Diurnal performance variations can be as pronounced as 26% in the course of a day

Circadian rhythms, among other factors, have been shown to regulate key physiological processes involved in athletic performance []. Personal best performance of athletes in the evening was confirmed across different sports []. Contrary to this view, we identified peak performance times in athletes to be different between human “larks” and “owls” (also called “morningness/eveningness types” [] or “chronotypes” [] and referred to as circadian phenotypes in this paper), i.e., individuals with well-documented genetic [] and physiological [] differences that result in disparities between their biological clocks and how they entrain to exogenous cues, such as the environmental light/dark cycle and social factors. We found time since entrained awakening to be the major predictor of peak performance times, rather than time of day, as well as significant individual performance variations as large as 26% in the course of a day. Our novel approach combining the use of an athlete-specific chronometric test, longitudinal circadian analysis, and physical performance tests to characterize relevant sleep/wake and performance parameters in athletes allows a comprehensive analysis of the link between the circadian system and diurnal performance variation. We establish that the evaluation of an athlete’s personal best performance requires consideration of circadian phenotype, performance evaluation at different times of day, and analysis of performance as a function of time since entrained awakening.

Circadian variation in the circulatory responses to exercise: relevance to the morning peaks in strokes and cardiac events.

Effects of regular training at the same time of day on diurnal fluctuations in muscular performance.

Analysis of personal best BLEEP test performance of all 20 subjects revealed average lowest performance at 07:00 a.m., intermediate performance values at 10:00 a.m., 1:00 p.m., and 10:00 p.m., and highest performance values at 4:00 p.m. and 7:00 p.m., with a considerable performance difference of 11.2% between the minimum and maximum average performance ( Figure 2 A) . Analysis considering circadian phenotype, however, revealed significant differences in peak performance, with the highest performance for ECTs at 12.19 ± 1.43 hr, ICTs at 15.81 ± 0.51 hr, and LCTs at 19.66 ± 0.67 hr ( Figures 2 B–2D). Diurnal changes in performance were 7.62% ± 1.18% in ECTs as compared to 10.03% ± 1.62% in ICTs and a striking 26.2% ± 3.97% in LCTs ( Figure 2 ). Analysis of the data as a function of time since entrained awakening, i.e., performance evaluated against time in hours after entrained wake-up time, diminished the time difference between peak performance times in ECTs and ICTs, with the highest average performance for ECTs at 5.60 ± 1.44 hr and ICTs at 6.54 ± 0.74 hr, i.e., being only 0.96 hr apart and not significantly different any longer (Kruskal-Wallis, p > 0.05; Figures 3 B, 3C, and 4 ) . Average LCT peak performance time, however, was 11.18 ± 0.93 hr after entrained wake-up and was significantly delayed as compared to ECT and ICT peak performance times (Kruskal-Wallis, p < 0.01; Figures 3 D and 4 ). Thus, our study of cardiovascular endurance, a major component of physical fitness, establishes that circadian phenotype and time since entrained awakening, i.e., the entrainment status of the circadian system reflecting internal biological time, are major determinants of diurnal athletic performance.

White bars represent ECTs, light-gray bars are ICTs, and LCTs are shown as dark-gray bars. Data are shown as Tukey boxplots; the line in the box indicates the median, the mean value is represented by the + symbol, and whiskers represent 1.5 times the interquartile or highest/lowest point distance. Statistical analysis was carried out using the Kruskal-Wallis test combined with Dunn’s multiple-comparison post test. ns, not significant; ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001.

BLEEP test performance values of all subjects (n = 20; A), ECTs (n = 5; B), ICTs (n = 10; C), and LCTs (n = 5; D) expressed as the percentage of individual personal best performance. The x axes show the time since awakening in hours. Symbols represent individual performance test results. Lines are second-order polynomial non-linear regressions. See also Figure S2 and Table S1

The x axes show the time of day in hours. Curve fits are second-order polynomial non-linear regressions. Symbols represent the median ± interquartile range. See also Figure S2 and Table S1

From these 121 athletes, 20 with comparable age and fitness levels and with circadian phenotypes matching the whole population—i.e., 25% versus 28% ECTs (n = 5), 50% versus 48% ICTs (n = 10), and 25% versus 24% LCTs (n = 5)—were selected to conduct BLEEP fitness tests at six different times of day. All 20 were field hockey players with an average age of 20.4 years competing at regional club level, with seven out of these 20 individuals additionally competing at international level. The BLEEP test is a progressive aerobic cardiovascular endurance test widely used by sports coaches to estimate athletes’ maximum oxygen uptake, i.e., cardiovascular fitness, one of the most important components of physical fitness [].

We recruited 121 competition level athletes (70 females and 51 males; average age 22.5 years) to complete the RB-UB chronometric test, a novel chronometric questionnaire specifically designed to study sleep/wake-related parameters and training, competition, and performance variables in athletes. After comprehensive analysis and scoring of selected parameters, all individuals were categorized as either early circadian phenotype (ECT), intermediate circadian phenotype (ICT), or late circadian phenotype (LCT); 28% of the individuals were ECTs (n = 34), 48% were ICTs (n = 58), and 24% were LCTs (n = 29). This circadian phenotyping methodology proved consistent with relevant circadian parameters, such as wake-up times, sleep-onset times, and sleep durations, validating behavioral circadian differences between the individual phenotypes; wake-up times, both on weekdays and weekends, were significantly different between the circadian phenotypes (Kruskal-Wallis, p < 0.0001). Significant differences were also seen in sleep-onset times (Kruskal-Wallis, p < 0.0001) and sleep durations (Kruskal-Wallis, p < 0.0001) ( Figures 1 and S1 ) .

White boxes represent early circadian phenotypes (ECTs), light-gray boxes are intermediate circadian phenotypes (ICTs), and late circadian phenotypes (LCTs) are shown as dark-gray boxes. Data are shown as Tukey boxplots; the line in the box indicates the median, the mean value is represented by the + symbol, and whiskers represent 1.5 times the interquartile or highest/lowest point distance. Statistical analysis was carried out using the Kruskal-Wallis test combined with Dunn’s multiple-comparison post test. ns, not significant;p < 0.05,p < 0.01,p < 0.001. See also Figure S1

Discussion

Our results shed new light on our understanding of personal best performance in athletes by showing (1) significant differences in peak performance times between circadian phenotypes, (2) time since entrained awakening to be the major and most reliable predictor of peak performance, and (3) significant individual performance variations up to 26% in the course of a day.

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McGuigan M.R. Circadian rhythms in exercise performance: implications for hormonal and muscular adaptation. A major impact of our study comes from the analysis of performance as a function of time since entrained awakening. While time of day analysis revealed that ECTs, ICTs, and LCTs performed best at different times of day, these results changed significantly as a function of time since entrained awakening. Time of day is an exogenous factor and is only partly related to the circadian physiology of an individual, and our data show that measurements of diurnal performance as a function of time of day have only limited value. Irrespective of the time of day, ECTs wake up earlier and go to sleep earlier than LCTs; thus, their individual periods of wakefulness, i.e., their biological days, differ significantly from each other. Our 10:00 a.m. performance test, for example, took place about 3 hr after the average wake-up time of the ECT participants and 2 hr after the entrained wake-up time of our ICT participants, but only 15 min after the entrained average wake-up time of the LCTs. Evaluating our data as a function of time since awakening revealed outstanding results; ECT and ICT performance curves were nearly identical, demonstrating that the time difference of peak performance between ECTs and ICTs was more or less entirely caused by the distinct phasing of their sleep/wake cycles. The diurnal performance curve of LCTs, however, showed a distinct shape and slope as compared to ECTs and ICTs, suggesting differences in the underlying physiology controlling performance. Possible explanations for this discrepancy come from endocrine studies showing that ECTs have higher cortisol levels in the morning and a distinct high-amplitude diurnal profile of cortisol, while LCTs have lowered cortisol in the morning and a flattened diurnal profile that cannot be explained by different wake-up times and sleep durations and thus suggest that intrinsic physiological features are responsible for these differences []. Cortisol production is controlled by circadian mechanisms [], and LCTs have been shown to have significantly delayed melatonin rhythms as compared to ECTs []. This suggests that the phase-shifted, i.e., delayed, circadian rhythm in LCTs may cause a partial suppression and delay of cortisol, which in turn negatively affects physical performance, as cortisol is essential for muscle function [].

We can exclude impact of age as well as external influences on the results of this study as all participants were of comparable age, completed daily sleep/wake diaries ( Figure S1 ), and comprehensive pre- and post-test forms with each performance test, monitoring a large number of variables, including sleep/wake times and food and caffeine intake. Additionally, to exclude any impact due to changes in sleep duration the night before the actual performance test, we re-analyzed all data by omitting all performance tests that took place earlier than entrained wake-up, i.e., that required a change in wake-up time and thus shortened sleep; this analysis confirmed the differences in peak performance times between the circadian phenotypes ( Table S1 ).

For ECTs and ICTs, peak performance times were similar about 5.5 hr and 6 hr after entrained wake-up, respectively, while LCTs reached their peak performance about 11 hr after their biological start of the day ( Figure 3 ). Thus, the differences in peak performance times are the consequence of both internal physiological mechanisms and differential entrainment of the circadian system to environmental cues. To further validate these striking results and ensure that these results were not specific to one particular performance test and/or the particular group of athletes selected, we conducted additional performance tests with an independently selected group of squash players showing highly comparable performance results ( Figure S3 ).

Our results are the first known performance data in athletes that have observed different peak performance between circadian phenotypes in both real time and time since awakening.

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Our results leave no doubt that the correct determination of an athlete’s personal best performance requires consideration of circadian phenotype, performance evaluation at different times of day, and analysis of performance as a function of time since entrained awakening. For an athlete to optimize performance, entrained wake-up time appears to be the most important and reliable predictor of optimal performance. It does not necessarily matter at what time of day personal best performance has to be achieved; what matters for an athlete is how many hours after entrained wake-up the competition or performance evaluation takes place. We herewith introduce novel tools for performance evaluation and enhancement, including a chronometric test specifically designed for athletes and longitudinal sleep/wake diaries that allow a detailed analysis of circadian disruptions, contributing factors, and internal biological time.