Digital Medicine System in Veterans With Severe Mental Illness: Feasibility and Acceptability Study
Bibliographic record
Abstract
BACKGROUND: Suboptimal medication adherence is a significant problem for patients with serious mental illness. Measuring medication adherence through subjective and objective measures can be challenging, time-consuming, and inaccurate. OBJECTIVE: The primary purpose of this feasibility and acceptability study was to evaluate the impact of a digital medicine system (DMS) among Veterans (patients) with serious mental illness as compared with treatment as usual (TAU) on medication adherence. METHODS: This open-label, 2-site, provider-randomized trial assessed aripiprazole refill adherence in Veterans with schizophrenia, schizoaffective disorder, bipolar disorder, or major depressive disorder. We randomized 26 providers such that their patients either received TAU or DMS for a period of 90 days. Semistructured interviews with patients and providers were used to examine the feasibility and acceptability of using the DMS. RESULTS: We enrolled 46 patients across 2 Veterans Health Administration sites: 21 (46%) in DMS and 25 (54%) in TAU. There was no difference in the proportion of days covered by medication refill over 3 and 6 months (0.82, SD 0.24 and 0.75, SD 0.26 in DMS vs 0.86, SD 0.19 and 0.82, SD 0.21 in TAU, respectively). The DMS arm had 0.85 (SD 0.20) proportion of days covered during the period they were engaged with the DMS (mean 144, SD 100 days). Interviews with patients (n=14) and providers (n=5) elicited themes salient to using the DMS. Patient findings described the positive impact of the DMS on medication adherence, challenges with the DMS patch connectivity and skin irritation, and challenges with the DMS app that affected overall use. Providers described an overall interest in using a DMS as an objective measure to support medication adherence in their patients. However, providers described challenges with the DMS dashboard and integrating DMS data into their workflow, which decreased the usability of the DMS for providers. CONCLUSIONS: There was no observed difference in refill rates. Among those who engaged in the DMS arm, the proportion of days covered by refills were relatively high (mean 0.85, SD 0.20). The qualitative analyses highlighted areas for further refinement of the DMS. TRIAL REGISTRATION: ClinicalTrials.gov NCT03881449; https://clinicaltrials.gov/ct2/show/NCT03881449.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".