A Digital Intervention Using Daily Financial Incentives to Increase Medication Adherence in Severe Mental Illness: Single-Arm Longitudinal Pilot Study
Bibliographic record
Abstract
BACKGROUND: Medication nonadherence is prevalent in severe mental illness and is associated with multiple negative outcomes. Mobile technology and financial incentives show promise to improve medication adherence; however, studies in mental health, especially with oral medications, are lacking. OBJECTIVE: The aim of this paper is to assess the feasibility and effectiveness of offering financial incentives through a mobile app based on behavioral economics principles to improve medication adherence in severe mental illness. METHODS: A 10-week, single-arm longitudinal pilot study was conducted. Patients earned rewards in the context of app-based adherence incentives. The reward was split into biweekly payments made in increments of US $15, minus any US $2 per day penalties for missed check-ins. Time-varying effect modeling was used to summarize the patients' response during the study. RESULTS: A total of 25 patients were enrolled in this pilot study, of which 72% (n=18) were female, and 48% (n=12) were of a White racial background. Median age was 24 (Q1-Q3: 20.5-30) years. Participants were more frequently diagnosed with schizophrenia and related disorders (n=9, 36%), followed by major depressive disorder (n=8, 32%). App engagement and medication adherence in the first 2 weeks were higher than in the last 8 weeks of the study. At study endpoint, app engagement remained high (n=24, Z=-3.17; P<.001), but medication adherence was not different from baseline (n=24, Z=-0.59; P=.28). CONCLUSIONS: Financial incentives were effectively delivered using an app and led to high engagement throughout the study and a significantly increased medication adherence for 2 weeks. Leveraging behavioral economics and mobile health technology can increase medication adherence in the short term. TRIAL REGISTRATION: ClinicalTrials.gov NCT04191876; https://clinicaltrials.gov/ct2/show/NCT04191876.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".