Developing a digital signature of care-related activities and burden
We propose to develop a digital signature that identifies the level of care partner burden associated with person living with dementia (PLWD)/care partner dyads living in the community. This project will use longitudinal data collected in studies using the Collaborative Aging Research Using Technology (CART) Initiative technology platform with care partner and PLWD participants. Inputs for the signature will include sensor-based measures of activity in the home (e.g., room transitions, activity pattern in the home) and health measures (e.g, activity level, weight, sleep). Univariate analysis will assess correlations of sensor measures to the primary outcome of burden level (ZBI-12). To develop the signature, machine and deep learning techniques will be used to determine the optimal combination of sensor measures that predict the level of burden and care.
Neil Thomas , Bruyère Research Institute
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