Evaluating User Perceptions of Mobile Medication Management Applications With Older Adults: A Usability Study
Bibliographic record
Abstract
BACKGROUND: Medication nonadherence has a significant impact on the health and wellbeing of individuals with chronic disease. Several mobile medication management applications are available to help users track, remember, and read about their medication therapy. OBJECTIVE: The objective of this study was to explore the usability and usefulness of existing medication management applications for older adults. METHODS: We recruited 35 participants aged 50 and over to participate in a 2-hour usability session. The average age ranged from 52-78 years (mean 67 years) and 71% (25/35) of participants were female. Each participant was provided with an iPad loaded with four medication management applications: MyMedRec, DrugHub, Pillboxie, and PocketPharmacist. These applications were evaluated using the 10 item System Usability Scale (SUS) and visual analog scale. An investigator-moderated 30-minute discussion followed, and was recorded. We used a grounded theory (GT) approach to analyze qualitative data. RESULTS: When assessing mobile medication management applications, participants struggled to think of a need for the applications in their own lives. Many were satisfied with their current management system and proposed future use only if cognition and health declined. Most participants felt capable of using the applications after a period of time and training, but were frustrated by their initial experiences with the applications. The early experiences of participants highlighted the benefits of linear navigation and clear wording (eg, "undo" vs "cancel") when designing for older users. While there was no order effect, participants attributed their poor performance to the order in which they tried the applications. They also described being a part of a technology generation that did not encounter the computer until adulthood. Of the four applications, PocketPharmacist was found to be the least usable with a score of 42/100 (P<.0001) though it offered a drug interaction feature that was among the favorite features of participants. The usability scores for MyMedRec (56/100), DrugHub (57/100), and Pillboxie (52/100) were not significantly different and participants preferred MyMedRec and DrugHub for their simple, linear interfaces. CONCLUSIONS: With training, adults aged 50 and over can be capable and interested in using mHealth applications for their medication management. However, in order to adopt such technology, they must find a need that their current medication management system cannot fill. Interface diversity and multimodal reminder methods should be considered to increase usability for older adults. Lastly, regulation or the involvement of older adults in development may help to alleviate generation bias and mistrust for applications.
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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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".