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.

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.

scholarly journals The Impact of Chronotype on the Sleep and Training Responses of Elite Female Australian Footballers

2021 ◽  
Vol 3 (4) ◽  
pp. 528-535
Author(s):  
Michele Lastella ◽  
Dean J. Miller ◽  
Manuella Quilelli ◽  
Spencer Roberts ◽  
Brad Aisbett ◽  
...  
Keyword(s):  
Perceived Exertion ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Self Report ◽  
Future Research ◽  
Training Time ◽  
Time In Bed ◽  
Circadian Preference ◽  
The Impact ◽  
And Training

The primary aims of the present study were to examine the impact of chronotype on sleep/wake behaviour, perceived exertion, and training load among professional footballers. Thirty-six elite female professional football player’s (mean ± SD: age, 25 ± 4 y; weight, 68 ± 7 kg) sleep and training behaviours were examined for 10 consecutive nights during a pre-season period using a self-report online player-management system and wrist activity monitors. All athletes completed the Morningness-Eveningness Questionnaire (rMEQ) on the first day of data collection. Eleven participants were morning types, seventeen participants were intermediate types, and three participants were evening types. Separate linear mixed models were conducted to assess differences in sleep, perceived exertion, and training behaviours between chronotype groups. Morning types woke up earlier (wake time: 07:19 ± 01:16 vs. 07:53 ± 01:01, p = 0.04) and reported higher ratings of perceived exertion compared to intermediate types (6.7 ± 1.1 vs. 5.9 ± 1.2, p = 0.01). No differences were observed between chronotype groups for bedtime, time in bed, total sleep time, sleep efficiency, training duration, or training load. In circumstances where professional female football players are required to train at a time opposing their natural circadian preference (e.g., morning type training in the evening), their perceived exertion during training may be higher than that of players that are training at a time that aligns with their natural circadian preference (e.g., evening type training in the evening). It is important for practitioners to monitor individual trends in training variables (e.g., rating of perceived exertion, training load) with relation to athlete chronotype and training time. Future research should examine the relationship between chronotype, training time, and rating of perceived exertion across different training durations.

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.

Defensive Strategy and Player Sex Impact Heart-Rate Responses During Games-Based Drills in Professional Basketball

2020 ◽  
pp. 1-7
Author(s):  
David Suárez-Iglesias ◽  
Rubén Dehesa ◽  
Aaron T. Scanlan ◽  
José A. Rodríguez-Marroyo ◽  
Alejandro Vaquera
Keyword(s):  
Heart Rate ◽  
Training Stimulus ◽  
Training Load ◽  
Defensive Strategy ◽  
Basketball Players ◽  
Professional Basketball ◽  
Defensive Strategies ◽  
Cardiovascular Stress ◽  
The Impact ◽  
And Training

Purpose: Games-based drills (GBD) are the predominant form of training stimulus prescribed to male and female basketball players. Despite being readily manipulated during GBD, the impact of defensive strategy on the sex-specific demands of GBD remains unknown. Therefore, the aim of this study was to quantify and compare the heart-rate (HR) responses experienced during 5v5 GBD using different defensive strategies (man-to-man defense vs zone defense [ZD] formations) according to player sex. Method: HR was recorded in 11 professional male and 10 professional female basketball players while performing 5v5 GBD with different defensive strategies (man-to-man defense or ZD). HR-based training load was also calculated using the summated heart-rate zones model. Results: During man-to-man defense, mean HR (), relative time (in percentage) spent working at 90% to 100% maximal HR (), and summated heart-rate zones () were greater (P < .05) in female players compared with males. During ZD, higher (P < .01) peak HR (), mean HR (), relative and absolute (in minutes) time spent working at 80% to 89% maximal HR ( and .03, respectively) and 90% to 100% maximal HR ( and .09, respectively), and summated heart-rate zones () were observed in female players compared with males. Conclusions: The defensive strategy employed during 5v5 full-court GBD influences HR responses and training load differently according to sex, where female players experience higher HR responses than males, especially when ZD are adopted. Basketball coaching staff can use these findings for the precise manipulation of team defenses during GBD to elicit desired cardiovascular stress on players.

The Impact of 3 Different-Length Between-Matches Microcycles on Training Loads in Professional Rugby League Players

2015 ◽  
Vol 10 (6) ◽  
pp. 767-773 ◽  
Author(s):  
Alexandre Moreira ◽  
Tom Kempton ◽  
Marcelo Saldanha Aoki ◽  
Anita C. Sirotic ◽  
Aaron J. Coutts
Keyword(s):  
Perceived Exertion ◽  
Training Session ◽  
Rugby League ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Endurance Athletes ◽  
Moderate Intensity ◽  
High Intensity Zone ◽  
Competition Period ◽  
The Impact

Purpose: To examine the impact of varying between-matches microcycles on training characteristics (ie, intensity, duration, and load) in professional rugby league players and to report on match load related to these between-matches microcycles. Methods: Training-load data were collected during a 26-wk competition period of an entire season. Training load was measured using the session rating of perceived exertion (session-RPE) method for every training session and match from 44 professional rugby league players from the same National Rugby League team. Using the category-ratio 10 RPE scale, the training intensity was divided into 3 zones (low <4 AU, moderate ≥4-≤7 AU, and high >7 AU). Three different-length between-matches recovery microcycles were used for analysis: 5−6 d, 7−8 d, and 9−10 d. Results: A total of 3848 individual sessions were recorded. During the shorter-length between-matches microcycles (5−6 d), significantly lower training load was observed. No significant differences for subsequent match load or intensity were identified between the various match recovery periods. Overall, 16% of the training sessions were completed at the low-intensity zone, 61% at the moderate-intensity zone, and 23% at the high-intensity zone. Conclusions: The findings demonstrate that rugby league players undertake higher training load as the length of between-matches microcycles is increased. The majority of in-season training of professional rugby league players was at moderate intensity, and a polarized approach to training that has been reported in elite endurance athletes does not occur in professional rugby league.

scholarly journals In-season internal and external training load quantification of an elite European soccer team

2018 ◽  
Author(s):  
Rafael Soares Oliveira ◽  
João Paulo Brito ◽  
Alexandre Martins ◽  
Bruno Mendes ◽  
Francisco Calvete ◽  
...  
Keyword(s):  
High Speed ◽  
Perceived Exertion ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Soccer Players ◽  
Soccer Teams ◽  
External Training ◽  
Daily Training ◽  
Player Positions ◽  
Level Of Effort

Elite soccer teams that participate in European competitions often have a difficult schedule, involving weeks in which they play up to three matches, which leads to acute and transient subjective, biochemical, metabolic and physical disturbances in players over the subsequent hours and days. Inadequate time recovery between matches can expose players to the risk of training and competing whilst not fully recovered. Controlling the level of effort and fatigue of players to reach higher performances during the matches is therefore critical. Therefore, the aim of the current study was to provide the first report of seasonal internal and external training load (TL) that included Hooper Index (HI) scores in elite soccer players during an in-season period. Sixteen elite soccer players were sampled, using global position system, session rating of perceived exertion (s-RPE) and HI scores during the daily training sessions throughout the 2015-2016 in-season period. Data were analysed across ten mesocycles (M: 1 to 10) and collected according to the number of days prior to a match. Total daily distance covered was higher at the start (M1 and M3) compared to the final mesocycle (M10) of the season. M1 (5589m) reached a greater distance than M5 (4473m) (ES = 9.33 [12.70, 5.95]) and M10 (4545m) (ES = 9.84 [13.39, 6.29]). M3 (5691m) reached a greater distance than M5 (ES = 9.07 [12.36, 5.78]), M7 (ES = 6.13 [8.48, 3.79]) and M10 (ES = 9.37 [12.76, 5.98]). High-speed running distance was greater in M1 (227m), than M5 (92m) (ES = 27.95 [37.68, 18.22]) and M10 (138m) (ES = 8.46 [11.55, 5.37]). Interestingly, the s-RPE response was higher in M1 (331au) in comparison to the last mesocycle (M10, 239au). HI showed minor variations across mesocycles and in days prior to the match. Every day prior to a match, all internal and external TL variables expressed significant lower values to other days prior to a match (p<0.01). In general, there were no differences between player positions. Conclusions: Our results reveal that despite the existence of some significant differences between mesocycles, there were minor changes across the in-season period for the internal and external TL variables used. Furthermore, it was observed that MD-1 presented a reduction of external TL (regardless of mesocycle) while internal TL variables did not have the same record during in-season match-day-minus.

Player Session Rating of Perceived Exertion: A More Valid Tool Than Coaches’ Ratings to Monitor Internal Training Load in Elite Youth Female Basketball

2020 ◽  
Vol 15 (4) ◽  
pp. 548-553 ◽  
Author(s):  
Corrado Lupo ◽  
Alexandru Nicolae Ungureanu ◽  
Riccardo Frati ◽  
Matteo Panichi ◽  
Simone Grillo ◽  
...  
Keyword(s):  
Body Mass ◽  
Perceived Exertion ◽  
Team Sports ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Basketball Players ◽  
Mixed Effect ◽  
Maximal Intensity ◽  
Session Duration ◽  
Post Hoc

Purpose: To monitor elite youth female basketball training to verify whether players’ and coaches’ (3 technical coaches and 1 physical trainer) session rating of perceived exertion (s-RPE) has a relationship with Edwards’ method. Methods: Heart rate of 15 elite youth female basketball players (age 16.7 [0.5] y, height 178 [9] cm, body mass 72 [9] kg, body mass index 22.9 [2.2] kg·m−2) was monitored during 19 team (268 individual) training sessions (102 [15] min). Mixed effect models were applied to evaluate whether s-RPE values were significantly (P ≤ .05) related to Edwards’ data, total session duration, maximal intensity (session duration at 90–100% HRmax), type of training (ie, strength, conditioning, and technique), and whether differences emerged between players’ and coaches’ s-RPE values. Results: The results showed that there is a relationship between s-RPE and Edwards’ methods for the players’ RPE scores (P = .019) but not for those of the trainers. In addition, as expected, both players’ (P = .014) and coaches’ (P = .002) s-RPE scores were influenced by total session duration but not by maximal intensity and type of training. In addition, players’ and coaches’ s-RPE values differed (P < .001)—post hoc differences emerged for conditioning (P = .01) and technique (P < .001) sessions. Conclusions: Elite youth female basketball players are better able to quantify the internal training load of their sessions than their coaches, strengthening the validity of s-RPE as a tool to monitor training in team sports.

scholarly journals Monitoring What Matters: A Systematic Process for Selecting Training-Load Measures

2017 ◽  
Vol 12 (s2) ◽  
pp. S2-101-S2-106 ◽  
Author(s):  
Sean Williams ◽  
Grant Trewartha ◽  
Matthew J. Cross ◽  
Simon P.T. Kemp ◽  
Keith A. Stokes
Keyword(s):  
Injury Risk ◽  
Perceived Exertion ◽  
Principal Component ◽  
Rugby Union ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Systematic Process ◽  
Daily Training ◽  
Monitoring Training ◽  
Generalized Linear Mixed Effects

Purpose:Numerous derivative measures can be calculated from the simple session rating of perceived exertion (sRPE), a tool for monitoring training loads (eg, acute:chronic workload and cumulative loads). The challenge from a practitioner’s perspective is to decide which measures to calculate and monitor in athletes for injury-prevention purposes. The aim of the current study was to outline a systematic process of data reduction and variable selection for such training-load measures.Methods:Training loads were collected from 173 professional rugby union players during the 2013–14 English Premiership season, using the sRPE method, with injuries reported via an established surveillance system. Ten derivative measures of sRPE training load were identified from existing literature and subjected to principal-component analysis. A representative measure from each component was selected by identifying the variable that explained the largest amount of variance in injury risk from univariate generalized linear mixed-effects models.Results:Three principal components were extracted, explaining 57%, 24%, and 9% of the variance. The training-load measures that were highly loaded on component 1 represented measures of the cumulative load placed on players, component 2 was associated with measures of changes in load, and component 3 represented a measure of acute load. Four-week cumulative load, acute:chronic workload, and daily training load were selected as the representative measures for each component.Conclusions:The process outlined in the current study enables practitioners to monitor the most parsimonious set of variables while still retaining the variation and distinct aspects of “load” in the data.

Effects of a 2-Week High-Intensity Training Camp on Sleep Activity of Professional Rugby League Athletes

2017 ◽  
Vol 12 (7) ◽  
pp. 928-933 ◽  
Author(s):  
Heidi R. Thornton ◽  
Grant M. Duthie ◽  
Nathan W. Pitchford ◽  
Jace A. Delaney ◽  
Dean T. Benton ◽  
...  
Keyword(s):  
Sleep Onset ◽  
Sleep Time ◽  
Rugby League ◽  
Training Camp ◽  
High Intensity Training ◽  
Time In Bed ◽  
Nighttime Sleep ◽  
Relationship Of ◽  
The Relationship ◽  
And Training

Purpose:To investigate the effects of a training camp on the sleep characteristics of professional rugby league players compared with a home period.Methods:During a 7-d home and 13-d camp period, time in bed (TIB), total sleep time (TST), sleep efficiency (SE), and wake after sleep onset were measured using wristwatch actigraphy. Subjective wellness and training loads (TL) were also collected. Differences in sleep and TL between the 2 periods and the effect of daytime naps on nighttime sleep were examined using linear mixed models. Pearson correlations assessed the relationship of changes in TL on individuals’ TST.Results:During the training camp, TST (–85 min), TIB (–53 min), and SE (–8%) were reduced compared with home. Those who undertook daytime naps showed increased TIB (+33 min), TST (+30 min), and SE (+0.9%). Increases in daily total distance and training duration above individual baseline means during the training camp shared moderate (r = –.31) and trivial (r = –.04) negative relationships with TST.Conclusions:Sleep quality and quantity may be compromised during training camps; however, daytime naps may be beneficial for athletes due to their known benefits, without being detrimental to nighttime sleep.

Monitoring Training Load and Well-Being During the In-Season Phase in National Collegiate Athletic Association Division I Men’s Basketball

2018 ◽  
Vol 13 (8) ◽  
pp. 1067-1074 ◽  
Author(s):  
Daniele Conte ◽  
Nicholas Kolb ◽  
Aaron T. Scanlan ◽  
Fabrizio Santolamazza
Keyword(s):  
Perceived Exertion ◽  
National Collegiate Athletic Association ◽  
Well Being ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Division I ◽  
Basketball Players ◽  
Men's Basketball ◽  
College Basketball ◽  
Basketball Coaches

Purpose: To characterize the weekly training load (TL) and well-being of college basketball players during the in-season phase. Methods: Ten (6 guards and 4 forwards) male basketball players (age 20.9 [0.9] y, stature 195.0 [8.2] cm, and body mass 91.3 [11.3] kg) from the same Division I National Collegiate Athletic Association team were recruited to participate in this study. Individualized training and game loads were assessed using the session rating of perceived exertion at the end of each training and game session, and well-being status was collected before each session. Weekly changes (%) in TL, acute-to-chronic workload ratio, and well-being were determined. Differences in TL and well-being between starting and bench players and between 1-game and 2-game weeks were calculated using magnitude-based statistics. Results: Total weekly TL and acute-to-chronic workload ratio demonstrated high week-to-week variation, with spikes up to 226% and 220%, respectively. Starting players experienced a higher (most likely negative) total weekly TL and similar (unclear) well-being status compared with bench players. Game scheduling influenced TL, with 1-game weeks demonstrating a higher (likely negative) total weekly TL and similar (most likely trivial) well-being status compared with 2-game weeks. Conclusions: These findings provide college basketball coaches information to optimize training strategies during the in-season phase. Basketball coaches should concurrently consider the number of weekly games and player status (starting vs bench player) when creating individualized periodization plans, with increases in TL potentially needed in bench players, especially in 2-game weeks.

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