An In-Home Medication Dispensing System to Support Medication Adherence for Patients With Chronic Conditions in the Community Setting: Prospective Observational Pilot Study
Bibliographic record
Abstract
BACKGROUND: Innovative digital technology systems that support and monitor real-time medication intake are now available commercially; however, there is limited knowledge of the use of such technology in patients' homes. One such smart medication dispenser, spencer, provides alerts to patients to take their medications and allows for tracking and reporting real-time medication adherence data. OBJECTIVE: The objectives of this study were to examine the use of a smart medication dispenser as a medication adherence and self-management support tool for community dwelling adults over a 6-month period, in addition to usability, usefulness, satisfaction, and impact on caregiver support. METHODS: This prospective, observational study invited community-dwelling adults aged 45 years and older taking at least one chronic medication and their caregivers to use this smart medication dispenser for their medication administration for 6 months. Adherence was defined as a dose intake within 2 hours post scheduled time. Real-time adherence data were collected using the smart medication dispenser and the AdhereNet platform. Usability, usefulness, and satisfaction were measured using the System Usability Scale and the Usefulness, Satisfaction, and Ease of Use questionnaire, respectively. Caregiver burden was measured on a visual analog scale at baseline and at the end of the 6-month study period. RESULTS: A total of 58 participants were recruited, of which 55% (32/58) were female with a mean age of 66.36 (SD 11.28; range 48-90) years. Eleven caregiver participants were recruited, of whom 91% (10/11) were female. The average monthly adherence over 6 months was 98% (SD 3.1%; range 76.5%-100%). The average System Usability score was 85.74 (n=47; SD 12.7; range 47.5-100). Of the 46 participants who provided data, 44 (96%) rated the product as easy, 43 (93%) as simple to use, and 43 (93%) were satisfied with the product. Caregiver burden prior to and following smart medication dispenser use for 6 months was found to be statistically significantly different (P<.001; CI 2.11-5.98). CONCLUSIONS: Smart medication adherence products such as spencer, when connected and clinically monitored, can be a useful solution for medication management and have the potential to improve caregiver burden.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.002 | 0.001 |
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".