Medication Dispensing Patterns Among Individuals With Serious Mental Illness Using a Remote Medication Dispensing and Adherence Monitoring Platform: A Cohort Study (Preprint)
Bibliographic record
Abstract
BACKGROUND: Medication adherence is poor among individuals with serious mental illness (SMI). Few studies have demonstrated the effectiveness of remote medication dispensing and adherence monitoring interventions among individuals with SMI. OBJECTIVE: This study aimed to understand medication dispensing rates for users of a remote medication dispensing and adherence monitoring device and to identify associated demographic and clinical characteristics. METHODS: In this cohort study, individuals' characteristics were measured at baseline, and dispensing records were followed from their enrollment and subsequent device installation as early as January 2019 until June 2023. Individuals were eligible to participate if they had an SMI diagnosis, were aged 18 to 64 years, were currently being prescribed psychiatric medications, and were receiving mental health services from a participating community mental health agency. Participants were recruited through a combination of self-selection and referrals from agency staff. Our intervention involved using a remote medication dispensing and adherence monitoring device to measure participants' daily medication dispensing. RESULTS: The final sample consisted of 99 participants. The mean age of the participants was 49 (SD 12.08) years; 64% (n=63) of the participants identified as men and 41% (n=41) as Black or African American. The overall dispensing rate was 92.9%, with 90 (91%) individuals having dispensing rates >80%. The results of the hierarchical Bayesian logistic regression model showed that participants adhered better to evening doses than morning doses (incidence rate ratio [IRR] 1.11, 95% credible interval [CrI] 1.06-1.16). Dispensing adherence was poorer on weekends than on weekdays (IRR 0.87, 95% CrI 0.83-0.91). For every additional year of using the device, the rate of adherence increased by 1% (IRR 1.01, 95% CrI 1.00-1.01). The rate of dispensing dropped by 22% after the onset of the COVID-19 pandemic (IRR 0.78, 95% CrI 0.71-0.86), and African American participants had a 29% lower rate of dispensing than White participants (IRR 0.71, 95% CrI 0.55-0.90). The rate of dispensing did not differ by age; sex; educational attainment; or the level of sadness, emotional and behavioral dyscontrol, cognitive function, or psychotic symptoms at baseline. CONCLUSIONS: The high adherence rate observed, regardless of baseline psychopathology levels, highlights the potential of remote medication dispensing and adherence monitoring devices to address adherence challenges in people with SMI. Observed variation in dispensing behavior by dose timing and contextual factors suggests opportunities for intervention, including aligning dosing schedules with patient routines, providing additional support during periods of disruption (eg, weekends or major life events), and tailoring strategies to address disparities across patient groups. These findings highlight the role of targeted, context-aware approaches to improve adherence in community-based SMI care. These findings support the integration of digital adherence monitoring within mental health services, especially in settings where traditional adherence support may be challenging. TRIAL REGISTRATION: ClinicalTrials.gov NCT03775044; https://clinicaltrials.gov/study/NCT03775044.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".