Abstract
s review aimed to synthesize evidence regarding interventions based on heart rate variability (HRV)-guided training for VO2maximprovements in endurance athletes and address the issues that impact this performance enhancement. The Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, EMBASE, CINAHL Complete, the Web of Science Core Collection, Global Health, Current Contents Connect, and the SciELO citation index were searched. Inclusion criteria were: randomized controlled trials; studies with trained athletes enrolled in any regular endurance training; studies that recruited men, women, and both sexes combined; studies on endurance training controlled by HRV; studies that measured performance with VO2max. A random-e ects meta-analysis calculating the e ect size (ES) was used. Moderator analyses (according to the athlete's level and gender) and metaregression (according to the number of participants in each group) were undertaken to examine di erences in ES. HRV-guided training and control training enhanced the athletes' VO2max (p<0.0001), but the ES for the HRV-guided training group was signi cantly higher (p<0.0001; ESHRVG-CG=0.187). The amateur level and female subgroup reported better and signi cant results (p<0.0001) for VO2max. HRV-guided training had a small (ES=0.402) but positive e ect on endurance athlete performance (VO2max), conditioned by the athlete's level and sex. Keywords:performance; heart rate variability; high-level athletes; maximal oxygen uptake 1. Introduction 1.1. Description of the Condition The key components in any training program are the volume (i.e., how much), intensity (i.e., how hard), and frequency (i.e., how
VO2max. HRV-guided training had a small (ES=0.402) but positive e ect on endurance athlete performance (VO2max), conditioned by the athlete's level and sex. Keywords:performance; heart rate variability; high-level athletes; maximal oxygen uptake 1. Introduction 1.1. Description of the Condition The key components in any training program are the volume (i.e., how much), intensity (i.e., how hard), and frequency (i.e., how often) of the exercise sessions, and the combination of these `training impulses' determines the magnitude of adaptive responses that improve the physical condition of an athlete or increase fatigue [1]. Combining these key elements to optimize training in athletes for better performance represents a relevant area of research within exercise physiology and sports medicine [2]. It is recognized that a standard training program applied to a group of athletes can induce diverse responses in terms of performance and physiological adaptations [3,4]. Therefore, individualization is recognized as a training principle [1] as well as the need to adjust training stimuli Int. J. Environ. Res. Public Health2020,17, 7999; doi:10.3390 /ijerph17217999 /journal/ijerph
Int. J. Environ. Res. Public Health2020,17, 7999 2 of 21 to the psychophysical load capacity and individual tolerance of each athlete, if individual responses to training and recovery loads are intended for optimal performance [5]. The maximal oxygen uptake (VO2max) is considered one of the main indicators for measuring an athlete's performance and cardiovascular adaptation to training loads [6]. The VO2maxis de ned as the largest volume of oxygen that the body can capture, use, and transport during intense exercise [7] and is a determining factor of endurance performance [7,8]. As Vesterinen et al. [4,9] state, although some athletes show great endurance performance improvements after standardized group training (even up to 40% in VO2max), other athletes show no changes or bene ts, and sometimes even show a decrease in endurance performance. In recent years, research has looked at whether heart rate variability (HRV)-guided training has positive e ects on athletic performance, given that this type of training allows daily adjustment of the training and recovery stimuli, individually based on HRV records [4,5,10]. 1.2. Description of the Intervention HRV is an indicator that enables the noninvasive analysis of autonomic nervous system activity in both its sympathetic and parasympathetic branches [11]. This is relevant if we consider that an important component of the interindividual variability in physiological responses to training is related to the balance between the parasympathetic (PNS) and sympathetic (SNS) activity of the autonomic nervous system (ANS) [12]. According to Huang et al. [13], HRV is considered the variation in the time interval between two consecutive heartbeats and obtained by calculating the time interval between two consecutive R waves (i.e., RR interval uctuation) in the electrocardiogram (ECG). Since the elapsed time between beats is not constant, high vagally related HRV values are associated with e cient ANS, promoting behavioral adaptation and cognitive exibility during stress [14], while low HRV is indicative of an ine cient ANS, resulting in maladaptive responses to stress and perceived threats [13]. HRV analysis is considered a useful method for measuring the heart's ability to adapt to endogenous and exogenous loads [15]; therefore, it can be
values are associated with e cient ANS, promoting behavioral adaptation and cognitive exibility during stress [14], while low HRV is indicative of an ine cient ANS, resulting in maladaptive responses to stress and perceived threats [13]. HRV analysis is considered a useful method for measuring the heart's ability to adapt to endogenous and exogenous loads [15]; therefore, it can be used for the individual assessment of responses to training loads and recovery adaptation [4,16]. High HRV measurements indicate more parasympathetic than sympathetic activation, which is indicative of better recovery and preparedness for facing high-intensity training sessions [17]. HRV-guided training starts with a preparation period of about four weeks, which serves as a standardized data collection phase to obtain the baseline HRV values (e.g., LnrMSSD; the natural logarithm of the square root of the mean value of the sum of the squares of the di erences between the adjacent RR intervals) and their normal range (upper and lower limits) for each athlete [9,18]. Once the normal range of HRV measurements has been established, the training prescribed (moderate- or high-intensity session) is based on this calculation, which is normally updated weekly [19]. Traditionally, the vagally related HRV index has been measured with ECG [20], and quanti ed by means of rMSSD [17]. Currently, the development and validation of new applications (i.e., smartphone applications: Kubios-HRV, Elite-HRV, Mobile Lab, or HRV4Training) facilitate daily HRV measurements and their quanti cation and, thus, the individual adaptation of training loads and recovery. 1.3. How the Intervention Might Work Bellenger et al. [21], in a recent systematic review with meta-analysis, highlighted the need to use monitoring systems that accurately re ect the athletes' adaptations to the training stimulus. Although there have been numerous research studies using the HRV measure to check wellness and training adaptation in athletes [22,23], these have not focused on performance improvement based on HRV-guided training but have followed training interventions based on a traditional and nonindividualized methodology. In contrast, evidence exists supporting the use of HRV-guided training for improved performance in endurance athletes. With this type of training monitoring, some studies have found signi
measure to check wellness and training adaptation in athletes [22,23], these have not focused on performance improvement based on HRV-guided training but have followed training interventions based on a traditional and nonindividualized methodology. In contrast, evidence exists supporting the use of HRV-guided training for improved performance in endurance athletes. With this type of training monitoring, some studies have found signi cant VO2maximprovements in athletes who have developed individualized endurance training programs based on daily HRV values. These studies alternated moderate-intensity sessions with high-intensity
Int. J. Environ. Res. Public Health2020,17, 7999 3 of 21 sessions [4,10] or even rest sessions, vigorous-intensity training, and moderate-intensity exercise [5]. However, Javaloyes et al. [18], in a program with similar characteristics developed with professional cyclists, found no signi cant improvements in VO2max. Likewise, signi cant improvements have been found among athletes following HRV-guided training in other variables; for example, for lactate in maximal test [10], speed in maximal test [4], time in maximal test [4,10], or muscle strength [24]. At the level of perceived recovery, signi cant improvements have also been found in variables such as general stress, emotional stress, lack of energy, and even overall mood disturbance [25]. HRV-guided training may, therefore, function as an alternative method for improving performance in resistance athletes. 1.4. Why Is This Review Important? In the search to improve athletic performance, di erent training methods have been tried and studied, such as intensi ed training [2] or submaximal tests [26]. However, it has also been recognized that the same training program followed by a group of athletes can provoke a wide range of reactions in terms of performance and physiological adaptations [3]. Overuse injuries occur due to repetitive submaximal loading of the musculoskeletal system when there is inadequate rest to allow for structural adaptation to take place [27]. In recent years, HRV-guided training has shown itself to be a promising method for improving di erent performance variables (e.g., VO2max) compared to prede ned training (traditional training) through the monitoring and individualization of endurance athletes' training [4,28]. HRV-guided training has been investigated in randomized trials on samples from di erent endurance sports, such as skiers [28], runners [4,25], and cyclists [18]), as well as athletes of di erent ages and levels: elite [18,28] and recreational endurance athletes [5,24,25]. Therefore, it is important to carry out a systematic review and meta-analysis of the di erent experimental studies conducted so far on endurance athletes in order to assess whether HRV-guided training is an e ective method for performance improvement. 2. Objectives As mentioned above, this review aimed to analyze the e ect of HRV-guided training
and recreational endurance athletes [5,24,25]. Therefore, it is important to carry out a systematic review and meta-analysis of the di erent experimental studies conducted so far on endurance athletes in order to assess whether HRV-guided training is an e ective method for performance improvement. 2. Objectives As mentioned above, this review aimed to analyze the e ect of HRV-guided training on VO2max in endurance athletes. We asked the following research questions regarding HRV-guided training in endurance athletes: Research Question 1: Does HRV-based training have an e ect on VO2max? Research Question 2: Is the e ect of this type of training superior to that of traditional training? Research Question 3: Is the level of the athletes decisive in obtaining an e ect on the VO2max? Research Question 4 : Does the e ect of HRV-guided training determine VO2maxscores according to the gender of the athlete? 3. Methods The methods detailed below are reported in accordance with the Campbell Collaboration policies and guidelines for systematic reviews [29]. 3.1. Criteria for Considering Studies for This Review (Eligibility Criteria) 3.1.1. Types of Studies We included randomized controlled trials (RCTs) and the rst period of cross-over RCTs and experimental studies using a random method for the treatment assignment in order to reduce the risk of allocation bias. We restricted study eligibility by language. We did not restrict study eligibility by publication status.
Int. J. Environ. Res. Public Health2020,17, 7999 4 of 21 3.1.2. Types of Participants We included studies with trained athletes enrolled in any form of regular endurance training (e.g., runners, triathletes, skiers, and cyclists). We included studies that recruited both men and women, or men and women separately. 3.1.3. Types of Interventions We included studies on endurance training controlled by heart rate variability to improve the athletes' performance. We considered designs comprising any dose, frequency, and duration. We also considered studies with the following types of comparisons: Endurance training controlled by HRV versus no speci c training intervention (e.g., habitual physical activity). Endurance training controlled by HRV versus another training intervention (e.g., traditional endurance training or another type of traditional training). Endurance training controlled by HRV versus another training intervention (i) versus a further training intervention (ii). Endurance training controlled by HRV (i) versus endurance training controlled by HRV (ii) versus another training intervention versus no speci c training intervention. 3.1.4. Types of Outcome Measures Primary Maximal oxygen consumption (VO2max) 3.2. Search Methods to Identify the Studies 3.2.1. Electronic Searches The register contains studies identi ed from the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, EMBASE, CINAHL Complete, the Web of Science Core Collection, Global Health, Current Contents Connect, and the SciELO citation index. The search is up to date as of 15 June 2020. The language was restricted, considering only English or Spanish. The terms used to search the databases were: (amateurOReliteORtrain*) AND (HRV-guided ORheart-rate variability guided). 3.2.2. Searching Other Resources We checked the reference lists of all the included studies and systematic reviews for additional references. We contacted experts in the eld and the authors of the included studies to identify additional unpublished studies. We also checked the results of completed trials registered on the US National Institutes of Health Ongoing Trials Register,, the World Health Organization International Clinical Trials Registry Platform (WHO ICTRP), and proceedings of conferences for relevant research. 3.3. Data Collection and Analysis We conducted the following data collection and analysis in accordance with the recommendations in the Cochrane Handbook for Systematic Reviews of
the results of completed trials registered on the US National Institutes of Health Ongoing Trials Register,, the World Health Organization International Clinical Trials Registry Platform (WHO ICTRP), and proceedings of conferences for relevant research. 3.3. Data Collection and Analysis We conducted the following data collection and analysis in accordance with the recommendations in the Cochrane Handbook for Systematic Reviews of Interventions [30]. 3.3.1. Selection of Studies Two review authors independently screened the titles and abstracts of all the retrieved references in Microsoft Excel 2018 (Microsoft, New York, NY, USA) for Windows. The full-text study reports were retrieved for all the citations that at least one review author considered potentially relevant. Two review
Int. J. Environ. Res. Public Health2020,17, 7999 5 of 21 authors independently screened the full-text articles and identi ed studies for inclusion; they also identi ed and recorded the reasons for excluding studies in the excluded studies characteristics. Any disagreements were resolved through discussion. The selection process is detailed in a PRISMA ow diagram [31]. 3.3.2. Data Extraction and Management We used a standardized piloted data collection form in Microsoft Excel 2018 for Windows and extracted the following study characteristics and outcome data: (i) Methods: study design; (ii) Participants: randomized number, study participants' mean age or age range, study location and setting, recruitment methods, inclusion and exclusion criteria, and type of endurance sport; (iii) Interventions: a description of the training intervention characteristics, the dose and duration of the training intervention, a description of the comparison intervention characteristics, the length of follow-up, the number of withdrawals, and the reasons for withdrawal; (iv) Outcomes: a description of the primary and secondary outcomes in the review that were reported in the trial and a listing of other outcomes collected in the trial; (v) Notes: the trial funding and notable con icts of interest of the trial authors; (vi) a `risk of bias' assessment. Two review authors independently extracted the outcome data from the included studies into Microsoft Excel 2018 spreadsheets and compared the data to identify any discrepancies in the data entries. Any disagreements were resolved by consensus. In the Characteristics of Included Studies section, we noted down if a trial did not report outcome data in a usable way. We then transferred all the outcome data into the Comprehensive Meta-Analysis software version 2.2.064 (Biostat, Englewood, NJ, USA) [32]. 3.3.3. Risk-of-Bias Assessment in the Included Studies Two review authors (M.C.P., A.G.G.) independently assessed the risk of bias for each included trial using the Cochrane risk-of-bias tool [30]. Any disagreements were resolved by discussion. The risk of biases were assessed for the following domains: random sequence generation (selection bias), allocation concealment (selection bias), blinding of participants and personnel (performance bias), blinding of outcome assessment for each outcome (detection bias), incomplete outcome data (attrition bias),
the risk of bias for each included trial using the Cochrane risk-of-bias tool [30]. Any disagreements were resolved by discussion. The risk of biases were assessed for the following domains: random sequence generation (selection bias), allocation concealment (selection bias), blinding of participants and personnel (performance bias), blinding of outcome assessment for each outcome (detection bias), incomplete outcome data (attrition bias), selective outcome reporting (reporting bias), and other biases (such as the validity of outcome measure and baseline comparability). Each potential source of bias was assessed as either high, low, or unclear, and a quotation from the study report was provided together with a justi cation for the judgment in the `risk of bias' tables. The judgments across the di erent studies were summarized for each of the domains listed. 3.3.4. Treatment E ect Measures The outcome data for each study were uploaded into the data tables of the Comprehensive Meta-Analysis software to calculate the treatment e ects. We used the mean di erence (MD) for continuous outcomes reported on the same scal, and the standardized mean di erence (SMD) for continuous outcomes measured on di erent scales in di erent trials (SMD=MHRV guided training Mcontrol group/Standard deviation) [33]. Uncertainty was expressed with 95% con dence intervals (CIs) for all the e ect estimates. 3.3.5. Assessment of Heterogeneity and Reporting Bias Heterogeneity was assessed qualitatively between studies in three ways: a visual examination of the forest plots, the Chi 2 test (p 0.10) for heterogeneity, and the I 2 statistic. The implications of the observed I 2 statistic value were considered as follows: 0% to 40%might not be important; 30% to 60%may represent moderate heterogeneity; 50% to 90%may represent substantial heterogeneity; 75% to 100%considerable heterogeneity [30]. Publication bias was assessed by examining the asymmetry of a funnel plot using Egger's test. If studies were distributed symmetrically around the mean e ect size (ES), there was an absence of publication bias [33]. Subgroup analysis was carried out
examining the asymmetry of a funnel plot using Egger's test. If studies were distributed symmetrically around the mean e ect size (ES), there was an absence of publication bias [33]. Subgroup analysis was carried out
Description
A systematic review analyzing HRV-guided training effects on VO2max in endurance athletes.