Older adults’ preferences for features of medication adherence technologies: a preference elicitation study
Bibliographic record
Abstract
Introduction: Older adults are at risk of medication non-adherence due to complex medication regimens and medication management challenges. Medication adherence technologies can help, but previous research demonstrates variability in usability and preferences of their features among older adults. Therefore, our objective is to examine older adults' preferences for medication adherence technology features and their trade-offs to guide the development of these technologies. This will facilitate serving older adults better by addressing their needs and preference. Methods: Guided by the Patient-Centered Benefit-Risk Framework, we conducted a questionnaire based preference elicitation study where older adults ranked 10 medication adherence technology features identified through qualitative interviews, then identified acceptable trade-offs. Recruitment of our sample was based on convenience sampling, and the inclusion criteria was older adults above 60 years and older and able to speak and read English. The ranking was evaluated by calculating the relative importance using relative importance index (RII) and the trade-offs were assessed using win rate analysis. Statistical significance was assessed using Kruskal Wallis analysis. Results: Thirty older adults were recruited, of which twenty-three (mean age 73 years, 47.8% males) participated. The 10 reported features were button size, screen size, device size, compartment division, setting time and alarm, alarm sound, user-friendly leaflet, battery operated, locking features, and number of steps to set up the device. Screen size was ranked highest with relative importance index (RII) of 0.75. Win-rate analysis of trade-offs revealed that a user-friendly leaflet was the most frequently selected feature with p value < 0.001 (Kruskal Wallis test). Conclusion: This study highlights the importance of understanding the preferences of older adults to guide selecting the medication adherence technology that better meet their needs, as well as developing tools supporting medication management and adherence. Clinical trial number: Not applicable. Supplementary Information: The online version contains supplementary material available at 10.1186/s44247-025-00222-z.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".