Feasibility, Usability, and Acceptability of an Adaptive Mobile Health Medication Adherence Intervention for Youth: Iterative Mixed Methods Human-Centered Design Study (Preprint)
Bibliographic record
Abstract
Background: Adolescents and young adults with chronic health conditions often struggle to adhere to their daily oral medications. Transdiagnostic mobile health (mHealth) interventions have the potential to promote medication adherence by reaching youth at a large scale. Objective: This study aimed at designing an adaptive medication adherence mHealth intervention (Adaptive Cell Phone Support), guided by iterative feasibility, usability, and acceptability feedback. A secondary objective was to explore changes in self-reported medication adherence during a field trial. Methods: Using human-centered design methods, researchers collaborated with a community advisory board of young adult patients to conduct 3 cycles of iterative design and usability testing. Adolescents and young adults aged 15-20 years (N=22) were recruited from a large pediatric hospital to user-test the intervention. Data collection included self-report questionnaires, think-aloud usability testing, semistructured interviews, and a 3-week field trial. Quantitative measures included the mHealth App Usability Questionnaire Ease of Use and Usefulness subscales, the Theoretical Framework of Acceptability Questionnaire, and visual analogue scales assessing medication adherence, as well as enrollment and engagement metrics. Qualitative data were analyzed using rapid assessment methods to identify actionable design insights, while quantitative data were analyzed using descriptive statistics and paired-samples t tests with a Holm-Bonferroni correction. Results: Enrollment was 63% and participants completed a mean of 67.2% (SD 23.5) of automated check-ins. Usability and acceptability ratings were relatively high across prototypes (eg, mHealth App Usability Questionnaire Ease of Use was mean 6.40, SD 0.64 for the initial prototype and mean 6.31, SD 0.61 for the third prototype, on a 7-point scale; Theoretical Framework of Acceptability was mean 4.33, SD 0.52 for the initial prototype and mean 4.50, SD 0.53 for the third prototype, on a 5-point scale). Qualitative data emphasized that the intervention was simple, easy to use, convenient, appropriate, and helpful for staying accountable for medication adherence, while also highlighting areas for improvement. Uncontrolled, 2-tailed, pre-post t tests estimated medium-sized improvements in self-reported medication adherence. However, only the percentage of time taking medications over the past month significantly increased (t21=3.26, d=0.70, 95% CI 0.22-1.16; P=.004). Conclusions: Integrated qualitative and quantitative results still suggest that more refinement is needed to optimize the intervention. Partnering with community members early in the development of an intervention may improve the ultimate feasibility, usability, and acceptability of digital health tools. Human-centered design offers a rapid, practical, and creative framework for identifying what works and what needs to be improved early in the lifecycle of a new intervention.
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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 | 0.030 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".