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
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
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定量分析 COVID-19 隔离对痴呆症患者家庭饮食习惯的影响
DOI：
10.1002/alz.065164
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Fletcher-Lloyd N
中科院分区：
文献类型：
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作者：
Fletcher-Lloyd N
自2019年冠状病毒大流行（COVID-19）开始以来，痴呆症患者（PLWD）报告称，在隔离期间，他们的家庭饮食习惯发生了变化。以前的研究仅通过问卷调查进行，使其受到研究人员的偏见。然而，远程监测技术使我们能够在真实的世界环境中量化PLWD的行为模式。我们使用的数据是英国痴呆症研究所护理研究和技术中心正在进行的项目的一部分。这项工作旨在更好地了解COVID-19封锁对PLWD家庭饮食习惯的影响。方法使用COVID-19作为自然实验，我们将英国首次COVID-19封锁期间整理的数据与COVID-19前同期进行比较。使用厨房运动和电器使用（冰箱门，水壶和烤箱）传感器观察家庭活动，在四个不同的日常时段（22：00，04：00，10：00和16：00）进行汇总，然后通过感官设备对家庭进行标准化，得到16个特征。使用这个数据集，我们为每个家庭独立训练了一个二元随机森林分类模型，使用10倍交叉验证评估性能，并计算排列特征重要性（PFI）。最后，使用每个家庭的PFI作为一种行为模式，反映在家里的饮食习惯的变化，作为一个功能的隔离，我们使用凝聚层次聚类（度量=相关性;方法=病房），以确定类似的行为变化在households.ResultOur队列包括25个家庭的PLWD。所有患者（11名女性：14名男性;研究开始时的平均年龄= 80.3 ± 6.78岁）均确诊为痴呆。在整个家庭中，二元分类准确率很高（平均值= 0.88 ± 0.08），精确度和召回率平衡良好（平均F1分数= 0.83 ± 0.14）。家庭PFIs的层次聚类产生4个主要分组，简单的统计观察显示每个依赖于不同的功能，二进制classification.ConclusionApplying相结合的监督和无监督学习方法在家庭监测数据有可能显着提高研究痴呆症护理。我们的研究结果表明，PLWD在家中饮食习惯的变化，以应对COVID-19隔离，是复杂的。
BackgroundSince 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.