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Diagnostic performance of actigraphy in Alzheimer’s disease using a machine learning classifier – a cross-sectional memory clinic study
Mathias Holsey Gramkow, Andreas Brink-Kjær, Frederikke Kragh Clemmensen, Nikolai Sulkjær Sjælland, Gunhild Waldemar, Poul Jennum, Steen Gregers Hasselbalch, Kristian Steen Frederiksen. Alzheimer s Research & Therapy, 2025-05-21
Topic: Amyloid, tau and blood-test diagnosis
Abstract Background Movement 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. Methods In 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 ® ) for 1 week. We derived movement patterns (walking, running, resting, etc.) from raw accelerometry data using a proprietary algorithm. By evaluating the movement patterns during day and nighttime, we calculated 510 activity-related features, including robustness and fragmentation of the circadian rhythm. These features were used to train a machine learning (ML) classifier using logistic regression. We evaluated the performance of our classifier by assessing the accuracy and precision of predictions. Results We 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). Conclusion Actigraphy accurately identifies different dementia etiologies and could serve as a supplement to diagnostic investigations in patients with suspected AD for differential diagnostic purposes.
Key findings
- Results We found that movement patterns as well as the robustness and fragmentation of the circadian rhythm differed significantly between groups.
Why it matters
In this cross-sectional memory clinic study, actigraphy with body-worn sensors showed that movement patterns and circadian rhythm differed significantly between people with Alzheimer's disease, dementia with Lewy bodies, cerebrovascular disease and healthy controls. It is a diagnostic study and does not test any treatment, cure or prevention.
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Study details
- Study designcross-sectional
- Participants29
- Effect size68.8%
- Evidence strengthLow
How this study scored 51/100
Published in a peer-reviewed journal, and it is a snapshot of people at one point in time. Scored zero for randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Alzheimer s Research & Therapy) |
| Journal's citation record | 1 / 4 | Alzheimer s Research & Therapy: journal h-index 146 (OpenAlex, 2026-10-29) |
| Study design | 4 / 12 | A snapshot of people at one point in time |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 4 / 5 | 2025 — within 3 year(s) of the newest study here |
| Sample size | 1 / 4 | 29 participants |
| Effect reported | 2 / 3 | Effect reported as a number, without an interval or test |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 2 / 7 | Cited 10 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 3.3 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 3 / 6 | Cited 6.4× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 1 / 6 | Cited by 2 other studies in this library |
| Still being cited | 1 / 4 | Cited 10 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Kristian Steen Frederiksen: h-index 37 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | University of Copenhagen: institution h-index 1,378 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 4 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 40399918) |
| Free to read | 2 / 2 | Free to read |
| Total | 51 / 100 | — |
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Read the original
- DOI: 10.1186/s13195-025-01751-5
- PubMed: 40399918
- Original source →
- Free full text: Alzheimer s Research & Therapy →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record names 8 authors; APA and Vancouver show the first six, BibTeX and the downloads carry all of them. Check the citation against the original before you use it.
APA 7th
Gramkow, M. H., Brink-Kjær, A., Clemmensen, F. K., Sjælland, N. S., Waldemar, G., Jennum, P., et al. (2025). Diagnostic performance of actigraphy in Alzheimer’s disease using a machine learning classifier – a cross-sectional memory clinic study. Alzheimer s Research & Therapy. https://doi.org/10.1186/s13195-025-01751-5
Vancouver
Gramkow MH, Brink-Kjær A, Clemmensen FK, Sjælland NS, Waldemar G, Jennum P, et al. Diagnostic performance of actigraphy in Alzheimer’s disease using a machine learning classifier – a cross-sectional memory clinic study. Alzheimer s Research & Therapy. 2025. doi:10.1186/s13195-025-01751-5. PMID: 40399918.
BibTeX
@article{gramkow2025-40399918,
author = {Gramkow, Mathias Holsey and Brink-Kjær, Andreas and Clemmensen, Frederikke Kragh and Sjælland, Nikolai Sulkjær and Waldemar, Gunhild and Jennum, Poul and Hasselbalch, Steen Gregers and Frederiksen, Kristian Steen},
title = {{Diagnostic performance of actigraphy in Alzheimer’s disease using a machine learning classifier – a cross-sectional memory clinic study}},
journal = {Alzheimer s Research \& Therapy},
year = {2025},
doi = {10.1186/s13195-025-01751-5},
pmid = {40399918},
url = {https://doi.org/10.1186/s13195-025-01751-5}
}
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