Examining Factors Associated With Medication Adherence in Youth With Bipolar Disorder
Bibliographic record
Abstract
Objective: To assess medication adherence and factors associated with poor adherence in youth with bipolar disorder (BD) followed from adolescence through young adulthood. Method: Participants with BD recruited through the Course and Outcome of Bipolar Youth (COBY) study were included in this study if they were prescribed psychotropic medications and had at least 3 follow-up assessments of medication adherence (N= 179, ages 12-36). Medication adherence had been evaluated for a median of 8 years using a questionnaire derived from the Coronary Artery Risk Development in Young Adults (CARDIA) study. For the longitudinal evaluation, adherence was measured as the percentage of follow-up assessments in which the participants did not endorse any of the nonadherence items included in the questionnaire. Concurrent and future predictors of poor adherence were assessed using both univariate and multivariate longitudinal analyses. Results: Among the participants, 51% reported poor adherence in more than 50% of their follow-up assessments. Younger age, family conflicts, polypharmacy, lower functioning, greater severity of mood symptoms, and comorbid disorders were associated with poor adherence in the univariate analyses. In the multivariate analyses, comorbid ADHD was the single most influential factor associated with concurrent and future poor adherence in all age groups. Participants' most reported reasons for poor adherence were forgetfulness (56%), negative attitudes toward medication treatment (10.5%), and disturbed daily routine (7%). Conclusions: Poor medication adherence is a significant problem in youth with BD with the most influential factor being the presence of comorbid ADHD. Thus, it is important to identify and appropriately treat comorbid ADHD to improve medication adherence and patients' prognosis. Providers should also recommend tools to enhance consistent medication intake and address patients' concerns and negative beliefs about their illness and treatment.
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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 | 0.001 |
| 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".