Davidoff Hannah, Van Kraaij Alex, Van den Bulcke Laura, Lutin Erika, Vandenbulcke Mathieu, Van Helleputte Nick, De Vos Maarten, Van Hoof Chris, Van Den Bossche Maarten
Department of Electrical Engineering, ESAT, KU Leuven, Heverlee, Belgium.
Imec, Heverlee, Belgium.
Innov Aging. 2024 Jun 5;8(7):igae057. doi: 10.1093/geroni/igae057. eCollection 2024.
The number of people with dementia is expected to triple to 152 million in 2050, with 90% having accompanying behavioral and psychological symptoms (BPSD). Agitation is among the most critical BPSD and can lead to decreased quality of life for people with dementia and their caregivers. This study aims to explore objective quantification of agitation in people with dementia by analyzing the relationships between physiological and movement data from wearables and observational measures of agitation.
The data presented here is from 30 people with dementia, each included for 1 week, collected following our previously published multimodal data collection protocol. This observational protocol has a cross-sectional repeated measures design, encompassing data from both wearable and fixed sensors. Generalized linear mixed models were used to quantify the relationship between data from different wearable sensor modalities and agitation, as well as motor and verbal agitation specifically.
Several features from wearable data are significantly associated with agitation, at least the < .05 level (absolute β: 0.224-0.753). Additionally, different features are informative depending on the agitation type or the patient the data were collected from. Adding context with key confounding variables (time of day, movement, and temperature) allows for a clearer interpretation of feature differences when a person with dementia is agitated.
The features shown to be significantly different, across the study population, suggest possible autonomic nervous system activation when agitated. Differences when splitting the data by agitation type point toward a need for future detection models to tailor to the primary type of agitation expressed. Finally, patient-specific differences in features indicate a need for patient- or group-level model personalization. The findings reported in this study both reinforce and add to the fundamental understanding of and can be used to drive the objective quantification of agitation.
预计到2050年，痴呆症患者人数将增至三倍，达到1.52亿，其中90%伴有行为和心理症状（BPSD）。激越属于最严重的BPSD之一，会导致痴呆症患者及其照护者的生活质量下降。本研究旨在通过分析可穿戴设备的生理和运动数据与激越的观察指标之间的关系，探索痴呆症患者激越的客观量化方法。
此处呈现的数据来自30名痴呆症患者，每位患者纳入研究1周，数据收集遵循我们之前发表的多模态数据收集方案。该观察方案采用横断面重复测量设计，涵盖可穿戴和固定传感器的数据。使用广义线性混合模型来量化不同可穿戴传感器模式的数据与激越之间的关系，以及具体的运动性激越和言语性激越之间的关系。
可穿戴数据的几个特征与激越显著相关，至少在<0.05水平（绝对β：0.224 - 0.753）。此外，根据激越类型或收集数据的患者不同，不同特征具有不同的信息量。加入关键混杂变量（一天中的时间、运动和温度）的背景信息，有助于在痴呆症患者激越时更清晰地解释特征差异。
在整个研究人群中显示出显著差异的特征表明，激越时可能存在自主神经系统激活。按激越类型划分数据时的差异表明，未来的检测模型需要针对所表现出的主要激越类型进行定制。最后，特征的患者特异性差异表明需要进行患者或组水平的模型个性化。本研究报告的结果既强化了对激越的基本理解，又增添了新的内容，可用于推动激越的客观量化。
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