Users’ Perspectives on mHealth Self-Management of Bipolar Disorder: Qualitative Focus Group Study
Bibliographic record
Abstract
BACKGROUND: Recent research indicates that current mHealth apps for bipolar disorders (BDs) show crucial shortcomings. They lack important functionality, are of inconsistent quality, and are insufficiently evidence-based. mHealth apps need to be better adapted to the needs of users. The perspectives of adult service users with BD regarding mHealth apps have not been well investigated. OBJECTIVE: The objective of this study was to examine the needs and expectations of adults with BD regarding mHealth apps. METHODS: Two focus group sessions were organized in which patients' views on self-management and design and functionality of an mHealth app for BD were assessed. During session 1, four focus groups were organized to identify users' needs regarding support for self-management. Session 2 contained three cocreation focus groups. Through this method, the desired functionality and design were explored. RESULTS: Participants indicated that they were in need of support in various ways. Not only support in psychoeducation, including daily routine, sleep pattern, maintaining social contacts, maintaining a healthy lifestyle, and avoidance of stimuli, was considered important for them but also gaining insight into their illness was found to be crucial. CONCLUSIONS: According to the participants, their illness-related information is a key factor in gaining insight into their mood pattern. Participants wanted a functional design that would increase daily use and prevent overstimulation. The results of this study should be taken into account when developing new mHealth apps.
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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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".