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Research Article

Effectiveness of Digital Health Interventions on Stress, Anxiety, and Depression in University Students: A Systematic Review and Meta-Analysis

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DOI:

10.3791/69949

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January 2nd, 2026

 ,  ,  ,  ,  ,  , 
1School of Public Health, Xinyu University

In This Article

Summary

This systematic review and meta-analysis demonstrate that digital health interventions significantly improve student mental health. Web-based programs are superior to apps, with a 4-8 week duration being optimal, positioning them as a valuable, scalable resource.

Abstract

Stress, anxiety, and depression are among the most common mental health problems experienced by university students. The aim of this study was to systematically review and analyze the effectiveness of digital health interventions in reducing stress, anxiety, and depression in university students.

Several databases (the Cochrane Library, Ovid Embase, Ovid MEDLINE, PubMed, Scopus, and Web of Science Core Collection databases) were searched for randomized controlled trials (RCTs) of digital health interventions published by June 30, 2024. The trials were reviewed, and outcome data were analyzed using random effects meta-analyses for each outcome.

A total of 22 RCTs involving 3,655 participants (3,041 analyzed) were included. The meta-analysis revealed that digital health interventions significantly reduced stress, anxiety, and depression in university students (stress: WMD = -1.79; 95% CI: -2.51, -1.07; P<0.001; anxiety: WMD = -1.73; 95% CI: -2.20, -1.25; P<0.001; depression: WMD = -2.05; 95% CI: -2.91, -1.19; P<0.001). Intervention duration and technique were significant moderators of effect size across all outcomes (all P<0.001). Interventions lasting 4 to 8 weeks demonstrated the greatest reductions in symptoms (stress: WMD = -3.7, 95% CI: -5.02, -2.39; anxiety: WMD = -2.77, 95% CI: -3.46, -2.08; depression: WMD = -4.1, 95% CI: -5.31, -2.89). The effect sizes were significantly greater when the interventions were compared to the passive control groups (stress: WMD = -2.46, 95% CI: -3.56, -1.36; anxiety: WMD = -2.32, 95% CI: -2.89, -1.75; depression: WMD = -2.61, 95% CI: -3.92, -1.29). In contrast, comparisons with active control groups yielded smaller, although still significant, effects for stress and depression and a nonsignificant effect for anxiety.

The findings indicate that digital health interventions are effective at reducing stress, anxiety, and depression in university students. These findings underscore the necessity of digital health interventions for promoting university students' mental health in higher education settings.

Introduction

The mental health of young people has attracted increasing attention and is now recognized as a global public health challenge1. University education aims to reinforce students' intellectual abilities and prepare them for productive and successful lives as adults2. However, during this particular phase in life, students frequently encounter multiple stressors, such as moving away from home, becoming more independent, taking on new responsibilities, and managing academic workloads3. Such stressors can adversely affect both physical and emotional well-being, leading to diminished academic performance, lower life satisfaction, reduced self-confidence, increased dropout rates, and, in severe cases, suicidal thoughts4. Indeed, a considerable proportion of university students report elevated levels of perceived stress, which is defined as the appraisal of environmental demands as exceeding one's coping abilities5. This population is particularly vulnerable to stress, anxiety, and depression, with late adolescence through early adulthood representing the peak period for the onset of mental health disorders6. Studies indicate that compared with non-students of the same age, one-third of university students have experienced or are currently experiencing severe mental health problems and higher levels of depression, anxiety, and distress3. A recent review indicated that the global prevalence of depression and anxiety symptoms among college students is high, at 33.6% and 39.0%, respectively7. Notably, approximately 75% of adults diagnosed with mental disorders experience initial symptoms before the age of 258. This early onset is particularly alarming given that suicide is a leading cause of death among young people aged 15-29 worldwide9. These findings collectively underscore the urgent need for effective interventions targeting stress, anxiety, and depression in university student populations.

Addressing the high prevalence of stress, anxiety, and depression among university students requires multifaceted approaches, including psychological counseling, pharmacotherapy, social support, physical exercise, and cognitive-behavioral interventions10,11. With rapid advances in information technology, evidence-based interventions are being increasingly delivered through scalable digital platforms (e.g., smartphone apps, web portals, and teletherapy systems), complementing traditional face-to-face services12. The term "digital health interventions" refers to responsive, technology-supported approaches that include personalized health communications, biometric or behavioral tracking, and just-in-time informational support13. These can be broadly categorized into two modalities: web-based platforms offering structured psychoeducational programs and mobile health (mHealth) applications delivering targeted interventions via smartphone-optimized interfaces.

Adolescents and young adults represent the most digitally engaged demographics worldwide, with a 70% internet penetration rate (versus 48% in the general population)14. Previous systematic reviews and meta-analyses have investigated the effects of online mindfulness-based interventions, physical activity interventions, computer-delivered and web-based interventions, and other psychotherapy interventions15 on reducing stress, anxiety, and depression among university students. For instance, a recent systematic review of nine RCTs involving 1,100 participants explored the effectiveness of online mindfulness interventions in improving mental health outcomes in this population16. However, the existing findings remain inconsistent. One study found that the mHealth application "Destressify" did not significantly improve stress, anxiety, or psychosocial functioning17. Similarly, Kvillemo et al. reported no statistically significant advantage of a mindfulness intervention over an active control condition18. While digital health interventions hold promise for promoting emotional well-being, enhancing treatment engagement, and offering a cost-effective alternative to conventional methods, a comprehensive synthesis of their overall efficacy is necessary to guide investment and implementation. To date, no systematic review or meta-analysis has integrated evidence across different types of digital health interventions targeting stress, anxiety, and depression specifically in university students.

To address this gap, we propose the following research questions: (1) Are digital health interventions effective for improving university students' stress, anxiety, and depression compared to active and passive control conditions? (2) What is the magnitude of the effect of digital health interventions on mental health outcomes in this population? (3) Which types of digital health technologies are most effective at alleviating depression, anxiety, and stress?

Therefore, the present study has three primary objectives. First, the evidence was evaluated regarding the effectiveness of digital health interventions in treating stress, anxiety, and depression in university students. Second, the efficacy of these interventions across reported outcomes was statistically summarized. Third, the quality of the available evidence was assessed. Additionally, given the diversity of the student population, we explored how participant characteristics may influence intervention outcomes.

To this end, we conducted a systematic review and three meta-analyses of randomized controlled trials (RCTs) measuring stress, anxiety, and depression outcomes. Through this work, we seek to support researchers in evaluating emerging evidence, identifying future research directions, and contributing to the development of effective digital treatment solutions for university students.

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Protocol

Study registration
This systematic review and meta-analysis protocol was registered on PROSPERO with a registration number of CRD 42024610457. This study was designed and conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines19.

Search strategy
A comprehensive literature review was conducted by two authors (XXZ and JZ), who searched the Cochrane Library, Ovid Embase, Ovid MEDLINE, PubMed, Scopus, and Web of Science Core Collection databases. Their search encompassed all relevant records from the start of each database's coverage up to June 30, 2024.

The search strategy employed a combination of controlled vocabulary (e.g., MeSH) and free-text terms pertaining to digital health interventions, depression, anxiety, and stress. To maximize the retrieval of pertinent studies, Boolean operators (AND, OR) were utilized. The search was restricted to publications in the English language. The complete search strategy is detailed in eTable 1 in Supplement 1. Citation Chaser was used to search the reference lists of the included studies and to retrieve articles that had cited the included studies to find additional relevant studies20.

Study selection and inclusion criteria
The inclusion and exclusion criteria for this systematic review were defined according to the Population, Intervention, Comparison, Outcomes, Study Design (PICOS)19 framework: Population (P): Studies involving university students, regardless of age, gender, or field of study. Intervention (I): Any digital health intervention primarily delivered via the internet or a mobile device and aimed at alleviating symptoms of stress, anxiety, or depression. This included, but was not limited to, web-based programs, mobile apps, and chatbot-delivered therapies. Comparison (C): Control groups including wait list controls, active controls (e.g., receiving nondigital psychoeducation), treatment as usual, or other digital health interventions. Outcomes (O): Studies had to report outcomes on at least one of the following, measured using a validated scale: stress (e.g., Perceived Stress Scale, PSS), anxiety (e.g., GAD-7), or depression (e.g., PHQ). Study Design (S): Only randomized controlled trials (RCTs) were included to ensure the highest quality of evidence.

Upon completion of the database search and duplicate removal, the titles and abstracts of the identified records were screened independently by two authors (XXZ and JZ). Any discrepancies encountered were adjudicated by a third reviewer (DZ). Subsequently, the full texts of the remaining articles were assessed for eligibility by two other independent reviewers (DZ and YFC), with a third reviewer (JZ) serving as an arbiter to reach a consensus on final inclusion.

The inclusion criteria encompassed all digital health intervention types (e.g., web-based programs, mobile applications) to facilitate a comprehensive analysis and direct comparison across technological modalities. Studies were required to report outcomes related to stress, anxiety, or depression using validated measurement scales. Studies were excluded if they possessed the following characteristics: lack of assessment of the relevant variables, population outside the intended scope, review articles, abstracts, editorials or letters, animal studies, or case reports. Studies that reported insufficient data for calculating effect sizes, even after attempts to obtain it from the authors, were excluded from the meta-analysis. Conference abstracts were excluded due to the lack of detailed information and in-depth methodology required for a robust assessment of quality and data extraction. Citations from all the databases were imported into an EndNote X21 library (Clarivate Analytics).

Appraisal of methodical quality
The quality of the included RCTs was independently assessed by two authors (DZ and YFC) according to the guidelines of the Cochrane reviews21. The evaluation contents include (1) random sequence generation, (2) allocation concealment, (3) blinding of participants and personnel, (4) blinding of outcome assessment, (5) incomplete outcome data, (6) selective reporting, and (7) other types of bias. Each included study was judged to have a "low", "high", or "unclear" risk of bias for each domain. If the researchers scored the four criteria as "low" and if no serious flaws were detected, then the study was scored as having a low risk of bias.

Extraction of the data
The following data from eligible studies were independently extracted by two authors (JZ and DZ): authors, year of publication, country, participant characteristics (including age, sample size, and distribution of groups), assessment tools employed for depression, anxiety, and stress, digital health interventions (delivery mode, treatment length, control group, intention to treat, attrition rate), and the related statistical data. The procedure for handling missing data (e.g., standard deviations) involved attempting to contact the corresponding authors twice within a four-week period. If no response is received, then SDs will be calculated from standard errors, confidence intervals, or P values provided in the studies, following the methods outlined in the Cochrane Handbook21. Any discrepancies or inconsistencies in the extracted data were resolved through discussion between the two reviewers (JZ and DZ). If a consensus could not be reached, then a third reviewer (XXZ) was consulted to make the final decision.

Data synthesis
For each outcome, the quantitative data were synthesized and are presented as the weighted mean difference (WMD) with a corresponding 95% confidence interval (CI). The WMDs were calculated by pooling the pre-to-post change scores (mean and SD) extracted from each eligible study. Heterogeneity across studies in direct comparisons was assessed using the I2 statistic, with values of 25%, 50%, and 75% conventionally denoting low, moderate, and high heterogeneity, respectively. A fixed effects model was applied when heterogeneity was not significant (I2< 50% and P > 0.1); otherwise, a random-effects model was employed.

The results were visualized using forest plots, which display the first author's name, publication year, sample size, effect estimate with its 95% CI, and associated P value for each study. Subgroup analyses were performed separately for each primary outcome (stress, anxiety, and depression). For each outcome, the analyses were stratified by the specific measurement instrument used (e.g., for stress: Connor-Davidson Resilience Scale (CD-RISC)22, Brief Resilience Scale (BRS)23, Perceived Stress Scale (PSS)24, Depression Anxiety Stress Scale (DASS)-Stress subscale25,26,27; for anxiety: Generalized Anxiety Disorder Scale (GAD)28, State-Trait Anxiety Inventory (STAI), DASS-Anxiety subscale27,29; for depression: Patient Health Questionnaire (PHQ)30, Beck Depression Inventory (BDI)31, DASS-Depression subscale26,27). Other subgroup variables included the following: (1) intervention technique: web- or app-based cognitive-behavioral therapy (CBT), mindfulness-based intervention (MBI), physical-activity intervention (PAI), and other psychological intervention (OPI) excluding CBT, MBI, and PAI; (2) guidance format: reminder only, feedback only, mixed (reminder + feedback), or none; (3) delivery mode: smartphone app, web-based platform/program, or other; (4) treatment duration: ≤ 4 weeks, > 4 to < 8 weeks, or ≥ 8 weeks; (5) recruitment pathway: online, mixed, on-campus, or unspecified; and (6) control group type: active or passive. Passive controls receive no intervention as they serving to control for the natural history of the condition and placebo effects. In contrast, active controls receive an alternative, standard, or placebo intervention to control for nonspecific effects of the intervention process. The assessment of publication bias involved examining funnel plot asymmetry and performing Egger's regression test. Bias was considered to be present if visual asymmetry was accompanied by a significant Egger's test result (P < 0.05). Egger's test was conducted solely for outcomes with 10 or more studies to ensure the test's power32. The primary data analysis, including the generation of forest and funnel plots, was performed using Review Manager (RevMan) version 5.3 software. Additionally, Stata version 18 was used to conduct the statistical assessment for publication bias (Egger's test).

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Results

A total of 20,975 potentially relevant studies were initially identified from the six databases. After 7,374 duplicate records were removed, 13,601 studies underwent screening. Of these, 13,548 were excluded based on their titles and abstracts. The remaining 53 studies underwent full-text review for eligibility. Following this assessment, 31 studies were excluded for the following reasons: (1) use of non-validated or inappropriate measurement scales (n = 7), (2) intervention not meeting the inclusion criteria (n = 5), (3...

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Discussion

The present meta-analysis identified 22 RCTs with 3,655 participants to examine the effectiveness of digital health interventions in reducing stress, anxiety, and depression among university students. As a scalable, low-cost, and readily accessible form of psychological support, digital health interventions have demonstrated strong implementation potential in the college population. Our systematic review and meta-analysis demonstrate that digital health interventions lead to significant reductions in students' stress...

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Disclosures

The authors declare that they have no conflicts of interest.

Acknowledgements

This study was supported by the Provincial First-Class Course in Medical Statistics, a project initiated by the Jiangxi Provincial Department of Education (003031604) and University Student Innovation and Entrepreneurship Program: Sound Healing: A Mobile Health Intervention for College Student Anxiety Based on the Multi-Theory Model (MTM) (2404230055). Xingxin Zhan and Liqin Huang: conceived and designed the research. Xingxin Zhan, Ju Zeng, Dan Zhu and Yifan Chen: performed the study and analyzed the data. Xingxin Zhan: wrote the paper. All authors revised the manuscript.

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Materials

List of materials used in this article
NameCompanyCatalog NumberComments
EndNote ClarivateX21Software used for literature management and screening
Review Manager (RevMan) The Cochrane Collaboration5.3Software used for meta-analysis
stataStataCorp LLC18Software used for statistical analysis and data management

References

  1. Pei, J., et al. Sociodemographic correlates of mental health treatment seeking among college students: A systematic review and meta-analysis. Psychiatr Serv. 75 (6), 556-569 (2024).
  2. Peng, P., et al. The prevalence and risk factors of mental problems in medical students during COVID-19 pandemic: A systematic review and meta-analysis. J Affect Disord. 321, 167-181 (2023).
  3. González-Martín, A. M., et al. Mindfulness to improve the mental health of university students: A systematic review and meta-analysis. Front Public Health. 11, 1284632(2023).
  4. Visier-Alfonso, M. E., et al. mental health, and protective factors in nursing students: An observational study. Nurse Educ Today. 139, 106258(2024).
  5. Ramu, S. D. G., Babu, G., Kumar, M., Shanmugam, J. Prevalence of depression and its determinants among undergraduate medical college students in Salem, Tamil Nadu. Indian J Comm Health. 23, 319-323 (2023).
  6. Koike, S., et al. A randomised controlled trial of repeated filmed social contact on reducing mental illness-related stigma in young adults. Epidemiol Psychiatr Sci. 27 (2), 199-208 (2018).
  7. Melendez-Torres, G. J., Anthony, R. E., Hewitt, G., Murphy, S., Moore, G. F. Prevalence of gambling behaviours and their associations with socioemotional harm among 11-16 year olds in Wales: Findings from the School Health Research Network survey. Eur J Public Health. 30 (3), 432-438 (2020).
  8. Hayes, D., et al. Promoting mental health and well-being in schools: Examining mindfulness, relaxation and strategies for safety and well-being in English primary and secondary schools-Study protocol for a multi-school, cluster randomised controlled trial (INSPIRE). Trials. 24 (1), 220(2023).
  9. Agyapong-Opoku, F., Agyapong-Opoku, N., Agyapong, B., Greenshaw, A. Suicidal behaviors among medical students: A scoping review of systematic reviews and meta-analyses. Behav Sci (Basel). 15 (9), 1215(2025).
  10. Amanvermez, Y., et al. Effects of self-guided stress management interventions in college students: A systematic review and meta-analysis. Internet Interv. 28, 100503(2022).
  11. Dawson, A. F., et al. Mindfulness-based interventions for university students: A systematic review and meta-analysis of randomised controlled trials. Appl Psychol Health Well Being. 12 (2), 384-410 (2020).
  12. López Steinmetz, L. C., Sison, M., Zhumagambetov, R., Godoy, J. C., Haufe, S. Machine learning models predict the emergence of depression in Argentinean college students during periods of COVID-19 quarantine. Front Psychiatry. 15, 1376784(2024).
  13. Nixon, L. S. B., Cassese, A., ten Hoor, G. Effects of the digital intervention StudentPOWR on the subjective wellbeing of students studying from home: A randomized wait-list control trial. Int J Appl Posit Psychol. 35, 165-188 (2024).
  14. O'logbon, J., Wickersham, A., Williamson, C., Leightley, D. The effectiveness of digital health technologies for reducing substance use among young people: A systematic review & meta-analysis. J Ment Health. 33 (5), 645-673 (2024).
  15. Wang, Y., Farb, N. A. S. Web-based training for post-secondary student well-being during the pandemic: A randomized trial. Anxiety Stress Coping. 36 (1), 1-17 (2023).
  16. Gong, X. G., et al. Effects of online mindfulness-based interventions on the mental health of university students: A systematic review and meta-analysis. Front Psychol. 14, 1073647(2023).
  17. Lee, R. A., Jung, M. E. Evaluation of an mHealth app (Destressify) on university students' mental health: Pilot trial. JMIR Ment Health. 5 (1), e2(2018).
  18. Kvillemo, P., Brandberg, Y., Bränström, R. Feasibility and outcomes of an internet-based mindfulness training program: A pilot randomized controlled trial. JMIR Ment Health. 3 (3), e33(2016).
  19. Page, M. A. O., et al. PRISMA 2020 explanation and elaboration: Updated guidance and exemplars for reporting systematic reviews. BMJ. 372, n160(2021).
  20. Haddaway, N. R., Grainger, M. J., Gray, C. T. Citationchaser: A tool for transparent and efficient forward and backward citation chasing in systematic searching. Res Synth Methods. 13 (4), 533-545 (2022).
  21. Cumpston, M., et al. Updated guidance for trusted systematic reviews: A new edition of the Cochrane Handbook for Systematic Reviews of Interventions. Cochrane Database Syst Rev. 10 (10), Ed000142(2019).
  22. Connor, K. M., Davidson, J. R. Development of a new resilience scale: The Connor-Davidson Resilience Scale (CD-RISC). Depress Anxiety. 18 (2), 76-82 (2003).
  23. Smith, B. W., et al. The brief resilience scale: Assessing the ability to bounce back. Int J Behav Med. 15 (3), 194-200 (2008).
  24. Lee, E. H. Review of the psychometric evidence of the Perceived Stress Scale. Asian Nurs Res (Korean Soc Nurs Sci). 6 (4), 121-127 (2012).
  25. Henry, J. D., Crawford, J. R. The short-form version of the Depression Anxiety Stress Scales (DASS-21): Construct validity and normative data in a large non-clinical sample. Br J Clin Psychol. 44 (Pt 2), 227-239 (2005).
  26. Osman, A., et al. The Depression Anxiety Stress Scales-21 (DASS-21): Further examination of dimensions, scale reliability, and correlates. J Clin Psychol. 68 (12), 1322-1338 (2012).
  27. Zanon, C., et al. Examining the dimensionality, reliability, and invariance of the Depression, Anxiety, and Stress Scale-21 (DASS-21) across eight countries. Assessment. 28 (6), 1531-1544 (2021).
  28. Spitzer, R. L., Kroenke, K., Williams, J. B., Löwe, B. A brief measure for assessing generalized anxiety disorder: The GAD-7. Arch Intern Med. 166 (10), 1092-1097 (2006).
  29. Parkitny, L., McAuley, J. The Depression Anxiety Stress Scale (DASS). J Physiother. 56 (3), 204(2010).
  30. Kroenke, K., et al. The PHQ-8 as a measure of current depression in the general population. J Affect Disord. 114 (1-3), 163-173 (2009).
  31. Von Glischinski, M., Von Brachel, R., Hirschfeld, G. How depressed is "depressed"? A systematic review and diagnostic meta-analysis of optimal cut points for the Beck Depression Inventory Revised (BDI-II). Qual Life Res. 28 (5), 1111-1118 (2019).
  32. Sterne, J. A., et al. Recommendations for examining and interpreting funnel plot asymmetry in meta-analyses of randomised controlled trials. BMJ. 343, d4002(2011).
  33. Mailey, E. L., et al. Internet-delivered physical activity intervention for college students with mental health disorders: A randomized pilot trial. Psychol Health Med. 15 (6), 646-659 (2010).
  34. Newman, M. G., Jacobson, N. C., Rackoff, G. N., Bell, M. J., Taylor, C. B. A randomized controlled trial of a smartphone-based application for the treatment of anxiety. Psychother Res. 31 (4), 443-454 (2020).
  35. Huberty, J., et al. Efficacy of the mindfulness meditation mobile app "Calm" to reduce stress among college students: Randomized controlled trial. JMIR mHealth uHealth. 7 (6), e14273(2019).
  36. Dorais, S., Gutierrez, D. The effectiveness of a centering meditation intervention on college stress and mindfulness: A randomized controlled trial. Front Psychol. 12, 720824(2021).
  37. Six, S. G., Byrne, K. A., Aly, H., Harris, M. W. The effect of mental health app customization on depressive symptoms in college students: Randomized controlled trial. JMIR Ment Health. 9 (8), e39516(2022).
  38. Fish, M. T., Saul, A. D. The gamification of meditation: A randomized-controlled study of a prescribed mobile mindfulness meditation application in reducing college students' depression. Simul Gaming. 50 (4), 419-435 (2019).
  39. Day, V., McGrath, P. J., Wojtowicz, M. Internet-based guided self-help for university students with anxiety, depression and stress: A randomized controlled clinical trial. Behav Res Ther. 51 (7), 344-351 (2013).
  40. Grégoire, S., et al. An online peer support program to improve mental health among university students: A randomized controlled trial. J Am Coll Health. 72 (7), 2001-2013 (2022).
  41. Bastien, L., et al. Peer-presented versus mental health service provider-presented mental health outreach programs for university students: Randomized controlled trial. JMIR Ment Health. 9 (7), e34168(2022).
  42. Ahmad, F., El Morr, C., Ritvo, P., Othman, N., Moineddin, R. An eight-week, web-based mindfulness virtual community intervention for students' mental health: Randomized controlled trial. JMIR Ment Health. 7 (2), e15520(2020).
  43. Karing, C. Long-term effects of combined mindfulness intervention and app intervention compared to single interventions during the COVID-19 pandemic: A randomized controlled trial. Front Psychol. 19 (15), 1355757(2024).
  44. Harrer, M., et al. Effectiveness of an internet- and app-based intervention for college students with elevated stress: Randomized controlled trial. J Med Internet Res. 20 (4), e136(2018).
  45. Richards, D., et al. Effectiveness of an internet-delivered intervention for generalized anxiety disorder in routine care: A randomised controlled trial in a student population. Internet Interv. 6, 80-88 (2016).
  46. Enrique Roig, A., et al. Assessing the efficacy and acceptability of a web-based intervention for resilience among college students: Pilot randomized controlled trial. JMIR Form Res. 4 (11), e20167(2020).
  47. Barcaccia, B., Medvedev, O. N., Pallini, S., Mastandrea, S., Fagioli, S. Examining mental health benefits of a brief online mindfulness intervention: A randomised controlled trial. Mindfulness. 15 (4), 835-843 (2024).
  48. Fazia, T., et al. Improving stress management, anxiety, and mental well-being in medical students through an online mindfulness-based intervention: A randomized study. Sci Rep. 13 (1), 8214(2023).
  49. Min, Y., Choi, Y. S., Kim, B. N. Filling the mental service gap on campus: An effectiveness trial testing the utility of app-based mindfulness psychological intervention for college students. Curr Psychol. 43 (17), 15434-15444 (2023).
  50. Jeong, S., Cha, C., Nam, S., Song, J. The effects of mobile technology-based support on young women with depressive symptoms: A block randomized controlled trial. Medicine. 103 (1), e36748(2024).
  51. Morris, J., et al. Internet-delivered cognitive behavior therapy for anxiety and insomnia in a higher education context. Anxiety Stress Coping. 29 (4), 415-431 (2015).
  52. Ditton, E., et al. Evaluation of an app-delivered psychological flexibility skill training intervention for medical student burnout and well-being: Randomized controlled trial. JMIR Ment Health. 10, e42566(2023).
  53. Andersson, C., et al. The effectiveness of smartphone compassion training on stress among Swedish university students: A pilot randomized trial. J Clin Psychol. 77 (4), 927-945 (2020).
  54. Tolin, D. F. Is cognitive-behavioral therapy more effective than other therapies? A meta-analytic review. Clin Psychol Rev. 30 (6), 710-720 (2010).
  55. Ramón-Arbués, E., et al. The prevalence of depression, anxiety and stress and their associated factors in college students. Int J Environ Res Public Health. 17 (19), 7001(2020).
  56. Li, J., Xu, C., Wan, K., Liu, Y., Liu, L. Mindfulness-based interventions to reduce anxiety among Chinese college students: A systematic review and meta-analysis. Front Psychol. 13, 1031398(2022).
  57. Yi, Q. F., et al. The experience of anxiety among Chinese undergraduate nursing students in the later period of their internships: Findings from a qualitative study. BMC Nurs. 21 (1), 70(2022).
  58. Lafreniere, L. S., Newman, M. G. Exposing worry's deceit: Percentage of untrue worries in generalized anxiety disorder treatment. Behav Ther. 51 (3), 413-423 (2020).
  59. Segal, Z. V., et al. Outcomes of online mindfulness-based cognitive therapy for patients with residual depressive symptoms: A randomized clinical trial. JAMA Psychiatry. 77 (6), 563-573 (2020).
  60. Drachev, S. N., et al. Perceived stress and associated factors in Russian medical and dental students: A cross-sectional study in North-West Russia. Int J Environ Res Public Health. 17 (15), 5390(2020).
  61. Eskildsen, A., et al. Cross-cultural adaptation and validation of the Danish consensus version of the 10-item Perceived Stress Scale. Scand J Work Environ Health. 41 (5), 486-490 (2015).
  62. Carter, S., Greenberg, J., Funes, C. J., Macklin, E. A., Vranceanu, A. M. Effects of a mind-body program on symptoms of depression and perceived stress among adults with neurofibromatosis type 2 who are deaf: A live-video randomized controlled trial. Complement Ther Med. 56, 102581(2021).
  63. Avila-Palencia, I., et al. The relationship between bicycle commuting and perceived stress: A cross-sectional study. BMJ Open. 7 (6), e013542(2017).
  64. Arumugam, V., et al. Effects of Ashwagandha (Withania somnifera) on stress and anxiety: A systematic review and meta-analysis. Explore (NY). 20 (6), 103062(2024).
  65. Toussaint, A., et al. Sensitivity to change and minimal clinically important difference of the 7-item Generalized Anxiety Disorder Questionnaire (GAD-7). J Affect Disord. 265, 395-401 (2020).
  66. Lenzen, M., Van Domburg, R., Hoeks, S., Kardys, I., Boersma, E. Tools & techniques methodology and statistics: Randomisation in clinical trials. EuroIntervention. 9 (3), 410-411 (2013).
  67. Plummer, F., Manea, L., Trepel, D., McMillan, D. Screening for anxiety disorders with the GAD-7 and GAD-2: A systematic review and diagnostic meta-analysis. Gen Hosp Psychiatry. 39, 24-31 (2016).
  68. Patel, J. S., et al. Measurement invariance of the Patient Health Questionnaire-9 (PHQ-9) depression screener in U.S. adults across sex, race/ethnicity, and education level: NHANES 2005-2016. Depress Anxiety. 36 (9), 813-823 (2019).
  69. Harrer, M., et al. Internet interventions for mental health in university students: A systematic review and meta-analysis. Int J Methods Psychiatr Res. 28 (2), e1759(2019).
  70. Higgins, J. P. T., Chandler, J., Cumpston, M., Li, T., Page, M. J., Welch, V. A. Cochrane Handbook for Systematic Reviews of Interventions Version 6.4. , Cochrane. (2023).
  71. Wang, Q., Zhang, W., An, S. A systematic review and meta-analysis of internet-based self-help interventions for mental health among adolescents and college students. Internet Interv. 34, 100690(2023).
  72. Antonopoulou, M., et al. Evaluating Mediterranean diet adherence in university student populations: Does this dietary pattern affect students' academic performance and mental health. Int J Health Plann Manage. 35 (1), 5-21 (2020).
  73. Herrero, R., et al. An internet-based intervention for improving resilience and coping strategies in university students: Study protocol for a randomized controlled trial. Internet Interv. 16, 43-51 (2019).
  74. Gallegos, C., Kausler, R., Alderden, J., Davis, M., Wang, L. Can artificial intelligence chatbots improve mental health?: A scoping review. Comput Inform Nurs. 42 (10), 731-736 (2024).
  75. Lattie, E. G., et al. Digital mental health interventions for depression, anxiety, and enhancement of psychological well-being among college students: Systematic review. J Med Internet Res. 21 (7), e12869(2019).
  76. Sun, X., Jahangir, A., Alghamdi, A. A., Liu, F. Use of AI-based mental health tools and psychological well-being among Chinese university students: A parallel mediation model of emotional self-efficacy and perceived autonomy. Sci Rep. 15 (1), 40305(2025).

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