Amsterdam School of Communication Research (ASCoR), University of Amsterdam, Amsterdam, Netherlands.
Department of Physiotherapy, Faculty of Health, Amsterdam University of Applied Sciences, Amsterdam, Netherlands.
JMIR Mhealth Uhealth. 2021 May 12;9(5):e13391. doi: 10.2196/13391.
The last decade has seen a substantial increase in the use of mobile health apps and research into the effects of those apps on health and health behaviors. In parallel, research has aimed at identifying population subgroups that are more likely to use those health apps. Current evidence is limited by two issues. First, research has focused on broad health apps, and little is known about app usage for a specific health behavior. Second, research has focused on comparing current users and current nonusers, without considering subgroups of nonusers.
We aimed to provide profile distributions of current users, previous users, and informed nonusers, and to identify predictor variables relevant for profile classification.
Data were available from 1683 people who participated in a Dutch walking event in Amsterdam that was held in September 2017. They provided information on demographics, self-reported walking behavior, and walking app usage, as well as items from User Acceptance of Information Technology, in an online survey. Data were analyzed using discriminant function analysis and multinomial logistic regression analysis.
Most participants were current walking app users (899/1683, 53.4%), while fewer participants were informed nonusers (663/1683, 39.4%) and very few were previous walking app users (121/1683, 7.2%). Current walking app users were more likely to report walking at least 5 days per week and for at least 30 minutes per bout (odds ratio [OR] 1.44, 95% CI 1.11-1.85; P=.005) and more likely to be overweight (OR 1.72, 95% CI 1.24-2.37; P=.001) or obese (OR 1.49, 95% CI 1.08-2.08; P=.005) as compared with informed nonusers. Further, current walking app users perceived their walking apps to be less boring, easy to use and retrieve information, and more helpful to achieve their goals. Effect sizes ranged from 0.10 (95% CI 0.08-0.30) to 1.58 (95% CI 1.47-1.70).
The distributions for walking app usage appeared different from the distributions for more general health app usage. Further, the inclusion of two specific subgroups of nonusers (previous users and informed nonusers) provides important information for health practitioners and app developers to stimulate continued walking app usage, including making information in those apps easy to understand and making it easy to obtain information from the apps, as well as preventing apps from becoming boring and difficult to use for goal attainment.
过去十年中，移动健康应用的使用显著增加，同时也有越来越多的研究关注这些应用对健康和健康行为的影响。在此期间，研究旨在确定更有可能使用这些健康应用的人群亚组。目前的证据受到两个问题的限制。首先，研究主要集中在广泛的健康应用上，对于特定健康行为的应用使用情况知之甚少。其次，研究主要集中在比较当前用户和当前非用户，而没有考虑非用户的亚组。
本研究旨在提供当前用户、以前用户和知情非用户的特征分布，并确定与特征分类相关的预测变量。
本研究的数据来自于 2017 年 9 月在荷兰阿姆斯特丹举行的一次步行活动中的 1683 人，他们通过在线调查提供了人口统计学、自我报告的步行行为和步行应用使用情况以及信息技术用户接受度的相关信息。数据使用判别函数分析和多项逻辑回归分析进行分析。
大多数参与者是当前的步行应用用户（899/1683，53.4%），而知情非用户（663/1683，39.4%）和以前的步行应用用户（121/1683，7.2%）较少。当前的步行应用用户更有可能报告每周至少行走 5 天，每次至少 30 分钟（比值比[OR]1.44，95%置信区间[CI]1.11-1.85；P=.005），并且更有可能超重（OR 1.72，95% CI 1.24-2.37；P=.001）或肥胖（OR 1.49，95% CI 1.08-2.08；P=.005）与知情非用户相比。此外，当前的步行应用用户认为他们的步行应用程序不那么无聊，使用和检索信息更容易，并且更有助于实现他们的目标。效应大小范围从 0.10（95% CI 0.08-0.30）到 1.58（95% CI 1.47-1.70）。
步行应用程序使用情况的分布与更广泛的健康应用程序使用情况的分布不同。此外，包括两个特定的非用户亚组（以前的用户和知情非用户）为健康从业者和应用程序开发人员提供了重要信息，以鼓励继续使用步行应用程序，包括使这些应用程序中的信息易于理解，以及使从应用程序中获取信息变得容易，同时防止应用程序变得无聊和难以实现目标。
