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Top 10 Stress Management Techniques for Students

Elizabeth Scott, PhD is an author, workshop leader, educator, and award-winning blogger on stress management, positive psychology, relationships, and emotional wellbeing.

Akeem Marsh, MD, is a board-certified child, adolescent, and adult psychiatrist who has dedicated his career to working with medically underserved communities.

Most students experience significant amounts of stress. This can significantly affect their health, happiness, relationships, and grades. Learning stress management techniques can help these students avoid negative effects in these areas.

Why Stress Management Is Important for Students

A study by the American Psychological Association (APA) found that teens report stress levels similar to adults. This means teens are experiencing significant levels of chronic stress and feel their stress levels generally exceed their ability to cope effectively .

Roughly 30% of the teens reported feeling overwhelmed, depressed, or sad because of their stress.

Stress can also affect health-related behaviors. Stressed students are more likely to have problems with disrupted sleep, poor diet, and lack of exercise. This is understandable given that nearly half of APA survey respondents reported completing three hours of homework per night in addition to their full day of school work and extracurriculars.

Common Causes of Student Stress

Another study found that much of high school students' stress originates from school and activities, and that this chronic stress can persist into college years and lead to academic disengagement and mental health problems.

Top Student Stressors

Common sources of student stress include:

• Extracurricular activities
• Social challenges
• Transitions (e.g., graduating, moving out , living independently)
• Relationships
• Pressure to succeed

High school students face the intense competitiveness of taking challenging courses, amassing impressive extracurriculars, studying and acing college placement tests, and deciding on important and life-changing plans for their future. At the same time, they have to navigate the social challenges inherent to the high school experience.

This stress continues if students decide to attend college. Stress is an unavoidable part of life, but research has found that increased daily stressors put college-aged young adults at a higher risk for stress than other age groups.

Making new friends, handling a more challenging workload, feeling pressured to succeed, being without parental support, and navigating the stresses of more independent living are all added challenges that make this transition more difficult. Romantic relationships always add an extra layer of potential stress.

Students often recognize that they need to relieve stress . However, all the activities and responsibilities that fill a student’s schedule sometimes make it difficult to find the time to try new stress relievers to help dissipate that stress.

10 Stress Management Techniques for Students

Here you will learn 10 stress management techniques for students. These options are relatively easy, quick, and relevant to a student’s life and types of stress .

Get Enough Sleep

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Students, with their packed schedules, are notorious for missing sleep. Unfortunately, operating in a sleep-deprived state puts you at a distinct disadvantage. You’re less productive, may find it more difficult to learn, and may even be a hazard behind the wheel.

Research suggests that sleep deprivation and daytime sleepiness are also linked to impaired mood, higher risk for car accidents, lower grade point averages, worse learning, and a higher risk of academic failure.

Don't neglect your sleep schedule. Aim to get at least 8 hours a night and take power naps when needed.

Use Guided Imagery

David Malan / Getty Images

Guided imagery can also be a useful and effective tool to help stressed students cope with academic, social, and other stressors. Visualizations can help you calm down, detach from what’s stressing you, and reduce your body’s stress response.

You can use guided imagery to relax your body by sitting in a quiet, comfortable place, closing your eyes, and imagining a peaceful scene. Spend several minutes relaxing as you enjoy mentally basking in your restful image.

Consider trying a guided imagery app if you need extra help visualizing a scene and inducting a relaxation response. Research suggests that such tools might be an affordable and convenient way to reduce stress.

Exercise Regularly

One of the healthiest ways to blow off steam is to get regular exercise . Research has found that students who participate in regular physical activity report lower levels of perceived stress. While these students still grapple with the same social, academic, and life pressures as their less-active peers, these challenges feel less stressful and are easier to manage.

Finding time for exercise might be a challenge, but there are strategies that you can use to add more physical activity to your day. Some ideas that you might try include:

• Doing yoga in the morning
• Walking or biking to class
• Reviewing for tests with a friend while walking on a treadmill at the gym
• Taking an elective gym class focused on leisure sports or exercise
• Joining an intramural sport

Exercise can help buffer against the negative effects of student stress. Starting now and keeping a regular exercise practice throughout your lifetime can help you live longer and enjoy your life more.

Take Calming Breaths

When your body is experiencing a stress response, you’re often not thinking as clearly as you could be. You are also likely not breathing properly. You might be taking short, shallow breaths. When you breathe improperly, it upsets the exchange of oxygen and carbon dioxide in your body.

Studies suggest this imbalance can contribute to various physical symptoms, including increased anxiety, fatigue, stress, emotional problems, and panic attacks.

A quick way to calm down is to practice breathing exercises . These can be done virtually anywhere to relieve stress in minutes.

Because they are fast-acting, breathing exercises are a great way to cope with moments of acute stress , such as right before an exam or presentation. But they can also help manage longer-lasting stress such as dealing with relationships, work, or financial problems.

Practice Progressive Muscle Relaxation (PMR)

Another great stress management technique for students that can be used during tests, before bed, or at other times when stress has you physically wound up is progressive muscle relaxation ( PMR ).

This technique involves tensing and relaxing all muscles until the body is completely relaxed. With practice, you can learn to release stress from your body in seconds. This can be particularly helpful for students because it can be adapted to help relaxation efforts before sleep for a deeper sleep.

Once a person learns how to use PMR effectively, it can be a quick and handy way to induce relaxation in any stressful situation, such as bouts of momentary panic before a speech or exam, dealing with a disagreement with your roommate, or preparing to discuss a problem with your academic advisor.

Listen to Music

A convenient stress reliever that has also shown many cognitive benefits, music can help relieve stress and calm yourself down or stimulate your mind depending on what you need in the moment.

Research has found that playing upbeat music can improve processing speed and memory. Stressed students may find that listening to relaxing music can help calm the body and mind. One study found that students who listened to the sounds of relaxing music were able to recover more quickly after a stressful situation.

Students can harness the benefits of music by playing classical music while studying, playing upbeat music to "wake up" mentally, or relaxing with the help of their favorite slow melodies.

Build Your Support Network

Halfpoint Images / Getty Images

Having emotional support can help create a protective buffer against stress. Unfortunately,  interpersonal relationships can also sometimes be a source of anxiety for students. Changes in friendships, romantic breakups, and life transitions such as moving away for college can create significant upheaval and stress for students.

One way to combat feelings of loneliness and make sure that you have people to lean on in times of need is to expand your support network and nurture your relationships.

Look for opportunities to meet new people, whether it involves joining study groups or participating in other academic, social, and leisure activities.

Remember that different types of relationships offer differing types of support . Your relationships with teachers, counselors, and mentors can be a great source of information and resources that may help you academically. Relationships with friends can provide emotional and practical support.

Widening your social circle can combat student stress on various fronts and ensure you have what you need to succeed.

Eat a Healthy Diet

Niedring/Drentwett / Getty Images

You may not realize it, but your diet can either boost your brainpower or sap you of mental energy. It can also make you more reactive to the stress in your life. As a result, you might find yourself turning to high-sugar, high-fat snacks to provide a temporary sense of relief.

A healthy diet can help combat stress in several ways. Improving your diet can keep you from experiencing diet-related mood swings, light-headedness, and more.

Unfortunately, students are often prone to poor dietary habits. Feelings of stress can make it harder to stick to a consistently healthy diet, but other concerns such as finances, access to cooking facilities, and time to prepare healthy meals can make it more challenging for students.

Some tactics that can help students make healthy choices include:

• Eating regularly
• Carrying a water bottle to class
• Keeping healthy snacks such as fruits and nuts handy
• Limiting caffeine, nicotine, and alcohol intake

Find Ways to Minimize Stress

One way to improve your ability to manage student stress is to look for ways you cut stress out of your life altogether. Evaluate the things that are bringing stress or anxiety into your life. Are they necessary? Are they providing more benefits than the toll they take on your mental health? If the answer is no, sometimes the best option is just to ditch them altogether.

This might mean cutting some extracurricular activities out of your schedule. It might mean limiting your use of social media. Or it might mean learning to say no to requests for your time, energy, and resources. 

While it might be challenging at first, learning how to prioritize yourself and your mental well-being is an important step toward reducing your stress.

Try Mindfulness

When you find yourself dealing with stress—whether it's due to academics, relationships, financial pressures, or social challenges—becoming more aware of how you feel in the moment may help you respond more effectively.

Mindfulness involves becoming more aware of the present moment. Rather than judging, reacting, or avoiding problems, the goal is to focus on the present, become more aware of how you are feeling, observe your reactions, and accept these feelings without passing judgment on them.

Research suggests that mindfulness-based stress management practices can be a useful tool for reducing student stress. Such strategies may also help reduce feelings of anxiety and depression.

A Word From Verywell

It is important to remember that stress isn't the same for everyone. Figuring out what works for you may take some trial and error. A good start is to ensure that you are taking care of yourself physically and emotionally and to experiment with different stress relief strategies to figure out what works best to help you feel less stressed.

If stress and anxiety are causing distress or making it difficult to function in your daily life, it is important to seek help. Many schools offer resources that can help, including face-to-face and online mental health services. You might start by talking to your school counselor or student advisor about the stress you are coping with. You can also talk to a parent, another trusted adult, or your doctor.

If you or a loved one are struggling with anxiety, contact the  Substance Abuse and Mental Health Services Administration (SAMHSA) National Helpline  at 1-800-662-4357 for information on support and treatment facilities in your area.

For more mental health resources, see our  National Helpline Database .

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Hershner SD, Chervin RD. Causes and consequences of sleepiness among college students . Nat Sci Sleep . 2014;6:73-84. doi:10.2147/NSS.S62907

Gordon JS, Sbarra D, Armin J, Pace TWW, Gniady C, Barraza Y. Use of a guided imagery mobile app (See Me Serene) to reduce COVID-19-related stress: Pilot feasibility study . JMIR Form Res . 2021;5(10):e32353. doi:10.2196/32353

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By Elizabeth Scott, PhD Elizabeth Scott, PhD is an author, workshop leader, educator, and award-winning blogger on stress management, positive psychology, relationships, and emotional wellbeing.

• Open access
• Published: 17 April 2024

Deciphering the influence: academic stress and its role in shaping learning approaches among nursing students: a cross-sectional study

• Rawhia Salah Dogham 1 ,
• Heba Fakieh Mansy Ali 1 ,
• Asmaa Saber Ghaly 3 ,
• Nermine M. Elcokany 2 ,
• Mohamed Mahmoud Seweid 4 &
• Ayman Mohamed El-Ashry   ORCID: orcid.org/0000-0001-7718-4942 5  

BMC Nursing volume  23 , Article number:  249 ( 2024 ) Cite this article

316 Accesses

Metrics details

Nursing education presents unique challenges, including high levels of academic stress and varied learning approaches among students. Understanding the relationship between academic stress and learning approaches is crucial for enhancing nursing education effectiveness and student well-being.

This study aimed to investigate the prevalence of academic stress and its correlation with learning approaches among nursing students.

Design and Method

A cross-sectional descriptive correlation research design was employed. A convenient sample of 1010 nursing students participated, completing socio-demographic data, the Perceived Stress Scale (PSS), and the Revised Study Process Questionnaire (R-SPQ-2 F).

Most nursing students experienced moderate academic stress (56.3%) and exhibited moderate levels of deep learning approaches (55.0%). Stress from a lack of professional knowledge and skills negatively correlates with deep learning approaches (r = -0.392) and positively correlates with surface learning approaches (r = 0.365). Female students showed higher deep learning approach scores, while male students exhibited higher surface learning approach scores. Age, gender, educational level, and academic stress significantly influenced learning approaches.

Academic stress significantly impacts learning approaches among nursing students. Strategies addressing stressors and promoting healthy learning approaches are essential for enhancing nursing education and student well-being.

Nursing implication

Understanding academic stress’s impact on nursing students’ learning approaches enables tailored interventions. Recognizing stressors informs strategies for promoting adaptive coping, fostering deep learning, and creating supportive environments. Integrating stress management, mentorship, and counseling enhances student well-being and nursing education quality.

Peer Review reports

Introduction

Nursing education is a demanding field that requires students to acquire extensive knowledge and skills to provide competent and compassionate care. Nursing education curriculum involves high-stress environments that can significantly impact students’ learning approaches and academic performance [ 1 , 2 ]. Numerous studies have investigated learning approaches in nursing education, highlighting the importance of identifying individual students’ preferred approaches. The most studied learning approaches include deep, surface, and strategic approaches. Deep learning approaches involve students actively seeking meaning, making connections, and critically analyzing information. Surface learning approaches focus on memorization and reproducing information without a more profound understanding. Strategic learning approaches aim to achieve high grades by adopting specific strategies, such as memorization techniques or time management skills [ 3 , 4 , 5 ].

Nursing education stands out due to its focus on practical training, where the blend of academic and clinical coursework becomes a significant stressor for students, despite academic stress being shared among all university students [ 6 , 7 , 8 ]. Consequently, nursing students are recognized as prone to high-stress levels. Stress is the physiological and psychological response that occurs when a biological control system identifies a deviation between the desired (target) state and the actual state of a fitness-critical variable, whether that discrepancy arises internally or externally to the human [ 9 ]. Stress levels can vary from objective threats to subjective appraisals, making it a highly personalized response to circumstances. Failure to manage these demands leads to stress imbalance [ 10 ].

Nursing students face three primary stressors during their education: academic, clinical, and personal/social stress. Academic stress is caused by the fear of failure in exams, assessments, and training, as well as workload concerns [ 11 ]. Clinical stress, on the other hand, arises from work-related difficulties such as coping with death, fear of failure, and interpersonal dynamics within the organization. Personal and social stressors are caused by an imbalance between home and school, financial hardships, and other factors. Throughout their education, nursing students have to deal with heavy workloads, time constraints, clinical placements, and high academic expectations. Multiple studies have shown that nursing students experience higher stress levels compared to students in other fields [ 12 , 13 , 14 ].

Research has examined the relationship between academic stress and coping strategies among nursing students, but no studies focus specifically on the learning approach and academic stress. However, existing literature suggests that students interested in nursing tend to experience lower levels of academic stress [ 7 ]. Therefore, interest in nursing can lead to deep learning approaches, which promote a comprehensive understanding of the subject matter, allowing students to feel more confident and less overwhelmed by coursework and exams. Conversely, students employing surface learning approaches may experience higher stress levels due to the reliance on memorization [ 3 ].

Understanding the interplay between academic stress and learning approaches among nursing students is essential for designing effective educational interventions. Nursing educators can foster deep learning approaches by incorporating active learning strategies, critical thinking exercises, and reflection activities into the curriculum [ 15 ]. Creating supportive learning environments encouraging collaboration, self-care, and stress management techniques can help alleviate academic stress. Additionally, providing mentorship and counselling services tailored to nursing students’ unique challenges can contribute to their overall well-being and academic success [ 16 , 17 , 18 ].

Despite the scarcity of research focusing on the link between academic stress and learning methods in nursing students, it’s crucial to identify the unique stressors they encounter. The intensity of these stressors can be connected to the learning strategies employed by these students. Academic stress and learning approach are intertwined aspects of the student experience. While academic stress can influence learning approaches, the choice of learning approach can also impact the level of academic stress experienced. By understanding this relationship and implementing strategies to promote healthy learning approaches and manage academic stress, educators and institutions can foster an environment conducive to deep learning and student well-being.

Hence, this study aims to investigate the correlation between academic stress and learning approaches experienced by nursing students.

Study objectives

Assess the levels of academic stress among nursing students.

Assess the learning approaches among nursing students.

Identify the relationship between academic stress and learning approach among nursing students.

Identify the effect of academic stress and related factors on learning approach and among nursing students.

Materials and methods

Research design.

A cross-sectional descriptive correlation research design adhering to the STROBE guidelines was used for this study.

A research project was conducted at Alexandria Nursing College, situated in Egypt. The college adheres to the national standards for nursing education and functions under the jurisdiction of the Egyptian Ministry of Higher Education. Alexandria Nursing College comprises nine specialized nursing departments that offer various nursing specializations. These departments include Nursing Administration, Community Health Nursing, Gerontological Nursing, Medical-Surgical Nursing, Critical Care Nursing, Pediatric Nursing, Obstetric and Gynecological Nursing, Nursing Education, and Psychiatric Nursing and Mental Health. The credit hour system is the fundamental basis of both undergraduate and graduate programs. This framework guarantees a thorough evaluation of academic outcomes by providing an organized structure for tracking academic progress and conducting analyses.

Participants and sample size calculation

The researchers used the Epi Info 7 program to calculate the sample size. The calculations were based on specific parameters such as a population size of 9886 students for the academic year 2022–2023, an expected frequency of 50%, a maximum margin of error of 5%, and a confidence coefficient of 99.9%. Based on these parameters, the program indicated that a minimum sample size of 976 students was required. As a result, the researchers recruited a convenient sample of 1010 nursing students from different academic levels during the 2022–2023 academic year [ 19 ]. This sample size was larger than the minimum required, which could help to increase the accuracy and reliability of the study results. Participation in the study required enrollment in a nursing program and voluntary agreement to take part. The exclusion criteria included individuals with mental illnesses based on their response and those who failed to complete the questionnaires.

socio-demographic data that include students’ age, sex, educational level, hours of sleep at night, hours spent studying, and GPA from the previous semester.

Tool two: the perceived stress scale (PSS)

It was initially created by Sheu et al. (1997) to gauge the level and nature of stress perceived by nursing students attending Taiwanese universities [ 20 ]. It comprises 29 items rated on a 5-point Likert scale, where (0 = never, 1 = rarely, 2 = sometimes, 3 = reasonably often, and 4 = very often), with a total score ranging from 0 to 116. The cut-off points of levels of perceived stress scale according to score percentage were low < 33.33%, moderate 33.33–66.66%, and high more than 66.66%. Higher scores indicate higher stress levels. The items are categorized into six subscales reflecting different sources of stress. The first subscale assesses “stress stemming from lack of professional knowledge and skills” and includes 3 items. The second subscale evaluates “stress from caring for patients” with 8 items. The third subscale measures “stress from assignments and workload” with 5 items. The fourth subscale focuses on “stress from interactions with teachers and nursing staff” with 6 items. The fifth subscale gauges “stress from the clinical environment” with 3 items. The sixth subscale addresses “stress from peers and daily life” with 4 items. El-Ashry et al. (2022) reported an excellent internal consistency reliability of 0.83 [ 21 ]. Two bilingual translators translated the English version of the scale into Arabic and then back-translated it into English by two other independent translators to verify its accuracy. The suitability of the translated version was confirmed through a confirmatory factor analysis (CFA), which yielded goodness-of-fit indices such as a comparative fit index (CFI) of 0.712, a Tucker-Lewis index (TLI) of 0.812, and a root mean square error of approximation (RMSEA) of 0.100.

Tool three: revised study process questionnaire (R-SPQ-2 F)

It was developed by Biggs et al. (2001). It examines deep and surface learning approaches using only 20 questions; each subscale contains 10 questions [ 22 ]. On a 5-point Likert scale ranging from 0 (never or only rarely true of me) to 4 (always or almost always accurate of me). The total score ranged from 0 to 80, with a higher score reflecting more deep or surface learning approaches. The cut-off points of levels of revised study process questionnaire according to score percentage were low < 33%, moderate 33–66%, and high more than 66%. Biggs et al. (2001) found that Cronbach alpha value was 0.73 for deep learning approach and 0.64 for the surface learning approach, which was considered acceptable. Two translators fluent in English and Arabic initially translated a scale from English to Arabic. To ensure the accuracy of the translation, they translated it back into English. The translated version’s appropriateness was evaluated using a confirmatory factor analysis (CFA). The CFA produced several goodness-of-fit indices, including a Comparative Fit Index (CFI) of 0.790, a Tucker-Lewis Index (TLI) of 0.912, and a Root Mean Square Error of Approximation (RMSEA) of 0.100. Comparative Fit Index (CFI) of 0.790, a Tucker-Lewis Index (TLI) of 0.912, and a Root Mean Square Error of Approximation (RMSEA) of 0.100.

Ethical considerations

The Alexandria University College of Nursing’s Research Ethics Committee provided ethical permission before the study’s implementation. Furthermore, pertinent authorities acquired ethical approval at participating nursing institutions. The vice deans of the participating institutions provided written informed consent attesting to institutional support and authority. By giving written informed consent, participants confirmed they were taking part voluntarily. Strict protocols were followed to protect participants’ privacy during the whole investigation. The obtained personal data was kept private and available only to the study team. Ensuring participants’ privacy and anonymity was of utmost importance.

Tools validity

The researchers created tool one after reviewing pertinent literature. Two bilingual translators independently translated the English version into Arabic to evaluate the applicability of the academic stress and learning approach tools for Arabic-speaking populations. To assure accuracy, two additional impartial translators back-translated the translation into English. They were also assessed by a five-person jury of professionals from the education and psychiatric nursing departments. The scales were found to have sufficiently evaluated the intended structures by the jury.

Pilot study

A preliminary investigation involved 100 nursing student applicants, distinct from the final sample, to gauge the efficacy, clarity, and potential obstacles in utilizing the research instruments. The pilot findings indicated that the instruments were accurate, comprehensible, and suitable for the target demographic. Additionally, Cronbach’s Alpha was utilized to further assess the instruments’ reliability, demonstrating internal solid consistency for both the learning approaches and academic stress tools, with values of 0.91 and 0.85, respectively.

Data collection

The researchers convened with each qualified student in a relaxed, unoccupied classroom in their respective college settings. Following a briefing on the study’s objectives, the students filled out the datasheet. The interviews typically lasted 15 to 20 min.

Data analysis

The data collected were analyzed using IBM SPSS software version 26.0. Following data entry, a thorough examination and verification were undertaken to ensure accuracy. The normality of quantitative data distributions was assessed using Kolmogorov-Smirnov tests. Cronbach’s Alpha was employed to evaluate the reliability and internal consistency of the study instruments. Descriptive statistics, including means (M), standard deviations (SD), and frequencies/percentages, were computed to summarize academic stress and learning approaches for categorical data. Student’s t-tests compared scores between two groups for normally distributed variables, while One-way ANOVA compared scores across more than two categories of a categorical variable. Pearson’s correlation coefficient determined the strength and direction of associations between customarily distributed quantitative variables. Hierarchical regression analysis identified the primary independent factors influencing learning approaches. Statistical significance was determined at the 5% (p < 0.05).

Table  1 presents socio-demographic data for a group of 1010 nursing students. The age distribution shows that 38.8% of the students were between 18 and 21 years old, 32.9% were between 21 and 24 years old, and 28.3% were between 24 and 28 years old, with an average age of approximately 22.79. Regarding gender, most of the students were female (77%), while 23% were male. The students were distributed across different educational years, a majority of 34.4% in the second year, followed by 29.4% in the fourth year. The students’ hours spent studying were found to be approximately two-thirds (67%) of the students who studied between 3 and 6 h. Similarly, sleep patterns differ among the students; more than three-quarters (77.3%) of students sleep between 5- to more than 7 h, and only 2.4% sleep less than 2 h per night. Finally, the student’s Grade Point Average (GPA) from the previous semester was also provided. 21% of the students had a GPA between 2 and 2.5, 40.9% had a GPA between 2.5 and 3, and 38.1% had a GPA between 3 and 3.5.

Figure  1 provides the learning approach level among nursing students. In terms of learning approach, most students (55.0%) exhibited a moderate level of deep learning approach, followed by 25.9% with a high level and 19.1% with a low level. The surface learning approach was more prevalent, with 47.8% of students showing a moderate level, 41.7% showing a low level, and only 10.5% exhibiting a high level.

Nursing students? levels of learning approach (N=1010)

Figure  2 provides the types of academic stress levels among nursing students. Among nursing students, various stressors significantly impact their academic experiences. Foremost among these stressors are the pressure and demands associated with academic assignments and workload, with 30.8% of students attributing their high stress levels to these factors. Challenges within the clinical environment are closely behind, contributing significantly to high stress levels among 25.7% of nursing students. Interactions with peers and daily life stressors also weigh heavily on students, ranking third among sources of high stress, with 21.5% of students citing this as a significant factor. Similarly, interaction with teachers and nursing staff closely follow, contributing to high-stress levels for 20.3% of nursing students. While still significant, stress from taking care of patients ranks slightly lower, with 16.7% of students reporting it as a significant factor contributing to their academic stress. At the lowest end of the ranking, but still notable, is stress from a perceived lack of professional knowledge and skills, with 15.9% of students experiencing high stress in this area.

Nursing students? levels of academic stress subtypes (N=1010)

Figure  3 provides the total levels of academic stress among nursing students. The majority of students experienced moderate academic stress (56.3%), followed by those experiencing low academic stress (29.9%), and a minority experienced high academic stress (13.8%).

Nursing students? levels of total academic stress (N=1010)

Table  2 displays the correlation between academic stress subscales and deep and surface learning approaches among 1010 nursing students. All stress subscales exhibited a negative correlation regarding the deep learning approach, indicating that the inclination toward deep learning decreases with increasing stress levels. The most significant negative correlation was observed with stress stemming from the lack of professional knowledge and skills (r=-0.392, p < 0.001), followed by stress from the clinical environment (r=-0.109, p = 0.001), stress from assignments and workload (r=-0.103, p = 0.001), stress from peers and daily life (r=-0.095, p = 0.002), and stress from patient care responsibilities (r=-0.093, p = 0.003). The weakest negative correlation was found with stress from interactions with teachers and nursing staff (r=-0.083, p = 0.009). Conversely, concerning the surface learning approach, all stress subscales displayed a positive correlation, indicating that heightened stress levels corresponded with an increased tendency toward superficial learning. The most substantial positive correlation was observed with stress related to the lack of professional knowledge and skills (r = 0.365, p < 0.001), followed by stress from patient care responsibilities (r = 0.334, p < 0.001), overall stress (r = 0.355, p < 0.001), stress from interactions with teachers and nursing staff (r = 0.262, p < 0.001), stress from assignments and workload (r = 0.262, p < 0.001), and stress from the clinical environment (r = 0.254, p < 0.001). The weakest positive correlation was noted with stress stemming from peers and daily life (r = 0.186, p < 0.001).

Table  3 outlines the association between the socio-demographic characteristics of nursing students and their deep and surface learning approaches. Concerning age, statistically significant differences were observed in deep and surface learning approaches (F = 3.661, p = 0.003 and F = 7.983, p < 0.001, respectively). Gender also demonstrated significant differences in deep and surface learning approaches (t = 3.290, p = 0.001 and t = 8.638, p < 0.001, respectively). Female students exhibited higher scores in the deep learning approach (31.59 ± 8.28) compared to male students (29.59 ± 7.73), while male students had higher scores in the surface learning approach (29.97 ± 7.36) compared to female students (24.90 ± 7.97). Educational level exhibited statistically significant differences in deep and surface learning approaches (F = 5.599, p = 0.001 and F = 17.284, p < 0.001, respectively). Both deep and surface learning approach scores increased with higher educational levels. The duration of study hours demonstrated significant differences only in the surface learning approach (F = 3.550, p = 0.014), with scores increasing as study hours increased. However, no significant difference was observed in the deep learning approach (F = 0.861, p = 0.461). Hours of sleep per night and GPA from the previous semester did not exhibit statistically significant differences in deep or surface learning approaches.

Table  4 presents a multivariate linear regression analysis examining the factors influencing the learning approach among 1110 nursing students. The deep learning approach was positively influenced by age, gender (being female), educational year level, and stress from teachers and nursing staff, as indicated by their positive coefficients and significant p-values (p < 0.05). However, it was negatively influenced by stress from a lack of professional knowledge and skills. The other factors do not significantly influence the deep learning approach. On the other hand, the surface learning approach was positively influenced by gender (being female), educational year level, stress from lack of professional knowledge and skills, stress from assignments and workload, and stress from taking care of patients, as indicated by their positive coefficients and significant p-values (p < 0.05). However, it was negatively influenced by gender (being male). The other factors do not significantly influence the surface learning approach. The adjusted R-squared values indicated that the variables in the model explain 17.8% of the variance in the deep learning approach and 25.5% in the surface learning approach. Both models were statistically significant (p < 0.001).

Nursing students’ academic stress and learning approaches are essential to planning for effective and efficient learning. Nursing education also aims to develop knowledgeable and competent students with problem-solving and critical-thinking skills.

The study’s findings highlight the significant presence of stress among nursing students, with a majority experiencing moderate to severe levels of academic stress. This aligns with previous research indicating that academic stress is prevalent among nursing students. For instance, Zheng et al. (2022) observed moderated stress levels in nursing students during clinical placements [ 23 ], while El-Ashry et al. (2022) found that nearly all first-year nursing students in Egypt experienced severe academic stress [ 21 ]. Conversely, Ali and El-Sherbini (2018) reported that over three-quarters of nursing students faced high academic stress. The complexity of the nursing program likely contributes to these stress levels [ 24 ].

The current study revealed that nursing students identified the highest sources of academic stress as workload from assignments and the stress of caring for patients. This aligns with Banu et al.‘s (2015) findings, where academic demands, assignments, examinations, high workload, and combining clinical work with patient interaction were cited as everyday stressors [ 25 ]. Additionally, Anaman-Torgbor et al. (2021) identified lectures, assignments, and examinations as predictors of academic stress through logistic regression analysis. These stressors may stem from nursing programs emphasizing the development of highly qualified graduates who acquire knowledge, values, and skills through classroom and clinical experiences [ 26 ].

The results regarding learning approaches indicate that most nursing students predominantly employed the deep learning approach. Despite acknowledging a surface learning approach among the participants in the present study, the prevalence of deep learning was higher. This inclination toward the deep learning approach is anticipated in nursing students due to their engagement with advanced courses, requiring retention, integration, and transfer of information at elevated levels. The deep learning approach correlates with a gratifying learning experience and contributes to higher academic achievements [ 3 ]. Moreover, the nursing program’s emphasis on active learning strategies fosters critical thinking, problem-solving, and decision-making skills. These findings align with Mahmoud et al.‘s (2019) study, reporting a significant presence (83.31%) of the deep learning approach among undergraduate nursing students at King Khalid University’s Faculty of Nursing [ 27 ]. Additionally, Mohamed &Morsi (2019) found that most nursing students at Benha University’s Faculty of Nursing embraced the deep learning approach (65.4%) compared to the surface learning approach [ 28 ].

The study observed a negative correlation between the deep learning approach and the overall mean stress score, contrasting with a positive correlation between surface learning approaches and overall stress levels. Elevated academic stress levels may diminish motivation and engagement in the learning process, potentially leading students to feel overwhelmed, disinterested, or burned out, prompting a shift toward a surface learning approach. This finding resonates with previous research indicating that nursing students who actively seek positive academic support strategies during academic stress have better prospects for success than those who do not [ 29 ]. Nebhinani et al. (2020) identified interface concerns and academic workload as significant stress-related factors. Notably, only an interest in nursing demonstrated a significant association with stress levels, with participants interested in nursing primarily employing adaptive coping strategies compared to non-interested students.

The current research reveals a statistically significant inverse relationship between different dimensions of academic stress and adopting the deep learning approach. The most substantial negative correlation was observed with stress arising from a lack of professional knowledge and skills, succeeded by stress associated with the clinical environment, assignments, and workload. Nursing students encounter diverse stressors, including delivering patient care, handling assignments and workloads, navigating challenging interactions with staff and faculty, perceived inadequacies in clinical proficiency, and facing examinations [ 30 ].

In the current study, the multivariate linear regression analysis reveals that various factors positively influence the deep learning approach, including age, female gender, educational year level, and stress from teachers and nursing staff. In contrast, stress from a lack of professional knowledge and skills exert a negative influence. Conversely, the surface learning approach is positively influenced by female gender, educational year level, stress from lack of professional knowledge and skills, stress from assignments and workload, and stress from taking care of patients, but negatively affected by male gender. The models explain 17.8% and 25.5% of the variance in the deep and surface learning approaches, respectively, and both are statistically significant. These findings underscore the intricate interplay of demographic and stress-related factors in shaping nursing students’ learning approaches. High workloads and patient care responsibilities may compel students to prioritize completing tasks over deep comprehension. This pressure could lead to a surface learning approach as students focus on meeting immediate demands rather than engaging deeply with course material. This observation aligns with the findings of Alsayed et al. (2021), who identified age, gender, and study year as significant factors influencing students’ learning approaches.

Deep learners often demonstrate better self-regulation skills, such as effective time management, goal setting, and seeking support when needed. These skills can help manage academic stress and maintain a balanced learning approach. These are supported by studies that studied the effect of coping strategies on stress levels [ 6 , 31 , 32 ]. On the contrary, Pacheco-Castillo et al. study (2021) found a strong significant relationship between academic stressors and students’ level of performance. That study also proved that the more academic stress a student faces, the lower their academic achievement.

Strengths and limitations of the study

This study has lots of advantages. It provides insightful information about the educational experiences of Egyptian nursing students, a demographic that has yet to receive much research. The study’s limited generalizability to other people or nations stems from its concentration on this particular group. This might be addressed in future studies by using a more varied sample. Another drawback is the dependence on self-reported metrics, which may contain biases and mistakes. Although the cross-sectional design offers a moment-in-time view of the problem, it cannot determine causation or evaluate changes over time. To address this, longitudinal research may be carried out.

Notwithstanding these drawbacks, the study substantially contributes to the expanding knowledge of academic stress and nursing students’ learning styles. Additional research is needed to determine teaching strategies that improve deep-learning approaches among nursing students. A qualitative study is required to analyze learning approaches and factors that may influence nursing students’ selection of learning approaches.

According to the present study’s findings, nursing students encounter considerable academic stress, primarily stemming from heavy assignments and workload, as well as interactions with teachers and nursing staff. Additionally, it was observed that students who experience lower levels of academic stress typically adopt a deep learning approach, whereas those facing higher stress levels tend to resort to a surface learning approach. Demographic factors such as age, gender, and educational level influence nursing students’ choice of learning approach. Specifically, female students are more inclined towards deep learning, whereas male students prefer surface learning. Moreover, deep and surface learning approach scores show an upward trend with increasing educational levels and study hours. Academic stress emerges as a significant determinant shaping the adoption of learning approaches among nursing students.

Implications in nursing practice

Nursing programs should consider integrating stress management techniques into their curriculum. Providing students with resources and skills to cope with academic stress can improve their well-being and academic performance. Educators can incorporate teaching strategies that promote deep learning approaches, such as problem-based learning, critical thinking exercises, and active learning methods. These approaches help students engage more deeply with course material and reduce reliance on surface learning techniques. Recognizing the gender differences in learning approaches, nursing programs can offer gender-specific support services and resources. For example, providing targeted workshops or counseling services that address male and female nursing students’ unique stressors and learning needs. Implementing mentorship programs and peer support groups can create a supportive environment where students can share experiences, seek advice, and receive encouragement from their peers and faculty members. Encouraging students to reflect on their learning processes and identify effective study strategies can help them develop metacognitive skills and become more self-directed learners. Faculty members can facilitate this process by incorporating reflective exercises into the curriculum. Nursing faculty and staff should receive training on recognizing signs of academic stress among students and providing appropriate support and resources. Additionally, professional development opportunities can help educators stay updated on evidence-based teaching strategies and practical interventions for addressing student stress.

Data availability

The datasets generated and/or analysed during the current study are not publicly available due to restrictions imposed by the institutional review board to protect participant confidentiality, but are available from the corresponding author on reasonable request.

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Acknowledgements

Our sincere thanks go to all the nursing students in the study. We also want to thank Dr/ Rasha Badry for their statistical analysis help and contribution to this study.

The research was not funded by public, commercial, or non-profit organizations.

Open access funding provided by The Science, Technology & Innovation Funding Authority (STDF) in cooperation with The Egyptian Knowledge Bank (EKB).

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Ayman M. El-Ashry & Rawhia S. Dogham: conceptualization, preparation, and data collection; methodology; investigation; formal analysis; data analysis; writing-original draft; writing-manuscript; and editing. Heba F. Mansy Ali & Asmaa S. Ghaly: conceptualization, preparation, methodology, investigation, writing-original draft, writing-review, and editing. Nermine M. Elcokany & Mohamed M. Seweid: Methodology, investigation, formal analysis, data collection, writing-manuscript & editing. All authors reviewed the manuscript and accept for publication.

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Dogham, R.S., Ali, H.F.M., Ghaly, A.S. et al. Deciphering the influence: academic stress and its role in shaping learning approaches among nursing students: a cross-sectional study. BMC Nurs 23 , 249 (2024). https://doi.org/10.1186/s12912-024-01885-1

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Mental health and the pandemic: What U.S. surveys have found

The coronavirus pandemic has been associated with worsening mental health among people in the United States and around the world . In the U.S, the COVID-19 outbreak in early 2020 caused widespread lockdowns and disruptions in daily life while triggering a short but severe economic recession that resulted in widespread unemployment. Three years later, Americans have largely returned to normal activities, but challenges with mental health remain.

Here’s a look at what surveys by Pew Research Center and other organizations have found about Americans’ mental health during the pandemic. These findings reflect a snapshot in time, and it’s possible that attitudes and experiences may have changed since these surveys were fielded. It’s also important to note that concerns about mental health were common in the U.S. long before the arrival of COVID-19 .

Three years into the COVID-19 outbreak in the United States , Pew Research Center published this collection of survey findings about Americans’ challenges with mental health during the pandemic. All findings are previously published. Methodological information about each survey cited here, including the sample sizes and field dates, can be found by following the links in the text.

The research behind the first item in this analysis, examining Americans’ experiences with psychological distress, benefited from the advice and counsel of the COVID-19 and mental health measurement group at Johns Hopkins Bloomberg School of Public Health.

At least four-in-ten U.S. adults (41%) have experienced high levels of psychological distress at some point during the pandemic, according to four Pew Research Center surveys conducted between March 2020 and September 2022.

Young adults are especially likely to have faced high levels of psychological distress since the COVID-19 outbreak began: 58% of Americans ages 18 to 29 fall into this category, based on their answers in at least one of these four surveys.

Women are much more likely than men to have experienced high psychological distress (48% vs. 32%), as are people in lower-income households (53%) when compared with those in middle-income (38%) or upper-income (30%) households.

In addition, roughly two-thirds (66%) of adults who have a disability or health condition that prevents them from participating fully in work, school, housework or other activities have experienced a high level of distress during the pandemic.

The Center measured Americans’ psychological distress by asking them a series of five questions on subjects including loneliness, anxiety and trouble sleeping in the past week. The questions are not a clinical measure, nor a diagnostic tool. Instead, they describe people’s emotional experiences during the week before being surveyed.

While these questions did not ask specifically about the pandemic, a sixth question did, inquiring whether respondents had “had physical reactions, such as sweating, trouble breathing, nausea, or a pounding heart” when thinking about their experience with the coronavirus outbreak. In September 2022, the most recent time this question was asked, 14% of Americans said they’d experienced this at least some or a little of the time in the past seven days.

More than a third of high school students have reported mental health challenges during the pandemic. In a survey conducted by the Centers for Disease Control and Prevention from January to June 2021, 37% of students at public and private high schools said their mental health was not good most or all of the time during the pandemic. That included roughly half of girls (49%) and about a quarter of boys (24%).

In the same survey, an even larger share of high school students (44%) said that at some point during the previous 12 months, they had felt sad or hopeless almost every day for two or more weeks in a row – to the point where they had stopped doing some usual activities. Roughly six-in-ten high school girls (57%) said this, as did 31% of boys.

On both questions, high school students who identify as lesbian, gay, bisexual, other or questioning were far more likely than heterosexual students to report negative experiences related to their mental health.

Mental health tops the list of worries that U.S. parents express about their kids’ well-being, according to a fall 2022 Pew Research Center survey of parents with children younger than 18. In that survey, four-in-ten U.S. parents said they’re extremely or very worried about their children struggling with anxiety or depression. That was greater than the share of parents who expressed high levels of concern over seven other dangers asked about.

While the fall 2022 survey was fielded amid the coronavirus outbreak, it did not ask about parental worries in the specific context of the pandemic. It’s also important to note that parental concerns about their kids struggling with anxiety and depression were common long before the pandemic, too . (Due to changes in question wording, the results from the fall 2022 survey of parents are not directly comparable with those from an earlier Center survey of parents, conducted in 2015.)

Among parents of teenagers, roughly three-in-ten (28%) are extremely or very worried that their teen’s use of social media could lead to problems with anxiety or depression, according to a spring 2022 survey of parents with children ages 13 to 17 . Parents of teen girls were more likely than parents of teen boys to be extremely or very worried on this front (32% vs. 24%). And Hispanic parents (37%) were more likely than those who are Black or White (26% each) to express a great deal of concern about this. (There were not enough Asian American parents in the sample to analyze separately. This survey also did not ask about parental concerns specifically in the context of the pandemic.)

Looking back, many K-12 parents say the first year of the coronavirus pandemic had a negative effect on their children’s emotional health. In a fall 2022 survey of parents with K-12 children , 48% said the first year of the pandemic had a very or somewhat negative impact on their children’s emotional well-being, while 39% said it had neither a positive nor negative effect. A small share of parents (7%) said the first year of the pandemic had a very or somewhat positive effect in this regard.

White parents and those from upper-income households were especially likely to say the first year of the pandemic had a negative emotional impact on their K-12 children.

While around half of K-12 parents said the first year of the pandemic had a negative emotional impact on their kids, a larger share (61%) said it had a negative effect on their children’s education.

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How Americans View the Coronavirus, COVID-19 Vaccines Amid Declining Levels of Concern

Online religious services appeal to many americans, but going in person remains more popular, about a third of u.s. workers who can work from home now do so all the time, how the pandemic has affected attendance at u.s. religious services, economy remains the public’s top policy priority; covid-19 concerns decline again, most popular.

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Research: More People Use Mental Health Benefits When They Hear That Colleagues Use Them Too

By: Laura M Giurge, Lauren C Howe, Zsofia Belovai, Guusje Lindemann, Sharon O'Connor

A study of 2,400 Novartis employees around the world found that simply hearing about others' struggles can normalize accessing support at work.

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• Publication Date: Apr 22, 2024
• Discipline: General Management
• Product #: H086RX-PDF-ENG

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Novartis has trained more than 1,000 employees as Mental Health First Aiders to offer peer-to-peer support for their colleagues. While employees were eager for the training, uptake of the program remains low. To understand why, a team of researchers conducted a randomized controlled trial with 2,400 Novartis employees who worked in the UK, Ireland, India, and Malaysia. Employees were shown one of six framings that were designed to overcome two key barriers: privacy concerns and usage concerns. They found that employees who read a story about their colleague using the service were more likely to sign up to learn more about the program, and that emphasizing the anonymity of the program did not seem to have an impact. Their findings suggest that one way to encourage employees to make use of existing mental health resources is by creating a supportive culture that embraces sharing about mental health challenges at work.

Apr 22, 2024

Discipline:

General Management

Harvard Business Review Digital Article

H086RX-PDF-ENG

1189 word count

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• v.28; 2022 Apr

Effects of self-guided stress management interventions in college students: A systematic review and meta-analysis

Yagmur amanvermez.

a Department of Clinical, Neuro and Developmental Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, the Netherlands

Ruiying Zhao

Pim cuijpers, leonore m. de wit, david d. ebert.

b Department of Sport and Health Sciences, Technical University of Munich, Munich, Germany

Ronald C. Kessler

c Department of Health Care Policy, Harvard Medical School, Boston, MA, USA

Ronny Bruffaerts

d Universitair Psychiatrisch Centrum, Center for Public Health Psychiatry, Katholieke Universiteit Leuven, Leuven, Belgium

Eirini Karyotaki

Associated data.

College students face several sources of stress. Self-guided stress management interventions offer an excellent opportunity for scaling up evidence-based interventions for self-management of these stresses. However, little is known about the overall effects of these interventions. Increasing this understanding is essential because self-guided stress management interventions might be a cost-effective and acceptable way of providing help to this important segment of the population during a critical life course stage.

We carried out a systematic literature search of bibliographical databases (PubMed, PsycINFO, Embase, and Cochrane Library) for randomized controlled trials (RCTs) of self-guided stress management interventions published up through April 2020. We conducted two separate meta-analyses for perceived stress, depression, and anxiety. The first included interventions for general college student samples. The second included studies for students with high levels of perceived stress.

The first meta-analysis included 26 studies with 29 intervention-control comparisons based on a total of 4468 students. The pooled effect size was small but statistically significant ( g  = 0.19; 95% CI [0.10, 0.29]; p  < 0.001). Results showed moderate heterogeneity across studies [ I 2  = 48%; 95% CI (19, 66%)]. The second meta-analysis, included four studies based on a total of 491 students with high levels of stress. The pooled effect size was small but statistically significant ( g  = 0.34; 95% CI [0.16, 0.52]; p  < 0.001). Results showed no heterogeneity across studies ( I 2  = 0%; 95% CI [0, 79%]), but risk of bias was substantial.

Our results suggest that self-guided stress management programs may be effective when compared to control conditions, but with small average effects. These programs might be a useful element of a multi-component intervention system. Given the psychological barriers to treatment that exist among many college students, self-help interventions might be a good first step in facilitating subsequent help-seeking among students reluctant to engage in other types of treatment. More studies should be conducted to investigate these interventions, sample specifications, mediating effects, and individual-level heterogeneity of effects.

• • College students encounter stressors in several domains.
• • Self-guided stress management interventions can be cost-effective and more acceptable.
• • Self-guided stress management programs may have a small effect on stress, depression, or anxiety.

1. Introduction

College students experience a range of stressors related to this specific phase in life such as leaving home, being more independent, gaining new responsibilities, and overcoming new academic demands ( Sussman and Arnett, 2014 ). A considerable proportion of college students report elevated levels of perceived stress defined as the appraisal of stressors as threats that exceed one's coping abilities and result in a feeling of being overwhelmed ( Cohen et al., 2020 ; Leppink et al., 2016 ). Prolonged psychological stress is closely associated with mental disorders ( Auerbach et al., 2018 ; Beiter et al., 2015 ; Karyotaki et al., 2020 ; Mortier et al., 2018 ), and also has consequences for academic performance ( Bruffaerts et al., 2018 ), campus engagement ( Salzer, 2012 ), and college drop-out ( Eisenberg et al., 2009 ). Chronic psychological stress may also lead to more serious mental health disorders later in life ( Cohen et al., 2007 ; De Girolamo et al., 2015 ). Moreover, the first onset of common mental disorders generally occurs during young adulthood ( Kessler et al., 2007 ). Therefore psychological interventions for college students may play a critical role in prevention and early intervention with these mental disorders ( Karyotaki et al., 2020 ).

Recent evidence suggests that stress management programs and psychological treatments for common mental disorders are both effective in decreasing perceived stress and clinically significant symptoms of anxiety and depression among college students ( Amanvermez et al., 2020 ; Cuijpers et al., 2016 ; Cuijpers et al., 2021 ; Harrer et al., 2019b ). However, college students' treatment uptake is low ( Bruffaerts et al., 2019 ) and access to psychological treatments for common mental disorders is limited due to several barriers such as system-related and scheduling issues ( Leviness et al., 2019 ; Marsh and Wilcoxon, 2015 ; Stallman and Shochet, 2009 ; Watkins et al., 2011 ). In addition, students can be reluctant to seek professional mental health treatment because of unwillingness to define themselves as having a mental disorder and fear of stigma ( Ebert et al., 2019 ; Marsh and Wilcoxon, 2015 ). Moreover, mental health needs may not be fulfilled sufficiently at university counseling centers since face-to-face interventions generally require resources such as trained personnel (e.g. therapist or coach) ( Leviness et al., 2019 ). Given these barriers, delivering evidence-based interventions designed to provide help with stress management in a self-help format might be a more practical and psychologically acceptable approach.

A self-help intervention is defined as a standardized intervention in which participants apply the intervention manual independently from a professional guide or therapist ( Cuijpers and Schuurmans, 2007 ). Self-help interventions are sometimes facilitated by a trained practitioner or in a group format to stimulate adherence and use group processes to enhance therapeutic effects. They can also be self-administered by individual users ( National Institute for Health and Care Excellence, 2020 ). Self-help interventions have enormous potential to maximize scalability because of their low cost and flexibility in terms of the times in which they can be used, and their ability to overcome system-related barriers (e.g. long waiting list) and attitudinal barriers (e.g. embarrassment, preference for self-management) ( Czyz et al., 2013 ; Levin et al., 2016 ; Mains and Scogin, 2003 ). Moreover, self-administered stress management interventions may be more acceptable to college students than to other segments of the population due to the greater digital literacy of college students ( Fairburn and Patel, 2017 ; Richardson et al., 2009 ).

Recent controlled studies found that self-help interventions can be effective in reducing depression and anxiety ( Andrews et al., 2018 ; Bennett et al., 2019 ; Cuijpers et al., 2019 ; Karyotaki et al., 2021 ). However, these studies generally focused on clinical or sub-clinical populations with depression or anxiety. Meta-analytical evidence is generally lacking for non-clinical samples who experience elevated levels of perceived stress. In addition, the systematic reviews that exist on this topic combine digital programs with and without human support ( Davies et al., 2014 ; Heber et al., 2017 ; Lattie et al., 2019 ). Given that supported self-help interventions are more expensive than their unsupported counterparts, it would be useful to have meta-analytic evidence on the effects of each separately. In the current report, we aimed to present such an analysis of self-help interventions for stress management delivered without human support, which we henceforth refer to as self-guided .

To date, the effects of self-guided stress management interventions at a meta-analytic level have generally been examined in samples of people with physical illness ( Ugalde et al., 2017 ) or adults seeking help for the management of work-related stress ( Richardson and Rothstein, 2008 ). However, there is growing literature investigating the effects of self-guided stress management interventions among unrestricted samples of college students. Therefore, we focused on individual self-guided stress management interventions, excluding both self-help groups and guided self-help interventions. In our study, we anticipated differences between college student samples in terms of inclusion characteristics, thus, we investigated the effects of stress management interventions for two college student samples separately: (1) those who were recruited regardless of their perceived stress scores (hereinafter referred to as unselected college students ), and (2) those who were recruited into studies delivered exclusively to students with high levels of perceived stress based on a cut-off score on a standardized stress scale (hereinafter referred to as preselected college students ).

The results of the systematic review and meta-analysis were reported following the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) Statement ( Liberati et al., 2009 ).

2.1. Protocol and registration

Methods of the planned search strategy, inclusion criteria, and data analysis were prospectively identified. This study has been preregistered in the Open Science Framework (OSF). The preregistered protocol can be retrieved via https://osf.io/23vck .

2.2. Eligibility criteria

We included studies that were 1) RCTs in which 2) a self-guided self-help stress management intervention compared to 3) a control condition (care-as-usual, waiting list, or attention control, etc.) in 4) a higher education setting. Included data were based on continuous outcomes for perceived stress, psychological distress, depression, and/or anxiety. Psychological stress/distress is considered a generic concept. In this study, we defined psychological stress/distress as a non-specific emotional and behavioral response resulting from external or internal demands ( Cohen et al., 1995, pp. 6–7 ; Cohen et al., 2016 ; Ridner, 2004 ). Therefore, we decided to examine symptoms of depression and anxiety as outcomes next to stress symptoms as they are often reported together and in some cases used interchangeably in studies testing the effectiveness of stress management programs. Further, depressive and anxiety symptoms are two of the most common emotional health problems, and thus, they are conceptually embedded within the definition of psychological distress ( Ridner, 2004 ). Despite their phenomenologically unique characteristics (e.g., depression is characterized as low positive affect, lack of motivation, loss of interest, or hopelessness, while anxiety is characterized by symptoms of physical hyperarousal such as restlessness or irritability), they share substantial non-specific components with each other ( Hammen, 2015 ; Henry and Crawford, 2005 ; Lovibond and Lovibond, 1995 ; Monroe, 2008 ; Watson et al., 1995 ). Given the strong associations between these emotional problems and the high comorbidity, in this meta-analysis, we included depression and anxiety outcomes to get a more comprehensive overview of the effectiveness of stress management interventions.

We defined two different eligibility criteria for two meta-analyses in terms of the target population. In the first meta-analysis, we only included studies with unselected college students. In the second study, we included studies preselected college students based on a cut-off score on a perceived stress scale. Self-guided stress management intervention is defined as a psychological intervention addressing psychological stress and coping skills in which individuals follow the program without the help of a care provider. We added self-guided stress management programs in any format including web-based, mobile phone applications, book, or audio. We included studies if participants were in contact with the research staff only with the purpose of technical support (e.g. if they had problems in accessing the platform in the online intervention) or data collection. Only RCTs in English were added to our study.

We excluded peer support groups and guided self-help interventions in which a coach/therapist provided personalized feedback on the student's progress. We excluded studies if the recruitment focus was not stress. Following this, we excluded studies addressing interventions for depressive and/or anxiety symptoms, worry, procrastination, sleep difficulties, or eating behaviors. We also excluded studies if they were not published in peer-reviewed journals (such as conference papers, or dissertations).

2.3. Search strategy

We retrieved publications from the four main bibliographic databases namely, PubMed, PsycINFO, Embase, and Cochrane Library up until 29.04.2020. We had already established this database to be used in another study examining the guided or in-person stress management interventions ( Amanvermez et al., 2020 ). We updated this database using the same search strings. We filtered results by RCTs and according to age groups. Full search strings are presented in Appendix A. We also searched a database which includes psychological interventions in college students that was developed by another research group. Details related to this database can be found at https://www.crd.york.ac.uk/PROSPERO/display_record.asp?ID=CRD42017068758 .

2.4. Selection of studies

After retrieving studies from the databases and removing duplicates, two researchers (YA and RZ) independently screened the studies based on the titles and abstracts. Following the first screening, full-texts of the selected studies were examined for eligibility. Disagreements between the two assessors were discussed with the senior researchers (EK, LDW, or PC).

2.5. Data extraction and classification

Two researchers independently extracted the data including 1) the characteristics of the studies (author, publication year, country), 2) characteristics of the interventions (theoretical background, format, length), 3) characteristics related to the study design (type of control condition, inclusion criteria of the participants, outcomes, time of assessments, compensation, study attrition rate), and 4) characteristics of the participants (age, percentage of the female students, target student population, recruitment strategy, type of the university). We extracted the percentage of female students to describe trends in gender distribution in the included studies because previous studies have shown that female participants were more likely to utilize psychological interventions ( Davies et al., 2014 ; Harrer et al., 2019b ).

We classified selected study characteristics to run subgroup analysis. Stress management programs were grouped according to the theoretical background under four categories based on common therapeutic strategies: 1) cognitive-behavioral therapy (CBT), 2) third-wave therapies (TW), 3) skills-training, and 4) mind-body interventions. Stress management programs including components such as cognitive restructuring or stress inoculation strategies were assessed under the CBT-based stress management programs ( Ong et al., 2004 ). Programs including techniques of acceptance or mindfulness concepts in combination with CBT strategies such as acceptance and commitment therapy (ACT), or mindfulness-based cognitive behavior therapy (MBCT) were classified under TW ( Hayes and Hofmann, 2017 ). Skills training programs included components to improve particular skills to manage stress (e.g. improving present control skills). Mind-body interventions were the programs that mainly used mindfulness, meditation, biofeedback, or relaxation techniques ( Astin et al., 2003 ; Ong et al., 2004 ). We classified the length of the intervention as brief, moderate, and long if they were delivered for 1–4 weeks, 5–8 weeks, and 8+ weeks, respectively. We have classified the modalities if they were delivered in online format (including web-based stress management programs, and/or mobile phone applications) or others (book and/or audio).

The number of participants, mean scores, and standard deviation of control and intervention conditions at post-test were extracted to calculate the effect size. If these data were not available, we extracted other available statistics (i.e., p- value, t score, or effect size) that allowed us to calculate the effect size. If no relevant information was reported for calculating effect size, then we contacted the author. If the author did not respond, we excluded this study from our analysis. We could not retrieve relevant data from the published reports of four studies. After contacting the authors, we could obtain the relevant data for three studies ( Nguyen-Feng et al., 2015 ; Nguyen-Feng et al., 2019 ; Walsh et al., 2019 ), which allowed us to calculate effect sizes. However, we could not retrieve the relevant data for one study which was excluded.

2.6. Quality assessment

The risk of bias was assessed by the revised Cochrane risk-of-bias tool for randomized trials (RoB 2) ( Sterne et al., 2019 ). RoB 2 includes five domains: (1) bias arising from the randomization process; (2) bias due to deviations from intended interventions; (3) bias due to missing outcome data; (4) bias in the measurement of the outcome; and (5) bias in the selection of the reported result ( Sterne et al., 2019 ). However, in the present study, the domain assessing the bias in the measurement of the outcome was omitted as all studies used self-reported measures. This issue inherently precludes the blinding of the outcome assessor. Therefore we assessed only four domains in the RoB 2 tool.

Two independent assessors (YA and RZ) assessed each study for these domains by answering the signaling questions. By doing this, we had results of low risk of bias, some concerns, or high risk of bias at the domain level. Then we determined the overall risk of bias of each study as low, some concerns, and high risk of bias. The overall risk of bias of the study was determined to be low if at least two domains had low risk and no high risk of bias at any domain. The overall risk of bias was considered high if at least two domains had a high risk of bias or all domains had some concerns. The studies that did not meet these criteria were evaluated as containing “some concerns” (e.g. studies having “some concerns” at three domains and low risk of bias at one domain). Discrepancies between assessments were discussed by two assessors. Senior researchers (EK, LDW, or PC) were consulted if discrepancies were not solved as a result of the discussion.

2.7. Meta-analyses

We conducted two separate meta-analyses using the Comprehensive Meta-Analysis (CMA) software package and the meta ( Schwarzer, 2007 ), metafor ( Viechtbauer, 2010 ), and dmetar ( Harrer et al., 2019a ) packages in R version 4.0.2. We calculated the effect size of each study using the CMA by pooling all continuous outcomes of perceived stress, anxiety, and/or depression within a study. Then we used R to calculate the pooled effect size, perform additional analyses including subgroup analyses, sensitivity analysis, and test publication bias.

We calculated the effect size of each study using the mean, standard deviation, and the number of students in the intervention and control conditions at post-test assessment. If multiple outcomes were reported within one study, first we calculated a synthetic effect size per study. If there is high association between targeted outcomes, this would result in high correlation between errors of these outcomes. In such case, combining outcomes first within the study and generating one effect size per study is recommended ( Borenstein et al., 2009 ). In our main analysis, we pooled the combined effect size from each study by taking into account the high correlation between these outcomes. Therefore we calculated one effect size (Hedges' g ) for each study separately. Then we pooled the effect sizes. As an additional analysis, we also examined the effect of stress management programs on each outcome separately. We conducted the meta-analyses under the random-effects model due to the wide variability of studies. An effect size of 0.2, 0.5, and 0.8 was interpreted as small, moderate, and large, respectively ( Cohen, 1988 ). We also calculated the number needed to treat (NNT) to improve the understandability of the findings ( Kraemer and Kupfer, 2006 ). Heterogeneity of the effect sizes across studies was calculated using I 2 ( Ioannidis et al., 2007 ). Heterogeneity was deemed to be low, moderate, and high if the I 2 value was quantified 25%, 50%, and 75% respectively ( Higgins et al., 2003 ). We also calculated the 95% CI around I 2 .

2.8. Publication bias

Publication bias was assessed by inspection of the funnel plot and testing the asymmetry of the funnel plot performing Egger's test of the intercept ( Egger et al., 1997 ). We estimated the number of missing studies from the funnel plot, and re-calculated the effect size after imputation the missing studies with Duval and Tweedie's trim and fill procedure ( Duval and Tweedie, 2000 ).

2.9. Additional analyses

A series of subgroup analyses using the mixed-effects model was conducted with the studies for unselected college students to examine whether the effects are different in terms of the study or intervention characteristics. We conducted subgroup analyses for the type of control (AC vs. WL vs. CAU), theoretical background (CBT vs. TW vs. skills training vs. mind-body), length of the studies (brief vs. moderate vs. long), intervention format (online vs. others), compensation (yes vs. no), recruitment strategies (campus vs. subject pool vs. online vs. mixed), and risk of bias assessment (high vs. some concerns vs. low). We also conducted a sensitivity analysis including only studies with a low risk of bias to obtain the most accurate findings.

3.1. Study selection

Our searches resulted in 5242 studies from the database search and 350 from the other source. After removing the duplicates, we had a total of 3846 studies. The records were screened based on the title and abstract and 3562 studies did not meet inclusion criteria and were excluded. Following this, we screened the full-text of 284 remaining studies for eligibility. As a result, we retrieved 26 studies with 29 comparisons for the unselected college samples and four studies for preselected students. Details of this process and reasons for the exclusion of the studies can be seen in Fig. 1 .

PRISMA flow diagram.

3.2. Study characteristics

Studies with unselected college students consisted of 4468 participants in total (intervention groups: N  = 2400; control groups: N  = 2068). Fourteen out of 26 studies compared the intervention to a waiting list (WL) group, while comparisons were made with an attention control (AC) in 10 studies, and care-as-usual (CAU) in two studies. Among 29 comparisons, 13 of them were mind-body interventions, seven interventions were based on TW approaches. There were six skills training and three CBT-based programs. Almost all studies were conducted in high-income countries. Half of the studies were conducted in the USA. Two studies were in the UK, New Zealand, Canada, and Australia. The rest were conducted in Sweden, France, Ireland, and Thailand. Study characteristics can be seen in Table 1 .

Study characteristics for studies with unselected college students.

Note . AC: active control; AUS: Australia; CAN: Canada; CAU: care-as-usual; CBT: cognitive behavior therapy; FR: France; IRE: Ireland; MB: mind-body; NZ: New Zealand; ST: skills training; SWE: Sweden; TH: Thailand; TW: third wave therapy; UK: United Kingdom; USA: United States of America; WL: waiting list.

In the meta-analysis of studies with preselected students, a total of 491 participants were included (intervention groups: N  = 235; control groups: N  = 256). Of four studies, two studies used AC as a comparison group and the other two studies used WL. Two interventions were based on skills training, one incorporated CBT techniques, and one was designed based on TW principles. With regard to the inclusion criteria of the participants, two studies used a cut-off score based on the Perceived Stress Scale (PSS-10) ( Cohen and Williamson, 1988 ). One study used the Depression Anxiety and Stress Scale (DASS-21) stress or anxiety sub-scale ( Lovibond and Lovibond, 1995 ), and one study used the Perceived Control Over Stressful Events Scale (PCOSES) ( Frazier et al., 2011 ). Three studies were conducted in the USA, and one study was in the UK. Study characteristics can be seen in Table 2 .

Study characteristics for studies with preselected college students.

Note . AC: active control, CAU: care-as usual, CBT: cognitive behavior therapy; DASS: Depression Anxiety Stress Scale; PCOSES: Perceived Control over Stressful Events Scale; PSS: Perceived Stress Scale; ST: skills training; TW: third wave therapy; UK: United Kingdom; USA: United States of America; WL: waiting list.

The study dropout rate was calculated using the number of participants who were included in the study but lost to post-test assessment. Overall, 27.77% (ranging from 0% to 62.59%) of the unselected college students, and 28.8% (ranging from 2.08 to 55.72%) of the pre-selected college students did not provide complete data.

3.3. Quality assessment

The overall risk of bias in all studies was considerable. Studies with unselected college students showed that the randomization process was handled/reported adequately in only five out of 26 studies. The risk of bias arising from the deviations from intended interventions showed that fourteen studies fulfilled the criteria for low risk. In fifteen studies, missing outcome data were properly managed or reported in a detailed way. We assessed only five studies as having a low risk of bias for the selection of the reported results. In total, eleven studies were judged to have an overall low risk of bias, ten studies had some concerns, and five studies had an overall high risk of bias. The visual demonstration of the risk of bias assessments can be seen in Figs. 2 and ​ and3 3 .

Risk of bias summary for studies with unselected college students.

Among the studies with preselected college students, the randomization process was either not reported elaborately or had some concerns. We assessed only one study having a low risk of bias in the domain of deviations from the intended interventions. Two out of four studies were assessed as having a low risk of bias for missing outcome data. Two studies were deemed to be having a low risk of bias for the selection of the reported result. Overall, there was a low risk of bias in one study, some concerns in two studies, and a high risk of bias in one study. The risk of bias assessments of the studies with preselected college students can be seen in Figs. 4 and ​ and5 5 in detail.

Risk of bias summary for studies with preselected college students.

3.4. Main analyses

The overall effect size of the self-guided stress management program in comparison to a control condition at post-test was small but statistically significant for the unselected student samples ( g  = 0.19; 95% CI [0.10, 0.29]; p  < 0.001; n = 29) with moderate heterogeneity across studies ( I 2  = 48%; 95% CI [19–66%]). NNT was 9.43. Based on inspection of the forest plot ( Fig. 6 ), we found two outliers ( Nguyen-Feng et al., 2017 , mindfulness intervention, and Paholpak et al., 2012 ). After removing the outliers, the effect size was still low ( g  = 0.23; 95% CI [0.14, 0.31]; p  < 0.001; n = 27; I 2  = 30%; 95% CI [0–56%]). As a result of separate analyses for each outcome, we found a small effect size for stress ( g  = 0.25; 95% CI [0.15–0.35]; p  < 0.001; n = 26), depression ( g  = 0.14; 95% CI [0.03–0.26]; p  = 0.016; n = 19), and anxiety ( g  = 0.11; 95% CI [0.00–0.21]; p  = 0.045; n = 19). Heterogeneity across studies was moderate for stress ( I 2  = 53%), depression ( I 2  = 59%), and anxiety ( I 2  = 46%). The prediction interval ranged from −0.15 to 0.54. All details are presented in Table 3 . We also found an indication of publication bias for studies with unselected samples based on the inspection of the funnel plot ( Fig. 8 ). Egger's test of the intercept was not significant (intercept: 0; 95% CI: −1.14–1.14, t  = 0, p  = 0.99) yet Duval and Tweedie's trim and fill procedure resulted in one imputed study. Adjusted effect size was small ( g  = 0.18, 95% CI [0.09; 0.28], p  < 0.001).

Forest plot for comparisons of self-guided stress management programs for unselected college students to control conditions at posttest.

Pooled effects of self-guided stress management programs for unselected college students on target outcomes compared with control groups.

Funnel plot of studies with unselected college students.

Meta-analysis of the studies with preselected college students yielded a low effect size ( g  = 0.34; 95% CI [0.16–0.52]; p  < 0.001; n = 4) with zero heterogeneity ( I 2  = 0%; 95% CI [0–79%]). NNT was 5.26. We found no outlier based on the inspection of the forest plot ( Fig. 7 ). We performed the separate analyses for outcomes and found a non-significant small effect size for stress ( g  = 0.27; 95% CI [−0.04, 0.58]; p  = 0.087; n = 3), significant small effect for depression ( g  = 0.29; 95% CI [0.10–0.49]; p  < 0.001; n = 3), and anxiety ( g  = 0.38; 95% CI [0.18–0.58]; p  = 0.003; n = 3). Our results yielded moderate heterogeneity for stress ( I 2  = 56%), zero heterogeneity for depression ( I 2  = 0%) and anxiety ( I 2  = 0%). The prediction interval ranged from −0.05 to 0.73. The results can be seen in Table 4 . Following the evaluation of the funnel plot in studies for preselected samples, findings did not suggest an indication of publication bias ( Fig. 9 ). Egger's test was significant (intercept: 6.173, 95% CI [3.92– 8.42], t  = 5.375, p  = 0.033) which suggest a publication bias. After performing Duval and Tweedie's trim and full procedure, we found no imputed studies.

Forest plot for comparisons of self-guided stress management programs for preselected college students to control conditions at posttest.

Pooled effects of self-guided stress management programs for preselected college students on target outcomes compared with control groups.

Funnel plot of studies with preselected college students.

3.5. Subgroup analyses, and sensitivity analysis

We performed several subgroup analyses for the studies with unselected samples. We found no significant associations between the theoretical background, control condition, length of the intervention, ITT, recruitment strategies, sending reminders, and the intervention format. We found a significant association ( p  = 0.04) between the effect size and compensation in favor of no compensation ( g  = 0.34; 95% CI [0.16–0.51]; n = 10) versus presence of compensation ( g  = 0.13; 95% CI [0.03– 0.23]; n = 19). All results are presented in Table 3 . We could not perform any subgroup analysis with the studies for preselected samples due to a lack of a sufficient number of studies to run such analysis.

Sensitivity analysis for studies only having an overall low risk of bias showed that effect size was low ( g  = 0.17; 95% CI [0.06–0.28]; p  = 0.003; n = 12). The number of studies with preselected samples did not permit us to run sensitivity analysis.

3.6. Meta-analyses of longer-term outcomes

Few studies assessed follow-up outcomes of the self-guided stress management programs, and follow-up time points varied widely across studies. Nevertheless, we pooled the findings of long-term effects up to 6-month follow-up for unselected samples, and found a non-significant effect ( g  = 0.01; 95% CI [−0.10–0.12]; p  = 0.821; n = 12, I 2  = 24%). Similarly, we ran the analysis only with studies assessing the outcomes up to 3-months follow-up and found no significant effect for unselected samples ( g  = 0.00; 95% CI [−0.13–0.13]; p  = 0.963; n = 9, I 2  = 36%). We calculated the long-term effects of self-guided stress management interventions up to 3-month in the preselected samples, and we found small and statistically significant effect size ( g  = 0.31; 95% CI [0.08–0.53]; p  = 0.007; n = 3, I 2  = 0%).

4. Discussion

We conducted two meta-analyses of the RCTs, one with unselected college samples and one with preselected college students with high levels of perceived stress, investigating the effectiveness of self-guided stress management interventions for college students. We included 26 studies with 29 comparisons for the unselected college students in the first meta-analysis and four studies for preselected students in the second meta-analysis. We found a small and statistically significant effect size of the self-guided stress management interventions for perceived stress, depression, and anxiety for the unselected student populations in comparison to the control condition. We also found a small and statistically significant effect size for the preselected students. The risk of bias was considerable in the included studies.

Systematic assessments of psychological interventions among college students have been studied for several mental health problems, however, to our knowledge, this is the first study pooling the results of all self-guided stress management interventions specifically for college students. Previous meta-analyses of self-guided programs found small effects in working populations ( Carolan et al., 2017 ; Stratton et al., 2017 ) and in general populations ( Heber et al., 2017 ) on stress and psychological distress. Although evidence of self-guided stress management programs for the non-clinical college student populations is limited, one meta-analysis of digital interventions reported a low effect size on stress, anxiety, and depression ( Harrer et al., 2019b ). Our results also align with other meta-analytic studies examining the internet-based interventions for non-clinical or subclinical depression/anxiety ( Deady et al., 2017 ; Zhou et al., 2016 ).

A possible explanation behind the low effects of self-guided interventions may be related to the lack of human reciprocal contact. Several studies have demonstrated the superiority of guided interventions over self-guided programs in improving symptoms of common mental disorders ( Baumeister et al., 2014 ; Domhardt et al., 2019 ; Karyotaki et al., 2021 ). Human support has been seen as a possible mechanism of change, as it was found to be associated with higher numbers of completed sessions and increased completer rates ( Domhardt et al., 2019 ; Karyotaki et al., 2021 ; Nixon et al., 2021 ), which could be related to better intervention outcomes ( Karyotaki et al., 2021 ). The superior effects of guided interventions may be related to the concept of supportive accountability defined as the social presence and/or dynamic interaction that stimulates the motivation to continue the intervention ( Mohr et al., 2011 ). In addition, as noted earlier, the therapeutic relationship has been suggested as a crucial common factor, which can stimulate behavioral change in psychological interventions ( Graves et al., 2017 ; Wampold, 2015 ). Thus, the lack of reflective processes and feedback while transferring the knowledge from the intervention to everyday life might limit the effects of the self-guided interventions ( Conley et al., 2016 ; Pleva and Wade, 2006 ; Rosen, 1993 ; Rozental et al., 2014 ).

However, we should note that most of the existing evidence around the relative effectiveness of guided and unguided interventions is derived from studies focusing on changes in depressive and anxiety symptoms. Therefore, it remains unclear whether guided and self-guided stress management programs produce differential effects. Future studies should examine this comparison in a head-to-head fashion to shed light into the differential effectiveness of guided and self-guided interventions for psychological stress/distress. It is also plausible that the effectiveness of self-guided stress management programs may be increased by the provision of on-demand support or personalized automated messages. For instance, individuals with high levels of stress may find guidance on-demand more beneficial than regular guidance as they can maintain control over the intervention and request social support whenever it is necessary. Previous studies on psychological problems, such as stress or perfectionism, showed that internet-based interventions with guidance on-demand yielded similar effects with guided interventions ( Zarski et al., 2016 ; Zetterberg et al., 2019 ). Moreover, in these studies, individuals contacted coaches rarely possibly due to the lack of perceived need for additional support. These findings do not necessarily imply lack of need for guided interventions because close monitoring and individualized feedback may be particularly relevant for certain groups like those who are at high risk of experiencing negative effects ( Rozental et al., 2014 ). Therefore, different types and intensities of guidance in stress management programs, as well as predictors of differential response with regard to different guidance formats should be investigated thoroughly in future studies. Moreover, technological advancements could offer innovative tools to increase the effects of self-guided interventions. Persuasive technology strategies, particularly using automated reminders may be another cost-effective strategy to improve intervention outcomes by increasing engagement ( Kelders et al., 2012 ; Brouwer et al., 2011 ). However, timing, frequency, content, and format (i.e., SMS, push notification, or email) of the automated reminders should be investigated with careful consideration since some participants may perceive them as stressful ( Dennison et al., 2013 ). Last but not least, it is possible that some individuals benefit equally from guided and self-guided interventions as we have seen in the case of depression ( Karyotaki et al., 2021 ). Such hypothesis, however, remains to be investigated in future research.

The low effect size could also be explained by sample characteristics. Our results showed that little improvement is possible in unselected student samples. In addition, studies targeting students with high levels of perceived stress yielded a slightly higher effect size than unselected student samples. Previously, higher baseline stress scores were found to be associated with the larger effect size of a web-based stress management program ( Coudray et al., 2019 ). Studies also provided evidence on the larger effects in preselected student samples than unselected ones ( Amanvermez et al., 2020 ; Conley et al., 2016 ; Harrer et al., 2019b). However, there are still unanswered questions about which students benefit most from self-guided stress management programs, as we did not directly test or compare this in the present study. Besides, it is not clear if stress management programs are also effective for people with subthreshold depression or anxiety, although recent RCT studies showed promising results ( Harrer et al., 2021 ; Weisel et al., 2018 ).

In this study, the study dropout rates were comparable to those reported by studies on internet-based interventions for common mental health problems ( Christensen et al., 2009 ; 1–50%; Harrer et al., 2019b ; 2–50%; Melville et al., 2010 ; 0–82%). Nevertheless, it is important to differentiate between study dropout and intervention dropout. Intervention dropout refers to the situation in which the participant stops following the intervention before receiving the recommended dose ( Donkin et al., 2011 ). It would have been important to investigate the possible impact of intervention dropout as it can be associated with the intervention outcomes ( Karyotaki et al., 2017 ). However, the substantial variability of measuring/reporting the intervention adherence across the included studies prevented us from investigating this issue. This has been found as a general problem in similar interventions ( Beintner et al., 2019 ). Future research should establish consensus about reporting criteria on adherence in digital intervention trials and investigate predictors and consequences of intervention dropout in self-guided stress management programs.

A large number of college students are experiencing stresses in several domains and scalable stress management interventions for college students are needed at universities ( Karyotaki et al., 2020 ). The present study indicated that unselected student samples and students with pre-existing stress symptoms can benefit in the aggregate from self-guided stress management programs. These results allowed us to advance our knowledge of the effects of these programs in more detail.

Despite these strengths, some limitations should be emphasized while interpreting our results. First, we could only retrieve four studies targeting preselected samples and the number of participants was small in this meta-analysis. These results are underpowered and more studies should be conducted with large sample sizes to confirm our findings. Second, the risk of bias was substantial in the included studies. This limits the reliability of our results. Also, we modified the RoB 2 tool by removing one domain related to bias in outcome measurement and altering the criteria for assessing the overall risk of bias in each study, as this tool did not fit perfectly with the psychotherapy research. Third, the ecological validity of our study is unclear because individuals in RCTs may be more motivated to follow the intervention compared to real-world users of self-help ( Cuijpers et al., 2010 ; Furmark et al., 2009 ). Moreover, we were not able to generalize our results to different (cultural) contexts. Our results generally were retrieved from the studies including predominantly female students pursuing a 4-year college education. This limits the representativeness of our results in students other than female, and non-college attending peers. Similarly, included studies were mainly conducted in high-income western countries, and our results may not be applicable for college students in low and middle-income countries, because there may be differences in sources of stress and contextual issues relating to the implementation of the interventions ( Evans-Lacko and Thornicroft, 2019 ). Fourth, we are not aware of research on individual participant differences in response to self-guided stress management interventions. We still do not know whether these aggregate effects represent small homogeneous effects across all students or if there is a subset of students for whom these interventions have substantial effects and a larger subset for whom the interventions have no effects or possibly even negative effects. Investigation of this individual-level heterogeneity is critical for advancing our understanding of the most appropriate role for self-help interventions among college students. Such knowledge may help us in targeting the interventions to those who are most likely to benefit. Fifth, all studies used self-report measurements for stress, depression, and anxiety. Therefore, we could not investigate the effects of self-guided stress management programs on the biological measurement of stress such as cortisol. Sixth, the field of self-help has changed dramatically as more interventions have been developed based on information and communications technology ( Haug et al., 2012 ; Rosen, 1993 ). Self-help tools such as books, websites, or mobile phone applications are used by large populations and barely evidence-based ( Rosen, 1993 ; Walsh et al., 2019 ). Therefore, the effects of the self-help tools apart from the ones used in RCTs are unclear.

Despite these limitations and the need for more high-quality research, our study showed that self-guided stress management interventions are potentially beneficial for college students in general and college students with elevated levels of stress. These results are important from the public health standpoint, as several implications can be addressed for both implementations of the programs in higher education and future research. Self-guided stress management programs are offering great promise for college students because a large group of students may benefit from psychological interventions at a low cost. Although self-guided stress management programs yielded a small effect size, and possibly will not be a substitute for in-person interventions for more severe cases, such programs can be offered for college students as first-line mental health support within a stepped care framework.

Self-help approaches can be successful to prevent or alleviate the high stress and associated mental problems among college students. Although we found small effects of these programs, the effects might be larger for specific subgroups of students. Students may have different responses to these programs based on their characteristics (e.g. pre-existing symptoms). To this end, an in-depth investigation should be done addressing the preferences, needs, and opinions of subgroups within college students. This also requires further research inquiring into the effects of these programs using individual-participant data. Exploring the changing mechanisms in these programs will also contribute to the implementation of these programs.

Lack of human support has been repeatedly mentioned as a limitation in similar programs. On the other hand, self-help may increase willingness to seek professional help among college students. Therefore, innovative methods are worth investigating such as the different ways of increasing adherence and transferring common factors associated with improvement in face-to-face/guided programs into self-guided interventions. Internet-based interventions may be preferable since more personalized and scalable interventions can be designed based on the unique goals and needs of individuals. As a result, the most accurate strategies can be developed to outreach college students who are in the need of mental health help but do not access the relevant resources due to barriers.

The first author of this manuscript is funded for her doctoral studies by the Ministry of National Education, the Republic of Turkey.

CRediT authorship contribution statement

Yagmur Amanvermez: Conceptualization, Writing – original draft, Writing – review & editing, Validation. Ruiying Zhao: Writing – review & editing, Validation. Pim Cuijpers: Conceptualization, Writing – review & editing, Validation. Leonore M. de Wit: Conceptualization, Writing – review & editing, Validation. David D. Ebert: Writing – review & editing, Validation. Ronald C. Kessler: Writing – review & editing, Validation. Ronny Bruffaerts: Writing – review & editing, Validation. Eirini Karyotaki: Conceptualization, Writing – review & editing, Validation.

Declaration of competing interest

In the past 3 years, Dr. Kessler was a consultant for Datastat, Inc., Holmusk, RallyPoint Networks, Inc., and Sage Therapeutics. He has stock options in Mirah, PYM, and Roga Sciences.

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.invent.2022.100503 .

Appendix A. Supplementary data

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Students seek wellness at Wells Library

By Michelle Crowe

April 24th, 2024

Written by Barb Berggoetz , Guest Journalist

 A student-focused partnership between IU Libraries and the IU School of Social Work gave students one more reason to visit the iconic Herman B Wells Library this academic year. Private wellness sessions led by advanced social work students use peer coaching methods to address issues of social isolation, stress, sleeping habits, time management, budgeting, and more. 

“Our students have some really serious issues come up in their lives,” said Diane Dallis-Comentale, Ruth Lilly Dean of University Libraries . “However, it does not take a crisis to interrupt academic progress and shift the focus away from learning and success. The right information at the right time can absolutely change a student’s experience.” 

With thousands of students coming into Wells Library each day, she said, this central campus building is a convenient and known place with many private collaboration spaces. With the new partnership, students benefit from the support, resources and referrals that master’s and bachelor’s students in social work can offer. In addition, Dallis points out that social work students learn from this experience and receive credit for their year-long practicums. “The libraries are very happy to have the School of Social Work as partners,” she said.

What students were offered 

There is a growing trend of libraries providing social work and mental health services to communities, and some public libraries are even hiring social workers, said Dallis-Comentale. Under the pilot program at IU Bloomington, free, individual sessions have been available to students Monday through Friday either by signing up at tables during the IU Libraries’ Friday Finish activities or dropping in at the designated consultation rooms. Some sessions are 30 to 60 minutes, and some are shorter. Students can have one or multiple sessions, depending on their needs. Virtual and in-person sessions are available. 

During this academic year, 64 sessions with students were conducted, according to Amanda McKinley, visiting wellness clinical professor overseeing the School of Social Work Wellness Program. Coaching sessions also are offered at the Jacobs School of Music and the McKinney School of Law in Indianapolis. 

Additionally, wellness coaches staffed a table in the Wells Lobby every Friday the Friday Finish was offered in both the spring and fall semester. Activities encouraged students to play games with coaches, consider their choices and habits, and talk about resources and ways to alleviate stress. “We want to get wellness information in the hands of as many people we can,” said McKinley. 

Both the Libraries and School of Social Work considered the pilot a success. “We’re already signed on to continue the program next year,” McKinley reported. 

The Friday Finish occurred multiple weeks at Wells Library in Fall 2023 and Spring 2024 semesters. The Social Work Wellness program was consistently there to discuss wellness topics with students.

What is wellness coaching? 

Coaching can focus on any dimension of well-being, including social, emotional, spiritual, intellectual, physical, environmental, occupational and financial. Interestingly, McKinley said, a lot of sessions so far have dealt with students’ social issues. “Students want to connect more with other students,” she explained. For example, international students have sought assistance with isolation as they are not physically close to their families and live in a different culture, McKinley said. When students come to sessions, coaches ask about the areas on which they want to focus and then help students come up with goals. “It’s very client driven. We’re guiding them along the way. We can make referrals for counseling, food pantries and other community services, too,” she said. 

Students can make an Wellness Appointment

The learning experience for social work students 

Since last semester, Melissa Bielawa, senior, has coached 34 students. She said she’s learned much about coaching, particularly about interacting with people her age, which often is beneficial. “They can see the coach can relate,” she said. “It helps us build rapport and have a better relationship with students off the bat.” 

Bielawa agreed students who she coached often wanted to work on social wellness and how to build up their own communities. She said some students want to work on time management, motivation and improving academic work. “It’s not therapy, but coaching,” she said. “We find what exact interventions will work for them."

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