Preferences for an Experience Sampling Method–Based Tool as an Adjunct to Usual Treatment in Patients With Problem Substance Use: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Mobile health tools that use the Experience Sampling Method (ESM) appear to be a promising tool to streamline and improve the treatment of substance use disorders. However, patient involvement in the development of these tools is uncommon, and research on the preferences of people being treated for substance use disorders has been scarce. In the scope of the European Union IMMERSE (Implementing Mobile Mental health Recording Strategy for Europe) consortium, an ESM-based tool for Digital Mobile Mental Health (DMMH) was first codeveloped and later tested in 4 European countries. OBJECTIVE: This study aimed to achieve an understanding of preferences for features of DMMH among mental health service users with problem substance use. METHODS: In 4 European countries, service users were recruited for a semistructured qualitative interview, which started with the presentation of a prototype of the DMMH. Data analysis was performed through directed qualitative content analysis. RESULTS: The analytical sample consisted of 12 (5 female, 6 male, and 1 nonbinary person) participants with problem substance use aged 18-50 years. There were 4 participants from Slovakia, 3 from Belgium, 4 from Germany, and 1 from Scotland. Patient preferences were classified into 7 categories: notifications, questions, user interface, functionality changes, visualizations, sharing data with clinicians, and sharing data with others. The proposed intensive notification schedule was deemed acceptable by service users as long as the questionnaire is short. Participants expressed a preference for open-text response options, Ecological Momentary Interventions, and options for individual customization of several elements of the tool. Data visualization was considered an important aid for communication with clinicians, with whom all participants wanted to share data obtained with DMMH. The possibility of sharing data with other people depended on the quality of the relationship with them. CONCLUSIONS: In the development of ESM-based mobile health tools for people with problem substance use, their preferences for content, functionality, and appearance of the tools should be considered so they match patients' treatment needs.
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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 | 0.046 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".