ArticleAlzheimer's research & therapy2025
Diagnostic performance of actigraphy in Alzheimer's disease using a machine learning classifier - a cross-sectional memory clinic study.
Article in Alzheimer's research & therapy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- AI-Based Measurement Tools for Intrinsic Capacity in Older Adults: Scoping Review.Journal of medical Internet research · 2026Article
- Glial cytokine modulation improves sleep and circadian disruption in female SAA knock-in mice of Alzheimer's-related pathology.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- AI-enabled digital phenotyping for Alzheimer's disease: a review of multimodal sensor integration and symptom trajectories.Alzheimer's research & therapy · 2026Review
- Wearable Sensor Technologies and Gait Analysis for Early Detection of Dementia: Trends and Future Directions.Sensors (Basel, Switzerland) · 2025Review
- Can objective measures of physical activity and sleep serve as early predictors of dementia among individuals with cognitive impairment?medRxiv : the preprint server for health sciences · 2025Article
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Authors and funding
8 authors.
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Abstract
backgroundMovement patterns, activity levels and circadian rhythm are altered in Alzheimer's disease (AD) and can be assessed by actigraphy using wearable sensors. We aimed to determine the diagnostic performance of actigraphy in AD in a memory clinic population by using a machine-learning classifier.
methodsIn our single-center cross-sectional study, 70 patients with AD (MCI-moderate dementia), dementia with Lewy bodies (DLB) (N = 29) and cerebrovascular disease (CVD) (N = 23), and 48 elderly healthy controls were included. Participants underwent actigraphy at home using two body-worn sensors (SENS Motion
resultsWe found that movement patterns as well as the robustness and fragmentation of the circadian rhythm differed significantly between groups. During the daytime, patients with AD performed less moderate activity and walked less than the healthy group. While we achieved a modest accuracy of 68.8% for differentiating AD and healthy, the performance was highest (accuracy: 80-89%; precision: 69-84%) when ML was applied to actigraphy data to differentiate dementia etiologies (AD vs. DLB + AD vs. CVD).
conclusionActigraphy accurately identifies different dementia etiologies and could serve as a supplement to diagnostic investigations in patients with suspected AD for differential diagnostic purposes.
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