Analysis Method of Real-World Digital Biomarkers for Clinical Impact in Cancer Patients
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View abstract on PubMed
Summary
This summary is machine-generated.Estimating daily steps from wearable devices in cancer patients helps predict clinical events. Higher daily step counts are linked to a lower risk of mortality and adverse clinical outcomes.
Area Of Science
- Digital Health
- Wearable Technology
- Clinical Trials
Background
- Consumer wearables offer promise for monitoring patient wellness in decentralized clinical trials.
- Handling and interpreting data from wearables present challenges, necessitating statistical and data science expertise.
- Gaps in wearable data require robust methods for accurate health outcome prediction.
Purpose Of The Study
- To develop and validate methods for estimating daily steps from Apple Watch data with data gaps.
- To analyze the association between estimated daily step counts and clinical events in cancer patients.
- To identify digital biomarkers of physical activity for predicting clinical outcomes.
Main Methods
- Utilized data from 50 cancer patients using the DigiBioMarC app and Apple Watch over 28 days.
- Developed three methods to estimate daily steps, addressing various data gap types based on duration and context.
- Employed Cox proportional hazards regression, decision tree modeling, and participant clustering to analyze associations.
Main Results
- A method accounting for data gap duration and context provided the most reliable daily step estimates.
- Daily step counts on sufficient measurement days predicted time to first clinical event (p=0.068).
- Higher daily step counts correlated with lower hazard probabilities for mortality and clinical events, with 83.3% accuracy in prediction.
Conclusions
- An effective method was developed to estimate daily steps from consumer wearable data with unknown gaps.
- Daily step counts from sufficient sampling days are strong predictors of clinical event timing and occurrence.
- Increased daily step counts in cancer patients are associated with a reduced hazard of death or clinical events.
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