Digital Technology Ownership, Usage, and Factors Predicting Downloading Health Apps Among Caucasian, Filipino, Korean, and Latino Americans: The Digital Link to Health Survey
Bibliographic record
Abstract
BACKGROUND: Interventions using mobile health (mHealth) apps have been effective in promoting healthy lifestyle behavior change and hold promise in improving health outcomes to thereby reduce health disparities among diverse racial/ethnic populations, particularly Latino and Asian American subgroups (Filipinos and Koreans) at high risk for diabetes and cardiovascular disease. Latinos and Asian Americans are avid digital technology owners and users. However, limited datasets exist regarding digital technology ownership and use, especially among specific racial/ethnic subgroups. Such information is needed to inform development of culturally tailored mHealth tools for use with lifestyle interventions promoting healthy behaviors for these at-risk racial/ethnic populations. OBJECTIVE: The intent of the study was to examine (1) digital technology ownership and usage, and (2) factors predicting downloading health apps for Caucasian, Filipino, Korean, and Latino American subgroups. METHODS: A cross-sectional survey conducted in August 2013 through December 2013 recruited 904 participants (Caucasians n=172, Filipinos n=250, Koreans n=234, and Latinos n=248), age >18 years, from California community events, clinics, churches, and online. English, Spanish, and Korean surveys were administered via paper or online. Descriptive statistics characterized the sociodemographics and digital technology ownership/usage of the 904 participants. Differences among groups in categorical variables were examined using chi-square statistics. Logistic regression was used to determine factors predicting downloading health apps. RESULTS: Overall, mean age was 44 years (SD 16.1), with 64.3% (581/904) female. Only 44.7% (404/904) of all participants reported English as their primary language (Caucasian 98.3%, 169/172; Filipino 67.6%, 169/250; Korean 9.4%, 22/234, and Latino 17.7%, 44/248. Overall, mobile phone ownership was 92.8% (839/904). Compared to all groups, Koreans were more likely to own a mobile phone (82.8%, 194/234), computer (91.4%, 214/234), or tablet (55.2%, 129/234), whereas Latinos (67.5%, 167/248; 65.3%, 162/248; 24.4%, 61/248, respectively) were least likely. Internet access via mobile phones (90.5%, 818/904) was higher than computers (78.6%, 711/904). Odds of downloading health apps increased with college (OR 2.62, 95% CI 1.44-4.80) or graduate school (OR 2.93, 95% CI 1.43-6.00) compared to some high school; and family history of heart attack (OR 2.02, 95% CI 1.16-3.51). Odds of downloading health apps were reduced with: race/ethnicity, Latino (OR 0.37, 95% CI 0.20-0.69), and Korean (OR 0.52, 95% CI 0.31-0.88) compared to Caucasians; increasing age (OR 0.96, 95% CI 0.95-0.97); and completing paper surveys (OR 0.50, 95% CI 0.34-0.75). CONCLUSIONS: This survey study uniquely targeted specific racial/ethnic subgroups. Results indicated that despite a narrowing racial/ethnic "digital divide", some disparities still exist, particularly among racial/ethnic groups with less education and whose primary language is not English. Findings will be used to inform development and evaluation of culturally tailored mHealth apps for use with interventions promoting healthy behavior change for Filipinos, Koreans, and Latinos.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".