Laing Sharon S, Alsayid Muhammad, Ocampo Carlota, Baugh Stacey
Nursing and Healthcare Leadership Program, University of Washington Tacoma, Tacoma, WA.
Health Services Department, University of Washington School of Public Health, Seattle, WA.
J Patient Cent Res Rev. 2018 Jul 30;5(3):204-217. doi: 10.17294/2330-0698.1622. eCollection 2018 Summer.
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.
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.
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).
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.
移动健康技术（mHealth）可减少健康差距，但仍需对低收入患者的健康行为进行研究。本研究评估了资源匮乏的安全网患者的移动健康知识与实践情况。
我们对华盛顿州及华盛顿特区社区卫生中心的164名低收入患者进行了一项包含47个项目的问卷调查。预测变量包括人口统计学因素：年龄、种族、族裔、收入。结果变量包括智能手机知识（将智能手机作为健康工具）、医疗应用程序知识（基于医疗的应用程序的可用性）、智能手机使用情况（是否曾使用智能手机促进健康）、健康应用程序使用情况（是否曾使用基于健康的应用程序）以及医疗应用程序使用情况（是否曾使用基于医疗的应用程序）。多变量逻辑回归分析评估了预测变量与结果变量之间的关系。
平均年龄为35.2岁（中位数：34岁），研究队列（N = 159）主要由女性（68%）、白人（36%）以及年收入低于20,000美元（63%）的人群组成。结果如下：分别有71%和58%的患者报告了解使用智能手机促进健康以及了解医疗应用程序；76%的患者使用智能手机促进健康，50岁及以上成年人使用智能手机促进健康的可能性显著低于年轻成年人（优势比[OR]：0.94，95%置信区间[CI]：0.88 - 0.99）；48%的患者使用健康应用程序，50岁及以上成年人使用健康应用程序的可能性低于年轻成年人（OR：0.95，95% CI：0.91 - 0.99），年收入低于20,000美元的受访者使用健康应用程序的可能性低于高收入者（OR：3.13，95% CI：1.02 - 9.57）；58%的患者使用医疗应用程序，西班牙裔/拉丁裔使用医疗应用程序的可能性显著高于非西班牙裔/拉丁裔（OR：6.38，95% CI：1.04 - 39.02）。
安全网患者使用移动设备促进健康。年龄和收入是重要的预测因素，这表明需要对技术进行更具针对性的设计，以实现广泛参与和健康公平。
