A Markov Chain Model for Identifying Changes in Daily Activity Patterns of People Living with Dementia

TL;DR

This study introduces a Markov chain model to detect behavioral changes in people living with dementia through in-home IoT monitoring, highlighting shifts in activity patterns during the COVID-19 pandemic to enable proactive care.

Contribution

The paper presents a novel application of Markov modelling to analyze remote activity data for identifying behavioral changes in dementia patients, advancing monitoring methods.

Findings

Increased daytime kitchen activity during COVID-19 pandemic.

Decreased night-time kitchen activity during COVID-19 pandemic.

Proposed Markov model effectively detects behavioral changes.

Abstract

Malnutrition and dehydration are strongly associated with increased cognitive and functional decline in people living with dementia (PLWD), as well as an increased rate of hospitalisations in comparison to their healthy counterparts. Extreme changes in eating and drinking behaviours can often lead to malnutrition and dehydration, accelerating the progression of cognitive and functional decline and resulting in a marked reduction in quality of life. Unfortunately, there are currently no established methods by which to objectively detect such changes. Here, we present the findings of an extensive quantitative analysis conducted on in-home monitoring data collected from 73 households of PLWD using Internet of Things technologies. The Coronavirus 2019 (COVID-19) pandemic has previously been shown to have dramatically altered the behavioural habits, particularly the eating and drinking habits, of PLWD. Using the COVID-19 pandemic as a natural experiment, we conducted linear mixed-effects modelling to examine changes in mean kitchen activity within a subset of 21 households of PLWD that were continuously monitored for 499 days. We report an observable increase in day-time kitchen activity and a significant decrease in night-time kitchen activity (t(147) = -2.90, p < 0.001). We further propose a novel analytical approach to detecting changes in behaviours of PLWD using Markov modelling applied to remote monitoring data as a proxy for behaviours that cannot be directly measured. Together, these results pave the way to introduce improvements into the monitoring of PLWD in naturalistic settings and for shifting from reactive to proactive care.