Wang Yunlong, König Laura M, Reiterer Harald
Department of Computer and Information Science, University of Konstanz, Konstanz, Germany.
Department of Psychology, University of Konstanz, Konstanz, Germany.
JMIR Form Res. 2021 Jan 27;5(1):e15369. doi: 10.2196/15369.
Prolonged sedentary behavior is related to a number of risk factors for chronic diseases. 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.
A 3-week user study was conducted with 16 participants who had the motivation to reduce their sedentary behavior. 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. Participants' sedentary behavior was estimated based on the sensor data of their smartphones, and their action plans and interaction with the app were also recorded by the app. Participants' intention to change their sedentary behavior and user experience of the app were assessed using questionnaires.
The data were analyzed using both traditional null hypothesis significance testing (NHST) and Bayesian statistics. 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 (Bayes factor, BF=1.92; median 0.52; 95% CI 0.04-1.25), whereas no change in sedentary time was more likely in the active control condition (BF=0.28; median 0.18; 95% CI 0.01-0.64). Furthermore, Bayesian analysis weakly suggested that the more frequently the users checked the app, the more likely they were to reduce their sedentary behavior (BF=1.49; r=-0.50).
Using a smartphone app to collect data on users' mobility patterns and provide real-time feedback using visualizations may be a promising method to induce changes in sedentary behavior and may be more effective than action planning alone. Replications with larger samples are needed to confirm these findings.
长时间久坐行为与多种慢性疾病风险因素相关。鉴于久坐行为在日常生活中普遍存在，需要简单实用的行为改变方案来避免对健康产生有害影响。
移动应用程序SedVis是基于健康行动过程方法开发的。该应用程序提供个人活动模式可视化（包括身体活动和久坐行为）以及久坐行为改变的行动计划。本研究的主要目的是调查活动模式可视化对用户改变久坐行为的行动计划的影响。次要目的是评估用户对可视化的参与度以及应用程序的用户体验。
对16名有减少久坐行为动机的参与者进行了为期3周的用户研究。参与者被分配到积极对照组（n = 8）或干预组（n = 8）。在为期1周的基线期内，所有参与者均无法使用应用程序中的功能。在接下来的2周干预期内，只有干预组可以使用可视化功能，而两组都被要求每天制定行动计划并减少久坐行为。根据参与者智能手机的传感器数据估算其久坐行为，应用程序还记录他们的行动计划以及与应用程序的交互情况。使用问卷评估参与者改变久坐行为的意图和应用程序的用户体验。
使用传统的零假设显著性检验（NHST）和贝叶斯统计对数据进行分析。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均无影响。SedVis中涉及可视化和行动计划的干预对减少参与者的久坐时长有积极影响，根据贝叶斯统计有微弱证据支持（贝叶斯因子，BF = 1.92；中位数0.52；95%可信区间0.04 - 1.25），而在积极对照条件下久坐时间更有可能没有变化（BF = 0.28；中位数0.18；95%可信区间0.01 - 0.64）。此外，贝叶斯分析微弱表明用户检查应用程序越频繁，就越有可能减少久坐行为（BF = 1.49；r = -0.50）。
使用智能手机应用程序收集用户活动模式数据并通过可视化提供实时反馈可能是一种诱导久坐行为改变的有前景的方法，并且可能比单独的行动计划更有效。需要更大样本量的重复研究来证实这些发现。
