Search

Abstract Background Sleep is a critical component of recovery, but it can be disrupted following prolonged endurance exercise. The objective of this study was to examine the capacity of male and female professional cyclists to recover between daily race stages while competing in the 2022 Tour de France and the 2022 Tour de France Femmes, respectively. The 17 participating cyclists (8 males from a single team and 9 females from two teams) wore a fitness tracker (WHOOP 4.0) to capture recovery metrics related to night-time sleep and autonomic activity for the entirety of the events and for 7 days of baseline before the events. The primary analyses tested for a main effect of ‘stage classification’—i.e., rest, flat, hilly, mountain or time trial for males and flat, hilly or mountain for females—on the various recovery metrics. Results During baseline, total sleep time was 7.2 ± 0.3 h for male cyclists (mean ± 95% confidence interval) and 7.7 ± 0.3 h for female cyclists, sleep efficiency was 87.0 ± 4.4% for males and 88.8 ± 2.6% for females, resting HR was 41.8 ± 4.5 beats·min−1 for males and 45.8 ± 4.9 beats·min−1 for females, and heart rate variability during sleep was 108.5 ± 17.0 ms for males and 119.8 ± 26.4 ms for females. During their respective events, total sleep time was 7.2 ± 0.1 h for males and 7.5 ± 0.3 h for females, sleep efficiency was 86.4 ± 1.2% for males and 89.6 ± 1.2% for females, resting HR was 44.5 ± 1.2 beats·min−1 for males and 50.2 ± 2.0 beats·min−1 for females, and heart rate variability during sleep was 99.1 ± 4.2 ms for males and 114.3 ± 11.2 ms for females. For male cyclists, there was a main effect of ‘stage classification’ on recovery, such that heart rate variability during sleep was lowest after mountain stages. For female cyclists, there was a main effect of ‘stage classification’ on recovery, such that the percentage of light sleep (i.e., lower-quality sleep) was highest after mountain stages. Conclusions Some aspects of recovery were compromised after the most demanding days of racing, i.e., mountain stages. Overall however, the cyclists obtained a reasonable amount of good-quality sleep while competing in these physiologically demanding endurance events. This study demonstrates that it is now feasible to assess recovery in professional athletes during multiple-day endurance events using validated fitness trackers.

The cortisol awakening response (CAR) is a distinct rise in cortisol that occurs upon awakening that is thought to contribute to arousal, energy boosting, and anticipation. There is some evidence to suggest that inadequate sleep may alter the CAR, but the relationship between sleep duration and CAR has not been systematically examined. Healthy males (n = 111; age: 23.0 ± 3.6 yrs) spent 10 consecutive days/nights in a sleep laboratory. After a baseline night (9 h time in bed), participants spent either 5 h (n = 19), 6 h (n = 23), 7 h (n = 16), 8 h (n = 27), or 9 h (n = 26) in bed for seven nights, followed by a 9 h recovery sleep. The saliva samples for cortisol assay were collected at 08:00 h, 08:30 h and 08:45 h at baseline, on experimental days 2 and 5 and on the recovery day. The primary dependent variables were the cortisol concentration at awakening (08:00 h) and the cortisol area under the curve (AUC). There was no effect of time in bed on either the cortisol concentration at awakening or cortisol AUC. In all the time in bed conditions, the cortisol AUC tended to be higher at baseline and lower on experimental day 5. Five consecutive nights of mild to moderate sleep restriction does not appear to affect the CAR in healthy male 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

Sleep loss causes mood disturbance in non-clinical populations under severe conditions, i.e., two days/nights of sleep deprivation or a week of sleep restriction with 4–5 h in bed each night. However, the effects of more-common types of sleep loss on mood disturbance are not yet known. Therefore, the aim of this study was to examine mood disturbance in healthy adults over a week with nightly time in bed controlled at 5, 6, 7, 8 or 9 h. Participants (n = 115) spent nine nights in the laboratory and were given either 5, 6, 7, 8 or 9 h in bed over seven consecutive nights. Mood was assessed daily using the Profile of Mood States (POMS-2). Mixed-linear effects models examined the effect of time in bed on total mood disturbance and subscales of anger-hostility, confusion-bewilderment, depression-dejection, fatigue-inertia, tension-anxiety, vigour-activity and friendliness. There was no effect of time in bed on total mood disturbance (F(4, 110.42) = 1.31, p = 0.271) or any of the subscales except fatigue-inertia. Fatigue-inertia was higher in the 5 h compared with the 9 h time in bed condition (p = 0.012, d = 0.75). Consecutive nights of moderate sleep loss (i.e., 5–7 h) does not affect mood but does increase fatigue in healthy males.

The study’s aim was to examine the effect of chronotype on cognitive performance during a single night shift. Data were collected from 72 (36f) young, healthy adults in a laboratory study. Participants had a 9 h sleep period (03:00–12:00) followed by an 8 h night shift (23:00–07:00). During the night shift, participants completed five test sessions, which included measures of subjective sleepiness, subjective alertness, and sustained attention (i.e., psychomotor vigilance task; PVT). Dim light melatonin onset (DLMO) was derived from saliva samples taken during the evening preceding the night shift. A tertile split of DLMO was used to determine three chronotype categories: earlier (DLMO = 20:22 ± 0:42), intermediate (DLMO = 21:31 ± 0:13), and later (DLMO = 22:54 ± 0:54). There were (a) significant main effects of test session (all p < 0.001); (b) no main effects of chronotype; and (c) no interaction effects between chronotype and test session on sleepiness, alertness, PVT response time, and PVT lapses. The results indicate that under controlled sleeping conditions, chronotype based on dim light melatonin onset did not affect nighttime performance. Differences in performance during night shift between chronotypes reported by field studies may be related to differences in the amount and/or timing of sleep before or between night shifts, rather than circadian timing.

Chronotype reflects circadian timing and can be determined from biological markers (e.g., dim light melatonin onset; DLMO), or questionnaires (e.g., Morningness-Eveningness Questionnaire; MEQ, or Munich Chronotype Questionnaire; MCTQ). The study’s aim was to quantify concordance between chronotype categorisations based on these measures. A total of 72 (36f) young, healthy adults completed the MEQ and MCTQ and provided saliva samples hourly in dim light during the evening in a laboratory. The corrected midpoint of sleep on free days (MSFsc) was derived from MCTQ, and tertile splits were used to define early, intermediate and late DLMO-CT, MEQ-CT and MSFsc-CT chronotype categories. DLMO correlated with MEQ score (r = −0.25, p = 0.035) and MSFsc (r = 0.32, p = 0.015). For early, intermediate and late DLMO-CT categories, mean(SD) DLMO were 20:25(0:46), 21:33(0:10) and 23:03(0:53). For early, intermediate and late MEQ-CT categories, mean(SD) MEQ scores were 60.5(5.3), 51.4(2.9) and 40.8 (5.0). For early, intermediate and late MSFsc-CT categories, mean(SD) MSFsc were 03:23(0:34), 04:37(0:12) and 05:55(0:48). Low concordance of categorisations between DLMO-CT and MEQ-CT (37%), and between DLMO-CT and MSFsc-CT (37%), suggests chronotype categorisations depend on the measure used. To enable valid comparisons with previous results and reduce the likelihood of misleading conclusions, researchers should select measures and statistical techniques appropriate to the construct of interest and research question.

To investigate associations between acute workload and in-game performance in basketball. Eight semi-professional, male basketball players were monitored during all training sessions (N = 28) and games (N = 18) across the season. External workload was determined using absolute (arbitrary units[AU]) and relative (AU·min-1) PlayerLoadTM (PL), and absolute (count) and relative (count·min-1) low-intensity, medium intensity, high-intensity, and total Inertial Movement Analysis (IMA) events (accelerations, decelerations, changes of-direction, and jumps). Internal workload was determined using absolute and relative Summated-Heart-Rate Zones workload, session-rating of perceived exertion, rating of perceived exertion, and time (min) spent working > 90% of maximal heart rate. In-game performance was indicated by the player efficiency statistic. Repeated measures correlations were used to determine associations between acute workload variables (across the previous 7 days) and player efficiency. Relative PL (r = 0.13, small) and high-intensity IMA events (r = 0.13, small) possessed the strongest associations with player efficiency of the investigated workload variables (P > 0.05). All other associations were trivial in magnitude (P > 0.05). Given the trivial-small associations between all external and internal workload variables and player efficiency, basketball practitioners should not rely solely on monitoring acute workloads to determine performance potential in players.

The purpose of this study was to examine player monitoring approaches used by basketball practitioners with a specific focus on the use of microsensors. An online survey was disseminated to basketball practitioners via international basketball-related organisations and social media channels. Multiple response, Likert-scale level of agreement, and open-ended questions captured data regarding if, and how player monitoring was performed, as well as barriers and facilitators to player monitoring, with an emphasis on the use of microsensors. Forty-four basketball practitioners completed the survey. Twenty-seven respondents (61%) implement player monitoring and thirteen (30%) use microsensors. Despite implementing player monitoring, over 85% of practitioners modify training based on their own observation. Respondents not currently monitoring players (39%) would commence monitoring if the tools or equipment were provided. 74% of respondents agree that microsensors are expensive. Only 56% of practitioners who use microsensors feel they have support for using the technology and analysing/interpreting the data. These findings suggest a low uptake of microsensors for player monitoring in basketball. Coaches and practitioners perceive player monitoring approaches to be cost-prohibitive and appear unsure of how player monitoring data should be used to optimise training outcomes for players.

Heart rate (HR) and HR variability (HRV) can be used to infer readiness to perform exercise in athletic populations. Advancements in the photoplethysmography technology of wearable devices such as WHOOP allow for the frequent and convenient measurement of HR and HRV, and therefore enhanced application in athletes. However, it is important that the reliability of such technology is acceptable prior to its application in practical settings. Eleven elite male water polo players (age 28.8 ± 5.3 years [mean ± standard deviation]; height 190.3 ± 3.8 cm; body mass 95.0 ± 6.9 kg; international matches 117.9 ± 92.1) collected their HR and HRV daily via a WHOOP strap (WHOOP 3.0, CB Rank, Boston, MA, USA) over 16 weeks ahead of the 2021 Tokyo Olympic Games. The WHOOP strap quantified HR and HRV via wrist-based photoplethysmography during overnight sleep periods. The weekly (i.e., 7-day) coefficient of variation in lnRMSSD (lnRMSSDCV) and HR (HRCV) was calculated as a measure of day-to-day variability in lnRMSSD and HR, and presented as a mean of the entire recording period. The mean weekly lnRMSSDCV and HRCV over the 16-week period was 5.4 ± 0.7% (mean ± 95% confidence intervals) and 7.6 ± 1.3%, respectively. The day-to-day variability in WHOOP-derived lnRMSSD and HR is within or below the range of day-to-day variability in alternative lnRMSSD (~3–13%) and HR (~10–11%) assessment protocols, indicating that the assessment of HR and HRV by WHOOP does not introduce any more variability than that which is naturally present in these variables.

The aim of this laboratory-based study was to examine the effect of sleep restriction on glucose regulation during nighttime sleep. Healthy males were randomly assigned to one of two conditions: 9 h in bed (n = 23, age = 24.0 year) or 5 h in bed (n = 18, age = 21.9 year). Participants had a baseline night with 9 h in bed (23:00–08:00 h), then seven nights of 9 h (23:00–08:00 h) or 5 h (03:00–08:00 h) in bed. Participants were mostly seated during the daytime but had three bouts of treadmill walking (4 km·h−1 for 10 min) at ~14:40 h, ~17:40 h, and ~20:40 h each day. On the baseline night and night seven, glucose concentration in interstitial fluid was assessed by using continuous glucose monitors, and sleep was assessed by using polysomnography. On night seven, compared to the 9 h group, the 5 h group obtained less total sleep (292 min vs. 465 min) and less REM sleep (81 min vs. 118 min), but their slow-wave sleep did not differ (119 min vs. 120 min), and their glucose concentration during sleep did not differ (5.1 mmol·L−1 vs. 5.1 mmol·L−1). These data indicate that sleep restriction does not cause elevated levels of circulating glucose during nighttime sleep when slow-wave sleep is maintained. In the future, it will be important to determine whether increased insulin is required to maintain circulating glucose at a normal level when sleep is restricted.

The primary aim of this study was to examine the validity of six commonly used wearable devices, i.e., Apple Watch S6, Garmin Forerunner 245 Music, Polar Vantage V, Oura Ring Generation 2, WHOOP 3.0 and Somfit, for assessing sleep. The secondary aim was to examine the validity of the six devices for assessing heart rate and heart rate variability during, or just prior to, night-time sleep. Fifty-three adults (26 F, 27 M, aged 25.4 ± 5.9 years) spent a single night in a sleep laboratory with 9 h in bed (23:00–08:00 h). Participants were fitted with all six wearable devices—and with polysomnography and electrocardiography for gold-standard assessment of sleep and heart rate, respectively. Compared with polysomnography, agreement (and Cohen’s kappa) for two-state categorisation of sleep periods (as sleep or wake) was 88% (κ = 0.30) for Apple Watch; 89% (κ = 0.35) for Garmin; 87% (κ = 0.44) for Polar; 89% (κ = 0.51) for Oura; 86% (κ = 0.44) for WHOOP and 87% (κ = 0.48) for Somfit. Compared with polysomnography, agreement (and Cohen’s kappa) for multi-state categorisation of sleep periods (as a specific sleep stage or wake) was 53% (κ = 0.20) for Apple Watch; 50% (κ = 0.25) for Garmin; 51% (κ = 0.28) for Polar; 61% (κ = 0.43) for Oura; 60% (κ = 0.44) for WHOOP and 65% (κ = 0.52) for Somfit. Analyses regarding the two-state categorisation of sleep indicate that all six devices are valid for the field-based assessment of the timing and duration of sleep. However, analyses regarding the multi-state categorisation of sleep indicate that all six devices require improvement for the assessment of specific sleep stages. As the use of wearable devices that are valid for the assessment of sleep increases in the general community, so too does the potential to answer research questions that were previously impractical or impossible to address—in some way, we could consider that the whole world is becoming a sleep laboratory.

The authors wish to correct the following errors in the original paper [...]

Night shift workers experience circadian misalignment and sleep disruption, which impact hunger and food consumption. The study aim was to assess the impact of chronotype on hunger and snack consumption during a night shift with acute sleep deprivation. Seventy-two (36f, 36m) healthy adults participated in a laboratory study. A sleep opportunity (03:00–12:00) was followed by a wake period (12:00–23:00) and a simulated night shift (23:00–07:00). Subjective measures of hunger, prospective consumption, desire to eat fruit, and desire to eat fast food were collected before (12:20, 21:50) and after (07:20) the night shift. Snack opportunities were provided before (15:10, 19:40) and during (23:50, 03:30) the night shift. A tertile split of the dim light melatonin onset (DLMO) distribution defined early (20:24 ± 0:42 h), intermediate (21:31 ± 0:12 h), and late chronotype (22:56 ± 0:54 h) categories. There were no main effects of chronotype on any subjective measure (p = 0.172–0.975), or on snack consumption (p = 0.420), and no interactions between chronotype and time of day on any subjective measure (p = 0.325–0.927) or on snack consumption (p = 0.511). Differences in circadian timing between chronotype categories were not associated with corresponding differences in hunger, prospective consumption, desire to eat fruit, desire to eat fast food, or snack consumption at any measurement timepoint.

COVID-19, the disease caused by the SARS-CoV-2 virus, can cause shortness of breath, lung damage, and impaired respiratory function. Containing the virus has proven difficult, in large part due to its high transmissibility during the pre-symptomatic incubation. The study's aim was to determine if changes in respiratory rate could serve as a leading indicator of SARS-CoV-2 infections. A total of 271 individuals (age = 37.3 ± 9.5, 190 male, 81 female) who experienced symptoms consistent with COVID-19 were included- 81 tested positive for SARS-CoV-2 and 190 tested negative; these 271 individuals collectively contributed 2672 samples (days) of data (1856 healthy days, 231 while infected with COVID-19 and 585 while negative for COVID-19 but experiencing symptoms). To train a novel algorithm, individuals were segmented as follows; (1) a training dataset of individuals who tested positive for COVID-19 (n = 57 people, 537 samples); (2) a validation dataset of individuals who tested positive for COVID-19 (n = 24 people, 320 samples); (3) a validation dataset of individuals who tested negative for COVID-19 (n = 190 people, 1815 samples). All data was extracted from the WHOOP system, which uses data from a wrist-worn strap to produce validated estimates of respiratory rate and other physiological measures. Using the training dataset, a model was developed to estimate the probability of SARS-CoV-2 infection based on changes in respiratory rate during night-time sleep. The model's ability to identify COVID-positive individuals not used in training and robustness against COVID-negative individuals with similar symptoms were examined for a critical six-day period spanning the onset of symptoms. The model identified 20% of COVID-19 positive individuals in the validation dataset in the two days prior to symptom onset, and 80% of COVID-19 positive cases by the third day of symptoms.

Objectives: To investigate the acute benefits of breaking up prolonged sitting with light-intensity physical activity on (i) glucose metabolism under conditions of sleep restriction, and (ii) cognitive deficits associated with sleep restriction. Methods: This counterbalanced, crossover trial consisted of two five-day (5 night) experimental conditions separated by a two-week washout period. On the first night, participants were given a 9-h sleep opportunity to allow the collection of steady-state baseline measures the following day. This was followed by three consecutive nights of sleep restriction (5-h sleep opportunity). In the sitting condition (SIT), participants remained seated between 1000 and 1800 h. In the physical activity condition (ACT), participants completed 3-min bouts of light-intensity walking every 30 min on a motorised treadmill between 1000 and 1800 h. At all other times, in both conditions, participants remained seated, except when walking to the dining room or to use the bathroom (max distance = 32 m). Six physically inactive, healthy males were randomised to one of two trial orders, 1) SIT then ACT, or 2) ACT then SIT. Continuous measures of interstitial glucose were measured at 5-min intervals. A cognitive and subjective test battery was administered every two hours during wake periods. Analyses were conducted using a series of linear mixed-effect ANOVAs. Results: No differences in interstitial glucose concentration or cognitive performance were observed between the SIT condition and the ACT condition. Participants reported higher levels of sleepiness, and felt less alert in the SIT condition compared with the ACT condition. Conclusions: There were no observable benefits of breaking up prolonged sitting on glucose metabolism under conditions of sleep restriction. These findings have implications for behaviour change interventions. Future studies will need to include larger, less homogenous study populations and appropriate control conditions (i.e., 8–9 h sleep opportunities). Keywords: Physical activity, Exercise, Sitting breaks, Sedentary behaviour, Fatigue, Sleepiness

This study examined the effectiveness of a circadian adaptation schedule in male cricketers after an 8.5 h eastward time zone change. Ten participants (aged 18.7 ± 0.9 y) were randomly assigned to a control group or an intervention group. Participants in the intervention group followed a light exposure schedule in which they were instructed to seek light in the three hours preceding, and avoid light in the three hours following their estimated core body temperature minimum. The rate of adaptation was assessed using the nightly excretion rate of urinary 6-sulphatoxymelatonin (aMT6s). General linear mixed models were conducted to assess the effect of condition (i.e., control and light intervention) on nocturnal secretion of aMT6s. Significant main effects of day (F(7, 35) = 10.4, p < 0.001) were reflected by an increase in nocturnal melatonin excretion (i.e., all participants gradually adapted to the destination time zone). Subjective jet lag decreased by day (F(7, 54) = 22.9, p < 0.001), bedtime was delayed by day (F(7, 54) = 3.1, p = 0.007) and get up time was earlier by day (F(7, 35) = 5.4, p < 0.001). On average, it took 7 days for all participants to return to baseline levels following transmeridian travel. Similarly, it took 7 days for subjective jet lag to alleviate. In the initial 4 days of the protocol, the intervention group registered higher levels of nocturnal urinary melatonin, however, there was no significant differences in the rate of adaptation between the groups. It is possible that participants did not adhere to the intervention or that they followed the intervention but it was ineffective.

The aims of this study were to: (1) compare actigraphy (ACTICAL) and a commercially available sleep wearable (i.e., WHOOP) under two functionalities (i.e., sleep auto-detection (WHOOP-AUTO) and manual adjustment of sleep (WHOOP-MANUAL)) for two-stage categorisation of sleep (sleep or wake) against polysomnography, and; (2) compare WHOOP-AUTO and WHOOP-MANUAL for four-stage categorisation of sleep (wake, light sleep, slow wave sleep (SWS), or rapid eye movement sleep (REM)) against polysomnography. Six healthy adults (male: n = 3; female: n = 3; age: 23.0 ± 2.2 yr) participated in the nine-night protocol. Fifty-four sleeps assessed by ACTICAL, WHOOP-AUTO and WHOOP-MANUAL were compared to polysomnography using difference testing, Bland–Altman comparisons, and 30-s epoch-by-epoch comparisons. Compared to polysomnography, ACTICAL overestimated total sleep time (37.6 min) and underestimated wake (−37.6 min); WHOOP-AUTO underestimated SWS (−15.5 min); and WHOOP-MANUAL underestimated wake (−16.7 min). For ACTICAL, sensitivity for sleep, specificity for wake and overall agreement were 98%, 60% and 89%, respectively. For WHOOP-AUTO, sensitivity for sleep, wake, and agreement for two-stage and four-stage categorisation of sleep were 90%, 60%, 86% and 63%, respectively. For WHOOP-MANUAL, sensitivity for sleep, wake, and agreement for two-stage and four-stage categorisation of sleep were 97%, 45%, 90% and 62%, respectively. WHOOP-AUTO and WHOOP-MANUAL have a similar sensitivity and specificity to actigraphy for two-stage categorisation of sleep and can be used as a practical alternative to polysomnography for two-stage categorisation of sleep and four-stage categorisation of sleep.

Heart rate (HR) and HR variability (HRV) infer readiness to perform exercise in athletic populations. Technological advancements have facilitated HR and HRV quantification via photoplethysmography (PPG). This study evaluated the validity of WHOOP’s PPG-derived HR and HRV against electrocardiogram-derived (ECG) measures. HR and HRV were assessed via WHOOP 2.0 and ECG over 15 opportunities during October–December 2018. WHOOP-derived pulse-to-pulse (PP) intervals were edited with WHOOP’s proprietary filter, in addition to various filter strengths via Kubios HRV software. HR and HRV (Ln RMSSD) were quantified for each filter strength. Agreement was assessed via bias and limits of agreement (LOA), and contextualised using smallest worthwhile change (SWC) and coefficient of variation (CV). Regardless of filter strength, bias (≤0.39 ± 0.38%) and LOA (≤1.56%) in HR were lower than the CV (10–11%) and SWC (5–5.5%) for this parameter. For Ln RMSSD, bias (1.66 ± 1.80%) and LOA (±5.93%) were lowest for a 200 ms filter and WHOOP’s proprietary filter, which approached or exceeded the CV (3–13%) and SWC (1.5–6.5%) for this parameter. Acceptable agreement was found between WHOOP- and ECG-derived HR. Bias and LOA in Ln RMSSD approached or exceeded the SWC/CV for this variable and should be interpreted against its own level of bias precision.

Background: Disturbed sleep may negatively influence physical health, cognitive performance, metabolism, and general wellbeing. Nutritional interventions represent a potential non-pharmacological means to increase sleep quality and quantity. Objective: (1) Identify an optimal suite of nutritional ingredients and (2) validate the effects of this suite utilising polysomnography, and cognitive and balance tests. Methods: The optimal and least optimal combinations of six ingredients were identified utilising 55 male participants and a Box–Behnken predictive model. To validate the model, 18 healthy, male, normal sleepers underwent three trials in a randomised, counterbalanced design: (1) optimal drink, (2) least optimal drink, or (3) placebo were provided before bed in a double-blinded manner. Polysomnography was utilised to measure sleep architecture. Cognitive performance, postural sway, and subjective sleep quality, were assessed 30 min after waking. Results: The optimal drink resulted in a significantly shorter sleep onset latency (9.9 ± 12.3 min) when compared to both the least optimal drink (26.1 ± 37.4 min) and the placebo drink (19.6 ± 32.0 min). No other measures of sleep, cognitive performance, postural sway, and subjective sleep quality were different between trials. Conclusion: A combination of ingredients, optimised to enhance sleep, significantly reduced sleep onset latency. No detrimental effects on sleep architecture, subjective sleep quality or next day performance were observed.

Cardiometabolic disease poses a serious health and economic burden worldwide and its prevalence is predicted to increase. Prolonged sitting, lack of physical activity, poor diet, and short sleep duration are ubiquitous behaviors in modern society, and all are independent risk factors in the development of cardiometabolic disease. Existing evidence demonstrates that breaking up prolonged periods of sitting is beneficial for cardiometabolic health, however, studies have not controlled for prior sleep duration. This article examines how prolonged sitting and short sleep duration independently contribute to cardiometabolic risk, and how breaking up sitting and obtaining adequate sleep may reduce this risk. We suggest that as prolonged sitting and short sleep duration influence the same cardiometabolic parameters, there is potential for short sleep to attenuate the positive impact of breaking up prolonged sitting with physical activity. Likewise, breaking up prolonged sitting and obtaining adequate sleep together could improve predictors of cardiometabolic disease, i.e., the combined effect may be stronger than either alone. To explore these perspectives, we propose a research agenda to investigate the relationship between breaking up prolonged sitting with physical activity and short sleep duration. This will provide an evidence-base for informing the design of interventions to reduce the burden of cardiometabolic disease on communities worldwide.

BACKGROUND: The independent and combined influence of smoking, alcohol consumption, physical activity, diet, sitting time, and sleep duration and quality on health status is not routinely examined. This study investigates the relationships between these lifestyle behaviors, independently and in combination, and health-related quality of life (HRQOL). METHODS: Adult members of the 10,000 Steps project (n = 159,699) were invited to participate in an online survey in November-December 2011. Participant socio-demographics, lifestyle behaviors, and HRQOL (poor self-rated health; frequent unhealthy days) were assessed by self-report. The combined influence of poor lifestyle behaviors were examined, independently and also as part of two lifestyle behavior indices, one excluding sleep quality (Index 1) and one including sleep quality (Index 2). Adjusted Cox proportional hazard models were used to examine relationships between lifestyle behaviors and HRQOL. RESULTS: A total of 10,478 participants provided complete data for the current study. For Index 1, the Prevalence Ratio (p value) of poor self-rated health was 1.54 (p = 0.001), 2.07 (p≤0.001), 3.00 (p≤0.001), 3.61 (p≤0.001) and 3.89 (p≤0.001) for people reporting two, three, four, five and six poor lifestyle behaviors, compared to people with 0-1 poor lifestyle behaviors. For Index 2, the Prevalence Ratio (p value) of poor self-rated health was 2.26 (p = 0.007), 3.29 (p≤0.001), 4.68 (p≤0.001), 6.48 (p≤0.001), 7.91 (p≤0.001) and 8.55 (p≤0.001) for people reporting two, three, four, five, six and seven poor lifestyle behaviors, compared to people with 0-1 poor lifestyle behaviors. Associations between the combined lifestyle behavior index and frequent unhealthy days were statistically significant and similar to those observed for poor self-rated health. CONCLUSIONS: Engaging in a greater number of poor lifestyle behaviors was associated with a higher prevalence of poor HRQOL. This association was exacerbated when sleep quality was included in the index.

Shift work has been associated with dietary changes. This study examined factors associated with the dietary profiles of shift workers from several industries (n = 118, 57 male; age = 43.4 ± 9.9 years) employed on permanent mornings, nights, or rotating 8-h or 12-h shifts. The dietary profile was assessed using a Food Frequency Questionnaire. Shift-related (e.g., sleep duration and fatigue), work-related (e.g., industry), and demographic factors (e.g., BMI) were measured using a modified version of the Standard Shift work Index. Mean daily energy intake was 8628 ± 3161 kJ. As a percentage of daily energy intake, all workers reported lower than recommended levels of carbohydrate (CHO, 45%–65%). Protein was within recommended levels (15%–25%). Permanent night workers were the only group to report higher than recommended fat intake (20%–35%). However, all workers reported higher than recommended levels of saturated fat (>10%) with those on permanent nights reporting significantly higher levels than other groups (Mean = 15.5% ± 3.1%, p < 0.05). Shorter sleep durations and decreased fatigue were associated with higher CHO intake (p ≤ 0.05) whereas increased fatigue and longer sleep durations were associated with higher intake of fat (p ≤ 0.05). Findings demonstrate sleep duration, fatigue, and shift schedule are associated with the dietary profile of shift workers.