Assessing the Use of Mobile Health Technology by Patients: An Observational Study in Primary Care Clinics
Bibliographic record
Abstract
BACKGROUND: There is significant potential for mobile health technology to improve health outcomes for patients with chronic diseases. However, there is a need for further development of mobile health technology that would help to improve the health of lower-income communities. OBJECTIVE: The study objective was to assess mobile phone and app usage among a culturally diverse patient population, and to determine whether patients would be interested in using mobile health technology to help manage their chronic diseases. METHODS: An observational study was conducted with patients of the Internal Medicine resident primary care clinics of Los Angeles County and University of Southern California (LAC+USC) Medical Center. Self-reported information regarding demographics, current mobile phone usage, current mobile health app and social media usage, barriers to using mobile phones or mobile health apps, and interest in using a mobile health app was collected. RESULTS: Ninety-one percent of patients owned a mobile phone, with 76% (169/223) of these reporting having a mobile phone with Internet capability. Fifty-seven percent of subjects used mobile apps on their mobile phones, and 32% (41/130) of these used mobile apps related to their health. Eighty-six percent (207/241) of respondents voiced interest in using a mobile app to improve their health, and 40% (88/221) stated they would use such an app daily. Patients stated they would find the mobile health app most useful for nutrition, exercise, and obtaining general information on medical conditions. CONCLUSIONS: Despite the fact that the majority of our primary care patients were of lower socioeconomic status, they utilized mobile phones with Internet and mobile app capabilities to a great extent. There was substantial interest among our patients in using mobile health technology to both manage chronic disease and improve overall health. Given that cultural, educational, and socioeconomic disparities strongly correlate with higher rates of chronic diseases such as obesity, diabetes and hypertension, access to culturally relevant mobile health tools may empower patients in these populations to improve health outcomes.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".