights from the Feature Selection Process

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.

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Top-ranking features selected for the HRV signal.

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Top-ranking features selected for the EDA and ACC signals.

4.2. Relaxation Method Suggestion by Analyzing the Physical Activity-Based Context

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 ).

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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.

4.3. Description of the Data Collection Procedure

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.

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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 .

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Time-line depicting eight days of the training event. Presentations, relaxations and lectures are highlighted.

4.3.1. Physiological Stress Data

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.

4.3.2. Ethics

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.

4.3.3. Questionnaire Self-Report Stress Data

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).

4.3.4. Stress Management Scheme Using 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.

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Application of James Gross’s Emotion Regulation model [ 4 ] in the context of stress management.

5. Experimental Results and Discussion

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).

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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.

5.1.2. Before and After Physiological Measurements for Evaluating Performance of Yoga and Mindfulness with Blood Pressure

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).

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.

5.2. Physiological Stress Level Detection with Wearables by Using Context Labels as the Class Label

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.

5.2.1. Effect of Different Physiological Signals on Stress Detection

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).

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).

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).

5.2.2. Effectiveness of Yoga, Mindfulness and Mobile Mindfulness (Pause)

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.

The classification accuracy of the relaxation sessions using stress management methods and stressful sessions using HRV.

6. Conclusions

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.

Acknowledgments

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.

Author Contributions

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.

Conflicts of Interest

The authors declare no conflict of interest.

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