Use of mobile health applications for health-promoting behavior among individuals with chronic medical conditions
Use of mobile health applications for health-promoting behavior among individuals with chronic medical conditions
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DOI：
10.1177/2055207619882181
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发表时间：
2019-10-01
期刊：
影响因子：
3.9
通讯作者：
Bhuyan, Soumitra S.
中科院分区：
文献类型：
--
作者：
Niahmood, Asos;Kedia, Satish;Bhuyan, Soumitra S.
背景慢性疾病（CC）是美国发病率和死亡率的主要原因。控制CC的策略包括针对不健康的行为，通常通过使用患者授权工具，如移动的健康（mHealth）技术。然而，没有确凿的证据表明，移动健康应用程序（应用程序）是有效的个人与慢性病自我管理的CC。我们使用了健康信息全国趋势调查（HINTS 5，第1周期，2017）的数据。分析了1864名拥有智能手机和/或平板电脑和至少一个CC的非机构化美国成年人（>= 18岁）的样本。使用多变量逻辑回归，我们评估了三种健康促进行为（HPB）的倾向，实现和需求预测因素：跟踪健康相关目标的进展，做出健康相关决策，以及与智能设备和mHealth应用程序所有者之间的医疗提供者进行健康相关的讨论。结果与没有mHealth应用程序的人相比，使用mHealth应用程序的人使用智能设备跟踪健康相关目标进展的几率显着更高（调整后的比值比（aOR）8.74，95%置信区间（CI）：5.66-13.50，P < .001），以做出健康相关的决定（aOR 1.77，95%CI：1.16-2.71，P <0.01）和与保健提供者的健康相关讨论（aOR 2.0，95%CI：1.26-3.19，P <0.01）。智能设备和移动健康应用程序用户中至少一种类型的HPB的其他重要因素包括年龄、性别、教育程度、职业状况、有定期提供者以及自我评估的一般健康状况。结论：移动健康应用程序与CC患者中HPB发生率的增加相关。然而，某些群体，如老年人，受数字鸿沟的影响最大，他们对移动健康应用程序的访问较少，因此无法利用这些工具。需要在不同人群和不同健康状况下进行严格的随机临床试验，以确定这些mHealth应用程序的有效性。医疗保健提供者应鼓励为患有CC的患者提供经过验证的mHealth应用程序。
Background Chronic medical conditions (CCs) are leading causes of morbidity and mortality in the United States. Strategies to control CCs include targeting unhealthy behaviors, often through the use of patient empowerment tools, such as mobile health (mHealth) technology. However, no conclusive evidence exists that mHealth applications (apps) are effective among individuals with CCs for chronic disease self-management. Methods We used data from the Health Information National Trends Survey (HINTS 5, Cycle 1, 2017). A sample of 1864 non-institutionalized US adults (>= 18 years) who had a smartphone and/or a tablet computer and at least one CC was analyzed. Using multivariable logistic regressions, we assessed predisposing, enabling, and need predictors of three health-promoting behaviors (HPBs): tracking progress on a health-related goal, making a health-related decision, and health-related discussions with a care provider among smart device and mHealth apps owners. Results Compared to those without mHealth apps, individuals with mHealth apps had significantly higher odds of using their smart devices to track progress on a health-related goal (adjusted odds ratio (aOR) 8.74, 95% confidence interval (CI): 5.66-13.50, P < .001), to make a health-related decision (aOR 1.77, 95% CI: 1.16-2.71, P < .01) and in health-related discussions with care providers (aOR 2.0, 95% CI: 1.26-3.19, P < .01). Other significant factors of at least one type of HPB among smart device and mHealth apps users were age, gender, education, occupational status, having a regular provider, and self-rated general health. Conclusion mHealth apps are associated with increased rates of HPB among individuals with CCs. However, certain groups, like older adults, are most affected by a digital divide where they have lower access to mHealth apps and thus are not able to take advantage of these tools. Rigorous randomized clinical trials among various segments of the population and different health conditions are needed to establish the effectiveness of these mHealth apps. Healthcare providers should encourage validated mHealth apps for patients with CCs.