Oakley-Girvan Ingrid, Zhai Yaya, Yunis Reem, Liu Raymond, Davis Sharon W, Kubo Ai, Aghaee Sara, Blankenship Jennifer M, Lyden Kate, Neeman Elad
Medable Inc., Strategy and Science Departments, Palo Alto, CA, USA.
Public Health Institute, The Data and Technology Proving Ground, Oakland, CA, USA.
Digit Biomark. 2025 Feb 3;9(1):40-51. doi: 10.1159/000543898. eCollection 2025 Jan-Dec.
Wearable technologies can enhance measurements completed from home by participants in decentralized clinical trials. These measurements have shown promise in monitoring patient wellness outside the clinical setting. However, there are challenges in handling data and its interpretation when using consumer wearables, requiring input from statisticians and data scientists. This article describes three methods to estimate daily steps to address gaps in data from the Apple Watch in cancer patients and uses one of these methods in an analysis of the association between daily step count estimates and clinical events for these patients.
A cohort of 50 cancer patients used the DigiBioMarC app integrated with an Apple Watch for 28 days. We identified different gap types in watch data based on their length and context to estimate daily steps. Cox proportional hazards regression models were used to determine the association between step count and time to death or time to first clinical event. Decision tree modeling and participant clustering were also employed to identify digital biomarkers of physical activity that were predictive of clinical event occurrence and hazard ratio to clinical events, respectively.
Among the three methods explored to address missing steps, the method that identified different step data gap types according to their duration and context yielded the most reasonable estimate of daily steps. Ten hours of waking time was used to differentiate between sufficient and insufficient measurement days. Daily step count on sufficient days was the most promising predictor of time to first clinical event ( = 0.068). This finding was consistent with participant clustering and decision tree analyses, where the participant clusters emerged naturally based on different levels of daily steps, and the group with the highest steps on sufficient days had the lowest hazard probability of mortality and clinical events. Additionally, daily steps on sufficient days can also be used as a predictor of whether a participant will have clinical events with an accuracy of 83.3%.
We have developed an effective way to estimate daily steps of consumer wearable data containing unknown data gaps. Daily step counts on days with sufficient sampling are a strong predictor of the timing and occurrence of clinical events, with individuals exhibiting higher daily step counts having reduced hazard of death or clinical events.
可穿戴技术能够提升分散式临床试验中参与者在家中完成的测量工作。这些测量结果在监测临床环境之外的患者健康状况方面展现出了前景。然而，在使用消费级可穿戴设备时，处理数据及其解读存在挑战，这需要统计学家和数据科学家的投入。本文介绍了三种估算每日步数的方法，以解决癌症患者苹果手表数据中的缺口问题，并在分析这些患者的每日步数估算与临床事件之间的关联时使用了其中一种方法。
一组50名癌症患者使用与苹果手表集成的DigiBioMarC应用程序28天。我们根据手表数据的长度和背景识别出不同类型的缺口，以估算每日步数。使用Cox比例风险回归模型来确定步数与死亡时间或首次临床事件时间之间的关联。还采用了决策树建模和参与者聚类来识别分别可预测临床事件发生和临床事件风险比的身体活动数字生物标志物。
在探索的三种解决缺失步数的方法中，根据持续时间和背景识别不同步数数据缺口类型的方法得出了最合理的每日步数估算。使用10小时的清醒时间来区分测量充足和不足的日子。充足日的每日步数是首次临床事件时间最有前景的预测指标（ = 0.068）。这一发现与参与者聚类和决策树分析一致，在这些分析中，参与者聚类基于不同的每日步数水平自然形成，充足日步数最高的组死亡和临床事件的风险概率最低。此外，充足日的每日步数还可以作为预测参与者是否会发生临床事件的指标，准确率为83.3%。
我们开发了一种有效的方法来估算包含未知数据缺口的消费级可穿戴设备数据的每日步数。采样充足日子的每日步数是临床事件发生时间和发生情况的有力预测指标，每日步数较高的个体死亡或临床事件的风险降低。
