Can HRV Biofeedback Improve Short-Term Effort Recovery? Implications for Intermittent Load Sports.

As intensity and physical demands continue to rise in sport competition, faster and better recovery becomes essential. The aim of this study was to assess the effects of HRV biofeedback (HRVB) while recovering from a submaximal aerobic exercise. Ten physically-active graduate students participated in the study, which was conducted in four sessions: exercise with free-breathing recovery, first resonance frequency (RF) detection, second RF detection, and exercise applying HRVB during recovery. Measurements included time spent running and recovering, HRV parameters, and recovery/exertion perceptions. The results indicate that using HRVB during recovery improves cardiac variability (RRmean, SDNN, RMSSD and LF; p < 0.01). HRVB also lowers recovery time (p < 0.05) and seems to be improving the perception of recovery (p = 0.087). Moreover, time spent exercising (p < 0.01) and perceived physical exertion (p < 0.05) were higher when applying HRVB. The improvement in the psychophysiological adaptation after intensive aerobic exercise provided by the HRVB is a valuable benefit, not only for competition-driven athletes, but also for the general population.

Forest bathing and hiking benefits for mental health during the COVID-19 pandemic in Mediterranean regions.

Forest bathing (FB) has evidenced positive effects on individuals' mental health and well-being, but its benefits have mainly been studied in Asian biomes. The present study aimed to evaluate whether its benefits are also generalisable to other forests and biomes of the world, such as the Mediterranean. Eighty-six healthy adults of the general population were assessed before and after a FB near Barcelona (Spain) during the COVID-19 pandemic. A control-hiking group of participants was also analysed to contrast the FB effects on anxiety, affect, mood states and mindfulness. Results show that the guided practice of FB in Mediterranean-Catalan forests increases mindfulness states and positive affect and reduces anxiety and negative affect, with effect sizes being large to very large. Hiking also induced significant changes in all variables tested, but FB showed higher effect sizes. An exploratory analysis also revealed a different profile of the FB participants compared to the hiking practitioners, being highly educated women living in urban areas and with lower basal levels of psychological well-being. Accordingly, it is concluded that both Mediterranean FB and hiking (to a lesser degree) might be cost-effective strategies to promote and restore psychological well-being after the COVID-19 pandemic and to promote sustainable tourism in Mediterranean biomes of the European forested and protected areas.

Agreement between two photoplethysmography-based wearable devices for monitoring heart rate during different physical activity situations: a new analysis methodology.

Wearables are being increasingly used to monitor heart rate (HR). However, their usefulness for analyzing continuous HR in research or at clinical level is questionable. The aim of this study is to analyze the level of agreement between different wearables in the measurement of HR based on photoplethysmography, according to different body positions and physical activity levels, and compared to a gold-standard ECG. The proposed method measures agreement among several time scales since different wearables obtain HR at different sampling rates. Eighteen university students (10 men, 8 women; 22 ± 2.45 years old) participated in a laboratory study. Participants simultaneously wore an Apple Watch and a Polar Vantage watch. ECG was measured using a BIOPAC system. HR was recorded continuously and simultaneously by the three devices, for consecutive 5-min periods in 4 different situations: lying supine, sitting, standing and walking at 4 km/h on a treadmill. HR estimations were obtained with the maximum precision offered by the software of each device and compared by averaging in several time scales, since the wearables obtained HR at different sampling rates, although results are more detailed for 5 s and 30 s epochs. Bland-Altman (B-A) plots show that there is no noticeable difference between data from the ECG and any of the smartwatches while participants were lying down. In this position, the bias is low when averaging in both 5 s and 30 s. Differently, B-A plots show that there are differences when the situation involves some level of physical activity, especially for shorter epochs. That is, the discrepancy between devices and the ECG was greater when walking on the treadmill and during short time scales. The device showing the biggest discrepancy was the Polar Watch, and the one with the best results was the Apple Watch. We conclude that photoplethysmography-based wearable devices are suitable for monitoring HR averages at regular intervals, especially at rest, but their feasibility is debatable for a continuous analysis of HR for research or clinical purposes, especially when involving some level of physical activity. An important contribution of this work is a new methodology to synchronize and measure the agreement against a gold standard of two or more devices measuring HR at different and not necessarily even paces.

Resonance frequency is not always stable over time and could be related to the inter-beat interval.

Heart Rate Variability Biofeedback (HRVB) is based on breathing at an optimal rate (or resonance frequency, RF) corresponding to the respiratory sinus arrhythmia (RSA). Our aim is to check whether the RF is a stable factor and analyse the HRV parameters individually per each breathing rate, comparing it with free slow breathing. A sample of 21 participants were trained in a test-retest HRVB protocol. The results indicated that RF changed between Test and Retest sessions in 66.7% of participants. This instability could be related to the average of interbeat interval (IBI). HRV time domain parameters (SDNN and RMSSD) were significantly higher for RF than for other breathing rates, including 6 breath/min and free slow breathing. Free slow breathing showed a lower heart rate averages than RF and other slow breathing rates. Overall, our study suggests the relevance of assessing RF individually and before each HRVB session, because the maximum cardiovascular benefits in terms of increasing HRV were found only at RF. Thus, breathing at the individualized and momentary frequency of resonance increases cardiac variability.

Perceived tiredness and heart rate variability in relation to overload during a field hockey World Cup.

The aim of the study was to examine the utility of perceived tiredness to predict cardiac autonomic response to overload among field hockey players during the 2006 World Cup. The French Society for Sports Medicine (SFMS) questionnaire was administered at the start of the Cup to evaluate perception of tiredness. Autonomic function was assessed nine days later at the semifinal match by time and frequency domain analysis of heart rate variability. An anxiety questionnaire was administered so that the effect of precompetitive anxiety on heart rate variability could be controlled. Results showed a negative correlation between perceived tiredness scores and time domain indexes, and a positive correlation of perceived tiredness scores and the high frequency component ratio (LF/HF ratio) of heart rate variability. Anxiety did not influence the precompetitive cardiac response despite somatic anxiety's correlation with sympathetic response (LF/HF ratio) and tiredness scores. Perceived tiredness predicted the autonomic cardiac response to competitive overload. Thus, the perceived tiredness assessment would be a good early marker of fatigue and overload states during competition.

mHealth technology for ecological momentary assessment in physical activity research: a systematic review.

OBJECTIVE: To systematically review the publications on ecological momentary assessment (EMA) relating to physical activity (PA) behavior in order to classify the methodologies, and to identify the main mHealth technology-based tools and procedures that have been applied during the first 10 years since the emergence of smartphones. As a result of this review, we want to ask if there is enough evidence to propose the use of the term "mEMA" (mobile-based EMA). DESIGN: A systematic review according to PRISMA Statement (PROSPERO registration: CRD42018088136). METHOD: Four databases (PsycINFO, CINALH, Medline and Web of Science Core Collection) were searched electronically from 2008 to February 2018. RESULTS: A total of 76 studies from 297 potential articles on the use of EMA and PA were included in this review. It was found that 71% of studies specifically used "EMA" for assessing PA behaviors but the rest used other terminology that also adjusted to the inclusion criteria. Just over half (51.3%) of studies (39) used mHealth technology, mainly smartphones, for collecting EMA data. The majority (79.5%) of these studies (31 out of 39) were published during the last 4 years. On the other hand, 58.8% of studies that only used paper-and-pencil were published during the first 3 years of the 10-year period analyzed. An accelerometer was the main built-in sensor used for collecting PA behavior by means of mHealth (69%). Most of the studies were carried out on young-adult samples, with only three studies in older adults. Women were included in 60% of studies, and healthy people in 82%. The studies lasted between 1 and 7 days in 57.9%, and between three and seven assessments per day were carried out in 37%. The most popular topics evaluated together with PA were psychological state and social and environmental context. CONCLUSIONS: We have classified the EMA methodologies used for assessing PA behaviors. A total of 71% of studies used the term "EMA" and 51.3% used mHealth technology. Accelerometers have been the main built-in sensor used for collecting PA. The change of trend in the use of tools for EMA in PA coincides with the technological advances of the last decade due to the emergence of smartphones and mHealth technology. There is enough evidence to use the term mEMA when mHealth technology is being used for monitoring real-time lifestyle behaviors in natural situations. We define mEMA as the use of mobile computing and communication technologies for the EMA of health and lifestyle behaviors. It is clear that the use of mHealth is increasing, but there is still a lot to be gained from taking advantage of all the capabilities of this technology in order to apply EMA to PA behavior. Thus, mEMA methodology can help in the monitoring of healthy lifestyles under both subjective and objective perspectives. The tendency for future research should be the automatic recognition of the PA of the user without interrupting their behavior. The ecological information could be completed with voice messages, image captures or brief text selections on the touch screen made in real time, all managed through smartphone apps. This methodology could be extended when EMA combined with mHealth are used to evaluate other lifestyle behaviors.

An application of fractional differintegration to heart rate variability time series.

Fractional differintegration is used as a new tool to characterize heart rate variability time series. This paper proposes and focuses in two indexes (αc and fnQ) derived from the fractional differintegration operator. Both indexes are applied to fractional Gaussian noise (fGn) and actual RR time series in order to test their behavior. In the analysis of monofractal time series, αc is linearly related with the Hurst exponent and the estimation of the exponent by the proposed index has lower variance than by using Detrended Fluctuation Analysis (DFA) or the periodogram. The other index fnQ quantifies how the time series adjust to a monofractal time series. Age, postural changes and paced breathing cause significant changes on fnQ while αc only shows significant changes due to posture. In the analyzed actual HRV time series, αc shows good correlation with the short term scaling exponent obtained by DFA, LF/HF and RMSSD while no correlations have been found for fnQ.

HRV based health&sport markers using video from the face.

Heart Rate Variability (HRV) is an indicator of health status in the general population and of adaptation to stress in athletes. In this paper we compare the performance of two systems to measure HRV: (1) A commercial system based on recording the physiological cardiac signal with (2) A computer vision system that uses a standard video images of the face to estimate RR from changes in skin color of the face. We show that the computer vision system performs surprisingly well. It estimates individual RR intervals in a non-invasive manner and with error levels comparable to those achieved by the physiological based system.