School of Electrical Engineering and Computer Science, The Pennsylvania State University, University Park, PA, USA.
Department of Kinesiology, The Pennsylvania State University, University Park, PA, USA.
Ann Behav Med. 2022 Nov 5;56(11):1188-1198. doi: 10.1093/abm/kaac051.
The COVID-19 pandemic adversely impacted physical activity, but little is known about how contextual changes following the pandemic declaration impacted either the dynamics of people's physical activity or their responses to micro-interventions for promoting physical activity.
This paper explored the effect of the COVID-19 pandemic on the dynamics of physical activity responses to digital message interventions.
Insufficiently-active young adults (18-29 years; N = 22) were recruited from November 2019 to January 2020 and wore a Fitbit smartwatch for 6 months. They received 0-6 messages/day via smartphone app notifications, timed and selected at random from three content libraries (Move More, Sit Less, and Inspirational Quotes). System identification techniques from control systems engineering were used to identify person-specific dynamical models of physical activity in response to messages before and after the pandemic declaration on March 13, 2020.
Daily step counts decreased significantly following the pandemic declaration on weekdays (Cohen's d = -1.40) but not on weekends (d = -0.26). The mean overall speed of the response describing physical activity (dominant pole magnitude) did not change significantly on either weekdays (d = -0.18) or weekends (d = -0.21). In contrast, there was limited rank-order consistency in specific features of intervention responses from before to after the pandemic declaration.
Generalizing models of behavioral dynamics across dramatically different environmental contexts (and participants) may lead to flawed decision rules for just-in-time physical activity interventions. Periodic model-based adaptations to person-specific decision rules (i.e., continuous tuning interventions) for digital messages are recommended when contexts change.
新冠疫情对身体活动产生了不利影响,但对于疫情宣布后环境变化如何影响人们身体活动的动态变化,以及他们对促进身体活动的微观干预措施的反应,知之甚少。
本文探讨了新冠疫情对数字信息干预促进身体活动反应动态的影响。
从 2019 年 11 月到 2020 年 1 月,招募了 22 名身体活动不足的年轻成年人(18-29 岁),他们佩戴 Fitbit 智能手表 6 个月。他们通过智能手机应用程序通知,每天接收 0-6 条消息,消息随机定时从三个内容库(多运动、少坐、励志名言)中选择。使用控制系统工程中的系统识别技术,在 2020 年 3 月 13 日宣布疫情后,识别个人对消息的身体活动动态模型。
疫情宣布后,工作日的日常步数明显减少(Cohen's d = -1.40),但周末没有减少(d = -0.26)。描述身体活动的反应(主导极点幅度)的整体速度均值在工作日(d = -0.18)或周末(d = -0.21)都没有显著变化。相比之下,在疫情宣布前后,干预反应的特定特征之间的秩序一致性有限。
在截然不同的环境背景(和参与者)下推广行为动态模型可能会导致及时的身体活动干预决策规则出现缺陷。当环境发生变化时,建议对数字消息进行基于模型的个性化决策规则的定期适应性调整(即连续调整干预)。