Abstract
The purposes of this study were: (1) to determine if smartphone-derived heart rate variability (HRV) could detect changes in training load during an overload microcycle and taper, and (2) to determine the reliability of HRV measured in the morning and measured immediately prior to the testing session. Twelve powerlifters (male = 10, female = 2) completed a 3-week resistance training program consisting of an introduction microcycle, overload microcycle, and taper. Using a validated smartphone application, daily measures of resting, ultra-short natural logarithm of root mean square of successive differences were recorded in the morning (LnRMSSDM) and immediately before the test session (LnRMSSDT) following baseline, post-overload, and post-taper testing. LnRMSSDM decreased from baseline (82.9 ± 13.0) to post-overload (75.0 ± 9.9, p = 0.019), while post-taper LnRMSSDM (81.9 ± 7.1) was not different from post-overload (p = 0.056) or baseline (p = 0.998). No differences in LnRMSSDT (p < 0.05) were observed between baseline (78.3 ± 9.0), post-overload (74.4 ± 10.2), and post-taper (78.3 ± 8.0). LnRMSSDM and LnRMSSDT were strongly correlated at baseline (ICC = 0.71, p < 0.001) and post-overload (ICC = 0.65, p = 0.010), whereas there was no relationship at post-taper (ICC = 0.44, p = 0.054). Bland–Altman analyses suggest extremely wide limits of agreement (Bias ± 1.96 SD) between LnRMSSDM and LnRMSSDT at baseline (4.7 ± 15.2), post-overload (0.5 ± 16.9), and post-taper (3.7 ± 15.3). Smartphone-derived HRV, recorded upon waking, was sensitive to resistance training loads across an overload and taper microcycles in competitive strength athletes, whereas the HRV was taken immediately prior to the testing session was not.
Highlights
Athlete monitoring is a strategy that many strength and conditioning coaches use to assess fatigue and adaptation with regard to training [1,2]
This study examined the effect of an intensified week of resistance training and taper on heart rate variability (HRV) and investigated the reliability between two HRV measures, immediately upon waking and immediately prior to a testing session
Bench press performance mirrored the pattern in LnRMSSDM
Summary
Athlete monitoring is a strategy that many strength and conditioning coaches use to assess fatigue and adaptation with regard to training [1,2]. While intense training is a major physiological stressor experienced by athletes, other factors such as sleep, nutrition, and emotional state may add to the overall stress imposed. The accumulation of these stressors will require sufficient recovery, or else noticeable decreases in performance may be experienced [2]. An effective monitoring tool for athletes in training would need to be sensitive enough to detect important perturbations in homeostasis and provide adequate information needed to alter training loads to optimize recovery. Monitoring the external load has the potential to provide the feedback needed for making informed decisions; it lacks the ability to assess the physiological and psychological responses to training, which is referred to as internal load
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