Bridging the Digital Divide in Psychological Therapies: Observational Study of Engagement With the SlowMo Mobile App for Paranoia in Psychosis
Bibliographic record
Abstract
BACKGROUND: Marginalized groups are more likely to experience problems with technology-related access, motivation, and skills. This is known as the "digital divide." Technology-related exclusion is a potential barrier to the equitable implementation of digital health. SlowMo therapy was developed with an inclusive, human-centered design to optimize accessibility and bridge the "digital divide." SlowMo is an effective, blended digital psychological therapy for paranoia in psychosis. OBJECTIVE: This study explores the "digital divide" and mobile app engagement in the SlowMo randomized controlled trial. METHODS: Digital literacy was assessed at baseline, and a multidimensional assessment of engagement (ie, adherence [via system analytics and self-report] and self-reported user experience) was conducted at 12 weeks after therapy. Engagement was investigated in relation to demographics (ie, gender, age, ethnicity, and paranoia severity). RESULTS: Digital literacy data demonstrated that technology use and confidence were lower in Black people and older people (n=168). The engagement findings indicated that 80.7% (96/119) of therapy completers met the a priori analytics adherence criteria. However, analytics adherence did not differ by demographics. High rates of user experience were reported overall (overall score: mean 75%, SD 17.1%; n=82). No differences in user experience were found for ethnicity, age, or paranoia severity, although self-reported app use, enjoyment, and usefulness were higher in women than in men. CONCLUSIONS: This study identified technology-related inequalities related to age and ethnicity, which did not influence engagement with SlowMo, suggesting that the therapy design bridged the "digital divide." Intervention design may moderate the influence of individual differences on engagement. We recommend the adoption of inclusive, human-centered design to reduce the impact of the "digital divide" on therapy outcomes. TRIAL REGISTRATION: ISRCTN Registry ISRCTN32448671; https://www.isrctn.com/ISRCTN32448671.
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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 | 0.001 | 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".