Mental and Emotional Self-Help Technology Apps: Cross-Sectional Study of Theory, Technology, and Mental Health Behaviors
Bibliographic record
Abstract
BACKGROUND: Mental and emotional self-help apps have emerged as potential mental illness prevention and treatment tools. The health behavior theory mechanisms by which these apps influence mental health-related behavior change have not been thoroughly examined. OBJECTIVE: The objective of this study was to examine the association between theoretical behavior change mechanisms and use of mental and emotional self-help apps and whether the use of such apps is associated with mental health behaviors. METHODS: This study utilized a cross-sectional survey of 150 users of mental or emotional health apps in the past 6 months. Survey questions included theory-based items, app engagement and likeability items, and behavior change items. Stata version 14 was used to calculate all statistics. Descriptive statistics were calculated for each of the demographic, theory, engagement, and behavior variables. Multiple regression analysis was used to identify factors associated with reported changes in theory and separately for reported changes in actual behavior after controlling for potentially confounding variables. RESULTS: Participants reported that app use increased their motivation, desire to set goals, confidence, control, and intentions to be mentally and emotionally healthy. Engagement (P<.001) was positively associated with the reported changes in theory items, whereas perceived behavior change was positively associated with theory (P<.001), engagement (P=.004), frequency of use of apps (P=.01), and income (P=.049). CONCLUSIONS: Participants reported that app use increased their motivation, desire to set goals, confidence, control, and intentions to be mentally and emotionally healthy. This increase in perceptions, beliefs, and attitudes surrounding their mental and emotional health was considerably associated with perceived change in behavior. There was a positive association between the level of engagement with the app and the impact on theory items. Future efforts should consider the value of impacting key theoretical constructs when designing mental and emotional health apps. As apps are evaluated and additional theory-based apps are created, cost-effective self-help apps may become common preventative and treatment tools in the mental health field.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".