Evidence map›Paper›PMID 41840158›Full record

ArticleCommunications medicine2026

Sleep and temperature data from wearable devices support noninvasive detection of diabetes mellitus in a large-scale, retrospective analysis.

Varun K Viswanath, Shreenithi Navaneethan, Jamison H Burks, Severine Soltani, Patrick Kasl, Wendy Hartogensis, Stephan Dilchert, Frederick M Hecht, Ashley E Mason, Edward J Wang and 1 more

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Article in Communications medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Varun K Viswanath *Department of Electrical and Computer Engineering, Jacobs School of Engineering, University of California, San Diego, CA, USA. vkviswan@ucsd.edu.ORCID http://orcid.org/0000-0001-6299-5777
Shreenithi Navaneethan *Shu Chien - Gene Lay Dept. of Bioengineering, Jacobs School of Engineering, University of California, San Diego, CA, USA.
Jamison H Burks *Shu Chien - Gene Lay Dept. of Bioengineering, Jacobs School of Engineering, University of California, San Diego, CA, USA.
Severine SoltaniShu Chien - Gene Lay Dept. of Bioengineering, Jacobs School of Engineering, University of California, San Diego, CA, USA.
Patrick KaslShu Chien - Gene Lay Dept. of Bioengineering, Jacobs School of Engineering, University of California, San Diego, CA, USA.
Wendy HartogensisOsher Center for Integrative Health, University of California, San Francisco, CA, USA.ORCID http://orcid.org/0000-0002-4979-8661
Stephan DilchertDepartment of Management, Zicklin School of Business, Baruch College, The City University of New York, New York, NY, USA.ORCID http://orcid.org/0000-0001-6733-576X
Frederick M HechtOsher Center for Integrative Health, University of California, San Francisco, CA, USA.ORCID http://orcid.org/0000-0002-5782-1171
Ashley E MasonOsher Center for Integrative Health, University of California, San Francisco, CA, USA.
Edward J WangDepartment of Electrical and Computer Engineering, Jacobs School of Engineering, University of California, San Diego, CA, USA.
Benjamin L SmarrShu Chien - Gene Lay Dept. of Bioengineering, Jacobs School of Engineering, University of California, San Diego, CA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDiabetes Mellitus is a common, chronic metabolic disorder affecting the cardiovascular system, autonomic nervous system, and sleep quality. Diabetes affects diverse physiological data including heart rate variability, distal body temperature, and sleep duration. We hypothesized that biologically informed features from wearable device data, combined with appropriate application of longitudinal data, can capture physiological covariates of diabetes and support the noninvasive detection of diabetes.

methodsWe obtained 4 months and 7 days of wearables data (Oura Ring) from 389 individuals self-reporting diabetes and 10,820 people self-reporting no diabetes diagnosis from the TemPredict database. We selected 36 features of sleep, circadian disruption, and distal body temperature from literature and evaluated whether time windows of these features could be classified to be from individuals self-reporting diabetes (N = 236) or self-reporting no diabetes diagnosis (N = 282).

resultsHere we show longer time windows of input perform better, with the best algorithm (21-nights) achieving 0.88 Area under ROC (AUROC) and 0.80 Area under Precision Recall (AUPRC) (0.30 improvement over random). Feature analyses reveal the importance of further derived distal body temperature features (increase AUROC by 0.0724), especially to differentiate other chronic conditions from diabetes. The model achieves 0.80 AUROC and 0.28 improvement over random in AUPRC in an imbalanced cohort drawn from 6,658 individuals, emulating a general population.

conclusionsThese results indicate the value of biologically informed features and longitudinal data for identifying people with diabetes and further, suggest that these methods could make such separations possible for other chronic conditions that affect sleep and inflammation.

Identifiers

PMID41840158
PMCPMC13079903

What Socratic holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.