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37/100

✓ Peer-reviewed alzhub usefulness score 37 out of 100. Digital health tools

Topic: Risk, prevention and care in population studies

So far, we included 42 patients in our study, that were monitored for at least 7 consecutive days and nights, to form the largest database of its kind in the world. We will present our current findings on potential digital biomarkers of agitation in dementia. Data will also be presented on which environmental triggers are involved in different types of agitation. Finally, we will elaborate on lessons learned during this project that can guide future research in this field. Multimodal sensing combined with machine learning techniques, could allow for more continuous, objective and quantifiable monitoring of agitation in dementia. This could lead to a better evaluation of interventions, lower need for potentially dangerous psychotropic drug use, less caregiver stress and lower economic costs. Our results support the initial hypothesis of population heterogeneity and they represent the base for personalized agitation detection and treatment evaluation using physiological markers and environmental triggers.

Cite this study APA, Vancouver, BibTeX · download .bib or .ris

APA 7th

Bono, M., Liu, Z., Van Helleputte, N., De Vos, M., & Van Den Bossche, M. J. A. (2025). Multimodal sensing and machine learning for continuous monitoring of agitation in dementia. Alzheimer s & Dementia. https://doi.org/10.1002/alz70857_101188

Vancouver

Bono M, Liu Z, Van Helleputte N, De Vos M, Van Den Bossche MJA. Multimodal sensing and machine learning for continuous monitoring of agitation in dementia. Alzheimer s & Dementia. 2025. doi:10.1002/alz70857_101188. PMID: 41445391.

BibTeX

@article{bono2025-41445391,
  author  = {Bono, Marta and Liu, Zhen and Van Helleputte, Nick and De Vos, Maarten and Van Den Bossche, Maarten J. A.},
  title   = {{Multimodal sensing and machine learning for continuous monitoring of agitation in dementia}},
  journal = {Alzheimer s \& Dementia},
  year    = {2025},
  doi     = {10.1002/alz70857_101188},
  pmid    = {41445391},
  url     = {https://doi.org/10.1002/alz70857_101188}
}

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Key findings

So far, we included 42 patients in our study, that were monitored for at least 7 consecutive days and nights, to form the largest database of its kind in the world.
We will present our current findings on potential digital biomarkers of agitation in dementia.
Data will also be presented on which environmental triggers are involved in different types of agitation.
Finally, we will elaborate on lessons learned during this project that can guide future research in this field.
Multimodal sensing combined with machine learning techniques, could allow for more continuous, objective and quantifiable monitoring of agitation in dementia.

Why it matters

This ongoing study has monitored 42 patients with dementia for at least seven consecutive days and nights using multimodal sensing and machine learning to look for digital markers of agitation. It is early work about monitoring a symptom, not about treating, curing or preventing Alzheimer's disease.

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Study details

Participants42
Evidence strengthLow

How this study scored 37/100

Published in a peer-reviewed journal. Scored zero for study design, effect reported and randomized / blinded because the record does not state it.

Component	Points	Why
Journal tier	7 / 10	Tier 3 — peer-reviewed journal (Alzheimer s & Dementia)
Journal's citation record	2 / 4	Alzheimer s & Dementia: journal h-index 228 (OpenAlex, 2026-10-29)
Study design	0 / 12	No study design recorded — scores zero, not guessed (not stated in the record — scored 0)
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	42 participants
Effect reported	0 / 3	No effect size reported — scores zero (not stated in the record — scored 0)
Studied in people	3 / 3	Studied in people (participant count stated)
Randomized / blinded	0 / 2	No randomization or blinding stated — scores zero (not stated in the record — scored 0)
Times cited	1 / 7	Cited 1 times (OpenAlex, 2026-10-10)
Citations per year	0 / 5	0.3 citations a year over 3 year(s) since 2025
Impact for its field and year	1 / 6	Cited 0.6× the average for papers of its field and year (OpenAlex FWCI)
Cited by studies here	0 / 6	No other study in this library cites it
Still being cited	0 / 4	Cited 1 times in 2024–2026 (2026 still in progress; OpenAlex)
Senior author's track record	1 / 4	Last-listed author Maarten J. A. Van Den Bossche: h-index 15 (OpenAlex, whole career)
Institution's track record	1 / 3	KU Leuven: institution h-index 1,386 (OpenAlex)
Multicenter collaboration	1 / 3	OpenAlex lists 2 institution(s) in 1 country
Indexed in PubMed	2 / 2	Indexed in PubMed (PMID 41445391)
Free to read	2 / 2	Free to read
Total	37 / 100	—

What each of these 20 components measures, and what this score is not →

Read the original

Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).

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