Patient and physician perspectives on the use and outcome measures of mHealth apps: Exploratory survey and focus group study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Objective: Factors that physicians and patients consider when making decisions about using or recommending health apps are not well understood. We explored these factors to better assess how to support such decision making. Methods: We conducted an exploratory cross-sectional study in Ontario using qualitative focus groups and quantitative surveys. 133 physicians and 94 community dwelling adults completed online surveys and we held two focus groups of nine community dwelling participants who had cardiovascular risk factors and an interest in using mHealth apps. Quantitative survey data was analyzed descriptively. Focus groups were audio-recorded and transcribed verbatim prior to inductive thematic content analysis. We integrated the results from the surveys and focus groups to understand factors that influence physicians' and patients' selection and use of such apps. Results: Physicians recommend apps to patients but the level of evidence they prefer to use to guide selection did not align with what they were currently using. Patients trusted recommendations and reviews from medical organizations and healthcare professionals when selecting apps and were motivated to continue using apps when they supported goal setting and tracking, data sharing, decision making, and empowerment. Conclusions: The findings highlight the significance of evaluating mHealth apps based on metrics that patients and physicians value beyond usage and clinical outcome data. Patients engage with apps that support them in confidently managing their health. Increased training and awareness of apps and creating a more rigorous evidence base showing the value of apps to supporting health goals will support greater adoption and acceptance of mHealth apps.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it