Alexa, let's train now! - A systematic review and classification approach to digital and home-based physical training interventions aiming to support healthy cognitive aging.

BACKGROUND: There is mounting evidence that regular physical activity is an important prerequisite for healthy cognitive aging. Consequently, the finding that almost one-third of the adult population does not reach the recommended level of regular physical activity calls for further public health actions. In this context, digital and home-based physical training interventions might be a promising alternative to center-based intervention programs. Thus, this systematic review aimed to summarize the current state of the literature on the effects of digital and home-based physical training interventions on adult cognitive performance. METHODS: In this pre-registered systematic review (PROSPERO; ID: CRD42022320031), 5 electronic databases (PubMed, Web of Science, PsycInfo, SPORTDiscus, and Cochrane Library) were searched by 2 independent researchers (FH and PT) to identify eligible studies investigating the effects of digital and home-based physical training interventions on cognitive performance in adults. The systematic literature search yielded 8258 records (extra 17 records from other sources), of which 27 controlled trials were considered relevant. Two reviewers (FH and PT) independently extracted data and assessed the risk of bias using a modified version of the Tool for the assEssment of Study qualiTy and reporting in EXercise (TESTEX scale). RESULTS: Of the 27 reviewed studies, 15 reported positive effects on cognitive and motor-cognitive outcomes (i.e., performance improvements in measures of executive functions, working memory, and choice stepping reaction test), and a considerable heterogeneity concerning study-related, population-related, and intervention-related characteristics was noticed. A more detailed analysis suggests that, in particular, interventions using online classes and technology-based exercise devices (i.e., step-based exergames) can improve cognitive performance in healthy older adults. Approximately one-half of the reviewed studies were rated as having a high risk of bias with respect to completion adherence (≤85%) and monitoring of the level of regular physical activity in the control group. CONCLUSION: The current state of evidence concerning the effectiveness of digital and home-based physical training interventions is mixed overall, though there is limited evidence that specific types of digital and home-based physical training interventions (e.g., online classes and step-based exergames) can be an effective strategy for improving cognitive performance in older adults. However, due to the limited number of available studies, future high-quality studies are needed to buttress this assumption empirically and to allow for more solid and nuanced conclusions.

Appraising the Methodological Quality of Sports Injury Video Analysis Studies: The QA-SIVAS Scale.

BACKGROUND: Video analysis (VA) is commonly used in the assessment of sports injuries and has received considerable research interest. Until now, no tool has been available for the assessment of study quality. Therefore, the objective of this study was to develop and evaluate a valid instrument that reliably assesses the methodological quality of VA studies. METHODS: The Quality Appraisal for Sports Injury Video Analysis Studies (QA-SIVAS) scale was developed using a modified Delphi approach including expert consensus and pilot testing. Reliability was examined through intraclass correlation coefficient (ICC3,1) and free-marginal kappa statistics by three independent raters. Construct validity was investigated by comparing QA-SIVAS with expert ratings by using Kendall's tau analysis. Rating time was studied by applying the scale to 21 studies and computing the mean time for rating per study article. RESULTS: The QA-SIVAS scale consists of an 18-item checklist addressing the study design, data source, conduct, report, and discussion of VA studies in sports injury research. Inter- and intra-rater reliability were excellent with ICCs > 0.97. Expert ratings revealed a high construct validity (0.71; p < 0.001). Mean rating time was 10 ± 2 min per article. CONCLUSION: QA-SIVAS is a reliable and valid instrument that can be easily applied to sports injury research. Future studies in the field of VA should adhere to standardized methodological criteria and strict quality guidelines.

Correlation properties of heart rate variability to assess the first ventilatory threshold and fatigue in runners.

PURPOSE: The short-term scaling exponent alpha1 of detrended fluctuation analysis (DFA-a1) of heart rate variability (HRV) has shown potential to delineate the first ventilatory threshold (VT1). The aims of this study were to investigate the accuracy of this method for VT1 determination in runners using a consumer grade chest belt and to explore the effects of acute fatigue. METHODS: We compared oxygen uptake (V̇O2) and heart rate (HR) at gas exchange VT1 to V̇O2 and HR at a DFA-a1 value of 0.75. Gas exchange and HRV data were obtained from 14 individuals during a treadmill run involving two incremental ramps. Agreement was assessed using Bland-Altman analysis and linear regression. RESULTS: Bland-Altman analysis between gas exchange and HRV V̇O2 and HR at VT1 during the first ramp showed a mean (95% limits of agreement) bias of -0.5 (-6.8 to 5.8) ml∙kg-1∙min-1, and -0.9 (-12.2 to 10.5) beats∙min-1, with R2 of 0.83 and 0.56, respectively. During the second ramp, the differences were -7.3 (-18.1 to 3.5) ml∙kg-1∙min-1 and -12.3 (-30.4 to 5.9) beats∙min-1, with R2 of 0.62 and 0.43, respectively. CONCLUSION: A chest-belt derived DFA-a1 of 0.75 is closely related to gas exchange VT1, with the variability in accuracy at an individual level being similar to gas exchange methods. This suggests this to be a useful method for exercise intensity demarcation. The altered relationship during the second ramp indicates that DFA-a1 is only able to accurately demarcate exercise intensity thresholds in a non-fatigued state, but also opens opportunities for fatigue-based training prescription.

The first ventilatory threshold determined with a nonlinear method (DFA-a1) to analyse heart rate variability derived from a chest-belt shows close agreement to the gas exchange first ventilatory threshold, with the variability in accuracy at an individual level being similar to gas exchange methods. This suggests this to be a useful method for exercise intensity demarcation.The altered relationship during fatigue indicates that DFA-a1 is only able to accurately demarcate exercise intensity thresholds in a non-fatigued state, but this also opens opportunities for fatigue-based training prescription.

Traumatic muscle injury.

Traumatic muscle injury represents a collection of skeletal muscle pathologies caused by trauma to the muscle tissue and is defined as damage to the muscle tissue that can result in a functional deficit. Traumatic muscle injury can affect people across the lifespan and can result from high stresses and strains to skeletal muscle tissue, often due to muscle activation while the muscle is lengthening, resulting in indirect and non-contact muscle injuries (strains or ruptures), or from external impact, resulting in direct muscle injuries (contusion or laceration). At a microscopic level, muscle fibres can repair focal damage but must be completely regenerated after full myofibre necrosis. The diagnosis of muscle injury is based on patient history and physical examination. Imaging may be indicated to eliminate differential diagnoses. The management of muscle injury has changed within the past 5 years from initial rest, immobilization and (over)protection to early activation and progressive loading using an active approach. One challenge of muscle injury management is that numerous medical treatment options, such as medications and injections, are often used or proposed to try to accelerate muscle recovery despite very limited efficacy evidence. Another challenge is the prevention of muscle injury owing to the multifactorial and complex nature of this injury.

The Between-Day Reliability of Correlation Properties of Heart Rate Variability During Running.

The short-term scaling exponent of detrended fluctuation analysis (DFA-a1) of heart rate variability may be a helpful tool to assess autonomic balance as a prelude to daily, individualized training. For this concept to be useful, between-session reliability should be acceptable. The aim of this study was to explore the reliability of DFA-a1 during a low-intensity exercise session in both a non-fatigued and a fatigued condition in healthy males and females. Ten participants completed two sessions with each containing an exhaustive treadmill ramp protocol. Before and after the fatiguing ramp, a standardized submaximal low-intensity exercise bout was performed during which DFA-a1, heart rate, and oxygen consumption (VO2) were measured. We compared between-session reliability of all metrics prior to the ramps (i.e., non-fatigued status) and after the first ramp (i.e., fatigued status). Intraclass correlation coefficients (ICC) with 95% confidence intervals (CI), the standard error of measurement, and the smallest worthwhile change (SWC) were determined. The ICC and SWC pre fatiguing ramp were 0.85 (95% CI 0.39-0.96) and 5.5% for DFA-a1, 0.85 (0.38-0.96) and 2.2% for heart rate, and 0.84 (0.31-0.96) and 3.1% for VO2. Post fatiguing ramp, the ICC and SWC were 0.55 (0.00-0.89) and 7.9% for DFA-a1, 0.91 (0.62-0.98) and 1.6% for heart rate, and 0.80 (0.17-0.95) and 3.0% for VO2. DFA-a1 shows generally acceptable to good between-session reliability with a SWC of 0.06 and 0.07 (5.5-7.9%) during non-fatigued and fatigued conditions. This suggests that this metric may be useful to inform on training readiness.

Heart Rate Variability-Derived Thresholds for Exercise Intensity Prescription in Endurance Sports: A Systematic Review of Interrelations and Agreement with Different Ventilatory and Blood Lactate Thresholds.

BACKGROUND: Exercise intensities are prescribed using specific intensity zones (moderate, heavy, and severe) determined by a 'lower' and a 'higher' threshold. Typically, ventilatory (VT) or blood lactate thresholds (LT), and critical power/speed concepts (CP/CS) are used. Various heart rate variability-derived thresholds (HRVTs) using different HRV indices may constitute applicable alternatives, but a systematic review of the proximity of HRVTs to established threshold concepts is lacking. OBJECTIVE: This systematic review aims to provide an overview of studies that determined HRVTs during endurance exercise in healthy adults in comparison with a reference VT and/or LT concept. METHODS: A systematic literature search for studies determining HRVTs in healthy individuals during endurance exercise and comparing them with VTs or LTs was conducted in Scopus, PubMed and Web of Science (until January 2022). Studies claiming to describe similar physiological boundaries to delineate moderate from heavy (HRVTlow vs. VTlow and/or LTlow), and heavy from severe intensity zone (HRVThigh vs. VThigh and/or LThigh) were grouped and their results synthesized. RESULTS: Twenty-seven included studies (461 participants) showed a mean difference in relative HR between HRVTlow and VTlow of - 0.6%bpm in weighted means and 0.02%bpm between HRVTlow and LTlow. Bias between HR at HRVTlow and VTlow was 1 bpm (limits of agreement (LoA): - 10.9 to 12.8 bpm) and 2.7 bpm (LoA: - 20.4 to 25.8 bpm) between HRVTlow and LTlow. Mean difference in HR between HRVThigh and VThigh was 0.3%bpm in weighted means and 2.9%bpm between HRVThigh and LThigh while bias between HR at HRVThigh and VThigh was - 4 bpm (LoA: - 17.9 to 9.9 bpm) and 2.5 bpm (LoA: - 12.1 to 17.1 bpm) between HRVThigh and LThigh. CONCLUSION: HRVTlow seems to be a promising approach for the determination of a 'lower' threshold comparable to VTlow and potentially for HRVThigh compared to VThigh, although the latter needs further empirical evaluation. LoA for both intensity zone boundaries indicates bias of HRVTs on an individual level. Taken together, HRVTs can be a promising alternative for prescribing exercise intensity in healthy, male athletes undertaking endurance activities but due to the heterogeneity of study design, threshold concepts, standardization, and lack of female participants, further research is necessary to draw more robust and nuanced conclusions.

[Motoric cognitive risk syndrome-Setting the course for dementia prevention?] / Motor-kognitives Risikosyndrom ­ Weichenstellung für die Demenzprävention?

BACKGROUND: The predicted increase in adults with dementia will pose a major challenge for the German healthcare system. To mitigate this challenge, the early detection of adults with an increased risk of dementia is crucial. In this context, the concept of motoric cognitive risk (MCR) syndrome has been introduced into the English literature but is currently relatively unknown in German-speaking countries. OBJECTIVE: What are the characteristics and diagnostic criteria of MCR? What is the impact of MCR on health-related parameters? What is the current state of evidence regarding the risk factors and prevention of the MCR? MATERIAL AND METHODS: We reviewed the English language literature concerning MCR, the associated risk factors, and protective factors, similarities or differences with the concept of mild cognitive impairment (MCI), and its influence on the central nervous system. RESULTS: The MCR syndrome is characterized by subjective cognitive impairment and a slower gait speed. Compared to healthy adults, adults with the MCR have a higher risk of dementia, falls, and mortality. Modifiable risk factors provide a starting point for specific multimodal lifestyle-related preventive interventions. CONCLUSION: As MCR can be easily diagnosed in practical settings, it could become an important concept for the early detection of adults with an increased risk of dementia in German-speaking countries, although further research is necessary to empirically confirm this assumption.

Video analysis of Achilles tendon ruptures in professional male football (soccer) reveals underlying injury patterns and provides strategies for injury prevention.

PURPOSE: In professional football (soccer), Achilles tendon ruptures are severe injuries. Video analysis promotes a better understanding of the underlying situational and biomechanical patterns, and provides a roadmap for future research to improve the management and prevention of Achilles tendon ruptures. The purpose of this study was to identify injury patterns contributing to acute Achilles tendon ruptures in professional male football players. METHODS: Professional male football players with an acute Achilles tendon rupture were identified using an online database. For every in-competition injury, the corresponding football match was detected. Video footage of the injury was accessed using Wyscout.com or publicly available video databases. Situational patterns and injury biomechanics of the injury frame were independently analysed by two reviewers using a standardised checklist and a motion analysis software. Finally, consensus was reached to describe the main injury patterns of Achilles tendon ruptures in professional male football players. RESULTS: The search identified video footage of 80 Achilles tendon ruptures in 78 players. Most injuries (94%) occurred through indirect or non-contact mechanisms. The kinematic analysis revealed characteristic joint positions at the time of injury consisting of hip extension, knee extension, ankle dorsiflexion, foot abduction, and foot pronation in most cases. The underlying direction of movement was from flexion to extension (knee) and from plantarflexion to dorsiflexion (ankle). Player actions identified as main injury patterns were stepping back (26%), landing (20%), running/sprinting (18%), jumping (13%), and starting (10%). CONCLUSION: Most Achilles tendon ruptures in professional male football players are closed-chain indirect or non-contact injuries. Sudden loading to the plantarflexor musculotendinous unit remains to be the main component for most cases. By achieving a better understanding of underlying injury mechanisms, this study provides new strategies for the prevention of Achilles tendon ruptures. LEVEL OF EVIDENCE: Level IV.

Improved Estimation of Exercise Intensity Thresholds by Combining Dual Non-Invasive Biomarker Concepts: Correlation Properties of Heart Rate Variability and Respiratory Frequency.

Identifying exercise intensity boundaries has been shown to be important during endurance training for performance enhancement and rehabilitation. Unfortunately, even though surrogate markers show promise when assessed on a group level, substantial deviation from gold standards can be present in each individual. The aim of this study was to evaluate whether combining two surrogate intensity markers improved this agreement. Electrocardiogram (ECG) and gas exchange data were obtained from 21 participants who performed an incremental cycling ramp to exhaustion and evaluated for first (VT1) and second (VT2) ventilatory thresholds, heart rate (HR) variability (HRV), and ECG derived respiratory frequency (EDR). HRV thresholds (HRVT) were based on the non-linear index a1 of a Detrended Fluctuation Analysis (DFA a1) and EDR thresholds (EDRT) upon the second derivative of the sixth-order polynomial of EDR over time. The average of HRVT and EDRT HR was set as the combined threshold (Combo). Mean VT1 was reached at a HR of 141 ± 15, HRVT1 at 152 ± 14 (p < 0.001), EDRT1 at 133 ± 12 (p < 0.001), and Combo1 at 140 ± 13 (p = 0.36) bpm with Pearson's r of 0.83, 0.78, and 0.84, respectively, for comparisons to VT1. A Bland-Altman analysis showed mean biases of 8.3 ± 7.9, -8.3 ± 9.5, and -1.7 ± 8.3 bpm, respectively. A mean VT2 was reached at a HR of 165 ± 13, HRVT2 at 167 ± 10 (p = 0.89), EDRT2 at 164 ± 14 (p = 0.36), and Combo2 at 164 ± 13 (p = 0.59) bpm with Pearson's r of 0.58, 0.95, and 0.94, respectively, for comparisons to VT2. A Bland-Altman analysis showed mean biases of -0.3 ± 8.9, -1.0 ± 4.6, and -0.6 ± 4.6 bpm, respectively. Both the DFA a1 and EDR intensity thresholds based on HR taken individually had moderate agreement to targets derived through gas exchange measurements. By combining both non-invasive approaches, there was improved correlation, reduced bias, and limits of agreement to the respective corresponding HRs at VT1 and VT2.

Validation of a non-linear index of heart rate variability to determine aerobic and anaerobic thresholds during incremental cycling exercise in women.

Studies highlight the usage of non-linear time series analysis of heart rate variability (HRV) using the short-term scaling exponent alpha1 of Detrended Fluctuation Analysis (DFA-alpha1) during exercise to determine aerobic and anaerobic thresholds. The present study aims to further verify this approach in women. Gas exchange and HRV data were collected from 26 female participants with different activity levels. Oxygen uptake (VO2) and heart rate (HR) at first (VT1) and second ventilatory thresholds (VT2) were compared with DFA-alpha1-based thresholds 0.75 (HRVT1) and 0.50 (HRVT2). Results: VO2 at VT1 and VT2 were 25.2 ml/kg/min (± 2.8) and 31.5 ml/kg/min (± 3.6) compared with 26.5 ml/kg/min (± 4.0) and 31.9 ml/kg/min (± 4.5) for HRVT1 and HRVT2, respectively (ICC3,1 = 0.77, 0.84; r = 0.81, 0.86, p < 0.001). The mean HR at VT1 was 147 bpm (± 15.6) and 167 bpm (± 12.7) for VT2, compared with 152 bpm (± 15.5) and 166 bpm (± 13.2) for HRVT1 and HRVT2, respectively (ICC3,1 = 0.87, 0.90; r = 0.87, 0.90, p < 0.001). Bland-Altman analysis for VT1 vs. HRVT1 showed a mean difference of - 1.3 ml/kg/min (± 2.4; LoA: 3.3, - 6.0 ml/kg/min) for VO2 and of - 4.7 bpm (± 7.8; LoA: 10.6, - 20.0 bpm) for HR. VT2 vs. HRVT2 showed a mean difference of - 0.4 ml/kg/min (± 2.3; LoA: 4.1, - 4.9 ml/kg/min) for VO2 and 0.5 bpm (± 5.7; LoA: 11.8, - 10.8 bpm) for HR. DFA-alpha1-based thresholds showed good agreement with traditionally used thresholds and could be used as an alternative approach for marking organismic transition zones for intensity distribution in women.

Fractal correlation properties of HRV as a noninvasive biomarker to assess the physiological status of triathletes during simulated warm-up sessions at low exercise intensity: a pilot study.

BACKGROUND: The non-linear index alpha 1 of Detrended Fluctuation Analysis (DFA a1) of heart rate variability, has been shown to be a marker of fatigue during endurance exercise. This report aims to explore its ability to assess the physiological status as a surrogate metric for "readiness to train" while performing simulated warm-up sessions the day after two different exercise sessions. METHODS: 11 triathletes were recruited to determine the first ventilatory threshold (VT1) during a baseline assessment and to perform 10-min of cycling at 90% of VT1 (simulating a warm-up bout) before (PRE) and within 36 h after (POST) light and heavy running exercise. RR intervals were recorded for DFA a1 analysis along with neuromuscular testing to verify the effects of the performed exercise sessions. In addition to common statistical methods, magnitude-based inferences (MBI) were applied to assess the changes in true score and thus also the practical relevance of the magnitude. RESULTS: Rating of perceived exertion for the heavy exercise session showed a significant higher rating as opposed to the light exercise session (p < 0.001, d = 0.89). In regard of MBIs, PRE versus POST comparisons revealed a significant reduced DFA a1 with large effect size after the heavy exercise session (p = 0.001, d = - 1.44) and a 99% chance that this negative change was clinically relevant. CONCLUSIONS: Despite inter-individual differences, DFA a1 offers potential to assess physiological status and guide athletes in their training as an easy-to-apply monitoring procedure during a standardized warm-up. A regular assessment including individual data history and statistical references for identification of response is recommended. Further data are necessary to confirm the results in a larger and more homogeneous population.

Estimation of Respiratory Frequency in Women and Men by Kubios HRV Software Using the Polar H10 or Movesense Medical ECG Sensor during an Exercise Ramp.

Monitoring of the physiologic metric, respiratory frequency (RF), has been shown to be of value in health, disease, and exercise science. Both heart rate (HR) and variability (HRV), as represented by variation in RR interval timing, as well as analysis of ECG waveform variability, have shown potential in its measurement. Validation of RF accuracy using newer consumer hardware and software applications have been sparse. The intent of this report is to assess the precision of the RF derived using Kubios HRV Premium software version 3.5 with the Movesense Medical sensor single-channel ECG (MS ECG) and the Polar H10 (H10) HR monitor. Gas exchange data (GE), RR intervals (H10), and continuous ECG (MS ECG) were recorded from 21 participants performing an incremental cycling ramp to failure. Results showed high correlations between the reference GE and both the H10 (r = 0.85, SEE = 4.2) and MS ECG (r = 0.95, SEE = 2.6). Although median values were statistically different via Wilcoxon testing, adjusted median differences were clinically small for the H10 (RF about 1 breaths/min) and trivial for the MS ECG (RF about 0.1 breaths/min). ECG based measurement with the MS ECG showed reduced bias, limits of agreement (maximal bias, -2.0 breaths/min, maximal LoA, 6.1 to -10.0 breaths/min) compared to the H10 (maximal bias, -3.9 breaths/min, maximal LoA, 8.2 to -16.0 breaths/min). In conclusion, RF derived from the combination of the MS ECG sensor with Kubios HRV Premium software, tracked closely to the reference device through an exercise ramp, illustrates the potential for this system to be of practical usage during endurance exercise.

Validity of the Polar H10 Sensor for Heart Rate Variability Analysis during Resting State and Incremental Exercise in Recreational Men and Women.

Heart rate variability (HRV) is frequently applied in sport-specific settings. The rising use of freely accessible applications for its recording requires validation processes to ensure accurate data. It is the aim of this study to compare the HRV data obtained by the Polar H10 sensor chest strap device and an electrocardiogram (ECG) with the focus on RR intervals and short-term scaling exponent alpha 1 of Detrended Fluctuation Analysis (DFA a1) as non-linear metric of HRV analysis. A group of 25 participants performed an exhaustive cycling ramp with measurements of HRV with both recording systems. Average time between heartbeats (RR), heart rate (HR) and DFA a1 were recorded before (PRE), during, and after (POST) the exercise test. High correlations were found for the resting conditions (PRE: r = 0.95, rc = 0.95, ICC3,1 = 0.95, POST: r = 0.86, rc = 0.84, ICC3,1 = 0.85) and for the incremental exercise (r > 0.93, rc > 0.93, ICC3,1 > 0.93). While PRE and POST comparisons revealed no differences, significant bias could be found during the exercise test for all variables (p < 0.001). For RR and HR, bias and limits of agreement (LoA) in the Bland−Altman analysis were minimal (RR: bias of 0.7 to 0.4 ms with LoA of 4.3 to −2.8 ms during low intensity and 1.3 to −0.5 ms during high intensity, HR: bias of −0.1 to −0.2 ms with LoA of 0.3 to −0.5 ms during low intensity and 0.4 to −0.7 ms during high intensity). DFA a1 showed wider bias and LoAs (bias of 0.9 to 8.6% with LoA of 11.6 to −9.9% during low intensity and 58.1 to −40.9% during high intensity). Linear HRV measurements derived from the Polar H10 chest strap device show strong agreement and small bias compared with ECG recordings and can be recommended for practitioners. However, with respect to DFA a1, values in the uncorrelated range and during higher exercise intensities tend to elicit higher bias and wider LoA.

Going digital - a commentary on the terminology used at the intersection of physical activity and digital health.

In recent years digital technologies have become a major means for providing health-related services and this trend was strongly reinforced by the current Coronavirus disease 2019 (COVID-19) pandemic. As it is well-known that regular physical activity has positive effects on individual physical and mental health and thus is an important prerequisite for healthy aging, digital technologies are also increasingly used to promote unstructured and structured forms of physical activity. However, in the course of this development, several terms (e.g., Digital Health, Electronic Health, Mobile Health, Telehealth, Telemedicine, and Telerehabilitation) have been introduced to refer to the application of digital technologies to provide health-related services such as physical interventions. Unfortunately, the above-mentioned terms are often used in several different ways, but also relatively interchangeably. Given that ambiguous terminology is a major source of difficulty in scientific communication which can impede the progress of theoretical and empirical research, this article aims to make the reader aware of the subtle differences between the relevant terms which are applied at the intersection of physical activity and Digital Health and to provide state-of-art definitions for them.

Fractal Correlation Properties of Heart Rate Variability as a Biomarker for Intensity Distribution and Training Prescription in Endurance Exercise: An Update.

While established methods for determining physiologic exercise thresholds and intensity distribution such as gas exchange or lactate testing are appropriate for the laboratory setting, they are not easily obtainable for most participants. Data over the past two years has indicated that the short-term scaling exponent alpha1 of Detrended Fluctuation Analysis (DFA a1), a heart rate variability (HRV) index representing the degree of fractal correlation properties of the cardiac beat sequence, shows promise as an alternative for exercise load assessment. Unlike conventional HRV indexes, it possesses a dynamic range throughout all intensity zones and does not require prior calibration with an incremental exercise test. A DFA a1 value of 0.75, reflecting values midway between well correlated fractal patterns and uncorrelated behavior, has been shown to be associated with the aerobic threshold in elite, recreational and cardiac disease populations and termed the heart rate variability threshold (HRVT). Further loss of fractal correlation properties indicative of random beat patterns, signifying an autonomic state of unsustainability (DFA a1 of 0.5), may be associated with that of the anaerobic threshold. There is minimal bias in DFA a1 induced by common artifact correction methods at levels below 3% and negligible change in HRVT even at levels of 6%. DFA a1 has also shown value for exercise load management in situations where standard intensity targets can be skewed such as eccentric cycling. Currently, several web sites and smartphone apps have been developed to track DFA a1 in retrospect or in real-time, making field assessment of physiologic exercise thresholds and internal load assessment practical. Although of value when viewed in isolation, DFA a1 tracking in combination with non-autonomic markers such as power/pace, open intriguing possibilities regarding athlete durability, identification of endurance exercise fatigue and optimization of daily training guidance.

The Movesense Medical Sensor Chest Belt Device as Single Channel ECG for RR Interval Detection and HRV Analysis during Resting State and Incremental Exercise: A Cross-Sectional Validation Study.

The value of heart rate variability (HRV) in the fields of health, disease, and exercise science has been established through numerous investigations. The typical mobile-based HRV device simply records interbeat intervals, without differentiation between noise or arrythmia as can be done with an electrocardiogram (ECG). The intent of this report is to validate a new single channel ECG device, the Movesense Medical sensor, against a conventional 12 channel ECG. A heterogeneous group of 21 participants performed an incremental cycling ramp to failure with measurements of HRV, before (PRE), during (EX), and after (POST). Results showed excellent correlations between devices for linear indexes with Pearson's r between 0.98 to 1.0 for meanRR, SDNN, RMSSD, and 0.95 to 0.97 for the non-linear index DFA a1 during PRE, EX, and POST. There was no significant difference in device specific meanRR during PRE and POST. Bland-Altman analysis showed high agreement between devices (PRE and POST: meanRR bias of 0.0 and 0.4 ms, LOA of 1.9 to -1.8 ms and 2.3 to -1.5; EX: meanRR bias of 11.2 to 6.0 ms; LOA of 29.8 to -7.4 ms during low intensity exercise and 8.5 to 3.5 ms during high intensity exercise). The Movesense Medical device can be used in lieu of a reference ECG for the calculation of HRV with the potential to differentiate noise from atrial fibrillation and represents a significant advance in both a HR and HRV recording device in a chest belt form factor for lab-based or remote field-application.