Self-Directed Engagement with a Mobile App (Sinasprite) and Its Effects on Confidence in Coping Skills, Depression, and Anxiety: Retrospective Longitudinal Study
Bibliographic record
Abstract
BACKGROUND: Inadequacies in mental health care coverage remain an enormous problem in the United States. Barriers include scarcity of accessible mental health care professionals. Use of a mental health mobile app incorporating social cognitive theory may help improve confidence in coping skills and improve anxiety and depression. Sinasprite is a mobile app that recruited users via self-referral and clinician referral. Users completed questionnaires to obtain demographic and medical histories. At baseline and 6-week follow-up, users completed the Patient Health Questionnaire 8 (PHQ-8), General Anxiety Disorder 7-Item (GAD-7), and the Coping Self-Efficacy Scale (CSE). It is unknown how self-directed use of a mobile app improves confidence in coping skills and its effects on self-reported depression and anxiety. OBJECTIVE: The objective of this study was to evaluate the Sinasprite database to assess self-directed engagement and how use of this mobile app impacted self-reported confidence in coping skills and severity of depression and anxiety. METHODS: This retrospective longitudinal study involved users recruited via clinician referral and self-referral through social media and news media. Questionnaires were used to record demographic, medical, and prescription medication histories. Mental health status was assessed via PHQ-8, GAD-7, and CSE questionnaires. A deidentified dataset reporting mobile app use data was provided to investigators. Individuals with verifiable usage data and at least one completed questionnaire at 6 weeks of use were included. Mann-Whitney U and Kruskal-Wallis tests were used to assess whether demographic data and psychotherapy were related to baseline questionnaire scores and usage. A Spearman rho (ρ) test was used to assess the relationship between improvement in the CSE and GAD-7 and PHQ-8 questionnaires. Changes in mental health status were assessed using Wilcoxon signed-rank test. A mixed-effects repeated-measures linear regression model assessed the main effects of time, concomitant counseling, and psychotropic prescription medication use on mental health status. RESULTS: Thirty-four users were eligible for inclusion in the analysis. Users were predominantly female, white, married, and college educated. At baseline, 35% (12/34) of respondents reported the use of individual/group counseling, and 38% (19/34) reported using prescription medications for their mental health. The median user completed 5.7 (interquartile range 2.7-14.1) trackable activities per week. Statistically significant improvements using a Wilcoxon signed-ranked test were observed in the PHQ-8 (P<.001), GAD-7 (P=.002), and CSE (P<.001) questionnaire scores. A strong positive correlation between improvement in the GAD-7 and CSE questionnaire scores (ρ=.572, P=.001, n=28) was observed. The mixed-effects repeated-measures regression model revealed a statistically significant effect of time on improvements in the PHQ-8 (P<.001), GAD-7 (P=.007), and CSE (P=.001). CONCLUSIONS: This 6-week retrospective study showed that self-directed use of the mobile app, Sinasprite, resulted in significant improvements in self-reported questionnaire scores reflecting depression, anxiety, and confidence in coping skills.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".