USER EXPERIENCE WITH MEDICATION ADHERENCE TECHNOLOGY: DETERMINING USABILITY BY CAPABILITIES
Bibliographic record
Abstract
Background: As populations worldwide grow older, the prevalence of chronic conditions and the complexity of managing multiple medications significantly increase. This challenge is further complicated by a range of barriers older adults face, including physical limitations, cognitive impairments, sensory issues, motivational challenges, and non-supportive environments. Such barriers can lead to a decline in capacity to self-manage medications, resulting in poor adherence to prescribed medication regimens, which in turn can cause increased hospitalizations and a decrease in quality of life. Medication Adherence Technologies (MATech), which range from simple electronic devices to more complex smart devices with connectivity and real-time monitoring capabilities, are recognized as one of the solutions to these challenges. However, the design and features of these technologies vary significantly, influencing how they are used by different users. Usability varies widely; some older adults may find certain features of these devices challenging to use due to their barriers. Hence, it is crucial to ensure that MATech are accessible and user-friendly for all older adults, regardless of their individual challenges. This study aims to identify the most suitable MATech for older adults with various physical, cognitive, sensory, motivational, and environmental limitations, tailored to their unique needs and abilities. \n \nObjectives: The primary objectives of this study were to evaluate the usability and user experience (UX) of thirteen MATech devices among older adults facing various barriers to medication self-management and to gather comprehensive feedback on the usability and features of these technologies. Secondary objectives included determining how different barriers affect the usability outcomes of these technologies and identifying design features that best meet the needs of this demographic to enhance their independence and well-being. \nMethods: The study used a mixed-methods approach to evaluate the usability of MATech. Eighty older adults, aged 60 and older, were recruited through convenience, purposive, and snowball sampling methods from various settings across Ontario, including academic and residential facilities. Data collection was conducted in three steps after obtaining informed consent from the participants. The first step involved measuring barriers to medication self-management using various scales such as the Self-Medication Assessment Tool (SMAT) for physical, cognitive, and vision barriers; the Whisper Test for hearing barriers; the Self-Efficacy for Medication Adherence Scale (SEAMS) for motivational barriers; and the Martin and Park Environmental Demands (MPED) Questionnaire for environmental barriers. The second step involved usability and user experience testing of three smart devices and ten electronic devices, to measure various performance-based metrics (task success rate, total task completion time, efficiency, error rate) and perception-based usability metrics (System Usability Scale (SUS) score, NASA-TLX workload score, Single Ease of Use Question (SEQ), and Subjective Mental Effort Question (SMEQ)). The third step consisted of in-depth qualitative interviews to explore feedback regarding the features of various MATech tested. Quantitative data were statistically analyzed using descriptive statistics and univariate and multivariate regression to assess usability across various devices, while qualitative responses were analyzed using inductive thematic analysis. \n \nResults: \nQuantitative Results: Cognitive impairments were identified in 20% of participants, physical limitations in 33.75%, hearing impairment (both ears) in 60%, and vision impairments in 11.25%. Backward stepwise multivariate regression analysis identified critical predictors for task success rates, including 'SEAMS score' (p<0.001) which measures motivational barrier positively influencing outcomes, whereas 'Low vision score' negatively affected success rates (p<0.001). Moreover, Old 'age' (p<0.001) and 'number of subtasks for product' (p<0.001) notably extended the total task completion times, and 'physical score' (p<0.001) increased error rates, suggesting necessary improvements in MATech design for better usability. While no predictors significantly impacted the SUS scores, the NASA TLX identified 'old age', 'vision impairment', and the ‘number of products tested’ as significant factors in perceived task load, particularly noting that using multiple products increased task load considerably, underscoring their profound impact on user experience and workload management. Predictive models were also developed to determine each participant's ability to successfully complete subtasks. For example, the model for a participant characterized by significant cognitive, physical, hearing, motivational and environmental impairments, but with high vision capacity, indicated high success probabilities for visually intensive subtasks such as "scroll the screen options" (92%) and "locate and touch an icon on a screen" (87%). Conversely, tasks requiring more physical interaction like "flip device" showed much lower success probabilities (45%). \nQualitative Findings: Five themes were identified: (1) the practicality of device design, (2) the impact of technological complexity, (3) the necessity for inclusivity in device functionality, which includes considerations for impairments, security, and privacy, (4) the influence of socio-economic and environmental factors, and (5) the importance of feedback for iterative design. \n \nDiscussion: The findings from this study underscore the critical importance of designing MATech that are not only functional but also tailored to the unique needs of older adults who face multiple barriers to effective medication management. Key findings from the regression analyses highlighted the importance of addressing physical and sensory impairments in MATech design, as these significantly influence user performance and error rates. Additionally, factors such as age and the complexity of device operations significantly influence usability and workload, suggesting the need for simpler, more intuitive designs that minimize cognitive and physical strain. Overall, the research emphasizes the need for a user-centered design approach in developing MATech, emphasizing simplicity, accessibility, and personalization to better support older adults in managing their medications effectively. This approach not only aids in improving medication adherence but also contributes to the broader goal of facilitating a more independent, quality life for older adults.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".