Effects of hour of training and exercise intensity on nocturnal autonomic modulation and sleep quality of amateur ultra-endurance runners

The training times of athletes could play a role in clinical sleep problems due to their associations with sleep difficulty scores

OBJECTIVES

Sleep is a key component of athletic recovery, yet training times could influence the sleep of athletes. The aim of the current study was to compare sleep difficulties in athletes across different training time groups (early morning, daytime, late evening, early morning plus late evening) and to investigate whether training time can predict sleep difficulties.

METHODS

Athletes from various sports who performed at a national-level (n = 273) answered the Athlete Sleep Screening Questionnaire (ASSQ) along with several other questionnaires related to demographics, exercise training, and mental health. From the ASSQ, a Sleep Difficulty Score (SDS) was calculated. Transformed SDS (tSDS) was compared across different training time categories using multiple one-way ANOVAs. A stepwise regression was then used to predict tSDS from various sleep-related factors.

RESULTS

SDSs ranged from none (31%), mild (38%), moderate (22%), and severe (9%). However, the one-way ANOVAs revealed training earlier or later vs. training daytime shifted the tSDS in a negative direction, a trend toward increased sleep difficulty. In particular, athletes training in the late evening (>20:00 or >21:00) had a significantly higher tSDS when compared to daytime training (p = .03 and p < .01, respectively). The regression model (p < .001) explained 27% of variance in the tSDS using depression score, age, training time, and chronotype score.

CONCLUSION

Among a heterogeneous sample of national-level athletes, 31% displayed moderate to severe SDSs regardless of their training time. However, when athletes trained outside daytime hours there was a tendency for the prevalence of sleep difficulties to increase.

Effects of morning and evening physical exercise on subjective and objective sleep quality: an ecological study

To optimise the relationship between exercise and sleep quality, the intensity of exercise and its proximity to sleep are key factors to manage. Although low-to-moderate exercises promote sleep quality, late-evening vigorous exercise instead of morning should still be avoided. It potentially impacts the objective and subjective markers of sleep quality. In the present study, we investigated the effects of vigorous morning and evening exercise on objective and subjective sleep features in an ecological context. A total of 13 recreational runners (mean [SD] age 27.7 [7.2] years, four females) performed a 45-60 min run (70% maximal aerobic velocity) either in the MORNING (30 min to 2 h after waking-up) or in the EVENING (2 h to 30 min before sleep). The two exercise conditions were separated by a REST day. After each condition, sleep was objectively assessed using an electroencephalographic headband and subjectively using the Spiegel Sleep Inventory. Compared with REST, both MORNING and EVENING exercise increased the time spent in non-rapid eye movement (NREM, +24.9 min and +22.7 min; p = 0.01, η2  = 0.11, respectively). Longer NREM duration was mainly due to sleep stage 2 extension after both MORNING (+20.8 min) and EVENING (+22.8 min) exercise relative to REST (p = 0.02, η2  = 0.12). No other effect of exercise on either objective or subjective sleep could be observed. Exercise, independently of the time at which it takes place, leads to extended NREM sleep without other effects on sleep quality. Considering the crucial role of exercise in achieving good health, sleep hygiene guidelines should be updated to promote exercise at any time of the day.

Hit the gym or hit the hay: can evening exercise characteristics predict compromised sleep in healthy adults?

Introduction: Recent sleep guidelines regarding evening exercise have shifted from a conservative (i.e., do not exercise in the evening) to a more nuanced approach (i.e., exercise may not be detrimental to sleep in circumstances). With the increasing popularity of wearable technology, information regarding exercise and sleep are readily available to the general public. There is potential for these data to aid sleep recommendations within and across different population cohorts. Therefore, the aim of this study was to examine if sleep, exercise, and individual characteristics can be used to predict whether evening exercise will compromise sleep. Methods: Data regarding evening exercise and the subsequent night's sleep were obtained from 5,250 participants (1,321F, 3,929M, aged 30.1 ± 5.2 yrs) using a wearable device (WHOOP 3.0). Data for females and males were analysed separately. The female and male datasets were both randomly split into subsets of training and testing data (training:testing = 75:25). Algorithms were trained to identify compromised sleep (i.e., sleep efficiency <90%) for females and males based on factors including the intensity, duration and timing of evening exercise. Results: When subsequently evaluated using the independent testing datasets, the algorithms had sensitivity for compromised sleep of 87% for females and 90% for males, specificity of 29% for females and 20% for males, positive predictive value of 32% for females and 36% for males, and negative predictive value of 85% for females and 79% for males. If these results generalise, applying the current algorithms would allow females to exercise on ~ 25% of evenings with ~ 15% of those sleeps being compromised and allow males to exercise on ~ 17% of evenings with ~ 21% of those sleeps being compromised. Discussion: The main finding of this study was that the models were able to predict a high percentage of nights with compromised sleep based on individual characteristics, exercise characteristics and habitual sleep characteristics. If the benefits of exercising in the evening outweigh the costs of compromising sleep on some of the nights when exercise is undertaken, then the application of the current algorithms could be considered a viable alternative to generalised sleep hygiene guidelines.

Different Intensities of Evening Exercise on Sleep in Healthy Adults: A Systematic Review and Network Meta-Analysis

The effects of different intensities of evening exercise on subsequent sleep remain contradictory. Thus, this systematic review and network meta-analysis aimed to compare and rank the effects of different intensities of acute evening exercise on sleep in healthy adults with good sleep. Articles were systematically searched journals indexed in the PubMed, Web of Science, Cochrane Library, Embase, and Scopus databases from inception to the 5th of May, 2022. The basic search terms included exercise, sleep and timing, which were combined with AND. Of the 12,203 retrieved, twenty-eight studies with 325 participants met the inclusion criteria. Results revealed that there were no significant differences in terms of impacts on sleep caused by different intensities of acute evening exercise, except that when compared to no exercise, acute evening high-intensity exercise decreased rapid eye movement sleep (mean difference [MD] = -1.95%, 95% credible interval [CI] = -3.58 to -0.35). Compared to no exercise, acute evening moderate-intensity exercise was ranked as the most potential method to improve sleep, displaying a trend to improve wake time after sleep onset (MD = -2.50 min, 95% CI = -8.17 to 1.62), sleep efficiency (MD = +0.41%, 95% CI = -0.71 to 1.66), the proportion of stage N1 (MD = -0.72%, 95% CI = -2.08 to 0.71) and N3 sleep (slow-wave sleep) (MD = +0.84%, 95% CI = -1.17 to 2.78). Acute evening low-intensity exercise displayed the greatest tendency to shorten sleep onset latency (MD = -1.02 min, 95% CI = -4.39 to 2.50) compared to no exercise. Overall, regardless of intensity, acute evening exercise completed before bedtime does not disrupt subsequent sleep in healthy young and middle-aged adults.

The Acute Effects of Aerobic Exercise on Nocturnal and Pre-Sleep Arousal in Patients with Unipolar Depression: Preplanned Secondary Analysis of a Randomized Controlled Trial

Unipolar depression is associated with insomnia and autonomic arousal. The aim of this study was to quantify the effect of a single bout of aerobic exercise on nocturnal heart rate variability and pre-sleep arousal in patients with depression. This study was designed as a two-arm, parallel-group, randomized, outcome assessor-blinded, controlled, superiority trial. Patients with a primary diagnosis of unipolar depression aged 18-65 years were included. The intervention consisted of a single 30 min moderate-intensity aerobic exercise bout. The control group sat and read for 30 min. The primary outcome of interest was RMSSD during the sleep period assessed with polysomnography. Secondary outcomes were additional heart rate variability outcomes during the sleep and pre-sleep period as well as subjective pre-sleep arousal. A total of 92 patients were randomized to either the exercise (N = 46) or the control (N = 46) group. Intent-to-treat analysis ANCOVA of follow-up sleep period RMSSD, adjusted for baseline levels and minimization factors, did not detect a significant effect of the allocation (β = 0.12, p = 0.94). There was no evidence for significant differences between both groups in any other heart rate variability measure nor in measures of cognitive or somatic pre-sleep arousal. As this is the first trial of its kind in this population, the findings need to be confirmed in further studies. Patients with depression should be encouraged to exercise regularly in order to profit from the known benefits on sleep and depressive symptoms, which are supported by extensive literature.

Deconstructing athletes' sleep: A systematic review of the influence of age, sex, athletic expertise, sport type, and season on sleep characteristics

PURPOSE

This systematic review aimed to describe objective sleep parameters for athletes under different conditions and address potential sleep issues in this specific population.

METHODS

PubMed and Scopus were searched from inception to April 2019. Included studies measured sleep only via objective evaluation tools such as polysomnography or actigraphy. The modified version of the Newcastle-Ottawa Scale was used for the quality assessment of the studies.

RESULTS

Eighty-one studies were included, of which 56 were classified as medium quality, 5 studies as low quality, and 20 studies as high quality. A total of 1830 athletes were monitored over 18,958 nights. Average values for sleep-related parameters were calculated for all athletes according to sex, age, athletic expertise level, training season, and type of sport. Athletes slept on average 7.2 ± 1.1 h/night (mean ± SD), with 86.3% ± 6.8% sleep efficiency (SE). In all datasets, the athletes' mean total sleep time was <8 h. SE was low for young athletes (80.3% ± 8.8%). Reduced SE was attributed to high wake after sleep onset rather than sleep onset latency. During heavy training periods, sleep duration and SE were on average 36 min and 0.8% less compared to pre-season and 42 min and 3.0% less compared to in-season training periods, respectively.

CONCLUSION

Athletes' sleep duration was found to be short with low SE, in comparison to the general consensus for non-athlete healthy adults. Notable sleep issues were revealed in young athletes. Sleep quality and architecture tend to change across different training periods.

Ambulatory heart rate variability in overweight and obese men after high-intensity interval exercise versus moderate-intensity continuous exercise

Despite the growing evidence for the beneficial effects of high-intensity interval exercise (HIIE) on vascular health of overweight and obese individuals, it is not clear whether the autonomic stress promoted by HIIE during the ambulatory period is higher than that by moderate-intensity continuous exercise (MICE). Therefore, this study compares the 24 hour (h) ambulatory heart rate variability (HRV) following HIIE and MICE in young overweight and obese men. Eleven untrained men aged 23.2 ± 1.9 years, with average body mass index (BMI) of 31.6 ± 3.9 kg/m² (three overweight and eight obese), underwent three exercise sessions: HIIE, MICE and Control (CT). HRV was recorded in the laboratory and for the next 24 h following laboratory recovery. There were no differences in HRV indices in the rest period between the sessions (p > .05). During the ambulatory period, the area under the curve (AUC) of the low-frequency band transformed into natural logarithm (Ln-LF) during sleep was higher after HIIE than after CT (43.31 ± 8.59 vs. 41.11 ± 7.28 ms²*h, p = .034). The HRV followed by MICE did not differ from other sessions (p > .05). In conclusion, the 24 h ambulatory HRV did not differ after HIIE and MICE. However, despite the small effect, HIIE increased the cardiac autonomic modulation during the sleep period, as a protector of cardiovascular system. Highlights24 h ambulatory HRV could add important information about the cardiovascular safety of the exercise protocols in overweight and obese individuals.There was no disturbances in the cardiac autonomic modulation during the awake period, regardless of the exercise protocol.LnLF index was higher in HIIE compared to CT in the sleep period.24 h ambulatory HRV did not differ between the HIIE and MICE sessions.

Effects of resistance training intensity on sleep quality and strength recovery in trained men: a randomized cross-over study

Resistance training (RT) variables can affect sleep quality, strength recovery and performance. The aim of this study was to examine the acute effect of RT leading to failure vs. non-failure on sleep quality (SQ), heart rate variability (HRV) overnight and one-repetition maximum (1-RM) performance 24 hours after training. Fifteen resistance-trained male athletes (age: 23.4 ± 2.4 years; height 178.0 ± 7.6 cm; weight: 78.2 ± 10.6 kg) performed two training sessions in a randomized order, leading to failure (4x10) or non-failure (5x8(10) repetitions), with 90 seconds for resting between sets at 75% 1-RM in bench press (BP) and half squat (HS). The day after, the participants completed the predicted 1-RM test for both exercises. In addition, the subjective and actigraphic SQ and HRV during sleep were measured after each training session. The day after the training protocol leading to failure, the 1-RM of BP (MD = 7.24 kg; -7.2%; p < 0.001) and HS (MD = 20.20 kg; -11.1%; p < 0.001) decreased. However, this parameter did not decrease after a non-failure RT session. No differences were observed between failure and non-failure training sessions on SQ and HRV; therefore, both types of training sessions similarly affected the SQ and the autonomic modulation during the night after the training session. This study provides an insight into the influence of different training strategies on SQ, strength performance and recovery after moderate- to high-demand training. This information could be useful especially for professional coaches, weightlifters and bodybuilders, due to the potential influence on the programming processes.

Sleep quality monitoring in individual sports athletes: parameters and definitions by systematic review

In the present review, we identify which instruments and parameters are used for sleep quality monitoring in individual sport athletes and which definitions were used for sleep quality parameters in this literature field. Systematic searches for articles reporting the qualitative markers related to sleep in team sport athletes were conducted in PubMed, Scopus and Web of Science online databases. The systematic review followed the Preferred Reporting Items for Systematic Reviews. The initial search returned 3316 articles. After the removal of duplicate articles, eligibility assessment, 75 studies were included in this systematic review. Our main findings were that the most widely used measurement instruments were Actigraphy (25%), Rating Likert Scales (16%) and Sleep Diary (13%). On sleep quality parameters (Sleep duration = 14%; Wake after sleep onset = 14%; Sleep Quality = 12%; Sleep Effciency = 11% and Sleep Latency = 9%), the main point is that there are different definitions for the same parameters in many cases reported in the literature. We conclude that the most widely used instruments for monitoring sleep quality were Actigraphy, Likert scales and Sleep diary. Moreover, the definitions of sleep parameters are inconsistent in the literature, hindering the understanding of the sleep-sport performance relationship.

The effect of evening cycling at different intensities on sleep in healthy young adults with intermediate chronobiological phenotype: A randomized, cross-over trial

This study investigated the effect of various cycling intensities on sleep-related parameters in healthy young adults with intermediate chronobiological phenotype. Ten recreationally trained male volunteers underwent an evening i) moderate-intensity continuous training (MICT; 45 min at 70% W_max), ii) high-intensity interval training (HIIT; 10 × 1 min at 90% W_max), iii) sprint interval training (SIT; 6 × 20 sec at 140% W_max) or iv) a non-exercise (CON) trial in randomized, counter-balanced and crossover order. At baseline, somatometric data, maximum oxygen uptake and chronotype were evaluated. Sleep-related indices and daily activity were recorded by a multi-sensor activity monitor. Total sleep time was longer after SIT compared to CON and MICT (p < 0.05). Sleep efficiency was higher in SIT than in CON (p < 0.05). Sleep onset latency did not differ among trials. Wake after sleep onset was decreased after SIT compared to CON (p= 0.049). No differences were found for bedtime among trials. Wake time was earlier in the MICT trial compared to CON (p = 0.026). Evening cycling exercise -independently of intensity- did not impair sleep of individuals with intermediate chronobiological phenotype. Furthermore, a single SIT session improved sleep quantity and continuation of individuals with this specific chronotype.

A critical review on sleep assessment methodologies in athletic populations: factors to be considered

A growing body of research focus on athletes' sleep in order to investigate the effects of sleep in sports performance and recovery or the prevalence of sleep disorders in athletes. At the same time, several sleep monitoring tools have been developed and used in athletic populations for fulfilling these purposes. This review aimed to provide critical assessment to the most used by athletes' methodological approaches and compared them with the gold standard approach. Advantages and disadvantages of the various sleep monitoring tools were critically discussed. Literature related to aspects of athletes' sleep was reviewed. From the shortlisted studies, several factors that seem to affect sleep in athletes were identified using objective methods such as polysomnography/electroencephalography and actigraphy. These factors were associated to sleep (eg such as sleep environment, familiarization procedures and napping) and daily habits (eg nutrition, fluid consumption, alcohol and caffeine intake, tobacco use). The selected studies that evaluated sleep objectively were screened according the reporting rates of these variables. The majority of the screened studies were found to underreport these variables. Practical issues were addressed and recommendations about reporting sleep-related factors were made in order to improve studies' quality assessment and allow for more robust comparisons between studies.

The Advantage of Supine and Standing Heart Rate Variability Analysis to Assess Training Status and Performance in a Walking Ultramarathon

Cardiac autonomic modulation of heart rate, assessed by heart rate variability (HRV), is commonly used to monitor training status. HRV is usually measured in athletes after awakening in the morning in the supine position. Whether recording during standing reveals additional information compared to supine remains unclear. We aimed to evaluate the association between short-duration HRV, assessed both in the supine and standing position, and a low-intensity long-duration performance (walking ultramarathon), as well as training experience. Twenty-five competitors in a 100 km walking ultramarathon underwent pre-race supine (12 min) and standing (6 min) HR recordings, whereas performance and subjective training experience were assessed post-race. There were no significant differences in both supine and standing HRV between finishers (n = 14) and non-finishers (n = 11, mean distance 67 km). In finishers, a slower race velocity was significantly correlated with a higher decrease in parasympathetic drive during position change [larger decrease in High Frequency power normalized units (HF_nu: r = −0.7, p = 0.01) and higher increase in the detrended fluctuation analysis alpha 1 index (DFA1: r = 0.6, p = 0.04)]. Highly trained athletes accounted for higher HF_nu during standing compared to poorly trained competitors (+11.5, p = 0.01). Similarly, greater training volume (total km/week) would predict higher HF_nu during standing (r = 0.5, p = 0.01). HRV assessment in both supine and standing position may provide additional information on the dynamic adaptability of cardiac autonomic modulation to physiologic challenges and therefore be more valuable for performance prediction than a simple assessment of supine HRV. Self-reported training experience may reliably associate with parasympathetic drive, therefore indirectly predicting long-term aerobic performance in ultramarathon walking races.

High-intensity exercise in the evening does not disrupt sleep in endurance runners

PURPOSE

To investigate the effect of early evening exercise training at different intensities on nocturnal sleep and cardiac autonomic activity in endurance-trained runners.

METHODS

Eight runners completed three experimental trials in a randomised, counterbalanced order. In the early evening (end of exercise 3.5 h before bedtime), participants performed either: (i) a 1 h high-intensity interval running session (HIGH, 6 × 5 min at 90% VO_2peak interspersed with 5 min recovery); (ii) a 1 h low-intensity running session (LOW, 60 min at 45% VO_2peak) or (iii) no exercise (CON). Subsequent nocturnal sleep was assessed using polysomnography, wristwatch actigraphy, and subjective sleep quality. A two-lead electrocardiogram recorded nocturnal cardiac autonomic activity.

RESULTS

Total sleep time increased after HIGH (477.4 ± 17.7 min, p = 0.022) and LOW (479.6 ± 15.6 min, p = 0.006) compared with CON (462.9 ± 19.0 min). Time awake was lower after HIGH (31.8 ± 18.5 min, p = 0.047) and LOW (30.4 ± 15.7 min, p = 0.008) compared with CON (46.6 ± 20.0 min). There were no differences between conditions for actigraphy and subjective sleep quality (p > 0.05). Nocturnal heart rate variability was not different between conditions, but average nocturnal heart rate increased after HIGH (50 ± 5 beats min^-1) compared with LOW (47 ± 5 beats min^-1, p = 0.02) and CON (47 ± 5 beats min^-1, p = 0.028).

CONCLUSION

When performed in the early evening, high-intensity exercise does not disrupt and may even improve subsequent nocturnal sleep in endurance-trained runners, despite increased cardiac autonomic activity. Additionally, low-intensity exercise induced positive changes in sleep behaviour that are comparable to those obtained following high-intensity exercise.

Impact of Caffeine Intake on 800-m Running Performance and Sleep Quality in Trained Runners

Background: Caffeine ingestion improves athletic performance, but impairs sleep quality. We aimed to analyze the effect of caffeine intake on 800-m running performance, sleep quality (SQ), and nocturnal cardiac autonomic activity (CAA) in trained runners. Methods: Fifteen male middle-distance runners participated in the study (aged 23.7 ± 8.2 years). In a randomized and comparative crossover study design, the athletes ingested a placebo (PL) or caffeine supplement (CAF; 6 mg∙kg⁻¹) one hour before an 800-m running time-trial test in the evening. During the night, CAA and SQ were assessed using actigraphy and a sleep questionnaire. A second 800-m running test was performed 24 h after the first. Time, heart rate, rating of perceived exertion, and blood lactate concentration were analyzed for each running test. Results: No significant differences in CAA and performance variables were found between the two conditions. However, CAF impaired sleep efficiency (p = 0.003), actual wake time (p = 0.001), and the number of awakenings (p = 0.005), as measured by actigraphy. Also, CAF impaired the questionnaire variables of SQ (p = 0.005), calm sleep (p = 0.005), ease of falling asleep (p = 0.003), and feeling refreshed after waking (p = 0.006). Conclusion: The supplementation with caffeine (6 mg∙kg⁻¹) did not improve the 800-m running performance, but did impair the SQ of trained runners.