Losing Sleep Over It: Sleep in Basketball Players Affected by Game But Not Training Workloads

2020 ◽  
Vol 15 (8) ◽  
pp. 1117-1124
Author(s):  
Jordan L. Fox ◽  
Aaron T. Scanlan ◽  
Robert Stanton ◽  
Cody J. O’Grady ◽  
Charli Sargent
Keyword(s):  
Heart Rate ◽  
Sleep Quality ◽  
Sleep Duration ◽  
Sleep Onset ◽  
Onset Time ◽  
Rating Of Perceived Exertion ◽  
Self Report ◽  
Basketball Players ◽  
Time In Bed ◽  
The Impact

Purpose: To examine the impact of workload volume during training sessions and games on subsequent sleep duration and sleep quality in basketball players. Methods: Seven semiprofessional male basketball players were monitored across preseason and in-season phases to determine training session and game workloads, sleep duration, and sleep quality. Training and game data were collected via accelerometers, heart-rate monitors, and rating of perceived exertion (RPE) and reported as PlayerLoad™ (PL), summated heart-rate zones, and session RPE (sRPE). Sleep duration and sleep quality were measured using wrist-worn activity monitors in conjunction with self-report sleep diaries. For daily training sessions and games, all workload data were independently sorted into tertiles representing low, medium, and high workload volumes. Sleep measures following low, medium, and high workloads and control nights (no training/games) were compared using linear mixed models. Results: Sleep onset time was significantly later following medium and high PL and sRPE game workloads compared with control nights (P < .05). Sleep onset time was significantly later following low, medium, and high summated heart-rate-zones game workloads, compared with control nights (P < .05). Time in bed and sleep duration were significantly shorter following high PL and sRPE game workloads compared with control nights (P < .05). Following low, medium, and high training workloads, sleep duration and quality were similar to control nights (P > .05). Conclusions: Following high PL and sRPE game workloads, basketball practitioners should consider strategies that facilitate longer time in bed, such as napping and/or adjusting travel or training schedules the following day.

Consecutive Days of Racing Does Not Affect Sleep in Professional Road Cyclists

2021 ◽  
pp. 1-4
Author(s):  
Charli Sargent ◽  
Shona L. Halson ◽  
David T. Martin ◽  
Gregory D. Roach
Keyword(s):  
Sleep Duration ◽  
Sleep Onset ◽  
Self Report ◽  
Activity Monitors ◽  
Sleep Diaries ◽  
Time In Bed ◽  
Road Cycling ◽  
Offset Time ◽  
The Impact

Purpose: Professional road cycling races are physiologically demanding, involving successive days of racing over 1 to 3 weeks of competition. Anecdotal evidence indicates that cyclists’ sleep duration either increases or deteriorates during these competitions. However, sleep duration in professional cyclists during stage races has not been assessed. This study examined the amount/quality of sleep obtained by 14 professional cyclists competing in the Australian Tour Down Under. Methods: Sleep was assessed using wrist activity monitors and self-report sleep diaries on the night prior to start of the race and on each night during the race. The impact of each day of the race on sleep onset, sleep offset, time in bed, sleep duration, and wake duration was assessed using separate linear mixed effects models. Results: During the race, cyclists obtained an average of 6.8 (0.9) hours of sleep between 23:30 and 07:27 hours and spent 13.9% (4.7%) of time in bed awake. Minor differences in sleep onset (P = .023) and offset times (P ≤.001) were observed during the week of racing, but these did not affect the amount of sleep obtained by cyclists. Interestingly, the 3 best finishers in the general classification obtained more sleep than the 3 worst finishers (7.2 [0.3] vs 6.7 [0.3] h; P = .049). Conclusions: Contrary to anecdotal reports, the amount of sleep obtained by cyclists did not change over the course of the 1-week race and was just below the recommended target of 7 to 9 hours for adults.

The Impact of Training Load on Sleep During a 14-Day Training Camp in Elite, Adolescent, Female Basketball Players

2020 ◽  
Vol 15 (5) ◽  
pp. 724-730 ◽  
Author(s):  
Michele Lastella ◽  
Gregory D. Roach ◽  
Grace E. Vincent ◽  
Aaron T. Scanlan ◽  
Shona L. Halson ◽  
...  
Keyword(s):  
Sleep Onset ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Adolescent Female ◽  
Moderate Increase ◽  
Basketball Players ◽  
Training Camp ◽  
Time In Bed ◽  
Daily Training ◽  
The Impact

Purpose: To quantify the sleep/wake behaviors of adolescent, female basketball players and to examine the impact of daily training load on sleep/wake behaviors during a 14-day training camp. Methods: Elite, adolescent, female basketball players (N = 11) had their sleep/wake behaviors monitored using self-report sleep diaries and wrist-worn activity monitors during a 14-day training camp. Each day, players completed 1 to 5 training sessions (session duration: 114 [54] min). Training load was determined using the session rating of perceived exertion model in arbitrary units. Daily training loads were summated across sessions on each day and split into tertiles corresponding to low, moderate, and high training load categories, with rest days included as a separate category. Separate linear mixed models and effect size analyses were conducted to assess differences in sleep/wake behaviors among daily training load categories. Results: Sleep onset and offset times were delayed (P < .05) on rest days compared with training days. Time in bed and total sleep time were longer (P < .05) on rest days compared with training days. Players did not obtain the recommended 8 to 10 hours of sleep per night on training days. A moderate increase in sleep efficiency was evident during days with high training loads compared with low. Conclusions: Elite, adolescent, female basketball players did not consistently meet the sleep duration recommendations of 8 to 10 hours per night during a 14-day training camp. Rest days delayed sleep onset and offset times, resulting in longer sleep durations compared with training days. Sleep/wake behaviors were not impacted by variations in the training load administered to players.

Effect of exercise on sleep and bi-directional associations with accelerometer-assessed physical activity in men with obesity

2020 ◽  
Author(s):  
Tatiana Plekhanova ◽  
Alex V. Rowlands ◽  
Melanie Davies ◽  
Charlotte L. Edwardson ◽  
Andrew Hall ◽  
...  
Keyword(s):  
Physical Activity ◽  
Sleep Quality ◽  
Aerobic Exercise ◽  
Sleep Duration ◽  
Exercise Intervention ◽  
Vigorous Physical Activity ◽  
Sleep Onset ◽  
Onset Time ◽  
Post Intervention

This study examined the effect of exercise training on sleep duration and quality and bidirectional day-to-day relationships between physical activity (PA) and sleep. Fourteen inactive men with obesity (49.2±7.9 years, BMI 34.9±2.8 kg/m²) completed a baseline visit, eight-week aerobic exercise intervention, and one-month post-intervention follow-up. PA and sleep were assessed continuously throughout the study duration using wrist-worn accelerometry. Generalised estimating equations (GEE) were used to examine associations between PA and sleep. Sleep duration increased from 5.2h at baseline to 6.6h during the intervention period and 6.5h at one-month post-intervention follow-up (p<0.001). Bi-directional associations showed that higher overall activity volume and moderate-to-vigorous physical activity (MVPA) were associated with earlier sleep onset time (p<0.05). Later timing of sleep onset was associated with lower overall volume of activity, most active continuous 30 minutes (M30CONT), and MVPA (p<0.05). Higher overall activity volume, M30CONT, and MVPA predicted more wake after sleep onset (WASO) (p<0.001), whereas greater WASO was associated with higher overall volume of activity, M30CONT, and MVPA (p<0.001). An aerobic exercise intervention increased usual sleep duration. Day-to-day, more PA predicted earlier sleep onset, but worse sleep quality and vice versa. Novelty: • Greater levels of physical activity in the day were associated with an earlier sleep onset time that night, whereas a later timing of sleep onset was associated with lower physical activity the next day in men with obesity • Higher physical activity levels were associated with worse sleep quality, and vice versa

Cancer-related insomnia: Evaluation by mobile technology.

2015 ◽  
Vol 33 (29_suppl) ◽  
pp. 107-107
Author(s):  
Brenda O'Connor ◽  
Pauline Ui Dhuibhir ◽  
Declan Walsh
Keyword(s):  
Sleep Quality ◽  
Sleep Duration ◽  
Median Duration ◽  
Metastatic Cancer ◽  
Eastern Cooperative Oncology Group ◽  
Sleep Onset ◽  
Severity Index ◽  
Sleep Efficiency ◽  
Subjective Sleep Quality ◽  
Time In Bed

107 Background: Insomnia is difficulty with sleep onset, maintenance, early morning wakening or non-restorative sleep. Cancer prevalence is 30-75%. Daytime consequences include fatigue. It is under-reported and impairs quality of life. Measurement previously required sleep laboratories. Technology advances help real-time measurement in the natural environment. This study investigated the feasibility and acceptability of a wireless device to evaluate sleep in cancer. Methods: Prospective observational study: Stage A: 10 consecutive in-patient hospice admissions; Stage B: 20 consecutive community patients Sleep quality was rated by Insomnia Severity Index (ISI). Participants used a wireless non-contact bedside monitor (SleepMinder) for 3 nights. Acceptability questionnaires were completed by participant and nurse (Stage A) or family (Stage B).Descriptive statistics were generated by Microsoft Excel. Results: 30 participants with metastatic cancer were recruited. Median age: 63 years (47-84). Median Eastern Cooperative Oncology Group (ECOG) performance score: 2 (0-3). In-patient (n=10): In 50%, sleep onset was delayed >30 minutes. Median duration: 8 hours. Median awakenings per night: 1 (0-8). Median sleep efficiency (proportion of time in bed spent asleep): 89% (74-100%). ISI score correlated with sleep duration in 70%. Participants and nurses reported 100% device acceptability. Community (n=20): Sleep onset was delayed >30 minutes in 25%. Median duration: 8 hours. Median awakenings per night: 3 (0-10). Median sleep efficiency: 91% (46-100). ISI score correlated with sleep duration in 90%. Participants and family reported 100% device acceptability. Conclusions: (1)A wireless monitor effectively measures sleep in cancer in both inpatient and community settings, (2) High acceptability supports clinical use, (3) Subjective sleep quality reports correlate with device, and (4) Further research: evaluate sleep improvement interventions with device.

scholarly journals The Impact of Sleep Timing, Sleep Duration, and Sleep Quality on Depressive Symptoms and Suicidal Ideation amongst Japanese Freshmen: The EQUSITE Study

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Atin Supartini ◽  
Takanori Honda ◽  
Nadzirah A. Basri ◽  
Yuka Haeuchi ◽  
Sanmei Chen ◽  
...  
Keyword(s):  
Depressive Symptoms ◽  
Suicidal Ideation ◽  
Sleep Quality ◽  
Sleep Duration ◽  
Sleep Onset ◽  
Poor Sleep ◽  
Sleep Onset Latency ◽  
Onset Latency ◽  
Poor Sleep Quality ◽  
The Impact

Aim. The aim of this study was to identify the impact of bedtime, wake time, sleep duration, sleep-onset latency, and sleep quality on depressive symptoms and suicidal ideation amongst Japanese freshmen.Methods. This cross-sectional data was derived from the baseline survey of the Enhancement of Q-University Students Intelligence (EQUSITE) study conducted from May to June, 2010. A total of 2,631 participants were recruited and completed the following self-reported questionnaires: the Pittsburgh Sleep Quality Index (PSQI), the Center for Epidemiologic Studies Depression Scale (CES-D), and the original Health Support Questionnaires developed by the EQUSITE study research team.Results. Of 1,992 participants eligible for analysis, 25.5% (n=507) reported depressive symptoms (CES-D total score ≥ 16), and 5.8% (n=115) reported suicidal ideation. The present study showed that late bedtime (later than 01:30), sleep-onset latency (≥30 minutes), and poor sleep quality showed a marginally significant association with depressive symptoms. Poor sleep quality was seen to predict suicidal ideation even after adjusting for depressive symptoms.Conclusion. The current study has important implications for the role of bedtime in the prevention of depressive symptoms. Improving sleep quality may prevent the development of depressive symptoms and reduce the likelihood of suicidal ideation.

741 Sleep and postpartum depression in healthy fathers: Perceptions of sleep quality predict severity of symptoms

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A289-A289
Author(s):  
Christopher Kalogeropoulos ◽  
Rebecca Burdayron ◽  
Christine Laganière ◽  
Marie-Julie Beliveau ◽  
Karine Dubois-Comtois ◽  
...  
Keyword(s):  
Depressive Symptoms ◽  
Sleep Quality ◽  
Sleep Duration ◽  
Sleep Onset ◽  
Self Report ◽  
Sleep Diary ◽  
Nocturnal Sleep ◽  
Postpartum Depressive Symptoms ◽  
Paternal Depressive Symptoms ◽  
Perceived Sleep Quality

Abstract Introduction Research on the link between sleep quality and depression in the postpartum period has focused primarily on mothers. Although fathers also experience poorer postpartum sleep and are at risk of developing depressive symptoms, they remain understudied. To date, the limited research focusing on paternal sleep and depression has relied on subjective measures of sleep, without objective verification. The current study implemented a multi-measure approach using subjective and objective indices to explore the relationship between sleep and depressive symptoms in fathers at 6 months postpartum. Methods Fifty-four healthy fathers participated in this cross-sectional study. Paternal sleep was assessed for 2 weeks utilizing: 1) a self-report daily sleep diary, 2) a self-report perceived sleep quality rating, and 3) actigraphy. Subjective indices via the sleep diary measured participants’ perception of their total nocturnal sleep duration and total number of awakenings (self-reported sleep duration and fragmentation). Perceived sleep quality ratings measured participants’ perceptions of how well they thought they slept. Objective sleep variables measured through actigraphy included: total nocturnal sleep duration, number of awakenings, sleep efficiency, and wake after sleep onset (WASO). Paternal depressive symptoms were assessed with the Center for Epidemiologic Studies – Depression Scale (CES-D). Results Regression analyses showed that subjective sleep variables (measured by the sleep diary) and objective sleep variables (measured by actigraphy) did not significantly predict postpartum depressive symptoms in fathers (p &gt; .05). However, self-reported perceived sleep quality significantly predicted postpartum depressive symptom severity in fathers (R2 = .172, p = .034). Conclusion These findings advance our understanding of the link between sleep and depression in fathers. The results highlight the important role of fathers’ perceptions of sleep quality, rather than the actual quality or quantity of their sleep (measured through the sleep diary or actigraphy), in the development of postpartum depressive symptoms. The multi-measure approach to sleep implemented in this study expanded our knowledge about how different facets of sleep relate to depression. These findings have important implications for the development of clinical interventions targeting paternal sleep and mood in the months following childbirth. Support (if any) Social-Science and Humanities Research Council (SSHRC) and Fonds de recherche du Québec – Santé (FRQS)

Sleep Quality but Not Quantity Altered With a Change in Training Environment in Elite Australian Rules Football Players

2017 ◽  
Vol 12 (1) ◽  
pp. 75-80 ◽  
Author(s):  
Nathan W. Pitchford ◽  
Sam J. Robertson ◽  
Charli Sargent ◽  
Justin Cordy ◽  
David J. Bishop ◽  
...  
Keyword(s):  
Sleep Quality ◽  
Repeated Measures ◽  
Perceived Exertion ◽  
Sleep Onset ◽  
Sleep Time ◽  
Rating Of Perceived Exertion ◽  
Training Environment ◽  
Time In Bed ◽  
Australian Rules Football ◽  
The Relationship

Purpose:To assess the effects of a change in training environment on the sleep characteristics of elite Australian Rules football (AF) players.Methods:In an observational crossover trial, 19 elite AF players had time in bed (TIB), total sleep time (TST), sleep efficiency (SE), and wake after sleep onset (WASO) assessed using wristwatch activity devices and subjective sleep diaries across 8-d home and camp periods. Repeated-measures ANOVA determined mean differences in sleep, training load (session rating of perceived exertion [RPE]), and environment. Pearson product–moment correlations, controlling for repeated observations on individuals, were used to assess the relationship between changes in sleep characteristics at home and camp. Cohen effect sizes (d) were calculated using individual means.Results:On camp TIB (+34 min) and WASO (+26 min) increased compared with home. However, TST was similar between home and camp, significantly reducing camp SE (–5.82%). Individually, there were strong negative correlations for TIB and WASO (r = -.75 and r = -.72, respectively) and a moderate negative correlation for SE (r = -.46) between home and relative changes on camp. Camp increased the relationship between individual s-RPE variation and TST variation compared with home (increased load r = -.367 vs .051, reduced load r = .319 vs –.033, camp vs home respectively).Conclusions:Camp compromised sleep quality due to significantly increased TIB without increased TST. Individually, AF players with higher home SE experienced greater reductions in SE on camp. Together, this emphasizes the importance of individualized interventions for elite team-sport athletes when traveling and/or changing environments.

259 Tracking naturalistic sleep over the menstrual cycle with a wearable in healthy young women

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A104-A104
Author(s):  
Elisabet Alzueta ◽  
Massimiliano de Zambotti ◽  
Teji Dulai ◽  
Benedetta Albinni ◽  
Katharine Simon ◽  
...  
Keyword(s):  
Heart Rate ◽  
Menstrual Cycle ◽  
Sleep Quality ◽  
Sleep Duration ◽  
Young Women ◽  
Sleep Onset ◽  
Sleep Onset Latency ◽  
Physiological Changes ◽  
Sleep Continuity ◽  
Self Reports

Abstract Introduction A woman’s menstrual cycle is characterized by hormonal changes that might affect sleep and therefore daily functionality. While some studies using self-reports have shown a lower sleep quality in the peri-menstruation phase, objective – in lab – studies have not found significant differences in sleep continuity during the menstrual cycle, but are limited by only a few recordings across the cycle. The aim of this study is to examine changes in sleep during the healthy menstrual cycle using a multi-sensory wearable, allowing continuous, objective, reliable and ecologically valid measurement. Methods 12 healthy young women (28.14 ± 2.33) were monitored using Oura ring – a sleep and activity tracker – during an entire menstrual cycle. Participants also reported mood, readiness, and sleep quality using a diary. Four phases of the menstrual cycle were compared (menstruation, periovulation, mid-luteal, and late-luteal). Ovulation day was determined using a urinary luteinizing hormone test. Results Ovulatory cycles were confirmed by the Oura ring, which showed a significant increase in average nocturnal heart rate and skin temperature during the post-ovulatory luteal phase relative to menstruation and periovulation. Oura ring measures of sleep continuity (Sleep Onset Latency, Wake After Sleep Onset) and self-reported sleep quality did not change across the 4 menstrual phases. We observed a trend for objective sleep duration, which tended to be shorter in the mid-luteal and late-luteal phases. We also observed a small reduction in perceived readiness and mood during these two phases. Conclusion Physiological changes (increase in heart rate and body temperature) in the postovulatory phase of the menstrual cycle are detectable with the Oura ring. Sleep features remain quite stable during the healthy, ovulatory menstrual cycle, apart from a trend for slightly shorter sleep duration in the post-ovulatory phases. In comparison to self-reports, which rely on retrospective memory and might be biased by perception and mood, wearable technologies seem to be a sensitive and informative tool to track sleep and physiological changes during the menstrual cycle. Support (if any) Supported by RF1AG061355 (Baker/Mednick)

The Impact of Contextual Factors on Game Demands in Starting, Semiprofessional, Male Basketball Players

2020 ◽  
Vol 15 (4) ◽  
pp. 450-456 ◽  
Author(s):  
Jordan L. Fox ◽  
Robert Stanton ◽  
Charli Sargent ◽  
Cody J. O’Grady ◽  
Aaron T. Scanlan
Keyword(s):  
Heart Rate ◽  
High Intensity ◽  
Perceived Exertion ◽  
Contextual Factors ◽  
Relative Number ◽  
Movement Analysis ◽  
Rating Of Perceived Exertion ◽  
Basketball Players ◽  
Mixed Effect ◽  
The Impact

Purpose: To quantify and compare external and internal game workloads according to contextual factors (game outcome, game location, and score-line). Methods: Starting semiprofessional, male basketball players were monitored during 19 games. External (PlayerLoad™ and inertial movement analysis variables) and internal (summated-heart-rate-zones and rating of perceived exertion [RPE]) workload variables were collected for all games. Linear mixed-effect models and effect sizes were used to compare workload variables based on each of the contextual variables assessed. Results: The number of jumps, absolute and relative (in min−1) high-intensity accelerations and decelerations, and relative changes-of-direction were higher during losses, whereas session RPE was higher during wins. PlayerLoad™ the number of absolute and relative jumps, high-intensity accelerations, absolute and relative total decelerations, total changes-of-direction, summated-heart-rate-zones, session RPE, and RPE were higher during away games, whereas the number of relative high-intensity jumps was higher during home games. PlayerLoad™, the number of high-intensity accelerations, total accelerations, absolute and relative decelerations, absolute and relative changes-of-direction, summated-heart-rate-zones, sRPE, and RPE were higher during balanced games, whereas the relative number of total and high-intensity jumps were higher during unbalanced games. Conclusions: Due to increased intensity, starting players may need additional recovery following losses. Given the increased external and internal workload volumes encountered during away games and balanced games, practitioners should closely monitor playing times during games. Monitoring playing times may help identify when players require additional recovery or reduced training volumes to avoid maladaptive responses across the in-season.

223 Sleep Duration and Sleep Quality in Chronic Cannabis Users and Non-Users

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A89-A89
Author(s):  
Caroline Tse ◽  
Alicia Stewart ◽  
Omar Ordaz-Johnson ◽  
Maya Herzig ◽  
Jacqueline Gagnon ◽  
...  
Keyword(s):  
Pilot Study ◽  
Sleep Quality ◽  
Sleep Duration ◽  
Sleep Latency ◽  
Sleep Onset ◽  
The United States ◽  
Cannabis Use ◽  
Sleep Diary ◽  
Random Factor ◽  
Time In Bed

Abstract Introduction Cannabis use is on the rise in the United States, with 10% of adults reporting cannabis use in the past 30 days. Users commonly report consuming cannabis to improve sleep despite the lack of research that supports an association between cannabis use and sleep. In this pilot study we sought to examine objective measures of sleep duration and sleep quality among non- and chronic-cannabis users, and any patterns in relation to the time since consumption of cannabis. Methods Chronic cannabis users (cannabis used 2 or more times/week) and non-users provided up to 2-weeks of actigraphy (ActiGraph wGT3X-BT), worn on the wrist and verified by sleep diary. Chronic cannabis users also reported the date, time, amount, and route of their cannabis use. Mixed-effects models with participant as a random factor were used to examine: 1) the relationship between daily sleep parameters in cannabis non-users vs. users; and 2) the elapsed time between cannabis use and time in bed in chronic cannabis users. Results Chronic cannabis users (n=6) and non-users (n=7) collectively provided 151 nights of sleep. Participant characteristics (38.5% female; age, 25.8 years ± 4 years; BMI, 23.4 kg/m2 ± 3.4 kg/m2) did not significantly differ between groups. Cannabis use was associated with decreased total sleep time (measured in hours, ß=-0.58, p&lt;0.001) and increased wake after sleep onset (WASO, ß=32.79, p=0.005), but not with the number of awakenings (ß=6.02, p=0.068). Among chronic cannabis users, cannabis use within two hours of bed was associated with increased sleep latency compared to use greater than two hours (ß=6.66, p=0.026). There was no association between time of cannabis use and WASO (p=0.621) or the number of awakenings (p=0.617). Conclusion In this pilot study of objectively measured sleep, we found that chronic cannabis use compared to non-use is associated with decreased sleep duration of otherwise healthy adults. Cannabis used closer to bedtime is associated with increased sleep latency. Additional studies that are able to assess the mode and dosage of use are needed to further understand the effects of cannabis and its components on sleep. Support (if any) KL2TR002370, AASM, Oregon Institute of Occupational Health Sciences

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