Abstract

AbstractBackgroundAgitation is a critical behavioral and psychological symptom of dementia, leading alone to increased caregiver burden, more frequent hospitalization, and earlier institutionalization. The results presented here provide preliminary insights into one facet of multimodal agitation detection, specifically analyzing the relationship between heart rate and agitation.MethodTo date, 26 people with dementia have been included in our study to detect agitation using a multimodal approach1. In this analysis, heart rate is derived from the wristwatch wearable measuring photoplethysmography (PPG). Data points were included in subsequent analysis if the signal quality index of the original PPG signal was > = 0.8 (on a scale from 0‐1). For each complete survey, the mean heart rate (HR) was extracted from 1‐minute intervals in a 6‐minute window centered on the survey timestamp (± 3 minutes). For each mean HR per minute, the presence or absence of agitation was derived from the Pittsburgh Agitation Scale integrated into the survey. The data was centered within‐patient. Due to the nested structure of the data, hierarchical mixed models were used to predict presence of agitation as a binary response variable.ResultThe resulting dataset of high‐quality HR data consists of 1286 data points, from 21 patients (Age mean: 81 (8.3), nfemale: 8) The 3‐parameter model built included mean heart rate as an explanatory fixed effect, and two random intercepts: day and patient. In the model, increased HR compared to a participant’s average HR over a week, was significantly associated with an increase in odds of agitation (p< 0.001). Every 1 beat increase in mean HR over a minute, from the participants’ mean heart rate, increased the odds of that 1‐minute interval indicating agitation by 7.68% (CI: 5.43‐10.1).ConclusionThese results provide insight into the relationship between heart rate and agitation, possibly indicating activation of the autonomic nervous system, due to the association of increased HR with increased odds of agitation. Analyses like these provide the basis for future agitation detection algorithms that account for a multi‐faceted patient response.1. Davidoff, et al, 2022, Innovation in Aging, Volume 6, Issue 7, 2022, igac064, https://doi.org/10.1093/geroni/igac064

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