Deciphering the effect of exercise timing on glucose metabolism: a randomised controlled trial

Abstract

Introduction Impaired glycaemic control is a major risk factor for cardiovascular disease, the leading cause of death worldwide (Roth et al., 2020). Reducing sedentary behaviour and increasing physical activity are key strategies for improving glycaemic control (Sparks et al., 2021). An intrinsic circadian system influences glucose metabolism, resulting in predictable 24-hour plasma glucose fluctuations, peaking during the biological night (Qian & Scheer, 2016). Relying on these fluctuations, the metabolic response might vary depending on when in the day an individual exercises. Guidelines providing evidence-based recommendations for physical activity emphasise frequency, intensity, time (duration), and type of exercise as the four foundational pillars for promoting health and well-being (DeSimone, 2019) but fail to address the potential role of exercise timing within the day. However, the established effects of the timing of behavioural factors such as sleep (Qian & Scheer, 2016) and nutrition (Wang et al., 2014), combined with evidence of diurnal variations in physical performance (Knaier et al., 2022), suggest that exercise timing might additionally influence exercise adaptations and associated health outcomes. Thus, the aim of this study was to investigate the effect of exercise timing on glycaemic control and to provide evidence to refine exercise recommendations and optimise metabolic health outcomes.

Methods Thirty-nine (34% female) non-diabetic adults (mean [standard deviation] age 68 [5] years) and body mass index 24.0 [2.6] kg/m²) participated in this double-blind, randomised controlled trial. Participants were allocated to one of five groups: control group (Con) or exercise at either 8:00, 12:00, 16:00 or 20:00 o'clock (i.e. E-08, E-12, E-16, E-20), which performed two strength and one endurance session per week across 12 weeks. A two-hour oral glucose tolerance test was performed after overnight fasting before and after the 12-week intervention or control condition. After ingestion of a solution containing 75 g of dextrose, serum glucose was measured at fasting and subsequently after 10, 20, 30, 60, 90, and 120 min. Serum glucose response was quantified over 120 min by calculating the area under the curve (AUC) using the trapezoidal method. To analyse the effect of treatment over time, linear mixed effects models were used, adjusting for sex, age and body mass index. The effect sizes were calculated using the baseline standard deviation of the parameter to provide a standardised measure. Their magnitudes were classified as follows: negligible (<0.2), small (0.2–0.49), moderate (0.5–0.79), large (0.8–1.19), and very large (>1.2) (Hopkins et al., 2009).

Results Thirty-two participants were analysed (Con: n=4, E08: n=6, E-12: n=7, E-16: n=3, E-20: n=12), with most dropouts due to missing more than two serum glucose values. Exercise adherence rate was excellent with 95% of all sessions. The exercise intervention reduced the AUC in all groups compared to Con with small to moderate effect sizes (95% confidence interval), being E-08: -0.47 (-1.31 to 0.37); E-12: -0.51, (-1.32 to 0.31); E-16: -0.54 (-1.53 to 0.46); and E-20: -0.64 (-1.38 to 0.11), respectively. Negative effect sizes (e.g., reduction in AUC) are considered beneficial for metabolic health. Yet, these effects were accompanied by considerable uncertainty. Pairwise comparisons of the exercise groups showed varying magnitudes of differences in AUC, likewise accompanied by considerable uncertainty. All comparisons including E-08 showed that AUC decreased more in E-08 than in the other groups, with effect sizes being E-12: 0.004 (-1.11 to 1.12), E-16: 0.39 (-1.20 to 1.99) and E-20: 0.16 (-0.86 to 1.17), classified as negligible or small. Similarly, comparisons including E-12 demonstrated greater reductions in AUC, with effect sizes classified as small for E-16: 0.39 (-2.00 to 1.22) and negligible for E-20: 0.15 (-1.15 to 0.85). The comparison of E-20 with E-16: 0.24 (-1.16 to 1.63) indicated a small effect size, suggesting that E-20 achieved a greater reduction in AUC.

Discussion/Conclusion Exercise at any time of day has the potential to improve glycaemic control in adults. Exercise timing may have a notable influence on metabolic adaptations, as distinct patterns were observed between exercise groups. This is possibly due to the influence of circadian rhythms on metabolic mechanisms. However, further in-depth understanding of the underlying metabolic mechanisms is needed to establish a robust scientific foundation for this approach. The inclusion of time of day as a potential fifth pillar in the guidelines for physical activity remains a worthwhile consideration.

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