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56/100

✓ Peer-reviewed alzhub usefulness score 56 out of 100. Digital health tools

Topic: Risk, prevention and care in population studies

The reviewed studies demonstrated that wearable sensors, particularly those employing multimodal data and personalized machine learning models, enable reliable detection of agitation symptoms and support timely, tailored interventions. The concept of digital phenotyping emerged as a promising approach for capturing complex behavioral signatures, while user-centered design was identified as essential for adoption and long-term compliance. The evidence identified in this scoping review indicates that wearable and multimodal sensor technologies may offer promising approaches for monitoring agitation in dementia, while acknowledging that the research remains in early stages. We recommend future research focus on large-scale, longitudinal validation and the expansion of these tools to other populations with communication challenges, such as individuals with autism spectrum disorder or traumatic brain injury. Systematic review registration: https://doi.org/10.17605/OSF.IO/DNHYM.

Key findings

The reviewed studies demonstrated that wearable sensors, particularly those employing multimodal data and personalized machine learning models, enable reliable detection of agitation symptoms and support timely, tailored interventions.
The concept of digital phenotyping emerged as a promising approach for capturing complex behavioral signatures, while user-centered design was identified as essential for adoption and long-term compliance.
The evidence identified in this scoping review indicates that wearable and multimodal sensor technologies may offer promising approaches for monitoring agitation in dementia, while acknowledging that the research remains in early stages.
We recommend future research focus on large-scale, longitudinal validation and the expansion of these tools to other populations with communication challenges, such as individuals with autism spectrum disorder or traumatic brain injury.
Systematic review registration: https://doi.org/10.17605/OSF.IO/DNHYM.

Why it matters

This scoping review found that wearable and multimodal sensor technologies may help detect agitation in dementia, but noted the research is still in early stages and needs large-scale, longitudinal validation. It concerns monitoring a symptom, not treating, curing or preventing Alzheimer's disease.

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Study details

Study designsystematic review
Evidence strengthHigh

How this study scored 56/100

Published in a peer-reviewed journal, and it is a systematic review of all the studies on the question. Scored zero for sample size, effect reported and studied in people because the record does not state it. We have no data on senior author's track record and institution's track record, so those were left out: it earned 52 of the 93 points we could judge it on, which is 56 out of 100.

Component	Points	Why
Journal tier	7 / 10	Tier 3 — peer-reviewed journal (Frontiers in Neurology)
Journal's citation record	2 / 4	Frontiers in Neurology: journal h-index 162 (OpenAlex, 2026-10-29)
Study design	12 / 12	A systematic review of all the studies on the question
Strength of evidence	5 / 5	Rated “high” in our summary
Peer-review validation	10 / 10	Peer-reviewed. Published as an article in a named peer-reviewed journal.
How recent	5 / 5	2026 — within 1 year(s) of the newest study here
Sample size	0 / 4	Participant count not stated — scores zero (not stated in the record — scored 0)
Effect reported	0 / 3	No effect size reported — scores zero (not stated in the record — scored 0)
Studied in people	0 / 3	Not stated whether this was studied in people — scores zero (not stated in the record — scored 0)
Randomised / blinded	0 / 2	A review of other studies — randomisation is not credited here
Times cited	1 / 7	Cited 4 times (OpenAlex, 2026-10-10)
Citations per year	1 / 5	2.0 citations a year over 2 year(s) since 2026
Impact for its field and year	4 / 6	Cited 12.9× 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	1 / 4	Cited 4 times in 2024–2026 (2026 still in progress; OpenAlex)
Senior author's track record	— / 4	OpenAlex's profile for last-listed author Mercy Mbogori-Kairichi holds only 5 work(s), too few to judge a track record — left out of this score
Institution's track record	— / 3	No institution listed in OpenAlex — left out of this score
Multi-centre collaboration	0 / 3	OpenAlex lists 0 institution(s) in 0 countries — scores zero (affiliation lists can be incomplete)
Indexed in PubMed	2 / 2	Indexed in PubMed (PMID 41743052)
Free to read	2 / 2	Free to read
Points	52 / 93	Scored on 18 of 20 parts. We have no data for the rest, so they are left out: 52 of the 93 points we could judge it on is 56 out of 100.
Total	56 / 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).

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. Check the citation against the original before you use it.

APA 7th

Malioukis, A., Snead, R. S., Marczika, J., Ambalavanan, R., Towett, G., & Mbogori-Kairichi, M. (2026). Digital biomarkers for early agitation detection in dementia: a scoping review of emerging wearable and smart technologies for personalized care. Frontiers in Neurology. https://doi.org/10.3389/fneur.2026.1683517

Vancouver

Malioukis A, Snead RS, Marczika J, Ambalavanan R, Towett G, Mbogori-Kairichi M. Digital biomarkers for early agitation detection in dementia: a scoping review of emerging wearable and smart technologies for personalized care. Frontiers in Neurology. 2026. doi:10.3389/fneur.2026.1683517. PMID: 41743052.

BibTeX

@article{malioukis2026-41743052,
  author  = {Malioukis, Alex and Snead, R Sterling and Marczika, Julia and Ambalavanan, Radha and Towett, Gideon and Mbogori-Kairichi, Mercy},
  title   = {{Digital biomarkers for early agitation detection in dementia: a scoping review of emerging wearable and smart technologies for personalized care}},
  journal = {Frontiers in Neurology},
  year    = {2026},
  doi     = {10.3389/fneur.2026.1683517},
  pmid    = {41743052},
  url     = {https://doi.org/10.3389/fneur.2026.1683517}
}

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