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Article

Sleep Matters: Profiling Sleep Patterns to Predict Sports Injuries in Recreational Runners

by
Jan de Jonge
1,2,3,* and
Toon W. Taris
3
1
Human Performance Management Group, Eindhoven University of Technology, NL-5600 Eindhoven, The Netherlands
2
UniSA Justice & Society, University of South Australia, Adelaide, SA 5001, Australia
3
Department of Social, Health and Organisational Psychology, Utrecht University, NL-3508 Utrecht, The Netherlands
*
Author to whom correspondence should be addressed.
Appl. Sci. 2025, 15(19), 10814; https://doi.org/10.3390/app151910814
Submission received: 22 August 2025 / Revised: 1 October 2025 / Accepted: 6 October 2025 / Published: 8 October 2025
(This article belongs to the Special Issue Human Performance and Health in Sport and Exercise—2nd Edition)
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Abstract

Running is one of the most popular recreational sports worldwide, yet it carries a high risk of sports injuries. While various risk factors have been identified, sleep has emerged as a potentially important but understudied contributor in recreational running. This study investigates whether distinct sleep profiles can predict sports injuries in recreational runners. A secondary analysis was conducted on survey data from 425 Dutch recreational runners. Latent profile analysis was applied to identify sleep risk profiles based on sleep duration, sleep quality, and sleep problems. Binary logistic regression tested the association between sleep profile membership and self-reported sports injuries, controlling for demographic and training variables. Findings revealed that four sleep profiles could be identified: Steady Sleepers, Poor Sleepers, Efficient Sleepers, and Fragmented Sleepers. Runners classified as Poor Sleepers were significantly more likely to report sports injuries than Steady Sleepers (OR = 1.78, 95% CI = 1.14–2.78; p = 0.01), with 68% injury probability. No significant differences were found for the other profiles. These findings underscore the importance of sleep as a multidimensional factor in injury prevention in recreational running, and suggest that interventions focusing on sleep duration and sleep quality may benefit running athletes’ health.

1. Introduction

Running is extremely popular among participants of recreational sports activities [1,2]. On a beautiful day in the forest or a park, you will undoubtedly encounter several runners. Worldwide, it is estimated that over 620 million people are running frequently, accompanied with a quickly rising number of running events, including both competitive races and casual fun runs [3]. In both the United States of America and the European Union, for example, approximately 60 million people participate in all kinds of running activities. Running is also extremely popular in The Netherlands, and it is one of the five most popular sports there (actually second in line), along with fitness, soccer, swimming, and walking [4]. Currently, in The Netherlands, approximately 1.7 million people run, and this number grows steadily. It is estimated that about 9% of the Dutch people older than 12 years run weekly. The attractiveness of running is that people can easily do it anywhere and anytime, and that it reaches a diverse audience. Running also offers all sorts of benefits, such as an active lifestyle, better health, healthier eating habits, and stress reduction [5]. Moreover, long-term running is linked to reduced disability in older age and a notable increase in life expectancy [6]. Unfortunately, besides its many advantages, running also has a significant disadvantage: It is a sport that is very prone to sports injuries [7,8]. Running-related sports injuries are notably prevalent, with rates reaching up to 90% (e.g., [7,9]). In The Netherlands, for instance, the incidence stands at 5.1 injuries per 1000 h of participation in running which is more than double the national average of 2.4 injuries per 1000 h of participation in all sports. Dutch runners experience approximately 690,000 sports injuries annually [8]. Following fitness and soccer, running ranks third in the country for the highest number of sports injuries. From a societal perspective, the financial burden of sports injuries across all disciplines is substantial. In The Netherlands alone, direct medical costs amounted to EUR 210 million annually. The broader economic impact, reflected in indirect costs such as work absenteeism, was even greater, totaling EUR 250 million per year [8]. Jungmalm et al. [10] aptly described sports injuries as the ‘primary enemy’ of runners, emphasizing that the public health benefits of keeping runners active and injury-free should not be underestimated.
Commonly identified risk factors of sports injuries include injury history, gender, age, body mass index, poor mood, acute training load, structural overtraining, inadequate nutrition, lack of physical and mental recovery, obsessive passion, and psychosocial stress (e.g., [11,12,13]). Sleep may be an additional factor associated with the risk of sports injuries [14]. Sleep can be described as a vital biological process that enables both the body and mind to recharge, promoting a sense of restfulness, revitalized, and alertness [15]. Sleep quality and, hence, poor sleep, are widely used concepts, but there is a lack of clear consensus on the precise definition [16,17]. Various components are used as indicators for overall sleep quality (also called ‘sleep health’ by Buysse [18]), such as total sleep time duration, sleep timing, (satisfaction with) sleep quality, sleep latency, sleep efficiency, alertness (or sleepiness), and sleep environment [16,17]. Globally, it has been agreed upon that shorter sleep latencies, fewer nighttime awakenings, reduced time spent awake between sleep onset and sleep offset, sufficient sleep duration, and higher sleep efficiency are key indicators of better sleep quality [19,20].
Sleep is well-known for its significance to athlete’s health that influences sports performance and recovery to a large extent [16,21]. There is increasing empirical evidence indicating that sleep plays a key role in the risk of sports injuries in athletes in general (e.g., [17,22,23]). For instance, a meta-analytic study by Gao et al. [22] revealed that adolescent athletes who chronically slept poorly were 1.58 times more likely to report sports injuries compared to people who slept well. Huang and Ihm [17] reviewed the literature on sleep and sports injuries, and concluded, in general, that evidence exists of an association between suboptimal sleep patterns and the risk of sports injuries. A narrative review study by Hatia et al. [23] indicated that research has consistently shown that poor sleep markedly increases the risk of sports injuries. Other reviews, however, have determined that the empirical evidence yields ambiguous or even null results, particularly prospective studies within specific subpopulations of athletes. For instance, a systematic review study among twelve prospective cohort studies found limited evidence in favor of a significant relation between inadequate sleep and subsequent sports injuries in adult athletic populations [24]. Although six out of twelve studies reported significant relations between poor sleep quality/quantity and increased risk of sports injuries, only two of these studies conducted multivariate tests between poor sleep and injuries while taking confounding factors into account. The other four studies did not control for confounding factors, which limits the strength of empirical evidence. Finally, Wilkes et al. [25] conducted a scoping review examining how sleep disturbances impact functional and physiological outcomes in collegiate athletes. While it is theoretically plausible that inadequate or poor-quality sleep may influence sports injury risk, the extant literature studied did not demonstrate clear associations. Among the six studies that investigated injury rate as an outcome variable, five reported no statistically significant relation between sleep parameters and sports injuries.
To date, only a limited number of studies have examined the relation between sleep and sports injuries in running or track and field athletes [24]. For instance, a cross-sectional study among 112 adolescent athletes revealed a positive relation between sleep duration and sports injuries (as registered by the school’s athletic trainers). Specifically, athletes who slept on average <8 h per night were 1.7 times more likely to have had a sports injury compared with athletes who slept for 8 h or more per night [26]. In a 6-month longitudinal study (two occasions) among 340 adolescent elite athletes (including 136 athletics people), sufficient sleep duration (>8 h per weekday) reduced the odds of a sports injury with 61% [27]. Johnston et al. [28] investigated longitudinal associations between sleep duration and new sports injuries in 95 endurance sporting participants, runners and triathletes inclusive. Following them for 52 weeks with weekly measures, new injury risk was negatively associated with >7 h per day sleep duration, and positively related to <7 h per day sleep duration. In a secondary analysis of two-wave longitudinal survey data of 89 student athletes including track and field athletes, Messman and his team [29] showed that baseline sleep disturbances were a significant predictor of follow-up sports injuries, controlling for gender, racial identity, sport in-season, and previous injury status.
To summarize, sleep plays a vital role in the recovery and adaptation processes that athletes undergo in response to training and competition-related demands. Inadequate or disrupted sleep is increasingly recognized as a potential contributor to elevated sports injury risk among athletes. However, evidence-based knowledge on the role of sleep in sports injuries in recreational runners is still in its infancy. Therefore, the aim of the present study is to investigate whether multidimensional sleep profiles predict sports injuries in recreational runners. If poor sleep profiles are validated as contributing factors to running-related sports injuries, they could represent a critical target for intervention strategies in recreational runners aimed at reducing sports injury risk.

2. Materials and Methods

2.1. Research Design and Procedure

This study is a secondary data analysis based on a survey that was part of a randomized controlled trial [30]. This survey aimed at investigating the health characteristics, such as fatigue, sleep problems, and sports injuries, of recreational runners in The Netherlands. Data collection was conducted by engaging an online survey using Qualtrics CoreXM software (https://www.qualtrics.com, accessed on 22 August 2025; Provo, UT, USA). Next to short-answer questions for demographics and training characteristics, the survey consisted of Likert-scale questions to measure the key variables. Filling out the online survey was only possible after providing informed consent. Specifically, recreational runners received information on confidential data treatment, aim and report of the study, and ethical issues. Moreover, detailed instructions were given how to fill out the survey to enhance validity of the answers [30]. Runners received a link to the online survey, which they could complete at their convenience. To achieve a convenient and relatively large sample, half and full marathon runners were initially recruited through different channels, including former participants of the Eindhoven Marathon, Dutch Facebook groups dedicated to running, and several Dutch athletics clubs.

2.2. Sample

The sample consisted of 425 recreational runners, both novice and experienced athletes. As far as gender is concerned, 57% was male and 43% was female. The sample’s mean age was almost 45 years (SD = 12). Average running experience was almost 12 years (SD = 11). Of the participants, 29% had attended a high school or vocational education at most, 41% held a bachelor’s degree, and 31% held a master’s degree or higher. Half of the participants performed group-based running at a club, and four out of ten trained with a running coach. Most of these demographics are in line with those of the general Dutch long distance running population [31].

2.3. Required Sample Size

Latent profile analysis (LPA) was used to determine whether different sleep risk profiles can be determined based on several sleep indicators. Sample size requirements and consequently power should be considered for planned LPA studies. However, there is currently no simple formula to estimate required sample size in LPA, as this is largely dependent on the number of latent profiles as well as the distance between the profiles, which is obviously unknown beforehand. Ferguson and her team [32] have suggested that samples of 300 to 500 people would be sufficient as a minimum sample size for LPA.
Given this, we performed a power analysis to determine the minimum sample size needed to have enough power to detect an effect based on our study’s key outcome; that is, self-reported sports injuries. Accordingly, we used G*Power 3.1.9.7 software [33] based on a 10% reduction in injury prevalence over the past year. Based on outcomes of earlier pilot data of recreational runners [13], we calculated an effect size (Cohen’s D) of 0.24 [34]. The minimum sample size required for the current study was 416 respondents, using a statistical power coefficient of 0.80 and a standard alpha of 0.05.

2.4. Instruments

2.4.1. Sleep

In line with the work of Buysse et al. [35] and Driller et al. [36], sleep was assessed using three different dimensions, namely (1) sleep duration, (2) sleep quality, and (3) sleep problems. The assumed 3-factor structure was verified by means of a factor analysis (oblique rotation), which explained 81% of the variance. Sleep duration was assessed using a well-validated single item with a ratio scale in hours, where runners reported the average number of hours they slept per night: “How many hours do you sleep on average per night?”. Such a single-item questionnaire of sleep time duration has often been used in large surveys or population-based epidemiologic studies with good psychometric properties [37]. Sleep quality was measured with one item that used a 5-point Likert scale, ranging from 1 ‘very bad’ to 5 ‘very good’: “How do you rate the quality of your sleep?”. A recent study showed that a single-item sleep quality questionnaire entails favorable measurement characteristics to assess sleep quality relative to more lengthier questionnaires [38]. Sleep problems were assessed using three different items developed and psychometrically tested by Appels et al. [39]. These items were (1) “Do you have problems falling asleep?”, (2) “Do you wake up during the night?”, and (3) “Do you usually wake up feeling tired and not well-rested?”. The three items were measured using a 3-point Likert scale with the following responses: 1 ‘no’, 2 ‘sometimes’, 3 ‘yes’. The internal consistency of this scale expressed in McDonald’s [40] omega was 0.63.

2.4.2. Sports Injuries

To minimize different interpretations of a sports injury, respondents received the following definition before the actual question: “A sports injury can be defined as an injury, impairment or wound, whether or not associated with pain, caused by or developed during a run training, that causes a restriction on running (in terms of frequency, speed, duration, distance, or intensity) or stoppage of running for at least seven days or three consecutive scheduled training sessions”. This definition is based on Bahr et al. [41] as well as Yamato et al. [42], and is commonly referenced in the sports medicine literature. Sports injuries were assessed by means of a single item that could be scored as 0 ‘no’ or 1 ‘yes’: “In the past year, have you experienced one or more sports injuries, as defined above, due to your running?”. Respondents reported their sports injuries retrospectively by themselves, which has proven to be a valid and reliable way of investigating this kind of injuries (e.g., [43]).

2.4.3. Demographic and Training Variables

Control variables were also included in the statistical analyses since they can distort the relation between the independent and dependent variables. The variables that were included in our analyses are gender (0 = male; 1 = female), age (in years), body mass index (BMI; kg/m2), body height (in centimeters), and running experience (in years). Empirical research has shown that particularly these variables could bias relations between sports injuries and athlete health outcomes (e.g., [22,24,44,45]).

2.5. Statistical Analyses

Firstly, sample means, standard deviations, and Pearson correlations were calculated using IBM SPSS Statistics v.29 (SPSS Inc., Chicago, IL, USA). Secondly, LPA was used to identify distinct risk profiles based on three sleep indicators (i.e., duration, quality, and problems) in predicting sports injuries [32]. LPA estimates latent profiles incrementally to determine the optimal number based on the input variables, following steps outlined by Ferguson et al. [32]. Initially, statistical assumptions (e.g., normality) were checked. Next, several models were estimated using the tidyLPA package in R v.4.5.1 [46], assuming equal variances and equal covariances between the indicators (Model 3 per Rosenberg et al. [46]). Each model was compared to the previous to determine the best-fitting solution. Thirdly, model selection relied on fit indices such as the Bayesian Information Criteria (BIC), the Sample-size Adjusted BIC (SABIC), and the Akaike’s Information Criterion (AIC), with lower values indicating a better model fit [47]. BIC changes were visualized using an elbow plot [47], aiding in more robust decision-making. Entropy, ranging from 0 to 1, was also considered; values ≥0.80 suggest minimal classification uncertainty [47]. Lastly, group sizes below 1% of the sample or fewer than 25 cases were deemed unsuitable [48]. The final profile solution had to be both statistically sound and substantively meaningful.
Finally, after the best-fitting model of latent profiles was determined, the association between these latent profiles and sports injuries was investigated, controlling for potential confounders. Within IBM SPSS Statistics v.29, binary logistic regression was used to test the relation between sleep profile membership and the binary injury outcome (0 = no injury, 1 = injury), while controlling for gender, age, BMI, body height, and running experience. We used the first latent profile as contrast (SPSS subcommand ‘indicator’), and both gender and profiles as categorical variables. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated. Omnibus model significance was evaluated using a Χ2-test. Nagelkerke’s R2 was used as a proxy for the overall explained variance of the binary logistic regression model. The significance level was set at p < 0.05 as minimum threshold.

3. Results

3.1. Means, Standard Deviations, and Correlations of Variables

Table 1 gives an overview of means (M), standard deviations (SD), and Pearson correlations of unstandardized variables. Some associations were intuitively plausible (e.g., between gender, age, BMI, body height, and running experience, as well as all sleep dimensions among themselves), yet others were more intriguing. For instance, gender was significantly related to all sleep dimensions, with women reporting more sleep issues than men. In addition, BMI was negatively associated with sleep duration, and positively associated with sports injuries. Moreover, two out of three sleep dimensions were significantly related to sports injuries. Sleep quality was negatively related to injuries, whereas sleep problems were positively related to injuries. Finally, six out of ten runners in our sample, or 60%, reported having had a sports injury in the past year.

3.2. Latent Risk Profiles for Sleep

A preliminary step of LPA consisted of checking normality assumptions for the sleep dimensions included. Conducting Shapiro–Wilk normality tests revealed that all three sleep dimensions formally violated the assumption of normality (W(425) < 0.98; p < 0.001). However, this kind of formal tests can be misleading in some cases [49], which means that more informal, visual tests should be performed, too. Based on histogram distributions and normal Q-Q plots, the distribution of sleep quality and sleep problems did not meet the normality assumption, but for sleep duration. Based on findings of both formal and informal tests, both sleep quality and sleep problems violated the assumption of normality. The two sleep dimensions were transformed accordingly. Since sleep quality was negatively skewed, it was transformed by a power transformation (square). In contrast, sleep problems were positively skewed, which led to a square root transformation. This resulted in better skewness levels for both sleep dimensions to come closer to a normal distribution. The newly transformed variables (i.e., (sleep quality)2 and √sleep problems) were used in further analyses, next to sleep duration.
The LPA started by adding profiles iteratively until criteria were not met anymore, which occurred beyond four profiles. In that case, models with five or more profiles consisted of classes which represented less than 1% of the sample size and, hence, were no longer adequately reflecting the data. The fit indices and statistics of the decision criteria up to six different profiles are presented in Table 2. According to the corresponding fit indices, the 4-profile model was superior to the other models. Specifically, the lowest BIC-value is 2965, the lowest SABIC-value is 2899, and the lowest AIC-value is 2881 for four latent profiles. In addition, an elbow plot for the BIC revealed that the ‘elbow’ cut-off point is exactly at four profiles. In line with the fit indices, the 4-profile solution fitted the data best. Further, entropy showed that the preferred solution was the model with four profiles as well (0.85). Finally, the smallest group size was acceptable since the minimum number of people in one group was 30 out of 425, which resulted in a smallest group size percentage of 7%.
Figure 1 shows the four different groups based on the three sleep dimensions with significant and interpretable differences. The first profile covered 48% of the sample and was referred to as Steady Sleepers. This group consists of runners with an average level of sleep duration, a slightly above average level of sleep quality, and a relatively lower level of sleep problems. The second profile represented 37% of the sample and was called Poor Sleepers, because this group of runners has a relatively lower value in sleep duration, a low value in sleep quality, and a relatively high level of sleep problems. The third profile included 8% of the sample and was named Efficient Sleepers. These runners have an average level of sleep duration while having a high level of sleep quality as well as a low level of sleep problems. The fourth profile covered 7% of the sample and was referred to as Fragmented Sleepers. This group consists of runners with an average level of sleep duration and a slightly above average level of both sleep quality and sleep problems. Table 3 presents several demographic and training variables per sleep profile. Generally, one-way ANOVAs showed significant differences across the sleep profiles, with the exception of BMI and body height. The Efficient Sleepers profile stood out in particular. This group of runners included a relatively higher proportion of older men with more running experience.

3.3. Association Between Sleep Profiles and Sports Injuries

Table 4 presents the binary logistic regression results for sports injuries. As mentioned in the Methods section, the first latent profile (i.e., Steady Sleepers) was used as a reference group (OR = 1.00). The overall regression model shows a significant model test: Χ2(8) = 16.03, p = 0.04. Specifically, the latent sleep profiles show that there was a significant positive relation between the Poor Sleepers profile and sports injuries (b = 0.58; OR = 1.78; 95% CI = 1.14–2.78; p = 0.01). Put differently, runners with a Poor Sleepers profile were 1.78 times more likely to report sports injuries compared to the Steady Sleepers group (OR = 1.00). As the OR is the ratio of probability, we calculated the probability of our outcome measure. The probability of sports injuries for the Poor Sleepers group is 68% accordingly. No significant differences were observed between the remaining profiles and the reference group. The Nagelkerke R2 shows that all predictor variables together were able to explain 5% of the variance in sports injuries. Finally, the classification table reveals that this prediction was correct 60% of the time.

4. Discussion

We examined the role of a combination of different indicators of sleep and sleep disorders in the prediction of sports injuries in recreational runners. First, the LPA method was used to determine whether different sleep risk profiles can be distinguished based on several sleep indicators in the prediction of sports injuries. Second, binary logistic regression was performed to test the association between sleep profiles and sports injuries, taking control variables such as gender and running experience into account. Results indicated that four sleep profiles fitted the data best; that is, (1) Steady Sleepers, (2) Poor Sleepers, (3) Efficient Sleepers, and (4) Fragmented Sleepers. Furthermore, findings revealed a positive association between the Poor Sleepers profile and sports injuries: Recreational runners with a Poor Sleepers profile were significantly more likely to report sports injuries compared to the Steady Sleepers group. No significant differences were detected between the other profiles and the reference group. Six out of ten runners, or 60%, reported having had a sports injury in the past year, which is in line with former research [7,9,13]. Finally, all predictor variables together were able to explain 5% of the variance in running-related sports injuries, and the regression model was accurately predicted in 60% of the time. Although these two figures are not very high, they are in line with the findings of other sports injury research (e.g., [13,50]) and interesting, also due to the multifactorial nature of sports injuries [51].

4.1. Theoretical Implications

Sleep is generally regarded by researchers as a crucial component of athletes’ recovery and sports injury prevention, although the empirical evidence is not conclusive. Implications of our study concern the contribution of the developed sleep profiles in their association with sports injuries. First, in line with Buysse et al. [35] and Driller et al. [36], we considered sleep as a multidimensional construct and measured three different sleep dimensions (i.e., sleep duration, sleep quality, and sleep problems). Using these dimensions, the LPA method was able to successfully distinguish four sleep profiles based on our data, which made sense from a theoretical point of view, too. The Steady Sleepers profile, for instance, aligns well with the ‘good sleeper’ label of Conte et al. [52]. In their study, good sleepers showed sufficient sleep duration as well as higher sleep continuity (fewer awakenings) and stability (fewer arousals and transitions) compared to bad sleepers. In addition, our Poor Sleepers profile fits the widely used ‘poor sleepers’ group in the extant (sports) literature (e.g., [21,24,25,53]). In a study by Ramlee et al. [54], for instance, poor sleepers were particularly classified by factors such as insufficient total sleep time, not feeling refreshed upon waking, and poor mood the following day. Knufinke and her team [53] characterized their poor sleepers mainly by lower total sleep time, low sleep quality and, to a lesser extent, sleep disorders. The Efficient Sleepers profile is well-aligned with the label of ‘elite sleepers’ introduced by Dong et al. [55]. Elite sleepers possess genes encoding proteins that influence sleep architecture and efficiency which allow them to receive restorative sleep in just 4 to 6 h. The brain of an elite sleeper is considered to complete essential sleep functions in a shorter time, challenging the ‘7–9 h sleep’ paradigm. In our sample, the Efficient Sleepers profile entails a relatively higher proportion of older men with more running experience. The fourth profile, labeled Fragmented Sleepers, matches with terms like ‘sleep fragmentation’ and ‘wakefulness after sleep onset’ [17]. Together, the four-profile classification underscores that the LPA method could both theoretically and methodologically contribute to this important line of research. This kind of studies could also advance our understanding of how different constellations of sleep relate to relevant outcomes in athletes (e.g., athlete’s health and sports injuries).
Second, and most importantly, our findings reveal that a poor sleep profile was positively associated with sports injuries in recreational runners. More specifically, controlled for potential confounders, runners characterized by a Poor Sleepers profile were 1.78 times more likely to report sports injuries compared to runners in the Steady Sleepers group, and reached a 68% injury probability. These results agree with a meta-analysis performed by Gao and colleagues [22]. They pooled together the odds of seven studies, and revealed that adolescent athletes with chronic sleep shortage were, in general, 1.58 times more likely to report sports injuries compared to people who slept well. Our findings also corroborate scarce empirical research on the association between sleep and sports injuries in running and track and field athletes. Particularly, this study’s OR of 1.78 closely fits the one found by Milewski et al. [26] in their study among 112 adolescent athletes (OR = 1.70). It also matches the 6-month prospective study of Von Rosen et al. [27] among 340 adolescent elite athletes (athletics inclusive) which reported that sufficient sleep duration (>8 h per weekday) lowered the odds of a sports injury with 61% (OR = 0.39). Finally, our findings are largely in line with Johnston and colleagues’ [28] 1-year longitudinal study in 95 endurance sporting participants (including runners and triathletes). They found that a 14-day lag sleep quantity <7 h per day increased the risk of a new sports injury by 51%, whereas a 14-day lag sleep quantity >7 h per day reduced a new injury risk by 37%. All these results highlight the importance of considering sleep in understanding the incidence of sports injuries in athletes such as recreational runners.

4.2. Limitations and Future Research

This study has some strengths and some limitations. To our knowledge, this is the first study that embraced a more comprehensive and alternative characterization of sleep using four sleep profiles based on the LPA method, allowing us to simultaneously take multiple sleep indicators into account. Another strength is that this is one of the scarce studies that investigated the relation between sleep and sports injuries in a relatively large sample of recreational runners. A notable limitation of our study is its cross-sectional research design, which restricts our capacity to draw strong causal inferences. For this reason, reverse causality could not be ruled out either. For instance, injured runners may sleep poorly due to inflammation, fatigue, pain, tissue repair, and medication effects [56,57]. Prospective studies with more objective sleep measures (e.g., actigraphy, wearables) and standardized records of injury history are highly recommended. Another critical note is that the self-reported sleep measures used here are questioned for their reliable and valid assessment of sleep health in running athletes [16]. Nevertheless, our sleep measures were not designed for clinical purposes but rather to identify dimensions where sleep-based interventions may be useful. In addition, given the considerable night-to-night variability in athletes’ sleep patterns, more frequent monitoring (e.g., using sleep diaries, polysomnography, actigraphy, and wearables) would enable researchers to distinguish the potentially distinct effects of acute sleep deprivation and chronic sleep insufficiency on sports injury risk. However, these so-called invasive measures place a higher burden upon running athletes and their sleep than survey measures [16]. A third limitation is that the current sample originated from a single European country, which implies that generalization of the findings awaits future investigation. A fourth limitation is the reliance on self-reported sports injuries, requiring runners to assess their own conditions without formal medical diagnoses. To mitigate this, participants were provided with a standardized definition of a sports injury, based on international consensus [41,42]. However, the nature and severity of injuries were not accounted for. For example, overtraining-related injuries may differ qualitatively from trauma-induced ones, and the seriousness of an injury can vary significantly. Future research would benefit from incorporating formal diagnostic assessments by medical professionals to enhance validity. Related to this, while our findings revealed a positive association between poor sleep and sports injuries, it is important to recognize that sleep disturbances may reflect underlying health and (over)training issues, and should not be considered solely as an independent risk factor for injury [17]. Future research should account for other important risk factors for sports injuries (e.g., training load, recovery, nutrition, and stress) and how they may interact with sleep. Finally, although age was controlled for in our analyses, future research should investigate whether sleep plays a more significant role in injury risk within specific age cohorts, such as children, adolescents, or older adults. Most existing studies have concentrated on adolescent athletes, and current findings suggest a potentially stronger link between sleep and sports injuries in that population [14]. Future research should explore other subgroups as well, such as gender, running distance, and running experience, to target personalized interventions.

4.3. Practical Implications

Study findings highlight the significance of prioritizing sleep as a risk factor for sports injuries in runners. Generally, our results showed that both sleep duration and sleep quality as well as sleep problems should be considered, as they represent important dimensions of a runner’s sleep that could be salient for injury risk. Moreover, if people return to running after a sports injury, ensuring adequate sleep duration and sleep quality is even more crucial for proper healing and tissue regeneration. Recreational runners may require as much, if not more, sleep than professionals. Unlike professional athletes, recreational runners often balance training with work, school, home, social, and personal responsibilities, increasing their need for restorative sleep [30]. Findings suggest that sleep should be treated as a performance priority, and not as an afterthought. As far as sleep duration is concerned, an international expert consensus generally recommends a range of 7 to 9 h per night for healthy adults, and 8 to 10 h per night for teenagers [45]. As passionate recreational runners consistently push their limits [13], it is no surprise they require more sleep than the average person to stay healthy and perform well. This implies that running athletes often benefit from being at the upper end of the recommended range, or even from exceeding it. Cunha and colleagues [58] recommend extending sleep duration through either longer nighttime sleep or regular daytime naps. For recreational runners who typically sleep around 7 h per night, a general guideline is to increase sleep by up to 2 additional hours over a period of 3 to 49 nights. Daytime naps ranging from 20 to 90 min at maximum and between 13.00 and 16.00 h can also be strategically used when needed. Short naps can help minimize the effects of sleep inertia, while longer naps may allow running athletes to complete a full sleep cycle, thereby promoting deeper recovery [59]. Further, a brief nap of 20 min may be most effective when the next task is short and involves frequent decision-making, whereas a longer nap can protect against muscle damage, even during more demanding activities [60]. Beyond enhancing total sleep time, naps have been shown to restore performance deficits to baseline levels following nights of partial sleep restriction [58].
Good sleep hygiene significantly enhances sleep quality and daytime alertness [45]. Key practices include avoiding caffeine after mid-afternoon, limiting heavy evening meals, and minimizing alcohol, especially before bedtime, as it disrupts sleep later in the night [61,62]. Additional strategies involve morning exposure to natural light, a calming bedtime routine, stress reduction, a consistent sleep schedule, and a cool, dark, quiet sleep environment. Reducing screen time before bed also supports better sleep [58].
A final recommendation is that training and sleep should align with an individual’s chronotype [45]. Adolescent runners usually tend to be evening chronotypes, making early training disruptive to sleep and recovery. Early-morning sessions can reduce sleep quality and increase fatigue [16]. To optimize recovery, runners should avoid extreme training times, prioritize consistent sleep duration, and create conditions that support restful, high-quality sleep, since quantity often underpins effective recovery.

5. Conclusions

This study contributes to the growing body of literature on the role of sleep in sports injury risk, specifically among recreational runners. Using latent profile analysis, we identified four distinct sleep profiles based on sleep duration, sleep quality, and sleep problems. These profiles offer a more nuanced understanding of sleep patterns beyond traditional single-variable approaches. Importantly, our findings revealed that runners classified as Poor Sleepers were significantly more likely to report sports injuries compared to Steady Sleepers, controlling for key demographic and training variables. This supports that poor sleep may be a meaningful risk factor for sports injuries in recreational athletes. While its cross-sectional design limits the possibilities for causal inference, the present study underscores the need for more longitudinal research to clarify the temporal dynamics between sleep disturbances and injury occurrence. Moreover, the findings highlight the importance of considering sleep as a multidimensional construct and suggest that targeted interventions addressing both sleep quality and sleep duration as well as sleep disorders may help reduce sports injury risk. Sleep quality and sleep duration are both important, but quantity often provides the bedrock. In summary, sleep should be recognized not only as a recovery tool, but also as a potential predictor of injury vulnerability in recreational sports.

Author Contributions

Conceptualization, J.d.J. and T.W.T.; methodology, J.d.J. and T.W.T.; software, J.d.J.; validation, T.W.T.; formal analysis, J.d.J.; investigation, J.d.J.; resources, J.d.J. and T.W.T.; data curation, J.d.J.; writing—original draft preparation, J.d.J.; writing—review and editing, T.W.T.; visualization, J.d.J. and T.W.T. All authors have read and agreed to the published version of the manuscript.

Funding

The present research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and American Psychological Association. The study protocol was profoundly evaluated by the Medical Ethics Committee of University Medical Center, Utrecht, The Netherlands, and waived from further ethical approval for studies involving humans (reference number: WAG/rgj/18/004801; approved 9 February 2018).

Informed Consent Statement

Informed consent was obtained from all running athletes involved in the study. Filling out the online questionnaire was only possible after agreeing with the informed consent.

Data Availability Statement

Empirical data are available upon reasonable request from the first author, and can be used for scientific reasons only.

Acknowledgments

The support of Luuk van Iperen, former doctoral student of Eindhoven University of Technology, with data collection and initial statistics is highly appreciated. The statistical assistance of Pieter Wanten, master’s student of Eindhoven University of Technology, with the LPA method is highly valued, too.

Conflicts of Interest

The authors declare no conflicts of interest.

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Applsci 15 10814 g001
Figure 1. Standardized mean values of the LPA 4-profile solution based on the three sleep dimensions (n = 425).
Figure 1. Standardized mean values of the LPA 4-profile solution based on the three sleep dimensions (n = 425).
Applsci 15 10814 g001
Table 1. Means, standard deviations, and Pearson correlations among the variables (n = 425).
Table 1. Means, standard deviations, and Pearson correlations among the variables (n = 425).
VariablesMSD12345678
1. Gender #0.430.50
2. Age44.6611.74−0.23 **
3. Body Mass Index22.832.53−0.23 **0.06
4. Body Height177.099.16−0.66 **0.020.09
5. Running Experience11.7010.54−0.13 **0.51 **−0.020.01
6. Sleep Duration7.300.850.12 *−0.19 **−0.16 **−0.04−0.01
7. Sleep Quality3.640.74−0.14 **0.020.010.070.15 **0.29 **
8. Sleep Problems1.600.510.17 **−0.01−0.03−0.15 **−0.10 *−0.27 **−0.70 **
9. Sports Injuries #0.600.49−0.050.020.11 *−0.03−0.05−0.02−0.10 *0.10 *
* significant at p < 0.05; ** significant at p < 0.01 (two-tailed). # these are binary variables; their means can be interpreted as a percentage.
Table 2. LPA fit indices and statistics for six different profiles (n = 425).
Table 2. LPA fit indices and statistics for six different profiles (n = 425).
LPA ProfilesBICSABICAICEntropy
13373334433361.00
23364332333120.66
33388333533190.38
42965289928810.85
52990291128890.70
63014292228970.64
Table 3. Means and standard deviations of demographic and training variables per sleep profile (n = 425).
Table 3. Means and standard deviations of demographic and training variables per sleep profile (n = 425).
VariablesSteady
Sleepers		Poor
Sleepers		Efficient
Sleepers		Fragmented
Sleepers		Group
Differences
M (SD)M (SD)M (SD)M (SD)F (3,422)
Gender #0.38 (0.49)0.53 (0.50)0.26 (0.44)0.43 (0.50)4.11 **
Age43.78 (12.23)44.78 (10.54)49.80 (12.41)43.97 (12.63)2.69 *
Body Mass Index22.97 (2.59)22.85 (2.49)22.52 (2.20)22.14 (2.67)1.12
Body Height177.83 (9.30)176.18 (9.00)177.17 (9.20)176.80 (8.99)0.98
Running Experience11.20 (10.15)10.74 (10.06)18.71 (12.93)11.93 (10.01)5.96 ***
* significant at p < 0.05; ** significant at p < 0.01; *** significant at p < 0.001 (two-tailed). # this is a binary variable; its mean can be interpreted as a percentage.
Table 4. Binary logistic regression analysis of sports injuries with the latent sleep profiles as predictors (n = 425).
Table 4. Binary logistic regression analysis of sports injuries with the latent sleep profiles as predictors (n = 425).
Sports Injuries
bSEOR (95% CI)Injury
Probability
Control Variables
Gender0.490.301.63 (0.91–2.92)
Age0.000.011.00 (0.98–1.02)
Body Mass Index0.080.041.08 (1.00–1.18)
Body Height−0.020.020.98 (0.95–1.01)
Running Experience−0.010.010.99 (0.97–1.01)
Predictor Variables
Steady Sleepers 1.0055%
Poor Sleepers0.58 **0.231.78 (1.14–2.78)68%
Efficient Sleepers0.280.391.32 (0.62–2.81)61%
Fragmented Sleepers0.170.401.18 (0.54–2.60)59%
Omnibus Model TestΧ2(8) = 16.03, p = 0.04
Nagelkerke R25%
Classification Table (Accuracy)60%
** significant at p < 0.01.
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de Jonge, J.; Taris, T.W. Sleep Matters: Profiling Sleep Patterns to Predict Sports Injuries in Recreational Runners. Appl. Sci. 2025, 15, 10814. https://doi.org/10.3390/app151910814

AMA Style

de Jonge J, Taris TW. Sleep Matters: Profiling Sleep Patterns to Predict Sports Injuries in Recreational Runners. Applied Sciences. 2025; 15(19):10814. https://doi.org/10.3390/app151910814

Chicago/Turabian Style

de Jonge, Jan, and Toon W. Taris. 2025. "Sleep Matters: Profiling Sleep Patterns to Predict Sports Injuries in Recreational Runners" Applied Sciences 15, no. 19: 10814. https://doi.org/10.3390/app151910814

APA Style

de Jonge, J., & Taris, T. W. (2025). Sleep Matters: Profiling Sleep Patterns to Predict Sports Injuries in Recreational Runners. Applied Sciences, 15(19), 10814. https://doi.org/10.3390/app151910814

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