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Article

Sleep Varies According to Game Venue but Not Season Period in Female Basketball Players: A Team-Based Observational Study

by
Aaron T. Scanlan
1,*,
Nathan Elsworthy
1,
Jordan L. Fox
2,
Emilija Stojanović
3,4,
Amalia Campos-Redondo
5,
Sergio J. Ibáñez
5 and
Cody J. Power
1
1
School of Health, Medical and Applied Sciences, Central Queensland University, Rockhampton, QLD 4701, Australia
2
Allied Health and Human Performance, University of South Australia, Adelaide, SA 5031, Australia
3
Faculty of Medical Sciences, Department of Physiology, University of Kragujevac, 34000 Kragujevac, Serbia
4
Faculty of Sport Science, Department of Training and Exercise Science, Ruhr University Bochum, 44780 Bochum, Germany
5
Faculty of Sport Sciences, University of Extremadura, 10071 Cáceres, Spain
*
Author to whom correspondence should be addressed.
Appl. Sci. 2025, 15(5), 2731; https://doi.org/10.3390/app15052731
Submission received: 19 December 2024 / Revised: 21 February 2025 / Accepted: 28 February 2025 / Published: 4 March 2025
(This article belongs to the Section Applied Biosciences and Bioengineering)
Browse Figures
Versions Notes

Abstract

:
Sleep is an essential part of the recovery process that may be jeopardized during specific contexts across the season. Therefore, this study aimed to examine the impact of key contextual factors—game venue and season period—on sleep in semi-professional, female basketball players. Sleep was monitored in players using wrist-worn activity monitors across the entire regular season. For game venue analyses, nights were categorized as a control, before and after home games, as well as before and after away games. For season period analyses, nights were arranged into evenly distributed four-week blocks as early, middle, and late periods of the regular season. Players slept significantly less on nights before away games (p < 0.05) than on other nights, which was attributed to significantly earlier wake times (p < 0.05). While sleep onset and offset times were significantly later during the middle and later season periods than the early season period (p < 0.05), sleep duration and quality remained consistent across periods. These results suggest players could experience disrupted sleep prior to away games, which has potential implications for performance in upcoming games. Coaches and performance staff may need to consider implementing suitable strategies to safeguard the sleep of their players in these scenarios.

1. Introduction

Basketball seasons impose extensive physiological [1], physical [1], and psychological [2] loading on players due to the regular training, competition, travel, and contractual commitments they involve. In turn, basketball players experience notable fatigue across the competition schedule [3], emphasizing the importance of implementing suitable recovery strategies and rest opportunities for players throughout the season. Indeed, basketball practitioners have indicated they place high importance on recovery strategies, predominantly to reduce injury risk, optimize readiness, reduce fatigue, prevent overtraining, and enhance the psychological well-being of their players [4]. Sleep is considered the most important recovery process among male and female team sport athletes [5,6]. Sleep variables have been significantly associated with in-game performance [7,8] and injury risk [9,10] in basketball players, emphasizing the importance of sleep within the sport. Consequently, expert consensus [11] recommends practitioners identify specific situations that may negatively impact sleep among their athletes to develop targeted interventions.
Various contextual factors may impose situations that impede basketball players from attaining adequate sleep duration and quality. For instance, different samples of basketball players have been reported to experience significantly poorer sleep following games [12], following training sessions when games are played on the following day [13], and on nights before games [14] compared to other nights. Further considering training and game contexts, competing away from home can impose added travel requirements and expose players to unfamiliar environments [11] as well as augment anxiety levels [15], which may further impact player sleep. Despite this logic, no significant differences in sleep variables between nights following games played at home and away venues have been reported in semi-professional, male [16] and professional, female basketball players [14]. However, control nights or nights before games were not factored in the comparisons conducted in these studies [14,16], limiting the rigor of evidence provided concerning the impact of game venue on sleep. In contrast, no significant effects were reported for sleep duration (hours) or efficiency (which represents sleep quality, determined as the ratio of sleep duration expressed relative to total time in bed as a percentage) between home and away games on nights before, following, and the day after games, nor control nights, in professional, female basketball players [8]. However, these studies [8,14,16] represent a relatively small evidence base with only one study [8] considering different nights surrounding home and away games, creating a need for more research on this topic to improve the current understanding.
Season period is another contextual factor that has been theorized to impact sleep among athletes, with variations in sleep variables documented across seasonal phases among different athlete samples in a recent review [17]. However, the impact of season period on sleep in basketball players remains to be definitively explored and warrants specific investigation given the constraints and challenges towards sleep that likely vary between sports [11]. To date, research has reported non-significant differences in subjective sleep quality between the off-season and regular season in male, high-school basketball players [18] and shown nightly self-reported sleep duration and quality to fluctuate widely across the entire season in collegiate, male basketball players [9]. However, the lack of statistical comparison across defined periods during the regular season and objective data acquired with validated methods were notable limitations in these studies [9,18]. In this regard, intensified periods of training and competition, varied levels of importance placed on specific games, and fluctuations in environmental conditions may promote varied sleep among players across chronic timeframes within the regular season [19]. Moreover, the accumulative physical, physiological, and psychological stress accrued by players may impact their sleep with season progression.
Therefore, a broader evidence base regarding the impact of logical contextual factors like game venue and season period on sleep is needed in basketball players, especially given that sports practitioners have indicated that a lack of knowledge is a major barrier to implementing appropriate sleep intervention strategies [20]. Moreover, sleep-related investigations are needed specifically in female athletes, considering their underrepresentation in the literature and the unique sleep issues they encounter compared to their male counterparts [21]. Consequently, this study aimed to examine the impact of the game venue (home vs. away) and season period (early vs. middle vs. late) on sleep in female basketball players.

2. Materials and Methods

2.1. Participants

Ten semi-professional, female basketball players from the same NBL1 North conference (state-level Australian competition) team were initially recruited to participate in this study. Players were informed of the risks and benefits of participation in this study and provided written informed consent. Inclusion criteria encompassed sufficient participation in training sessions and games across the season, suitable compliance in following sleep monitoring procedures, absence of any injuries or health conditions preventing safe participation, and absence of any sleep-related disorder or medication use that could impact sleep behaviors. In this regard, two players were excluded due to insufficient participation during games (i.e., <2 min of playing time on average), and data from another player were excluded due to poor compliance with sleep monitoring procedures (i.e., wore a sleep monitor for <50% of sleep opportunities). Consequently, only data from seven players were included in the final analyses (age: 20 ± 2 years, stature: 178 ± 8 cm, body mass: 74 ± 14 kg). All players were confirmed to be free from any injuries or health conditions that could prevent safe participation in this study (via the Adult-Pre-Exercise Screening System) and disclosed they had not been clinically diagnosed with a sleep disorder nor were taking medication that could impact their sleep behaviors. All procedures were approved by the Central Queensland University Human Research Ethics Committee (no. 0000023323).

2.2. Design and Procedures

A longitudinal, observational pilot study was performed where players had their sleep monitored on all nights across the 2021 NBL1 regular season (May to August). The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement was followed in writing in this study [22]. The full regular season schedule (which was shorter than typical due to wider COVID-related restrictions) for the team monitored in this study is shown in Figure 1, where 21 training sessions and 14 games were completed. Regarding game venue, 6 games were played at home and 8 games were played at an away venue.
Player sleep was monitored using activity monitors (Readiband™, Fatigue Science, Vancouver, BC, Canada) worn on the non-dominant wrist on all nights across the regular season. Players were instructed to always wear the monitor, except when showering, swimming, or participating in training sessions or games. All sleep data were extracted and organized into a custom Microsoft Excel spreadsheet (version 16; Microsoft Corporation, Redmond, WA, USA) for processing and analysis. The following sleep variables were determined from the acquired data: sleep onset (hours:minutes [hh:mm], clock time that each player fell asleep), sleep offset (clock time [hh:mm] that each player woke before getting up), sleep duration (time [min] spent asleep between sleep onset and sleep offset), wake after sleep onset (WASO, time [min] spent awake between sleep onset and sleep offset), and sleep efficiency (sleep duration expressed as a percentage of the time spent in bed, which was determined as the time between when an attempt was made to initiate sleep while in bed and getup time following the sleep period) [23]. Readibands™ have previously demonstrated acceptable validity in detecting sleep and wake (sensitivity = 94%, specificity = 40%, and accuracy = 90%) compared to polysomnography [24]. Moreover, total sleep time, WASO, and sleep efficiency gathered via Readibands™ have shown acceptable inter-device reliability (intra-class coefficient = 0.80–0.99) [25] and validity (p > 0.05) [24,26] compared to polysomnography.

2.3. Statistical Analyses

For game venue analyses, nights were categorized (n = 5) as a control (neither training nor games conducted on these days or the day immediately prior and after) immediately before home games, following home games, immediately before away games, and following away games. During weeks (n = 3, Figure 1) where multiple away games were played on subsequent days, only the first game was used in analyses for nights immediately before away games, given that other nights surrounding games in these scenarios were categorized as following away games. For season period analyses, data were split across all nights arranged into even 4-week blocks (n = 3) representing the early, middle, and late periods of the regular season [27].
Sleep data were imported into RStudio (v4.1.3; R Core Team) from Microsoft Excel for cleaning and analyses. Data were arranged in long form, with rows representing independent observations and sleep variables presented in columns. Linear mixed-effects models (LMM) were built to assess differences in sleep variables between nights separately for game venue and season period analyses. A customized script was developed for all analyses with models built using the lmerTest package [28] in RStudio. For the game venue model, ‘night type’ (n = 5) was entered as a fixed effect, while for the season period model, ‘period’ (n = 3) was entered as a fixed effect. ‘Player’ was entered as a random effect in both models to account for repeated data points taken from the same players. To assess data normality, histograms and Q-Q plots were produced from residual values and visually checked using the see [29] and performance [30] packages. No statistical assumptions for using LMM were violated, supporting their use in our analyses. Tukey’s Honestly Significant Difference tests were utilized for post hoc comparisons, with pairwise differences between night types (game venue analyses) and season periods determined using the emmeans [31] package for calculating the estimated marginal means. Furthermore, Hedge’s gav effect size with 95% confidence limits was also determined for all pairwise comparisons [32,33]. In this regard, effect size magnitudes were interpreted according to established descriptors with accompanying thresholds: trivial = <0.20; small = 0.20–0.59; moderate = 0.60–1.19; large = 1.20–1.99; or very large = ≥2.00 [34]. Descriptive data were calculated as the estimated marginal means (with 95% confidence limits) as well as the mean ± standard deviation (SD) for all variables, with statistical significance set at p ≤ 0.05 for analyses.

3. Results

3.1. Game Venue

The number of individual player samples for each category in the game venue analyses was 70 for control nights, 32 for nights immediately before home games, 28 for nights following home games, 13 for nights immediately before away games, and 33 for nights following away games. Estimated marginal means (with 95% confidence limits) for each sleep variable in each night category are shown in Table 1, while the mean ± standard deviation alongside individual data points for each sleep variable in each night category are shown in Figure 2. Analyses showed significant differences in sleep duration, with players sleeping 98 min less on nights before away games than on control nights (p < 0.001, gav = 1.19 [0.60, 1.78], moderate effect) and 88 min less on nights before away games than before home games (p = 0.013, gav = 1.07 [0.42, 1.72], moderate effect). Significant differences were also evident for sleep onset and offset times, with players falling asleep 55–71 min later after home games compared to control nights (p = 0.001, gav = 0.90 [0.46, 1.35], moderate effect), nights before home games (p < 0.001, gav = 1.15 [0.65, 1.66], moderate effect), and nights after away games (p = 0.007, gav = 0.89 [0.38, 1.40], moderate effect), and awakening 69–99 min earlier before away games compared to all other nights (control nights: p < 0.001, gav = 1.27 [0.67, 1.86], large effect; nights before home games: p = 0.014, gav = 1.06 [0.41, 1.71], moderate effect; nights after home games: p = 0.001, gav = 1.34 [0.68, 2.00], large effect; nights after away games: p = 0.037, gav = 0.94 [0.30, 1.58], moderate effect). Non-significant (p > 0.05) differences were apparent between all nights for WASO (gav = 0.00–0.37, trivial-to-small effects) and sleep efficiency (gav = 0.08–0.54, trivial-to-small effects).

3.2. Season Period

The number of individual samples for each category in the season period analyses was 122 for the early period, 123 for the middle period, and 136 for the late period. Marginal means (with 95% confidence intervals) for each sleep variable in each season period are shown in Table 2, while the mean ± standard deviation alongside individual data points for each sleep variable in each season period are shown in Figure 3. Analyses revealed significant differences in sleep onset and offset times. Specifically, players fell asleep 30 min later during the middle period (p = 0.017, gav = 0.44 [0.19, 0.69], small effect) and 34 min later during the later period (p = 0.010, gav = 0.26 [0.02, 0.53], small effect) compared to the early period, and awoke 34 min later during the middle period compared to the early period (p < 0.001, gav = 0.48 [0.23, 0.73], small effect). In contrast, non-significant (p > 0.05) trivial-to-small differences were evident between season periods for sleep duration (gav = 0.01–0.08), WASO (gav = 0.02–0.17), and sleep efficiency (gav = 0.07–0.22).

4. Discussion

This study examined the independent effects of novel contextual factors—game venue and season period—on sleep in female basketball players. Notable findings that were observed included: (1) players attained less than the recommended sleep duration and quality in specific scenarios surrounding games at different venues; (2) nights before away games had a significantly reduced sleep duration compared to other nights with an earlier awakening evident on the following morning; (3) although sleep patterns were altered following the early season period with later sleep onset and offset times, sleep duration and quality were consistent across chronic season periods. These key findings add useful evidence to the limited body of literature concerning the sleep behaviors of female basketball players, with potential implications for end-users in practice.
On average, although players attained an adequate sleep duration (≥7 h) and quality (sleep efficiency ≥ 85%) on control nights and nights before home games according to established recommendations [35,36], they did not meet these requirements in other scenarios across the schedule. More precisely, players attained inadequate sleep on nights after home games (duration: 6.6 h; efficiency: 82.8%), nights before away games (duration: 5.8 h; efficiency: 81.4%), and nights after away games (duration: 6.8 h; efficiency: 83.6%). These descriptive findings are less than those reported for habitual sleep in a meta-analysis of 14 studies examining elite (Olympic, international, professional national, or Division I collegiate levels) female athletes (mean [95% confidence intervals], duration: 7.8 [7.4, 8.2 h]; efficiency: 87% [85, 89%]) [37]. Likewise, our data are less than that documented in professional female basketball players competing in the Australian Women’s National Basketball League, who were monitored for a month during the season (mean ± SD all days, duration: 8.1 ± 1.6 h; efficiency: 92 ± 5%) [38], four seven-day periods during the pre-season and competitive season phases (mean ± SD for habitual days, duration: 7.4 ± 1.5 h; efficiency: 88 ± 6%) [14], and two seasons (mean ± SD all days, duration: 7.6 ± 1.5 h; efficiency: 92 ± 4%) [8] in separate studies. Lower sleep duration and quality in our study may be related to the novel challenges faced by semi-professional teams compared to professional teams, as studied previously [8,14,38]. Semi-professional teams likely operate on a lower budget with fewer resources to combat factors that may impact sleep during the season, such as adopting appropriate nutritional choices, ensuring optimal travel quality (e.g., transport mode, departure times, sleep environment when traveling), and players having added work or study commitments [11]. Moreover, common issues such as post-game media and team commitments [13], as well as rumination over individual or team performance [39], may have also underpinned the inadequate sleep on nights following both home and away games. Indeed, when comparing sleep between nights, our study adds to the limited basketball research [8], considering the night before games separately for playing at home and away, yielding some interesting insights.
We observed players to be particularly susceptible to poor sleep on nights before away games, attaining ~1.5 h less sleep than on control nights and nights before home games (p < 0.01, moderate effects) and ~1 h less sleep than on nights following games (p > 0.05, moderate effect). These findings may be underpinned by disruptions to normal sleep patterns among players, given they woke ~1.2–1.5 h earlier (p < 0.05) on mornings following away games than other mornings on average. Earlier wake times in scenarios when playing away might have been due to various reasons in the players we monitored. For instance, given players were traveling to the away destination on ‘game day’, they may have awoken earlier to meet scheduled departure times to the away location. Moreover, players may have experienced increased nervousness and thoughts before upcoming away games, given that normal routines (such as those during home games) may be disrupted, promoting earlier awakening [40]. In this regard, added stress in such scenarios may promote sleep-related issues like bruxism [41] and insomnia [42]. Although existing basketball research predominantly shows non-significant differences (p > 0.05) in sleep duration and efficiency between nights following away and home games in professional, female (home vs. away, duration: 7.4 ± 1.8 h vs. 7.6 ± 1.3 h; efficiency: 92 ± 4% vs. 93 ± 4%) [8] and semi-professional, male players (home vs. away, duration: 6.4 ± 2.2 h vs. 5.3 ± 2.3 h; efficiency: 83 ± 7% vs. 86 ± 5% [16]) as we did, our findings contrast previous findings in some cases. In this regard, professional, female players have been observed to attain significantly less sleep on nights following away games than on nights following home games (7.7 h vs. 6.8 h), attributed to early return travel on the following morning [14]—further emphasizing the importance of optimal travel scheduling. Considering nights prior to home and away games, the only previous study on this topic reported contrasting findings to those we observed, with non-significant variations between game venues (home vs. away, duration: 7.7 ± 1.7 h vs. 7.9 ± 1.6 h; efficiency: 93 ± 4% vs. 92 ± 5%) among professional, female players [8]. Variations in findings between studies might be due to player-related variability across samples [19] or team-related factors such as varied access to support staff and resources to accommodate player preparation, travel conditions, and accommodation settings conducive to sleep [43]. Nevertheless, while disturbances to sleep on the night prior to competition have been readily identified in the wider sports science literature [39], our findings suggest these may be context-specific in some cases, with particular risk evident when competing at away venues.
While sleep variables have been shown to fluctuate on a nightly basis across the season in collegiate, male players [9], no research has statistically compared sleep during different phases of the regular season. In fact, the need for research exploring the effects of cumulative travel [43] and load demands [12] accrued across the season on sleep has been highlighted in the literature. In this way, we showed some slight deviations in sleep patterns where players had significantly (p < 0.05, small effects) later sleep onset times (middle and later compared to early season period) and wake times (middle compared to early season period) with season progression. These variations between season periods may have been attributed to the increased travel requirements earlier in the season, with five out of the eight away games held in the early season period (Figure 1). In this regard, players may have adjusted their sleeping patterns in line with the team schedule when traveling and being situated in locations away from home. However, these findings did not translate into any notable variations in sleep duration or efficiency across season periods, suggesting sleep was relatively stable across chronic four-week blocks. Indeed, similar observations have been made in professional, male rugby league players, with their sleeping patterns shifting later when transitioning between pre-season and competitive season phases without any overall impact on sleep duration and efficiency [44]. Our initial findings suggest that variations in sleep may manifest across more acute than chronic periods during the regular season; however, further research is encouraged, given the limited evidence available concerning sleep behaviors during chronic season phases in basketball.
Although our findings are novel, they should be considered in light of the inherent limitations. Firstly, our study was a team-based descriptive study consisting of a small sample of semi-professional female basketball players monitored across the season. Consequently, given the variability in sleep across female athletes [37], our results might not be indicative of all semi-professional players. Likewise, similar investigations should be conducted in other player samples representing different competition levels, different sports, and males, given the discrepancies in sleep reported due to these factors [17]. Secondly, all away games were arranged in condensed schedules across three separate weeks (two or three games played on consecutive days). Accordingly, we considered nights before away games only prior to the first game, given that subsequent nights were categorized as nights following away games, which restricted the number of player samples for nights before away games. Thirdly, additional measures of psychological stress as potential mechanism variables were not able to be measured in this study but warrant consideration in future research given the reported associations between psychological stress and sleep variables in athletes [45,46]. Fourthly, the activity monitors used in our study were not able to discern between different sleep stages, which may provide more nuanced insight regarding changes in sleep surrounding training and competition schedules in athletes [17]. Finally, we were not able to monitor menstrual cycle status among players as a potential mechanism variable to explain variations in sleep across nights. In this regard, future longitudinal research spanning multiple seasons should be conducted considering menstrual cycle status in line with recommendations [47], given the variations in sleep that can occur across menstrual cycle phases [48,49].
From a practical perspective, our novel findings offer important considerations for end-users in basketball settings. For instance, disrupted sleep on the night prior to competition may reduce player performance in subsequent games, given that meta-analytic evidence [50,51] indicates a single night of restricted sleep—especially due to earlier awakening as observed in our study—is detrimental to physical performance on the following day. In turn, declines in performance following poor sleep are particularly apparent in the afternoon and evening [50,51], which is concerning given that games are typically scheduled later in the day as evident in our study. Consequently, basketball coaches and performance staff should implement strategies that may help protect the sleep of their players when playing away where feasible, especially given that many athletes do not adopt specific approaches to optimize sleep [39,40]. To combat the earlier wake times prior to away games, suitable strategies to optimize sleep when playing away should be considered like: (1) providing frequent sleep education sessions to players, coaches, and performance staff [35], especially to improve their understanding of potential sleep hygiene strategies [52]; (2) encouraging naps and banking of sleep if faced with unavoidable disruptions [35]; and (3) planning travel that protects sleeping opportunities for normal wake times to be achieved. In turn, when traveling, teams may also seek to provide conducive sleep environments in player rooms (e.g., consideration of light, noise, temperature, and comfort [52]), ensure teammates assigned to the same room will complement the sleep of one another (e.g., compatibility in chronotype and personality), and effectively plan next-day travel if playing consecutive away games. Future intervention studies are encouraged to assess the efficacy of different sleep-related strategies.

5. Conclusions

Our findings indicate that while sleep variables were relatively consistent when aggregated across chronic phases during the regular season, players were susceptible to poor sleep on particular nights surrounding games. In this way, players obtained significantly less sleep on nights before away games, which was attributed to awakening significantly earlier on the following day compared to other night categories.

Author Contributions

Conceptualization, A.T.S., C.J.P., N.E. and S.J.I.; methodology, C.J.P., A.T.S. and J.L.F.; software, N.E.; formal analysis, A.T.S. and N.E.; investigation, C.J.P.; data curation, A.T.S., N.E. and C.J.P.; writing—original draft preparation, A.T.S.; writing—review and editing, E.S., J.L.F., S.J.I., A.C.-R. and C.J.P. All authors have read and agreed to the published version of the manuscript.

Funding

C.P. was supported by an Australian Government Research Training Program Scholarship in collecting data for this study.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Central Queensland University Human Research Ethics Committee (approval no. 0000023323) on 24 February 2022.

Informed Consent Statement

Informed consent was obtained from all participants involved in the study.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors upon request.

Acknowledgments

The voluntary contributions of the coaching staff and players within the monitored team were integral in completing this study.

Conflicts of Interest

The authors declare no conflicts of interest.

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Applsci 15 02731 g001
Figure 1. The training and game schedule for the semi-professional, female basketball players monitored in this study. Note: letters next to each row correspond to each day of the week from Monday (M) to Sunday (S) in order; court image indicates on-court team training session; shaded basketball indicates away game; transparent basketball indicates home game; all training sessions commenced at 18:30; all games on Fridays commenced at 18:30; all games on Saturdays commenced at 18:00; all games on Sundays commenced at 12:00.
Figure 1. The training and game schedule for the semi-professional, female basketball players monitored in this study. Note: letters next to each row correspond to each day of the week from Monday (M) to Sunday (S) in order; court image indicates on-court team training session; shaded basketball indicates away game; transparent basketball indicates home game; all training sessions commenced at 18:30; all games on Fridays commenced at 18:30; all games on Saturdays commenced at 18:00; all games on Sundays commenced at 12:00.
Applsci 15 02731 g001
Applsci 15 02731 g002
Figure 2. Mean ± standard deviation (large shapes and lines) alongside individual data points (small shapes) for (A) sleep onset, (B) sleep offset, (C) sleep duration, (D) wake after sleep onset (WASO), and (E) sleep efficiency across different nights according to game venue throughout the regular season in semi-professional, female basketball players.
Figure 2. Mean ± standard deviation (large shapes and lines) alongside individual data points (small shapes) for (A) sleep onset, (B) sleep offset, (C) sleep duration, (D) wake after sleep onset (WASO), and (E) sleep efficiency across different nights according to game venue throughout the regular season in semi-professional, female basketball players.
Applsci 15 02731 g002
Applsci 15 02731 g003
Figure 3. Mean ± standard deviation (large shapes and lines) alongside individual data points (small shapes) for (A) sleep onset, (B) sleep offset, (C) sleep duration, (D) wake after sleep onset (WASO), and (E) sleep efficiency across different nights according to season period throughout the regular season in semi-professional, female basketball players.
Figure 3. Mean ± standard deviation (large shapes and lines) alongside individual data points (small shapes) for (A) sleep onset, (B) sleep offset, (C) sleep duration, (D) wake after sleep onset (WASO), and (E) sleep efficiency across different nights according to season period throughout the regular season in semi-professional, female basketball players.
Applsci 15 02731 g003
Table 1. Estimated marginal means (95% confidence limits) for sleep variables across different nights according to game venue throughout the regular season in semi-professional, female basketball players.
Table 1. Estimated marginal means (95% confidence limits) for sleep variables across different nights according to game venue throughout the regular season in semi-professional, female basketball players.
VariableNight Type
ControlBefore HomeAfter HomeBefore AwayAfter Away
Sleep onset (hh:mm)23:46 (23:12, 00:19) #23:30 (22:55, 00:05) #00:41 (00:05, 01:17)23:47 (23:10, 00:22) #23:46 (23:10, 00:22) #
Sleep offset (hh:mm)07:41 (07:13, 08:08) *07:25 (06:53, 07:57) *07:46 (07:13, 08:19) *06:07 (05:22, 6:52)07:16 (06:43, 07:49) *
Sleep duration (min)444 (402, 485) *434 (390, 479) *397 (352, 443)346 (290, 403)408 (362, 453)
Wake after sleep onset (min)32.0 (16.7, 47.3)39.9 (23.6, 56.1)29.6 (13.0, 46.2)29.5 (9.5, 49.4)38.5 (1.9, 55.0)
Sleep efficiency (%)86.6 (82.4, 90.8)84.5 (79.9, 88.3)82.8 (78.0, 87.5)81.4 (75.3, 87.5)83.6 (78.8, 88.3)
Note: # indicates significantly earlier than nights after home games (p < 0.01); * indicates significantly greater or later than nights before away games (p < 0.05).
Table 2. Marginal means (95% confidence limits) for sleep variables across different nights according to season period throughout the regular season in semi-professional, female basketball players.
Table 2. Marginal means (95% confidence limits) for sleep variables across different nights according to season period throughout the regular season in semi-professional, female basketball players.
VariableSeason Period
EarlyMiddleLate
Sleep onset (hh:mm)23:18 (22:41, 23:55)23:48 (23:11, 00:25) †23:52 (23:17, 00:28) †
Sleep offset (hh:mm)07:12 (06:46, 07:38)07:46 (07:20, 08:12) †07:26 (07:01, 07:52)
Sleep duration (min)440 (400, 481)441 (400, 481)435 (394, 475)
Wake after sleep onset (min)29.7 (17.4, 41.9) 34.1 (21.9, 46.3)34.5 (22.5, 46.6)
Sleep efficiency (%)86.2 (83.1, 89.3)85.5 (81.2, 87.2)84.2 (81.2, 87.2)
Note: † indicates significantly later than the early period (p < 0.05).
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MDPI and ACS Style

Scanlan, A.T.; Elsworthy, N.; Fox, J.L.; Stojanović, E.; Campos-Redondo, A.; Ibáñez, S.J.; Power, C.J. Sleep Varies According to Game Venue but Not Season Period in Female Basketball Players: A Team-Based Observational Study. Appl. Sci. 2025, 15, 2731. https://doi.org/10.3390/app15052731

AMA Style

Scanlan AT, Elsworthy N, Fox JL, Stojanović E, Campos-Redondo A, Ibáñez SJ, Power CJ. Sleep Varies According to Game Venue but Not Season Period in Female Basketball Players: A Team-Based Observational Study. Applied Sciences. 2025; 15(5):2731. https://doi.org/10.3390/app15052731

Chicago/Turabian Style

Scanlan, Aaron T., Nathan Elsworthy, Jordan L. Fox, Emilija Stojanović, Amalia Campos-Redondo, Sergio J. Ibáñez, and Cody J. Power. 2025. "Sleep Varies According to Game Venue but Not Season Period in Female Basketball Players: A Team-Based Observational Study" Applied Sciences 15, no. 5: 2731. https://doi.org/10.3390/app15052731

APA Style

Scanlan, A. T., Elsworthy, N., Fox, J. L., Stojanović, E., Campos-Redondo, A., Ibáñez, S. J., & Power, C. J. (2025). Sleep Varies According to Game Venue but Not Season Period in Female Basketball Players: A Team-Based Observational Study. Applied Sciences, 15(5), 2731. https://doi.org/10.3390/app15052731

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