What influences engagement with a bipolar disorder self-management app? A qualitative investigation of use of the PolarUs app
Bibliographic record
Abstract
Interventions delivered via smartphone apps may support individuals with bipolar disorder (BD) to learn about and implement evidence-based self-management strategies in the context of their daily lives. However, app usage rates are often suboptimal. The subjective experience of users may provide insights into factors influencing engagement (and disengagement) with an mHealth intervention. The present study describes a qualitative investigation of the experiences of people with BD who participated in the evaluation of a novel app-based intervention for BD self-management, the PolarUs app. Twenty-five individuals with BD were provided with access to an app-based self-management intervention over a three-month study period, and were later interviewed about personal experiences of engagement with the intervention, including attempts to enact self-management strategies. Thematic analysis was used to identify important aspects of the experience of engaging with a self-management app. Three themes describing drivers of engagement with the PolarUs app and associated features were generated: 1) Motivations, 2) Salience, and 3) Perceived effort. Drivers of engagement were shaped by contextual influences, summarised in four themes: 1) The smartphone ecosystem, 2) Daily life, 3) Mood symptoms, and 4) Involvement in a research study. The findings of this research generate insights into how individuals with BD engage with app-based interventions. Lived experience perspectives can inform the design of engaging app-based interventions for BD. Further, these findings emphasise the importance of considering the context in which people use self-management apps for BD for both research studies and implementation.
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".