Comparing Subjective With Objective Sleep Parameters Via Multisensory Actigraphy in German Physical Education Students

Does a self-reported sleep duration reflect actigraphy reported sleep duration in female football players?

Sleep is often compromised in female athletes, and the monitoring of female athletes' sleep is an important preventative and educational tool. With self-reporting of sleep common practice for athletes as part of a daily wellness assessment, there is a need to understand whether sleep indices are being reported accurately, and thus whether self-report data are useful. This study aimed to compare the agreement between self-reported and actigraphy reported sleep duration in female football players, with the intention of informing best practice for athlete monitoring. Twenty-two female footballers (mean age 19.5 ± 1.3 years) provided a daily self-report across 7 days, whilst also wearing an actigraph across the same testing period. Agreement between the two measures was assessed using Bland-Altman limits of agreement, with acceptable limits of agreement defined as <30 minutes. Results showed evident disagreement between the two methods, with a mean bias of -0.54 (32 min, 95% CI -0.66 to -0.43) and a potential disagreement range of over 2 h (Lower 95% limits of agreement -1.49 to upper 95% limits of agreement 0.40). Coaches using self-reported sleep durations as a monitoring tool for female footballers should interpret the results with caution and be aware of the potential for inaccuracies in this measure. As such, where possible, coaches should consider other methods of sleep monitoring, rather than solely relying on a self-report, to ensure they are operating with optimal practice within situational constraints.

Subjective Measures of Workload and Sleep in Australian Army Recruits; Potential Utility as Monitoring Tools

INTRODUCTION

Subjective measures may offer practitioners a relatively simple method to monitor recruit responses to basic military training (BMT). Yet, a lack of agreement between subjective and objective measures may presents a problem to practitioners wishing to implement subjective monitoring strategies. This study therefore aims to examine associations between subjective and objective measures of workload and sleep in Australian Army recruits.

MATERIALS AND METHODS

Thirty recruits provided daily rating of perceived exertion (RPE) and differential RPE (d-RPE) for breathlessness and leg muscle exertion each evening. Daily internal workloads determined via heart rate monitors were expressed as Edwards training impulse (TRIMP) and average heart rate. External workloads were determined via global positioning system (PlayerLoadTM) and activity monitors (step count). Subjective sleep quality and duration was monitored in 29 different recruits via a customized questionnaire. Activity monitors assessed objective sleep measures. Linear mixed-models assessed associations between objective and subjective measures. Akaike Information Criterion assessed if the inclusion of d-RPE measures resulted in a more parsimonious model. Mean bias, typical error of the estimate (TEE) and within-subject repeated measures correlations examined agreement between subjective and objective sleep duration.

RESULTS

Conditional R2 for associations between objective and subjective workloads ranged from 0.18 to 0.78, P < 0.01, with strong associations between subjective measures of workload and TRIMP (0.65-0.78), average heart rate (0.57-0.73), and PlayerLoadTM (0.54-0.68). Including d-RPE lowered Akaike Information Criterion. The slope estimate between objective and subjective measures of sleep quality was not significant. A trivial relationship (r = 0.12; CI -0.03, 0.27) was observed between objective and subjective sleep duration with subjective measures overestimating (mean bias 25 min) sleep duration (TEE 41 min).

CONCLUSIONS

Daily RPE offers a proxy measure of internal workload in Australian Army recruits; however, the current subjective sleep questionnaire should not be considered a proxy measure of objective sleep measures.

Evidence that heat acclimation training may alter sleep and incidental activity

This randomized cross-over study tested the hypothesis that heat acclimation training would detrimentally affect sleep variables and alter incidental physical activity compared to a thermoneutral training control condition. Eight recreationally trained males (V̇O2peak 49±4.9 mL.kg-1.min-1) completed two separate interventions separated by at least 31 days: 5 consecutive day training blocks of moderate-intensity cycling (60 min·day-1 at 50% peak power output) in a hot (34.9±0.7 °C and 53±4 % relative humidity) and a temperate (22.2±2.6 °C; 65±8 % relative humidity) environment. Wrist-mounted accelerometers were worn continuously for the length of the training blocks and recorded physical activity, sleep quality and quantity. Data were analysed in a Bayesian framework, with the results presented as the posterior probability that a coefficient was greater or less than zero. Compared to the temperate training environment, heat acclimation impaired sleep efficiency (Pr β<0 = .979) and wake on sleep onset (Pr β>0 = .917). Daily sedentary time was, on average, 35 min longer (Pr β>0 = .973) and light physical activity time 18 min shorter (Pr β>0 = .960) during the heat acclimation period. No differences were observed between conditions in sleep duration, subjective sleep quality, or moderate or vigorous physical activity. These findings may suggest that athletes and coaches need to be cognisant that heat acclimation training may alter sleep quality and increase sedentary behaviour.HighlightsFive consecutive days of heat training negatively affected some objective measures of sleep quality and incidental physical activity in recreationally trained athletes.Athletes and coaches need to be aware of the potential unintended consequences of using heat acclimation on sleep behaviours.

Sleep behavior and training load in adolescent elite basketball players during COVID-19 pandemic development

The COVID-19 pandemic caused strong restrictions in organized sports. Specifically, adolescent athletes, whose daily routine is usually determined by school and practice schedules, experienced a sudden lifestyle change. The aim of this study was to examine sleep and training patterns of German elite adolescent basketball players (N = 115, 15.70 ± 1.22 years, female = 32.17%) during different phases of the 2021 lockdown. Sleep and training behavior were documented by a subjective monitoring over 10 days at three different lockdown phases in February, April, and June/July. A linear mixed model approach was conducted for the statistical analyses. Sleep duration and time in bed decreased between phase 1 (prohibition of organized sports) and phase 3 (normal training conditions), while sleep efficiency and quality increased. There was no change in training duration over time, but in training intensity, which peaked in the third phase. This study contains meaningful evidence to analyze the lockdown-related return to practice process. Athletes were able to maintain training duration with alternative protocols during pandemic conditions. Interestingly, athletes slept less under non-lockdown conditions, but reported a better sleep quality. It is recommended to consider athletes' individual preferences when planning training and recovery schedules.

Effect of sleep on weight loss and adherence to diet and physical activity recommendations during an 18-month behavioral weight loss intervention

BACKGROUND/OBJECTIVES

To examine the association between indices of sleep quantity and quality with dietary adherence, physical activity adherence, and weight loss during a behavioral weight loss intervention.

METHODS

Adults (n = 156) with overweight and obesity (40 ± 9 years, 84% female, BMI: 34.4 ± 4.2 kg/m²) participated in an 18-month behavioral weight loss intervention which prescribed a reduced calorie diet (1200-1800 kcal/d) and increased physical activity (300 min/wk). Body weight, indices of sleep (SenseWear armband; SWA), energy intake (EI, 3-day food records), and moderate-to-vigorous physical activity (SWA) were measured at baseline, 6, 12, and 18 months. Linear mixed effects models examined the association between sleep and weight change over time. Additional models were adjusted for covariates including age, BMI, sex, race, ethnicity, study completion, randomization, EI, and physical activity. Secondary analyses examined the association between sleep and adherence to diet and physical activity recommendations.

RESULTS

Mean weight loss was 7.7 ± 5.4, 8.4 ± 7.9, and 7.1 ± 9.0 kg at 6, 12, and 18 months, respectively. Lower sleep efficiency, higher wake after sleep onset (WASO), more awakenings, and higher sleep onset latency (SOL) were significantly associated with attenuated weight loss (p < 0.05). Lower sleep efficiency, more awakenings, and higher SOL remained significantly associated with blunted weight loss after adjustment for covariates (p < 0.05). Later waketime, longer time in bed, longer sleep duration, higher WASO, more awakenings, and higher SOL were associated with lower odds of achieving ≥300 min/wk of moderate-to-vigorous physical activity, adjusted for covariates (FDR p < 0.05).

CONCLUSIONS

Future studies should evaluate whether incorporating strategies to improve sleep health within a behavioral weight loss intervention leads to improved adherence to diet and physical activity recommendations and enhanced weight loss.

CLINICAL TRIALS IDENTIFIER

NCT01985568.

Sleep Restriction in Elite Soccer Players: Effects on Explosive Power, Wellbeing, and Cognitive Function

Purpose: To investigate the cognitive, physical, and perceptual effects of sleep restriction (SR) in soccer players following a night match. Methods: In a crossover design, nine male soccer players from the English Premier League 2 (age, 21 ± 5 years; height, 1.80 ± 0.75 m; body mass, 74.2 ± 6.8 kg) recorded their sleep quality and quantity with sleep logs and a subjective survey after two night matches (19:00); one where sleep duration was not altered (CON) and one where sleep was restricted by a later bed-time (SR). Countermovement jump height (CMJ), subjective wellbeing (1-5 likert scale for mood, stress, fatigue, sleep, and soreness), and cognitive function were measured at baseline and the morning following the match (+12 h; M + 1). Results: Bed-time was later in SR than CON (02:36 ± 0.17 vs. 22:43 ± 29; P = .0001; ηp² = 0.999) and sleep duration was shorter in SR than CON (5.37 ± 0.16 vs. 8.59 h ± 0.36; P = .0001; ηp² = 0.926). CMJ decreased by ~8% after the match in both SR and CON (P = .0001; ηp² = 0.915) but there were no differences between the conditions (P > .05; ηp² = 0.041-0.139). Wellbeing was rated worse after both matches (P = .0001; ηp² = 0.949) but there were no differences between the trials (P > .05; ηp² = 0.172-257). SR did not influence cognitive function (P > .05; interaction effects, ηp² = 0.172-257). Conclusion: SR following a nighttime soccer match does not impair CMJ performance, subjective wellbeing, or cognitive function the following morning.

Is unemployment associated with inefficient sleep habits? A cohort study using objective sleep measurements

Unemployed people could be at risk of developing inefficient sleep habits by spending excessive time in bed, as they lack a structuring activity. This could impact their mental health and reintegration into labour. This study aims to analyse possible associations between employment status and sleep parameters using actigraphy. Subjects (148 employed and 50 unemployed) were drawn from a German population-based cohort. Sleep parameters were measured with the SenseWear Bodymedia Pro 3 armband. Comparison of means concerning sleep duration, sleep efficiency, time of sleep and sleep fragmentation was performed separately for week days and weekends. Multiple linear regression analysis was performed to analyse group differences controlling for covariates. Finally, we defined cut-off scores for each sleep variable, and analysed the distribution of subjects above and below these values. Unemployed people did not sleep significantly longer than employed people. However, on week days, they displayed night sleep efficiency reduced by on average > 5% points, they lay down for 28 min longer, had later mid sleep time (38 min) and sleep offset (55 min), as well as more frequent awakenings after sleep onset accounting for being awake 28 min longer (all p ≤ 0.005). Sleep in unemployed subjects compared with employed subjects aged 41-64 years was less efficient, more fragmented and shifted to a later point of the night. Results support prior findings that unemployment has a negative influence on sleep quality. Unemployed individuals could benefit from intervention programmes aiming at the adoption of healthier sleep habits.

Influence of Perceptions of Sleep on Well-Being in Youth Athletes

ABSTRACT

Sawczuk, T, Jones, B, Scantlebury, S, and Till, K. Influence of perceptions of sleep on well-being in youth athletes. J Strength Cond Res 35(4): 1066-1073, 2021-To date, most research considering well-being questionnaires has only considered the training stress imposed on the athlete, without evaluating the questionnaire's relationship with a measure of recovery (e.g., sleep). This study aimed to assess the influence of sleep duration (Sduration), sleep quality (Squality), and sleep index (Sindex; Sduration × Squality) on well-being in youth athletes, while accounting for the known training stressors of training load and exposure to match play. Forty-eight youth athletes (age 17.3 ± 0.5 years) completed a daily questionnaire including well-being (DWBno-sleep; fatigue, muscle soreness, stress, and mood) measures, Perceived Recovery Status Scale (PRS), the previous day's training loads, Sduration, and Squality every day for 13 weeks. Linear mixed models assessed the impact of Sduration, Squality, and Sindex on DWBno-sleep, its individual subscales, and PRS. Sduration had a small effect on DWBno-sleep (d = 0.31; ±0.09), fatigue (d = 0.42; ±0.11), and PRS (d = 0.25; ±0.09). Squality had a small effect on DWBno-sleep (d = 0.47; ±0.08), fatigue (d = 0.53; ±0.11), stress (d = 0.35; ±0.07), mood (d = 0.41; ±0.09), and PRS (d = 0.37; ±0.08). Sindex had a small effect on DWBno-sleep (d = 0.44; ±0.08), fatigue (d = 0.55; ±0.11), stress (d = 0.29; ±0.07), mood (d = 0.37; ±0.09), and PRS (d = 0.36; ±0.09). The results indicate that an athlete's perceptions of sleep are associated with deviations in well-being measures and should be used as an input to the monitoring process rather than as part of the outcome well-being score. The sleep index is suggested as a potential input because it provides information on both the duration and quality of the sleep experienced.

Changes in sleep pattern and sleep quality during COVID-19 lockdown

INTRODUCTION

To mitigate the spread of the pandemic coronavirus infection (COVID-19), governments across the world have adopted "lockdowns" which have confined many individuals to their homes. This disrupts normal life routines, elements of which are important circadian cues. The pandemic is also associated with new stressors, altered roles, and uncertainties about health and economic security, which are also likely to affect sleep. The current study is an online survey of sleep experience, routines, physical activity, and symptoms of anxiety and depression, to study the alterations associated with the lockdown.

MATERIALS AND METHODS

The survey was conducted in early May 2020 using a questionnaire circulated through social media platforms. Questions related to demographic characteristics, current and previous sleep schedules, routine, and working patterns. Insomnia (Insomnia Severity Index - 4), Stress (Perceived Stress Scale - 4), anxiety and depressive symptoms (Patient Health Questionnaire - 4) and physical activity (International Physical Activities Questionnaire) were assessed using standardized instruments.

RESULTS

A total of 958 valid responses were received. Compared to the prelockdown period, there was a shift to a later bedtime and waking time, with a reduction in night-time sleep and an increase in day-time napping. These effects were visible across occupational groups, but mostly affected working individuals except health professionals. Sleep quality deteriorated across groups. Reductions in sleep duration were associated with depressive symptoms.

CONCLUSIONS

The COVID-19 lockdown is associated with changes in sleep schedule and in the quantity and quality of night-time sleep. Although these changes are associated with elevated rates of emotional symptoms, it is unclear from these cross-sectional results, whether sleep deterioration produces psychological distress, or vice versa.

Portable PSG for sleep stage monitoring in sports: Assessment of SOMNOwatch plus EEG

Current sport-scientific studies mostly neglect the assessment of sleep architecture, although the distribution of different sleep stages is considered an essential component influencing an athlete's recovery and performance capabilities. A mobile, self-applied tool like the SOMNOwatch plus EEG might serve as an economical and time-friendly alternative to activity-based devices. However, self-application of SOMNOwatch plus EEG has not been validated against conventional polysomnography (PSG) yet. For evaluation purposes, 25 participants (15 female, 10 male; M _age = 22.92 ± 2.03 years) slept in a sleep laboratory on two consecutive nights wearing both, conventional PSG and SOMNOwatch plus EEG electrodes. Sleep parameters and sleep stages were compared using paired t-tests and Bland-Altman plots. No significant differences were found between the recordings for Sleep Onset Latency, stages N1 to N3 as well as Rapid Eye Movement stage. Significant differences (Bias [95%-confidence interval]) were present between Total Sleep Time (9.95 min [-29.18, 49.08], d = 0.14), Total Wake Time (-13.12 min [-47.25, 23.85], d = -0.28), Wake after Sleep Onset (-11.70 min [-47.25, 23.85], d = -0.34) and Sleep Efficiency (2.18% [-7.98, 12.34], d = 0.02) with small effect sizes. Overall, SOMNOwatch plus EEG can be considered a valid and practical self-applied method for the examination of sleep. In sport-scientific research, it is a promising tool to assess sleep architecture in athletes; nonetheless, it cannot replace in-lab PSG for all clinical or scientific purposes.

Observing Ramadan and sleep-wake patterns in athletes: a systematic review, meta-analysis and meta-regression

Objective

To evaluate the effect of observing Ramadan on athletes’ sleep patterns.

Design

Systematic review and meta-analysis.

Data sources

The entire content of PubMed/MEDLINE and Web of Science.

Eligibility criteria for selecting studies

Single-group, prepost and cross-over design studies conducted in athletes aged ≥18 years, training at least twice a week and published in English before 12 July 2018 were included. Studies assessing sleep quantity, quality, daytime sleepiness and/or daily naps based on objective or subjective methods were deemed eligible.

Study appraisal

The methodological quality was assessed using ‘QualSyst’.

Results

Of 13 selected articles, 7 were of strong quality, 3 were moderate and 3 were weak. 11 studies evaluated total sleep time (TST); this decreased during Ramadan in 4 studies, increased in 1 and remained unchanged in 6. Pooled TST findings indicated a moderate effect size (− 0.97, SE=0.37, 95% CI −1.69 to −0.25, t=−2.64, p=0.01) with significant heterogeneity but no publication bias. Meta-regressions showed no effects of study year, age, sample size, type of sport or competition level, but there were effects of country (with France and Tunisia being the most affected countries and Turkey the least affected, Q=32.14, p<0.0001) and study design (Q=7.74, p=0.02). Four studies measured self-reported sleep quality and it decreased in three studies. One study of sleep architecture reported more frequent waking and more light sleep during Ramadan. Daily nap duration was increased in two studies, but daytime sleepiness remained unchanged in four studies.

Conclusion

When athletes continue to train at least two times/week while observing Ramadan, TST is decreased compared with athletes’ baseline levels.

Sleep-Related Issues for Recovery and Performance in Athletes

The body of research that reports the relevance of sleep in high-performance sports is growing steadily. While the identification of sleep cycles and diagnosis of sleep disorders are limited to lab-based assessment via polysomnography, the development of activity-based devices estimating sleep patterns provides greater insight into the sleep behavior of athletes in ecological settings. Generally, small sleep quantity and/or poor quality appears to exist in many athletic populations, although this may be related to training and competition context. Typical sleep-affecting factors are the scheduling of training sessions and competitions, as well as impaired sleep onset as a result of increased arousal prior to competition or due to the use of electronic devices before bedtime. Further challenges are travel demands, which may be accompanied by jet-lag symptoms and disruption of sleep habits. Promotion of sleep may be approached via behavioral strategies such as sleep hygiene, extending nighttime sleep, or daytime napping. Pharmacological interventions should be limited to clinically induced treatments, as evidence among healthy and athletic populations is lacking. To optimize and manage sleep in athletes, it is recommended to implement routine sleep monitoring on an individual basis.

The influence of training load, exposure to match play and sleep duration on daily wellbeing measures in youth athletes

This study assessed the influence of training load, exposure to match play and sleep duration on two daily wellbeing measures in youth athletes. Forty-eight youth athletes (age 17.3 ± 0.5 years) completed a daily wellbeing questionnaire (DWB), the Perceived Recovery Status scale (PRS), and provided details on the previous day's training loads (TL) and self-reported sleep duration (sleep) every day for 13 weeks (n = 2727). Linear mixed models assessed the effect of TL, exposure to match play and sleep on DWB and PRS. An increase in TL had a most likely small effect on muscle soreness (d = -0.43;± 0.10) and PRS (d = -0.37;± 0.09). Match play had a likely small additive effect on muscle soreness (d = -0.26;± 0.09) and PRS (d = -0.25;± 0.08). An increase in sleep had a most likely moderate effect on sleep quality (d = 0.80;± 0.14); a most likely small effect on DWB (d = 0.45;± 0.09) and fatigue (d = 0.42;± 0.11); and a likely small effect on PRS (d = 0.25;± 0.09). All other effects were trivial or did not reach the pre-determined threshold for practical significance. The influence of sleep on multiple DWB subscales and the PRS suggests that practitioners should consider the recovery of an athlete alongside the training stress imposed when considering deviations in wellbeing measures.

Sleep monitoring of a six-day microcycle in strength and high-intensity training

This study examined the effect of microcycles in eccentric strength and high-intensity interval training (HIT) on sleep parameters and subjective ratings. Forty-two well-trained athletes (mean age 23.2 ± 2.4 years) were either assigned to the strength (n = 21; mean age 23.6 ± 2.1 years) or HIT (n = 21; mean age 22.8 ± 2.6 years) protocol. Sleep monitoring was conducted with multi-sensor actigraphy (SenseWear Armband™, Bodymedia, Pittsburg, PA, USA) and sleep log for 14 days. After a five-day baseline phase, participants completed either eccentric accented strength or high-intensity interval training for six days, with two training sessions per day. This training phase was divided into two halves (part 1 and 2) for statistical analyses. A three-day post phase concluded the monitoring. The Recovery-Stress Questionnaire for Athletes was applied at baseline, end of part 2, and at the last post-day. Mood ratings were decreased during training, but returned to baseline values afterwards in both groups. Sleep parameters in the strength group remained constant over the entire process. The HIT group showed trends of unfavourable sleep during the training phase (e.g., objective sleep efficiency at part 2: mean = 83.6 ± 7.8%, F3,60 = 2.57, P = 0.06, [Formula: see text] = 0.114) and subjective improvements during the post phase for awakenings (F3,60 = 2.96, P = 0.04, [Formula: see text] = 0.129) and restfulness of sleep (F3,60 = 9.21, P < 0.001, [Formula: see text] = 0.315). Thus, the HIT protocol seems to increase higher recovery demands than strength training, and sufficient sleep time should be emphasised and monitored.