A Guided, Internet-Based Stress Management Intervention for University Students With High Levels of Stress: Feasibility and Acceptability Study
Bibliographic record
Abstract
BACKGROUND: Transitioning to adulthood and challenges in university life can result in increased stress levels among university students. Chronic and severe stress is associated with deleterious psychological and physiological effects. Digital interventions could succeed in approaching and helping university students who might be at risk; however, the experiences of students with internet-based stress management interventions are insufficiently understood. OBJECTIVE: This study aims to explore the feasibility; acceptability; and changes in perceived stress, depressive symptoms, and quality of life from baseline to posttest assessment of a 5-session, internet-based stress management intervention guided by an e-coach, developed for university students experiencing high levels of stress. METHODS: A single-arm study was conducted. Students were recruited from different channels, mainly from a web survey. Students were eligible if they (1) scored ≥20 on the Perceived Stress Scale-10, (2) were aged ≥18 years, and (3) were studying at one of the participating universities. Feasibility and acceptability of the intervention were investigated using several indications, including satisfaction (Client Satisfaction Questionnaire-8) and usability (System Usability Scale-10). We also investigated the indicators of intervention adherence using use metrics (eg, the number of completed sessions). Our secondary goal was to explore the changes in perceived stress (Perceived Stress Scale-10), depressive symptoms (Patient Health Questionnaire-9), and quality of life (EQ-5D-5L scale) from baseline to posttest assessment. In addition, we conducted semistructured interviews with intervention completers and noncompleters to understand user experiences in depth. For all primary outcomes, descriptive statistics were calculated. Changes from baseline to posttest assessment were examined using 2-tailed paired sample t tests or the Wilcoxon signed rank test. Qualitative data were analyzed using thematic analysis. RESULTS: Of 436 eligible students, 307 (70.4%) students started using the intervention. Overall, 25.7% (79/307) completed the core sessions (ie, sessions 1-3) and posttest assessment. A substantial proportion of the students (228/307, 74.3%) did not complete the core sessions or the posttest assessment. Students who completed the core sessions reported high satisfaction (mean 25.78, SD 3.30) and high usability of the intervention (mean 86.01, SD 10.25). Moreover, this group showed large reductions in perceived stress (Cohen d=0.80) and moderate improvements in depression score (Cohen d=0.47) and quality of life (Cohen d=-0.35) from baseline to posttest assessment. Qualitative findings highlight that several personal and intervention-related factors play a role in user experience. CONCLUSIONS: The internet-based stress management intervention seems to be feasible, acceptable, and possibly effective for some university students with elevated stress levels. However, given the high dropout rate and qualitative findings, several adjustments in the content and features of the intervention are needed to maximize the user experience and the impact of the intervention. TRIAL REGISTRATION: Netherlands Trial Register 8686; https://onderzoekmetmensen.nl/nl/trial/20889. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1016/j.invent.2021.100369.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".