Associations between glycemic variability, sleep quality, and daily steps in subjects without diabetes using wearable devices
Open accessAbstract
Background: Since there are limited studies on the associations between glycemic variability (GV) and sleep quality or physical activity in subjects without diabetes, we evaluated the associations between GV, as assessed by continuous glucose monitoring (CGM), and both sleep quality and daily steps using wearable devices in healthy individuals. Methods: Forty participants without diabetes were monitored by both an intermittently scanned CGM and a smartwatch-type activity tracker for 2 weeks. The standard deviation (SD) and coefficient of variation (CV) of glucose were evaluated as indices of GV. The activity tracker was used to calculate each participant's average step count per day. We also calculated sleep duration, sleep efficiency, and sleep latency based on data from the activity tracker. Spearman's correlation coefficient was used to assess the association between GV and sleep indices or daily steps. For each participant, periods were divided into quartiles according to step counts throughout the day. We compared mean parameter differences between the periods of lowest quartile and highest quartile (lower 25% and upper 25%). Results: SD glucose was significantly positively correlated with sleep latency (R = 0.23, P < 0.05). There were no significant correlations among other indices in GV and sleep quality (P > 0.05). SD glucose and CV glucose levels in the upper 25% period of daily steps were lower than those in the lower 25% period in each participant (both, P < 0.01). Conclusion: In subjects without diabetes, GV evaluated by intermittently scanned CGM was positively associated with the time to fall asleep. Furthermore, GV in the days of larger daily steps was decreased compared to the days of smaller daily steps in each participant.
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Commonly used features are supported by prior work to be relevant to the effects of DM.
“commonly used features supported by prior work to be relevant to the effects of DM31–33,35–38,45,49”
From Sleep and temperature data from wearable devices support noninvasive detection of diabetes mellitus in a large-scale, retrospective analysis (2026) · cites this paper as [49]
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- Authors
- Inaishi, J., Kashiwagi, K., Kinoshita, S., Wada, Y., Hanashiro, S., Shiga, K., Kitazawa, M., Tsutsumi, S., Yamakawa, H., & Kishimoto, T.
- Affiliations
- Keio University (JP)
- National Institute of Public Health (JP)
- Keio University Hospital (JP)
- Northwell Health (US)
- Corresponding author
- Kashiwagi, Kazuhiro
- ORCID
- Venue
- Metabolism Open
- Publisher
- Elsevier BV
- Published
- 2023-12
- Volume
- 20
- Pages
- 100263
- ISSN
- 2589-9368 (ISSN-L)
- PMC
- PMC10700801
- OpenAlex
- W4388774146
- Cited by
- 9
- References
- 14
- Citations by year
- 2026: 4 · 2025: 2 · 2024: 2 · 2023: 1
- FWCI
- 1.85
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Metabolism Open (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Keio University
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Health Sciences › Medicine › Physiology › Physical Activity and Health
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and related disorders
- Keywords
- glycemic variability, sleep quality, sleep latency, daily step count, physical activity, continuous glucose monitoring, wearable activity trackers, healthy individuals
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