Daytime physical activity and nighttime glucose levels in individuals with pregnancy hyperglycemia: linking wearable activity trackers to continuous glucose monitoring

Introduction Continuous glucose monitoring (CGM) offers a unique opportunity to assess Q6 glucose patterns across the 24-hour day, including nighttime. In individuals with pregnancy hyperglycemia, evidence suggests that optimizing nocturnal glucose levels reduces the risk of large-for-gestational-age births and future metabolic dysfunction. However, the behavioral correlates of nocturnal glucose levels remain poorly understood. Continuous movement devices assess physical activity (PA) and sedentary behavior (SED) across 24-hour days, and to the best of our knowledge, have not been paired with CGM data in individuals with pregnancy hyperglycemia. This secondary analysis of a feasibility trial explored the association of day-time PA and SED with nighttime (i.e., 12–6 AM) interstitial glucose levels in individuals with gestational diabetes mellitus (GDM) or gestational glucose intolerance (GGI). Methods Participants (N = 13; ~31 weeks gestation) wore a Dexcom G6 CGM and ActiGraph Insight Watch for 7 days. Mixed effects models examined associations between daytime moderate-tovigorous physical activity (MVPA), light physical activity (LPA), and sedentary behavior (SED) with nocturnal glucose metrics, including mean glucose, time in range (TIR; 60–99 mg/dL), and area under the curve (AUC). Results Adjusted models revealed that each 10-minute increase in MVPA was associated with 0.86 mg/dL [95% confidence interval (CI) 0.002, 1.73] higher mean glucose and 313 mg/ dL*min (CI 0.98, 624.55) higher AUC. No associations were observed for total activity, LPA, or SED. Discussion These findings illustrate the feasibility and potential of combining CGM with activity monitor data, and the need to further integrate dietary intake data. Improvements in glucose and activity monitoring technology hold great promise for improving scientific and clinical understanding and supporting the development of personalized, data-driven glucose management tools during pregnancy.