The experience of self-monitoring using the PolarUs bipolar disorder self-management app: a qualitative report of impacts and unmet needs
Bibliographic record
Abstract
BACKGROUND: Self-monitoring is key to detecting and preventing mood episodes in bipolar-disorder (BD). An increasing number of smartphone apps have been developed to facilitate this aspect of self-management; however, the feasibility and efficacy of these interventions is heterogenous. To improve understanding of the subjective experience of app-based self-monitoring interventions for BD, the present study describes a qualitative investigation of the perspectives of individuals participating in an evaluation of a novel self-management app. METHODS: Twenty-five individuals with BD were given access to PolarUs, an app-based self-management intervention, and were later questioned about perceptions of and engagement with this tool. Thematic analysis was used to identify important aspects of the experience of self-monitoring as part of an app-based intervention. RESULTS: Four themes describing experiences of self-monitoring were generated. These included increased self-awareness, the use of self-monitoring to guide self-management, positive and negative emotional responses to self-monitoring, and unmet needs for self-monitoring apps. Three subthemes describing unmet needs were identified, including the provision of proactive coping suggestions, support reviewing data, and tracking additional symptoms, behaviours and life areas. CONCLUSIONS: The present study highlights the importance of taking the subjective experience of users into account during the development, evaluation, and implementation of app-based monitoring interventions in BD. Implications for the use of passively collected data and personalisation of app delivery are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".