PDF(Pubmed)
Abstract：
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: Several features from wearable data are significantly associated with agitation, at least the p < .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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: Several features from wearable data are significantly associated with agitation, at least the p < .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.
UNASSIGNED: 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.
摘要：
■预计到2050年，痴呆症患者的数量将增加两倍，达到1.52亿，其中90％伴有行为和心理症状（BPSD）。躁动是最关键的BPSD之一，可能导致痴呆症患者及其护理人员的生活质量下降。本研究旨在通过分析可穿戴设备的生理和运动数据与观察性躁动措施之间的关系，探索痴呆症患者躁动的客观量化。
■这里提供的数据来自30名痴呆症患者，每个包括1周，按照我们先前发布的多模态数据收集协议收集。这个观测协议有一个横截面重复措施设计，包含来自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来自不同可穿戴传感器模态的数据与躁动之间的关系，特别是运动和言语激动。
■可穿戴数据中的几个特征与激动密切相关，至少p<.05水平(绝对β：0.224-0.753)。此外,根据患者的躁动类型或数据的收集,不同的特征可提供信息.添加具有关键混杂变量的上下文(一天中的时间、运动,和温度）可以更清晰地解释痴呆症患者躁动时的特征差异。
■显示出明显不同的功能，在整个研究人群中，提示烦躁不安时可能会激活自主神经系统。按搅拌类型划分数据时的差异表明需要将来的检测模型来定制所表达的主要搅拌类型。最后,患者特有的特征差异表明需要对患者或组级别的模型进行个性化.这项研究报告的发现既加强又增加了对躁动的基本理解，并可用于驱动对躁动的客观量化。
■这里提供的数据来自30名痴呆症患者，每个包括1周，按照我们先前发布的多模态数据收集协议收集。这个观测协议有一个横截面重复措施设计，包含来自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来自不同可穿戴传感器模态的数据与躁动之间的关系，特别是运动和言语激动。
■可穿戴数据中的几个特征与激动密切相关，至少p<.05水平(绝对β：0.224-0.753)。此外,根据患者的躁动类型或数据的收集,不同的特征可提供信息.添加具有关键混杂变量的上下文(一天中的时间、运动,和温度）可以更清晰地解释痴呆症患者躁动时的特征差异。
■显示出明显不同的功能，在整个研究人群中，提示烦躁不安时可能会激活自主神经系统。按搅拌类型划分数据时的差异表明需要将来的检测模型来定制所表达的主要搅拌类型。最后,患者特有的特征差异表明需要对患者或组级别的模型进行个性化.这项研究报告的发现既加强又增加了对躁动的基本理解，并可用于驱动对躁动的客观量化。
