Effectiveness of a Mobile App Intervention for Anxiety and Depression Symptoms in University Students: Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Depression and anxiety symptoms are common among university students, but many do not receive treatment. This is often because of lack of availability, reluctance to seek help, and not meeting the diagnostic criteria required to access services. Internet-based interventions, including smartphone apps, can overcome these issues. However, a large number of apps are available, each with little evidence of their effectiveness. OBJECTIVE: This study aims to evaluate for the first time the effectiveness of a self-guided mobile app, Feel Stress Free, for the treatment of depression and anxiety symptoms in students. METHODS: A web-based randomized controlled trial compared a cognitive behavioral therapy (CBT)-based mobile app Feel Stress Free with a wait-list control. University students self-identified as experiencing symptoms of anxiety or depression and were randomized to 6 weeks of intervention (n=84) or control (n=84), unblinded. The app is self-guided and incorporates behavioral relaxation activities, mood tracking and thought challenging, and minigames. Participants completed the Hospital Anxiety and Depression Scale online at baseline and every fortnight. RESULTS: At week 6, the primary end point, there was evidence that the Feel Stress Free app reduced depression symptoms (mean difference -1.56; 95% CI -2.67 to -0.44; P=.006) but only very weak evidence that it reduced anxiety symptoms (mean difference -1.36; 95% CI -2.93 to 0.21; P=.09). At week 4, there was evidence to support the effectiveness of the intervention for anxiety symptoms (mean difference -1.94; 95% CI -3.11 to -0.77; P=.001) and, though weaker, depression symptoms (mean difference -1.08; 95% CI -2.12 to -0.04; P=.04). At week 6, 83% (34/41) of participants indicated that they were using the app weekly or more frequently. CONCLUSIONS: The Feel Stress Free app is a promising mobile intervention for treating symptoms of anxiety and depression in students and overcomes many of the barriers to traditional CBT. Further research is needed to establish its effectiveness at and beyond 6 weeks. TRIAL REGISTRATION: ClinicalTrials.gov NCT03032952; https://clinicaltrials.gov/ct2/show/NCT03032952.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".