Baroudi Loubna, Zernicke Ronald Fredrick, Tewari Muneesh, Carlozzi Noelle E, Choi Sung Won, Cain Stephen M
Department of Mechanical Engineering, University of Michigan-Ann Arbor, 2505 Hayward St, Ann Arbor, MI, 48109, United States, 1 7342626353.
Department of Orthopedic Surgery, University of Michigan-Ann Arbor, Ann Arbor, MI, United States.
JMIR Mhealth Uhealth. 2025 Mar 21;13:e46149. doi: 10.2196/46149.
Consumer-grade wearables allow researchers to capture a representative picture of human behavior in the real world over extended periods. However, maintaining users' engagement remains a challenge and can lead to a decrease in compliance (eg, wear time in the context of wearable sensors) over time (eg, "wearables' abandonment").
In this work, we analyzed datasets from diverse populations (eg, caregivers for various health issues, college students, and pediatric oncology patients) to quantify the impact that wear time requirements can have on study results. We found evidence that emphasizes the need to account for participants' wear time in the analysis of consumer-grade wearables data. In Aim 1, we demonstrate the sensitivity of parameter estimates to different data processing methods with respect to wear time. In Aim 2, we demonstrate that not all research questions necessitate the same wear time requirements; some parameter estimates are not sensitive to wear time.
We analyzed 3 Fitbit datasets comprising 6 different clinical and healthy population samples. For Aim 1, we analyzed the sensitivity of average daily step count and average daily heart rate at the population sample and individual levels to different methods of defining "valid" days using wear time. For Aim 2, we evaluated whether some research questions can be answered with data from lower compliance population samples. We explored (1) the estimation of the average daily step count and (2) the estimation of the average heart rate while walking.
For Aim 1, we found that the changes in the population sample average daily step count could reach 2000 steps for different methods of analysis and were dependent on the wear time compliance of the sample. As expected, population samples with a low daily wear time (less than 15 hours of wear time per day) showed the most sensitivity to changes in methods of analysis. On the individual level, we observed that around 15% of individuals had a difference in step count higher than 1000 steps for 4 of the 6 population samples analyzed when using different data processing methods. Those individual differences were higher than 3000 steps for close to 5% of individuals across all population samples. Average daily heart rate appeared to be robust to changes in wear time. For Aim 2, we found that, for 5 population samples out of 6, around 11% of individuals had enough data for the estimation of average heart rate while walking but not for the estimation of their average daily step count.
We leveraged datasets from diverse populations to demonstrate the direct relationship between parameter estimates from consumer-grade wearable devices and participants' wear time. Our findings highlighted the importance of a thorough analysis of wear time when processing data from consumer-grade wearables to ensure the relevance and reliability of the associated findings.
消费级可穿戴设备使研究人员能够在较长时间内获取现实世界中人类行为的代表性图景。然而，保持用户的参与度仍然是一项挑战，并且随着时间的推移（例如“可穿戴设备被弃用”）可能导致依从性下降（例如，在可穿戴传感器的情况下为佩戴时间）。
在本研究中，我们分析了来自不同人群（例如，患有各种健康问题的护理人员、大学生和儿科肿瘤患者）的数据集，以量化佩戴时间要求对研究结果可能产生的影响。我们发现有证据强调在分析消费级可穿戴设备数据时需要考虑参与者的佩戴时间。在目标1中，我们展示了参数估计对不同数据处理方法在佩戴时间方面的敏感性。在目标2中，我们证明并非所有研究问题都需要相同的佩戴时间要求；一些参数估计对佩戴时间不敏感。
我们分析了3个Fitbit数据集，这些数据集包含6个不同的临床和健康人群样本。对于目标1，我们分析了在人群样本和个体层面上，平均每日步数和平均每日心率对使用佩戴时间定义“有效”天数的不同方法的敏感性。对于目标2，我们评估了一些研究问题是否可以通过来自依从性较低的人群样本的数据来回答。我们探讨了（1）平均每日步数的估计和（2）步行时平均心率的估计。
对于目标1，我们发现，对于不同的分析方法，人群样本平均每日步数的变化可能达到2000步，并且取决于样本的佩戴时间依从性。正如预期的那样，每日佩戴时间较短（每天佩戴时间少于15小时）的人群样本对分析方法的变化最为敏感。在个体层面，我们观察到，在分析的6个人群样本中的4个样本中，当使用不同的数据处理方法时，约15%的个体的步数差异高于1000步。在所有人群样本中，接近5%的个体的个体差异高于3000步。平均每日心率似乎对佩戴时间的变化具有较强的耐受性。对于目标2，我们发现，在6个人群样本中的5个样本中，约11%的个体有足够的数据来估计步行时的平均心率，但没有足够的数据来估计他们的平均每日步数。
我们利用来自不同人群的数据集来证明消费级可穿戴设备的参数估计与参与者的佩戴时间之间的直接关系。我们的研究结果强调了在处理来自消费级可穿戴设备的数据时，对佩戴时间进行全面分析的重要性，以确保相关研究结果的相关性和可靠性。
