Psychophysiological markers of athlete burnout: a call to arms

Abstract

Burnout is a growing problem in high-performance sport and has negative consequences for athletes (e.g., mental ill-health). It is therefore important to effectively monitor athlete burnout to aid intervention efforts. While self-report measures are available (e.g., athlete burnout questionnaire), the limitations associated with these measures (e.g., social desirability bias) means that objective physiological markers may also be useful. Thus, this article critically discusses potential biomarkers of athlete burnout, drawing on research inside and outside of sport to offer an overview of the current state-of-the-art in this research area. First, it outlines what athlete burnout is, its deleterious consequences, and discusses existing psychological assessments. The article then critically discusses literature on hypothalamic-pituitary-adrenal axis (e.g., salivary cortisol) and autonomic nervous system (e.g., heart rate variability) indices of burnout, highlighting some promising biomarkers for future research (e.g., salivary cortisol at bedtime, vagally-mediated heart rate variability at rest). Finally, the article concludes by highlighting key considerations and offering recommendations for future research (e.g., use of more homogenous methods in assessing burnout and physiological parameters). As a result, the intention of this article is to spark more higher quality research on the psychophysiology of athlete burnout, thereby helping tackle this prominent issue in high-performance sport.

Keywords

Biomarkers - Exhaustion - Mental Health - Sport Devaluation - Well-Being

Introduction

Burnout is a multifaceted phenomenon that was first identified as a problem in caregivers (e. g., doctors, nurses) [1] [2]. In the 1970s, it was speculated that athletes may be susceptible to burnout [3], and these concerns grew in the mid-1980s and early 1990s [4] [5]. As research on athlete burnout accumulated, Raedeke [6] drew on the original work of Maslach and proposed a sport-specific conceptualization characterized by three dimensions: (1) physical and emotional exhaustion (i. e., perceived depletion of one’s emotional and physical resources resulting from training and competition), sport devaluation (i. e., negative feelings towards oneʼs sport), and reduced sense of accomplishment (i. e., a negative evaluation of one’s sporting abilities and achievements). Studies have shown that ~10% of athletes may be experiencing moderate to severe burnout at any given time [7] [8], and the prevalence of athlete burnout is on the rise [9]. This is worrying given that it has been linked with various deleterious outcomes (e. g., dropout from sport) [10]. For instance, in a recent meta-analysis, Glandorf et al. [11] found that higher athlete burnout was associated with poorer mental health (e. g., higher depression symptomology) and well-being (e. g., lower life satisfaction). Given that burnout is a growing problem that athletes face, and it has clear detrimental impacts on health and well-being, more emphasis is needed on accurate identification, prevention, and treatment [12]. To aid such efforts, it is crucial that valid and reliable burnout measures are developed.

Various questionnaires have been used to assess athlete burnout (e. g., Shirom Melamed Burnout Measure; SMBM) [13], with the most common being the Athlete Burnout Questionnaire (ABQ) [14]. Although the ABQ aligns with Raedeke’s [6] conceptualization and is recommended for use with athletes [15], it has been criticized [7]. For example, there are concerns about how it measures emotional and physical exhaustion [16], with some arguing that its items fail to directly assess either construct (e. g., “I feel wiped out from sport”). As a result, other tools have emerged tackling some of these issues (e. g., items from the Athlete Burnout Scale more directly assess physical exhaustion) [17]. Although self-report measures can help monitor athlete burnout, their utility in isolation may be limited. For instance, with assessments like the ABQ, no reliable cut-offs have been established, making it difficult to determine if an athlete is experiencing moderate to severe burnout symptoms and in need of intervention [7]. Moreover, athletes may find it challenging to admit feelings of exhaustion, negativity about their sport, and performance-related concerns due to fear of judgment (e. g., by coaches) [18]. This could lead athletes to conceal their true feelings and respond in a way that masks their vulnerabilities, introducing social desirability bias. Indeed, athlete burnout still carries some stigma [19], meaning that the accuracy of self-report data might be negatively impacted by bias (e. g., self-presentation) [20]. As such, practitioners (e. g., sports scientists) may struggle to rely solely on self-report data and might benefit from drawing on additional sources of information, such as objective measurements (e. g., biomarkers) [11].

At present, very little is known about the psychophysiology of athlete burnout, including its etiology and potential biomarkers (e. g., salivary cortisol) [15]. Thus, the aim of this article is to provide a novel summary and critique of existing literature on potential biomarkers of burnout (e. g., heart rate variability; HRV), largely drawing on research outside of sport to offer vital information that can aid future research and guide applied practice.

Psychophysiological markers of burnout

Hypothalamic-pituitary-adrenal axis (HPA) measures

Most studies searching for a biomarker of burnout have used HPA axis measures [21]. Despite its involvement in other processes (e. g., fighting illness or infection), the HPA axis is best known as a stress regulation system; and when a stressor is perceived, it is activated by the limbic part of the brain (e. g., amygdala) and results in the sequential release of corticotropin hormone from the hypothalamus, adrenocorticotropic hormone from the pituitary gland, and cortisol from the adrenal cortex [22]. This release of hormones into the blood mobilizes substrates for energy metabolism, supplying the body with the energy required to actively cope with the stressor [23]. As the end-product of the HPA axis, most research has focused on cortisol, largely through saliva samples but less commonly via blood (serum), hair, or urine [24]. Cortisol levels often increase in response to a stressor [25] and tend to follow a diurnal pattern in which they steadily increase in the second half of the night, peak shortly after waking (i. e., within 30 to 60 minutes), and then gradually decrease across the day [26]. Due to this diurnal rhythm, and the fact that cortisol levels are affected by many factors (e. g., diet, sleep) [27], there are complexities in measuring it, and various approaches have been used including collecting single (e. g., pre-work) or multiple (e. g., cortisol awakening response) morning samples, and samples taken serially across the day (e. g., afternoon, evening) [21].

Research linking burnout with salivary cortisol assessed via one-off morning samples has revealed mixed results [22]. While some studies have shown null effects [e. g., 28] or a negative association [e. g., 29, 30], more than half of these studies found that burnout was associated with elevated morning cortisol [e. g., 31, 32, 33, 34]. For example, well-powered studies by Deneva et al. [33] and Wingenfeld et al. [34] found that burnt-out medical professionals (303 doctors and 279 nurses, respectively) had higher morning cortisol than their colleagues. However, in both studies, cortisol samples were taken at the start of a workday (i. e., at approximately 7 am) and thus did not reflect, or account for, waking levels or time. This makes the findings difficult to interpret given cortisol’s diurnal rhythm (e. g., the typical increase observed 30 to 60 minutes after waking) and that various uncaptured factors could have impacted cortisol concentrations in the time between waking and arriving at work (e. g., ambient light level, anticipation of the day ahead, caffeine consumption) [35]. Indeed, variations in sample timing and a lack of control for confounding factors likely explains the mixed findings across studies employing single morning samples. For instance, samples were reportedly taken at 7 AM in two studies [30] [34], 8 AM in two studies [28] [32], between 6 AM and 8 AM in one study [33], and at an unspecified time in two studies [29] [31]. Given these issues, expert consensus guidelines strongly advise against one-off morning samples, instead recommending multiple morning samples (e. g., to assess the cortisol awakening response) while controlling for crucial confounding factors like time of awakening [35].

The cortisol awakening response, defined as the rapid rise in cortisol levels that typically occurs in the morning after waking (usually within 30 to 60 minutes), has been the subject of much research and can be assessed in numerous ways (e. g., from area-under-the-curve metrics to change scores) [36]. The findings from research assessing burnout in relation to the cortisol awakening response have also been equivocal [36], with studies reporting null effects [e. g., 37], positive associations [e. g., 38], or negative associations [e. g., 39]. Indeed, these mixed findings are likely due, at least in part, to inconsistencies in how the awakening response has been assessed. For example, while Sjors and Jónsdóttir [39] utilized change scores (i. e., difference between awakening and 15 minutes later), McCanlies et al. [37] and Traunmuller et al. [38] used area-under-the-curve metrics (i. e., ground and increment) from a greater number of samples (e. g., awakening and 15, 30, and 45 minutes later). Despite the unclear results, it is worth noting that one of the more rigorous (e. g., confounding factors such as awakening time, cigarette smoking, drug use, etc. were controlled for in the analyses and/or protocol) and largest datasets collected to date by Marchand and colleagues [40] [41] [42] reported that higher burnout was related to a blunted cortisol awakening response (i. e., less change from waking to 30 minutes after waking) in a sample of 401 Canadian day-shift workers. Indeed, this finding mirrors the conclusion from an earlier meta-analysis by Chida and Steptoe [43], as well as sport-based research linking salivary cortisol to a concept related to burnout, overtraining syndrome [e. g., 44]. As such, the cortisol awakening response might be worth examining in future work alongside other potential biomarkers (e. g., diurnal cortisol).

Further to research centered on morning cortisol, studies have investigated if burnout impacts salivary cortisol secretion across the rest of the day (e. g., afternoon, evening) [21]. Overall, the findings on diurnal cortisol have been mixed, with some studies reporting null effects [e. g., 45], and others revealing a positive [e. g., 46] or negative [e. g., 47] association. Again, this is likely due, at least in part, to variations in how and when diurnal cortisol was assessed. For instance, studies differed in whether they analyzed diurnal cortisol via one-off samples (e. g., at the end of a work shift) [46], a single metric (e. g., diurnal slope from awakening to bedtime) [37], or multiple samples while factoring in morning cortisol levels (e. g., waking, 30 minutes later, 11 AM, 3 PM, and 8 PM) [45]. However, despite the equivocal results, it is worth noting that when focused solely on studies using non-clinical samples of a relatively large size (i. e., ≥ 200 participants) that measured burnout in a manner most consistent with athlete burnout (i. e., via Maslach Burnout Inventory; MBI) [48], a clearer picture emerges. Indeed, Marchand et al. [41] collected salivary cortisol from 401 Canadian workers at awakening, 30 minutes later, 2 PM, 4 PM, and bedtime, and found that higher burnout (and particularly exhaustion) was linked with lower cortisol in the afternoon and evening, with the largest effect at bedtime. Similarly, among 197 police officers, McCanlies et al. [37] found that greater exhaustion and cynicism were associated with less diurnal cortisol secretion, with samples collected at lunchtime, dinner, and bedtime. In contrast, Wingenfeld et al. [34] found that burnt-out nurses had higher salivary cortisol than colleagues across all samples (i. e., 11:30 AM, 5:30 PM, and 8 PM). This conflicting finding, which mirrors research linking hypercortisolism with depression – a condition thought to overlap with burnout [36] – could be due to methodological differences between studies (e. g., populations).

Many reviews published to date have concluded that there is no single compelling HPA axis-related marker of burnout, attributing this to the high methodological heterogeneity and weaknesses inherent in the literature (but it might be that burnout is not reflected in any HPA-axis measures) [21]. First, the way in which burnout has been conceptualized has varied between studies [36]. For example, while some studies have used Maslach’s [48] multidimensional definition (i. e., emotional exhaustion, cynicism, personal inefficacy) [49], others have explored burnout via the dimensions (e. g., cognitive weariness, emotional exhaustion, physical fatigue; [50]) developed by Shirom and Melamed [e. g., 51]. Second, studies have differed in how they have treated burnout data [52], either analyzing it in a continuous fashion [e. g., 41] or, most often, grouping participants using cut-points that differ from study-to-study [e. g., 53]. Third, the population has varied greatly between studies [24], ranging from non-clinical (e. g., teachers) to clinical (e. g., patients with exhaustion disorder; [54]) samples. Fourth, studies have differed in when biomarker data was collected (e. g., 15, 30, 45, or 60 minutes after waking) [36], and if they controlled statistically for confounding factors (e. g., age) [21]. Fifth, most studies have used small samples and cross-sectional designs, which limit statistical power and causal understanding [22]. Given these issues, future work evaluating HPA-axis related markers of burnout should strive for greater methodological homogeneity (e. g., in burnout questionnaire use) and employ stronger methods (e. g., longitudinal study designs). Unfortunately, the few studies that have been conducted in sport also suffer from the issues noted above (e. g., small sample sizes; see [Table 1]).

Table 1 A summary of studies linking burnout with salivary or serum cortisol and HRV in athletic samples.

Authors (Year)	Study Design	Sample (n)	BO Measure	Sample Timing	Key Results	Effect Sizes	Main Limitations
Davis et al. (2018)	Cross-sectional	82 athletes (55 male, 27 female; M age =20 years)	ABQ	Salivary cortisol sampled at 7–9 am, 10–11 am, 1–3pm, or 6–8 pm based on training timing Also, before, immediately after, and 20 minutes after, a physical test *No other biomarkers assessed	BO was not significantly associated with change in cortisol	r=0.10	Small sample size Cross-sectional or snapshot design Did not assess BO dimensions, only a global score Unclear precisely when cortisol was sampled No control for confounding factors
Dobson et al. (2020)	Repeated measures observational	13 female swimmers (M age =19 years)	ABQ at beginning of season, in period of overload training, and after a taper (11 weeks between each measure)	Resting lnRMSSD assessed for 10 minutes in supine position at three time points via Finapres (lnRMSSD from last 5 minutes used in analyses) *HR also assessed	BO was highest in tapering period and lnRMSSD was lowest in overload period Change in BO not significantly associated with change in lnRMSSD *Change in BO also not significantly associated with change in HR	Not available	Small sample size Did not assess BO dimensions, only a global score Equipment used to assess HRV was not ECG-based No control for confounding factors
Landolt et al. (2019)	Cross-sectional	32 professional jockeys (14 male, 18 female; M age =19 years)	MBI	Salivary cortisol sample at awakening and 30 minutes after in low- and high-stress period of season *sAA-AR also assessed	No significant associations between BO dimensions and CAR apart from positive relationship between cynicism and CAR in high-stress period *Professional inefficacy significantly associated with sAA-AR	r between BO dimensions and CAR in low stress period ranged from 0.06 to 0.27 r between BO dimensions and CAR in high stress period ranged from 0.22 to 0.37	Small sample size Cross-sectional or snapshot design Did not assess athlete BO or a global BO score No control for confounding factors
Martin et al. (2022)	Longitudinal	40 NCAA swimmers (17 male, 23 females; M age = 20 years)	ABQ	Serum cortisol at beginning and end of a 6-week training period, no specific time of day stated *CK, MG, and testosterone also assessed	BO was positively associated with change in the ratio of testosterone to cortisol, which was mostly driven by a ↓ in cortisol *BO not significantly associated with change in CK or MG	r=0.34	Small sample size Did not assess BO dimensions, only a global BO score Unclear precisely when cortisol was sampled in blood No control for confounding factors
Monfared et al. (2021)	Cross-sectional	42 youth athletes (14 males, 28 females; M age = 15 years)	ABQ	Salivary cortisol sample 30 minutes before and immediately before practice, but no specific time of day stated *BVP, GSR, and RR also assessed	Significant positive association between BO and change in cortisol *BO not significantly associated with GSR, but negatively associated with BVP and RR	β=0.24	Small sample size Cross-sectional or snapshot design Did not assess BO dimensions, only a global BO score Unclear precisely when cortisol was sampled No control for confounding factors
Souza et al. (2018)	Case-control	12 BO and 12 control soccer and futsal players (gender not stated but M age = 21 years)	ABQ	Salivary cortisol sample at 8 am *DHEA-S and testosterone also assessed	No significant differences in salivary cortisol between groups *No significant differences in DHEA-S or testosterone between groups	d=0.32	Small sample size Cross-sectional or snapshot design Failure to sample at multiple time points No control for confounding factors

Notes: ABQ, athlete burnout questionnaire; BO, burnout; BVP, blood volume pulse; CAR, cortisol awakening response; CK, creatine kinase; DHEA-S, dehydroepiandrosterone sulphate; GSR, galvanic skin response; HR, heart rate; lnRMSSD, natural log of RMSSD; MBI, Maslach burnout inventory; MG, myoglobin; NCAA, national collegiate athletic association; RMSSD, root mean square of successive differences of r-to-r intervals; RR, respiration rate; sAA-AR, Salivary alpha-amylase awakening responses.

Like the research noted outside of sport, the few studies conducted in sport have revealed mixed results. Indeed, most research has reported null effects between athlete burnout and cortisol [e. g., 55]. For instance, Souza et al. [28] split soccer players into burnt out and control groups based on ABQ scores and found no significant between-group differences in morning salivary cortisol (assessed via one sample at 8 AM). Similarly, among professional jockeys, Landolt et al. [56] found few significant associations between burnout dimensions assessed via the MBI and cortisol awakening response, with only a positive relationship emerging between cynicism and the cortisol awakening response during a high-stress period of the season. Monfared et al. [57] also reported a significant positive correlation, with higher burnout (measured via the ABQ) associated with greater increases in salivary cortisol from 30 minutes to immediately before training in 42 youth athletes. Finally, in a study with 40 collegiate-level swimmers, Martin et al. [58] collected serum cortisol samples at the beginning and end of a six-week training block and found that burnout was significantly and positively associated with a change in testosterone to cortisol ratio, an effect that was mostly driven by a reduction in cortisol concentrations. Thus, to date, no single compelling HPA-axis related biomarker has emerged from the literature focused specifically on athletic populations.

In summary, the results of research linking burnout with HPA-axis measures (e. g., cortisol) have been relatively unclear. However, some of the largest and more rigorous studies have hinted that biomarkers such as elevated morning (i. e., pre-work) cortisol levels, a blunted cortisol awakening response, and diminished salivary cortisol secretion in the afternoon and evening (e. g., at bedtime) might warrant further investigation [41]. It therefore remains for future research to assess these physiological markers alongside other indices (e. g., HRV).

Autonomic nervous system (ANS) metrics

Many studies searching for a biomarker of burnout have used ANS metrics [21]. The ANS plays a pivotal role in maintaining balance in key regulatory functions (e. g., metabolism, temperature), and is an important system that enables the body to react flexibly and effectively in response to stressors [59]. The ANS has two main branches: the sympathetic nervous system (SNS) which aids the rapid mobilization of energy to actively combat a stressor (i. e., fight vs. flight response), and the parasympathetic nervous system (PNS) which facilitates energy conservation and restoration following a stressor [60]. These two systems are normally in dynamic balance, but chronic stress results in the SNS persistently dominating the PNS, thereby placing excessive demands on the body and inhibiting energy conservation and recovery [61]. This stress-related ANS imbalance (i. e., SNS hyperactivity and PNS hypoactivity) has most commonly been examined via HRV, defined as variations in the time intervals between successive heart beats [62]. Both ANS branches impact HRV via the sinoatrial node, with SNS and PNS activity accelerating and decelerating heart rate, respectively [63]. Due to differences in chemical signaling at the sinoatrial node, the PNS can modulate heart rate via the vagus nerve on a shorter timescale than the SNS (i. e., milliseconds versus seconds), meaning that high-frequency changes in HRV offer a relatively purer measure of PNS activity, while low-frequency changes reflect a mixture of SNS and PNS activity [64].

Research linking burnout with low-frequency HRV parameters has revealed equivocal results [52] [59]. Indeed, while some studies have reported null effects [e. g., 38], others have found positive [e. g., 65] or negative [e. g., 66] associations. For instance, Thielmann et al. [67] performed a 24-hour electrocardiogram (ECG) recording with 414 employees working with patient or child populations and found no differences between low, moderate, and high burnout groups in low-frequency HRV. While May et al. [68] used a similar 24-hour ECG recording protocol with 88 female undergraduate students, they found that higher burnout was associated with greater low-frequency HRV after controlling for depression symptomology. In contrast, Lennartsson et al. [69] found that low-frequency HRV, assessed over a 300-second recording session in a supine position, was lower in a clinical burnout group compared to non-clinical high and low burnout groups. This unclear pattern of results could be due to low-frequency HRV metrics reflecting a blend of SNS and PNS activity, which contrasts with high-frequency HRV metrics that more purely indicate PNS activity (or vagal tone) [70]. Indeed, given the less clear physiological origin of low-frequency HRV (e. g., baroreflex activity, mix of SNS vs. PNS activity), researchers are encouraged to focus more on metrics linked to PNS activity [70].

Studies relating burnout with HRV metrics reflective of PNS activity have also revealed mixed findings [21] [59]. Indeed, studies using indices from the frequency-domain (e. g., high-frequency HRV) have either reported null effects [e. g., 71], positive associations [e. g., 29], or negative associations [e. g., 72]. However, frequency-domain HRV measures are susceptible to the influence of movement and respiration [59] [73], which might have clouded the results. To combat this, some studies have assessed only time-domain HRV metrics reflective of PNS activity (e. g., root mean square of successive differences; RMSSD) [70]. For instance, well-powered and high-quality studies have emerged from the Dresden Burnout Study [74]. In the first, Kanthak et al. [75] measured HRV among 410 healthy adults during three conditions (i. e., an emotionally arousing situation, a recumbent recovery period, and seated rest) and found that higher burnout (and especially exhaustion) was associated with lower RMSSD in most conditions after controlling for covariates (e. g., age). Subsequently, among 167 healthy adults, Wekenborg et al. [76] found that lower RMSSD during seated rest predicted higher burnout (and particularly exhaustion) 12 months later after accounting for covariates (e. g., gender). Finally, in a longitudinal study of 378 healthy adults, Wekenborg et al. [77] found that lower RMSSD predicted higher exhaustion, but not vice versa (or global burnout), after controlling for covariates (e. g., body mass index). Thus, RMSSD might offer a promising biomarker of burnout, and particularly the dimension of emotional exhaustion [78].

Although RMSSD may potentially mark burnout, when looking at the burnout-HRV literature overall, the findings have been mixed, likely due to high methodological heterogeneity (but possibly because burnout is not reflected in ANS-related indices) [52]. First, the way in which burnout has been assessed has varied between studies [21]. Indeed, while the MBI has been used most [e. g., 79], other instruments such as the Copenhagen burnout inventory [80] have also been used [e. g., 66]. Second, HRV recording equipment has differed across studies [81], ranging from gold-standard ECG devices [e. g., 82] to chest- or wrist-worn sensors [e. g., 83] and mobile applications using photoplethysmography [e. g., 84]. Third, the time periods used to record HRV data has varied [59], with most studies using 24-hour recordings [e. g., 38] but some using shorter measurement periods during rest [e. g., 85] or work-related tasks [e. g., 86]. Fourth, a vast array of HRV metrics has been used across studies, often with little justification [52]. For example, May et al. [68] found a negative association between burnout and very low-frequency HRV, with limited consideration of the metrics’ physiological origin (e. g., thermoregulation) [70]. Fifth, most studies have used absolute HRV values that do not account for participants’ normative values and, as such, have not explored other promising metrics (e. g., HRV coefficient of variation) [59]. Finally, many studies have been cross-sectional with small samples, thereby limiting causal understanding [21]. Given these issues, future work examining ANS-related indices of burnout should strive for greater methodological homogeneity (e. g., in equipment use) and utilize stronger methods (e. g., larger sample sizes). Unfortunately, there is a dearth of research on burnout and HRV in sport, and what studies do exist, suffer from similar issues (see [Table 1]). For example, in a rare study, Dobson et al. [87] found no link between burnout and RMSSD among 13 female swimmers.

In summary, the results of studies searching for an ANS-related indicator of burnout (e. g., HRV) have been equivocal. However, the most well-powered and rigorous studies imply that indices of PNS hypoactivity such as lower RMSSD during seated rest might hold some promise [e. g., 75,76]. Future research therefore needs to further assess this biomarker while taking into consideration the key issues central to this complex field of study.

Considerations for future research

While this review will help the selection of biomarkers for further investigation (e. g., bedtime salivary cortisol; [41]), such decisions should be theoretically grounded. Given that burnout is theorized to develop when there is a persistent mismatch between situational demands and personal coping resources [78], the biopsychosocial model (BPSM) of challenge and threat might offer a neat theoretical framework to explore potential biomarkers [88]. Indeed, according to the BPSM, a threat appraisal, where an individual feels that situational demands exceed their coping resources, is associated with a distinct physiological profile [89]. Specifically, compared to a challenge appraisal (i. e., when coping resources are deemed to match or exceed situational demands), a threat appraisal is marked by lower vagally-mediated HRV (e. g., RMSSD) and higher cortisol at rest [90]. Moreover, in response to an acutely stressful event (e. g., sporting competition), a threat appraisal results in cardiovascular reactivity characterized by relatively lower cardiac output (i. e., amount of blood ejected by the heart per minute) and higher total peripheral resistance (i. e., net constriction in the blood vessels) [89]. Thus, the BPSM could help researchers select and integrate potential biomarkers of burnout in future investigations, resulting in studies that are more theoretically underpinned. However, it should be noted that other frameworks exist [12], some of which go beyond stress-based explanations and offer additional causes of athlete burnout (e. g., unidimensional identity).

In addition to the predictions of the BPSM [91], other pertinent frameworks could guide future research on burnout biomarkers. Indeed, borrowing from models of allostatic load [92], a common hypothesis is that the early stages of burnout are marked by higher HPA-axis and ANS activity (e. g., hypercortisolism and SNS hyperactivity) [21], while over time these systems become exhausted due to prolonged or recurrent stress, resulting in more severe burnout being marked by lower HPA-axis and ANS activity (e. g., hypocortisolism and lower PNS activity) [36]. Thus, future research should assess if different biomarkers are needed to detect athletes at varying stages of burnout. To help with this feat, researchers could draw on initial conceptual models that suggested that burnout dimensions develop sequentially (i. e., emotional exhaustion → cynicism → professional inefficacy) [93]. As well as testing these theory-driven predictions, future work could explore if burnout biomarkers are more likely to emerge in response to challenges (e. g., intense exercise) [22]. Indeed, research has hinted that burnout may be marked by hypocortisolism in response to acute stress [e. g., 94], a response that differs from the typical increase in cortisol seen in reaction to a stressor [25].

As noted previously, the literature exploring burnout biomarkers has been hampered by cross-sectional designs and small samples [21]. Thus, future research should use longitudinal designs with larger samples [22]. Historically, this has been difficult given the expensive, time-consuming, laboratory-based, and expertise-reliant nature of biomarker testing (e. g., cortisol) [95]. However, new technology is emerging (e. g., microsensors) that might help researchers determine if biomarkers (e. g., cortisol, HRV) are useful in identifying burnt out athletes [96]. For example, due to learnings from the COVID-19 pandemic, companies are developing rapid assay-based tests that could, in the future, enable accurate, reliable, and cost-effective assessments of cortisol in day-to-day life [97]. While many of these diagnostic tests are only at the proof-of-concept stage, technology already exists that allows for the valid and expedient measurement of vagally-mediated HRV (e. g., camera-based photoplethysmography via applications such as HRV4Training) [98]. This technology might offer an effective way of integrating burnout biomarker testing into practice given that athletes are used to wearing devices (e. g., heart rate monitors) to supply sport scientists with data (e. g., related to physical training load). Although sport may provide an excellent “natural laboratory” to assess burnout biomarkers (e. g., cortisol, HRV), more research is needed into the acceptability of such testing and how it might integrate with existing practices (e. g., well-being monitoring) [99]. There is also a risk that, due to the relatively high cost of this technology (vs. self-report measures), such biomarker testing is only available to support elite professional athletes operating at the highest levels in their sport, with lower-level and talented youth athletes missing out.

As mentioned previously, high methodological heterogeneity has hindered burnout biomarker research [36]. Future work therefore needs to be more consistent in the methods used to assess burnout (e. g., conceptualization, measurement) and physiological markers (e. g., equipment, sample timing). Regarding the former and in line with past recommendations [100], future studies that examine burnout as a health problem in athletes should use a definition where exhaustion is central [e. g., 50] and use questionnaires that align with this conceptualization (e. g., SMBQ) [13]. That said, given recent research showing that athlete burnout is on the rise predominately due to increases in sport devaluation and a reduced sense of accomplishment (vs. emotional and physical exhaustion) [9], future investigations might also wish to use other additional questionnaires (e. g., ABQ) [14]. Furthermore, to encourage more complex approaches (e. g., experience sampling methods), researchers could develop expedient single-item measures (e. g., “Right now I feel exhausted”) [49]. Also, rather than dividing athletes into groups (e. g., high vs. low) using arbitrary cut-points or median splits, future work should examine associations between burnout and biomarkers continuously and report findings for global burnout as well as its subdimensions [7]. Indeed, this is important given that some biomarkers (e. g., RMSSD) have been more strongly linked to certain dimensions (e. g., exhaustion) [77]. While uniformity in physiological recording is biomarker-dependent, future research should use valid and reliable equipment (e. g., ECG devices for vagally mediated HRV), comparable measurement times and methods (e. g., cortisol samples taken via passive drool upon awakening, 30 minutes after waking, 2 PM, 4 PM, and bedtime), and similar metrics in analyses (e. g., RMSSD). As well as striving for greater homogeneity in the methods utilized, future work should strictly adhere to best-practice guidelines [70] [101].

Future burnout biomarker research can also be improved by more routinely controlling for confounding factors [21], either by accounting for covariates in protocols (e. g., asking participants not to consume caffeine two hours before measuring vagally mediated HRV) [70], or controlling for them statistically (e. g., adjusting for awakening time when assessing cortisol) [101]. Confounders of particular interest are likely to include age, gender, physical fitness, sleep time, and stage of season [36]. For age, research has shown that HRV declines with age, particularly for metrics reflecting PNS activity (e. g., RMSSD) [102]. Moreover, for gender, studies have suggested that chronically stressed females may display larger increases in cortisol upon awakening than males [e. g., 103]. Furthermore, for physical fitness, research has shown that highly fit athletes have higher resting HRV than less fit athletes [e. g., 104]. Additionally, for sleep, research has suggested that shorter sleep times may be associated with lower cortisol at awakening [e. g., 105]. Finally, for stage of season, research has shown that RMSSD might be higher among athletes during periods of overload training than at other times (e. g., tapering) [87]. Thus, to confidently link certain biomarkers to athlete burnout, future research needs to better account and control for important confounding factors [21]. Indeed, one crucial factor, which is central to most sport contexts, is the exercise associated with training and competition. Given that many biomarkers are impacted acutely by exercise (e. g., cortisol) [106], it is possible that physiological indices measured at rest and away from training or competition temporally (e. g., at bedtime) might hold most promise in identifying athlete burnout.

In summary, the key considerations for future research on physiological markers of athlete burnout include: (1) ensuring the selection of biomarkers is underpinned by empirical evidence, pertinent theory, and takes into account stage of burnout development; (2) using technology that enables biomarker data to be collected longitudinally from large samples; (3) adopting more homogenous methods in the assessment of burnout and physiological parameters; and (4) routinely controlling for confounding factors (see [Table 2]). The final point is particularly important given the myriad of factors that can impact biomarkers such as cortisol and HRV (e. g., diet, illness, medication) [35] [70]. If the factors listed above are adequately considered, a clearer picture should emerge of any physiological indices of athlete burnout.

Table 2 Key considerations for future research on biomarkers of athlete burnout.

Ensure selected biomarkers are:

Evidence-based (i. e., aligned with previous research)

Theoretically grounded (e. g., BPSM of challenge and threat)

Appropriate for burnout stage (i. e., early versus latter stages)

Use technology that enables:

Researchers to use stronger study designs (e. g., longitudinal)

Data to be collected from larger samples (i. e., ≥ 200 participants)

Cost-effective and efficient data acquisition (e. g., HRV4Training)

Adopt consistent methods when:

Measuring burnout via self-report (e. g., ABQ and SMBQ)

Assessing biomarkers (e. g., timing of saliva samples)

Analysing physiological data (e. g., ECG signals for HRV)

Control for confounding factors, such as:

Chronological age

Biological sex or gender

Physical fitness

Sleep time and/or quality

Stage of season (e. g., tapering)

Exercise or physical activity

Conclusion

Athlete burnout is a growing issue in high-performance sport and, given its negative consequences (e. g., mental ill-health), it is important to effectively monitor it to aid intervention efforts. While questionnaires are available (e. g., ABQ), the limitations associated with these subjective measures (e. g., social desirability bias) means that objective physiological markers may also be useful. Thus, this article summarized and critiqued existing literature on potential burnout biomarkers. Drawing largely on work outside of sport, it revealed a mixed picture, with few clear physiological indices, potentially due to the high methodological heterogeneity and weaknesses inherent in the literature (e. g., varying burnout measures, small sample sizes) but possibly because burnout does not leave a physiological footprint (or trace). While biomarkers are unlikely to offer a panacea for identifying burnt out athletes, some indices may be worthy of further exploration, including elevated morning cortisol levels, blunted cortisol awakening responses, diminished cortisol secretion in the afternoon and evening (e. g., at bedtime), and lower vagally mediated HRV (i. e., RMSSD) during seated rest. Important recommendations were also made for future research, including the need to adopt more homogenous methods in assessing burnout and physiological parameters (e. g., cortisol), and the use of technology that enables biomarker data to be collected longitudinally from larger samples (e. g., rapid assay-based tests). As a result, it is hoped that this article will spark a step change in research on the psychophysiology of athlete burnout and guide applied practice.

Conflict of Interest

Dr Isoard-Gautheur and Professor Gustafsson have no conflicts of interest to declare relevant to the content of this article. Although Dr Moore wrote this article while on secondment with an organisation interested in developing a biomarker test of burnout, this organisation did not have any input regarding the content of the article, which was drafted completely independently by the authors listed.

Acknowledgement

The research informing this article was funded by the University of Bath and developed based on the International Journal of Sports Medicine ethical standards (see Harriss et al., 2022).

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Publication History

Received: 05 January 2024

Accepted after revision: 02 October 2024

Accepted Manuscript online:
02 October 2024

Article published online:
20 November 2024