Analysis Method of Real-World Digital Biomarkers for Clinical Impact in Cancer Patients.

Abstract

Introduction: 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.

Methods: 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.

Results: 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 (p = 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%.

Conclusion: 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.