Hair Hormones and Heart Rate Variability as Chronic Stress Biomarkers in a Female Long-Distance Runner: A 22-Month Longitudinal Case Study

Hair Hormones and Heart Rate Variability as Chronic Stress Biomarkers in a Female Long-Distance Runner: A 22-Month Longitudinal Case Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Hair Hormones and Heart Rate Variability as Chronic Stress Biomarkers in a Female Long-Distance Runner: A 22-Month Longitudinal Case Study Kyoka Shimizu, Genta Ochi, Keita Oneyama, Yumi Okamoto, Tomomi Fujimoto, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7872377/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Although heart rate variability (HRV) and hair hormone assessments have been proposed as biomarkers to evaluate chronic stress and mental health in athletes, whether combining these biomarkers can predict future mental health decline and performance deterioration remains unclear. In this study, we sought to establish this by evaluating waking HRV and hair hormone concentrations (cortisol, oxytocin, and cortisol/oxytocin ratio) in one elite female long-distance runner over 22 months. We assessed mental health once monthly using the six-item Kessler Psychological Distress Scale and Profile of Mood States Second Edition (POMS2). In addition, self-reported training load, training volume, and athletic performance were assessed as physical and performance indices. Heart rate and HRV were each measured three days per week in both resting (supine) and standing (upright) positions upon waking. Hair samples were collected monthly for hormone analysis. Cross-correlation analysis identified 10 significant time-lagged relationships, and Granger causality testing revealed three significant predictive relationships. Waking standing root mean square of successive differences (RMSSD) predicted future POMS2 vigor-activity scores, hair oxytocin concentration predicted future POMS2 fatigue-inertia scores, and hair cortisol concentration predicted future athletic performance. The findings suggest that combining HRV and hair hormone assessments provides complementary information across multiple timescales, potentially serving as an early warning system for preventing mental health decline and performance deterioration in athletes. Hair hormones heart rate variability chronic stress mental health Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction In modern competitive sports, comprehensive condition monitoring of both physical and mental states that extends beyond simple training load management is essential for optimizing athletic performance. In long-distance running events and marathons, extended exercise duration and substantial energy expenditure imply that athletes’ overall condition significantly influences performance outcomes. Stress is a critical factor that can impair athletic condition, necessitating appropriate stress assessment and management to prevent overtraining syndrome characterized by injury, motivational decline, and sustained performance deterioration (Meeusen et al., 2013 ). Elite athletes face intense mental and physical demands, potentially increasing their susceptibility to mental health decline and risk-taking behaviors (Hughes & Leavey, 2012 ). Beyond physical and competitive stress, elite athletes also encounter public pressure from media, mental strain from non-competitive environments, and fear of early career termination due to injury or demotivation. These stressors can lead to decreased performance, mental health problems, burnout, and dropout (Bruner et al., 2008 ; Fletcher & Wagstaff, 2009 ; Hanton et al., 2005 ). While burnout occurs in approximately 10% of elite athletes (Hughes & Leavey, 2012 ), the prevalence of overtraining has been reported at 20–60%, with distance runners most severely affected. Therefore, evaluating stress and predicting condition are crucial for maximizing performance in long-distance runners. Heart rate variability (HRV) assessment has been proposed as a useful method to evaluate the balance between physical and mental stress in athletes (Thayer et al., 2012 ). HRV is a physiological indicator of emotional regulation related to mental health, reflecting autonomic regulation of the heart (Thayer et al., 2012 ). Previous research highlights the possibility of predicting mental health decline based on declining HRV (Chalmers et al., 2014 ; Zou et al., 2018 ). Several studies have examined the usefulness of HRV-based monitoring for athletes (Egan-Shuttler et al., 2020 ; Morales et al., 2014 ; Rodrigues et al., 2021 ), primarily using HRV as an indicator of mental and physical stress. However, limited evidence exists regarding its utility in predicting mental health decline. Our prior research conducted on a single elite athlete over 20 months demonstrated that waking standing HRV had moderate negative relationships with depression indices measured by the six-item Kessler Psychological Distress Scale (K6) and fatigue-inertia scores measured by the Profile of Mood States Second Edition (POMS2) (Matsuura & Ochi, 2023 ). Traditional methods of cortisol assessment (saliva, blood, urine) reflect relatively short time periods, making it difficult to examine long-term cumulative glucocorticoid burden. Hair segment analysis provides a retrospective index of cumulative hormone secretion over periods of up to several months (Davenport et al., 2006 ; Gow et al., 2010 ). In a cross-sectional study, endurance athletes exhibited higher hair cortisol concentrations compared to controls, revealing positive correlations between hair cortisol and training volume indicators (Skoluda et al., 2012 ). Furthermore, a longitudinal study of female collegiate soccer players confirmed that physical rather than psychological stressors were associated with increased hair cortisol concentrations, demonstrating that increased training load elevates hair cortisol levels over time (Sato et al., 2024 ). These findings suggest that repeated physical stress of intensive training and competitive races is associated with elevated cortisol exposure over prolonged periods. Hair oxytocin measurement has emerged as a novel biomarker to assess long-term neuroendocrine activity. Oxytocin, a peptide hormone associated with social cognition and attachment (Bielsky & Young, 2004 ), exerts physiological effects that counteract those of cortisol. It is secreted during novel events (Ross & Young, 2009 ; K. Uvnäs-Moberg, 1998 ) and stress responses (Hoehne et al., 2022 ), and binds to hypothalamic receptors to exert inhibitory effects on the hypothalamic–pituitary–adrenal (HPA) axis (Evenepoel et al., 2023 ; Neumann et al., 2000 ; Parker et al., 2005 ). While oxytocin has been shown to attenuate stress responses (Gibbs, 1986 ) and produce anxiolytic effects (Kerstin Uvnäs-Moberg & Petersson, 2005 ), most studies have examined only transient effects. Thus, the relationship between chronic oxytocin levels and stress remains unclear. However, recent animal studies have reported a positive correlation between hair oxytocin and cortisol (López-Arjona et al., 2021 ), suggesting that hair oxytocin concentration may increase under chronic stress conditions as a compensatory mechanism. Similar to cortisol, hair oxytocin can be measured using comparable extraction methods, potentially revealing whether individuals with higher hair oxytocin levels exhibit suppressed cortisol accumulation and reduced negative effects on mood and performance. A previous preprint study suggested that higher hair oxytocin concentrations may attenuate training load-induced cortisol elevation (Ochi et al., 2025 ), indicating that the cortisol/oxytocin ratio may serve as a more comprehensive indicator of the balance between stress and adaptive responses. While HRV provides information about acute autonomic regulation and current mental health status, hair hormones offer a retrospective window into cumulative stress exposure over months. Therefore, the combination of these biomarkers may provide complementary information, with HRV reflecting dynamic regulatory capacity and hair hormones reflecting cumulative physiological burden. Previous research has not examined whether the combination of HRV and hair hormone biomarkers can predict future mental health decline and performance deterioration in athletes or investigated the temporal relationships between these biomarkers and psychological/performance outcomes using time-series analysis methods such as cross-correlation analysis and Granger causality testing. Therefore, this study aimed to investigate whether combining HRV measurements and hair hormone assessments (cortisol, oxytocin, and cortisol/oxytocin ratio) can serve as predictive biomarkers for future mental health decline and performance deterioration in an elite female long-distance runner. This 22-month longitudinal single-subject study sought to examine temporal relationships between training load, hair hormones, HRV, mental health indicators, and athletic performance using cross-correlation analysis; identify predictive relationships using Granger causality testing; and evaluate whether these biomarkers can prospectively predict mental health and performance outcomes. We hypothesized that hair hormone concentrations and HRV would show time-lagged relationships with mental health and performance measures, and that prior values of these biomarkers would predict future mental health status and athletic performance. Methods Participant A female collegiate long-distance runner (aged 19–20 years during the study period) with experience competing in the Japan Intercollegiate Championships was the only participant in this project. She was a non-smoker with no chronic physical or mental health problems at the start of the study. All procedures were approved by the Institutional Ethics Committee of Niigata University of Health and Welfare and were conducted in accordance with the latest version of the Declaration of Helsinki (approval number: 19366–240809). Procedures Data were collected throughout the 22 months of the study period (covering training and competitive phases: from September 2023 to June 2025). Psychological scales assessing mental health and competitive stressors were applied every month. Heart rate and HRV upon waking were measured thrice weekly. Hair samples for cortisol and oxytocin analysis were collected monthly. Self-reported training load, time, distance, and volume were continuously monitored using heart rate-based measurement systems. Hair Hormone Measurements Hair samples were collected from the back of the participant’s head with minimal variation (Sauvé et al., 2007 ). To assess chronic stress levels during the previous month, we chose a 1-cm section (10 mg) from the scalp end of the collected hair for analysis, drawing on previous studies (Greff et al., 2019 ; Ochi et al., 2025 ; Sato et al., 2024 ). Hair samples were collected using scissors and weighed using an electronic balance (HT84R; Shinko Denshi, Japan). Because human hair grows approximately 1 cm per month (Loussouarn, 2001 ; Wennig, 2000 ), the hair samples from the measurement periods were assumed to be completely different. The hair was washed twice with isopropanol for 3 min at room temperature to remove any sweat or sebum secretions adhering to the hair surface, air-dried, and weighed. The washed hair samples were then outsourced to Air Plants Bio Co., Ltd. (Japan) for measurement, with the measurement methods being identical to those used in previous studies (Ochi et al., 2025 ; Sato et al., 2024 ). Due to the insufficient volume of hair samples during certain periods, oxytocin measurements could not be obtained for some months, resulting in missing data for oxytocin concentration and the cortisol/oxytocin ratio. Heart Rate Variability (HRV) Measurements Waking HRV was measured using the Polar H10 sensor and a Vantage V3 watch (Polar Electro Oy, Kempele, Finland). Waking resting heart rate and root mean square of successive differences (RMSSD) were measured thrice weekly at rest (supine position), whereas waking standing heart rate and RMSSD were measured in standing position upon waking up. The monthly average of the three data sets was used for analysis. The participant went to bed wearing H10 on her chest, starting the night before the measurement day. Upon waking, she followed the instructions provided by Vantage V3 to perform the measurement. The data thus collected were stored in Polar Flow from Vantage V3, which stores average heart rate per minute and RMSSD. Psychological Measurements The participant completed the POMS2 and K6 monthly. The Japanese versions of POMS2 (Yokoyama & Watanabe, 2015 ) was used. In this study, only the vigor-activity and fatigue-inertia subscales were used to reduce the participant's psychological burden. Each subscale comprises multiple items rated on a five-point Likert scale, with higher scores indicating greater vigor or fatigue. Similarly, the Japanese version of the K6 scale was used (Furukawa et al., 2008 ) to screen for psychological distress (Kessler et al., 2002 ). The respondent answered six items rated on a five-point Likert scale, with scores ranging from 0 to 4. A higher total score (range: 0–24) indicated worse mental health conditions. Competition Stressor Scale To measure competitive stress, the Competition Stressor Scale (Asanuma et al., 2015 ) was used. It comprises 28 questions that record the frequency of stressors over the preceding month, with each item rated on a four-point scale from 0 ("not at all") to 3 ("very often"). It comprises five factors: "interpersonal relationships" (0–24 points), "competition results" (0–9 points), "evaluation from one's surroundings" (0–12 points), "expectations and pressure from others" (0–15 points), and "motivation loss" (0–21 points). Higher scores indicate higher stress levels. Training Load Measurements Self-reported training load was calculated as the product of monthly training hours and training intensity. An 11-point scale from 0 to 10, was used to measure the rate of perceived exertion (Foster et al., 2001 ). Training time, distance, and volume were measured using the Polar H10 sensor and a Vantage V3 watch (Polar Electro Oy, Kempele, Finland). Training volume (in kcal) was calculated by the Polar flow web service based on the training session intensity measured using heart rate and training time. Performance Measurement The participant competed in 5000m and 10000m events, and her race times were recorded. To enable comparison across different distances, athletic performance was quantified using World Athletics points, an internationally standardized scoring system that allows comparison of performances across different track and field events. Points were calculated from race times using the World Athletics scoring tables (World Athletics, 2025), which converts performances into a common metric regardless of event distance. Statistical analyses All analyses were performed using R software (version 4.4.2). Given the single-subject repeated measures design (N = 1, 22 time points), mean, standard deviation (SD), minimum, maximum, and median were calculated for all variables. To examine time-lagged relationships, cross-correlation analysis was performed with a maximum lag of 2 time points (approximately 2 months). Missing values were imputed using linear interpolation. Correlations exceeding the 95% confidence interval (calculated as ± 1.96/√n) were considered noteworthy. To assess predictive relationships between variables, Granger causality tests were conducted using vector autoregression models with lags up to 2 time points. The F-statistic and corresponding p-value were calculated for each predictor–outcome pair. Results Time Series Visualization Figure 1 – 4 depict the time series data for all measured variables across the 22-month study period, organized by measurement category. Figure 1 reveals training load indicators: (a) self-reported training load, (b) training time, (c) training distance, and (d) training volume. Figure 2 displays hair hormone biomarkers: (a) hair cortisol concentration, (b) hair oxytocin concentration, and (c) cortisol/oxytocin ratio. Figure 3 presents waking physiological indicators: (a) waking resting heart rate, (b) waking resting RMSSD, (c) waking standing heart rate, and (d) waking standing RMSSD. Figure 4 illustrates mental health indicators: (a) K6, (b) POMS2 fatigue-inertia, (c) POMS2 vigor-activity, and (d) World Athletics points. Across the 22-month period, self-reported training load ranged from 5,280 to 16,170 arbitrary units (mean = 11,758.18, SD = 3,632.45, 22 measurements). Training time, distance, and volume ranged from 1,550.35 to 2,359.75 min (mean = 1,972.69, SD = 228.20, 21 measurements), 179.58 to 485.16 km (mean = 381.05, SD = 88.92, 21 measurements), and 10,305 to 18,154 kcal (mean = 13,911.29, SD = 2,119.42, 21 measurements), respectively. Hair cortisol and oxytocin concentrations ranged from 3.86 to 21.71 pg/mg (mean = 11.60, SD = 4.79, 22 measurements) and from 1.80 to 3.96 pg/mg (mean = 2.71, SD = 0.65, 16 measurements), respectively. The cortisol/oxytocin ratio ranged from 2.47 to 7.77 (mean = 5.24, SD = 1.62, 16 measurements). Waking resting heart rate and RMSSD ranged from 35 to 42.5 bpm (mean = 38.37, SD = 2.01, 21 measurements) and from 101 to 167 ms (mean = 124.65, SD = 15.92, 21 measurements), respectively. Waking standing heart rate and RMSSD ranged from 36 to 47.5 bpm (mean = 41.08, SD = 3.01, 21 measurements) and from 80.75 to 153 ms (mean = 119.43, SD = 19.07, 21 measurements), respectively. K6 scores ranged from 1 to 21 (mean = 11.68, SD = 4.75, 22 measurements), POMS2 fatigue-inertia scores ranged from 6 to 17 (mean = 10.68, SD = 2.73, 22 measurements), POMS2 vigor-activity scores ranged from 5 to 19 (mean = 11.41, SD = 4.12, 22 measurements), and World Athletics points ranged from 938 to 1043 (mean = 995.40, SD = 38.24, 10 measurements). Monthly time series data for training load indicators over the 22-month study period: (a) self-reported training load, (b) training time, (c) training distance, (d) training volume. Monthly time series data for hair hormone biomarkers over the 22-month study period: (a) hair cortisol concentration, (b) hair oxytocin concentration, (c) cortisol/oxytocin ratio. Monthly time series data for waking physiological indicators over the 22-month study period: (a) waking resting heart rate, (b) waking resting RMSSD, (c) waking standing heart rate, (d) waking standing RMSSD. Monthly time series data for mental health indicators over the 22-month study period: (Aa) K6, (b) POMS2 fatigue-inertia, (c) POMS2 vigor-activity, (d) World Athletics points. Detailed Correlation Analysis Detailed correlation analyses for all variable relationships are provided in the supplementary materials. Below, we focus on the key temporal relationships identified through cross-correlation analysis and Granger causality testing. Cross-Correlation Analysis (Time-Lagged Relationships) Cross-correlation analysis revealed 11 significant time-lagged relationships exceeding the 95% confidence interval (Fig. 5 ). Of these, four relationships exhibited simultaneous correlations (lag = 0): training volume and hair cortisol concentration (r = .761), training volume and waking standing heart rate (r = − .526), self-reported training load and hair oxytocin concentration (r = .496), and training time and hair cortisol concentration (r = .444). Seven relationships exhibited time-lagged patterns. Hair cortisol concentration revealed a lagged relationship with World Athletics points (max r = .583, lag = -2), indicating that the latter leads the former by 2 months. Hair cortisol concentration also revealed a predictive relationship with K6 (max r = .547, lag = + 2), indicating that the former predicts the latter two months later. Cortisol/oxytocin ratio revealed similar patterns with World Athletics points (max r = .507, lag = -2) and K6 (max r = .490, lag = + 2). Training volume predicted cortisol/oxytocin ratio one month later (max r = .463, lag = + 1). Waking standing RMSSD revealed a lagged relationship with POMS2 vigor-activity (max r = .448, lag = -1), indicating that the latter leads the former by one month. These findings suggest temporal dynamics in the relationships between training load, hair hormone biomarkers, and mental health outcomes. Cross-correlation functions for 10 significant time-lagged relationships between training load, hair hormone biomarkers, physiological indicators, and mental health outcomes: (a) Training Volume → Hair Cortisol Concentration, (b) Hair Cortisol Concentration → World Athletics Points, (c) Hair Cortisol Concentration → K6, (d) Training Volume → Waking Standing HR, (e) Cortisol/Oxytocin Ratio → World Athletics Points, (f) Self-Reported Training Load → Hair Oxytocin Concentration, (g) Cortisol/Oxytocin Ratio → K6, (h) Training Volume → Cortisol/Oxytocin Ratio, (i) Waking Standing RMSSD → POMS2 Vigor-Activity, (j) Training Time → Hair Cortisol Concentration. Dashed lines indicate 95% confidence intervals. Positive lags indicate that the predictor leads the outcome; negative lags indicate that the outcome leads the predictor. Granger Causality Testing Granger causality tests identified three significant predictive relationships (Table 1 ). Waking standing RMSSD at previous two months significantly predicted POMS2 vigor-activity scores (F(2,16) = 7.885, p = .0051, 21 measurements). Similarly, hair oxytocin concentration at previous two months significantly predicted POMS2 fatigue-inertia scores (F(2,11) = 6.269, p = .0197, 16 measurements) and hair cortisol concentration at previous two months significantly predicted athletic performance scores (F(2,5) = 14.708, p = .0282, 10 measurements). While these Granger causality results indicate statistical significance, they should be interpreted cautiously given the limited sample size (N = 1 participant with 10–22 time points). Table 1 Significant Granger Causality Relationships Predictor Outcome F statistic P value Lag Standing Heart Rate Variability POMS Vigor-Activity 7.885 0.0051 2 Hair Oxytocin Concentration POMS Fatigue-Inertia 6.269 0.0197 2 Hair Cortisol Concentration World Athletics Points 14.708 0.0282 2 Discussion In this study, we conducted a 22-month condition assessment of one female collegiate long-distance runner to clarify whether combining HRV and hair hormone measurements (cortisol, oxytocin, and cortisol/oxytocin ratio) can predict future mental health decline and performance deterioration in athletes. Cross-correlation analysis identified 10 significant time-lagged relationships between biomarkers, training load, mental health indices, and athletic performance. Granger causality testing revealed three significant predictive relationships: waking standing RMSSD predicted future POMS2 Vigor-Activity scores, hair oxytocin concentration predicted future POMS2 Fatigue-Inertia scores, and hair cortisol concentration predicted future athletic performance. These findings suggest that combining HRV and hair hormone assessments may be effective as an early warning system for preventing mental health decline and performance deterioration in athletes. By contrast, we did not find consistent predictive relationships between training load or competitive stressors and biomarker changes, suggesting that the relationship between external stressors and physiological stress responses is complex and may be mediated by individual coping resources. Our prior single-case study in an elite wheel gymnastics athlete demonstrated that waking standing RMSSD had moderate negative linear relationships with K6 depression index (r = − .54) and POMS2 Fatigue-Inertia (r = − .61) over 20 months (Matsuura & Ochi, 2023 ). The present study in a long-distance runner partially replicates these findings, showing weak negative correlations between waking standing RMSSD and K6 (r = − .191, 21 measurements) and POMS2 Fatigue-Inertia (r = − .195, 21 measurements) (Supplementary Materials, Figure S2). While the direction of these relationships is consistent across both athletes, the strength of correlations was weaker in the current study. This difference may reflect sport-specific characteristics or individual differences in autonomic regulation patterns. Importantly, the current study extends our prior correlational findings by demonstrating that waking standing RMSSD predicts future POMS2 Vigor-Activity scores (F(2,16) = 7.885, p = .0051), providing evidence for a prospective predictive relationship rather than merely concurrent association. This advancement from correlation to prediction represents a critical step toward establishing HRV as an early warning biomarker for mental health decline in athletes. Previous research has suggested that predicting mental health decline based on declining HRV is possible (Chalmers et al., 2014 ; Zou et al., 2018 ), and our Granger causality findings provide longitudinal evidence supporting this claim in the athletic context. The utility of HRV monitoring in endurance athletes has been demonstrated across multiple studies, with evidence that training adaptation is reflected in HRV changes (Plews et al., 2013) and that decreased HRV accompanies overtraining (Mourot et al., 2004). Our finding that HRV predicts future vigor may reflect this regulatory capacity's influence on maintaining positive mood states over time. Our finding that hair cortisol concentration predicted athletic performance (F(2,5) = 14.708, p = .0282) extends previous findings in endurance athletes. Skoluda et al. ( 2012 ) demonstrated that endurance athletes exhibited 46% higher hair cortisol concentrations compared to controls, with training volume positively correlating with hair cortisol levels. Sato et al. ( 2024 ) confirmed this pattern in female footballers. The current study advances these findings by demonstrating prospective predictive relationships over 22 months in a female long-distance runner. The strong correlation between hair cortisol and training volume (r = .756) confirms that intensive aerobic training elevates long-term cortisol exposure (Sato et al., 2024 ; Skoluda et al., 2012 ). This pattern has been observed in Japanese female long-distance runners via salivary cortisol (Tsunekawa et al., 2022; Ushiki et al., 2020), though the present study is the first examining hair cortisol in this population. Cross-correlation analysis revealed a bidirectional temporal relationship: World Athletics Points led hair cortisol by 2 months (max r = .583, lag = -2), while hair cortisol also predicted future performance. This pattern likely reflects training periodization and tapering. During intensive training phases, elevated cortisol exposure is incorporated into growing hair over weeks to months. Subsequent tapering reduces training volume to allow recovery. Hormonal balance improves within one week of tapering, but performance improvements require an additional 1–2 weeks (Papacosta et al., 2013). Therefore, the 2-month lag likely captures intensive training (elevated hair cortisol) followed by tapering and peak performance. The finding that hair oxytocin concentration predicted future POMS2 Fatigue-Inertia scores (F(2,11) = 6.269, p = .0197) is novel, as few studies have examined hair oxytocin in athletic populations. Oxytocin is involved in social bonding, stress buffering, and inhibition of HPA axis activity (Neumann et al., 2000 ). Higher oxytocin levels may reflect greater social support resources and more effective stress recovery mechanisms, protecting against fatigue accumulation over time. Cross-correlation analysis revealed that self-reported training load and hair oxytocin concentration showed simultaneous positive correlation (r = .496, lag = 0), and that hair oxytocin showed moderate positive relationships with training volume (r = .504, 16 measurements). This pattern suggests that despite increased training stress, oxytocin levels were maintained or increased in parallel with training demands, possibly reflecting adaptive coping mechanisms or social support from training partners and coaches. The moderate negative correlation between hair oxytocin and cortisol/oxytocin ratio (r = − .610) indicates that higher oxytocin levels shift the hormonal balance away from stress-dominance toward a more balanced physiological state. This ratio may be critical for maintaining mental health and preventing burnout during intensive training periods. Recent preprint suggests that oxytocin may reduce the accumulation and effects of chronic stress (Ochi et al., 2025 ), and our findings provide preliminary evidence that this protective effect may extend to athletic populations experiencing sustained training loads. Cross-correlation analysis revealed that training volume predicted cortisol/oxytocin ratio at 1 month later (max r = .463, lag = + 1), suggesting that increased training load shifts the hormonal balance toward a more stress-dominant profile with a temporal delay. This time-lagged relationship highlights the cumulative nature of training stress effects and the importance of monitoring both cortisol and oxytocin to capture the full hormonal response profile. The cortisol/oxytocin ratio may serve as an integrated biomarker reflecting the relative balance between stress (cortisol) and recovery/social support (oxytocin) systems, providing information beyond what either hormone offers independently. Several limitations should be noted. First, this is a single-case study (N = 1), limiting generalizability to other athletes, sports, or populations. However, the intensive longitudinal design with 22 monthly measurements provides strong within-person evidence of temporal relationships and addresses the limitation of high interindividual variability that complicates group-level analyses of athlete stress responses. Single-subject designs are particularly valuable when individual differences are large and within-person processes may differ from between-person patterns, as is often the case in athletic populations with diverse sport demands, training histories, and coping resources (Strahler & Luft, 2019). The N-of-1 design allows for idiographic assessment that captures individual-specific stress-response patterns, which may be obscured in nomothetic group-level analyses. Replication in larger samples of long-distance runners and athletes across different endurance sports is needed to establish generalizability. Second, the measurement frequency (monthly for hair hormones and psychological assessments) may not capture shorter-term dynamics of stress and recovery cycles within training mesocycles or microcycles. While HRV was measured three times weekly, future studies should consider more frequent psychological assessments and potentially shorter hair segments to examine dynamics at finer temporal resolutions. Third, we did not measure some potentially relevant variables such as sleep quality, nutrition status, menstrual cycle phase, social support quality, and life stressors outside of sport. These factors may mediate or moderate the relationships observed between biomarkers, training load, and mental health outcomes. The role of social support is particularly relevant given oxytocin's established function in social bonding and stress buffering. Fourth, the mechanisms linking hair hormones to brain function and behavior remain unclear. Similarly, whether hair oxytocin reflects central or peripheral oxytocin activity, and how accurately it indexes brain oxytocin relevant to psychological function, requires further investigation. Fifth, while Granger causality testing provides evidence of temporal precedence and predictive relationships, it does not establish true causality or rule out unmeasured confounding variables. Experimental manipulations or interventions targeting these biomarkers are needed to confirm causal mechanisms. Conclusion This study demonstrates that combining HRV measurements with hair hormone assessments provides complementary information about athlete stress, mental health, and performance across multiple timescales. Waking standing RMSSD predicted future vigor, hair oxytocin predicted future fatigue, and hair cortisol predicted future athletic performance. These findings suggest that multi-modal biomarker assessment may serve as an early warning system for mental health decline and performance deterioration in endurance athletes. Future research should replicate these findings in larger samples, investigate underlying mechanisms, and develop practical monitoring protocols for athlete mental health surveillance and overtraining prevention. Declarations Consent Statement: The participant in this study provided written informed consent to participate in the research. The study procedures were fully explained to the participant, and she voluntarily agreed to participate in this longitudinal case study. The study protocol was approved by the Ethics Committee of Niigata University of Health and Welfare, and all procedures were conducted in accordance with the Declaration of Helsinki. Disclosure of interest The authors have no competing interests relevant to the content of this article. Funding This work was supported by Japan Society for the Promotion of Science (JSPS) grants JP25K21657 (G.O.), Grant-in-Aid for Scientific Research 2024 from the Japan Racing Association (JKA; G.O.) and Grant-in-Aid for Scientific Research from Niigata University of Health and Welfare, 2024, 2025 (G.O.) Author Contribution KS and GO conceptualized the research, designed the methodology, conducted the investigation, performed the formal analysis, wrote the original draft, and reviewed and edited the manuscript. KS conducted the investigation and acquired all data. YO, TF, and KY supervised and reviewed the manuscript. All authors have read, reviewed, and approved the final manuscript. Acknowledgement We would like to thank Naru Tsuchida and Sosuke Nakano for their assistance with the washing procedures during the preprocessing of hair samples for measurement. We would also like to thank Editage (www.editage.com) for English language editing. 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V., Contreras-Aguilar, M. D., Martínez-Miró, S., Cerón, J. J., & Martínez-Subiela, S. (2021). A procedure for oxytocin measurement in hair of pig: Analytical validation and a pilot application. Biology , 10 (6), 527. https://doi.org/10.3390/biology10060527 Loussouarn, G. (2001). African hair growth parameters. British Journal of Dermatology , 145 (2), 294–297. https://doi.org/10.1046/j.1365-2133.2001.04350.x Matsuura, Y., & Ochi, G. (2023). The potential of heart rate variability monitoring for mental health assessment in top wheel gymnastics athletes: A single case design. Applied Psychophysiology and Biofeedback , 48 (3), 335–343. https://doi.org/10.1007/s10484-023-09585-3 Meeusen, R., Duclos, M., Foster, C., Fry, A., Gleeson, M., Nieman, D., Raglin, J., Rietjens, G., Steinacker, J., Urhausen, A., & American College of Sports Medicine. (2013). European College of Sport Science, &. 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Oxytocin may reduce the accumulation and effects of chronic stress: An exploratory study using hair samples. In bioRxiv (p. 2025.04.15.649015). https://doi.org/10.1101/2025.04.15.649015 Parker, K. J., Buckmaster, C. L., Schatzberg, A. F., & Lyons, D. M. (2005). Intranasal oxytocin administration attenuates the ACTH stress response in monkeys. Psychoneuroendocrinology , 30 (9), 924–929. https://doi.org/10.1016/j.psyneuen.2005.04.002 Rodrigues, G. D., Gurgel, J. L., Gonçalves, T. R., & Soares, P. P. D. S. (2021). The physical capacity of rowing athletes cannot reverse the influence of age on heart rate variability during orthostatic stress. Anais da Academia Brasileira de Ciências , 93 (Suppl. 3), e20201677. https://doi.org/10.1590/0001-3765202120201677 Ross, H. E., & Young, L. J. (2009). Oxytocin and the neural mechanisms regulating social cognition and affiliative behavior. Frontiers in Neuroendocrinology , 30 (4), 534–547. https://doi.org/10.1016/j.yfrne.2009.05.004 Sato, M., Sasaki, M., Shima, T., Ikegami, R., Sato, D., & Ochi, G. (2024). Hair cortisol is a physiological indicator of training stress for female footballers. European Journal of Applied Physiology , 124 (12), 3719–3728. https://doi.org/10.1007/s00421-024-05571-7 Sauvé, B., Koren, G., Walsh, G., Tokmakejian, S., & Van Uum, S. H. M. (2007). Measurement of cortisol in human hair as a biomarker of systemic exposure. Clinical and Investigative Medicine Médecine Clinique et Experimentale , 30 (5), E183–E191. https://doi.org/10.25011/cim.v30i5.2894 Skoluda, N., Dettenborn, L., Stalder, T., & Kirschbaum, C. (2012). Elevated hair cortisol concentrations in endurance athletes. Psychoneuroendocrinology , 37 (5), 611–617. https://doi.org/10.1016/j.psyneuen.2011.09.001 Thayer, J. F., Ahs, F., Fredrikson, M., Sollers, J. J. 3rd, & Wager, T. D. (2012). A meta-analysis of heart rate variability and neuroimaging studies: Implications for heart rate variability as a marker of stress and health. Neuroscience and Biobehavioral Reviews , 36 (2), 747–756. https://doi.org/10.1016/j.neubiorev.2011.11.009 Uvnäs-Moberg, K. (1998). Oxytocin may mediate the benefits of positive social interaction and emotions. Psychoneuroendocrinology , 23 (8), 819–835. https://doi.org/10.1016/s0306-4530(98)00056-0 Uvnäs-Moberg, K., & Petersson, M. (2005). Oxytocin, ein Vermittler von Antistress, Wohlbefinden, sozialer Interaktion, Wachstum und Heilung [Oxytocin, a mediator of anti-stress, well-being, social interaction, growth and healing]. Zeitschrift Für Psychosomatische Medizin Und Psychotherapie , 51 (1), 57–80. https://doi.org/10.13109/zptm.2005.51.1.57 Wennig, R. (2000). Potential problems with the interpretation of hair analysis results. Forensic Science International , 107 (1–3), 5–12. https://doi.org/10.1016/s0379-0738(99)00146-2 World Athletics (2025, April 3). World Athletics scoring tables updated for 2025 . https://worldathletics.org/news/news/scoring-tables-2025 Yokoyama, K., & Watanabe, K. (2015). Japanese translation of POMS 2: Profile of Mood States . Kaneko Shobo. Zou, L., Sasaki, J. E., Wei, G. X., Huang, T., Yeung, A. S., Neto, O. B., Chen, K. W., & Hui, S. S. C. (2018). Effects of Mind-Body exercises (Tai chi/yoga) on heart rate variability parameters and perceived stress: A systematic review with meta-analysis of randomized controlled trials. Journal of Clinical Medicine , 7 (11), 404. https://doi.org/10.3390/jcm7110404 Additional Declarations No competing interests reported. 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Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn modern competitive sports, comprehensive condition monitoring of both physical and mental states that extends beyond simple training load management is essential for optimizing athletic performance. In long-distance running events and marathons, extended exercise duration and substantial energy expenditure imply that athletes\u0026rsquo; overall condition significantly influences performance outcomes. Stress is a critical factor that can impair athletic condition, necessitating appropriate stress assessment and management to prevent overtraining syndrome characterized by injury, motivational decline, and sustained performance deterioration (Meeusen et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Elite athletes face intense mental and physical demands, potentially increasing their susceptibility to mental health decline and risk-taking behaviors (Hughes \u0026amp; Leavey, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Beyond physical and competitive stress, elite athletes also encounter public pressure from media, mental strain from non-competitive environments, and fear of early career termination due to injury or demotivation. These stressors can lead to decreased performance, mental health problems, burnout, and dropout (Bruner et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Fletcher \u0026amp; Wagstaff, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Hanton et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). While burnout occurs in approximately 10% of elite athletes (Hughes \u0026amp; Leavey, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), the prevalence of overtraining has been reported at 20\u0026ndash;60%, with distance runners most severely affected. Therefore, evaluating stress and predicting condition are crucial for maximizing performance in long-distance runners.\u003c/p\u003e\u003cp\u003eHeart rate variability (HRV) assessment has been proposed as a useful method to evaluate the balance between physical and mental stress in athletes (Thayer et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). HRV is a physiological indicator of emotional regulation related to mental health, reflecting autonomic regulation of the heart (Thayer et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Previous research highlights the possibility of predicting mental health decline based on declining HRV (Chalmers et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Zou et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Several studies have examined the usefulness of HRV-based monitoring for athletes (Egan-Shuttler et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Morales et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Rodrigues et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), primarily using HRV as an indicator of mental and physical stress. However, limited evidence exists regarding its utility in predicting mental health decline. Our prior research conducted on a single elite athlete over 20 months demonstrated that waking standing HRV had moderate negative relationships with depression indices measured by the six-item Kessler Psychological Distress Scale (K6) and fatigue-inertia scores measured by the Profile of Mood States Second Edition (POMS2) (Matsuura \u0026amp; Ochi, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e Traditional methods of cortisol assessment (saliva, blood, urine) reflect relatively short time periods, making it difficult to examine long-term cumulative glucocorticoid burden. Hair segment analysis provides a retrospective index of cumulative hormone secretion over periods of up to several months (Davenport et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Gow et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In a cross-sectional study, endurance athletes exhibited higher hair cortisol concentrations compared to controls, revealing positive correlations between hair cortisol and training volume indicators (Skoluda et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Furthermore, a longitudinal study of female collegiate soccer players confirmed that physical rather than psychological stressors were associated with increased hair cortisol concentrations, demonstrating that increased training load elevates hair cortisol levels over time (Sato et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These findings suggest that repeated physical stress of intensive training and competitive races is associated with elevated cortisol exposure over prolonged periods.\u003c/p\u003e\u003cp\u003eHair oxytocin measurement has emerged as a novel biomarker to assess long-term neuroendocrine activity. Oxytocin, a peptide hormone associated with social cognition and attachment (Bielsky \u0026amp; Young, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), exerts physiological effects that counteract those of cortisol. It is secreted during novel events (Ross \u0026amp; Young, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; K. Uvn\u0026auml;s-Moberg, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) and stress responses (Hoehne et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and binds to hypothalamic receptors to exert inhibitory effects on the hypothalamic\u0026ndash;pituitary\u0026ndash;adrenal (HPA) axis (Evenepoel et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Neumann et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Parker et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). While oxytocin has been shown to attenuate stress responses (Gibbs, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1986\u003c/span\u003e) and produce anxiolytic effects (Kerstin Uvn\u0026auml;s-Moberg \u0026amp; Petersson, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), most studies have examined only transient effects. Thus, the relationship between chronic oxytocin levels and stress remains unclear. However, recent animal studies have reported a positive correlation between hair oxytocin and cortisol (L\u0026oacute;pez-Arjona et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), suggesting that hair oxytocin concentration may increase under chronic stress conditions as a compensatory mechanism. Similar to cortisol, hair oxytocin can be measured using comparable extraction methods, potentially revealing whether individuals with higher hair oxytocin levels exhibit suppressed cortisol accumulation and reduced negative effects on mood and performance. A previous preprint study suggested that higher hair oxytocin concentrations may attenuate training load-induced cortisol elevation (Ochi et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), indicating that the cortisol/oxytocin ratio may serve as a more comprehensive indicator of the balance between stress and adaptive responses.\u003c/p\u003e\u003cp\u003eWhile HRV provides information about acute autonomic regulation and current mental health status, hair hormones offer a retrospective window into cumulative stress exposure over months. Therefore, the combination of these biomarkers may provide complementary information, with HRV reflecting dynamic regulatory capacity and hair hormones reflecting cumulative physiological burden. Previous research has not examined whether the combination of HRV and hair hormone biomarkers can predict future mental health decline and performance deterioration in athletes or investigated the temporal relationships between these biomarkers and psychological/performance outcomes using time-series analysis methods such as cross-correlation analysis and Granger causality testing.\u003c/p\u003e\u003cp\u003eTherefore, this study aimed to investigate whether combining HRV measurements and hair hormone assessments (cortisol, oxytocin, and cortisol/oxytocin ratio) can serve as predictive biomarkers for future mental health decline and performance deterioration in an elite female long-distance runner. This 22-month longitudinal single-subject study sought to examine temporal relationships between training load, hair hormones, HRV, mental health indicators, and athletic performance using cross-correlation analysis; identify predictive relationships using Granger causality testing; and evaluate whether these biomarkers can prospectively predict mental health and performance outcomes. We hypothesized that hair hormone concentrations and HRV would show time-lagged relationships with mental health and performance measures, and that prior values of these biomarkers would predict future mental health status and athletic performance.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eParticipant\u003c/h2\u003e\u003cp\u003eA female collegiate long-distance runner (aged 19\u0026ndash;20 years during the study period) with experience competing in the Japan Intercollegiate Championships was the only participant in this project. She was a non-smoker with no chronic physical or mental health problems at the start of the study. All procedures were approved by the Institutional Ethics Committee of Niigata University of Health and Welfare and were conducted in accordance with the latest version of the Declaration of Helsinki (approval number: 19366\u0026ndash;240809).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eProcedures\u003c/h3\u003e\n\u003cp\u003eData were collected throughout the 22 months of the study period (covering training and competitive phases: from September 2023 to June 2025). Psychological scales assessing mental health and competitive stressors were applied every month. Heart rate and HRV upon waking were measured thrice weekly. Hair samples for cortisol and oxytocin analysis were collected monthly. Self-reported training load, time, distance, and volume were continuously monitored using heart rate-based measurement systems.\u003c/p\u003e\n\u003ch3\u003eHair Hormone Measurements\u003c/h3\u003e\n\u003cp\u003eHair samples were collected from the back of the participant\u0026rsquo;s head with minimal variation (Sauv\u0026eacute; et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). To assess chronic stress levels during the previous month, we chose a 1-cm section (10 mg) from the scalp end of the collected hair for analysis, drawing on previous studies (Greff et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ochi et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Sato et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Hair samples were collected using scissors and weighed using an electronic balance (HT84R; Shinko Denshi, Japan). Because human hair grows approximately 1 cm per month (Loussouarn, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Wennig, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), the hair samples from the measurement periods were assumed to be completely different. The hair was washed twice with isopropanol for 3 min at room temperature to remove any sweat or sebum secretions adhering to the hair surface, air-dried, and weighed. The washed hair samples were then outsourced to Air Plants Bio Co., Ltd. (Japan) for measurement, with the measurement methods being identical to those used in previous studies (Ochi et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Sato et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Due to the insufficient volume of hair samples during certain periods, oxytocin measurements could not be obtained for some months, resulting in missing data for oxytocin concentration and the cortisol/oxytocin ratio.\u003c/p\u003e\n\u003ch3\u003eHeart Rate Variability (HRV) Measurements\u003c/h3\u003e\n\u003cp\u003eWaking HRV was measured using the Polar H10 sensor and a Vantage V3 watch (Polar Electro Oy, Kempele, Finland). Waking resting heart rate and root mean square of successive differences (RMSSD) were measured thrice weekly at rest (supine position), whereas waking standing heart rate and RMSSD were measured in standing position upon waking up. The monthly average of the three data sets was used for analysis. The participant went to bed wearing H10 on her chest, starting the night before the measurement day. Upon waking, she followed the instructions provided by Vantage V3 to perform the measurement. The data thus collected were stored in Polar Flow from Vantage V3, which stores average heart rate per minute and RMSSD.\u003c/p\u003e\n\u003ch3\u003ePsychological Measurements\u003c/h3\u003e\n\u003cp\u003eThe participant completed the POMS2 and K6 monthly. The Japanese versions of POMS2 (Yokoyama \u0026amp; Watanabe, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) was used. In this study, only the vigor-activity and fatigue-inertia subscales were used to reduce the participant's psychological burden. Each subscale comprises multiple items rated on a five-point Likert scale, with higher scores indicating greater vigor or fatigue. Similarly, the Japanese version of the K6 scale was used (Furukawa et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) to screen for psychological distress (Kessler et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The respondent answered six items rated on a five-point Likert scale, with scores ranging from 0 to 4. A higher total score (range: 0\u0026ndash;24) indicated worse mental health conditions.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eCompetition Stressor Scale\u003c/h2\u003e\u003cp\u003eTo measure competitive stress, the Competition Stressor Scale (Asanuma et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) was used. It comprises 28 questions that record the frequency of stressors over the preceding month, with each item rated on a four-point scale from 0 (\"not at all\") to 3 (\"very often\"). It comprises five factors: \"interpersonal relationships\" (0\u0026ndash;24 points), \"competition results\" (0\u0026ndash;9 points), \"evaluation from one's surroundings\" (0\u0026ndash;12 points), \"expectations and pressure from others\" (0\u0026ndash;15 points), and \"motivation loss\" (0\u0026ndash;21 points). Higher scores indicate higher stress levels.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eTraining Load Measurements\u003c/h3\u003e\n\u003cp\u003eSelf-reported training load was calculated as the product of monthly training hours and training intensity. An 11-point scale from 0 to 10, was used to measure the rate of perceived exertion (Foster et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Training time, distance, and volume were measured using the Polar H10 sensor and a Vantage V3 watch (Polar Electro Oy, Kempele, Finland). Training volume (in kcal) was calculated by the Polar flow web service based on the training session intensity measured using heart rate and training time.\u003c/p\u003e\n\u003ch3\u003ePerformance Measurement\u003c/h3\u003e\n\u003cp\u003eThe participant competed in 5000m and 10000m events, and her race times were recorded. To enable comparison across different distances, athletic performance was quantified using World Athletics points, an internationally standardized scoring system that allows comparison of performances across different track and field events. Points were calculated from race times using the World Athletics scoring tables (World Athletics, 2025), which converts performances into a common metric regardless of event distance.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analyses\u003c/h2\u003e\u003cp\u003eAll analyses were performed using R software (version 4.4.2). Given the single-subject repeated measures design (N\u0026thinsp;=\u0026thinsp;1, 22 time points), mean, standard deviation (SD), minimum, maximum, and median were calculated for all variables. To examine time-lagged relationships, cross-correlation analysis was performed with a maximum lag of 2 time points (approximately 2 months). Missing values were imputed using linear interpolation. Correlations exceeding the 95% confidence interval (calculated as \u0026plusmn;\u0026thinsp;1.96/\u0026radic;n) were considered noteworthy. To assess predictive relationships between variables, Granger causality tests were conducted using vector autoregression models with lags up to 2 time points. The F-statistic and corresponding p-value were calculated for each predictor\u0026ndash;outcome pair.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eTime Series Visualization\u003c/h2\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e depict the time series data for all measured variables across the 22-month study period, organized by measurement category. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e reveals training load indicators: (a) self-reported training load, (b) training time, (c) training distance, and (d) training volume. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e displays hair hormone biomarkers: (a) hair cortisol concentration, (b) hair oxytocin concentration, and (c) cortisol/oxytocin ratio. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents waking physiological indicators: (a) waking resting heart rate, (b) waking resting RMSSD, (c) waking standing heart rate, and (d) waking standing RMSSD. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates mental health indicators: (a) K6, (b) POMS2 fatigue-inertia, (c) POMS2 vigor-activity, and (d) World Athletics points. Across the 22-month period, self-reported training load ranged from 5,280 to 16,170 arbitrary units (mean\u0026thinsp;=\u0026thinsp;11,758.18, SD\u0026thinsp;=\u0026thinsp;3,632.45, 22 measurements). Training time, distance, and volume ranged from 1,550.35 to 2,359.75 min (mean\u0026thinsp;=\u0026thinsp;1,972.69, SD\u0026thinsp;=\u0026thinsp;228.20, 21 measurements), 179.58 to 485.16 km (mean\u0026thinsp;=\u0026thinsp;381.05, SD\u0026thinsp;=\u0026thinsp;88.92, 21 measurements), and 10,305 to 18,154 kcal (mean\u0026thinsp;=\u0026thinsp;13,911.29, SD\u0026thinsp;=\u0026thinsp;2,119.42, 21 measurements), respectively. Hair cortisol and oxytocin concentrations ranged from 3.86 to 21.71 pg/mg (mean\u0026thinsp;=\u0026thinsp;11.60, SD\u0026thinsp;=\u0026thinsp;4.79, 22 measurements) and from 1.80 to 3.96 pg/mg (mean\u0026thinsp;=\u0026thinsp;2.71, SD\u0026thinsp;=\u0026thinsp;0.65, 16 measurements), respectively. The cortisol/oxytocin ratio ranged from 2.47 to 7.77 (mean\u0026thinsp;=\u0026thinsp;5.24, SD\u0026thinsp;=\u0026thinsp;1.62, 16 measurements). Waking resting heart rate and RMSSD ranged from 35 to 42.5 bpm (mean\u0026thinsp;=\u0026thinsp;38.37, SD\u0026thinsp;=\u0026thinsp;2.01, 21 measurements) and from 101 to 167 ms (mean\u0026thinsp;=\u0026thinsp;124.65, SD\u0026thinsp;=\u0026thinsp;15.92, 21 measurements), respectively. Waking standing heart rate and RMSSD ranged from 36 to 47.5 bpm (mean\u0026thinsp;=\u0026thinsp;41.08, SD\u0026thinsp;=\u0026thinsp;3.01, 21 measurements) and from 80.75 to 153 ms (mean\u0026thinsp;=\u0026thinsp;119.43, SD\u0026thinsp;=\u0026thinsp;19.07, 21 measurements), respectively. K6 scores ranged from 1 to 21 (mean\u0026thinsp;=\u0026thinsp;11.68, SD\u0026thinsp;=\u0026thinsp;4.75, 22 measurements), POMS2 fatigue-inertia scores ranged from 6 to 17 (mean\u0026thinsp;=\u0026thinsp;10.68, SD\u0026thinsp;=\u0026thinsp;2.73, 22 measurements), POMS2 vigor-activity scores ranged from 5 to 19 (mean\u0026thinsp;=\u0026thinsp;11.41, SD\u0026thinsp;=\u0026thinsp;4.12, 22 measurements), and World Athletics points ranged from 938 to 1043 (mean\u0026thinsp;=\u0026thinsp;995.40, SD\u0026thinsp;=\u0026thinsp;38.24, 10 measurements).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eMonthly time series data for training load indicators over the 22-month study period: (a) self-reported training load, (b) training time, (c) training distance, (d) training volume.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eMonthly time series data for hair hormone biomarkers over the 22-month study period: (a) hair cortisol concentration, (b) hair oxytocin concentration, (c) cortisol/oxytocin ratio.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eMonthly time series data for waking physiological indicators over the 22-month study period: (a) waking resting heart rate, (b) waking resting RMSSD, (c) waking standing heart rate, (d) waking standing RMSSD.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eMonthly time series data for mental health indicators over the 22-month study period: (Aa) K6, (b) POMS2 fatigue-inertia, (c) POMS2 vigor-activity, (d) World Athletics points.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eDetailed Correlation Analysis\u003c/h2\u003e\u003cp\u003eDetailed correlation analyses for all variable relationships are provided in the supplementary materials. Below, we focus on the key temporal relationships identified through cross-correlation analysis and Granger causality testing.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eCross-Correlation Analysis (Time-Lagged Relationships)\u003c/h2\u003e\u003cp\u003eCross-correlation analysis revealed 11 significant time-lagged relationships exceeding the 95% confidence interval (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Of these, four relationships exhibited simultaneous correlations (lag\u0026thinsp;=\u0026thinsp;0): training volume and hair cortisol concentration (r\u0026thinsp;=\u0026thinsp;.761), training volume and waking standing heart rate (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.526), self-reported training load and hair oxytocin concentration (r\u0026thinsp;=\u0026thinsp;.496), and training time and hair cortisol concentration (r\u0026thinsp;=\u0026thinsp;.444). Seven relationships exhibited time-lagged patterns. Hair cortisol concentration revealed a lagged relationship with World Athletics points (max r\u0026thinsp;=\u0026thinsp;.583, lag = -2), indicating that the latter leads the former by 2 months. Hair cortisol concentration also revealed a predictive relationship with K6 (max r\u0026thinsp;=\u0026thinsp;.547, lag\u0026thinsp;=\u0026thinsp;+\u0026thinsp;2), indicating that the former predicts the latter two months later. Cortisol/oxytocin ratio revealed similar patterns with World Athletics points (max r\u0026thinsp;=\u0026thinsp;.507, lag = -2) and K6 (max r\u0026thinsp;=\u0026thinsp;.490, lag\u0026thinsp;=\u0026thinsp;+\u0026thinsp;2). Training volume predicted cortisol/oxytocin ratio one month later (max r\u0026thinsp;=\u0026thinsp;.463, lag\u0026thinsp;=\u0026thinsp;+\u0026thinsp;1). Waking standing RMSSD revealed a lagged relationship with POMS2 vigor-activity (max r\u0026thinsp;=\u0026thinsp;.448, lag = -1), indicating that the latter leads the former by one month. These findings suggest temporal dynamics in the relationships between training load, hair hormone biomarkers, and mental health outcomes.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eCross-correlation functions for 10 significant time-lagged relationships between training load, hair hormone biomarkers, physiological indicators, and mental health outcomes: (a) Training Volume \u0026rarr; Hair Cortisol Concentration, (b) Hair Cortisol Concentration \u0026rarr; World Athletics Points, (c) Hair Cortisol Concentration \u0026rarr; K6, (d) Training Volume \u0026rarr; Waking Standing HR, (e) Cortisol/Oxytocin Ratio \u0026rarr; World Athletics Points, (f) Self-Reported Training Load \u0026rarr; Hair Oxytocin Concentration, (g) Cortisol/Oxytocin Ratio \u0026rarr; K6, (h) Training Volume \u0026rarr; Cortisol/Oxytocin Ratio, (i) Waking Standing RMSSD \u0026rarr; POMS2 Vigor-Activity, (j) Training Time \u0026rarr; Hair Cortisol Concentration. Dashed lines indicate 95% confidence intervals. Positive lags indicate that the predictor leads the outcome; negative lags indicate that the outcome leads the predictor.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eGranger Causality Testing\u003c/h2\u003e\u003cp\u003eGranger causality tests identified three significant predictive relationships (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Waking standing RMSSD at previous two months significantly predicted POMS2 vigor-activity scores (F(2,16)\u0026thinsp;=\u0026thinsp;7.885, p\u0026thinsp;=\u0026thinsp;.0051, 21 measurements). Similarly, hair oxytocin concentration at previous two months significantly predicted POMS2 fatigue-inertia scores (F(2,11)\u0026thinsp;=\u0026thinsp;6.269, p\u0026thinsp;=\u0026thinsp;.0197, 16 measurements) and hair cortisol concentration at previous two months significantly predicted athletic performance scores (F(2,5)\u0026thinsp;=\u0026thinsp;14.708, p\u0026thinsp;=\u0026thinsp;.0282, 10 measurements). While these Granger causality results indicate statistical significance, they should be interpreted cautiously given the limited sample size (N\u0026thinsp;=\u0026thinsp;1 participant with 10\u0026ndash;22 time points).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSignificant Granger Causality Relationships\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredictor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOutcome\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF statistic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLag\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStanding Heart Rate Variability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePOMS Vigor-Activity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.885\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHair Oxytocin Concentration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePOMS Fatigue-Inertia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.269\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0197\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHair Cortisol Concentration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWorld Athletics Points\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14.708\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0282\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we conducted a 22-month condition assessment of one female collegiate long-distance runner to clarify whether combining HRV and hair hormone measurements (cortisol, oxytocin, and cortisol/oxytocin ratio) can predict future mental health decline and performance deterioration in athletes. Cross-correlation analysis identified 10 significant time-lagged relationships between biomarkers, training load, mental health indices, and athletic performance. Granger causality testing revealed three significant predictive relationships: waking standing RMSSD predicted future POMS2 Vigor-Activity scores, hair oxytocin concentration predicted future POMS2 Fatigue-Inertia scores, and hair cortisol concentration predicted future athletic performance. These findings suggest that combining HRV and hair hormone assessments may be effective as an early warning system for preventing mental health decline and performance deterioration in athletes. By contrast, we did not find consistent predictive relationships between training load or competitive stressors and biomarker changes, suggesting that the relationship between external stressors and physiological stress responses is complex and may be mediated by individual coping resources.\u003c/p\u003e\u003cp\u003eOur prior single-case study in an elite wheel gymnastics athlete demonstrated that waking standing RMSSD had moderate negative linear relationships with K6 depression index (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.54) and POMS2 Fatigue-Inertia (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.61) over 20 months (Matsuura \u0026amp; Ochi, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The present study in a long-distance runner partially replicates these findings, showing weak negative correlations between waking standing RMSSD and K6 (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.191, 21 measurements) and POMS2 Fatigue-Inertia (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.195, 21 measurements) (Supplementary Materials, Figure S2). While the direction of these relationships is consistent across both athletes, the strength of correlations was weaker in the current study. This difference may reflect sport-specific characteristics or individual differences in autonomic regulation patterns. Importantly, the current study extends our prior correlational findings by demonstrating that waking standing RMSSD predicts future POMS2 Vigor-Activity scores (F(2,16)\u0026thinsp;=\u0026thinsp;7.885, p\u0026thinsp;=\u0026thinsp;.0051), providing evidence for a prospective predictive relationship rather than merely concurrent association. This advancement from correlation to prediction represents a critical step toward establishing HRV as an early warning biomarker for mental health decline in athletes. Previous research has suggested that predicting mental health decline based on declining HRV is possible (Chalmers et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Zou et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and our Granger causality findings provide longitudinal evidence supporting this claim in the athletic context. The utility of HRV monitoring in endurance athletes has been demonstrated across multiple studies, with evidence that training adaptation is reflected in HRV changes (Plews et al., 2013) and that decreased HRV accompanies overtraining (Mourot et al., 2004). Our finding that HRV predicts future vigor may reflect this regulatory capacity's influence on maintaining positive mood states over time.\u003c/p\u003e\u003cp\u003eOur finding that hair cortisol concentration predicted athletic performance (F(2,5)\u0026thinsp;=\u0026thinsp;14.708, p\u0026thinsp;=\u0026thinsp;.0282) extends previous findings in endurance athletes. Skoluda et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) demonstrated that endurance athletes exhibited 46% higher hair cortisol concentrations compared to controls, with training volume positively correlating with hair cortisol levels. Sato et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) confirmed this pattern in female footballers. The current study advances these findings by demonstrating prospective predictive relationships over 22 months in a female long-distance runner. The strong correlation between hair cortisol and training volume (r\u0026thinsp;=\u0026thinsp;.756) confirms that intensive aerobic training elevates long-term cortisol exposure (Sato et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Skoluda et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This pattern has been observed in Japanese female long-distance runners via salivary cortisol (Tsunekawa et al., 2022; Ushiki et al., 2020), though the present study is the first examining hair cortisol in this population.\u003c/p\u003e\u003cp\u003eCross-correlation analysis revealed a bidirectional temporal relationship: World Athletics Points led hair cortisol by 2 months (max r\u0026thinsp;=\u0026thinsp;.583, lag = -2), while hair cortisol also predicted future performance. This pattern likely reflects training periodization and tapering. During intensive training phases, elevated cortisol exposure is incorporated into growing hair over weeks to months. Subsequent tapering reduces training volume to allow recovery. Hormonal balance improves within one week of tapering, but performance improvements require an additional 1\u0026ndash;2 weeks (Papacosta et al., 2013). Therefore, the 2-month lag likely captures intensive training (elevated hair cortisol) followed by tapering and peak performance.\u003c/p\u003e\u003cp\u003eThe finding that hair oxytocin concentration predicted future POMS2 Fatigue-Inertia scores (F(2,11)\u0026thinsp;=\u0026thinsp;6.269, p\u0026thinsp;=\u0026thinsp;.0197) is novel, as few studies have examined hair oxytocin in athletic populations. Oxytocin is involved in social bonding, stress buffering, and inhibition of HPA axis activity (Neumann et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Higher oxytocin levels may reflect greater social support resources and more effective stress recovery mechanisms, protecting against fatigue accumulation over time. Cross-correlation analysis revealed that self-reported training load and hair oxytocin concentration showed simultaneous positive correlation (r\u0026thinsp;=\u0026thinsp;.496, lag\u0026thinsp;=\u0026thinsp;0), and that hair oxytocin showed moderate positive relationships with training volume (r\u0026thinsp;=\u0026thinsp;.504, 16 measurements). This pattern suggests that despite increased training stress, oxytocin levels were maintained or increased in parallel with training demands, possibly reflecting adaptive coping mechanisms or social support from training partners and coaches. The moderate negative correlation between hair oxytocin and cortisol/oxytocin ratio (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.610) indicates that higher oxytocin levels shift the hormonal balance away from stress-dominance toward a more balanced physiological state. This ratio may be critical for maintaining mental health and preventing burnout during intensive training periods. Recent preprint suggests that oxytocin may reduce the accumulation and effects of chronic stress (Ochi et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and our findings provide preliminary evidence that this protective effect may extend to athletic populations experiencing sustained training loads.\u003c/p\u003e\u003cp\u003eCross-correlation analysis revealed that training volume predicted cortisol/oxytocin ratio at 1 month later (max r\u0026thinsp;=\u0026thinsp;.463, lag\u0026thinsp;=\u0026thinsp;+\u0026thinsp;1), suggesting that increased training load shifts the hormonal balance toward a more stress-dominant profile with a temporal delay. This time-lagged relationship highlights the cumulative nature of training stress effects and the importance of monitoring both cortisol and oxytocin to capture the full hormonal response profile. The cortisol/oxytocin ratio may serve as an integrated biomarker reflecting the relative balance between stress (cortisol) and recovery/social support (oxytocin) systems, providing information beyond what either hormone offers independently.\u003c/p\u003e\u003cp\u003eSeveral limitations should be noted. First, this is a single-case study (N\u0026thinsp;=\u0026thinsp;1), limiting generalizability to other athletes, sports, or populations. However, the intensive longitudinal design with 22 monthly measurements provides strong within-person evidence of temporal relationships and addresses the limitation of high interindividual variability that complicates group-level analyses of athlete stress responses. Single-subject designs are particularly valuable when individual differences are large and within-person processes may differ from between-person patterns, as is often the case in athletic populations with diverse sport demands, training histories, and coping resources (Strahler \u0026amp; Luft, 2019). The N-of-1 design allows for idiographic assessment that captures individual-specific stress-response patterns, which may be obscured in nomothetic group-level analyses. Replication in larger samples of long-distance runners and athletes across different endurance sports is needed to establish generalizability. Second, the measurement frequency (monthly for hair hormones and psychological assessments) may not capture shorter-term dynamics of stress and recovery cycles within training mesocycles or microcycles. While HRV was measured three times weekly, future studies should consider more frequent psychological assessments and potentially shorter hair segments to examine dynamics at finer temporal resolutions. Third, we did not measure some potentially relevant variables such as sleep quality, nutrition status, menstrual cycle phase, social support quality, and life stressors outside of sport. These factors may mediate or moderate the relationships observed between biomarkers, training load, and mental health outcomes. The role of social support is particularly relevant given oxytocin's established function in social bonding and stress buffering. Fourth, the mechanisms linking hair hormones to brain function and behavior remain unclear. Similarly, whether hair oxytocin reflects central or peripheral oxytocin activity, and how accurately it indexes brain oxytocin relevant to psychological function, requires further investigation. Fifth, while Granger causality testing provides evidence of temporal precedence and predictive relationships, it does not establish true causality or rule out unmeasured confounding variables. Experimental manipulations or interventions targeting these biomarkers are needed to confirm causal mechanisms.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrates that combining HRV measurements with hair hormone assessments provides complementary information about athlete stress, mental health, and performance across multiple timescales. Waking standing RMSSD predicted future vigor, hair oxytocin predicted future fatigue, and hair cortisol predicted future athletic performance. These findings suggest that multi-modal biomarker assessment may serve as an early warning system for mental health decline and performance deterioration in endurance athletes. Future research should replicate these findings in larger samples, investigate underlying mechanisms, and develop practical monitoring protocols for athlete mental health surveillance and overtraining prevention.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eConsent Statement: The participant in this study provided written informed consent to participate in the research. The study procedures were fully explained to the participant, and she voluntarily agreed to participate in this longitudinal case study. The study protocol was approved by the Ethics Committee of Niigata University of Health and Welfare, and all procedures were conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e\u003cp\u003e\u003ch2\u003eDisclosure of interest\u003c/h2\u003e\u003cp\u003eThe authors have no competing interests relevant to the content of this article.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis work was supported by Japan Society for the Promotion of Science (JSPS) grants JP25K21657 (G.O.), Grant-in-Aid for Scientific Research 2024 from the Japan Racing Association (JKA; G.O.) and Grant-in-Aid for Scientific Research from Niigata University of Health and Welfare, 2024, 2025 (G.O.)\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eKS and GO conceptualized the research, designed the methodology, conducted the investigation, performed the formal analysis, wrote the original draft, and reviewed and edited the manuscript. KS conducted the investigation and acquired all data. YO, TF, and KY supervised and reviewed the manuscript. All authors have read, reviewed, and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe would like to thank Naru Tsuchida and Sosuke Nakano for their assistance with the washing procedures during the preprocessing of hair samples for measurement. We would also like to thank Editage (www.editage.com) for English language editing.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAsanuma, T., Takeda, F., Monma, T., \u0026amp; Hotoge, S. (2015). 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(2015). \u003cem\u003eJapanese translation of POMS 2: Profile of Mood States\u003c/em\u003e. Kaneko Shobo.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZou, L., Sasaki, J. E., Wei, G. X., Huang, T., Yeung, A. S., Neto, O. B., Chen, K. W., \u0026amp; Hui, S. S. C. (2018). Effects of Mind-Body exercises (Tai chi/yoga) on heart rate variability parameters and perceived stress: A systematic review with meta-analysis of randomized controlled trials. \u003cem\u003eJournal of Clinical Medicine\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(11), 404. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/jcm7110404\u003c/span\u003e\u003cspan address=\"10.3390/jcm7110404\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Hair hormones, heart rate variability, chronic stress, mental health","lastPublishedDoi":"10.21203/rs.3.rs-7872377/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7872377/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAlthough heart rate variability (HRV) and hair hormone assessments have been proposed as biomarkers to evaluate chronic stress and mental health in athletes, whether combining these biomarkers can predict future mental health decline and performance deterioration remains unclear. In this study, we sought to establish this by evaluating waking HRV and hair hormone concentrations (cortisol, oxytocin, and cortisol/oxytocin ratio) in one elite female long-distance runner over 22 months. We assessed mental health once monthly using the six-item Kessler Psychological Distress Scale and Profile of Mood States Second Edition (POMS2). In addition, self-reported training load, training volume, and athletic performance were assessed as physical and performance indices. Heart rate and HRV were each measured three days per week in both resting (supine) and standing (upright) positions upon waking. Hair samples were collected monthly for hormone analysis. Cross-correlation analysis identified 10 significant time-lagged relationships, and Granger causality testing revealed three significant predictive relationships. Waking standing root mean square of successive differences (RMSSD) predicted future POMS2 vigor-activity scores, hair oxytocin concentration predicted future POMS2 fatigue-inertia scores, and hair cortisol concentration predicted future athletic performance. The findings suggest that combining HRV and hair hormone assessments provides complementary information across multiple timescales, potentially serving as an early warning system for preventing mental health decline and performance deterioration in athletes.\u003c/p\u003e","manuscriptTitle":"Hair Hormones and Heart Rate Variability as Chronic Stress Biomarkers in a Female Long-Distance Runner: A 22-Month Longitudinal Case Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-22 18:53:03","doi":"10.21203/rs.3.rs-7872377/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6b475bd2-dac4-49f1-a3db-f7ed590148be","owner":[],"postedDate":"October 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-22T18:53:05+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-22 18:53:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7872377","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7872377","identity":"rs-7872377","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}