Evaluating the Effects of a Self-Help Mobile Phone App on Worry and Rumination Experienced by Young Adults: Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Delivery of preventative interventions via mobile phone apps offers an effective and accessible way to address the global priority of improving the mental health of adolescents and young adults. A proven risk factor for anxiety and depression is elevated worry and rumination, also known as repetitive negative thinking (RNT). OBJECTIVE: This was a prevention mechanism trial that aimed to investigate whether an RNT-targeting self-help mobile phone app (MyMoodCoach) reduces worry and rumination in young adults residing in the United Kingdom. A secondary objective was to test whether the app reduces symptoms of anxiety and depression and improves well-being. METHODS: A web-based, single-blind, 2-arm parallel-group randomized controlled trial was conducted with 236 people aged between 16 and 24 years, who self-reported high levels of worry or rumination. Eligible participants were randomized to an active intervention group (usual practice, plus up to 6 weeks of using the RNT-targeting mobile app, n=119) or a waitlist control group (usual practice with no access to the app until after 6 weeks, n=117). The primary outcome was changes in worry and rumination 6 weeks after randomization. Secondary outcomes included changes in well-being and symptoms of anxiety and depression after 6 weeks and changes in all measures after 12 weeks. RESULTS: =0.06) at 6-week follow-up, relative to the waitlist control. Similar differences were observed for well-being (P<.001), anxiety (P=.03), and depression (P=.04). The waitlist control group also showed improvement when given access to the app after 6 weeks. Improvements observed in the intervention group after 6 weeks of using the app were maintained at the 12-week follow-up point. CONCLUSIONS: The MyMoodCoach app had a significant positive effect on worry and rumination, well-being, anxiety, and depression in young adults, relative to waitlist controls, providing proof-of-principle that an unguided self-help app can effectively reduce RNT. This app, therefore, has potential for the prevention of anxiety and depression although longer-term effects on incidence need to be directly evaluated. TRIAL REGISTRATION: ClinicalTrials.gov NCT04950257; https://www.clinicaltrials.gov/ct2/show/NCT04950257. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/s12888-021-03536-0.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| 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 | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".