Exploring the relationship between the usability of a goal-oriented mobile health application and non-usage attrition in patients with multimorbidity: A blended data analysis approach
Bibliographic record
Abstract
BACKGROUND: Mobile health applications are increasingly used to support the delivery of health care services to a variety of patients. Based on data obtained from a pragmatic trial of the electronic Patient Reported Outcome (ePRO) app designed to support goal-oriented care primary care, this study aims to (1) examine how patient-reported usability changed over the one-year intervention period, and (2) explore participant attrition rate of the electronic Patient Reported Outcome app over one year study period. METHODS: We performed a secondary analysis of 44 older adults with complex chronic needs enrolled in the electronic Patient Reported Outcome-digital health intervention. App usage and attrition were measured using device-generated usage logs; usability was measured using the patient-reported post-study system usability questionnaire collected at 3, 6, 9, and 12 months. Research memos were used to interpret potential contextual contributing factors to patients' overall usage and usability score pattern. A data triangulation method of both quantitative and qualitative data was used to analyze and interpret study findings. RESULTS: While there was gradual attrition in the use of the ePRO app, patients' usability scores remained consistent throughout the study period. Qualitative memos suggested patients' encounters with technical difficulties and relationship dynamics with primary providers influenced patients' adherence to the ePRO app. CONCLUSION: This study highlights that the patient-provider relationship is a key determining factor that influences complex patients' continued engagement with a Mobile health app. The finding calls attention to the measurement of usability of a Mobile health app, its impact on attrition, and contributing factors that influence patients' attrition. Trial registration: Clinicaltrials.gov Identified NCT02917954.
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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 | 0.052 | 0.125 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".