Behavioral Patterns of Older Adults in Assisted Living

TLDR

The study examines at‑home activity rhythms and identifies a dozen behavioral patterns from a pilot monitoring study of 22 assisted‑living residents. Custom software using a statistical predictive algorithm modeled circadian activity rhythms from in‑home monitoring data, estimating room‑based occupancy and motion events to detect deviations. The system can detect activity deviations that warn caregivers of potential health changes, enabling timely diagnostic and intervention actions.

Abstract

In this paper, we examine at-home activity rhythms and present a dozen of behavioral patterns obtained from an activity monitoring pilot study of 22 residents in an assisted living setting with four case studies. Established behavioral patterns have been captured using custom software based on a statistical predictive algorithm that models circadian activity rhythms (CARs) and their deviations. The CAR was statistically estimated based on the average amount of time a resident spent in each room within their assisted living apartment, and also on the activity level given by the average n.umber of motion events per room. A validated in-home monitoring system (IMS) recorded the monitored resident's movement data and established the occupancy period and activity level for each room. Using these data, residents' circadian behaviors were extracted, deviations indicating anomalies were detected, and the latter were correlated to activity reports generated by the IMS as well as notes of the facility's professional caregivers on the monitored residents. The system could be used to detect deviations in activity patterns and to warn caregivers of such deviations, which could reflect changes in health status, thus providing caregivers with the opportunity to apply standard of care diagnostics and to intervene in a timely manner.