Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
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Journal of affective disorders
Exploring temperamental and clinical predictors of lithium treatment outcomes in bipolar disorder using diverse machine learning approaches
BACKGROUND: Lithium is a core treatment for bipolar disorder (BD), yet clinical response varies across patients. Testing accessible predictors of lithium response is critical for personalization strategies in real-world settings. METHODS: In this cross-sectional study, 179 individuals with BD were assessed using a broad range of standardized clinical instruments and the Brief TEMPS-M to evaluate affective temperaments. Lithium response was retrospectively classified via the Alda Scale. Multivariate linear regressions and three supervised machine learning classifiers (random forest, elastic net, and XGBoost) were applied to identify predictors of treatment response. Class imbalance was addressed by down-sampling during training. RESULTS: Responders (12.3 % of participants) were more likely to be employed, diagnosed with BD-II, had fewer lifetime depressive and hypomanic episodes, fewer suicide attempts, and significantly lower anxious temperament scores. In multivariable models, lifetime psychotic and mixed features, as well as higher counts of hypomanic and manic episodes, were independently associated with poorer lithium response. Machine learning models confirmed the importance of these variables, possibly with remarkably high predictive validity (random forest AUC = 1.00; elastic net and XGBoost F1 = 0.86). Hyperthymic temperament was positively associated with lithium response, whereas cyclothymic and depressive traits were associated with poorer lithium response. CONCLUSIONS: The present exploratory study should be intended as a primer for larger samples and more balanced replication studies employing reliable machine learning techniques, allowing for firm conclusions about suggestive clinical predictors of lithium response in BD, and their complex interaction.
16 Apr 2026
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Cultural Adaptation of a Digital Mobile App for Bipolar Disorder (PolarUs): Protocol for a Qualitative Co-Design Study
BACKGROUND: Bipolar disorder (BD) affects approximately 40 million people worldwide and is a chronic, potentially disabling mood disorder. Although effective treatments exist, access to evidence-informed psychosocial care remains limited, particularly for culturally and linguistically diverse populations, contributing to persistent global treatment gaps. Digital mental health interventions (DMHIs), such as smartphone apps, offer a promising means to improve access to self-management support and quality of life (QoL), an outcome prioritized by people with BD and in clinical guidelines. However, most apps for BD lack quality and are not culturally adapted or co-designed with people with BD, limiting relevance and engagement. PolarUs (mobile app) is an evidence-informed DMHI developed using co-design with people with BD. The app is structured on the core 14 domains from the Quality of Life in BD scale, the only BD-tailored scale, combined with psychoeducation on self-management strategies and QoL. A recent pilot study demonstrated promising QoL, clinical, and feasibility outcomes. OBJECTIVE: This study aims to culturally and linguistically adapt the PolarUs app into French, Chinese, and Spanish for the North American context using qualitative and co-design methods. METHODS: Guided by community-based participatory research principles, whereby end users are engaged throughout the research process, and the Ecological Validity Framework of Bernal et al, we will engage advisory groups of people with lived experience from each linguistic community throughout the cultural adaptation process. Semimonthly virtual meetings will support systematic cultural adaptation of the self-management strategies, affirmations, and resources while maintaining fidelity to core evidence-based components. This will include cultural tailoring of app content and the identification of culturally appropriate resources. Advisory groups will also contribute to the cointerpretation of findings and the co-design of culturally appropriate recruitment and implementation strategies of PolarUs for a future clinical trial. Meetings will be recorded and coanalyzed as research data with advisory groups using qualitative reflexive thematic analysis to capture advisory group perspectives and experiences. RESULTS: This study was funded in October 2024. As of January 31, 2026, we enrolled 7 participants, and the results are expected to be published in the fall of 2026. CONCLUSIONS: The findings will support the development of a culturally appropriate DMHI for BD for additional linguistic communities, advance cultural adaptation methodologies, and inform preparation for a future clinical trial. This study will produce the first culturally adapted, BD-specific DMHI developed through co-design using a community-based participatory research approach with multilingual end users from traditionally underserved communities, advancing equitable access, engagement, and scalability of DMHIs for BD and digital health care more broadly.
10 Apr 2026
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