Purpose To assess the validity of RR intervals and short-term heart rate variability (HRV) data obtained from the Polar V800 heart rate monitor, in comparison to an electrocardiograph (ECG). Method Twenty participants completed an active orthostatic test using the V800 and ECG. An improved method for the identification and correction of RR intervals was employed prior to HRV analysis. Agreement of the data was assessed using intra-class correlation coefficients (ICC), Bland–Altman limits of agreement (LoA), and effect size (ES). Results A small number of errors were detected between ECG and Polar RR signal, with a combined error rate of 0.086 %. The RR intervals from ECG to V800 were significantly different, but with small ES for both supine corrected and standing corrected data (ES 0.999 for both supine and standing corrected intervals. When analysed with the same HRV software no significant differences were observed in any HRV parameters, for either supine or standing; the data displayed small bias and tight LoA, strong ICC (>0.99) and small ES (≤0.029). Conclusions The V800 improves over previous Polar models, with narrower LoA, stronger ICC and smaller ES for both the RR intervals and HRV parameters. The findings support the validity of the Polar V800 and its ability to produce RR interval recordings consistent with an ECG. In addition, HRV parameters derived from these recordings are also highly comparable.

The research base for rock climbing has expanded substantially in the past three decades as worldwide interest in the sport has grown. An important trigger for the increasing research attention has been the transition of the sport to a competitive as well as recreational activity and the potential inclusion of sport climbing in the Olympic schedule. The International Rock Climbing Research Association (IRCRA) was formed in 2011 to bring together climbers, coaches and researchers to share knowledge and promote collaboration. This position statement was developed during and after the 2nd IRCRA Congress which was held in Pontresina, in September 2014. The aim of the position statement is to bring greater uniformity to the descriptive and statistical methods used in reporting rock climbing research findings. To date there is a wide variation in the information provided by researchers regarding the climbers’ characteristics and also in the approaches employed to convert from climbing grading scales to a numeric scale suitable for statistical analysis. Our paper presents details of recommended standards of reporting that should be used for reporting climber characteristics and provides a universal scale for the conversion of climbing grades to a number system for statistical analysis.

There is a need for standard practice in the collection and processing of RR interval data recorded using heart rate monitors (HRM) in research. This paper assessed the validity of RR intervals and heart rate variability (HRV) data obtained using a HRM during incremental exercise, and artefact correction methods. Eighteen participants completed an active orthostatic test and incremental running V[Combining Dot Above]O2MAX test, while simultaneous recordings using a Polar V800 HRM and an electrocardiogram were made. Artefacts were corrected by deletion; degree zero, linear, cubic and spline interpolation; and Kubios HRV software. Agreement was assessed using percentage bias, effect size (ES), intra-class correlation coefficients (ICC) and Bland–Altman limits of agreement (LoA). Artefacts increased relative to exercise intensity, to a peak of 4.46% during 80-100% V[Combining Dot Above]O2MAX. Correction of RR intervals was necessary with unacceptably increased bias, LoA and ES and reduced ICC in all but resting recordings. All correction methods resulted in data with reduced percentage bias and ES for resting and 60% V[Combining Dot Above]O2MAX, even with correction, large amounts of variation were present in HRV measures of root mean square of the successive differences (RMSSD), low to high frequency ratio, Poincaré dispersion perpendicular to the axis (SD1) and sample entropy. Linear interpolation produced RR intervals with the lowest bias and ES. However, caution should be given to HRV parameters at high exercise intensities, as large amounts of variation were still present. Recommendations for minimising artefacts are discussed, along with guidelines for their identification, correction and reporting.

Forest Bathing, where individuals use mindfulness to engage with nature, has been reported to increase heart rate variability and benefit wellbeing. To date, most Forest Bathing studies have been conducted in Asia. Accordingly, this paper reports the first pragmatic controlled trial of Forest Bathing in the United Kingdom, comparing Forest Bathing with a control comprising an established wellbeing intervention also known to increase heart rate variability called Compassionate Mind Training. Sixty-one university staff and students (50 females, 11 males) were allocated to (i) Forest Bathing, (ii) Compassionate Mind Training or (iii) Forest Bathing combined with Compassionate Mind Training. Wellbeing and heart rate variability were measured at baseline, post-intervention and three-months follow-up. There were improvements in positive emotions, mood disturbance, rumination, nature connection and compassion and 57% of participants showed an increase in heart rate variability. There were no significant differences between conditions, showing that Forest Bathing had equivalence with an established wellbeing intervention. The findings will help healthcare providers and policy makers to understand the effects of Forest Bathing and implement it as a feasible social prescription to improve wellbeing. Future research needs to involve clinical populations and to assess the effects of Forest Bathing in a fully powered randomised controlled trial.

Recent study points to the value of a non-linear heart rate variability (HRV) biomarker using detrended fluctuation analysis (DFA a1) for aerobic threshold determination (HRVT). Significance of recording artefact, correction methods and device bias on DFA a1 during exercise and HRVT is unclear. Gas exchange and HRV data were obtained from 17 participants during an incremental treadmill run using both ECG and Polar H7 as recording devices. First, artefacts were randomly placed in the ECG time series to equal 1, 3 and 6% missed beats with correction by Kubios software’s automatic and medium threshold method. Based on linear regression, Bland Altman analysis and Wilcoxon paired testing, there was bias present with increasing artefact quantity. Regardless of artefact correction method, 1 to 3% missed beat artefact introduced small but discernible bias in raw DFA a1 measurements. At 6% artefact using medium correction, proportional bias was found (maximum 19%). Despite this bias, the mean HRVT determination was within 1 bpm across all artefact levels and correction modalities. Second, the HRVT ascertained from synchronous ECG vs. Polar H7 recordings did show an average bias of minus 4 bpm. Polar H7 results suggest that device related bias is possible but in the reverse direction as artefact related bias.

Past attempts to define an anaerobic threshold (AnT) have relied upon gas exchange kinetics, lactate testing and field-based evaluations. DFA a1, an index of heart rate (HR) variability (HRV) fractal correlation properties, has been shown to decrease with exercise intensity. The intent of this study is to investigate whether the AnT derived from gas exchange is associated with the transition from a correlated to uncorrelated random HRV pattern signified by a DFA a1 value of 0.5. HRV and gas exchange data were obtained from 15 participants during an incremental treadmill run. Comparison of the HR reached at the second ventilatory threshold (VT2) was made to the HR reached at a DFA a1 value of 0.5 (HRVT2). Based on Bland–Altman analysis and linear regression, there was strong agreement between VT2 and HRVT2 measured by HR (r = 0.78, p < 0.001). Mean VT2 was reached at a HR of 174 (±12) bpm compared to mean HRVT2 at a HR of 171 (±16) bpm. In summary, the HR associated with a DFA a1 value of 0.5 on an incremental treadmill ramp was closely related to that of the HR at the VT2 derived from gas exchange analysis. A distinct numerical value of DFA a1 representing an uncorrelated, random interbeat pattern appears to be associated with the VT2 and shows potential as a noninvasive marker for training intensity distribution and performance status.