Characterizing and predicting person-specific, day-to-day, fluctuations in walking behavior.
Characterizing and predicting person-specific, day-to-day, fluctuations in walking behavior.
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DOI：
10.1371/journal.pone.0251659
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发表时间：
2021
期刊：
影响因子：
3.7
通讯作者：
Godino J
中科院分区：
文献类型：
--
作者：
Chevance G;Baretta D;Heino M;Perski O;Olthof M;Klasnja P;Hekler E;Godino J
尽管体育活动对健康有积极影响，但据估计，世界上有三分之一的人口活动不足。先前的研究主要调查了短时间内的总体水平上的身体活动，例如，在基线时的3至7天内和干预后的几个月内。为了制定有效的干预措施，我们需要更好地了解身体活动的时间动态。我们在这里提出了一种方法来研究步行行为在“高分辨率”，并通过捕捉具体的和日常的步行行为的变化。我们分析了151名超重或肥胖的年轻人的每日步数，他们平均佩戴了226天的加速度计（约25，000次观察）。然后，我们使用递归分割算法来描述变化的模式，这里是在研究过程中突然的行为收益和损失。这些行为的增加或减少被定义为相对于每个参与者持续至少7天的平均步数水平增加或减少30%。在识别收益和损失之后，使用动态复杂性算法计算每个参与者的个人时间序列的波动强度，以识别突然收益或损失的潜在预警信号。结果表明，步行行为的变化表现出不连续的变化，可以描述为突然的增益和损失。平均而言，参与者在研究中经历了六次突然的收益或损失。我们还观察到步行行为的临界波动（一种早期预警信号）与随后几天突然发生的行为丧失之间存在显著的正相关。总之，这项研究表明，步行行为可以很好地理解下的动态范式。研究结果还提供了支持的发展“及时适应”的行为干预措施的基础上检测的早期预警信号的突然行为损失。
Despite the positive health effect of physical activity, one third of the world’s population is estimated to be insufficiently active. Prior research has mainly investigated physical activity on an aggregate level over short periods of time, e.g., during 3 to 7 days at baseline and a few months later, post-intervention. To develop effective interventions, we need a better understanding of the temporal dynamics of physical activity. We proposed here an approach to studying walking behavior at “high-resolution” and by capturing the idiographic and day-to-day changes in walking behavior. We analyzed daily step count among 151 young adults with overweight or obesity who had worn an accelerometer for an average of 226 days (~25,000 observations). We then used a recursive partitioning algorithm to characterize patterns of change, here sudden behavioral gains and losses, over the course of the study. These behavioral gains or losses were defined as a 30% increase or reduction in steps relative to each participants’ median level of steps lasting at least 7 days. After the identification of gains and losses, fluctuation intensity in steps from each participant’s individual time series was computed with a dynamic complexity algorithm to identify potential early warning signals of sudden gains or losses. Results revealed that walking behavior change exhibits discontinuous changes that can be described as sudden gains and losses. On average, participants experienced six sudden gains or losses over the study. We also observed a significant and positive association between critical fluctuations in walking behavior, a form of early warning signals, and the subsequent occurrence of sudden behavioral losses in the next days. Altogether, this study suggests that walking behavior could be well understood under a dynamic paradigm. Results also provide support for the development of “just-in-time adaptive” behavioral interventions based on the detection of early warning signals for sudden behavioral losses.
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影响因子：
2.3
作者：
Cavill, Nick;Richardson, Debra;Rutter, Harry
通讯作者：
Rutter, Harry
影响因子：
5.9
作者：
Helmich, Marieke A.;Wichers, Marieke;Snippe, Evelien
通讯作者：
Snippe, Evelien
影响因子：
6.1
作者：
de Mooij, Susanne M. M.;Blanken, Tessa F.;van der Maas, Han L. J.
通讯作者：
van der Maas, Han L. J.
影响因子：
5.5
作者：
Hekler EB;Michie S;Pavel M;Rivera DE;Collins LM;Jimison HB;Garnett C;Parral S;Spruijt-Metz D
通讯作者：
Spruijt-Metz D
DOI：
10.1186/s40798-018-0157-9
发表时间：
2018-09-03
期刊：
Sports medicine - open
影响因子：
--
作者：
Gal R;May AM;van Overmeeren EJ;Simons M;Monninkhof EM
通讯作者：
Monninkhof EM