Relationships between smart ring-measured sleep variables and next-day basketball game performance: A case series

Abstract

Sleep is critical to athlete health and sport performance.

Purpose

To evaluate the relationship between objective sleep data and next-day basketball game performance.

Methods

Collegiate men basketball athletes (n = 4) wore smart ring sleep trackers the night before games (n = 22). Sleep variables included hours of total sleep (TS), rapid eye movement sleep duration (RM), deep sleep duration (DS), and heart rate variability (HRV). Game performance was evaluated using field goal percentage (FG%) and player efficiency (EFF).

Results

TS, RM, DS, and HRV demonstrated very weak relationships with FG% (p > 0.05) and EFF (p > 0.05). Individual player analysis revealed one athlete exhibited strong to moderate correlations for TS-EFF (r = 0.67), RS-EFF (r = 0.41), TS-FG% (r = 0.51), and RM-FG% (r = 0.67) (p < 0.05). Sleep metrics had no effect on FG% (R²= 0.025) and EFF (R²= 0.053) (p > 0.05).

Conclusions

Results suggest a lack of group-level associations between smart ring-measured sleep variables and next-day game performance. This highlights the need for individualized sleep monitoring rather than relying solely on objective metrics.

Keywords

Athlete monitoring heart rate variability recovery strategies

Introduction

Growing evidence suggests that sleep may be one of the most important modifiable lifestyle factors for athletes seeking to optimize health and performance. Sufficient sleep has been associated with cognitive function, cardiovascular and metabolic health, and sport-related outcomes (i.e., injury reduction, skill development).¹ The recommendation of adequate sleep is >7 h.² Goldman et al.³ concluded that collegiate athletes do not meet this minimum, which may be detrimental to performance. It is estimated that approximately 40% of collegiate athletes obtain <7 h of sleep during weekdays, while 51% report high levels of daytime sleepiness.⁴ In particular, sports, like basketball, that require speed, tactical strategy, and technical skill are the most sensitive to sleep changes and thus, insufficient sleep may result in consequential performance alterations.⁵ Despite the proposed importance of sleep on athletic performance, few studies have investigated the impact of sleep on in-game performance in basketball.

An increase in sleep duration (508 min per night vs 401 min at baseline) has been found to be associated with improvements in sprint times (−0.7 s), reaction times, free throws (+9%) and 3-point shooting accuracy (+9.2%) in collegiate men basketball players.⁴ Further, research in semi-professional men's basketball showed that the night before competition, subjective sleep quality was positively associated with free-throw accuracy, rebounds, assists, steals, offensive ratings, and player efficiency (EFF).⁶ However, conflicting findings were reported in collegiate women basketball players, where sleep duration and EFF were not associated with in-game performance.⁷ More research is warranted to explore the role of sleep on sport performance, especially in collegiate athletes, who are faced with unique training and game demands, competition scheduling, travel requirements, and academic responsibilities. Therefore, the purpose of this case series was to explore the relationship between acute sleep metrics and next-day game performance in collegiate men basketball players.

Methods

Participants

National Collegiate Athletic Association (NCAA) Division I men's basketball players (n = 4; age = 19.8 ± 1.26 years, height = 189.2 ± 3.0 cm, mass = 82.5 ± 5.0 kg) participated in this prospective, case series study. The sample size was inherently limited due to the small roster size of the basketball team (n = 10). Additionally, adherence to wearing the smart ring device was a primary challenge, contributing to a reduced final sample. Several athletes were inconsistent in wearing the device, leading to missing data and further restricting the number of valid observations for analysis; thus, we chose to include only athletes in the final analysis who wore the device consistently for >60% of all games. Inclusion criteria included: aged 18–22; medically cleared for intercollegiate athletic participation; and played ≥ 15 min per game. Athletes who did not meet these criteria were excluded. Players had the risks and benefits explained beforehand, signed an institutionally approved written consent form, and completed a medical history form. Study procedures were conducted in accordance with the requirements of the Declaration of Helsinki and approved by the University (1674043).

Sleep

Nocturnal sleep and heart rate variability (HRV) were assessed using a commercially available, validated⁸ biometric tracking ring (Oura Ring Inc., Oulu, Finland). The Oura Ring uses a 3D accelerometer and infrared photoplethysmography to measure body signals such as movement, body temperature, HRV, and respiration. These metrics are combined to provide a comprehensive overview about sleep and wakefulness. The Oura Ring has shown to accurately detect total sleep time, heart rate, and HRV (r²= 0.980–0.996) in physically active populations.^9–11 Participants were instructed to wear their rings on the nights before games (n = 22), for a total of 88 player observations. Sleep metrics collected were total sleep duration (TS), rapid eye movement duration (RM), deep sleep duration (DS), and HRV.

Basketball performance

In-game (n = 10) performance metrics included field goal percentage (FG%) and player efficiency (EFF). EFF was calculated as follows: (Points + Rebounds + Assists + Steals + Blocks − Missed Field Goals − Missed Free Throws - Turnovers).

Statistical analysis

All sleep and basketball metrics were reported as mean ± standard deviation. Normality was assessed using the Shapiro-Wilks test, and non-normally distributed variables were log-transformed to assume a normal distribution. After transformation, normality was re-evaluated to ensure that the assumption of normality was met for Pearson correlation analysis. Pearson correlations assessed group and individual relationships among TS, RM, DS, HRV, FG%, and EFF (p < 0.05). Correlation coefficients assessed relationships and were interpreted as: very weak: < 0.20, weak: 0.20–0.39, moderate: 0.40–0.59, strong: 0.60–0.79, and very strong: > 0.80. Two separate linear mixed models were conducted to examine the effects of TS, RM, DS, and HRV FG% and EFF, accounting for intra-individual variability (p < 0.05). Analyses were completed using R Studio (version 4.3, R Studio, Boston, MA, USA).

Results

Sleep and basketball descriptive game statistics are reported in Table 1.

Table 1.

The results of sleep metrics and basketball performance metrics.

	Sleep Metrics				Performance Metrics
	TS (hrs)	RM (hrs)	DS (hrs)	HRV (ms)	FG%	EFF
All	7.6 ± 1.1	1.7 ± 0.5	1.4 ± 0.4	92.3 ± 18.2	41.7 ± 0.2	10.0 ± 6.8
Athlete 1	7.8 ± 0.7	2.1 ± 0.3	1.4 ± 0.3	99.0 ± 15.9	43.7 ± 0.2	12.9 ± 5.6
Athlete 2	7.1 ± 1.8	1.8 ± 0.6	1.1 ± 0.4	86.7 ± 9.3	33.6 ± 0.3	7.7 ± 6.5
Athlete 3	7.8 ± 0.9	1.4 ± 0.4	1.3 ± 0.4	108.9 ± 14.0	39.0 ± 0.2	9.1 ± 8.5
Athlete 4	7.4 ± 0.7	1.5 ± 0.3	1.6 ± 0.3	76.7 ± 12.6	47.9 ± 0.3	9.8 ± 5.7

Values are present as Mean ± SD; TS: Total Sleep Duration; RM: Rapid Eye Movement Duration; DS: Deep Sleep Duration; HRV: Heart Rate Variability; hrs: hours; ms: millisecond; FG%: Field Goal Percentage; EFF: Efficiency Rating.

Correlations among sleep and game performance metrics are summarized in Table 2. There were no relationships among sleep (TS, RS, DS, HRV) and in-game performance (FG%, EFF) (p > 0.05). However, there were unique individual responses, as one athlete demonstrated significant correlations (p < 0.05) between TS-EFF (r = 0.48), RM-EFF (r = 0.51), and RS-FG% (r = 0.47).

Table 2.

Correlations among sleep and game performance metrics.

	TS	RM	DS	HRV	FG%	EFF
TS		0.55***	0.37***	0.22*	0.12	0.20
RM	0.55***		0.36***	0.05	0.11	0.19
DS	0.37***	0.36***		−0.04	0.11	0.04
HRV	0.22*	0.05	−0.04		−0.05	0.01
FG%	0.12	0.11	0.11	−0.05		0.71***
EFF	0.20	0.19	0.04	0.01	0.71***

*p < 0.05.

***p < 0.001.

Linear mixed model analysis showed that TS (F = 0.490, p = 0.485), RM (F = 0.085, p = 0.772), DS (F = 0.188, p = 0.665), and HRV (F = 0.406, p = 0.526) had no effect on FG% (R²= 0.025). EFF was also not influenced (R²= 0.053) by TS (F = 1.280, p = 0.261), RM (F = 0.999, p = 0.321), DS (F = 0.324, p = 0.571), and HRV (F = 0.071, p = 0.790).

Discussion

The primary aim of this case series was to explore sleep behavior and the relationship between objective sleep metrics and next-day game performance in collegiate men's basketball players. Main findings demonstrate a lack of relationships among total sleep, rapid eye movement sleep, deep sleep, HRV, basketball efficiency, and field goal % when evaluated at the group-level.

Similarly, other studies in basketball players failed to observe relationships between sleep and basketball performance when objective actigraphy was used to measure sleep;^6,7 Fox et al.⁶ did report a positive association between subjective sleep quality (via sleep diaries), with next-day free-throw accuracy, rebounds, assists, steals, offensive ratings, and EFF. Thus, athletes may perform better when they feel they have slept well, even if objective measures indicate otherwise. Confidence in sleep quality may influence performance, regardless of actual sleep duration or efficiency. In fact, objective and subjective sleep quality appear to provide conflicting information at times regarding sleep outcomes as supported by weak correlations (r = 0.22–0.28) between objective measures of sleep efficiency versus subjective sleep quality.¹² It is possible that higher perceptions of perceived sleep quality play a role in favorable mental status (i.e., mood, attention, emotional regulation, reduced anxiety), which may translate into positive performance outcomes.¹³ As such, monitoring subjective sleep quality and perceptions of recovery may be critical to optimizing player performance.

A possible explanation for the lack of associations between objective sleep metrics and basketball performance observed in the current study may be the athletes’ adequate sleep duration. Players were obtaining 7–8 h of sleep, which falls within the recommendations set forth for young adults by the National Sleep Foundation.² Interestingly, this is in alignment with research examined in a recent meta-analysis that athletes typically achieve ≥7 h of total sleep the night before competition, but their sleep is often compromised on other days during the week.¹⁴ Therefore, it is crucial to monitor sleep patterns throughout the entire week, as a single night of adequate sleep may not provide an accurate reflection of overall recovery and its relationship with performance. The cumulative effect of sleep across several days could be a more influential factor in performance than sleep just prior to competition.

Rather, sleep restriction (<7 h per night) may be a greater indicator of performance, subsequently leading to reductions in decision making and reaction time.^15,16 Therefore, sleep duration may have a limited influence on performance when the recommended 7–9 h per night is obtained. Larger associations between sleep duration and performance may be evident when sleep duration is restricted or extended beyond recommended sleep duration.⁶ For example, Gong et al. showed in a recent meta analysis that sleep deprivation resulted in impaired explosive power (d = −0.95, p < 0.001), speed (d = −0.6, p = 0.029), high intensisty intermittent performance (d = −1.57, p = 0.024), aerobic endurance (d = −0.54, p < 0.001), and skill control (d = −1.06, p = 0.002).¹⁷

While no group-level associations were observed, a large degree of inter-individual variability existed in the relationships between sleep and game performances. For example, Athlete 2 appeared to have the lowest total sleep and deep sleep durations, along with higher variances in sleep, compared to the other athletes. He was the only player to show positive, moderate associations between sleep and basketball performance, specifically between total sleep duration and game efficiency (r = 0.48), RM sleep duration and efficiency (r = 0.51), and RM sleep duration and field goal % (r = 0.47). This highlights the importance of individualized sleep strategies for athletes.

Despite achieving adequate sleep, additional sleep may offer favorable benefits for sport performance, particularly in certain individuals who may require additional sleep to feel rested. Mah et al.⁴ employed a sleep extension protocol in basketball athletes who were averaging 7–8 h of sleep per night. Throughout the 5–7 week sleep extension period, athletes averaged 10 h of sleep. This chronic increase in sleep duration resulted in improved free throw and 3-point accuracy, reaction time, sprint speed, and sleepiness.⁴ This underscores the importance of consistently achieving sufficient sleep, and making sleep a priority before game days. Furthermore, this relationship between sleep duration via extended sleep and additional was confirmed in a recent systematic review,¹⁸ where more sleep resulted in improved attention, simple reaction time, multiple choice reaction time, juggling performance, mental rotation test, lower reaction test, and psychomotor vigilance tasks.^14,19–22

This case series has several limitations that should be addressed. First, the small sample size (n = 4) limits the statistical power and generalizability of the findings. The small roster size of the basketball team and the challenge of ensuring consistent adherence to wearing the sleep-tracking device further reduced the final sample. Another limitation is the reliance on a single tracking device (Oura Ring) to assess sleep, which, while validated, may not fully capture all aspects of sleep in response to training and competition, particularly subjective perceptions of sleep quality. The location of HR detection from the ring finger, may affect the accuracy and reliability of the HRV data. Further, several factors beyond sleep may influence basketball performance and HRV, including nutritional intake and psychological stress. These variables have the potential to confound the relationship between sleep and game performance by independently affecting cognitive function, recovery, and physiological readiness. However, in this study, we did not have access to data on these potential confounders, which limits our ability to account for their influence on the observed relationships. Despite its limitations, this study has several strengths. The use of the Oura Ring provides reliable and objective measurements of sleep and HRV, which are crucial for understanding athletes’ recovery. The inclusion of both group-level and individual-level analyses allowed for a more nuanced exploration of how sleep may impact performance, highlighting individual variability in response to sleep patterns.

Practical application

Given the individual variability observed in the study, an individualized approach should be taken to monitoring sleep needs and recovery strategies. To individualize sleep, teams can establish baseline sleep assessments and periodically re-evaluate throughout the season. Athletes struggling with consistency in sleep duration or quality should receive personalized recommendations, such as modifying pre-sleep routines, optimizing sleep environments, and adjusting training or travel schedules when possible. In addition, education of the importance of sleep for recovery and performance should be integrated, along with strategies to mitigate sleep disruptions. It may be beneficial for sport practitioners to track both objective (e.g., wearable devices) and subjective (e.g., sleep diaries, perceived recovery scales) sleep data to provide a comprehensive understanding of their athletes’ sleep behaviors and needs. Practitioners should encourage their athletes to achieve the recommended minimum of 7–8 h of sleep each night. Further, napping (40–90 min) could be preventive against performance degradation, and thus, a short or long nap could be beneficial for improving performance, attention, fatigue, soreness, and mood.^19,21 However, continued exploration of the role of sleep in relation to individual performance is recommended, with future studies incorporating larger sample sizes, additional subjective measures to monitor sleep, and track measures of nutritional intake and psychological stress to better isolate the independent effects of sleep on athletic performance.

Conclusion

This case series highlights the complex relationship between sleep and sport performance in collegiate men basketball athletes. Group data showed no significant correlations between objective sleep parameters and in-game performance, suggesting there is no direct link between sleep metrics and game performance; however, individual variability exists. Furthermore, the athletes in this study were obtaining adequate sleep, which may have limited the observable effects of sleep on performance. It is important to recognize that the conclusions drawn from this study are conditional due to the small sample size and the exploratory nature of the research question. The findings should be interpreted with caution, as more robust research is needed to allow for generalization across broader athletic populations. Continued exploration of the role of sleep in relation to individual performance is recommended.

Footnotes

Acknowledgements

We would like to thank Keon Marsh for helping to organize this study, and the basketball players for their participation in this study.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Jennifer B Fields

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