Sharon S Laing1,2, Muhammad Alsayid2,3, Carlota Ocampo4, Stacey Baugh4. 1. Nursing and Healthcare Leadership Program, University of Washington Tacoma, Tacoma, WA. 2. Health Services Department, University of Washington School of Public Health, Seattle, WA. 3. Department of Medicine, University of Massachusetts Medical School, Worcester, MA. 4. Department of Psychology, Trinity Washington University, Washington, DC.
Abstract
PURPOSE: Mobile health technology (mHealth) can reduce health disparities, but research on the health behaviors of low-income patients is needed. This study evaluates mHealth knowledge and practices of low-resource safety-net patients. METHODS: We administered a 47-item questionnaire to 164 low-income patients accessing services at community health centers in the state of Washington and Washington, DC. Predictor variables included demographic factors: age, race, ethnicity, income. Outcome variables were smartphone knowledge (smartphones as a wellness tool), medical app knowledge (availability of medical-based apps), smartphone practices (ever used smartphones for wellness), health apps practices (ever used health-based apps), and medical apps practices (ever used medical-based apps). Multivariate logistic regression assessed relationships between predictor and outcome variables. RESULTS: Mean age was 35.2 years (median: 34), and study cohort (N=159) consisted of mostly women (68%), white race (36%), and income of <$20,000/year (63%). Outcomes: 71% and 58% reported knowledge of using smartphones for wellness and knowledge of medical apps, respectively; 76% used smartphones for wellness, with adults 50+ years of age significantly less likely than younger adults (odds ratio [OR]: 0.94, 95% confidence interval [CI]: 0.88-0.99); 48% used health apps, with adults 50+ years of age less likely than younger adults (OR: 0.95, 95% CI: 0.91-0.99) and respondents earning <$20,000/year less likely than higher earners (OR: 3.13, 95% CI: 1.02-9.57); and 58% used medical apps, with Hispanics/Latinos significantly more likely than non-Hispanics/Latinos (OR: 6.38, 95% CI: 1.04-39.02). CONCLUSIONS: Safety-net patients use mobile devices for health promotion. Age and income are important predictive factors, suggesting a more tailored design of the technology is required for broad engagement and health equity.
PURPOSE: Mobile health technology (mHealth) can reduce health disparities, but research on the health behaviors of low-income patients is needed. This study evaluates mHealth knowledge and practices of low-resource safety-net patients. METHODS: We administered a 47-item questionnaire to 164 low-income patients accessing services at community health centers in the state of Washington and Washington, DC. Predictor variables included demographic factors: age, race, ethnicity, income. Outcome variables were smartphone knowledge (smartphones as a wellness tool), medical app knowledge (availability of medical-based apps), smartphone practices (ever used smartphones for wellness), health apps practices (ever used health-based apps), and medical apps practices (ever used medical-based apps). Multivariate logistic regression assessed relationships between predictor and outcome variables. RESULTS: Mean age was 35.2 years (median: 34), and study cohort (N=159) consisted of mostly women (68%), white race (36%), and income of <$20,000/year (63%). Outcomes: 71% and 58% reported knowledge of using smartphones for wellness and knowledge of medical apps, respectively; 76% used smartphones for wellness, with adults 50+ years of age significantly less likely than younger adults (odds ratio [OR]: 0.94, 95% confidence interval [CI]: 0.88-0.99); 48% used health apps, with adults 50+ years of age less likely than younger adults (OR: 0.95, 95% CI: 0.91-0.99) and respondents earning <$20,000/year less likely than higher earners (OR: 3.13, 95% CI: 1.02-9.57); and 58% used medical apps, with Hispanics/Latinos significantly more likely than non-Hispanics/Latinos (OR: 6.38, 95% CI: 1.04-39.02). CONCLUSIONS: Safety-net patients use mobile devices for health promotion. Age and income are important predictive factors, suggesting a more tailored design of the technology is required for broad engagement and health equity.
Entities:
Keywords:
knowledge; mobile health promotion; practices; safety-net patients; smartphone; telehealth
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