Development and Preliminary Testing of App-Based Culturally-Adapted Psychoeducation for Bipolar Disorder in Pakistan
Bibliographic record
Abstract
Aims Bipolar disorder (BD) leads to marked disability, morbidity, and premature death. Although pharmacological agents are an essential part of BD treatment, psychosocial interventions have played an important role in enhancing treatment adherence, functioning and quality of life in patients with BD. Building on a successful pilot randomised controlled trial (RCT) of a Culturally adapted PsychoEducation (CaPE) intervention for BD, CaPE is currently being evaluated in a large multicenter RCT for its clinical and cost-effectiveness across Pakistan. However, innovations are urgently needed due to limited human resources and disproportionately high clinical needs to bring effective interventions to scale. This study aims to develop and test a mHealth iteration of CaPE, digital CaPE (dCaPE), to be delivered via a mobile app. Methods The study will utilise a two-phased approach to i) develop a user-centred dCaPE mobile application and ii) assess the feasibility and preliminary efficacy of dCaPE for people with BD in a randomised controlled trial in Pakistan. For application development, we have conducted discussion groups with stakeholders i.e., mental health professionals (psychiatrists, psychologists, nurses) (n = 8) and patients and carers (n = 10) to gauge their valuable insights for app design, visual elements, cultural sensitivity, motivational and mood-monitoring features, and app functionality to improve user experience. Results The findings from discussion groups informed the importance of visual elements, specifically font size and style. Participants recommended the use of soft and soothing colours like white, grey, and soft shades of pink to prevent overstimulation. Additionally, participants highlighted the need for culturally and linguistically inclusive features, including emojis and audio messages for effective engagement and to address the challenge of low literacy. The mHealth approach was deemed highly valuable, especially given the prevalence of mental health challenges and associated stigma. Endorsed by participants, the dCaPE application will offer customized psychoeducation messages along with daily 5-item (mood, energy, sleep, medication, and irritability) screening, a weekly comprehensive test for manic and depressive episodes based on DSM–5 criteria; weekly reminders to regulate sleep and eating habits, and visual representations of weekly mood monitoring reports with the incentive of badges or rewards for goal achievers. Conclusion This research has the potential to enhance clinical outcomes, social and occupational functioning, and the overall quality of life for BD patients while addressing substantial mental health treatment gaps with impact and implications extending to various low-resource settings.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".