ORCID Profile
0000-0003-3869-4346
Current Organisation
Central Queensland University
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Biological psychology not elsewhere classified | Occupational and workplace health and safety | Other psychology not elsewhere classified | Human resources and industrial relations
Publisher: Cold Spring Harbor Laboratory
Date: 06-07-2023
DOI: 10.1101/2023.07.06.23292280
Abstract: 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 109 th edition of the Tour de France (2022) and the 1 st edition of the Tour de France Femmes (2022), respectively. The 17 participating cyclists were 8 males from a single team (aged 28.0 ± 2.5 years [mean ± 95% confidence interval]) and 9 females from two separate teams (aged 26.7 ± 3.1 years). Cyclists wore a fitness tracker (WHOOP 4.0) to capture recovery metrics primarily related to night-time sleep and autonomic activity. Data were collected 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 type’ – i.e., rest, flat, hilly, mountain or time trial for males and flat, hilly or mountain for females – on the various recovery metrics. During baseline, total sleep time at night was 7.2 ± 0.3 h for male cyclists and 7.7 ± 0.3 h for female cyclists, sleep efficiency (i.e., total sleep time as a percentage of time in bed) was 87.0 ± 4.4 % for males and 88.8 ± 2.6 % for females, resting heart rate 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 108.7 ± 17.0 ms for males and 119.8 ± 26.4 ms for females. During their respective events, total sleep time at night 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 heart rate 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 type’ on recovery, such that heart rate variability during sleep was lowest after mountain stages. For female cyclists, there was also a main effect of ‘stage type’ on recovery, such that the percentage of light sleep in a sleep period (i.e., lower-quality sleep) was highest after mountain stages. Some aspects of recovery were compromised in cyclists 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 highly demanding endurance events. This study demonstrates that it is now feasible to assess recovery metrics in professional athletes during multiple-day endurance events using validated fitness trackers.
Publisher: MDPI AG
Date: 11-07-2022
Abstract: The aim of this study was to examine sleep/wake behaviour and sleep strategies before, during and after ultra-marathon running events exceeding 100 miles (161 km). A total of 119 athletes completed a web-based questionnaire regarding their habitual sleep/wake behaviour before, during, and after ultra-marathon participation. Event-specific data were grouped by race distance categories 100–149 miles (161–240 km), 150–199 miles (241–321 km), and ≥200 miles (322 km). Athletes commonly reported not sleeping throughout the duration of their races (74%). However, for events that were ≥200 miles, athletes reported more sleep opportunities, longer sleep duration, and more total sleep when compared to events that were 100–149 miles in distance (p ≤ 0.001). This suggests that for races of shorter distances, the benefit of continuous racing outweighs the negative impact of continuous wakefulness/sleep deprivation. However, for longer races (≥200 miles), there is an apparent tradeoff between sleep deprivation and race strategy, whereby athletes cannot sustain a desired level of performance without obtaining sleep. This is consistent with established sleep/wake behaviour models suggesting that sleep need increases as wakefulness increases, or in this case, as race duration increases. For athletes participating in ultra-marathons, sleep management education and/or consultation with a sleep scientist prior to racing may be beneficial. Future research should examine the optimal strategies concerning the frequency and duration of sleep during ultra-marathons and the subsequent impact on performance.
Publisher: MDPI AG
Date: 22-08-2022
DOI: 10.3390/S22166317
Abstract: 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.
Publisher: Informa UK Limited
Date: 28-06-2022
DOI: 10.1080/07420528.2022.2093213
Abstract: Chronotype can be defined as an overt expression of circadian rhythmicity in an in idual that dictates tendencies towards being a morning or evening person - also referred to as 'morningness' or 'eveningness.' Chronotypes generally impact preferred bed and wake times, in addition to a range of personal and social factors. This study examined how matching/mismatching chronotypes within relationships impact sexual satisfaction and sleep quality. A s le of 32 couples (52% females, 38.3 ± 11.7 years) each completed an online survey that assessed chronotype (reduced Morningness Eveningness Questionnaire), sleep (Pittsburgh Sleep Quality Index), and sexual satisfaction (Index of Sexual Satisfaction). Partner surveys were matched to identify whether chronotypes were matching or mismatching. Couples with matched chronotypes reported greater sexual satisfaction than those with mismatched chronotypes, F(1, 58) = 19.57, p < .001. Matched couples also reported better sleep quality than couples whose chronotypes were mismatched, F(1,62) = 48.02, p < .001. The in idual chronotype did not seem to impact on sleep quality or sexual satisfaction. To improve sleep quality and sexual satisfaction, strategies (e.g., circadian phase advance or delay) could be used to increase circadian alignment between members of a couple.
Publisher: Informa UK Limited
Date: 03-06-2018
DOI: 10.1080/07420528.2018.1466795
Abstract: This study examined the efficacy of daytime napping to supplement night-time sleep in athletes. Twelve well-trained male soccer players completed three conditions in a randomised, counterbalanced order: 9 h in bed overnight with no daytime nap (9 h + 0 h) 8 h in bed overnight with a 1-h daytime nap (8 h + 1 h) and 7 h in bed overnight with a 2-h daytime nap (7 h + 2 h). Sleep was assessed using polysomnography. The total amount of sleep obtained in the three conditions was similar, i.e. 8.1 h (9 h + 0 h), 8.2 h (8 h + 1 h), and 8.0 h (7 h + 2 h). Daytime napping may be an effective strategy to supplement athletes' night-time sleep.
Publisher: Public Library of Science (PLoS)
Date: 10-12-2020
DOI: 10.1371/JOURNAL.PONE.0243693
Abstract: 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 in iduals (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 in iduals collectively contributed 2672 s les (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, in iduals were segmented as follows (1) a training dataset of in iduals who tested positive for COVID-19 (n = 57 people, 537 s les) (2) a validation dataset of in iduals who tested positive for COVID-19 (n = 24 people, 320 s les) (3) a validation dataset of in iduals who tested negative for COVID-19 (n = 190 people, 1815 s les). 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 in iduals not used in training and robustness against COVID-negative in iduals with similar symptoms were examined for a critical six-day period spanning the onset of symptoms. The model identified 20% of COVID-19 positive in iduals 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.
Publisher: Informa UK Limited
Date: 09-05-2020
DOI: 10.1080/17461391.2019.1611934
Abstract: The aim of this study was to examine the effect of single bouts of moderate-intensity aerobic exercise and moderate-intensity resistance exercise performed in the evening on the sleep of healthy young males. The study employed a repeated-measures, counterbalanced, crossover design with three conditions (control, evening aerobic exercise, evening resistance exercise). Twelve male participants (mean ± SD age: 21.9 ± 2.7 yr) attended the laboratory on three occasions separated by one day between each visit. Between 20:45 h and 21:30 h, participants completed either no exercise, 30 min of aerobic exercise at 75%HRmax, or 30 min of resistance exercise corresponding to 75% of 10-repetition maximum. A 9-h sleep opportunity was provided between 23:00 h and 08:00 h. Core body temperature was measured using ingestible temperature capsules and sleep was measured using polysomnography. Core body temperature was higher during the aerobic exercise and resistance exercise compared to control (
Publisher: Informa UK Limited
Date: 29-05-2018
DOI: 10.1080/07420528.2018.1466787
Abstract: This study examined the difference between athletes' self-reported and objective sleep durations during two nap opportunities. Twelve well-trained male soccer players' sleep durations were assessed using polysomnography and a self-report question during a 60- and 120-min nap opportunity. Participants underestimated sleep compared to objective sleep assessments for both the 60-min nap opportunity (p = 0.004) and 120-min nap opportunity (p = 0.001). Soccer players underestimated their sleep duration by approximately 10 min per hour of nap opportunity. It is yet to be determined if athletes are likely to underestimate sleep duration during their main nighttime sleep period.
Publisher: Cold Spring Harbor Laboratory
Date: 09-06-2020
DOI: 10.1101/2020.06.07.20124685
Abstract: The COVID-19 pandemic incited unprecedented restrictions on the behavior of society. The aims of this study were to quantify changes to sleep/wake behavior and exercise behavior, as well as changes in physiological markers of health during COVID-19 physical distancing. A retrospective analysis of 5,436 US-based subscribers to the WHOOP platform (mean age = 40.25 ± 11.33 1,536 females, 3,900 males) was conducted covering the period from January 1st, 2020 through May 15th, 2020. This time period was separated into a 68-day baseline period and a 67-day physical distancing period. To provide context and allow for potential confounders (e.g., change of season), data were also extracted from the corresponding time periods in 2019. As compared to baseline, during physical distancing, all subjects fell asleep earlier (−0.25 hours), woke up later (0.48 hours), obtained more sleep (+0.35 hours) and reduced social jet lag (−0.21 hours). Contrasting sleep behavior was seen in 2019, with subjects falling asleep and waking up at a similar time (−0.01 hours -0.05 hours), obtaining less sleep (−0.14 hours) and maintaining social jet lag (0.01 hours) in corresponding periods. In iduals exercised more intensely during physical distancing by increasing the time spent in high heart rate zones. In 2020, resting heart rate decreased (−0.9 beats per minute) and heart rate variability increased (+0.98 milliseconds) during physical distancing when compared to baseline. However, similar changes were seen in 2019, suggesting the variation may not be related to the introduction of physical distancing mandates. The findings suggest that changes in societal commitments (e.g., daily commute working from home) during physical distancing may have resulted in changes to health-related behavior (i.e., increased exercise intensity and longer sleep duration). As the COVID-19 pandemic eases, maintenance of certain aspects of physical distancing (e.g., working from home) may allow for positive changes to sleep/wake and exercise behaviors.
Publisher: Public Library of Science (PLoS)
Date: 12-08-2021
DOI: 10.1371/JOURNAL.PONE.0256063
Abstract: The COVID-19 pandemic incited unprecedented restrictions on the behavior of society. The aims of this study were to quantify changes to sleep/wake behavior and exercise behavior, as well as changes in physiological markers of health during COVID-19 physical distancing. A retrospective analysis of 5,436 US-based subscribers to the WHOOP platform (mean age = 40.25 ± 11.33 1,536 females, 3,900 males) was conducted covering the period from January 1st, 2020 through May 15th, 2020. This time period was separated into a 68-day baseline period and a 67-day physical distancing period. To provide context and allow for potential confounders (e.g., change of season), data were also extracted from the corresponding time periods in 2019. As compared to baseline, during physical distancing, all subjects fell asleep earlier (-0.15 hours), woke up later (0.29 hours), obtained more sleep (+0.21 hours) and reduced social jet lag (-0.13 hours). Contrasting sleep behavior was seen in 2019, with subjects falling asleep and waking up at a similar time (-0.01 hours -0.03 hours), obtaining less sleep (-0.14 hours) and maintaining social jet lag (+0.06 hours) in corresponding periods. In iduals exercised more intensely during physical distancing by increasing the time spent in high heart rate zones. In 2020, resting heart rate decreased (-0.90 beats per minute) and heart rate variability increased (+0.98 milliseconds) during physical distancing when compared to baseline. However, similar changes were seen in 2019 for RHR (-0.51 beats per minute) and HRV (+2.97 milliseconds), suggesting the variation may not be related to the introduction of physical distancing mandates. The findings suggest that in iduals improved health related behavior (i.e., increased exercise intensity and longer sleep duration) during physical distancing restrictions. While positive changes were seen to cardiovascular indicators of health, it is unclear whether these changes were a direct consequence of behavior change.
Publisher: MDPI AG
Date: 18-04-2022
DOI: 10.3390/S22083090
Abstract: The authors wish to correct the following errors in the original paper [...]
Publisher: Wiley
Date: 17-10-2019
DOI: 10.1111/JSR.12903
Abstract: Sleep inertia is the transitional state marked by impaired cognitive performance and reduced vigilance upon waking. Exercising before bed may increase the amount of slow-wave sleep within the sleep period, which has previously been associated with increased sleep inertia. Healthy males (n = 12) spent 3 nights in a sleep laboratory (1-night washout period between each night) and completed one of the three conditions on each visit - no exercise, aerobic exercise (30 min cycling at 75% heart rate), and resistance exercise (six resistance exercises, three sets of 10 repetitions). The exercise conditions were completed 90 min prior to bed. Sleep was measured using polysomnography. Upon waking, participants completed five test batteries every 15 min, including the Karolinska Sleepiness Scale, a Psychomotor Vigilance Task, and the Spatial Configuration Task. Two separate linear mixed-effects models were used to assess: (a) the impact of condition and (b) the amount of slow-wave sleep, on sleep inertia. There were no significant differences in sleep inertia between conditions, likely as a result of the similar sleep amount, sleep structure and time of awakening between conditions. The amount of slow-wave sleep impacted fastest 10% reciprocal reaction time on the Psychomotor Vigilance Task only, whereby more slow-wave sleep improved performance however, the magnitude of this relationship was small. Results from this study suggest that exercise performed 90 min before bed does not negatively impact on sleep inertia. Future studies should investigate the impact of exercise intensity, duration and timing on sleep and subsequent sleep inertia.
Publisher: MDPI AG
Date: 20-05-2021
DOI: 10.3390/S21103571
Abstract: 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.
Publisher: MDPI AG
Date: 04-03-2022
Abstract: The aim of this study was to examine the sleep–wake behaviour of 200-mile ultra-marathon runners before, during, and after a competition. A longitudinal, observational study was conducted to collect the sleep data of four (two females mean age: 45.5 ± 3.1 years) runners competing in a 200-mile ultra-marathon (N = 4). Wrist-worn activity monitors, in conjunction with self-report sleep diaries, were used to measure sleep, beginning seven days prior to the race and concluding seven days following the race (2–19 June 2021). Descriptive analysis of runners’ subjective and objective sleep data was conducted. All runners completed the 200-mile event in an average of 82.5 ± 7.1 h. On average, runners obtained 4.7 ± 3.0 h of sleep from 4.8 ± 2.4 sleep episodes, averaging 59.9 ± 49.2 min of sleep per episode. Runners averaged 6.0 ± 1.3 h of sleep per night in the week before the competition and 6.3 ± 1.3 h per night in the week following the competition. Runners in the 200-mile (326 km) ultra-marathon drastically restricted their sleep. However, obtained sleep, the number of sleep episodes, and sleep episode length were greater than those previously reported with 100-mile (161 km) runners. In-race sleep data suggest an increased need for sleep as race duration increases. Interestingly, runners obtained less than the recommended ~8 h of sleep per night, in both pre-race and post-race phases of the competition.
Publisher: Informa UK Limited
Date: 26-07-2020
Publisher: MDPI AG
Date: 25-10-2021
DOI: 10.3390/APP11219962
Abstract: 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.
Publisher: Informa UK Limited
Date: 11-05-2018
DOI: 10.1080/07420528.2018.1466800
Abstract: The validity of a commercially available wearable device for measuring total sleep time was examined in a s le of well-trained young athletes during night-time sleep periods and daytime naps. Participants wore a FitBit HR Charge on their non-dominant wrist and had electrodes attached to their face and scalp to enable polysomnographic recordings of sleep in the laboratory. The FitBit automatically detected 24/30 night-time sleep periods but only 6/20 daytime naps. Compared with polysomnography, the FitBit overestimated total sleep time by an average of 52 ± 152 min for night-time sleep periods, and by 4 ± 8 min for daytime naps. It is important for athletes and practitioners to be aware of the limitations of wearable devices that automatically detect sleep duration.
Publisher: Informa UK Limited
Date: 26-02-2022
DOI: 10.1080/24733938.2021.1890812
Abstract: The aims of the present study were to (1) quantify sleep behaviours of soccer referees and (2) compare sleep behaviours between nights before training, before matches, and after matches. Fourteen professional soccer referees from the A-League (mean±SD age 34 ± 4 years sex: 11 males, 3 females) participated in this observational study. Referees' sleep behaviours were examined using sleep diaries and wrist activity monitors for 31 consecutive nights during the 2018-2019 A-League season. Separate linear mixed models were conducted to assess differences in sleep behaviours between nights before training, before matches, and after matches. On average, referees did not obtain recommended sleep durations across the in-season (mean±SD sleep duration: 6.4 h ± 0.7 h). Referees went to bed later, spent less time in bed, and slept significantly less post-matches compared to pre-training and pre-match nights ( Referees were particularly susceptible to inadequate sleep on nights following training and matches. The findings related to poor sleep behaviours highlight the importance of implementing monitoring systems to understand the sleep behaviours of referees, with further research encouraged to ascertain the efficacy of various sleep hygiene practices to optimise sleep in this population.
Publisher: MDPI AG
Date: 06-09-2022
DOI: 10.3390/S22186723
Abstract: 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.
Start Date: 2023
End Date: 12-2025
Amount: $347,427.00
Funder: Australian Research Council
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