Efficacy of the Mental Health App Intellect to Reduce Stress: Randomized Controlled Trial With a 1-Month Follow-up
Bibliographic record
Abstract
Background Excessive stress is a major global health concern, particularly in young adults. Short skills-focused self-guided interventions (SGIs) on smartphones are a scalable way to improve stress-coping skills at the population level. Objective In this randomized controlled trial, we aimed to examine the possible efficacy of a recently developed stress-coping SGI (Intellect) in improving psychological distress, relative to an active control group and 2 potential moderators of this predicted relationship (ie, psychological mindedness [PM] and coping self-efficacy [CSE]). Methods University students (N=321) were randomly assigned to either an 8-day SGI on stress-coping or an active control group. Self-reported measures were obtained at baseline, after the intervention, and at the 1-month follow-up. The primary outcome was psychological stress (Psychological Stress Measure-9). Secondary outcomes were anxiety (Generalized Anxiety Disorder-7) and depressive symptoms (Patient Health Questionnaire-9). PM and CSE were assessed as potential moderators at baseline. Results The final sample (n=264) included 188 (71.2%) female, 66 (25%) male, 7 (2.7%) nonbinary, and 3 (1.1%) others participants with a mean age of 22.5 (SD 5.41) years. The intervention group reported significantly lower perceived stress (partial eta–squared [ηp2]=0.018; P=.03) and anxiety (ηp2=0.019; P=.03) levels after intervention relative to the active control group. The effects on perceived stress levels remained statistically significant at the 1-month follow-up (ηp2=0.015; P=.05). Students with the lowest CSE and highest PM experienced the fastest decline in perceived stress levels (β=6.37, 95% Cl 2.98-9.75). Improvements in anxiety levels were not observed at 1-month follow-up. Similarly, no intervention effects were found for depression levels at postintervention and follow-up periods. Conclusions This study provides evidence that the Intellect stress-coping SGI is effective in reducing perceived stress and anxiety levels among university students. Mobile health apps are brief, scalable, and can make important contributions to public mental health. Trial Registration ClinicalTrials.gov NCT04978896; https://www.clinicaltrials.gov/ct2/show/NCT04978896
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".