A Markov Chain Model for Identifying Changes in Daily Activity Patterns
  of People Living with Dementia
Nan Fletcher-Lloyd Alina-Irina Serban Magdalena Kolanko David Wingfield Danielle Wilson Ramin Nilforooshan Payam Barnaghi Eyal Soreq
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.