Mobile Device Ownership, Current Use, and Interest in Mobile Health Interventions Among Low-Income Older Chinese Immigrants With Type 2 Diabetes: Cross-sectional Survey Study
Bibliographic record
Abstract
BACKGROUND: Chinese immigrants suffer a disproportionately high type 2 diabetes (T2D) burden and tend to have poorly controlled disease. Mobile health (mHealth) interventions have been shown to increase access to care and improve chronic disease management in minority populations. However, such interventions have not been developed for or tested in Chinese immigrants with T2D. OBJECTIVE: This study aims to examine mobile device ownership, current use, and interest in mHealth interventions among Chinese immigrants with T2D. METHODS: In a cross-sectional survey, Chinese immigrants with T2D were recruited from Chinese community centers in New York City. Sociodemographic characteristics, mobile device ownership, current use of social media software applications, current use of technology for health-related purposes, and interest in using mHealth for T2D management were assessed. Surveys were administered face-to-face by bilingual study staff in the participant's preferred language. Descriptive statistics were used to characterize the study sample and summarize technology use. RESULTS: The sample (N=91) was predominantly female (n=57, 63%), married (n=68, 75%), and had a high school education or less (n=58, 64%); most participants had an annual household income of less than US $25,000 (n=63, 69%) and had limited English proficiency (n=78, 86%). The sample had a mean age of 70 (SD 11) years. Almost all (90/91, 99%) participants had a mobile device (eg, basic cell phones, smart devices), and the majority (n=83, 91%) reported owning a smart device (eg, smartphone or tablet). WeChat was the most commonly used social media platform (65/91, 71%). When asked about their top source for diabetes-related information, 63 of the 91 participants (69%) reported health care providers, followed by 13 who reported the internet (14%), and 10 who reported family, friends, and coworkers (11%). Less than one-quarter (21/91, 23%) of the sample reported using the internet to search for diabetes-related information in the past 12 months. About one-third of the sample (34/91, 37%) reported that they had watched a health-related video on their cell phone or computer in the past 12 months. The majority (69/91, 76%) of participants reported interest in receiving an mHealth intervention in the future to help with T2D management. CONCLUSIONS: Despite high mobile device ownership, the current use of technology for health-related issues remained low in older Chinese immigrants with T2D. Given the strong interest in future mHealth interventions and high levels of social media use (eg, WeChat), future studies should consider how to leverage these existing low-cost platforms and deliver tailored mHealth interventions to this fast-growing minority group.
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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 | 0.002 | 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 | 0.001 | 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 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".