# Poor Sleep Quality Is Associated With an Increased Risk of Running-Related Injuries: A Prospective Study of 339 Runners Over Six Months

Mathias Goldberg, Yann Le Mat, Mélanie Metra, Thomas De Sousa, Pascal Edouard, Guillaume Y Millet, Benoit Pairot de Fontenay, Ursula Debarnot

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## To cite this version:

Mathias Goldberg, Yann Le Mat, Mélanie Metra, Thomas De Sousa, Pascal Edouard, et al., Poor Sleep Quality Is Associated With an Increased Risk of Running-Related Injuries: A Prospective Study of 339 Runners Over Six Months. Scandinavian Journal of Medicine and Science in Sports, 2025, 35 (11), 10.1111/sms.70164. hal-05419371

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ORIGINAL ARTICLE

# Poor Sleep Quality Is Associated With an Increased Risk of Running-Related Injuries: A Prospective Study of 339 Runners Over Six Months

Mathias Goldberg<fnref n="1" /> ID | Yann Le Mat² ID | Mélanie Metra² ID | Thomas de Sousa<fnref n="1" /> ID | Pascal Edouard²,³ ID |
Guillaume Y. Millet²,⁴ ID | Benoit Pairot de Fontenay<fnref n="1" />,⁵ ID | Ursula Debarnot<fnref n="1" />,⁴ ID

**Correspondence:** Mathias Goldberg (mathias.goldberg@univ-lyon1.fr)

**Received:** 23 July 2025 | **Revised:** 28 October 2025 | **Accepted:** 28 October 2025

**Funding:** The authors received no specific funding for this work.

**Keywords:** epidemiology | injury prevention | sleep assessment | track and field | trail running

## ABSTRACT

Association between sleep and running-related injuries (RRI) risk remains still unclear. This prospective cohort study investigated whether sleep quality and quantity, along with training load and complementary recovery factors, were associated with the risk of RRI. Over a 26 week period, adult runners, with at least 1 year of running experience, weekly reported their RRI and completed the Hooper_m questionnaire assessing sleep (quality, quantity, latency), fatigue, muscle soreness, perceived stress, through a smartphone application. We also measured training load through participants' personal watches. We analyzed the association between RRI, Hooper_m variables, and training load using time-to-event models. We compared Hooper_m and training load through the 2 weeks preceding an RRI (RRI-1, RRI-2) and injury-free weeks using linear mixed models. A total of 339 runners (16.2% females, age: $43.4 \pm 10.2$, 92.9% competing regularly) from 24 countries were included. Lower sleep quality was significantly associated with an increased risk of RRI (HRR 1.36, 95% 1.04 to 1.78; $p=0.02$). Compared to no-RRI weeks, muscle soreness was higher during RRI-2 ($p=0.02$) and RRI-1 ($p<0.001$), whereas fatigue was elevated during RRI-1 ($p=0.04$). No further significant results were observed among the other factors. Poor sleep quality was associated with an increased risk of RRI and may represent an early warning sign. Elevated fatigue and muscle soreness may signal a heightened risk of injury in the weeks following this onset. While interpreted with caution, these findings highlight the importance for runners to improve sleep quality and regularly monitor recovery, to proactively reduce the likelihood of RRI.

## 1 | Introduction

Running is one of the most widely practiced types of physical activity worldwide. While its regular practice offers numerous health benefits [1, 2], running-related injuries (RRI) remain a

major concern [3, 4]. Given its complex and multifactorial nature [5], a better understanding of the etiology of RRI is essential for the development of effective prevention strategies [6]. A substantial proportion of these injuries are classified as overuse injuries, characterized by a gradual onset, resulting from an imbalance

<footnote n="1">¹Université Claude Bernard Lyon 1, LIBM, Inter-University Laboratory of Human Movement Sciences, Villeurbanne, EA, France | ²Université Jean Monnet Saint-Etienne, LIBM, Inter-University Laboratory of Human Movement Biology, Saint-Etienne, EA, France | ³Department of Clinical and Exercise Physiology, Sports Medicine Unit, University Hospital of Saint-Etienne, Saint-Etienne, France | ⁴Institut Universitaire de, Paris, France | ⁵Ramsay Santé, Clinique de la Sauvegarde, Lyon, France</footnote>

<footnote>Benoit Pairot de Fontenay and Ursula Debarnot are contributed equally to this work.</footnote>
between training loads and recovery capacity [3, 7, 8]. Training factors [5, 9, 10], recovery parameters [11, 12], and their balance have been explored as risk factors, but the findings remain inconclusive, prompting further investigation into the major recovery-related factor.

Among recovery factors, sleep is a key regulator of physiological and psychological functions, with growing evidence supporting its role in enhancing performance and reducing RRI [13, 14]. Retrospective studies have highlighted associations between inadequate sleep, whether due to poor quality or insufficient quantity, and an increased injury risk [15, 16], whereas evidence from prospective studies remains inconclusive [11, 12]. Collectively, these studies present several limitations, such as the assessment of a single sleep outcome over months [15, 16] and a narrow focus on sleep quantity [12, 15]. Moreover, the heterogeneous inclusion criteria and the reliance on retrospective and interindividual analysis designs further limit causal inferences [11, 12, 15, 16]. Thus, the relationship between sleep and RRI remains insufficiently understood, while sleep may pathophysologically play a relevant role in RRI onset, which warrants further investigation.

Addressing these knowledge gaps requires prospective studies employing regular, self-reported data collection over extended periods and a more advanced approach to sleep analysis (i.e., more than one sleep parameter) [17]. To date, no longitudinal study has comprehensively evaluated the sleep parameters on RRI alongside training load and complementary recovery factors. Moreover, time-based analyses that facilitate both between-subject and within-subject comparisons in adult runners remain unexplored. Therefore, the present study aimed to investigate whether sleep quality and quantity, along with training load and complementary recovery factors, are associated with the risk of RRI. Specifically, we hypothesized that (1) lower sleep quality and quantity would be associated with an elevated risk of RRI; (2) a short-term decline in sleep quality and quantity within the weeks prior to an RRI would increase the likelihood of its occurrence; and (3) higher training load and altered recovery-related parameters including muscle soreness, fatigue, and perceived stress, would increase the risk of RRI.

# 2 | Materials and Methods

## 2.1 | Study Design and Protocol

We conducted a prospective longitudinal observational study among runners with data collected at baseline (i.e., during the first week of follow-up) and weekly up to 26 weeks (6 months) using a smartphone application specifically developed for the project (*TRAINIMM*, V.3.5.7), from March 2022 to December 2023 (i.e., a 95 week data collection window). The primary aim of this study was to assess the association between sleep and RRI. The report of this study followed the STROBE guidelines for Sports Injury and Illness Surveillance [18]. Participants were informed about the procedures and provided written consent prior to inclusion in the study. This study was conducted in accordance with local and international regulations regarding human subject research and received approval from our local Ethical Committee (*Commission recherche de Terre d'éthique*,

Institutional Review Board: IORG0007394; IRBN072020/CHUSTE).

## 2.2 | Population

Runners were recruited via our participant network and the running community through a trilingual (English, French, and Spanish) promotional video shared on social media. Runners were eligible for inclusion if they met all of the following criteria: (1) agreed to participate, provided written informed consent, and reported no medical contraindications to running; (2) completed the full baseline questionnaire; (3) responded to the RRI questionnaire for at least three consecutive weeks (i.e., participants who did not complete at least three consecutive weeks were excluded, and their data were not considered in the analyses); (4) had at least 1 year's running experience; (5) performed running workouts at least twice a week; and (6) reported that running was their main sport (i.e., more than 50% of weekly training volume spent in running).

## 2.3 | Data Collection

At the enrolment, runners had to complete baseline questionnaires assessing anthropometric characteristics, running-related habits (i.e., running experience, usual weekly volume, distance, number of trainings), general health status, and chromotype using the Morningness-Eveningness Questionnaire (MEQ) [19] and sleep quality using the Pittsburgh Sleep Quality Index (PSQI) [20] (*Supporting Information*). The MEQ and PSQI were used for descriptive purposes only and were not part of the inclusion criteria.

Following enrolment, weekly monitoring was carried out prospectively, and runners received a notification each Sunday at 10am to complete questionnaires. If unanswered, two reminders' notifications were sent in the evening (6pm), and the following morning (8am). The weekly questionnaires collected data on their RRI, training load, sleep quality and quantity, as well as muscle soreness, fatigue and perceived stress. Runners reported whether they had experienced pain during the past week, and if so, completed follow-up questions to assess whether the condition met criteria for a RRI. A RRI was defined as any pain affecting running practice, resulting in partial limitation (distance, speed, duration or number of training sessions) or total inability to train or compete for at least 7 days or three consecutive sessions (i.e., time loss), or requiring consultation with a doctor or healthcare professional [21, 22]. Information about the type, localization and previous similar RRI were also reported in this questionnaire. Training load was evaluated through weekly training volume, defined as the total number of hours practiced per week across all sports, provided that running represented at least 50% of this volume, and rating of perceived exertion (RPE). Runners could either (1) connect the application to their STRAVA account or running watch (*Garmin, Polar*, and *Sunto* brands were available), or (2) manually report their training sessions into *TRAINIMM*. After each training, runners were also required to report their RPE on a 1-10 scale to evaluate self-perceived training intensity. To evaluate sleep and other recovery factors
on a weekly basis, only subjective measures were considered in this study, consistent with standard practice in similar research protocols [12, 23–25]. Although objective metrics from running watches were available, they were excluded due to the lack of validated reliability and inter-brands variability in algorithms and measurement standards, which limited their suitability for meaningful comparison within this study. To extend the sleep assessment, a modified version of the Hooper and Mackinnon questionnaire (1995) [26] (Hooper<sub>m</sub>) was used. This questionnaire assessed fatigue (1–7 scale; “how fatigued are you?”), sleep quality (1–6 scale; “how was your sleep last night?”), sleep quantity (1–7 scale; “how many hours did you sleep last night?”), muscle soreness (1–7 scale; “rate your level of muscle soreness?”), and perceived stress (1–7 scale; “how are you feeling psychologically?”). An additional item assessing sleep latency (1–6 scale; “how long it took you to fall asleep?”) was completed to estimate time of sleep onset, as sleep latency is a recognized marker of sleep disturbances and can indicate difficulties falling asleep due to stress, anxiety, or pain (*Supporting Information*). For all items, lower scores indicated a good perceived state.

## 2.4 | Outcomes

The primary outcome was the time to first RRI, and independent outcomes were sleep quality, quantity, and latency (i.e., the time to fall asleep), muscle soreness, fatigue, perceived stress, and training load.

## 2.5 | Patient and Public Involvement Statement

There was no patient involvement in the study design or data collection due to the observational and noninterventional nature of the research.

## 2.6 | Statistical Analyses

Statistical analyses were conducted over a maximum follow-up period of 6 months from inclusion. We carried out statistical analysis using R software V.4.4.2. Significance was accepted at *p* < 0.05. Runners were included if they responded for at least three consecutive weeks (i.e., a minimum of 11.5% of the total follow-up period). A descriptive analysis of the study population was carried out at baseline and over the follow-up period. Data were calculated as mean and standard deviation for continuous variables, and as number and percentage for categorical variables. Epidemiological outcomes were calculated: proportion of runners with at least one RRI (i.e., prevalence); number of RRIs per athlete over the 26 weeks; number of RRIs per 1000 h of practice (i.e., incidence rate); time to the first RRI; and the number of weeks with RRI per 1000 h of practice (i.e., burden) [17].

We conducted two levels of analysis using distinct statistical approaches to evaluate if sleep quality and quantity, training load, and complementary recovery factors were associated with the risk of RRI. First, a time-to-event analysis was conducted to identify factors associated with the time to first RRI, using a between-subject analysis. Cox proportional hazards regression

models were used to analyze the association between the primary outcome (time in weeks to the first RRI), and potential associate factors collected (1) at baseline: Sex, age; and (2) weekly during the follow-up: Hooper<sub>m</sub> questions (i.e., sleep quality, quantity and latency, muscle soreness, fatigue, perceived stress) and training volume. For the weekly monitoring data, mean values for each variable were calculated from the start of follow-up until the occurrence of the first RRI or until the participants were right-censored. Participants were considered right censored if they stopped filling out the RRI questionnaire [23]. To consider missing data, median imputation was applied for the Hooper<sub>m</sub> and training volume due to the low overall proportion of missing data (< 5% of the total dataset) and the ordinal nature of the variables, as previously conducted in a similar prospective study protocol [12]. Univariate Cox regression analyses were conducted for each potential associate factor. Following the selection approach described by Hosmer et al. [27] and as previously applied [23], variables with a *p* < 0.25 were observed and included as covariates in the final multivariate model. Hazard rate ratios (HRRs) with a 95% CI were reported for each variable. Additionally, sensitivity analyses were conducted by comparing Cox regression results from the imputed dataset with those obtained from complete-case data, where all rows with missing values were excluded, to assess the impact of imputation on the robustness of the findings.

Secondly, a within-subject analysis was conducted to examine the potential factors associated during the 2 weeks preceding RRI onset, as performed by Johnston et al. [12]. To do so, we identified all week triplets with responses to the RRI questionnaire across the 6 month follow-up. Only two triplets corresponding to the following sequences were retained (98.3%): no-RRI/no-RRI/no-RRI or no-RRI (RRI-2)/no-RRI (RRI-1)/RRI; models with nonterminal RRI were excluded (*Supplementary file*). Once the right triplets have been selected, no-RRI, RRI-1 and RRI-2 weeks were kept for analyses. No missing data were observed in this analysis. Linear mixed models were used to evaluate differences between no-RRI, RRI-1 and RRI-2 among the weekly variables collected during the follow-up, including Hooper<sub>m</sub> and training volume. Post hoc comparisons were performed using Tukey's analysis. Effect sizes (η<sup>2</sup>) were calculated to interpret the magnitude of the mean differences between the 3-week types (small [0.01], medium [0.06], and large [0.14]) [28].

# 3 | Results

## 3.1 | Population and Epidemiological Analysis

Of the 1982 runners registered, 339 were included in the statistical analysis (Figure 1). Participants came from 24 countries, of which 81.7% came from France. Both male and female participants were included in this study, with males representing 83.8% of the sample. Baseline characteristics and epidemiological outcomes are presented in Tables 1 and 2.

## 3.2 | Risk Factor Interindividual Analysis

For this analysis, 339 runners were included and followed for an average of 4.2 ± 2.6 weeks before being right-censored (52
<figure><img src="image_4.png" /><figcaption>**FIGURE 1** | Flow diagram of this study. MEQ: Morningness–Eveningness chronotype questionnaire.</figcaption></figure>

runners experienced an RRI, and 287 runners discontinued
filling out questionnaires). During this period, 37 out of 1423
data points (2.6%) were missing for the Hooper_m, 102 out of
1423 (7.2%) were missing for training volume, and these were
imputed by the median values. The time-to-event analysis re-
vealed that lower sleep quality was significantly associated
with an increased risk of RRI at any given time (HRR 1.36,
95% 1.04 to 1.78; *p* = 0.02). Importantly, each one-point in-
crease in the sleep quality score (meaning poorer sleep qual-
ity) was associated with a 36% increase in the risk of injury. No
other factors were significantly associated with RRI (Table 3).
Although the initial aim was to compute the session RPE
(Training volume*RPE) as a weekly indicator of training load,
this was finally not feasible because fewer than 10% of partic-
ipants provided RPE responses and RRI descriptions. Cox re-
gression analyses on complete-case and imputed data showed
minimal differences in log hazard ratios (≤0.027) with over-
lapping confidence intervals. The log-HRs were highly cor-
related (*r* = 0.987), and *p* value changes were minor, without
affecting significance.

3.3 | Intraindividual Analysis Through the Weeks
Preceding an RRI and Injury-Free Weeks

Of the 339 runners included, 14 had missing data and were ex-
cluded from this analysis, resulting in a final sample of 325 runners
(272 [83.7%] males and 53 [16.3%] females, age: 43.5 ± 10.1 years).
In this dataset, 1853 weeks were categorized as no-RRI, 56 as
RRI-1, and 52 as RRI-2. Differences were observed for fatigue
and muscle soreness across conditions (p=0.046 and p<0.001,
respectively). More precisely, compared to no-RRI weeks, muscle
soreness was higher during RRI-2 (p = 0.02) and RRI-1 (p < 0.001),
whereas fatigue was elevated during RRI-1 (p = 0.04). No other
differences were found between week categories (Table 4).

4 | Discussion

This study provided the first prospective investigation into RRI,
sleep quality, and quantity over a 6 month period in adult run-
ners, while also accounting for the training volume, muscle
<table><caption>**TABLE 1** | Baseline characteristics of the included population (n=339). Data are presented as mean and standard deviation (SD) for continuous variables with minimum (Min) and maximum (Max) values indicated, with number and percentage (%) for categorical variables.</caption><thead><tr><th>N=339</th><th>Mean (SD) or number (%)</th><th>Min—Max</th></tr></thead><tbody><tr><td>Anthropometrics values</td><td></td><td></td></tr><tr><td>Female runners</td><td>55 (16.2)</td><td>/</td></tr><tr><td>Male runners</td><td>284 (83.8)</td><td>/</td></tr><tr><td>Age (year)</td><td>43.4 (10.2)</td><td>21–72</td></tr><tr><td>Height (cm)</td><td>175.0 (8.5)</td><td>153–197</td></tr><tr><td>Body weight (kg)</td><td>70.5 (10.8)</td><td>41.4–119</td></tr><tr><td>BMI (kg·m<sup>-2</sup>)</td><td>22.9 (2.7)</td><td>16.2–43.1</td></tr><tr><td>Sleep</td><td></td><td></td></tr><tr><td>PSQI (score)</td><td>6.6 (3.1)</td><td>1–18</td></tr><tr><td>PSQI > 5</td><td>181 (55.2)</td><td>/</td></tr><tr><td>Sleep quantity ≥9 h</td><td>11 (3.2)</td><td>/</td></tr><tr><td>Sleep quantity ≥7 h to <9 h</td><td>251 (74.0)</td><td>/</td></tr><tr><td>Sleep quantity <7 h</td><td>77 (22.7)</td><td>/</td></tr><tr><td>Chronotype</td><td></td><td></td></tr><tr><td>MEQ (score)</td><td>60.4 (6.2)</td><td>37–73</td></tr><tr><td>Evening type</td><td>0 (0)</td><td>/</td></tr><tr><td>Moderately evening type</td><td>1 (0.002)</td><td>/</td></tr><tr><td>Intermediate chronotype</td><td>117 (34.5)</td><td>/</td></tr><tr><td>Moderately morning type</td><td>198 (58.4)</td><td>/</td></tr><tr><td>Morning type</td><td>23 (6.8)</td><td>/</td></tr><tr><td>Running practice</td><td></td><td></td></tr><tr><td>Experience (years of practice)</td><td>11.6 (9.7)</td><td>1–60</td></tr><tr><td>Competitive</td><td>315 (92.9)</td><td>/</td></tr><tr><td>Recreational</td><td>24 (7.1)</td><td>/</td></tr><tr><td>Main discipline—5/10 km (road)</td><td>25 (7.4)</td><td>/</td></tr><tr><td>Main discipline—half marathon (road)</td><td>33 (9.7)</td><td>/</td></tr><tr><td>Main discipline—marathon (road)</td><td>39 (11.5)</td><td>/</td></tr><tr><td>Main discipline—trail <80 km</td><td>99 (29.2)</td><td>/</td></tr></tbody></table>

(Continues)

<table><caption>**TABLE 1** | (Continued)</caption><thead><tr><th>N=339</th><th>Mean (SD) or number (%)</th><th>Min—Max</th></tr></thead><tbody><tr><td>Main discipline—trail > 80 km</td><td>121 (35.7)</td><td>/</td></tr><tr><td>Main discipline—no or mixed</td><td>21 (6.2)</td><td>/</td></tr><tr><td>Training frequency (number/week)</td><td>4.1 (2.3)</td><td>2–19</td></tr><tr><td>Training volume (hour/week)</td><td>7.3 (3.1)</td><td>2–21</td></tr></tbody></table>

Abbreviations: BMI: body mass index; MEQ: Morningness–Eveningness Chronotype Questionnaire; PSQI: Pittsburgh Sleep Quality Index.

soreness, fatigue, and perceived stress. Findings revealed that worse sleep quality was significantly associated with an increased risk of RRI, although the dropout rate should be considered when interpreting this association. Moreover, increased levels of muscle soreness and fatigue were observed during the previous weeks preceding an RRI, indicating that these markers may serve as early warning signs of insufficient recovery. However, contrary to our initial hypothesis, neither sleep quantity nor training volume was significantly associated with RRI.

## 4.1 | Interindividual Analysis: Lower Sleep Quality Was Associated With Increased RRI Risk

Our results, showing an association between lower sleep quality and increased RRI risk, are consistent with a previous study [16]. However, their assessment solely relied on a single retrospective PSQI score, which aggregates multiple sleep-related variables into one measure with respect to the month before. While our research is the first prospective within-runner study linking sleep quality to RRI over several weeks, the high dropout rate and shortened follow-up period warrant caution when interpreting this association. Already reported across various sports [14, 29], the high prevalence of poor sleep quality observed at baseline (55.2%) and confirmed through weekly assessments highlights a persistent issue in runners. This ongoing impairment is problematic given its potential contribution to increased injury risk. Yet, the physiological mechanisms linking subjective sleep quality to RRI remain unclear. It is not established whether sleep disturbances play a causal role in injury occurrence or reflect underlying factors. It can be suggested that poor sleep quality may disrupt the training–recovery balance contributing to increased injury risk [30]. As RRI results from a combination of various factors [5], modifying one of them such as improving sleep quality, may potentially reduce the risk of RRI [30]. Several targeted sleep hygiene education interventions based on weekly sleep quality monitoring may improve sleep quality, especially for individuals with the lowest sleep quality scores [31, 32], and may be implemented as a key priority for future injury prevention programs. This relationship may also be bidirectional, as increased training load can impair sleep, as shown in football players by Barba et al. [33], where higher training intensity was associated with poorer sleep. While training volume was not associated with injury in the present study, this suggests that poor sleep
may not only contribute to injury risk but could also reflect periods of high training stress, fatigue, or overreaching, which may represent an early stage in the development of injuries, highlighting the complex interplay between training load, recovery, and sleep in athletes.

While no significant association between sleep quantity and RRI was found here, earlier studies reported an increased injury risk in athletes sleeping less than 7h per night [12, 15]. However, these

<table><caption>**TABLE 2** | Epidemiological analysis. Data are presented as mean and standard deviation (SD) for continuous and number and percentage (%) for categorical variables.</caption><thead><tr><th></th><th>Runners n = 339<br>7926 weeks</th></tr></thead><tbody><tr><td>Outcomes response rates</td><td></td></tr><tr><td>RRI only</td><td>3190 (40.2)</td></tr><tr><td>RRI, Hooper<sub>m</sub>, and Training<br>volume</td><td>5462 (68.9)</td></tr><tr><td>RRI</td><td></td></tr><tr><td>Prevalence (proportion)</td><td>146 (43.1)</td></tr><tr><td>RRI per runner (number)</td><td>1.2 (2.0)</td></tr><tr><td>Incidence (number/1000 h)</td><td>7.9</td></tr><tr><td>Time to first RRI (weeks)</td><td>3.7 (5.7)</td></tr><tr><td>Burden (weeks/1000 h)</td><td>2.7 (2.3)</td></tr></tbody></table>

Note: NB: “7926 weeks” indicates the total number of weeks included in the analysis. “RRI only = 3,190 (40.2%)” indicates the number of weeks with RRI questionnaires completed and the percentage of responses compared to the total number of weeks included in this subgroup. “RRI, Hooper<sub>m</sub> & Training volume” indicates the number of weeks with RRI, Hooper<sub>m</sub>, or training volume questionnaires completed, and the percentage of responses compared to the total number of weeks included in this subgroup.

studies relied on fixed sleep duration thresholds (i.e., less or more than 7h), which may not adequately capture the individual variability in sleep needs of adult athletes [34]. In contrast, our analysis treated sleep quantity as a continuous variable, enabling a nuanced assessment of injury risk, though week-averaging may have masked some variations. The present study involved a larger, exclusively runner population, particularly trail runners who trained extensively. Compared to a previous study involving various endurance athletes, in which runners were more frequently injured [12], our population may be particularly exposed due to its training exposition. Discrepancies across studies may stem from varying RRI definitions (e.g., time loss vs. non-time loss injuries or a lack of clear criteria [8]), differences in study design (prospective vs. retrospective [5]), and heterogeneity in the tools and accuracy of sleep assessments. The link between sleep quantity and RRI remains unclear, as sleep needs can vary significantly between individuals based on factors such as age, physiology, and sleep opportunities [35], suggesting that no direct relationship between sleep quantity and RRI can be established.

## 4.2 | Intraindividual Analysis: Muscle Soreness and Fatigue, as Early Indicators of Training-Recovery Imbalance

Compared to injury-free weeks, muscle soreness was higher in 2-week and 1-week period before injury, and fatigue increased during the week preceding the injury. Whereas no association was observed with sleep quantity, quality, and training volume as previously reported [11, 23], runners reported higher fatigue and soreness before being injured, suggesting a state of potential overreaching. Monitoring fatigue, also possibly assessed as the subjective state of fitness [23], could be a useful indicator to detect athletes with a higher risk of RRI. Elevated pain and fatigue levels can be a sign of an insufficient recovery state that would lead to injury [36, 37]. This suggests that perceived fatigue and

<table><caption>**TABLE 3** | Results of the time to event analysis analyzing the association between the primary outcome (i.e., RRI onset) and potential associate factors at baseline and weekly during the 26-week follow-up period.</caption><thead><tr><th rowspan="2"></th><th colspan="3">Univariate analysis</th><th colspan="3">Multivariate analysis</th></tr><tr><th>HRR</th><th>(95% CI)</th><th>p</th><th>HRR</th><th>(95% CI)</th><th>p</th></tr></thead><tbody><tr><td>Baseline parameters</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Sex (reference male)</td><td>0.77</td><td>(0.40 to 1.47)</td><td>0.431</td><td>—</td><td>—</td><td>—</td></tr><tr><td>Age</td><td>0.98</td><td>(0.95 to 1.01)</td><td>0.248</td><td>0.98</td><td>(0.95 to 1.01)</td><td>0.287</td></tr><tr><td>Weekly factors</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Fatigue</td><td>1.02</td><td>(0.82 to 1.29)</td><td>0.820</td><td>—</td><td>—</td><td>—</td></tr><tr><td>Muscle soreness</td><td>1.09</td><td>(0.90 to 1.32)</td><td>0.370</td><td>—</td><td>—</td><td>—</td></tr><tr><td>Perceived stress</td><td>1.03</td><td>(0.84 to 1.27)</td><td>0.752</td><td>—</td><td>—</td><td>—</td></tr><tr><td>Sleep quantity</td><td>1.14</td><td>(0.88 to 1.48)</td><td>0.300</td><td>—</td><td>—</td><td>—</td></tr><tr><td>Sleep quality</td><td><strong>1.36</strong></td><td><strong>(1.08 to 1.73)</strong></td><td><strong>0.010</strong></td><td><strong>1.36</strong></td><td><strong>(1.04 to 1.77)</strong></td><td><strong>0.023</strong></td></tr><tr><td>Sleep latency</td><td>1.22</td><td>(0.89 to 1.66)</td><td>0.211</td><td>1.00</td><td>(0.71 to 1.40)</td><td>0.996</td></tr><tr><td>Training volume</td><td>1.00</td><td>(1.00 to 1.00)</td><td>0.486</td><td>—</td><td>—</td><td>—</td></tr></tbody></table>

Note: Bold values are for significant HRR. HRR above 1 indicates a tendency to have an increased RRI risk. Abbreviations: CI, confidence intervals; HRR, hazard rate ratio.
<table><caption>TABLE 4 | Difference between factors among RRI-1, RRI-2, and no-RRI weeks (mean and ± SD).</caption><thead><tr><th rowspan="2"></th><th colspan="3">Weeks</th><th colspan="2">Differences</th></tr><tr><th>no-RRI<sup>a</sup></th><th>RRI-1<sup>b</sup></th><th>RRI-2<sup>c</sup></th><th>p</th><th>η<sup>2</sup></th></tr></thead><tbody><tr><td>Hooper<sub>m</sub></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Sleep Quality (score)</td><td>2.9 ± 1.1</td><td>3.2 ± 1.0</td><td>2.9 ± 1.1</td><td>0.17</td><td>0.002</td></tr><tr><td>Sleep Quantity (score)</td><td>4.7 ± 1.2</td><td>4.8 ± 1.2</td><td>4.6 ± 1.2</td><td>0.37</td><td>0.001</td></tr><tr><td>Sleep Latency (score)</td><td>1.9 ± 0.8</td><td>2.0 ± 0.8</td><td>2.0 ± 0.7</td><td>0.56</td><td>0.001</td></tr><tr><td>Fatigue (score)</td><td>3.6 ± 1.2<sup>b</sup></td><td>4.0 ± 1.2<sup>a</sup></td><td>3.8 ± 1.1</td><td><strong>0.046<sup>a</sup></strong></td><td>0.003</td></tr><tr><td>Perceived stress (score)</td><td>2.9 ± 1.3</td><td>3.2 ± 1.3</td><td>2.9 ± 1.2</td><td>0.54</td><td>0.001</td></tr><tr><td>Muscle soreness (score)</td><td>2.6 ± 1.4<sup>b,c</sup></td><td>3.4 ± 1.3<sup>a</sup></td><td>3.2 ± 1.4<sup>a</sup></td><td><strong>&lt;0.001<sup>**</sup></strong></td><td>0.01</td></tr><tr><td>Training load</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Training volume (h)</td><td>6.9 ± 7.6</td><td>6.1 ± 3.6</td><td>6.9 ± 5.0</td><td>0.68</td><td>0.001</td></tr></tbody></table>

muscle soreness may serve as an early indicator of imbalance in the training-recovery process. Psychological symptoms are also prominent indicators of overreaching, and sleep latency has been assessed here as a potential marker of presleep anxiety [38]. However, no association between sleep latency, perceived stress, and RRI was found here, even though a type II error due to limited power cannot be ruled out. Identifying the key recovery-related factors that interfere with optimal tissue adaptation remains crucial for injury prevention [6]; muscle soreness and fatigue appear to be among the most relevant.

## 4.3 | Limitations

Although this study provides novel insights into the sleep-related factors associated with RRI among runners, some limitations should be acknowledged. First, despite the use of appropriate statistical methods, the relatively moderate sample size, low response rate, and participant dropout over time may reduce the strength of the findings. In addition to the missing data observed during the weekly follow-up, the missing data rate (from 1143 to 339 athletes with incomplete datasets; ~70% reduction) is comparable with previous studies as Edouard et al. [23] reported a decrease from 840 to 320 participants (~62% reduction), Hayes et al. [39] from 297 to 97 (~67% reduction), Joachim et al. [11] from 434 to 161 (~63% reduction, > 75% data completion), and von Rosen et al. [25, 30] from 732 to 496 (~32% reduction, with 19.6% missing data). However, most of these studies were conducted with adolescent and college student athletes, who are more likely to complete the questionnaires due to social and institutional pressures. In adult populations, even if some participants may be conscientious, everyday lifestyles and work constraints limit the ability to complete questionnaires regularly. Nonetheless, we addressed this limitation with full transparency. All instances of missing data were systematically reported, and we employed validated statistical techniques such as median imputation to manage incomplete

responses. Sensitivity analyses confirmed the robustness of our findings, with minimal differences between imputed and complete-case datasets. This degree of transparency is often underreported in comparable longitudinal research, particularly in sport and injury epidemiology, where data attrition is common [11, 17] but rarely scrutinized. Second, due to missing data, prior running injuries, a known predictor of RRI [23], or training load using RPE could not be evaluated. Third, the inclusion of runners with varying practices likely introduced variability in training loads that may have masked potential associations with RRI. Road and trail runners require specific physiological demands, particularly due to variations in terrain and elevation in trail [40]. Moreover, the small number of long sleepers (3.2%) likely limited the power to detect potential associations with RRI, even though some evidence suggests that long sleep duration may also be linked to adverse outcomes [41]. Additionally, injuries and recovery-related factors were assessed through subjectively self-report. While self-reported injuries are recognized as one of the three injury categories in the IOC consensus statement [18], widely used for prospective injury data collection [42], and clear criteria were used to define injuries, the lack of medical diagnosis remains a limitation, as the way athletes determine the status of their injuries may be linked to their behavior regarding injury management and participation restriction.

## 5 | Perspectives

Our study provides novel and meaningful insights into factors associated with RRI, highlighting a significant association between lower sleep quality and increased injury risk. Fatigue and muscle soreness were also found to be significantly higher within 1 and 2 weeks preceding an RRI, respectively, compared to injury-free periods. These findings, while acknowledging some methodological limitations, emphasize the importance of improving sleep quality among runners and regularly

<footnote><sup>a</sup>no-RRI.</footnote>

<footnote><sup>b</sup>RRI-1.</footnote>

<footnote><sup>c</sup>RRI-2.</footnote>

<footnote><sup>*</sup>(p ≤ 0.05).</footnote>

<footnote><sup>**</sup>(p ≤ 0.001), differences between conditions are underlined with letters.</footnote>
monitoring recovery status. Fatigue and muscle soreness may serve as early indicators of elevated injury risk.

To address this, weekly monitoring through wearables and brief questionnaires targeting sleep and other recovery-related indicators (e.g., fatigue and muscle soreness) may help to identify recovery deficits and advise both theoretical (e.g., sleep hygiene education, regular sleep-wake schedules adapted to chronotype) and practical interventions (e.g., relaxation techniques). Crucially, interventions should align with individual lifestyle habits to be sustainable over time.

Integrating sleep specialists into running organizations, alongside nutritionists, could reinforce athlete education. Researchers should also disseminate these findings to coaches and runners, promoting useful tracking while avoiding excessive or anxiety-driven monitoring. Building on previous work [43], a similar study could also be designed to test whether individualized feedback based on early warning signs can reduce injury risk. Future longitudinal studies should reduce data loss by implementing proactive follow-up strategies (i.e., employing someone to directly contact runners who are being lost), providing personal feedback, such as prevention advice and performance insights, and incorporating objective sleep monitoring for at least 6 months to better explore links between sleep architecture, particularly slow-wave sleep (i.e., restorative sleep) [13] and injury risk.

## Author Contributions

M.G., Y.L.M., P.E., G.Y.M., B.P.D.F., and U.D. conceived the study; Y.L.M. developed the application, and M.G., Y.L.M., and M.M. participated in the data collection; M.G., Y.L.M., M.M., and T.S. performed data treatment; M.G., Y.L.M., M.M., T.S., P.E., G.Y.M., B.P.F., and U.D. discussed and performed data analyses; M.G. drafted the manuscript, and all coauthors contributed substantially to interpreting the results, provided important revisions, and approved the manuscript. All authors understand that they are accountable for all aspects of the work and ensure the accuracy and integrity of this manuscript.

## Acknowledgments

The authors would like to thank all athletes involved in the study follow-up. The authors also acknowledge Ursula Debarnot and Benoit Pirot de Fontenay as co-last authors in recognition of their significant contributions to this research.

## Ethics Statement

This study was conducted in accordance with local and international regulations regarding human subject research and received approval from our local Ethical Committee (Commission recherche de Terre d'éthique, Institutional Review Board: IORG0007394; IRBN072020/CHUSTE). Participants were informed about the procedures and provided written consent prior to inclusion.

## Conflicts of Interest

P.E. is an Associate Editor for the British Journal of Sports Medicine, the BMJ Open Sports and Exercise Medicine, and the Scandinavian

Journal of Medicine & Science in Sports. G.Y.M. is an Associate Editor for Medicine and Science in Sports and Exercise and an Editorial member for Sports Medicine.

## Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

## References

1. L. C. Hespanhol Junior, J. D. Pillay, W. van Mechelen, and E. Verhagen, "Meta-Analyses of the Effects of Habitual Running on Indices of Health in Physically Inactive Adults," *Sports Medicine* 45, no. 10 (2015): 1455-1468, https://doi.org/10.1007/s40279-015-0359-y.

2. D. Lee, A. G. Brellenthin, P. D. Thompson, X. Sui, I. M. Lee, and C. J. Lavie, "Running as a Key Lifestyle Medicine for Longevity," *Progress in Cardiovascular Diseases* 60, no. 1 (2017): 45-55, https://doi.org/10.1016/j.pcad.2017.03.005.

3. L. C. Hespanhol Junior, W. van Mechelen, and E. Verhagen, "Health and Economic Burden of Running-Related Injuries in Dutch Trailrunners: A Prospective Cohort Study," *Sports Medicine* 47, no. 2 (2017): 367-377, https://doi.org/10.1007/s40279-016-0551-8.

4. S. Videbæk, A. M. Bueno, R. O. Nielsen, and S. Rasmussen, "Incidence of Running-Related Injuries Per 1000 h of Running in Different Types of Runners: A Systematic Review and Meta-Analysis," *Sports Medicine* 45, no. 7 (2015): 1017-1026, https://doi.org/10.1007/s40279-015-0333-8.

5. A. Fredette, J.-S. Roy, K. Perreault, F. Dupuis, C. Napier, and J.-F. Esculier, "The Association Between Running Injuries and Training Parameters: A Systematic Review," *Journal of Athletic Training* 57, no. 7 (2021): 650-671, https://doi.org/10.4085/1062-6050-0195.21.

6. W. van Mechelen, H. Hlobil, and H. C. G. Kemper, "Incidence, Severity, Aetiology and Prevention of Sports Injuries: A Review of Concepts," *Sports Medicine* 14, no. 2 (1992): 82-99, https://doi.org/10.2165/00007256-199214020-00002.

7. A. D. Lopes, L. C. Hespanhol, S. S. Yeung, and L. O. P. Costa, "What Are the Main Running-Related Musculoskeletal Injuries?," *Sports Medicine* 42, no. 10 (2012): 891-905, https://doi.org/10.1007/BF03262301.

8. K. G. Roos and S. W. Marshall, "Definition and Usage of the Term "Overuse Injury" in the US High School and Collegiate Sport Epidemiology Literature: A Systematic Review," *Sports Medicine* 44, no. 3 (2014): 405-421, https://doi.org/10.1007/s40279-013-0124-z.

9. L. C. Hespanhol Junior, L. O. Pena Costa, and A. D. Lopes, "Previous Injuries and Some Training Characteristics Predict Running-Related Injuries in Recreational Runners: A Prospective Cohort Study," *Journal of Physiotherapy* 59, no. 4 (2013): 263-269, https://doi.org/10.1016/S1836-9553(13)70203-0.

10. D. van Poppel, G. G. M. Scholten-Peeters, M. van Middelkoop, B. W. Koes, and A. P. Verhagen, "Risk Models for Lower Extremity Injuries Among Short- and Long Distance Runners: A Prospective Cohort Study," *Musculoskeletal Science & Practice* 36 (2018): 48-53, https://doi.org/10.1016/j.msksp.2018.04.007.

11. M. R. Joachim, B. C. Heiderscheit, and S. A. Kliethermes, "Changes in Sleep, Stress, and Fatigue Were Not Prospectively Associated With Running-Related Injuries Among High School Cross Country Runners," *Sports Health: A Multidisciplinary Approach* 16, no. 2 (2023): 19417381231217347, https://doi.org/10.1177/19417381231217347.

12. R. Johnston, R. Cahalan, L. Bonnett, et al., "General Health Complaints and Sleep Associated With New Injury Within an Endurance Sporting Population: A Prospective Study," *Journal of Science and Medicine in Sport* 23, no. 3 (2020): 252-257, https://doi.org/10.1016/j.jsams.2019.10.013.
13. S. L. Halson and L. E. Juliff, "Sleep, Sport, and the Brain," in *Progress in Brain Research*, vol. 234 (Elsevier, 2017), 13–31.

14. N. P. Walsh, S. L. Halson, C. Sargent, et al., "Sleep and the Athlete: Narrative Review and 2021 Expert Consensus Recommendations," *British Journal of Sports Medicine* 55, no. 7 (2021): 356–368, https://doi.org/10.1136/bjsports-2020-102025.

15. M. D. Milewski, D. L. Skaggs, G. A. Bishop, et al., "Chronic Lack of Sleep Is Associated With Increased Sports Injuries in Adolescent Athletes," *Journal of Pediatric Orthopedics* 34, no. 2 (2014): 129–133, https://doi.org/10.1097/BPO.0b013e3182878d91.

16. S. H. Mousavi, J. M. Hijmans, H. Minoonejad, R. Rajabi, and J. Zwerver, "Factors Associated With Lower Limb Injuries in Recreational Runners: A Cross-Sectional Survey Including Mental Aspects and Sleep Quality," *Journal of Sports Science and Medicine* 20, no. 2 (2021): 204–215, https://doi.org/10.52082/jssm.2021.204.

17. P. Edouard, P. E. Dandrieux, D. Blanco, et al., "How Do Sports Injury Epidemiological Outcomes Vary Depending on Athletes' Response Rates to a Weekly Online Questionnaire? An Analysis of 39-Week Follow-Up From 391 Athletics (Track and Field) Athletes," *Scandinavian Journal of Medicine & Science in Sports* 34, no. 3 (2024): e14589, https://doi.org/10.1111/sms.14589.

18. R. Bahr, B. Clarsen, W. Derman, et al., "International Olympic Committee Consensus Statement: Methods for Recording and Reporting of Epidemiological Data on Injury and Illness in Sport 2020 (Including STROBE Extension for Sport Injury and Illness Surveillance (STROBE-SIIS))," *British Journal of Sports Medicine* 54, no. 7 (2020): 372–389, https://doi.org/10.1136/bjsports-2019-101969.

19. J. A. Horne and O. Östberg, "A Self-Assessment Questionnaire to Determine Morningness-Eveningness in Human Circadian Rhythms," *International Journal of Chronobiology* 4, no. 2 (1976): 97–110.

20. D. J. Buysse, C. F. Reynolds, T. H. Monk, S. R. Berman, and D. J. Kupfer, "The Pittsburgh Sleep Quality Index: A New Instrument for Psychiatric Practice and Research," *Psychiatry Research* 28, no. 2 (1989): 193–213, https://doi.org/10.1016/0165-1781(89)90047-4.

21. T. Timpka, J. M. Alonso, J. Jacobsson, et al., "Injury and Illness Definitions and Data Collection Procedures for Use in Epidemiological Studies in Athletics (Track and Field): Consensus Statement," *British Journal of Sports Medicine* 48, no. 7 (2014): 483–490, https://doi.org/10.1136/bjsports-2013-093241.

22. T. P. Yamato, B. T. Saragotto, and A. D. Lopes, "A Consensus Definition of Running-Related Injury in Recreational Runners: A Modified Delphi Approach," *Journal of Orthopaedic and Sports Physical Therapy* 45, no. 5 (2015): 375–380, https://doi.org/10.2519/jospt.2015.5741.

23. P. Edouard, J. Tondut, K. Hollander, et al., "Risk Factors for Injury Complaints Leading to Restricted Participation in Athletics (Track and Field): A Secondary Analysis of Data From 320 Athletes Over One Season," *BMJ Open Sport & Exercise Medicine* 9 (2023): e001718, https://doi.org/10.1136/bmjsem-2023-001718.

24. S. Sonesson, Ö. Dahlström, N. K. Panagodage Perera, and M. Häglund, "Risk Factors for Injury and Illness in Youth Floorball Players – A Prospective Cohort Study," *Physical Therapy in Sport* 59 (2023): 92–102, https://doi.org/10.1016/j.ptsp.2022.11.008.

25. P. von Rosen, A. Frohm, A. Kottorp, C. Fridén, and A. Heijne, "Too Little Sleep and an Unhealthy Diet Could Increase the Risk of Sustaining a New Injury in Adolescent Elite Athletes," *Scandinavian Journal of Medicine & Science in Sports* 27, no. 11 (2017): 1364–1371, https://doi.org/10.1111/sms.12735.

26. S. L. Hooper, L. T. Mackinnon, A. Howard, R. D. Gordon, and A. W. Bachmann, "Markers for Monitoring Overtraining and Recovery," *Medicine and Science in Sports and Exercise* 27, no. 1 (1995): 106–112, https://doi.org/10.1249/00005768-199501000-00019.

27. D. W. Hosmer, S. Lemeshow, and R. X. Sturdivant, *Applied Logistic Regression*, 3rd ed. (John Wiley & Sons, 2013).

28. J. Cohen, *Statistical Power Analysis for the Behavioral Sciences* (Routledge, 1988), https://doi.org/10.4324/9780203771587.

29. R. Swinbourne, N. Gill, J. Vaile, and D. Smart, "Prevalence of Poor Sleep Quality, Sleepiness and Obstructive Sleep Apnoea Risk Factors in Athletes," *European Journal of Sport Science* 16, no. 7 (2016): 850–858, https://doi.org/10.1080/17461391.2015.1120781.

30. P. von Rosen, A. Frohm, A. Kottorp, C. Fridén, and A. Heijne, "Multiple Factors Explain Injury Risk in Adolescent Elite Athletes: Applying a Biopsychosocial Perspective," *Scandinavian Journal of Medicine & Science in Sports* 27, no. 12 (2017): 2059–2069, https://doi.org/10.1111/sms.12855.

31. F. Pasquier, R. Pla, L. Bosquet, F. Sauvet, and M. Nedelec, "The Impact of Multisession Sleep-Hygiene Strategies on Sleep Parameters in Elite Swimmers," *International Journal of Sports Physiology and Performance* 18, no. 11 (2023): 1304–1312, https://doi.org/10.1123/ijspp.2023-0018.

32. J. Gooderick, R. Clash, H. Fisher, N. Maxwell, and M. Hayes, "The Use of Individualised, Media-Based Sleep Hygiene Education for Professional Female Footballers," *European Journal of Sport Science* 25, no. 2 (2025): e12247, https://doi.org/10.1002/ejsc.12247.

33. E. Barba, D. Casamichana, and J. Castellano, "How Different Training Sessions Affect the Sleep of Professional Soccer Players," *Biology of Sport* 42, no. 2 (2025): 125–133, https://doi.org/10.5114/biolsport.2025.142644.

34. A. Vlahoyiannis, G. Aphamis, G. C. Bogdanis, G. K. Sakkas, E. Andreou, and C. D. Giannaki, "Deconstructing Athletes' Sleep: A Systematic Review of the Influence of Age, Sex, Athletic Expertise, Sport Type, and Season on Sleep Characteristics," *Journal of Sport and Health Science* 10, no. 4 (2021): 387–402, https://doi.org/10.1016/j.jshs.2020.03.006.

35. M. Hirshkowitz, K. Whiton, S. M. Albert, et al., "National Sleep Foundation's Updated Sleep Duration Recommendations: Final Report," *Sleep Health* 1 (2015): 233–243, https://doi.org/10.1016/j.sleh.2014.12.010.

36. M. Sterling, G. Jull, and A. Wright, "The Effect of Musculoskeletal Pain on Motor Activity and Control," *Journal of Pain* 2, no. 3 (2001): 135–145, https://doi.org/10.1054/jpai.2001.19951.

37. A. Trecroci, G. Boccolini, M. Duca, D. Formenti, and G. Alberti, "Mental Fatigue Impairs Physical Activity, Technical and Decision-Making Performance During Small-Sided Games," *PLoS One* 15, no. 9 (2020): e0238461, https://doi.org/10.1371/journal.pone.0238461.

38. A. Montero, D. Stevens, R. Adams, and M. Drummond, "Sleep and Mental Health Issues in Current and Former Athletes: A Mini Review," *Frontiers in Psychology* 13 (2022): 868614, https://doi.org/10.3389/fpsyg.2022.868614.

39. L. E. Hayes, A. Boulos, and A. I. Cruz Jr, "Risk Factors for In-season Injury in Varsity Collegiate Cross-Country Athletes: An Analysis of One Season in 97 Athletes," *Journal of Sports Medicine and Physical Fitness* 59, no. 9 (2019): 1536–1543, https://doi.org/10.23736/s0022-4707.19.09221-1.

40. F. Sabater Pastor, T. Besson, M. Berthet, et al., "Elite Road vs. Trail Runners: Comparing Economy, Biomechanics, Strength, and Power," *Journal of Strength and Conditioning Research* 37, no. 1 (2023): 181–186, https://doi.org/10.1519/JSC.0000000000004226.

41. M. Jike, O. Itani, N. Watanabe, D. J. Buysse, and Y. Kaneita, "Long Sleep Duration and Health Outcomes: A Systematic Review, Meta-Analysis and Meta-Regression," *Sleep Medicine Reviews* 39 (2018): 25–36, https://doi.org/10.1016/j.smrv.2017.06.011.

42. B. Clarsen, R. Bahr, G. Myklebust, et al., "Improved Reporting of Overuse Injuries and Health Problems in Sport: An Update of the Oslo Sport Trauma Research Center Questionnaires," *British Journal of Sports Medicine* 54, no. 7 (2020): 390–396, https://doi.org/10.1136/bjspo-rts-2019-101337.
43. P.-E. Dandrieux, L. Navarro, D. Blanco, et al., "Association Between the Use of Daily Injury Risk Estimation Feedback (I-REF) Based on Machine Learning Techniques and Injuries in Athletics (Track and Field): Results of a Prospective Cohort Study Over an Athletics Season," *BMJ Open Sport & Exercise Medicine* 11, no. 1 (2025): e002331, https://doi.org/10.1136/bmjsem-2024-002331.

**Supporting Information**

Additional supporting information can be found online in the
Supporting Information section. Data S1: Supporting Information