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57/100

✓ Peer-reviewed alzhub usefulness score 57 out of 100. Digital health tools

Topic: Risk, prevention and care in population studies

Light Gradient-boosting Machine (LightGBM) achieved the highest performance in identifying agitation over an 8-day period with a sensitivity of 71.32% ± 7.38 and specificity of 75.28% ± 7.38. Implementing the traffic-light system for stratification increased specificity to 90.3% ± 7.55 and improved all metrics. Key features for identifying agitation included low nocturnal respiratory rate, heightened alertness during sleep, and increased indoor illuminance, as revealed by statistical and feature importance analysis. Using our interactive tool, we identified indoor lighting and temperature adjustments as the most promising and feasible intervention options within our cohort. simulation of non-pharmacological interventions, facilitating the design of personalised interventions that can improve in-home dementia care. Funding: This study is funded by the UK Dementia Research Institute [award number UK DRI-7002] through UK DRI Ltd, principally funded by the Medical Research Council (MRC), and the UKRI Engineering and Physical Sciences Research Council (EPSRC) PROTECT Project (grant number: EP/W031892/1). Infrastructure support for this research was provided by the NIHR Imperial Biomedical Research Centre (BRC) and the UKRI Medical Research Council (MRC). P.B. is also funded by the Great Ormond Street Hospital and the Royal Academy of Engineering. C.S. is supported by the UK Dementia Research Institute [award number UK DRI-5209], a UKRI Future Leaders Fellowship [MR/MR/X032892/1] and the Edmond J. Safra Foundation. R.N. is funded by UK Dementia Research Institute [award number UK DRI-7002] and the UKRI Engineering and Physical Sciences Research Council (EPSRC) PROTECT Project (grant number: EP/W031892/1). M.B. and A.K.S. are funded by the UK Dementia Research Institute [award number UKDRI-7002 and UKDRI-5209]. N.F.L., A.C., C.W. and S.K. are funded by the UK Dementia Research Institute [award number UK DRI-7002].

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

APA 7th

Bafaloukou, M., Schalkamp, A., Fletcher‐Lloyd, N., Capstick, A., Walsh, C., Sandor, C., et al. (2025). An interpretable machine learning tool for in-home monitoring of agitation episodes in people living with dementia: a proof-of-concept study. EClinicalMedicine. https://doi.org/10.1016/j.eclinm.2024.103032

Vancouver

Bafaloukou M, Schalkamp A, Fletcher‐Lloyd N, Capstick A, Walsh C, Sandor C, et al. An interpretable machine learning tool for in-home monitoring of agitation episodes in people living with dementia: a proof-of-concept study. EClinicalMedicine. 2025. doi:10.1016/j.eclinm.2024.103032. PMID: 39896868.

BibTeX

@article{bafaloukou2025-39896868,
  author  = {Bafaloukou, Marirena and Schalkamp, Ann‐Kathrin and Fletcher‐Lloyd, Nan and Capstick, Alexander and Walsh, Chloe and Sandor, Cynthia and Kouchaki, Samaneh and Nilforooshan, Ramin and others},
  title   = {{An interpretable machine learning tool for in-home monitoring of agitation episodes in people living with dementia: a proof-of-concept study}},
  journal = {EClinicalMedicine},
  year    = {2025},
  doi     = {10.1016/j.eclinm.2024.103032},
  pmid    = {39896868},
  url     = {https://doi.org/10.1016/j.eclinm.2024.103032}
}

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Key findings

Light Gradient-boosting Machine (LightGBM) achieved the highest performance in identifying agitation over an 8-day period with a sensitivity of 71.32% ± 7.38 and specificity of 75.28% ± 7.38.
Implementing the traffic-light system for stratification increased specificity to 90.3% ± 7.55 and improved all metrics.
Key features for identifying agitation included low nocturnal respiratory rate, heightened alertness during sleep, and increased indoor illuminance, as revealed by statistical and feature importance analysis.
Using our interactive tool, we identified indoor lighting and temperature adjustments as the most promising and feasible intervention options within our cohort. simulation of non-pharmacological interventions, facilitating the design of personalised interventions that can improve in-home dementia ca
Funding: This study is funded by the UK Dementia Research Institute [award number UK DRI-7002] through UK DRI Ltd, principally funded by the Medical Research Council (MRC), and the UKRI Engineering and Physical Sciences Research Council (EPSRC) PROTECT Project (grant number: EP/W031892/1).

Why it matters

In this proof-of-concept study, a machine learning tool identified agitation episodes in people with dementia over an 8-day period with a sensitivity of 71.32% and specificity of 75.28%, rising to 90.3% specificity with a traffic-light stratification system. It is an early monitoring and intervention-design tool, not a treatment, cure, or prevention of Alzheimer's disease.

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Study details

Study designlongitudinal study
Participants63
Effect size71.32%; 75.28%; 90.3%
Evidence strengthModerate

How this study scored 57/100

Published in a peer-reviewed journal, and it is a longitudinal study following the same people over time. Scored zero for randomized / blinded because the record does not state it.

Component	Points	Why
Journal tier	7 / 10	Tier 3 — peer-reviewed journal (EClinicalMedicine)
Journal's citation record	2 / 4	EClinicalMedicine: journal h-index 150 (OpenAlex, 2026-10-29)
Study design	7 / 12	A longitudinal study following the same people over time
Strength of evidence	3 / 5	Rated “moderate” 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	63 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)
Randomized / blinded	0 / 2	No randomization or blinding stated — scores zero (not stated in the record — scored 0)
Times cited	2 / 7	Cited 7 times (OpenAlex, 2026-10-10)
Citations per year	1 / 5	2.3 citations a year over 3 year(s) since 2025
Impact for its field and year	3 / 6	Cited 4.4× 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 7 times in 2024–2026 (2026 still in progress; OpenAlex)
Senior author's track record	2 / 4	Last-listed author Payam Barnaghi: h-index 42 (OpenAlex, whole career)
Institution's track record	2 / 3	Imperial College London: institution h-index 1,744 (OpenAlex)
Multicenter collaboration	2 / 3	OpenAlex lists 5 institution(s) in 1 country
Indexed in PubMed	2 / 2	Indexed in PubMed (PMID 39896868)
Free to read	2 / 2	Free to read
Total	57 / 100	—

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