The work represents an integrative single-case study of a 50-year-old, moderate performance-oriented sport climber in a typical mesocycle of training. To the authors' knowledge, this was the first study to investigate the complex relationships between ANS and tissue characteristics using a combination of CCF, multivariate LM taking into account time-delayed effects and cause-effect relationships between 30 24-hour measurements.
A cluster analysis of the derived HRV parameters revealed two clearly distinguishable clusters. Following the studies of several authors, we related HRV1 to SNS activity and HRV2 to PNS activity (Tarvainen et al. 2014; Pham et al. 2021; Miyatsu et al. 2023). Miyatsu et al. (2023) examined HRV at rest after stressful events in 30 military athletes and performed the same cluster analysis. The analysis revealed identical clusters as in the present study, therefore it is assumed that both clusters represent different aspects of the ANS.
All HRV1 parameters were significantly negatively correlated with TLFD at the same time. LF showed the best goodness-of-fit index and could explain 50% of the variance in the LM. In the literature, LF is described as one of the HRV parameters that probably best reflects SNS activity (Tarvainen et al. 2014; Pham et al. 2021; Miyatsu et al. 2023). However, it is also influenced by the PNS (see limitations section for a detailed discussion). During the study period, a TLFD drop of up to 19 mm was observed from one day to the next, accompanied by a threefold increase in LF. Interestingly, although the RPE showed no significant effect on TLFD in the LM, the largest drop was observed on two days of intense bouldering sessions, which were categorized as "very hard" by the participant. A study by Maspers et al. (1991) investigated the influence of the SNS on fluid filtration during intense exercise in cats. They found that an exercise-induced increase in capillary pressure, which led to filtration of plasma fluid into the interstitial tissue, was counteracted by increased SNS activity. It has also been described that increased SNS activity enhances vascular permeability in subcutaneous adipose tissue (Bartness et al. 2014). It is likely that these mechanisms significantly alter the fluid dynamics between the layers of TLF and their vicinity. Hyaluronic acid in the loose connective tissue is the key component that separates the dense layers and enables gliding between them (Fede et al. 2018). A loss of plasma fluid likely leads to an increase in the viscosity of hyaluronic acid and is associated with a reduction in hydrodynamic lubrication and enhanced friction (Cowman et al. 2015). The HRV2 parameters also showed negative correlations with TLFD, but were not significant. Therefore, we hypothesize that the observed reciprocal coupling of HRV1 and TLFD is mainly due to SNS-driven fluid dynamics.
Surprisingly, positive correlations between HRV and TLFD five days later were found. With the exception of SD2, which primarily reflects the long-term HRV variations of SNS and PNS (Noronha Osório et al. 2019), only the HRV2 parameters showed significant effects here. It is known from previous studies that PNS activity may reflect the state of recovery after exercise (Chen et al. 2011; Kassiano et al. 2021). However, these studies only investigated short-term effects. The finding that there is a positive effect on connective tissue characteristics after five days is therefore new. Based on the assumption that the underlying mechanism of the negative SNS-TLFD correlation at lag 0 is a loss of fluid in the interstitium, parasympathetic activation with increased plasma volume could be associated with the TLF recovery response as a counter-reaction, as described by the supercompensation theory (Stanley et al. 2013).
Although not significant in the LM, RPE showed a negative correlation with SD2 in the CCF five days after higher perceived exertion. Losnegard et al. (2021), who examined 160 endurance athletes, described a strong relationship between RPE and HRV. However, HRV was measured immediately after exercise, and the relationship with morning resting HRV is probably weaker (Coelho et al. 2019). Nevertheless, given the results of this study, trainers should be aware that higher levels of RPE can lead to ANS impairment even after longer periods of up to five days.
Mood, as measured by the STAI-6, showed no effect on HRV. Dell'Acqua et al. (2021) demonstrated that depressed mood, rumination and HRV were interrelated in healthy individuals, with HRV playing a moderating role between the other variables. The association between rumination and depressive symptoms was higher in individuals with reduced SDNN and HF. These results emphasize the complex cascade of interdependencies of biopsychosocial variables. Although no direct cause-effect relationship between mood and HRV was found in this study, it cannot be assumed that there is no moderated or mediated relationship.
There was a significant positive effect of DISE on HRV after two days. Two daily stressful events involving family disputes stood out in particular. It is well known from other time series studies in the field of psychoneuroimmunology that the sympatho-adrenomedullary system and/or the hypothalamic-pituitary-adrenal axis react dynamically in the days following an emotionally meaningful daily stressor (Schubert et al. 2012; Schubert and Hagen 2018; Singer et al. 2021; Seizer et al. 2023). This is accompanied by a delayed decrease in the concentration of neopterin, a cellular immune parameter, about two days later (Schubert et al. 2012). The results of this study are consistent with the observations in the current study, showing that the ANS responds both dynamically and consistently to a significant stressor in the participant. An increase in HRV2, which likely represents the PNS, was also observed, although not significantly. In the literature, the SNS/PNS coupling is often described reciprocally (Mueller et al. 2022). However, SNS/PNS co-activation was observed in the recovery phase after an acute stress task (Weissman and Mendes 2021) and its coupling, considering time-varying study methods, seems to be rather dynamic and presumably dependent on the type of stressor (Callara et al. 2021).
Remarkably, HRV1 parameters, particularly LF, was found to mediate the effect of DISE two days earlier on the decrease in TLFD by almost 98%. Thus, it could be hypothesized that a negative stressful event, such as a heated argument between the athlete and a family member, leads to a significant decrease in TLFD (DISE(day 0)↑ →SNS(day 2)↑→TLFD(day 2)↓). It appears that this mechanism was accompanied by an HRV1-coupled HRV2 increase, which could be interpreted as SNS/PNS co-activation. Here, RMSDD, a PNS parameter, mediated the effect of DISE on an increase in TLFD five days after the initial LF-mediated reduction in fascial deformability.
There is evidence that the PNS may reduce peripheral pro-inflammatory cytokines such as interleukin-6, interleukin-1ß and TNFα, even if the target organ is not vagally innervated (Pereira and Leite 2016). The PNS is known to regulate proinflammatory cytokines in plasma that correlate with central and peripheral inflammation (Felger et al. 2020), which strongly influences the viscosity of hyaluronan and its lubricating properties in relation to TLF (Zullo et al. 2017). Therefore, we hypothesize that the co-activation of the PNS after the daily stressful event is a systemic inflammatory regulatory response leading to a delayed PNS-driven increase in plasma volume (Stanley et al. 2013). These mechanisms could lead to a kind of supercompensation with increased TLFD, which the mediation analysis probably reflects statistically (DISE(day 0)↑ →PNS(day2)↑→TLFD(day5)↑).
The probably ANS-regulated changes in hydrodynamic tissue lubrication could have implications for the ability of the TLF to support the back muscles. Bojairami and Driscoll (2022) found a 75% contribution of the TLF to static spinal stability. Brandl et al. (2023b) found that the TLFD correlates with maximum power in deadlifts in athletes with r = .88. Considering the results of the current study and adding them to the previous findings on the biomechanical properties of the TLF, the importance of the influence of daily stressful events becomes apparent. It can therefore be expected that initially not only a lower performance can be assumed, but also a higher risk of injury and/or a higher susceptibility to infection, which has been shown in previous studies (Schubert et al. 2012; Schubert and Hagen 2018; Singer et al. 2021; Seizer et al. 2023). Coaches and trainers should be aware of these mechanisms and consider monitoring daily stressful events and HRV during training. Changes in the first few days following such an event along with elevated HRV values could be an indication of fascial tissue restrictions and an increased risk of performance and health impairments.
Limitations
The study had a number of limitations.
First, there are some studies showing that the SNS variables identified based on cluster analysis, particularly LF, also reflect PNS to some extent and not exclusively sympathetic tone (Thomas et al. 2019; Lalanza et al. 2023). Therefore, our results could not firmly distinguish between an actual increase in SNS activity or a possible PNS withdrawal. However, the study was able to demonstrate a cause-effect relationship between HRV1 and TLFD. Considering that up to 40% of the total innervation of TLF is sympathetic postganglionic nerve fibers and the lack of description of parasympathetic innervation (Tesarz et al. 2011; Mense 2019; Fede et al. 2022), the authors assume that the immediate effects are SNS-related.
Second, breathing directly influences the measured HRV parameters during data acquisition. Among other things, low respiratory rates below 0.15 Hz lead to an impairment of the LF (Shaffer and Ginsberg 2017). In addition, HRV alterations caused by respiratory sinus arrhythmia can occur, a phenomenon that encompasses breathing-related heart rate fluctuations. This can arise in particular when the subject accentuates their own inspiration and expiration (Kuusela 2012). Both were taken into account in this study. The data were analyzed with regard to low breathing frequencies and the statistics were controlled for breathing rate. In addition, a standardization checklist was used to address all points relating to possible inadequacies in HRV recording (Catai et al. 2020).
Third, instead of a multi-channel electrocardiogram, a consumer chest strap device was used to record HRV data. Data collection during an athlete's "life as it is lived" required the use of an easy-to-use method. The chest strap sensor was an obvious choice, as the athlete was already familiar with it. Schaffarczyk et al. (2022) compared the Polar H10 sensor used here with a clinical 12-channel electrocardiogram and found nearly perfect ICCs of 1.0 for heart beats and heart rates at rest and an ICC > .85 for the nonlinear short-term scaling exponent alpha 1 of Detrended Fluctuation Analysis, a parameter describing complex cardiac autonomic regulation. These results indicate good concurrency validity of the sensor used in this study with established laboratory devices under resting conditions.
Fourth, the self-reported results are limited to subjective retrospective ratings, which can lead to response bias (Bolger et al. 2003). Here, future studies should also consider interview-based data collection and hermeneutic interview analysis to identify emotionally meaningful everyday incidents, as seen e.g. in studies in the field of psychoneuroimmunology (Schubert et al. 2012; Schubert 2024).
Fifth, the observed time series was relatively short (30 days) and limited to one measurement per day. This was mainly due to limitations in the acquisition of HRV data, which is highly dependent on circadian rhythmicity and strict requirements to avoid bias (Catai et al. 2020). Therefore, despite using a time series approach, the results of the study only reflect measurements focused on a morning resting state. Previous research suggests that stress-induced ANS changes are more dynamic than the results of this study suggest. One possible option for taking such dynamic processes into account could therefore be the use of long-term (24 h) HRV recordings (Pham et al. 2021).
Finally, a particular limitation is the exploratory nature of this study, which is accompanied by design-related boundaries (n-of-one). Further replications are therefore necessary and the results may not be generalizable to a larger cohort. However, if the study conditions are close to the natural environment of the participants (“life as it is lived”), the degree of ecological validity of the study increases (Schubert et al. 2012; Schubert 2024) and thus the generalizability with regard to the conditions and the protocol (Reis 2018). Therefore, the recruitment of a free-living athlete could even be a significant advantage. In contrast to many studies conducted in controlled laboratory settings with RCT-appropriate sample sizes, this study considers the effects of real-world stressors on ANS and TLFD responses in addition to training load, allowing for a more comprehensive view over a specific period of time, such as a mesocycle in this case. In addition, the study provides a comprehensive assessment of stress and HRV markers, providing a holistic approach that enhances understanding of the complex, time-varying interdependencies between daily stressful events, HRV and tissue characteristics, in contrast to the limited scope of previous studies that focus on data selection at a specific point in time.