Davidoff Hannah, Van Kraaij Alex, Lutin Erika, Van den Bulcke Laura, Vandenbulcke Mathieu, Van Helleputte Nick, De Vos Maarten, Van Hoof Chris, Van Den Bossche Maarten
ESAT, Department of Electrical Engineering, KU Leuven, Leuven, Belgium.
imec, Heverlee, Belgium.
JMIR Form Res. 2025 Aug 27;9:e60274. doi: 10.2196/60274.
Among the most critical behavioral and psychological symptoms of dementia, agitation can lead to decreased quality of life of people with dementia and their caregivers. Monitoring triggers of agitation and its subtypes could enable early detection or prediction of agitated moments, which could be used to guide preventive or mitigating interventions. However, at this point in time, limited research exists on quantifying environmental triggers of agitation or its subtypes.
In this paper, we aim to quantify the relationships between specific environmental factors and agitation as well as specific agitation subtypes, such as motor and verbal agitation.
Using a cross-sectional repeated measures design, 37 people with dementia, admitted to a specialized neuropsychiatric ward for patients with dementia and severe behavioral and psychological problems, were each included for 1 week. During this period, the Pittsburgh Agitation Scale was filled in by the nurses on the ward following an experience sampling methodology to assess a patient's agitation level on a momentary basis. Continuous environmental data (light, sound, and temperature) were collected from fixed sensors mounted on the ward. Generalized linear mixed models were used to quantify relationships between environmental variables and outcome variables (agitation, motor agitation, and verbal agitation). These models accounted for the hierarchical nature of our dataset as well as confounding factors, such as time of day and the room-level location of the patient. The time window for analysis was selected through a comparison of β coefficient estimates across various window lengths. Models were built up sequentially, per outcome variable, using selected features per environmental modality.
We found that different environmental factors captured in the window of 33 to 12 minutes before the agitation moment were most informative for different subtypes of agitation: mean light level (β=-0.61, 95% CI -1.12 to -0.10; P=.02) for motor agitation and SD of sound level (β=0.68, 95% CI 0.34-1.02; P<.001) for verbal agitation. Contextual factors such as time of day (β range=0.51-0.94; P<.05 to <.001) and room-level location (β range=0.85-1.08; P<.01 to <.001) were also significant predictors of agitation.
Integrating the key differences between predictors of verbal and motor agitation, respectively, the higher SD in sound level and the lower mean light level, in a model predicting the occurrence of subtype-specific agitation, could substantially improve model performance. Overall, these findings can aid in the development of predictive models for agitation based on environmental data and enable subsequent just-in-time interventions, improving the quality of life for both patients and caregivers.
在痴呆症最关键的行为和心理症状中,激越会导致痴呆症患者及其照护者的生活质量下降。监测激越及其亚型的触发因素能够实现对激越时刻的早期检测或预测,这可用于指导预防或缓解干预措施。然而,目前关于量化激越及其亚型的环境触发因素的研究有限。
在本文中,我们旨在量化特定环境因素与激越以及特定激越亚型(如运动性激越和言语性激越)之间的关系。
采用横断面重复测量设计,纳入37名入住专门的神经精神科病房的痴呆症患者,这些患者患有痴呆症且存在严重行为和心理问题,每位患者纳入为期1周的研究。在此期间,病房护士按照经验取样法填写匹兹堡激越量表,以即时评估患者的激越水平。从病房安装的固定传感器收集连续的环境数据(光线、声音和温度)。使用广义线性混合模型量化环境变量与结果变量(激越、运动性激越和言语性激越)之间的关系。这些模型考虑了我们数据集的分层性质以及混杂因素,如一天中的时间和患者所在的房间位置。通过比较不同窗口长度的β系数估计值来选择分析的时间窗口。针对每个结果变量,依次使用每个环境模式的选定特征建立模型。
我们发现,在激越时刻前33至12分钟的窗口内捕获的不同环境因素对不同亚型的激越最具信息量:运动性激越的平均光照水平(β=-0.61,95%置信区间-1.12至-0.10;P=0.02)和言语性激越的声级标准差(β=0.68,95%置信区间0.34 - 1.02;P<0.001)。一天中的时间(β范围=0.51 - 0.94;P<0.05至<0.001)和房间位置(β范围=0.85 - 1.08;P<0.01至<0.001)等背景因素也是激越的重要预测因素。
在预测特定亚型激越发生的模型中,分别整合言语性激越和运动性激越预测因素的关键差异,即较高的声级标准差和较低的平均光照水平,可显著提高模型性能。总体而言,这些发现有助于基于环境数据开发激越预测模型,并实现后续的即时干预,从而提高患者和照护者的生活质量。