A Prospective Study of Usability and Workload of Electronic Medication Adherence Products by Older Adults, Caregivers, and Health Care Providers
Bibliographic record
Abstract
BACKGROUND: A decreased capacity to self-manage medications results in nonadherence, medication errors, and drug-related problems in older adults. Previous research identified 80 electronic medication adherence products available to assist patients with self-management of medications. Unfortunately, the usability and workload of these products are unknown. OBJECTIVE: This study aimed to examine the usability and workload of a sample of electronic medication adherence products. METHODS: In a prospective, mixed methods study, a sample of older adults, health care professionals, and caregivers tested the usability and workload of 21 electronic medication adherence products. Each participant tested 5 products, one at a time, after which they completed the system usability scale (SUS) and NASA-task load index (NASA-TLX), instruments that measure the usability and workload involved in using a product. Higher SUS scores indicate more user-friendliness, whereas lower NASA-TLX raw scores indicate less workload when using a product. RESULTS: Electronic medication adherence products required a mean of 12.7 steps (range 5-20) for the appropriate use and took, on average, 15.19 min to complete the setup tasks (range 1-56). Participants were able to complete all steps without assistance 55.3% of the time (103 out of the 186 tests were completed by 39 participants; range 0%-100%). The mean SUS and NASA-TLX raw scores were 52.8 (SD 28.7; range 0-100) and 50.0 (SD 25.7; range 4.2-99.2), respectively, revealing significant variability among the electronic medication adherence products. The most user-friendly products were found to be TimerCap travel size (mean 78.67, SD 15.57; P=.03) and eNNOVEA Weekly Planner with Advanced Auto Reminder (mean 78.13, SD 14.13; P=.049) as compared with MedReady 1700 automated medication dispenser (mean 28.63, SD 21.24). Similarly, MedReady (72.92, SD 18.69) was found to be significantly more work intensive when compared with TimerCap (29.35, SD 20.35; P=.03), e-pill MedGlider home medication management system (28.43, SD 20.80; P=.02), and eNNOVEA (28.65, SD 14.97; P=.03). The e-pill MedTime Station automatic pill dispenser with tipper (71.77, SD 21.98) had significantly more workload than TimerCap (P=.04), MedGlider (P=.03), and eNNOVEA (P=.04). CONCLUSIONS: This study demonstrated that variability exists in the usability and workload of different electronic medication adherence products among older adults, caregivers, and clinicians. With few studies having investigated the usability and workload of electronic medication adherence products, no benchmarks exist to compare the usability and workload of these products. However, our study highlights the need to assess the usability and workload of different products marketed to assist with medication taking and provides guidance to clinicians regarding electronic medication adherence product recommendations for their patients. Future development of electronic medication adherence products should ensure that the target populations of patients are able to use these products adequately to improve medication management.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".