Detecting Smartwatch-Based Behavior Change in Response to a Multi-Domain Brain Health Intervention

In this study, we introduce and validate a computational method to detect lifestyle change that occurs in response to a multidomain healthy brain aging intervention. To detect behavior change, digital behavior markers are extracted from smartwatch sensor data and a permutation-based change detection algorithm quantifies the change in marker-based behavior from a preintervention, 1-week baseline. To validate the method, we verify that changes are successfully detected from synthetic data with known pattern differences. Next, we employ this method to detect overall behavior change for n = 28 brain health intervention subjects and n = 17 age-matched control subjects. For these individuals, we observe a monotonic increase in behavior change from the baseline week with a slope of 0.7460 for the intervention group and a slope of 0.0230 for the control group. Finally, we utilize a random forest algorithm to perform leave-one-subject-out prediction of intervention versus control subjects based on digital marker delta values. The random forest predicts whether the subject is in the intervention or control group with an accuracy of 0.87. This work has implications for capturing objective, continuous data to inform our understanding of intervention adoption and impact.