Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, United States.
Interdisciplinary Center for Network Science and Applications, University of Notre Dame, Notre Dame, IN, United States.
JMIR Mhealth Uhealth. 2019 Mar 12;7(3):e11075. doi: 10.2196/11075.
Moderate-vigorous physical activity (MVPA) offers extensive health benefits but is neglected by many. As a result, a wide body of research investigating physical activity behavior change has been conducted. As many of these studies transition from paper-based methods of MVPA data collection to fitness trackers, a series of challenges arise in extracting insights from these new data.
The objective of this research was to develop a framework for preprocessing and extracting MVPA trends from wearable fitness tracker data to support MVPA behavior change studies.
Using heart rate data collected from fitness trackers, we propose Physical Activity Trend eXtraction (PATX), a framework that imputes missing data, recalculates personalized target heart zones, and extracts MVPA trends. We tested our framework on a dataset of 123 college study participants observed across 2 academic years (18 months) using Fitbit Charge HRs. To demonstrate the value of our frameworks' output in supporting MVPA behavior change studies, we applied it to 2 case studies.
Among the 123 participants analyzed, PATX labeled 41 participants as experiencing a significant increase in MVPA and 44 participants who experienced a significant decrease in MVPA, with significance defined as P<.05. Our first case study was consistent with previous works investigating the associations between MVPA and mental health. Whereas the second, exploring how individuals perceive their own levels of MVPA relative to their friends, led to a novel observation that individuals were less likely to notice changes in their own MVPA when close ties in their social network mimicked their changes.
By providing meaningful and flexible outputs, PATX alleviates data concerns common with fitness trackers to support MVPA behavior change studies as they shift to more objective assessments of MVPA.
适度剧烈的身体活动（MVPA）有广泛的健康益处，但很多人却忽视了这一点。因此，大量研究致力于改变身体活动行为。随着许多研究从基于纸张的 MVPA 数据收集方法向健身追踪器转变，从这些新数据中提取见解会带来一系列挑战。
本研究旨在开发一个从可穿戴健身追踪器数据中预处理和提取 MVPA 趋势的框架，以支持 MVPA 行为改变研究。
我们使用健身追踪器收集的心率数据，提出了 Physical Activity Trend eXtraction（PATX），这是一个框架，可填补缺失数据、重新计算个性化目标心率区，并提取 MVPA 趋势。我们在使用 Fitbit Charge HR 的 123 名大学生参与者的数据集上测试了我们的框架，观察时间跨越两个学年（18 个月）。为了展示我们框架输出在支持 MVPA 行为改变研究中的价值，我们将其应用于两个案例研究。
在分析的 123 名参与者中，PATX 将 41 名参与者标记为 MVPA 显著增加，44 名参与者标记为 MVPA 显著减少，显著性定义为 P<.05。我们的第一个案例研究与先前研究一致，这些研究调查了 MVPA 与心理健康之间的关联。而第二个案例研究则探讨了个体如何感知自己的 MVPA 水平与朋友的关系，得出了一个新的观察结果，即当社交网络中的亲密关系模仿他们的变化时，个体不太可能注意到自己的 MVPA 变化。
通过提供有意义且灵活的输出，PATX 缓解了健身追踪器中常见的数据问题，为更客观地评估 MVPA 提供了支持，从而支持 MVPA 行为改变研究。
