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Using Wearable Sensors to Measure and Predict Personal Circadian Lighting Exposure in Nursing Home Residents: Model Development and Validation
Shevvaa Beiglary, Yanxiao Feng, Nan Wang, Neda Ghaeili Ardabili, Ying‐Ling Jao, Yo‐Jen K Liao, Yuxin Li, Julian Wang. JMIR Aging, 2025-09-11
Topic: Risk, prevention and care in population studies
The calibration models for photopic lux and correlated color temperature demonstrated strong accuracy, with an adjusted R² of 0.858 and 0.982, respectively, ensuring reliable sensor measurements. Predictive models for circadian stimulus were developed using both simple regression and machine learning techniques. The random forest model outperformed linear regression, achieving an adjusted R² of 0.915 and a cross-validation R² of 0.857, demonstrating high generalization capability. Upon the implementation of these models, significant individual variations in circadian light exposure were found in the study, highlighting the significance of customized lighting evaluations. These results confirm the effectiveness of wearable sensors, combined with the developed calibration and predictive modeling, in accurately assessing personal circadian light exposure and supporting lighting-related health care research. This study introduces an effective and scalable approach to circadian light assessment using wearable sensors and predictive modeling. By replacing labor-intensive and costly spectrometer measurements, the proposed methodology enables continuous, cost-effective monitoring in health care environments. However, challenges related to sensor wearability, durability, and user compliance were identified, underscoring the need for further sensor design refinements. Future research should focus on refining sensor integration, expanding case studies, and developing adaptive lighting interventions to enhance circadian health in vulnerable populations.
Key findings
- The calibration models for photopic lux and correlated color temperature demonstrated strong accuracy, with an adjusted R² of 0.858 and 0.982, respectively, ensuring reliable sensor measurements.
- Predictive models for circadian stimulus were developed using both simple regression and machine learning techniques.
- The random forest model outperformed linear regression, achieving an adjusted R² of 0.915 and a cross-validation R² of 0.857, demonstrating high generalization capability.
- Upon the implementation of these models, significant individual variations in circadian light exposure were found in the study, highlighting the significance of customized lighting evaluations.
- These results confirm the effectiveness of wearable sensors, combined with the developed calibration and predictive modeling, in accurately assessing personal circadian light exposure and supporting lighting-related health care research.
Why it matters
Wearable sensors combined with calibration and predictive modeling accurately assessed personal circadian light exposure in nursing home residents, with a random forest model achieving an adjusted R² of 0.915. This supports lighting-related health care research but does not address treating, curing, or preventing Alzheimer's disease.
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Study details
- Evidence strengthLow
How this study scored 37/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (JMIR Aging) |
| Journal's citation record | 0 / 4 | JMIR Aging: journal h-index 49 (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 | 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 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 2 / 7 | Cited 8 times (OpenAlex, 2026-10-10) |
| Citations per year | 1 / 5 | 2.7 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 2 / 6 | Cited 2.9× 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 8 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Julian Wang: h-index 26 (OpenAlex, whole career) |
| Institution's track record | 2 / 3 | Northwestern University: institution h-index 1,592 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 3 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 41055091) |
| Free to read | 2 / 2 | Free to read |
| Total | 37 / 100 | — |
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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. 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
Beiglary, S., Feng, Y., Wang, N., Ardabili, N. G., Jao, Y., Liao, Y. K., et al. (2025). Using Wearable Sensors to Measure and Predict Personal Circadian Lighting Exposure in Nursing Home Residents: Model Development and Validation. JMIR Aging. https://doi.org/10.2196/72338
Vancouver
Beiglary S, Feng Y, Wang N, Ardabili NG, Jao Y, Liao YK, et al. Using Wearable Sensors to Measure and Predict Personal Circadian Lighting Exposure in Nursing Home Residents: Model Development and Validation. JMIR Aging. 2025. doi:10.2196/72338. PMID: 41055091.
BibTeX
@article{beiglary2025-41055091,
author = {Beiglary, Shevvaa and Feng, Yanxiao and Wang, Nan and Ardabili, Neda Ghaeili and Jao, Ying‐Ling and Liao, Yo‐Jen K and Li, Yuxin and Wang, Julian},
title = {{Using Wearable Sensors to Measure and Predict Personal Circadian Lighting Exposure in Nursing Home Residents: Model Development and Validation}},
journal = {JMIR Aging},
year = {2025},
doi = {10.2196/72338},
pmid = {41055091},
url = {https://doi.org/10.2196/72338}
}
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