A Mobile App Designed to Promote Shared Decision-Making in the Treatment of Psychotic Disorders: Feasibility and Acceptability Study
Bibliographic record
Abstract
Background: Strengthening shared decision-making in mental health care may improve the quality of services and treatment outcomes, but its implementation in services for severe mental disorders is currently lacking. Objective: This study aims to explore the feasibility and acceptability of iTandem (University of Oslo), a mobile app designed to promote shared decision-making in the treatment of psychotic disorders. In addition, the study aims to investigate mechanisms that potentially contribute to the intended effect of the app. iTandem is a therapy supplement that facilitates patient involvement in decisions regarding treatment goals and focus areas. It is designed for personalized use and contains 8 optional modules: sleep, medication, recovery, mood, psychosis, activity, substance use, and feedback concerning therapy. Methods: Patients undergoing assessment or treatment for psychotic disorders and their clinicians were recruited for the study. Patients and clinicians jointly used iTandem as part of standard treatment in a 6-week trial. We used a mixed-methods study design with a clear emphasis on qualitative methods. Feasibility and acceptability were assessed through descriptive statistics based on preintervention and postintervention questionnaires and app usage data, in addition to text responses to open-ended items. We conducted a reflexive thematic analysis of postintervention interviews to elaborate these measures and to explore mechanisms potentially contributing to achieving shared decision-making when using iTandem. Results: A total of 9 patients and 8 clinicians completed the trial. The participants evaluated iTandem as a user-friendly and acceptable tool, but there were considerable variations in how the app was integrated into treatment and in perceptions of its clinical value. The thematic analysis suggests that iTandem has the potential to facilitate shared decision-making through supporting cognition and shifting the patient's role. We also identified scaffolding structures, an analogy of personalized support, as a precondition for these mechanisms and for the overall feasibility and acceptability of iTandem. Conclusions: iTandem was generally perceived as a feasible and acceptable tool in the treatment of patients with psychotic disorders. Our findings suggest that nonclinical aspects, such as support structures, are important to the feasibility and acceptability of such digital interventions and patients' aptness for digitalized treatment in general. Future research should explore related nonclinical aspects further instead of defining potential target groups based on diagnoses and symptom severity alone.
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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 | 0.011 | 0.034 |
| 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) | 0.004 | 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".