Evidence map›Paper›PMID 38974775›Full record

ArticleInnovation in aging2024

Physiological Profiling of Agitation in Dementia: Insights From Wearable Sensor Data.

Hannah Davidoff, Alex Van Kraaij, Laura Van den Bulcke, Erika Lutin, Mathieu Vandenbulcke, Nick Van Helleputte, Maarten De Vos, Chris Van Hoof, Maarten Van Den Bossche

Abstract read
In one paragraph

Article in Innovation in aging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Hannah DavidoffDepartment of Electrical Engineering, ESAT, KU Leuven, Heverlee, Belgium.ORCID https://orcid.org/0000-0003-2111-1538
Alex Van KraaijOnePlanet Research Center, Wageningen, Netherlands.ORCID https://orcid.org/0000-0002-8592-8611
Laura Van den BulckeGeriatric Psychiatry, University Psychiatric Center KU Leuven, Leuven, Belgium.ORCID https://orcid.org/0000-0003-4477-4107
Erika LutinImec, Heverlee, Belgium.ORCID https://orcid.org/0000-0002-9254-7374
Mathieu VandenbulckeGeriatric Psychiatry, University Psychiatric Center KU Leuven, Leuven, Belgium.ORCID https://orcid.org/0000-0001-9765-1499
Nick Van HelleputteImec, Heverlee, Belgium.ORCID https://orcid.org/0000-0002-7511-1923
Maarten De VosDepartment of Electrical Engineering, ESAT, KU Leuven, Heverlee, Belgium.ORCID https://orcid.org/0000-0002-3482-5145
Chris Van HoofDepartment of Electrical Engineering, ESAT, KU Leuven, Heverlee, Belgium.ORCID https://orcid.org/0000-0002-4645-3326
Maarten Van Den BosscheGeriatric Psychiatry, University Psychiatric Center KU Leuven, Leuven, Belgium.ORCID https://orcid.org/0000-0002-2989-6600

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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 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.

Indexed as

Agitation detectionAgitation physiologyAutonomic nervous systemPersonalized modelsWearable sensors

Identifiers

PMID38974775
PMCPMC11227003

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.