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

1960 

Contributed Abstract 

Paper 

Deji Suolang (1), Brady West (2)

(1) University of Michigan - Ann Arbor, N/A, (2) Institute for Social Research, N/A

Brady West  
Institute for Social Research

Deji Suolang  
University of Michigan - Ann Arbor

Deji Suolang  
University of Michigan - Ann Arbor

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 and self-reported values. Multiple imputation estimates and standard errors resembled the NHANES estimates and were consistent across different subgroups. Self-reports and imputed sensor values were used to predict health conditions as a means for evaluating data quality. Models with sensor values exhibited smaller deviance. The study adds to the existing literature on combining multiple data sources and provides 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

Missing Data Methods/Non-response Bias Analysis