Patient Engagement With and Perspectives on a Mobile Health Home Spirometry Intervention: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Patient engagement attrition in mobile health (mHealth) remote patient monitoring (RPM) programs decreases program benefits. Systemic disparities lead to inequities in RPM adoption and use. There is an urgent need to understand patients' experiences with RPM in the real world, especially for patients who have stopped using the programs, as addressing issues faced by patients can increase the value of mHealth for patients and subsequently decrease attrition. OBJECTIVE: This study sought to understand patient engagement and experiences in an RPM mHealth intervention in lung transplant recipients. METHODS: Between May 4, 2020, and November 1, 2022, a total of 601 lung transplant recipients were enrolled in an mHealth RPM intervention to monitor lung function. The predictors of patient engagement were evaluated using multivariable logistic and linear regression. Semistructured interviews were conducted with 6 of 39 patients who had engaged in the first month but stopped using the program, and common themes were identified. RESULTS: Patients who underwent transplant more than 1 year before enrollment in the program had 84% lower odds of engaging (odds ratio [OR] 0.16, 95% CI 0.07-0.35), 82% lower odds of submitting pulmonary function measurements (OR 0.18, 95% CI 0.09-0.33), and 78% lower odds of completing symptom checklists (OR 0.22, 95% CI 0.10-0.43). Patients whose primary language was not English had 78% lower odds of engaging compared to English speakers (OR 0.22, 95% CI 0.07-0.67). Interviews revealed 4 prominent themes: challenges with devices, communication breakdowns, a desire for more personal interactions and specific feedback with the care team about their results, understanding the purpose of the chat, and understanding how their data are used. CONCLUSIONS: Care delivery and patient experiences with RPM in lung transplant mHealth can be improved and made more equitable by tailoring outreach and enhancements toward non-English speakers and patients with a longer time between transplant and enrollment. Attention to designing programs to provide personalization through supplementary provider contact, education, and information transparency may decrease attrition rates.
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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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".