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Physiological Profiling of Agitation in Dementia: Insights From Wearable Sensor Data.
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.
Afiliação
  • Davidoff H; Department of Electrical Engineering, ESAT, KU Leuven, Heverlee, Belgium.
  • Van Kraaij A; Imec, Heverlee, Belgium.
  • Van den Bulcke L; OnePlanet Research Center, Wageningen, Netherlands.
  • Lutin E; Geriatric Psychiatry, University Psychiatric Center KU Leuven, Leuven, Belgium.
  • Vandenbulcke M; Neuropsychiatry, Research Group Psychiatry, Department of Neurosciences, Leuven Brain Institute, KU Leuven, Leuven, Belgium.
  • Van Helleputte N; Imec, Heverlee, Belgium.
  • De Vos M; Geriatric Psychiatry, University Psychiatric Center KU Leuven, Leuven, Belgium.
  • Van Hoof C; Neuropsychiatry, Research Group Psychiatry, Department of Neurosciences, Leuven Brain Institute, KU Leuven, Leuven, Belgium.
  • Van Den Bossche M; Imec, Heverlee, Belgium.
Innov Aging ; 8(7): igae057, 2024.
Article em En | MEDLINE | ID: mdl-38974775
ABSTRACT
Background and

Objectives:

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. Research Design and

Methods:

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.

Results:

Several features from wearable data are significantly associated with agitation, at least the p < .05 level (absolute ß 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. Discussion and Implications 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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Innov Aging Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Bélgica

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Innov Aging Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Bélgica
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