ArticleAlzheimer's & dementia (Amsterdam, Netherlands)2022
Wearable multimodal sensors for the detection of behavioral and psychological symptoms of dementia using personalized machine learning models.
Article in Alzheimer's & dementia (Amsterdam, Netherlands), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 2 of them syntheses that pooled it.
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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.
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.
Who cites it
26 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Stage-Wise IoT Solutions for Alzheimer's Disease: A Systematic Review of Detection, Monitoring, and Assistive Technologies.Sensors (Basel, Switzerland) · 2025Pooled it
- Digital biomarkers for early agitation detection in dementia: a scoping review of emerging wearable and smart technologies for personalized care.Frontiers in neurologyPooled it
- Efficacy of Brexpiprazole on Neuropsychiatric Symptoms and Impact on Caregivers: Pooled Neuropsychiatric Inventory (NPI) Analysis in Patients With Agitation Associated With Dementia due to Alzheimer's Disease.Journal of geriatric psychiatry and neurology · 2026Trial
- Detecting Distress in Cognitively Impaired People to Prevent Suffering: Protocol for an Observational Feasibility Study of a Radar-Based Technology Augmented with Photoplethysmographic Sensors and Audio Signals (SURREAL).Sensors (Basel, Switzerland) · 2026Article
- A Multi-Sensor, Multi-Movement Exploratory Study of Motion Tape Strain Data for Low Back Pain Classification.Sensors (Basel, Switzerland) · 2026Article
- Autonomic Non-Responsiveness in HRV Biofeedback: A Narrative Conceptual Review and Future Directions for AI-Guided Closed-Loop Adaptive Systems.Medicina (Kaunas, Lithuania) · 2026Review
- A Mixed Methods Feasibility Study of a Clinical Decision Support System Designed to Assess Risk for Aggression and Prevent Aggressive Behaviour.International journal of mental health nursing · 2026Article
- Designing AI-Enabled Video Monitoring Clinician Dashboard for Neuropsychiatric Symptoms: A Survey of User Needs.The American journal of geriatric psychiatry. Open science, education, and practice · 2026Article
- Multimodal passive smartphone sensing in older adults: a guide for clinical scientists based upon an ongoing cohort study.Innovation in aging · 2026Article
- Wearable monitoring during music-based interventions in dementia: physiological and behavioral observations from a pilot study.Frontiers in human neuroscience · 2026Article
- Neuropsychiatric symptoms in Alzheimer's disease: Bridging mechanisms, management, and emerging innovations.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2026Review
- Bridging Gaps in Sundown Syndrome Research: a Scoping Review and Roadmap for Future Multimodal Approaches.Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists · 2025Article
- Using Artificial Intelligence-Based Technologies for the Early Detection of Behavioral and Psychological Symptoms of Dementia: Scoping Review.JMIR aging · 2025Article
- Digital Twin Cognition: AI-Biomarker Integration in Biomimetic Neuropsychology.Biomimetics (Basel, Switzerland) · 2025Review
- Wearable Technologies for Health Promotion and Disease Prevention in Older Adults: Systematic Scoping Review and Evidence Map.Journal of medical Internet research · 2025Article
- Using Apple Watches to Monitor Health and Behaviors of Individuals With Cognitive Impairment: A Case Series Study.The journals of gerontology. Series A, Biological sciences and medical sciences · 2025Article
- An interpretable machine learning tool for in-home monitoring of agitation episodes in people living with dementia: a proof-of-concept study.EClinicalMedicine · 2025Article
- Article
- Examining the Relationships Between Indoor Environmental Quality Parameters Pertaining to Light, Noise, Temperature, and Humidity and the Behavioral and Psychological Symptoms of People Living With Dementia: Scoping Review.Interactive journal of medical research · 2024Article
- Using AI-Based Technologies to Help Nurses Detect Behavioral Disorders: Narrative Literature Review.JMIR nursing · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Behavioral and psychological symptoms of dementia (BPSD) signal distress or unmet needs and present a risk to people with dementia and their caregivers. Variability in the expression of these symptoms is a barrier to the performance of digital biomarkers. The aim of this study was to use wearable multimodal sensors to develop personalized machine learning models capable of detecting individual patterns of BPSD. Methods: Older adults with dementia and BPSD (n = 17) on a dementia care unit wore a wristband during waking hours for up to 8 weeks. The wristband captured motion (accelerometer) and physiological indicators (blood volume pulse, electrodermal activity, and skin temperature). Agitation or aggression events were tracked, and research staff reviewed videos to precisely annotate the sensor data. Personalized machine learning models were developed using 1-minute intervals and classifying the presence of behavioral symptoms, and behavioral symptoms by type (motor agitation, verbal aggression, or physical aggression). Results: Behavioral events were rare, representing 3.4% of the total data. Personalized models classified behavioral symptoms with a median area under the receiver operating curve (AUC) of 0.87 (range 0.64-0.95). The relative importance of the different sensor features to the predictive models varied both by individual and behavior type. Discussion: Patterns of sensor data associated with BPSD are highly individualized, and future studies of the digital phenotyping of these behaviors would benefit from personalization.
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