Associations between glycemic variability, sleep quality, and daily steps in subjects without diabetes using wearable devices.
Glycemic variability
Intermittently scanned continuous glucose monitoring
Sleep quality
Wearable device
Journal
Metabolism open
ISSN: 2589-9368
Titre abrégé: Metabol Open
Pays: England
ID NLM: 101767753
Informations de publication
Date de publication:
Dec 2023
Dec 2023
Historique:
received:
03
11
2023
revised:
10
11
2023
accepted:
12
11
2023
medline:
11
12
2023
pubmed:
11
12
2023
entrez:
11
12
2023
Statut:
epublish
Résumé
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. 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%). 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). 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.
Sections du résumé
Background
UNASSIGNED
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
UNASSIGNED
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
UNASSIGNED
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
UNASSIGNED
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.
Identifiants
pubmed: 38077241
doi: 10.1016/j.metop.2023.100263
pii: S2589-9368(23)00035-X
pmc: PMC10700801
doi:
Types de publication
Journal Article
Langues
eng
Pagination
100263Informations de copyright
© 2023 The Authors. Published by Elsevier Inc.
Références
PLoS One. 2023 Oct 4;18(10):e0291923
pubmed: 37792730
JAMA. 2006 Apr 12;295(14):1681-7
pubmed: 16609090
J Diabetes Sci Technol. 2017 Jul;11(4):780-790
pubmed: 28317402
Metabolism. 2018 Jul;84:56-66
pubmed: 29510179
Diabetes Technol Ther. 2023 Oct 13;:
pubmed: 37870460
Diabetologia. 2021 Oct;64(10):2159-2169
pubmed: 34136937
Diabetol Metab Syndr. 2021 Sep 23;13(1):102
pubmed: 34556157
BMC Endocr Disord. 2022 Jan 11;22(1):20
pubmed: 35016646
Sports Med Health Sci. 2021 Oct 11;3(4):183-193
pubmed: 35783368
J Diabetes Investig. 2021 Jun;12(6):940-949
pubmed: 33058513
Nutr Metab Cardiovasc Dis. 2014 Mar;24(3):309-14
pubmed: 24418379
Biol Sport. 2019 Jun;36(2):141-148
pubmed: 31223191
Neuromolecular Med. 2008;10(3):169-78
pubmed: 18224460
Int J Mol Sci. 2014 Oct 13;15(10):18381-406
pubmed: 25314300