EVALUATING INDIVIDUAL TRAINING ADAPTATION WITH SMARTPHONE-DERIVED HEART RATE VARIABILITY IN A COLLEGIATE FEMALE SOCCER TEAM ANDREW A. FLATT1 1 2
AND
MICHAEL R. ESCO1,2
Department of Kinesiology, Exercise Physiology Laboratory, The University of Alabama, Tuscaloosa, Alabama; and Department of Kinesiology, Human Performance Laboratory, Auburn University Montgomery, Montgomery, Alabama
ABSTRACT
INTRODUCTION
Flatt, AA and Esco, MR. Evaluating individual training adaptation with Smartphone-derived heart rate variability in a collegiate female soccer team. J Strength Cond Res 30(2): 378–385, 2016—Monitoring individual responses throughout training may provide insight to coaches regarding how athletes are coping to the current program. It is unclear if the evolution of heart rate variability (HRV) throughout training in team-sport athletes can be useful in providing early indications of individual adaptation. This study evaluated relationships between changes in resting cardiac autonomic markers derived from a novel smartphone device within the first 3 weeks of a 5-week conditioning program and the eventual change in intermittent running performance at week 5 among 12 collegiate female soccer players. Change variables from weeks 1 to 3 of the weekly mean and weekly coefficient of variation for resting heart rate (ΔRHRmean and ΔRHRcv, respectively) and logtransformed root mean square of successive R-R intervals multiplied by 20 (ΔLn rMSSDmean and ΔLn rMSSDcv, respectively) were compared with changes in Yo-Yo Intermittent Recovery Test Level 1 performance (ΔYo-Yo). A very large and significant correlation was found between ΔYo-Yo and ΔLn rMSSDcv (r = 20.74; p = ,0.01) and a large nonsignificant correlation was found with ΔLn rMSSDmean (r = 0.50; p = 0.096). This study suggests that a decrease in Ln rMSSDcv within the first 3 weeks of training is a favorable response, indicative of positive adaptation. Collecting daily HRV data with a smartphone application using ultrashort HRV measures seems useful for athlete monitoring.
KEY WORDS HRV, vagal, parasympathetic, Yo-Yo, Women, intermittent running
Address correspondence to Andrew A. Flatt,
[email protected]. 30(2)/378–385 Journal of Strength and Conditioning Research Ó 2016 National Strength and Conditioning Association
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tandardized training programs within team-sport settings often produce mixed results with high responders and low responders often getting lost in averaged data reports (28). As a result, an increased desire for the individualization of training in team-sport settings has given rise to a variety of athlete-monitoring strategies enabling coaches to better manage fatigue and manipulate training prescription on an individual basis (21). Resting heart rate (RHR) and heart rate variability (HRV) are promising monitoring variables that may serve as indicators of physiological adaptation to training (7). Derived from R-R interval recordings, HRV provides a noninvasive means of assessing cardiac autonomic status (12). Changes in aerobic performance after training have been related to resting vagal-HRV indices at baseline (23,24,40). Additionally, the changes in vagal-HRV in response to training have been positively associated with improvements in various fitness markers (e.g., maximum oxygen consumption, intermittent running capacity, and maximum aerobic speed) in a variety of individual and team-sport athletes (6,9,15,16,20,22,24,29,37). However, little research exists that has evaluated early changes in resting HR–derived indices as they relate to subsequent performance adaptation in teamsport athletes. This research is needed to determine if early RHR and HRV responses help discriminate athletes who are coping well with training and those who are not especially because changes in autonomic activity have been found to occur after only 3 weeks of an exercise program (20). Early adaptation of HRV may potentially aid the decision-making process of training modification for individual athletes (7,34). Recent articles suggest that resting HRV data are most meaningful when averaged weekly in favor of solitary values acquired less frequently (i.e., once weekly or monthly) (27,30,34). This is because even the most reliable vagalHRV index, the logarithm of the root mean square of successive R-R interval differences (Ln rMSSD), has been shown to display a coefficient of variation (CV) of 10–12% or greater (2,6,11). This calls into question the validity of an isolated HRV value for reflecting training responses in athletes and may account for conflicting results (8). The weekly
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among a female collegiate soccer team during 5 weeks of offseason training. It was hypothesized that individual Ln rMSSD changes assessed after 3 weeks would be more sensitive than RHR (32) to eventual fitness changes measured at week 5.
TABLE 1. Typical microcycle structure for offseason training.*
Monday Tuesday Wednesday Thursday Friday Saturday Sunday
AM
PM
RT — — RT Pr — —
Pr, C — Pr C — — —
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METHODS Experimental Approach to the Problem
This is a single-group correlation study design, where relationships between weeks 1 and 3 changes in weekly mean and CV of Ln rMSSD and RHR values and changes in Yo-Yo Intermittent Recovery Test Level 1 (Yo-Yo IR T1) performance from weeks 0 to 5 were quantified with Pearson product-moment correlations. Changes in weekly mean and CV of Ln rMSSD and RHR values were the independent variables, whereas the change in Yo-Yo IR T-1 performance was the dependent variable.
*RT = resistance training session; Pr = soccer practice session; C = conditioning; — = rest.
CV reflects the day-to-day variation in HRV and is believed to represent the fatigue and recovery process from training (7) with higher CV values possibly indicating lower fitness and higher fatigue and lower CV values indicative of higher fitness and recovery (6,11). Therefore, it has been recommended that the weekly mean and CV of Ln rMSSD be evaluated together to draw meaningful associations with training status (7). The requirement for daily HRV measures places a highcompliance demand on athletes (6,33) limiting the practicality of this tool among sport teams. To facilitate more convenient HRV data collection, the development of valid smartphone applications (18) capable of providing Ln rMSSD data and investigation into shortened Ln rMSSD recording methodology (17) serves to enhance the practical applicability of HRV monitoring in field settings. However, the usefulness of smartphone-derived HRV using ultrashort recordings (i.e., ;1 minute) to assess individual training adaptation in team-sport athletes has not been investigated. The purpose of this study was to evaluate the relationship between early changes in weekly mean and CV of RHR and Ln rMSSD derived from a smartphone application and eventual changes in intermittent running performance
Subjects
Twelve members (age = 22 6 2.3 years; height = 165.05 6 5.5 cm; weight = 60.6 6 6.2 kg; body fat = 27.3 6 5.2%; V_ O2max 46.1 6 3.3) from a National Association for Intercollegiate Athletics female soccer team participated in this investigation. Before data collection, all subjects provided written informed consent and completed health history questionnaires devised by the associated institutions’ sports physician. All subjects received prior medical clearance from the team physician to participate in soccer training. To be included in this study, subjects had to be a collegiate female soccer player between the ages of 19 and 25 years with no injury or illness that would preclude their involvement in vigorous exercise training. This study was granted ethical approval by the institutional review board for human participants. Procedures
Descriptors. Descriptive data were collected during the week before the training program. Dual energy x-ray absorptiometry (GE Lunar Prodigy, version 10.50.086; GE Lunar Corporation, Madison, WI, USA) was used to assess body fat percentage. _ O2max) was assessed with Maximum oxygen consumption (V a graded maximal exercise test on a treadmill (Trackmaster;
TABLE 2. Conditioning program.* Week 0 1 2 3 4 5
Monday
Sets
Min
%MAS
Thursday
Sets
Min
%MAS
— MAS Test Intensive Intensive Intensive Intensive
— — 2 2 3 3
— — 5 6 5 6
— — 120 120 120 120
Yo-Yo IR T1 Extensive Extensive Extensive Extensive Yo-Yo IR T1
— 2 2 3 3 —
— 6 7 6 7 —
— 100/70 100/70 100/70 100/70 —
*Min = minutes per set; MAS = maximum aerobic speed; Yo-Yo IRT1 = Yo-Yo intermittent recovery test level 1.
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Smartphone-Derived HRV in Female Soccer Players Full Vision, Inc., Carrollton, TX, USA) following the Bruce protocol, whereas oxygen consumption was evaluated with a metabolic cart (ParvoMedics TrueOne 2400 metabolic cart; Sandy, UT, USA). Training Structure. This study took place during the first half of the spring semester 2014 offseason that ran from early February to the second week of March (i.e., spring break) and consisted of soccer practices and strength and conditioning training. Resistance training (RT) and conditioning (C) were performed twice weekly on Mondays and Thursdays separated by at least 4 hours. Soccer practices were held 3 times per week on Monday, Wednesday, and Friday. This microcycle structure is similar to inseason training schedules reported previously with a collegiate female soccer team (1). Typical durations for RT sessions and conditioning sessions were between 45 and 60 minutes, whereas soccer practice sessions lasted between 90 and 120 minutes. Table 1 displays a typical weekly microcycle from the training program. All strength and conditioning sessions were programmed and implemented by the researchers who are Certified Strength and Conditioning Specialists. Conditioning Protocol. All C sessions were prescribed based on the subject’s maximum aerobic speed (MAS) derived from a 1.6-km run time to completion test as described by Baker (4). Monday C sessions were intensive, involving linear sprints at 120% MAS with a work to rest ratio of 15:15 seconds. Thursday C sessions were extensive, involving continuous running around a rectangular course at 100% MAS across the length of the field and 70% MAS across the width of the field using a 15:15 seconds of work to active rest ratio. Conditioning program details are displayed in Table 2. Training Load. The session rating of perceived exertion (sRPE) has previously been shown to be suitable for training load quantification in collegiate female soccer players participating in routine RT, conditioning, and soccer training sessions (1). Within 15–30 minutes after each training session, sRPE values were acquired individually for all subjects. The sRPE values were summed intraindividually each week then averaged for each of the 5 weeks (Figure 1). Fitness Test. On Thursday afternoon between the hours of 4:00 and 6:00 PM before week 1 and at the same time and place on Thursday of week 5, the participants performed the Yo-Yo test. All athletes had experience with this test from routine fitness testing before participating in this study. The Yo-Yo test was administered by an audio speaker system in accordance with standardized guidelines (5). The test involves progressive 2 3 20 meter shuttle runs with a 10-second walking period (2 3 5 m) between shuttles. The total meter distance covered by each athlete during the test was recorded for analysis.
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Figure 1. Bar graph representing the weekly training load through session rating of perceived exertion reported in arbitrary units.
HRV Recording. Daily HRV was self-measured by the athletes each morning after waking and elimination throughout weeks 1 and 3 of offseason training. HRV data were collected using a chest-strap transmitter (T-31 Non-Coded; Polar, Kemple, Finland) and validated (18) smartphone application with the accompanying receiver that inserts into the headphone slot of a smartphone or tablet device (ithlete; HRV Fit Ltd., Southampton, United Kingdom). These materials were provided to each athlete for individual use by the researchers. Daily HRV recording involved athletes moistening and fitting the chest-strap transmitter around their upper trunk at the level of the xiphoid process and performing the HRV measure while resting comfortably and motionless in a supine position on their bed. When a stable heart rate is detected by the application, the user initiates the 55-second HRV recording. R-R intervals are processed by the software and the RHR and Ln rMSSD is automatically calculated and displayed for the user. The application modifies the Ln rMSSD value by multiplying it by twenty (Ln rMSSD 3 20) to provide the nonexpert user a more easily interpretable figure on a ;100 point scale. On completion of an HRV measure, the athletes were instructed to use the export data feature of the application to e-mail daily results to the researchers for analysis. Raw R-R intervals are not extractable from the application, and therefore manual inspection of the data is not possible. However, the application is equipped with an R-R interval processing algorithm that excludes excessively short (i.e., ,500 milliseconds) or long (i.e., .1800 milliseconds) intervals to control for irregular heartbeats. Athletes were familiarized with the HRV recording procedure at a team meeting and were given a 3-day trial period before the start of the study to perform waking HRV measurements. Athlete feedback from this trial period indicated that spontaneous breathing was more comfortable than the paced breathing cadence provided by the application. Because Ln rMSSD has been shown to be consistent with paced and nonpaced breathing (35), all HRV recordings were completed under spontaneous breathing conditions.
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TABLE 3. Individual and group RHR, Ln rMSSDx20 and Yo-Yo IR T-1 values.*† RHRmean Subject
Ln rMSSDmean
Ln rMSSDcv
Yo-Yo IR T-1
Week 3
Week 1
Week 3
Week 1
Week 3
Week 1
Week 3
Week 1
Week 5
61.0 55.7 59.5 63.6 60.0 69.9 64.3 61.2 47.6 62.7 58.1 60.3 60.3 6 5.3 0.97 20.02
56.2 55.0 63.3 61.2 60.1 64.6 64.1 63.1 49.2 74.4 62.5 55.4 60.4 6 5.5
10.5 10.6 4.7 8.8 3.4 6.0 9.6 4.4 4.7 9.3 5.2 8.4 7.1 6 2.6 0.77 20.13
6.0 9.5 13.0 4.2 3.4 10.7 8.1 5.5 5.9 12.1 8.2 3.3 7.5 6 3.3
90.3 91.1 83.1 88.2 80.0 85.6 90.4 86.9 107.0 90.0 95.0 85.1 89.4 6 2.0 0.30 20.59
95.3 92.6 83.8 92.9 86.9 89.6 88.7 86.5 105.7 85.4 92.1 86.4 90.5 6 1.7
10.4 4.0 5.1 9.4 17.0 3.5 3.4 6.1 2.3 4.8 3.5 3.4 6.1 6 1.2 0.64 20.57
8.1 5.8 12.4 4.3 9.1 5.6 5.2 11.4 2.1 8.7 3.7 3.6 6.7 6 0.9
960 840 960 600 1120 1240 1160 1200 1280 1080 840 1520 1066.6 6 243.8 ,0.01† 20.75
1200 1280 960 920 1600 1440 1360 960 1480 1120 1080 1600 1250.0 6 247.2
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*RHRmean = weekly mean resting heart rate; RHRcv = coefficient of variation of weekly resting heart rate; Ln rMSSDmean = weekly mean log-transformed root mean square of successive R-R intervals multiplied by 20; Ln rMSSDcv = coefficient of variation of weekly log-transformed root mean square of successive R-R intervals multiplied by 20; Yo-Yo IR T-1 = Yo-Yo intermittent recovery test level 1. †Significant difference.
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1 2 3 4 5 6 7 8 9 10 11 12 Mean 6 SD p Cohen’s d
RHRcv
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Smartphone-Derived HRV in Female Soccer Players
TABLE 4. Correlations between the RHR, Ln rMSSD, and Yo-Yo IR T-1 change variables.*† ΔYo-Yo
ΔRHRmean ΔRHRcv ΔLn rMSSDmean ΔLn rMSSDcv
R
p
20.31 20.32 0.50 20.74†
0.341 0.307 0.096 0.006
*ΔYo-Yo = change in Yo-Yo performance from weeks 0 to 5; ΔRHRmean = change in mean resting heart rate from week 1 to week 3; ΔRHRcv = change in the coefficient of variation of resting heart rate from weeks 1 to 3; ΔLn rMSSDmean = change in mean log-transformed root mean square of successive R-R intervals multiplied by 20 from weeks 1 to 3; ΔLn rMSSDcv = change in the coefficient of variation of log-transformed root mean square of successive R-R intervals multiplied by 20 from weeks 1 to 3. †Significant correlation.
RHR and HRV Analysis. The RHR and Ln rMSSD values were averaged for each athlete individually for weeks 1 and 3. In addition, the CV of RHR and Ln rMSSD for each athlete was calculated for weeks 1 and 3 as follows; CV = ([SD/mean] 3 100). Change variables were calculated for each individual for mean and CV values from weeks 1 and 3 (ΔRHRmean and ΔRHRcv, respectively, and ΔLn rMSSDmean and ΔLn rMSSDcv, respectively). Statistical Analyses
Group mean and SD were calculated from individual weekly mean and CV of RHR and Ln rMSSD values and compared with paired samples T-tests. Cohen’s d (14) effect sizes were calculated using thresholds described by Hopkins et al. (25) as follows: 0–0.2 was trivial, 0.2–0.6 was small, 0.6–1.2 was moderate, 1.2–2.0 was large, .2.0 was very large. Mean and SD were also compared using the same statistics describe above for before and after Yo-Yo (Yo-Yo-pre and Yo-Yopost, respectively) values. Pearson product-moment correlations were used to quantify the relationships between changes in before and after Yo-Yo performance (ΔYo-Yo) with ΔRHRmean and ΔRHRcv. Pearson product-moment
Figure 2. Scatter plots representing the relationships between changes in resting heart rate derived indices from weeks 1–3 and changes in Yo-Yo test performance from weeks 0–5. ΔRHRmean = change in mean resting heart rate from weeks 1 to 3; ΔRHRcv = change in the CV of resting heart rate from weeks 1 to 3; ΔLn rMSSDmean = change in mean log-transformed root mean square of successive R-R intervals multiplied by 20 from weeks 1 to 3; ΔLn rMSSDcv = change in the CV of log-transformed root mean square of successive R-R intervals multiplied by 20 from weeks 1 to 3; ΔYo-Yo = change in Yo-Yo performance from weeks 0 to 5.
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RESULTS There was a significant increase in Yo-Yo values after the 5-week training program (p , 0.01), and the effect size was considered moderate (Cohen’s d = 20.75). Compared with week 1, mean values were not significantly different (p . 0.05) for any of the RHR and HRV values at week 3 with effect sizes ranging from trivial to moderate (Cohen’s d = 20.02 to 20.59). Group and individual RHR, Ln rMSSD, and Yo-Yo values are reported in Table 3. A very large correlation was found between ΔLn rMSSDcv and ΔYo-Yo (r = 20.74; p = 0.006). In addition, a large correlation was found between ΔLn rMSSDmean and ΔYo-Yo although not statistically significant (r = 0.50; p = 0.096). Nonsignificant moderate correlations were found between ΔRHRmean and ΔYo-Yo (r = 20.31; p = 0.324) and ΔRHRcv and ΔYo-Yo (r = 20.32; p = 0.246). The results of the correlation analysis are displayed in table 4, and scatter plots can be viewed in Figure 2.
DISCUSSION This study evaluated relationships between changes in resting cardiac autonomic markers derived from a novel smartphone device within the first 3 weeks of a 5-week conditioning program and the eventual change in intermittent running performance among a collegiate female soccer team. The 2 main findings were as follows: (a) the very large relationship between ΔYo-Yo and ΔLn rMSSDcv, which indicated that the participants who showed a decrease in Ln rMSSDcv from weeks 1 to 3 experienced a greater improvement in Yo-Yo performance posttraining and (b) acquisition of daily HRV data with a smartphone application using ultrashort recordings provided meaningful training status information. This supports the efficacy of shortened and more convenient HRV recording procedures (,2 minutes) compared with traditionally used methodology that requires up to 10-minute HRV recordings for evaluating training adaptation in athletes (6,15,16,29). The CV reflects the daily fluctuation in Ln rMSSD across a training week and is believed to reflect the stress and recovery process in response to training (7). For example, Schmitt et al. (36) found that fatigued elite endurance athletes displayed lower HRV and higher interindividual variation in HRV parameters compared with those who were nonfatigued. Buchheit et al. (11) reported higher Ln rMSSDcv in less fit athletes throughout a 3-week training camp in young soccer players. It was found that in contrast to other HR measures (e.g., exercise heart rate and heart rate recovery), only Ln rMSSDcv was associated with a faster
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MAS (r = 0.52, p = 0.002) (11). Athletes characterized with lower day-to-day variability (i.e., Ln RMSSDcv) experienced less homeostatic perturbation likely because of a greater capacity for training stress (11). However, these HR measures were derived from postexercise recordings (11) unlike this study that evaluated resting HR measures. In a recent case study, Ln rMSSDcv was associated with a faster 8-km race time in a collegiate male runner (19). However, along with a decrease in the weekly mean, a reduced Ln rMSSDcv was associated with the development of nonfunctional overreaching in an elite female triathlete (32). Based on these previous studies (11,36), it is conceivable that the decrease in Ln rMSSDcv observed in this study indicated adequate recovery, appropriate training loads, and positive adaptation. In contrast, the athletes characterized with an increased Ln rMSSDcv were possibly experiencing higher fatigue and inadequate recovery. Boullosa et al. (6) found that after an 8-week training program, nocturnal Ln rMSSDcv correlated very strongly with posttraining Yo-Yo performance in 8 professional male soccer players (r = 20.898, p = ,0.01). These findings by Boullosa et al. (6) are complemented by the results of this study demonstrating that early changes in Ln rMSSDcv may indicate which athletes are adapting well to training. Recent work by de Freitas et al. (16) found a strong correlation between changes in resting Ln rMSSD (derived from solitary recordings) and changes in Yo-Yo performance in 11 elite futsal players (r = 0.64, p = ,0.05) after a 5-week training program. This study showed a similar although nonsignificant relationship when assessing Ln rMSSDmean (r = 0.50) at the midpoint of training as opposed to posttraining. Our results suggest that cardiac autonomic adaptations that occur within 3 weeks of training may provide early indications of fitness changes in female team-sport athletes. Changes in cardiac autonomic activity in response to training are possibly mediated by the appropriateness of interindividual training load or underlying genetic factors (22). Of importance to the strength and conditioning coach are the potential factors that influence daily HRV changes and thus Ln rMSSDmean and CV values. Acute decreases in vagal-HRV indices seem to be mode and intensity related (31,38). Low-intensity aerobic work can stimulate parasympathetic activity within 24 hours (13), whereas intense conditioning or RT can require between 48 and 72 hours to return to baseline (13,31,38). Recovery modalities such as cold-water immersion have been shown to stimulate parasympathetic reactivation after training and are associated with improved rating of perceived sleep quality in athletes (3). Additionally, hydration status and plasma volume changes (10) and non–training-related factors such as sleep quality and psychosocial stress may also contribute to greater acute fluctuation in HRV (7,39). Ensuring adequate nutrition and sleep practices in addition to limiting non– training-related stressors may therefore help facilitate recovery and training adaptation. VOLUME 30 | NUMBER 2 | FEBRUARY 2016 |
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Smartphone-Derived HRV in Female Soccer Players Throughout longitudinal training, moderate training loads tend to increase vagal-HRV, whereas intensified training loads tend to cause a decrease (7,34,38). In cases of excessive training load leading to nonfunctional overreaching, increases in vagal-HRV have been observed (27). Therefore, HRV changes are context dependent, and coaches must consider additional factors such as performance and psychometrics to effectively assess training status with HRV (7,34). Coaches may wish to investigate further the potential causes of Ln rMSSD changes throughout training to determine what means of action to pursue to accommodate individual athletes because this may be handled with training load manipulation (31) or modifying lifestyle factors as described above (39). Future research should assess how training load manipulation (i.e., reduction or supplementation with active recovery) affects HRV responses and subsequent performance adaptations. Worth mention is the effectiveness of the MAS-based conditioning program adapted from Baker (4) for inducing significant improvements in Yo-Yo performance in just 5 weeks. The Yo-Yo test is useful for evaluating one’s capacity to perform repeated bouts of intense running and to assess changes in performance (5). This test has also been shown to serve as an indicator of physical match performance in female soccer players (26). The MAS-based conditioning protocol seems to be an effective and easily implemented program that coaches may consider adopting to improve intermittent running capacity in female soccer players in relatively short time periods (e.g., 5 weeks). The limitations of this study pertain to the small sample size and acquisition of HRV data from only 2 of the 5 weeks. Future research with larger sample sizes and more frequent HRV data collection may provide more insight into the evolution of HRV and its association with changes in fitness. Additionally, future research should assess the effectiveness of manipulating training load or recovery modalities for athletes demonstrating unfavorable HRV changes to support training adaptation. This study showed that a reduced Ln rMSSDcv after 3 weeks of training was largely associated with eventual changes in intermittent running performance. It seems that a reduction in daily fluctuations in Ln rMSSD is indicative of positive adaptation to training in female collegiate team-sport athletes. Acquisition of daily HRV data with a smartphone application using ultrashort recordings seems suitable for athlete monitoring.
PRACTICAL APPLICATIONS The evolution of an athlete’s HRV trend throughout training may provide coaches and sport scientists an early indication of individual fitness adaptation in female team-sport athletes. A reduction in the Ln rMSSDcv and increase in Ln rMSSDmean seem to be a favorable response, associated with greater eventual improvements in intermittent running performance. Modification of training and lifestyle factors to maintain a favorable cardiac autonomic profile throughout training may support physiological adaptations to training.
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Athlete’s demonstrating an increase in day-to-day variation in Ln rMSSD and a decreased Ln rMSSDmean may not be coping well with training and may require further assessment to determine the appropriate means of action for intervention. Appropriate interventions may include reduced training load, recovery modalities such as postexercise cold-water immersion, ensuring adequate hydration and nutrition practices, and reducing non–training-related stressors. Acquiring daily HRV data from athletes with a smartphone application using ultrashort measures seem to be useful for monitoring training effects in female team sport athletes. This likely provides a more convenient and affordable alternative to traditional HRV field tools.
ACKNOWLDEGMENTS The authors would like to thank the athletes for volunteering their time to participate in this study. The authors have received HRV tools from HRV Fit Ltd., for future research projects, established after the completion of this study.
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