What influences engagement with a bipolar disorder self-management app? A qualitative investigation of use of the PolarUs app
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it