论文标题
通过可视化个人流动模式和行动计划（SEDVIS）来支持久坐行为改变的智能手机应用：开发和试点研究
A Smartphone App to Support Sedentary Behavior Change by Visualizing Personal Mobility Patterns and Action Planning (SedVis): Development and Pilot Study
论文作者
论文摘要
鉴于日常生活中久坐行为的盛行率很高，需要简单而实用的行为改变解决方案，以避免有害的健康影响。移动应用SEDVI是根据健康行动过程方法开发的。该应用程序提供个人移动模式可视化（用于体育锻炼和久坐行为）以及久坐行为改变的行动计划。该研究的主要目的是研究移动性模式可视化对用户改变其久坐行为的行动计划的影响。次要目的是评估用户参与应用程序的可视化和用户体验。在为期3周的用户研究中，将参与者分配给主动对照组（n = 8）或干预组（n = 8）。在1周的基准期间，没有一个参与者无法访问应用程序中的功能。在接下来的2周干预期内，只有干预组可以访问可视化，而两组都要求每天制定行动计划并减少其久坐的行为。结果表明，根据NHST和贝叶斯统计，Sedvis中的可视化对参与者的行动计划没有影响。涉及塞德维斯（Sedvis）的可视化和行动计划的干预措施对减少参与者久坐的时间有积极影响，根据贝叶斯统计数据，证据较弱，而在主动控制条件下久坐时间没有变化。此外，贝叶斯分析薄弱地表明，用户检查应用程序的频率越多，他们越有可能减少久坐行为。
Given the high prevalence of sedentary behavior in daily life, simple yet practical solutions for behavior change are needed to avoid detrimental health effects. The mobile app SedVis was developed based on the health action process approach. The app provides personal mobility pattern visualization (for both physical activity and sedentary behavior) and action planning for sedentary behavior change. The primary aim of the study is to investigate the effect of mobility pattern visualization on users' action planning for changing their sedentary behavior. The secondary aim is to evaluate user engagement with the visualization and user experience of the app. In a 3-week user study, participants were allocated to either an active control group (n=8) or an intervention group (n=8). In the 1-week baseline period, none of the participants had access to the functions in the app. In the following 2-week intervention period, only the intervention group was given access to the visualizations, whereas both groups were asked to make action plans every day and reduce their sedentary behavior. The results suggested that the visualizations in SedVis had no effect on the participants' action planning according to both the NHST and Bayesian statistics. The intervention involving visualizations and action planning in SedVis had a positive effect on reducing participants' sedentary hours, with weak evidence according to Bayesian statistics, whereas no change in sedentary time was more likely in the active control condition. Furthermore, Bayesian analysis weakly suggested that the more frequently the users checked the app, the more likely they were to reduce their sedentary behavior.
