Leveraging Wearables Data to Improve Self-Reports in Survey Research: An Imputation-Based Approach

Oregon Convention Center 

The integration of wearable sensor data in survey research has the potential to mitigate the recall and response errors that are typical in self-report data. However, such studies are often constrained in scale by implementation challenges and associated costs. This study used NHANES data, which includes both self-report responses and wearable sensor data measuring physical activity, to multiply impute sensor values for NHIS, a larger survey relying solely on interviews. Imputations were performed on synthetic populations to fully account for the complex sample design features.

Cross-validation demonstrated the robust predictive performance of the imputation model. The results showed disparities between sensor estimates and survey self-reports, and these discrepancies vary by different subgroups. Imputed estimates in NHIS closely mirrored the observed values in NHANES but tended to have higher standard errors. After the imputation, self-reports and sensor data in the combined dataset were used to predict health conditions as a means for evaluating data quality. Models with sensor values showed smaller deviance and higher coefficients of determination. The study advanced the existing literature on combining multiple data sources and provided insights into the use of sensor data in survey research.

missing data imputation

wearable sensor data

self-report survey

data integration

NHANES

NHIS 

Survey Research Methods Section