Waking Up to the Issue! Research Inattention and Sex-Related Differences Warrant More Sleep Studies in Female Athletes.

Understanding sleep patterns and behaviors of athletes is essential for developing targeted sleep-based interventions for implementation in practice. However, more than double the number of sleep studies have examined male athletes compared with female athletes, making the current understanding of sleep patterns, behaviors, and interventions among athletes disproportionately indicative of men. Consequently, this review demonstrates the need for more female-specific sleep data among athlete populations due to research inattention and sex-related differences. Specifically, this review identifies variations in sleep patterns and behaviors between male and female athletes, as well as physiological and lifestyle factors that potentially affect sleep patterns and behaviors across the lifespan, specifically in female athletes. In this regard, evidence suggests some female athletes experience longer sleep durations and better objective sleep quality, but similar or worse subjective sleep quality compared with male athletes. Additionally, scheduling training in the morning or throughout the day may benefit sleep in some female athletes. Considering sleep disorders, women may be at greater risk for insomnia and restless legs syndrome compared with men, which may be attributed to pregnancy, as well as a higher prevalence of anxiety and depressive symptoms, iron deficiency without anemia, and use of psychotropic medication among women. Finally, the menstrual cycle, menstrual disorders, oral contraceptive use, and the postpartum period have been shown to exert detrimental effects on sleep patterns and behaviors and should theoretically be considered when monitoring and managing sleep in female athletes.

Experiences of Rural and Metropolitan Background Applicants in Preparing for and Completing a Regionally Focused Multiple Mini-interview for Admission into a Regional Medical Program.

BACKGROUND: To better target rural background and rurally interested applicants during medical school admission, it is increasingly common for rural medical programs to include multiple mini-interview (MMI) scenarios designed to screen for rural interest. It remains unclear whether the inclusion of regionally/rurally focused MMI scenarios positively impacts the selection of rural background applicants and evidence is limited regarding why rural background applicants may perform worse on the MMI. Therefore, this study explored how rural and metropolitan applicants prepare for and perceive the MMI for admission to a regional medical pathway. METHODS: A mixed-methods survey was sent to provisional entry regional pathway medical school applicants who had completed an MMI. The survey was distributed before any offers of admission had been released. RESULTS: Rural applicants spent less time and money preparing for the MMI and felt less prepared (P < 0.05). However, time and money spent, and resources used to prepare were not associated with feeling more prepared (all P > 0.05). Respondents mostly felt that the MMI process aligned with their expectations (83%), is fair (64%), and helps a rural program select the most suitable applicants (61%). Rural applicants generally felt that they had an advantage over other applicants (61%) while most metropolitan applicants did not (23%; P = 0.002). DISCUSSION: Applicants to a regional medical pathway are generally supportive of the MMI process. It appears valuable for applicants to prepare for the MMI by understanding the format and requirements; however, investing substantial time and money does not underpin feeling better prepared. MMI scenarios which include a regional focus are perceived to advantage rural applicants.

A Narrative Review of Health Status and Healthcare Delivery in the Oil and Gas Industry: Impacts on Employees, Employers, and Local Communities.

Workers in the oil and gas industry are exposed to numerous health risks, ranging from poor health behaviours to the possibility of life-threatening injuries. Determining the most appropriate models of healthcare for the oil and gas industry is difficult, as strategies must be acceptable to multiple stakeholders, including employees, employers, and local communities. The purpose of this review was to broadly explore the health status and needs of workers in the oil and gas industry and healthcare delivery models relating to primary care and emergency responses. Database searches of PubMed, EMBASE, CINAHL, PsycINFO, and Scopus were conducted, as well as grey literature searches of Google, Google Scholar, and the International Association of Oil and Gas Producers website. Resource-sector workers, particularly those in 'fly-in fly-out' roles, are susceptible to poor health behaviours and a higher prevalence of mental health concerns than the general population. Evidence is generally supportive of organisation-led behaviour change and mental health-related interventions. Deficiencies in primary care received while on-site may lead workers to inappropriately use local health services. For the provision of emergency medical care, telehealth and telemedicine lead to favourable outcomes by improving patient health status and satisfaction and reducing the frequency of medical evacuations.

Short-term medical student placements completed consecutively at a rural general practice positively impact chronic disease management.

INTRODUCTION: Little is known about how medical school placements in rural areas impact key stakeholders such as patients, host organisations and the wider rural community. With engagement from rural communities crucial to the success of rural medical training, this case study sought to demonstrate the benefit that rural clinical placements can have on rural general practices (systems) and likely impacts on communities (health outcomes). Specifically, we describe how a series of consecutive short-term student placements in a single rural practice were the drivers of a series of clinical audits and interventions resulting in improved management of chronic disease. METHODS: Data for this project were obtained from student research reports completed as part of a rural and remote medicine rotation at an Australian medical school. For this series of projects, eight consecutive students were based at the same rural medical centre, with each attending for 6 weeks across a 15-month period, completing a report for a quality improvement activity and evaluating the outcomes. Each project related to chronic kidney disease (CKD), with CKD chosen based on the needs of the medical centre and the higher burden of this disease in rural areas. Each project was developed and delivered in consultation with the practice, taking into account student interest and skills, and related projects completed prior or concurrently. Projects were related to database management (n=2), alignment between CKD management and best-practice guidelines (n=3), patient health literacy (n=3), and a summary and staff perceptions of the preceding quality improvement activities (n=1). RESULTS: The combination of student projects led to tangible improvements in CKD management at a rural general practice. All doctors at the medical centre (n=4) reported using the database management tools implemented by the students and felt the interventions were sustainable, long-term solutions for ensuring clinical investigations are not being delayed or missed. Following the various interventions completed by the students, clinician knowledge and implementation of best-practice CKD management increased, and some patients became more aware of their condition and how to manage it. CONCLUSION: This case study provides evidence that short-term rural clinical placements for medical students have the potential to greatly improve health care and clinical practice in rural and remote communities, when designed around a consistent topic within a medical practice. Outcomes of the student projects in combination demonstrate that addressing CKD management longitudinally led to improvements in administrative processes, clinical practices, and patient awareness and accountability, despite each student only being at the medical centre for a short period of time. Similar approaches to structuring rural clinical placements and defining community projects for medical students should be considered more broadly.

Medical student interest and participation in research at one rural clinical school: Insights from the last six years.

INTRODUCTION: Limited evidence is currently available relating to research acitivity of medical students training in regional or rural areas. OBJECTIVE: To describe medical student interest and participation in research at The University of Queensland Rural Clinical School (UQRCS). DESIGN: Annual student expression of interest surveys were collated with records of student research participation maintained at UQRCS from 2017-2022. Additionally, a systematic search was conducted to identify student outputs not captured in internal records. Frequencies and proportions were calculated for all descriptive data along with proportions of students who engaged with a project, and projects that led to a peer-reviewed publication. FINDINGS: At UQRCS commencement, 55% of research-interested students reported having basic research skills. Thirty-nine percent of research-interested students engaged with a project, most commonly literature reviews (47%) or audits (29%). Thirty-three percent of completed projects led to a peer-reviewed manuscript. DISCUSSION: Students who engage with a project are unlikely to receive a project in their preferred clinical area and should be informed about the transferrability of research skills. Most students have basic research skills when commencing a project and therefore require ongoing support and mentorship from staff/supervisors. CONCLUSION: Publication rates of medical students in regional/rural areas are comparable to those reported by metropolitan medical schools (~30%).

Sleep Patterns Fluctuate Following Training and Games across the Season in a Semi-Professional, Female Basketball Team.

Quantifying athlete sleep patterns may inform development of optimal training schedules and sleep strategies, considering the competitive challenges faced across the season. Therefore, this study comprehensively quantified the sleep patterns of a female basketball team and examined variations in sleep between nights. Seven semi-professional, female basketball players had their sleep monitored using wrist-worn activity monitors and perceptual ratings during a 13-week in-season. Sleep variables were compared between different nights (control nights, training nights, training nights before games, nights before games, non-congested game nights, and congested game nights), using generalized linear mixed models, as well as Cohen's d and odds ratios as effect sizes. Players experienced less sleep on training nights before games compared to control nights, training nights, nights before games, and congested game nights (p < 0.05, d = 0.43-0.69). Players also exhibited later sleep onset times on non-congested game nights compared to control nights (p = 0.01, d = 0.68), and earlier sleep offset times following training nights before games compared to all other nights (p < 0.01, d = 0.74-0.79). Moreover, the odds of players attaining better perceived sleep quality was 88% lower on congested game nights than on nights before games (p < 0.001). While players in this study attained an adequate sleep duration (7.3 ± 0.3 h) and efficiency (85 ± 2%) on average across the in-season, they were susceptible to poor sleep on training nights before games and following games. Although limited to a team-based case series design, these findings suggest basketball coaches may need to reconsider scheduling team-based, on-court training sessions on nights prior to games and consider implementing suitable psychological and recovery strategies around games to optimize player sleep.

External and Internal Load Variables Encountered During Training and Games in Female Basketball Players According to Playing Level and Playing Position: A Systematic Review.

BACKGROUND: Despite the growing global participation of females in basketball and number of studies conducted on the topic, no research has summarized the external and internal load variables encountered by female basketball players during training and games. OBJECTIVE: To collate existing literature investigating external and internal load variables during training and games in female basketball players according to playing level (club, high-school, representative, collegiate, semi-professional, and professional) and playing position (backcourt and frontcourt players). METHODS: A systematic review of the literature was performed using PubMed, SPORTDiscus, and Web of Science to identify studies published from database inception until June 11, 2021. Studies eligible for inclusion were observational and cross-sectional studies, published in English, reporting external and/or internal load variables during training sessions and/or games. Methodological quality and bias were assessed for each study prior to data extraction using a modified Downs and Black checklist. Weighted means according to playing level and playing position were calculated and compared if a load variable was reported across two or more player samples and were consistent regarding key methodological procedures including the seasonal phase monitored, minimum exposure time set for including player data (playing time during games), approach to measure session duration, and approach to measure session intensity. RESULTS: The search yielded 5513 studies of which 1541 studies were duplicates. A further 3929 studies were excluded based on title and abstract review, with 11 more studies excluded based on full-text review. Consequently, 32 studies were included in our review. Due to the wide array of methodological approaches utilized across studies for examined variables, comparisons could only be made according to playing level for blood lactate concentration during games, revealing backcourt players experienced higher lactate responses than frontcourt players (5.2 ± 1.9 mmol·L-1 vs. 4.4 ± 1.8 mmol·L-1). CONCLUSIONS: Inconsistencies in the methods utilized to measure common load variables across studies limited our ability to report and compare typical external and internal loads during training and games according to playing level and position in female basketball players. It is essential that standardized methodological approaches are established for including player data as well as measuring session duration (e.g., total time, live time) and intensity (e.g., consistent rating of perceived exertion scales, intensity zone cut points) in future female basketball research to permit meaningful interpretation and comparisons of load monitoring data across studies.

Are acute player workloads associated with in-game performance in basketball?

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.

Weekly Training Demands Increase, but Game Demands Remain Consistent Across Early, Middle, and Late Phases of the Regular Season in Semiprofessional Basketball Players.

PURPOSE: To compare weekly training, game, and overall (training and games) demands across phases of the regular season in basketball. METHODS: Seven semiprofessional, male basketball players were monitored during all on-court team-based training sessions and games during the regular season. External monitoring variables included PlayerLoad™ and inertial movement analysis events per minute. Internal monitoring variables included a modified summated heart rate zones model calculated per minute and rating of perceived exertion. Linear mixed models were used to compare training, game, and overall demands between 5-week phases (early, middle, and late) of the regular season with significance set at P ≤ .05. Effect sizes were calculated between phases and interpreted as: trivial, <0.20; small, 0.20 to 0.59; moderate, 0.60 to 1.19; large, 1.20 to 1.99; very large, ≥2.00. RESULTS: Greater (P > .05) overall inertial movement analysis events (moderate-very large) and rating of perceived exertion (moderate) were evident in the late phase compared with earlier phases. During training, more accelerations were evident in the middle (P = .01, moderate) and late (P = .05, moderate) phases compared with the early phase, while higher rating of perceived exertion (P = .04, moderate) was evident in the late phase compared with earlier phases. During games, nonsignificant, trivial-small differences in demands were apparent between phases. CONCLUSIONS: Training and game demands should be interpreted in isolation and combined given overall player demands increased as the season progressed, predominantly due to modifications in training demands given the stability of game demands. Periodization strategies administered by coaching staff may have enabled players to train at greater intensities late in the season without compromising game intensity.

Acute caffeine supplementation improves jumping, sprinting, and change-of-direction performance in basketball players when ingested in the morning but not evening.

This study compared the effects of acute caffeine supplementation (3 mg/kg) administered in the morning and evening on performance-related variables in basketball players. Eleven, national-level, adolescent male basketball players underwent field-based fitness testing on four occasions: morning (10:00) with caffeine ingestion (AMCAFF), morning (10:00) with placebo ingestion (AMPLAC), evening (21:00) with caffeine ingestion (PMCAFF), and evening (21:00) with placebo ingestion (PMPLAC). Fitness testing included of a countermovement jump without arm swing (CMJ), CMJ with arm swing (CMJAS), squat jump (SJ), Lane Agility Drill (LAD), 20-m linear sprint, and Suicide Run with (SRD) and without dribbling (SR). Data were analysed using two-way repeated measures analyses of variance and paired t-tests, with effect sizes (ES) also determined for all pairwise comparisons. Follow-up t-test comparisons revealed that AMCAFF produced small-moderate, significant (p<0.001), improvements in CMJ (ES = 0.51), CMJAS (ES = 0.40), SJ (ES = 0.51), and SR (ES = -0.45) compared to AMPLAC. AMCAFF also produced a moderate, significantly (p<0.001) faster LAD (ES = -0.61) compared to PMCAFF. PMPLAC demonstrated small-moderate, significant (p<0.05) improvements in CMJ (ES = 0.43), CMJAS (ES = 0.48), and 20-m sprint (ES = -0.63) compared to AMPLAC. In contrast, AMPLAC resulted in large, significantly (p<0.001), faster SRD (ES = -1.46) and SR (ES = -1.59) compared to PMPLAC. Given the ergogenic effects of caffeine during basketball-specific fitness tests appear to be influenced by time of ingestion, basketball practitioners should consider administering caffeine only to players in the morning to improve vertical jump, sprinting, and change-of-direction performance, with no beneficial effects observed with caffeine ingestion in the evening.Highlights The effect of caffeine supplementation on basketball-specific performance related variables were mediated by ingestion time in elite, adolescent basketball players.AMCAFF produced small-moderate improvements in vertical jump, change-of-direction, 20-m linear sprint, and repeated-sprint performance compared to AMPLAC while PMCAFF produced trivial differences in each performance-related variable compared to PMPLAC.Comparisons between ingestion times in the placebo condition revealed vertical jump height and 20-m sprint speed were impaired in the morning compared to the evening, but these time-dependent differences were eliminated when caffeine was consumed in the morning.Basketball practitioners should consider administering caffeine only to players in the morning to improve vertical jump, sprinting, and change-of-direction performance, with no beneficial effects observed with caffeine ingestion in the evening.

The Effect of Game-Related Contextual Factors on Sleep in Basketball Players.

ABSTRACT: Fox, JL, Scanlan, AT, Stanton, R, and Sargent, C. The effect of game-related contextual factors on sleep in basketball players. J Strength Cond Res 36(10): 2942-2945, 2022-The purpose of this study was to determine whether contextual factors including game location, game outcome, and score-line margin influence subsequent nightly sleep duration and quality in basketball players. Seven semiprofessional, male basketball players were monitored across one competitive season using self-reported sleep diaries and activity monitors. Linear mixed models and effect sizes (ESs) were used to compare sleep duration and quality following games based on game location (played at home or away venues), game outcome (win or loss), and score-line margin (balanced [≤ 8-point margin] or unbalanced [≥ 9-point margin]). Sleep onset was later following away games (ES = 0.79 ± 0.86 [ moderate ], p = 0.12), losses (ES = 0.63 ± 0.79 [ moderate ], p = 0.18), and unbalanced games (ES = 1.36 ± 0.90 [ large ], p = 0.01). Wake time was later following losses (ES = 1.14 ± 0.85 [ moderate ], p = 0.02). Sleep fragmentation was higher following wins (ES = 0.65 ± 0.74 [ moderate ], p = 0.17). Sleep efficiency was higher following losses (ES = 0.70 ± 0.80 [ moderate ], p = 0.14). All other differences in sleep variables yielded trivial - small ESs ( p > 0.05). Because of the later sleep onset times of players, basketball practitioners should consider avoiding early morning travel or training sessions following away games, losses, and unbalanced games, to maintain similar nightly sleep durations in light of the contextual factors encountered. Basketball practitioners may also need to ensure player sleep is optimized following wins compared with losses given the lower quality observed.

The Application of Accelerometer-Derived Moving Averages to Quantify Peak Demands in Basketball: A Comparison of Sample Duration, Playing Role, and Session Type.

ABSTRACT: Fox, JL, Conte, D, Stanton, R, McLean, B, and Scanlan, AT. The application of accelerometer-derived moving averages to quantify peak demands in basketball: A comparison of sample duration, playing role, and session type. J Strength Cond Res 35(12S): S58-S63, 2021-The purpose of this was to study compare peak external workload intensities in basketball using accelerometer-derived moving averages between different sample durations (0.5-5 minutes), session types (training vs. game-play), and playing roles (starting vs. bench players). Five starting and 3 bench players were monitored over a 15-week competitive season using accelerometers. For all training sessions and games, peak external workload intensities were determined using accelerometer-derived moving averages for PlayerLoad per minute (PL·min-1) across sample durations of 0.5, 1, 2, 3, 4, and 5 minutes. Linear mixed-models and effect sizes (ESs) were used to compare peak PL·min-1 between sample durations, session type, and playing role. Peak PL·min-1 was significantly different between all sample durations (p < 0.05; ES = 0.88-5.45), with higher intensities evident across shorter sample durations. In starting players, peak intensities were significantly higher during games compared with training for all sample durations (p < 0.05; ES = 0.69-0.93). Peak game intensities were higher in starting players using all sample durations (p > 0.05; ES = 0.69-1.43) compared with bench players. Shorter sample durations produced higher peak PL·min-1. Peak intensities were higher during games than training in starting players, indicating training may not adequately prepare players for the most demanding passages of game-play.

Comparing Weekly Training and Game Demands According to Playing Position in a Semiprofessional Basketball Team.

PURPOSE: To compare weekly training and game demands according to playing position in basketball players. METHODS: A longitudinal, observational study was adopted. Semiprofessional, male basketball players categorized as backcourt (guards; n = 4) and frontcourt players (forwards/centers; n = 4) had their weekly workloads monitored across an entire season. External workload was determined using microsensors and included PlayerLoad™ (PL) and inertial movement analysis variables. Internal workload was determined using heart rate to calculate absolute and relative summated-heart-rate-zones workload and rating of perceived exertion (RPE) to calculate session-RPE workload. Comparisons between weekly training and game demands were made using linear mixed models and effect sizes in each positional group. RESULTS: In backcourt players, higher relative PL (P = .04, very large) and relative summated-heart-rate-zones workload (P = .007, very large) were evident during training, while greater session-RPE workload (P = .001, very large) was apparent during games. In frontcourt players, greater PL (P < .001, very large), relative PL (P = .019, very large), peak PL intensities (P < .001, moderate), high-intensity inertial movement analysis events (P = .002, very large), total inertial movement analysis events (P < .001, very large), summated-heart-rate-zones workload (P < .001, very large), RPE (P < .001, very large), and session-RPE workload (P < .001, very large) were evident during games. CONCLUSIONS: Backcourt players experienced similar demands between training and games across several variables, with higher average workload intensities during training. Frontcourt players experienced greater demands across all variables during games than training. These findings emphasize the need for position-specific preparation strategies leading into games in basketball teams.

Not All About the Effort? A Comparison of Playing Intensities During Winning and Losing Game Quarters in Basketball.

PURPOSE: To compare peak and average intensities encountered during winning and losing game quarters in basketball players. METHODS: Eight semiprofessional male basketball players (age = 23.1 [3.8] y) were monitored during all games (N = 18) over 1 competitive season. The average intensities attained in each quarter were determined using microsensors and heart-rate monitors to derive relative values (per minute) for the following variables: PlayerLoad, frequency of high-intensity and total accelerations, decelerations, changes of direction, jumps, and total inertial movement analysis events combined, as well as modified summated-heart-rate-zones workload. The peak intensities reached in each quarter were determined using microsensors and reported as PlayerLoad per minute over 15-second, 30-second, 1-minute, 2-minute, 3-minute, 4-minute, and 5-minute sample durations. Linear mixed models and effect sizes were used to compare intensity variables between winning and losing game quarters. RESULTS: Nonsignificant (P > .05), unclear-small differences were evident between winning and losing game quarters in all variables. CONCLUSIONS: During winning and losing game quarters, peak and average intensities were similar. Consequently, factors other than the intensity of effort applied during games may underpin team success in individual game quarters and therefore warrant further investigation.

Call to Action: Recommendations to Improve the Methodological Reporting of Games-Based Drills in Basketball Research.

PURPOSE: Games-based drills are the predominant form of training adopted during basketball practice. As such, researchers have begun to quantify the physical, physiological, and perceptual demands of different games-based drill formats. However, study methodology has not been systematically reported across studies, limiting the ability to form conclusions from existing research. The authors developed this call to action to draw attention to the current standard of methodological reporting in basketball games-based drill research and establish a systematic reporting standard the authors hope will be utilized in future research. The Basketball Games-Based Drill Methodical Reporting Checklist (BGBDMRC) was developed to encourage the systematic reporting of games-based drill methodology. The authors used the BGBDMRC to evaluate the current methodological reporting standard of studies included in their review published in the International Journal of Sports Physiology and Performance, "A Systematic Review of the External and Internal Workloads Experienced During Games-Based Drills in Basketball Players" (2020), which highlighted this issue. Of the 17 studies included in their review, only 38% (±18%) of applicable checklist items were addressed across included studies, which is problematic as checklist items are essential for study replication. CONCLUSIONS: The current standard of methodological reporting in basketball games-based drill research is insufficient to allow for replication of examined drills in future research or the application of research outcomes to practice. The authors implore researchers to adopt the BGBDMRC to improve the quality and reproducibility of games-based drill research and increase the translation of research findings to practice.

Individualized and Fixed Thresholds to Demarcate PlayerLoad Intensity Zones Produce Different Outcomes.

ABSTRACT: Scanlan, AT, Fox, JL, Milanovic, Z, Stojanovic, E, Stanton, R, and Dalbo, VJ. Individualized and fixed thresholds to demarcate PlayerLoad intensity zones produce different outcomes. J Strength Cond Res 35(7): 2046-2052, 2021-Individualized approaches to derive intensity zones are yet to be examined using microsensor metrics in basketball. The purpose of this study was to quantify and compare the time spent in different PlayerLoad intensity zones using individualized and fixed approaches during basketball training. Thirteen semiprofessional, male basketball players were monitored across the preparatory training phase. Microsensors recorded the time spent in 6 PlayerLoad intensity zones using individualized and fixed approaches. Individualized zones were calculated relative to the peak instantaneous PlayerLoad response observed in each player across training. Fixed zones were determined following predefined cut-points set in proprietary software. The majority of training time was spent in zones 1-2 (98-99%), with a low proportion of time detected in zones 3-6 (1-2%) across approaches. The fixed approach produced greater training time in zones 2 (11.9 ± 1.2 vs. 9.3 ± 2.4 minutes; very likely large) and 3 (1.7 ± 0.8 vs. 0.8 ± 0.9 minutes; very likely moderate), was an unclear small increase in time spent in zone 1 were apparent using individualized thresholds (76.5 ± 11.2 vs. 72.9 ± 9.6 minutes). Almost certain similar time was spent in zones 4-6 across approaches. Individual analyses showed deviations across players; however, fixed PlayerLoad thresholds produced higher training time in zones 2 (moderate to very large), 3 (moderate to very large), 4 (moderate to large), and 5 (moderate) than the individualized approach in most players. Variations in outcomes between individualized and fixed approaches must be considered when quantifying the time spent working in PlayerLoad intensity zones.

The Association Between Sleep and In-Game Performance in Basketball Players.

PURPOSE: To investigate the associations between sleep and competitive performance in basketball. METHODS: A total of 7 semiprofessional, male players were monitored across the in-season. On nights prior to competition, sleep duration and quality were assessed using actigraphs and sleep diaries. The data were accumulated over 1 (night 1), 2 (nights 1-2 combined), 3 (nights 1-3 combined), and 4 (nights 1-4 combined) nights prior to competition. Performance was reported as player statistics (field goal and free-throw accuracy, rebounds, assists, steals, blocks, and turnovers) and composite performance statistics (offensive rating, defensive rating, and player efficiency). Linear regression analyses with cluster-robust standard errors using bootstrapping (1000 replications) were performed to quantify the association between sleep and performance. RESULTS: The night before competition, subjective sleep quality was positively associated with offensive rating and player efficiency (P < .05). CONCLUSIONS: Strategies to increase subjective sleep quality the night before competition should be considered to increase the likelihood of successful in-game performance, given its association with composite performance metrics.

The Concurrent Validity of Session-Rating of Perceived Exertion Workload Obtained Face-to-Face Versus Via an Online Application: A Team Case Study.

PURPOSE: To compare the concurrent validity of session-rating of perceived exertion (sRPE) workload determined face-to-face and via an online application in basketball players. METHODS: Sixteen semiprofessional, male basketball players (21.8 [4.3] y, 191.2 [9.2] cm, 85.0 [15.7] kg) were monitored during all training sessions across the 2018 (8 players) and 2019 (11 players) seasons in a state-level Australian league. Workload was reported as accumulated PlayerLoad (PL), summated-heart-rate-zones (SHRZ) workload, and sRPE. During the 2018 season, rating of perceived exertion (RPE) was determined following each session via individualized face-to-face reporting. During the 2019 season, RPE was obtained following each session via a phone-based, online application. Repeated-measures correlations with 95% confidence intervals were used to determine the relationships between sRPE collected using each method and other workload measures (PL and SHRZ) as indicators of concurrent validity. RESULTS: Although all correlations were significant (P < .05), sRPE obtained using face-to-face reporting demonstrated stronger relationships with PL (r = .69 [.07], large) and SHRZ (r = .74 [.06], very large) compared with the online application (r = .29 [.25], small [PL] and r = .34 [.22], moderate [SHRZ]). CONCLUSIONS: Concurrent validity of sRPE workload was stronger when players reported RPE in an individualized, face-to-face manner compared with using a phone-based online application. Given the weaker relationships with other workload measures, basketball practitioners should be cautious when using player training workloads predicated on RPE obtained via online applications.

The Relationships Between External and Internal Workloads During Basketball Training and Games.

PURPOSE: To investigate the relationships between external and internal workloads using a comprehensive selection of variables during basketball training and games. METHODS: Eight semiprofessional, male basketball players were monitored during training and games for an entire season. External workload was determined as PlayerLoad™: total and high-intensity accelerations, decelerations, changes of direction, and jumps and total low-intensity, medium-intensity, high-intensity, and overall inertial movement analysis events. Internal workload was determined using the summated-heart-rate zones and session rating of perceived exertion models. The relationships between external and internal workload variables were separately calculated for training and games using repeated-measures correlations with 95% confidence intervals. RESULTS: PlayerLoad was more strongly related to summated-heart-rate zones (r = .88 ± .03, very large [training]; r = .69 ± .09, large [games]) and session rating of perceived exertion (r = .74 ± .06, very large [training]; r = .53 ± .12, large [games]) than other external workload variables (P < .05). Correlations between total and high-intensity accelerations, decelerations, changes of direction, and jumps and total low-intensity, medium-intensity, high-intensity, and overall inertial movement analysis events and internal workloads were stronger during training (r = .44-.88) than during games (r = .15-.69). CONCLUSIONS: PlayerLoad and summated-heart-rate zones possess the strongest dose-response relationship among a comprehensive selection of external and internal workload variables in basketball, particularly during training sessions compared with games. Basketball practitioners may therefore be able to best anticipate player responses when prescribing training drills using these variables for optimal workload management across the season.

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

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.