Psychophysiological factors are not directly associated to Heart Rate Variability in Athletes: A Meta-Analysis

Henrique M. Lapo1, Mara Patrícia T. Chacon-Mikahil1,2, Amanda V. Sardeli1,2,3*

1Laboratory of Exercise Physiology, Scholl of Physical Education, University of Campinas SP, Brazil

2Gerontology Program – Scholl of Medical Sciences, University of Campinas, SP, Brazil

3Department of Inflammation and Ageing, University of Birmingham, Birmingham, UK


Objective: Although heart rate variability (HRV) has been a useful and accessible tool to monitor recovery from athletes’ training, it is not clear if it reflects changes in psychophysiological factors. The aim of these study is to identify, through a systematic review and meta-analyses, whether the psychophysiological factors are associated with alteration in heart rate variability (HRV) in sports.

Methods: We searched in four databases (PubMed; Scopus; Cochrane; Web of Science) for studies assessing the association of root mean square of successive differences between normal heartbeats (RMSSD) with a variety of psychophysiological outcomes in athletes of any modality.

Results: After initial search, we selected 12 studies with 27 study arms for analysis. First, we combined 19 study arms in a meta-analysis testing the correlation between HRV and the psychophysiological factors at baseline (Meta 1). Second, we combined 9 study arms in a meta-analysis testing the correlation between HRV and changes in psychophysiological factors within a training period (Meta 2). We analyzed the following psychophysiological factors: stress, sleep deprivation, fatigue, muscular soreness, mood, and hormonal changes (cortisol). summary, there was no significant association between the HRV and the psychophysiological factors in both meta-analyses (Meta 1: r = 0.084, P = 0.167; I² = 20%, P-value for heterogeneity = 0.215; and Meta 2: r = 0.268, P = 0.131, I² = 65.2%, P-value for heterogeneity = 0.003).

Conclusion: We were not able to confirm the association between HRV and any psychophysiological factors by meta-analysis, but it could be due to inherent limitations of this type of analyses. To test whether this associations truly exist, future meta-analysis will need to include studies with much larger sample size and standardize the methods between studies to reduce heterogeneity. Longitudinal studies will be fundamental to understand the causal relationship between these factors to ultimately improve training monitoring tools for better recovery in athletes.


Introduction

The analysis of oscillations between consecutive R waves intervals, named heart rate variability (HRV), informs about the function of autonomic nervous system (ANS), associated with intern organism function1. Changes in HRV assessed in resting conditions have been associated to health outcomes and performance of athletes2-4. During situations that challenge the organism homeostasis, like exercise, there is an increase in the afferent feedback from exercised muscles, arterial and pulmonary baroceptors, chemoreflex, and activation of central command that initially leads to a parasympathetic withdrawal with a sequential increase in sympathetic modulation5. This increase in sympathetic modulation is related to the increased intensity of the effort, and leads to increased heart rate (HR), and decreased HRV, in order to meet the transitory organism energetic needs5.

Nonetheless, resting HRV is mainly modulated by the intrathoracic pressure changes caused by the respiratory flow, reflecting the physiological oscillation on the ANS output, which can be used as a general homeostatic assessment2. Via frequent HRV resting assessment in athletes, it is possible to estimate the level of chronic recuperation from a training session or competition, compared to the same HRV in different periods until it returns to homeostatic values.

In this way, HRV could be used to predict a potential chronic loss of homeostasis in athletes, which can lead to poor outcomes, as a non-functional overreaching state and loss of performance6. It is not clear to which extent chronic periods of physical overload or other psychological stressors would be more determinant of HRV reduction. Recently, a meta-analysis showed that pre-competition period overload but not the increase in volume and intensity, volume or post-competition overload caused an impairment in parasympathetic modulation in athletes7. Given that pre-competition period is usually preceded by a taper phase in which the athlete does not undergo intense physical training, it led the authors to hypothesize that physiological aspects before the competition, such as pre-competitive anxiety, could be determinant of the HRV reductions in this context. In fact, the HRV has been inversely associated with the level of pre-competitive anxiety8.

Sleep and sports stress are other psychophysiological factors that may play a role in sports competition outcomes and can result in immunological, cognitive, and physiological changes in athletes9. Morales et al.10 with the objective to compare physiological and performance markers, before and after the final mesocycle of training, found a considerable correlation between the HRV changes and the variation of stress in athletes.

Considering athletes undergoing recurrent infections might have impaired homeostatic regulation11,12, which interferes with training and ability to compete13, it is important to monitor the athlete’s homeostasis on a daily basis. It has been debated that those recurrent cases of infection in athletes are not dependent on physical training overload per se, but rather it is dependent on multifactorial aspects in the context of sports, such as travelling, sleep interruption, psychological stress, and exposure to crowds14.

A few studies investigated the relationship between some of these multifactorial aspects with HRV10,14,15. The literature would benefit from the integration of those aspects considering individuals changes in ANS along time, instead of just cross-sectional relationships. Therefore, although heart rate variability (HRV) has been a useful and accessible tool to monitor recovery from athletes’ training, it is not clear if it reflects changes in psychophysiological factors, and a meta-analysis of the literature could integrate the previous findings.

Among these multifactorial aspects present in training and competition, we highlight the psychological factors that interfere with the final performance of athletes, like stress, sleep deprivation, fatigue, muscular soreness, mood, and hormonal changes (cortisol). All these factors have a potential to impair homeostasis and changing ANS state, increasing the predominance of sympathetic modulation16,17. In this way, HRV in athletes can be modulated by a variety of those factors simultaneously.

The association of these factors with HRV during the training and competition has never been tested by a meta-analysis. Thus, our objective was to identify, through a systematic review and meta-analyses, whether these psychophysiological factors (fatigue, soreness, stress, mood, sleep, and cortisol) are associated with alteration in HRV in the sports training context. This will support the understanding of what kind of stressors influence HRV, and ultimately it will allow sports’ coaches to improve training prescription by constant HRV monitoring. As soon as HRV variation can be detected, the easier to identify potential stressors individually.

JMHCP-24-1323-fig1

Abstract Figure: HRV

Methods

We searched PubMed, Scopus, Cochrane, and Web of Science databases on 04th April 2022 to test the correlation between the root mean square of successive differences between normal heartbeats (RMSSD) and psychophysiological factors in athletes. The same syntaxes constructed for PubMed were utilized as a model for the equivalence in other databases (PROSPERO CRD42020181966) (see Supplementary Material).

Eligibility Criteria

Studies were eligible if they were interventions or observational studies and were in English, Portuguese, or Spanish language. No restriction to the date of publication was applied.

Study Selection

All the articles selected passed by a screening process composed of two phases. In the first, only the abstract and title were screened, and in the second, the complete reading of the articles was done. Inclusion Criteria: 1) studies including athletes of any sports modality; 2) studies testing the association between HRV and psychophysiological factors (measured by questionnaires or biomarkers) in any period of training or competition; and 3) HRV assessed at rest condition. Exclusion Criteria: 1) Non-original studies; 2) studies with athletes with disabilities; 3) Studies that analyzed HRV post-exercise; 4) studies that did not show any psychophysiological factors.

Data Extraction

We collected data on the correlation coefficient between RMSSD and psychophysiological factors (r values and number of subjects in the studies). We also collected secondary information, such as the position of HRV assessment (supine and seated), the modality practiced by the athletes, the sex of the participants (male, female or mixed), and the moment of HRV collection (rest or sleep). We chose RMSSD within different HRV indexes because RMSSD represents the parasympathetic activity, it can be collected by 1 to 10 minutes protocol, it shows a small day-to-day variation, and it can be analyzed by simple free software that contributes to standardization and reliability of the results we find in the literature18.

Statistical Analysis

Two meta-analyses were performed on Comprehensive Meta Analyses 3.0 software. First, we performed a meta-analysis for the correlation of baseline psychophysiological factors (fatigue, soreness, stress, mood, sleep, and cortisol) and baseline RMSSD (Meta 1). Second (Meta 2), we performed a meta-analysis for the correlation between the changes of psychophysiological factors and changes in RMSSD within a period of training (post-overload period, which could be by a training or competition period, minus pre-moment of overload). When the heterogeneity between the studies was significant (p ≤ 0.05), we adopted the random effects model. On the other hand, when the heterogeneity was not significant (p > 0.05), we maintained the fixed effects model. The Egger test was used to identify the possible risk of publication bias, analyzed by the p-value of 2 tailed test since these analyses present a bilateral hypothesis.

Results

Twelve studies with 27 study arms were included in the meta-analyses (Figure 1 - flow chart of the studies). In Meta 1, we performed a meta-analysis for 9 studies with 19 study arms14,15,19-23 and in Meta 2, we performed a meta-analysis for 4 studies with 9 study arms10,24-26. These studies included male and female athletes of different sports modalities, such as soccer, swimming, Brazilian jiu-jitsu, lacrosse, and synchronized swimming (Table 1).

JMHCP-24-1323-fig2

Figure 1: Flowchart of the study’s selection

HRV = Heart Rate Variability; RMSSD = Root Mean Square of Successive Differences Between Normal Heartbeats

 

Table 1: Characteristics of the studies

Correlation with HRV

Author, year (condition)

Total

sample

size (F)

Mean

age (SD)

 

Sex

 

Sports modality

HRV Body position

HRV

condition

HRV time of collection

 

Subgroup: r and p-value

 

 

Baseline data

 

Sanchez, 2013

 

18

 

26.67

(3.43)

 

M

 

Soccer

 

Supine

 

Rest

 

5 minutes

Iceberg Status / Fatigue: r

= 0.45; p >0.05

Changed Status/Fatigue: r =

-0.52; p >0.05

 

Cervantes, 2009

17

24.47

F

Hockey

Supine

Rest

5 minutes

Fatigue: r = -0.318, p = 0.214

 

 

Flatt, 2017

 

10

 

21.6 (2)

 

M

 

Soccer

 

Supine

 

Rest

 

3 minutes

Stress: r = -0.24; Sleep: r = 0.14; Soreness: r = -0.38; Mood: r = -0.38; Fatigue: r

= -0.55; *

 

Núñez-Espinosa,

2021

25 (13)

13.5

(1.4)

M/F

Swimming

Supine

Rest

5 minutes

Fatigue: r = 0.47, p = 0.021

 

Hauer, 2020

12

26.8

(5.6)

M

Lacrosse

Supine

Rest

5 minutes

Stress: r = -0.326, p >0.05

 

Mishica, 2021

8 (5)

16 (1)

M/F

Country Skiing/ Biathlon

Supine

Sleep

3 minutes

Cortisol: r = -0.545, p = 0.001

 

Sekiguchi,

2019

10

19 (1)

F

Cross Country

Supine

Sleep

NR

Sleep: r = -0.13, p > 0.05

 

 

 

 

Lima-Borges, 2018

 

 

 

30

 

 

 

12-17*

 

 

 

M/F

 

 

 

Swimming

 

 

 

Supine

 

 

 

Rest

 

 

 

20 minutes

GP-S stress: r = -0.259, p

=0.167; GP-G stress: r = - 0.233, p = 0.214; SP-S

stress: r = -0.043, p = 0.822; SP-G stress: r = -0.128, p = 0.499; CP-S stress: r =

0.056, p = 0.768; CP-G

stress: r = 0.017, p = 0.928

 

 

Delta

Lizuka, 2020

(Days 1-4 vs.

Days 5-8)

 

8 (4)

 

23 (2.8)

 

M/F

 

Badminton

 

Seated

 

Rest

 

5 minutes

 

Fatigue: r = -0.77, p = 0.027

 

Flatt, 2016 (Low Load vs. High Load)

 

8

 

20.2

(1.8)

 

F

 

Soccer

 

Seated

 

Rest

 

1 minute

Stress: r = 0.06; Sleep: r = 0.34; Soreness: r = 0.54;

Mood: r = -0.03; Fatigue: r

= 0.56; *

 

Morales, 2019 (Pre-T vs. Post

T)

 

16

23.25

(5.07)

 

F

 

Soccer

 

Supine

 

Rest

 

5 minutes

G stress: r = -0.61, p = 0.01; S stress: r = 0.58, p = 0.01

 

Tramunt, 2018 (pre 2 W PC vs. post 2 W

PC)

 

12

 

21.5

(3.5)

 

F

 

Synchronized Swimming

 

Supine

 

Rest

 

10 minutes

 

Cortisol: r = 0.41, p > 0.05

The articles included in Meta 2 tested the influence of a variety of interventions, such as a training period, pre- competition, competition, or test period. The HRV data were collected during rest conditions in all studies, with exception of two studies that collected HRV during sleep15,23. Most of HRV analyses occurred in the supine position, with exception for two studies that only analyzed in the seated position24,26. Although Meta 1 was homogeneous (Q = 21.241; DF(Q) = 17; P= 0.215; I² = 19.968%), there was no significant association between the basal HRV and the basal psychophysiological factors in the Meta 1 (Figure 2), since the 95% confidence interval, in black, crossed the null line (R= - 0,084; P= 0,167).

JMHCP-24-1323-fig3

Figure 2: Forest plot of meta-analysis 1

Changed Status = Athletes that show alterations in tension, depression, hostility, fatigue, and confusion in POMS questionnaire; CP = Competitive Period; F = fixed effects; GP = General period; Iceberg Status = Athletes that show small levels in tension, depression, hostility, fatigue, and confusion in POMS questionnaire; Outcome = psychophysiological factors analyzed;  R = random effects; Relative Weight = weight of the result in the study for the subgroup analysis;  SP = Specific Period; Total = sample of the study

 

There was also no significant association between the delta of HRV and the delta of psychophysiological factors on Meta 2 (Figure 3), since the 95% confidence interval cross the null line (R= -0, 268; P= 0,131). However, this meta-analysis was significant heterogeneous (Q = 23.013; DF(Q) = 8; P= 0.003; I² = 65.23%), suggesting potential confounding factors might be explaining differences between studies. Furthermore, all the subgroups within each meta-analysis did not show a significant correlation between the HRV and any of the psychophysiological aspects (Figures 2 and 3). None of the meta-analyses showed a significant p-value for the risk of publications bias (Meta 1, P= 0.119 and Meta 2, P= 0.878)

JMHCP-24-1323-fig4

Figure 3: Forest plot of meta-analysis 2

F= fixed effect; General = general stress score; Outcome = psychophysiological factors analyzed; R = random effects; Relative Weight = weight of the result in the study for the subgroup analysis; Specific = sport specific stress scores; Total = sample of the study

 

Discussion

Different factors might have contributed to the non-significant association between HRV and the psychophysiological factors in athletes. One, is the fact that many athletes develop an ability to cope with different types of stress; this could support the maintenance of high HRV even under stressful and/or fatigued conditions.

The regulation of the autonomic nervous system is complex, based on feedback mechanisms from a diversity of brain areas, such as the hypothalamus, amygdala, and medial pre-frontal cortex27. As for the complexity of these systems, Hans Seyle28, presents a model of three stages to respond to a stressful situation. The first one is the alarm reaction where the organism reacts for one stress situation with a fight or flight answer (sympathetic activity). In the second stage, called resistance stage, the body start to adapt itself to the stress situation. This is characterized by the return of part of the physiological function to its baseline condition by the parasympathetic reactivation, even though the blood level of glucose, cortisol and adrenaline are still high. The third stage is the exhaustion one, they occur when the adaptation that result in a parasympathetic predominance during the increased cortisol and adrenaline are lost, back to a sympathetic predominance.

Because of very frequent exposure to stress29 and the importance of keeping homeostasis under stressful situations, it is very common that athletes undergo specific types of training to counteract stress reactions, such as methods of visualization and simulation of positive feelings30. Observing Hans Seyle’s model, it is possible that because of a parasympathetic predominance of athletes31, caused by the high training level, this population presents a better stage two28, and thus prevents the reduction in the HRV in a chronic way during a big and long situation of stress, like a training or competition period. The lack of association between HRV and psychophysiological markers found here, can be connected to other adaptations that exercise and systematic physical training can cause in the ANS of the athletes’ organism. During exercise, the hypothalamus–pituitary–adrenal axis is activated, also influenced by the increase in cortisol levels, and this leads to higher sympathetic activation, stimulating a sequential increase in heart rate (HR)32. It is well known that physical training leads to a chronic increase in HRV in athletes33, and potentially this could represent a resistance to stress. On the other hand, it is known that not all athletes will have the same positive response34.

With the chronic practice of physical exercise, there is a possible increase in the basal concentration of cortisol level32, which could lead to different adaptations that benefit stress control. One of these alterations is the improvement of resistance for this higher concentration of basal cortisol, given that regular training causes an increase or maintains the level of mineral corticoid receptors and of the glucocorticoids that recaptures the released cortisol32. At the same time, this increases in basal cortisol increases the synthesis and release of dopamine, which promotes beneficial actions in the cerebral cortex, regulating, for example, the brain-derived neurotrophic factor that keeps synaptic plasticity32. Like that, an enhancement in welfare could influence the subjective psychophysiological questionnaires in the included studies.

Together with this, physical exercise promotes a reduction in the concentration of dopamine receptors and in norepinephrine transporters that promote a longer time of dopaminergic action in the Pre-Frontal Medial Cortex32. Considering the majority of the studies used subjective psychophysiological questionnaires, this longer dopaminergic action could promote a sense of higher wellness state that in turns would underestimate their real physiological state.

Considering exercise training can increase resting dopamine levels in animals, and it stimulates the brain-derived neurotrophic factor production, a possible hyperactivation of parasympathetic modulation in athletes could influence the relation between HRV and the psychophysiological factors investigated in this study. We hypothesized that this hyper parasympathetic activity sustained a stable HRV status, even though some of the psychophysiological factors were altered, so most of the athletes could better cope with stress.

Considering the articles in Meta 2 tested the influence of a variety of interventions, such as a training period, pre-competition, competition, or test period; these differences could also explain the heterogeneity in this analysis. Future meta-analysis with more studies could cluster this data by different types of training overload, caused by an increase in intensity or volume for example, to chase more homogeneous analyses. This separation was not possible here due to the low number of studies.

Another potential limitation of this study was the variation in assessments. The HRV was measured in different periods, positions and daily time of collection in the studies, together with the use of different psychophysiological tools, like distinct questionnaires as Rest-Q (recovery and stress), POMS (mood) or visual scales (stress). This leads to variations to measure the same outcome, that could explain the current results.

Considering each study's results, the only one that showed a positive and significant correlation between HRV and a psychophysiological factor was the article from Núñez-Espinosa21, that investigated fatigue (r = 0.470; P= 0.017). This study used fatigue tests like Stroop test, that could have reduced the variability between individuals in this study, compared to studies that used questionnaires.

The small number of studies, especially for some of the outcomes (cortisol, sleep, mood, and soreness) is another limitation of this study. Finally, the non-significant risk of publication bias means that studies with poor estimations did not influence the meta-analyses true effects.

Considering our results and limitations, it was not possible to conclude that an association between HRV and psychophysiological factors exists. The lack of correlation between those variables here, do not completely deny the existence of association between them, given all the limitations of this type of analysis that we just discussed. Future research needs to find other ways to integrate the assessment tools in the literature. It is possible that different psychophysiological factors could change specific HRV indexes, such the High Frequency or Low Frequency components of HRV.

It is important to take account that all the variables investigated here are highly variable between individuals35, and the association between them could be tested by other type of analysis such as comparing HRV in groups that increased or did not increased stress, withing a certain period of training. A longitudinal study with more than one time-point can be also useful to understand the individual variations.

Conclusion

The results of this study weren’t significant enough to conclude that there is an association between HRV and psychophysiological factors, suggesting we need to improve the methodology of use of this tool, to extrapolate its meaning and support training monitoring. The difficulty in observing this association might have been caused by one or more of the aspects discussed above, like stress coping mechanisms in athletes or the low number of studies included. Future studies need to keep investigating other means by which we can improve our understanding of psychophysiological factors regulating homeostasis in athletes. 

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Supplementary Material

Syntax for PubMed Search:

(((("RMSSD"OR"LnRMSSD"OR"HRV"[tiab] OR "biofeedback,psycology"[mh] OR "heart-rate variability"[tiab] OR "HRV analysis"[tiab])) AND (("Fatigue Syndrome, Chronic"[mh] OR "Chronic Fatigue Syndrome" [tiab] OR "overload"[tiab] OR "overtrainig"[tiab] OR "subjective fatigue" [tiab] OR "perceptual fatigue"[tiab] OR "fatigue"[tiab] OR "perfomance parameters"[tiab] OR "total score fatigue" OR "TSF" [tiab] OR "fatigue and wellbeing questionnare from mc lean" OR "brief questionnare from mc lean" OR "eletronic questionnaire adptaded from mc lean" OR "Stress, Psychological"[tiab] OR "stress, physiological"[mh] OR "stress score"[tiab]; OR "psychological stresses"[tiab] OR "monitoring stress"[tiab] OR "stress recovery"[tiab] OR "stress tolerance"[tiab] OR "stroop task" OR "RESTQ-SPORT" OR "DALDA" OR ("recovery of function"[tiab] and "physiology"[tiab]) OR ("recovery of function"[tiab] and "physiology"[tiab]) OR "Sleep"[mh] OR "sleep"[tiab] OR "'ASRM"OR"athlete self-report measures of recovery status" OR "recovery status"[tiab] OR "disorder, mood"[tiab] OR "profile of mood state questionnaire "OR" profile of mood state "[tiab] OR "POMS" OR "Myalgia"[mh] OR "muscle pain"[tiab] OR "muscle, skeletal / physiology"[tiab] OR "muscle soreness"[tiab] OR "Psychometrics"[mh] OR "psychometrics"[tiab] OR "psychometrics questionnare" OR "psychometrics parameters"[tiab] OR "psychometric indices"[tiab] OR "psychometric response"[tiab] OR "cortisol"[tiab] OR "salivary cortisol"[tiab] OR "percption of recovery"[tiab] ))) AND (("Athletes"[mh] OR "players"[tiab] OR "sport"[tiab] OR "Athletic Performance"[tiab] OR "Exercise"[tiab] OR "elite players"[tiab] OR "sports"[mh] OR "athletic"[tiab])).

 

Article Info

Article Notes

  • Published on: December 04, 2024

Keywords

  • Heart Rate Variability
  • Psychological Factors
  • Athletes
  • Sports

*Correspondence:

Dr. Amanda V. Sardeli,
Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK.
Email: a.veigasardeli@bham.ac.uk

Copyright: ©2024 Sardeli AV. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License.