A Mobile Health App (ChillTime) Promoting Emotion Regulation in Dual Disorders: Acceptability and Feasibility Pilot Study
Bibliographic record
Abstract
BACKGROUND: A growing number of studies highlight the importance of emotion regulation in the treatment and recovery of individuals with psychosis and concomitant disorders such as substance use disorder (SUD), for whom access to integrated dual-disorder treatments is particularly difficult. In this context, dedicated smartphone apps may be useful tools to provide immediate support to individuals in need. However, few studies to date have focused on the development and assessment of apps aimed at promoting emotional regulation for people with psychosis. OBJECTIVE: The aim of this study was to evaluate the feasibility, acceptability, and potential clinical impact of a dedicated app (ChillTime) for individuals with psychotic disorders and concurrent SUD. The app design process followed recommendations for reducing cognitive effort on a mobile app. A total of 20 coping strategies regrouped in four categories (behavioral, emotional, cognitive, spiritual) were included in the app. METHODS: This open pilot study followed a pre-post design. After the initial assessment, researchers asked participants to use the app as part of their treatment over a 30-day period. Feasibility was determined by the frequency of use of the app and measured using the number of completed strategies. Acceptability was determined by measuring ease of use, ease of learning, satisfaction, and perceived utility at the end of the 30-day study period based on responses to satisfaction questionnaires. Clinical scales measuring emotion regulation, substance use (ie, type of substance, amount taken, and frequency of use), and various psychiatric symptoms were administered at the beginning and end of the 30-day period. RESULTS: A total of 13 participants were recruited from two first-episode psychosis clinics in Montreal, Quebec, Canada. All participants were symptomatically stable, were between 18 and 35 years of age (mostly men; 70% of the sample), and had a schizophrenia spectrum disorder with a comorbid substance use diagnosis. A total of 11 participants completed the study (attrition<20%). Approximately half of the participants used the tool at least 33% of the days (11-21 days). Cognitive and emotion-focused techniques were rated the highest in terms of usefulness and were the most frequently used. The majority of participants gave positive answers about the ease of use and the ease of learning the tool. A nonsignificant association of ChillTime use with negative symptoms and drug use was observed. No other statistically significant changes were observed. CONCLUSIONS: The ChillTime app showed good feasibility (approximately half of the participants used the tool at least 33% of the days) and acceptability among people with schizophrenia spectrum disorder and SUD. Trends suggesting a potential impact on certain clinical outcomes will need to be replicated in larger-sample studies before any conclusion can be drawn.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".