Exploring the utility of unobtrusive methods of acquiring heart rate variability at rest and following exercise

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Use heart rate variability (HRV) for monitoring cardio-autonomic perturbations in response to physical stimuli has increased in popularity and demand. However, the utility of measures of HRV to reflect acute changes in muscle recovery from exercise is controversial. As criterion short-term HRV recordings are performed with a gold standard electrocardiogram (ECG), less obtrusive methods have been developed for improved practicality of HRV measures. Three studies were performed to investigate the accuracy of less obtrusive methods for acquiring HRV and the utility of HRV for tracking changes in muscular performance recovery. The first study involved a systematic review and meta-analysis on the accuracy of portable devices for acquiring HRV. Twenty-three studies yielded 301 effects and revealed that HRV measures acquired from portable devices differed from those obtained from ECG (ES=0.23, 95% CI: 0.05, 0.42), although this effect was small and highly heterogeneous (I2=78.6%, 95% CI: 76.2%, 80.7%). Moderator analysis revealed that HRV metric (p<0.001), position (p=0.033), and biological sex (β=0.45, 95% CI: 0.30, 0.61; p<0.001), but not portable device, modulated the degree of absolute error. Within metric, absolute error was significantly higher when expressed as SDNN (ES=0.44) compared to any other metric but was no longer significantly different after a sensitivity analysis removed outliers. Likewise, the error associated with the tilt/recovery position was significantly higher than any other position and remained significantly different without outliers in the model. In the second study, the time course in recovery between criterion short-term HRV measures and acute muscular performance 72 hours following an exhaustive bout of resistance training was investigated. All HRV metrics had a significant interaction with muscular performance (performance) over time (p < .01) indicating change scores in performance and HRV following the physiological stressor were not parallel and did not track. Mean change scores in all HRV metrics significantly differed from performance across time (p < .05), except the standard deviation of all normal-to-normal R-R intervals (SDNN), low frequency power (LF), and the standard deviation of long-term HRV from the Poincaré plot (SD2) at the 0.5-hr mark, and high frequency power (HF) at the 24-hr time point. Furthermore, repeated measures correlation analysis indicated a lack of intra-individual association between the change in performance and HRV over time (all < .45). In the third study, the agreement between ultra-short and criterion short-term HRV measures surrounding a bout of exhaustive resistance training was investigated. Results displayed the highest levels of agreement from the log-transformed (ln) root mean square of successive R-R differences (lnRMSSD) [LOA = -0.91– 0.69, ICC = .91, p = .082, ES = 0.15] and the standard deviation of the points through the width of the plot (lnSD1) [LOA = -0.90 – 0.72, ICC = .91, p = .156, ES = 0.13] compared to all other metrics.

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