Examining the effects of engagement with an app-based mental health intervention: a secondary analysis of a randomized control trial with treatment non-compliance
Bibliographic record
Abstract
BACKGROUND: Minder is a mental health and substance use mobile application found to have a small but significant effects in a recent randomized trial. Poor engagement has been identified as a common threat to the effectiveness of digital mental health tools that is not accounted for in intention-to-treat analyses. The objective of this study is to conduct a prespecified secondary analyses to identify factors associated with engagement and examine the impact of engagement on trial outcomes. METHODS: 1489 students were randomized to either the intervention (n = 743) or waitlist control (n = 746). Primary outcomes were changes in anxiety (General Anxiety Disorder 7 (GAD-7)), depression (Patient Health Questionnaire 9 (PHQ-9)), and alcohol consumption (US Alcohol Use Disorders Identification Test-Consumption Scale (USAUDIT-CS)) at 30-days. Secondary outcomes included frequency of substance use and mental wellbeing (Short Warwick-Edinburgh Mental Wellbeing Scale (SWEMWS)). A Complier Average Causal Effect (CACE) analysis was conducted using 3 separate criteria reflecting differing engagement levels: (1) a binary measure: use of any app component, (2) a continuous measure: number of unique days of app use, and (3) an ordinal measure: number of components accessed within the app. RESULTS: 80.4% of participants used at least one app feature. Statistically significant differences were observed in app utilization across gender, ethnicity, having a history of depression or anxiety, higher baseline PHQ-9, higher SWEMWS, and poor/fair overall self-assessed mental and physical health. Any use of Minder was associated with significantly lower scores on the GAD-7 (adjusted group mean difference = - 1.09, 95% CI - 1.60 to - 0.57; P < .01) and PHQ-9 (adjusted group mean difference = - 0.84, 95% CI - 1.41 to - 0.27; P < .01) with increasing number of unique utilization days or components accessed associated with increased reductions. Any use of Minder was associated with significantly higher scores on the SWEMWS (adjusted group mean difference = 0.93, 95% CI 0.46 to 1.39; P < .01) and lower frequency of cannabis use (adjusted group mean difference = - 0.15, 95% CI - 0.23 to - 0.06; P < .01) with increased app utilization associated with larger improvements. CONCLUSIONS: The CACE analysis identified significant dose-response relationships indicating that increased use of the Minder app leads to larger effects that can reach levels of clinical significance. TRIAL REGISTRATION: ClinicalTrials.gov NCT05606601 (November 3, 2022); https://clinicaltrials.gov/ct2/show/NCT05606601 .
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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 | 0.039 | 0.070 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.016 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 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".