Improving the Utility, Safety, and Ethical Use of a Passive Mood-Tracking App for People With Bipolar Disorder Using Coproduction: Qualitative Focus Group Study
Bibliographic record
Abstract
BACKGROUND: Coproduction with users of new digital technology, such as passive mood monitoring, is likely to improve its utility, safety, and successful implementation via improved design and consideration of how such technology fits with their daily lives. Mood-monitoring interventions are commonly used by people with bipolar disorder (BD) and have promising potential for digitization using novel technological methods. OBJECTIVE: This study aims to explore how a passive behavioral monitoring platform, Remote Assessment of Disease and Relapse, would meet the needs of people with BD by specifically considering purpose and function, diversity of need, personal preference, essential components and potential risks, and harms and mitigation strategies through an iterative coproduction process. METHODS: A total of 17 people with BD were recruited via national charities. We conducted 3 web-based focus groups as a part of an iterative coproduction process in line with responsible research and innovation principles and with consideration of clinical challenges associated with BD. Data were analyzed thematically. Results were cross-checked by someone with lived experience of BD. RESULTS: Focus groups were transcribed and analyzed using thematic analysis. Six themes were identified as follows: (1) the purpose of using the app, (2) desired features, (3) when to use the app, (4) risks of using the app, (5) sharing with family and friends, and (6) sharing with health care professionals. CONCLUSIONS: People with BD who are interested in using passive technology to monitor their mood wish to do so for a wide variety of purposes, identifying several preferences and potential risks. Principally, people with BD wished to use this novel technology to aid them in self-managing their BD with greater insight and a better understanding of potential triggers. We discuss key features that may aid this functionality and purpose, including crisis plans and sharing with others. Future development of passive mood-monitoring technologies should not assume that the involvement of formal mental health services is desired.
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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 | 0.017 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".