Chelsea L. Ratcliff is with the Department of Communication Studies, University of Georgia, Athens. Melinda Krakow is with the University of Mississippi Medical Center, Jackson, and during the study was also with the Health Communication and Informatics Research Branch, National Cancer Institute, Bethesda, MD. Alexandra Greenberg-Worisek was with Mayo Clinic College of Medicine, Rochester, MN, during the study. Bradford W. Hesse served as branch chief for the Health Communication and Informatics Research Branch, National Cancer Institute, Bethesda, when this work began; he has since retired.
Am J Public Health. 2021 Jul;111(7):1348-1351. doi: 10.2105/AJPH.2021.306282. Epub 2021 May 20.
To examine prevalence and predictors of digital health engagement among the US population. We analyzed nationally representative cross-sectional data on 7 digital health engagement behaviors, as well as demographic and socioeconomic predictors, from the Health Information National Trends Survey (HINTS 5, cycle 2, collected in 2018; n = 2698-3504). We fitted multivariable logistic regression models using weighted survey responses to generate population estimates. Digitally seeking health information (70.14%) was relatively common, whereas using health apps (39.53%) and using a digital device to track health metrics (35.37%) or health goal progress (38.99%) were less common. Digitally communicating with one's health care providers (35.58%) was moderate, whereas sharing health data with providers (17.20%) and sharing health information on social media (14.02%) were uncommon. Being female, younger than 65 years, a college graduate, and a smart device owner positively predicted several digital health engagement behaviors (odds ratio range = 0.09-4.21; value range < .001-.03). Many public health goals depend on a digitally engaged populace. These data highlight potential barriers to 7 key digital engagement behaviors that could be targeted for intervention.
调查美国人群中数字健康参与的流行程度和预测因素。
我们分析了来自健康信息国家趋势调查（HINTS 5，第 2 轮，于 2018 年收集；n=2698-3504）的 7 种数字健康参与行为以及人口统计学和社会经济预测因素的全国代表性横断面数据。我们使用加权调查回复拟合多变量逻辑回归模型，以生成人口估计值。数字方式寻求健康信息（70.14%）相对常见，而使用健康应用程序（39.53%）和使用数字设备跟踪健康指标（35.37%）或健康目标进展（38.99%）则不太常见。与医疗保健提供者进行数字沟通（35.58%）中等常见，而与提供者共享健康数据（17.20%）和在社交媒体上共享健康信息（14.02%）则不常见。女性、年龄小于 65 岁、大学毕业和拥有智能设备者对几种数字健康参与行为具有积极预测作用（比值比范围为 0.09-4.21；P 值范围<.001-.03）。
许多公共卫生目标都依赖于数字化参与的人群。这些数据突出了 7 种关键数字参与行为可能存在的障碍，可以针对这些行为进行干预。
