Abstract TP93: A Bayesian Hierarchical Time Series Model to Forecast Patient Recovery Outcomes After an Acute Stroke When Activity Monitor Data is Incomplete

Abstract

Background: Activity monitor data is a useful tool in clinical trials to monitor patient recovery after stroke. These data offer important information on activity, sedentary and sleep behaviors throughout recovery. However, not all participants wear activity monitors, or their activity data may be incomplete. The purpose of this study was to develop a model-based approach to obtain individual patient-level longitudinal forecast estimates of activity and sleep behaviors for both those with and without complete activity data. Methods: Participants wore an ActivPal (steps/day) and Actigraph (total sleep/day) monitors for 3-7 days at 10-60-90 days post stroke. We developed a Bayesian hierarchical time series model to forecast daily steps and sleep time by participant. We included a logistic growth curve in which we use predictors comprising age, sex, race, marital status, smoking, BMI, cognition, gait speed, depression, self-reported sleep, and patients’ previous time trends to forecast patient specific future time trends during the first 90 days following stroke. Results: Data were collected on a total sample of 114 participants. Out of the 114 participants, 73 participants had recorded sleep data, and 76 participants had step data reported, which are not mutually exclusive groups. Out of 114 total participants, 41 participants did not have any sleep or step data recorded. We obtained patient-specific time trends for both patients with and without monitor data. To assess model predictive performance, we compared predicted estimates to observed data using a validation study and found that on average, there was a difference of 0.52 daily steps (counts). Additionally, on average, there was a difference of 0.46 between observed and estimated daily sleep count (minutes). Discussion: The contribution of our study is a model-based approach to accurately predict patient-specific longitudinal step and sleep behaviors based on predictor variables and previous time trends for patients in the subacute stroke recovery phase. Our approach may be extended to other applications using activity monitor data. A limitation of this study is the increased uncertainty surrounding forecast estimates for periods/days without data, which will be addressed in future work.