A quantitative analysis of the impact of COVID‐19 quarantining on in‐home eating and drinking habits in a cohort of people living with dementia

Abstract

AbstractBackgroundSince the start of the coronavirus pandemic in 2019 (COVID‐19), people living with dementia (PLWD) have reported experiencing changes in their in‐home eating and drinking habits during quarantine. Previous research has been conducted solely through questionnaires, making it subject to researcher bias. However, remote monitoring technologies allow us the unprecedented ability to quantify behavioural patterns of PLWD in real‐world environments. We used data collected as part of an ongoing project at the UK Dementia Research Institute’s Care Research and Technology Centre. This work aims to understand better the effects of COVID‐19 quarantining on the in‐home eating and drinking habits of PLWD.MethodUsing COVID‐19 as a natural experiment, we compared data collated during the UK’s first COVID‐19 lockdown with the same period pre‐COVID. Household activity was observed using kitchen motion and appliance usage (refrigerator door, kettle, and oven) sensors, summed across four different daily periods (22:00, 04:00, 10:00, and 16:00), and then standardized across households by sensory device, resulting in 16 features. With this dataset, we trained a binary random forest classification model for each household independently, assessing performance using 10‐fold cross‐validation and also computing permutation feature importances (PFIs). Finally, using the PFIs of each household as a behavioural pattern reflecting in‐home eating and drinking habit changes as a function of quarantine, we used agglomerative hierarchical clustering (metric = correlation; method = ward) to identify similar behavioural changes across households.ResultOur cohort included 25 households of PLWD. All patients (11 female : 14 male; mean age at study start = 80.3 ± 6.78 years) had established diagnoses of dementia. Across households, binary classification accuracy was high (mean = 0.88 ± 0.08), with precision and recall balanced well (mean F1‐score = 0.83 ± 0.14). Hierarchical clusters of the household PFIs produced 4 main groupings that simple statistical observations showed each relied on distinct features for binary classification.ConclusionApplying combined supervised and unsupervised learning approaches to in‐home monitoring data has the potential to dramatically improve research for dementia care. Our findings show that in‐home eating and drinking habit changes among PLWD, in response to the COVID‐19 quarantine, are complex.