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Background
Advancements in wearable technology have provided practitioners and researchers with the ability to conveniently measure various health and/or fitness indices. Specifically, portable devices have been devised for convenient recordings of heart rate variability (HRV). Yet, their accuracies remain questionable.
Objective
The aim was to qu...
Citations
... However, portable heart rate monitors (e.g., chest belts) are widely spread and have better ease of use for monitoring ITL during everyday training. Given the consistent evidence demonstrating a small amount of absolute error in HRV measurements obtained from the measurement of inter-beat-intervals through one-lead ECG via portable heart rate monitors when compared to multi-lead ECG recordings [36,52], data was collected using a HR monitor (Polar M430) with sensor (Polar H10). The acclimatization phase was 5 min, followed by a 5 min resting measurement, the recommended duration for short-term recordings [40,53]. ...
... Although there is consistent evidence that HRV measurements obtained from the measurement of interbeat-intervals through one-lead ECG via portable HR monitors shows a small amount of error compared to HRV derived from multi-lead ECG recordings [36,52], further research is required to investigate the test-retest reliability of vm-HRV during exergame-based training and motor-cognitive training in general. In particular, future research should further investigate the reliability and validity of vm-HRV during exergame-based training and motor-cognitive training in general with a specific focus on comparing different measurement methodologies (e.g., measurement durations, technologies (i.e., measurement of inter-beat-intervals through onelead ECG via portable heart rate monitor compared to multi-lead ECG recordings as well as different recording devices) as well as different analysis methodologies (e.g., beat correction and noise handling algorithms, or computation methods to calculated vm-HRV parameters), particularly focusing on ultra-short-term HRV measurements. ...
... First, the measurement of vm-HRV was done using a one-lead ECG via portable HR monitor, and multi-lead ECG data was not collected in parallel to assess the agreement of the outcome measures with ECG data, although multi-lead ECG is considered the gold standard for measuring HRV [51]. This approach was chosen due to consistent evidence demonstrating a small amount of absolute error in HRV measurements obtained from the measurement of inter-beat-intervals through one-lead ECG via portable HR monitors when compared to multi-lead ECG recordings [36,52]. Additionally, portable HR monitors (e.g., chest belts) are widely spread and have good ease of use for monitoring ITL during everyday training. ...
BACKGROUND:
Vagally-mediated heart rate variability (vm-HRV) shows promise as a biomarker of internal training load (ITL) during exergame-based training or motor-cognitive training in general. This study evaluated the test-retest reliability of vm-HRV during exergaming in healthy older adults (HOA) and its validity to monitor ITL.
METHODS:
A within-subjects (repeated-measures) randomized study was conducted that included baseline assessments and 4 measurement sessions. Participants played 5 exergames at 3 standardized levels of external task demands (i.e., “easy”, “challenging”, and “excessive”) in random order for 90 s. Test-retest reliability was assessed on the basis of repeated-measures analyses of variance (ANOVA), intraclass correlation coefficients (ICC3,1), standard errors of measurement (SEM), and smallest detectable differences (SDD). Validity was determined by examining the effect of game level on vm-HRV in the ANOVA.
RESULTS:
Fourty-three HOA (67.0 ± 7.0 years; 58.1% females (25 females, 18 males); body mass index = 23.7 ± 3.0 kg·m⁻²) were included. Mean R-R time intervals (mRR) and parasympathetic nervous system tone index (PNS-Index) exhibited mostly good to excellent relative test-retest reliability with no systematic error. Mean SEM% and SDD% were 36.4% and 100.7% for mRR, and 44.6% and 123.7% for PNS-Index, respectively. Significant differences in mRR and PNS-Index were observed between standardized levels of external task demands, with mostly large effect sizes (mean r = 0.847). These results persisted irrespective of the type of neurocognitive domain trained and when only motoric and cognitive demands were manipulated while physical intensity was kept constant. The remaining vm-HRV parameters showed inconsistent or poor reliability and validity.
CONCLUSION:
Only mRR and PNS-Index demonstrated reliable measurement and served as valid biomarkers for ITL during exergaming at a group level. Nonetheless, the presence of large SEMs hampers the detection of individual changes over time and suggests insufficient precision of these measurements at the individual level. Future research should further investigate the reliability and validity of vm-HRV with a specific focus on comparing different measurement methodologies and exercise conditions, particularly focusing on ultra-short-term HRV measurements, and investigate the potential implications (i.e., superiority to other markers of ITL or monitoring strategies?) of using vm-HRV as a biomarker of ITL.
... The methodological quality of the included studies was evaluated using the "Standard for Reporting Diagnostic Accuracy Studies Guidelines for Heart Rate Variability Research" (STARD HRV ), which is designed to assess the methodological quality of studies with a focus on HRV measurement [35]. This tool includes 25 parameters with a maximum score of 25 points. ...
... The study quality of HRV methodology was evaluated with a slightly modified version [36] of the STARD HRV [35]. The average score of the STARD HRV was 14.6/25 and ranged from 8 [58] to 18 [46,51,54]. ...
In modern soccer, fitness and fatigue monitoring tools tend to be focused on noninvasive, time‐efficient and player‐friendly measures. Heart rate variability (HRV) has been suggested as an effective method for monitoring training response and readiness to perform. However, there is still a lack of consensus on HRV monitoring when it comes to soccer. Thus, this scoping review aims to map existing evidence on HRV in professional and semiprofessional soccer settings, and to identify knowledge gaps to inform future research directions. A search of databases (PubMed, Scopus, Web of Science, Google Scholar) according to the PRISMA‐ScR statement was employed. Studies were screened for eligibility on inclusion criteria: (1) HRV was among the topics discussed in the article; (2) adult professional or semiprofessional soccer players were involved in the study; (3) both male and female participants; (4) no geographical area exclusion; (5) articles published in English; and (6) article full text available. The search of the selected databases revealed 8456 records. The titles and abstracts of all articles were retrieved for screening of eligibility, leaving 30 articles for further consideration. Following screening against set criteria, a total of 25 studies were included in this review, the sample size of which ranged from 6 to 124 participants. The participants in the included studies were professional and semiprofessional soccer players, interviewed clubs staff, and practitioners. Along with other monitoring strategies, morning vagally mediated HRV analysis via (ultra)short‐term orthostatic measurements may be an efficient way to assess training adaptations and readiness to perform in professional and semiprofessional soccer players. Further research is required to make definitive recommendations.
... The PPG signal can be used to detect HR and HRV -but more specifically, the signal provides pulse rate (PR) and pulse rate variability (PRV). Most commercially available portable devices show a low absolute error under resting conditions, but should always be validated against reference measures to clarify the accuracy of data parameters and maximize real word application value (18,38). It is essential to evaluate the validity depending on the setting, measurement duration, paradigm and cohort that is investigated (43), because modulators such as the analyzed metric, body position, or individual characteristics of the population can cause deviations in HRV measurements from different devices (18). ...
... Most commercially available portable devices show a low absolute error under resting conditions, but should always be validated against reference measures to clarify the accuracy of data parameters and maximize real word application value (18,38). It is essential to evaluate the validity depending on the setting, measurement duration, paradigm and cohort that is investigated (43), because modulators such as the analyzed metric, body position, or individual characteristics of the population can cause deviations in HRV measurements from different devices (18). For applied settings, different portable high-resolution systems have been validated such as chest strap sensor systems for ECG-accurate electrophysiological recordings coupled via Bluetooth connection with receiving devices (smartwatches, smartphone via apps) for acquisition during rest and physical exercise conditions (18). ...
... It is essential to evaluate the validity depending on the setting, measurement duration, paradigm and cohort that is investigated (43), because modulators such as the analyzed metric, body position, or individual characteristics of the population can cause deviations in HRV measurements from different devices (18). For applied settings, different portable high-resolution systems have been validated such as chest strap sensor systems for ECG-accurate electrophysiological recordings coupled via Bluetooth connection with receiving devices (smartwatches, smartphone via apps) for acquisition during rest and physical exercise conditions (18). Finally, PPG short-term measurements (metric: RMSSD, see figure 1) with the index finger and smartphone camera (light signal: flashing light) can currently also be recommended depending on the specific solution under resting conditions (3,52,66). ...
English:
Heart rate variability (HRV) operationalizes the successive beat-to-beat fluctuations over a defined period of time, is derived from the time series of successive R-R intervals using various context-dependent metrics, and reflects the complex dynamic modulation of the heart’s chronotropic response to physiological and/or pathological perturbations. HRV metrics are used as markers of human cardiovascular health and risk stratification, or as measures of load quantification, exercise response and performance, respectively. However, a valid use of HRV in the fields of sports medicine and exercise science requires careful consideration of the specific measurement principle of the recording device, standardized assessment, preprocessing, analysis, and context-sensitive interpretation.
German:
Die Herzfrequenzvariabilität (HRV) operationalisiert die aufeinanderfolgenden Schlag-zu-Schlag-Schwankungen über einen bestimmten Zeitraum, wird aus der Zeitreihe aufeinanderfolgender R-R-Intervalle unter Verwendung verschiedener kontextabhängiger Metriken abgeleitet und spiegelt die komplexe dynamische Modulation der chronotropen Reaktion des Herzens auf physiologische und/oder pathologische Störungen wider. HRV-Metriken werden als Marker für die kardiovaskuläre Gesundheit des Menschen und zur Risikostratifizierung bzw. als Maß für die Quantifizierung von Beanspruchung und Leistungsfähigkeit verwendet. Eine sinnvolle Verwendung der HRV in den Bereichen Sportmedizin und Trainingswissenschaft erfordert jedoch eine sorgfältige Berücksichtigung des spezifischen Messprinzips des Aufzeichnungsgeräts, eine standardisierte Erhebung, Vorverarbeitung, Analyse und kontext-sensitive Interpretation.
... Расчет ВРС проводился по регистрируемым R-R-интервалам с вычислением временных и спектральных характеристик в соответствии с действующими стандартами. При выборе показателей для анализа использовались рекомендации Европейского кардиологического общества и Североамериканского общества кардиостимуляции и электрофизиологии (Task Force of The European Society of Cardiology and The North American Society of Pacingand Electrophysiology) с поправкой на выбранные способ и время регистрации: HR -частота сердечных сокращений (ЧСС), уд./мин; RMSSD -корень квадратный из суммы квадратов разностей последовательных пар интервалов R-R, мс; pNN50 -количество пар кардиоинтервалов с разностью более 50 мс, % к общему количеству кардиоинтервалов в массиве; CV -нормированный по HR коэффициент вариации полного массива кардиоинтервалов, %; SI -стресс-индекс -степень напряжения регуляторных систем (степень преобладания активности центральных механизмов регуляции над автономными), а также спектральные составляющие метода частотного домена в абсолютных значениях, мс 2 : HF -высокочастотная, формирующаяся дыхательными волнами в диапазоне 0,15-0,4 Гц; LF -низкочастотная, связанная с медленными колебаниями в диапазоне 0,15-0,04 Гц и VLF<0,04 Гц; соотношение LF/HF [11,12]. Для оценки степени централизации управления сердечным ритмом использовался индекс напряжения регуляторных систем (ИН), предложенный Р.М.Баевским [13]. ...
The purpose of our study was to study the influence of professional activity on the functional state of the body of medical workers of emergency medical teams (SMP). Materials and methods of research. The study involved 65 medical workers from mobile ambulance teams. The distribution of those examined by gender was 19 men and 46 women; by position – 16 doctors and 49 paramedics. The study was carried out from May to October 2021. The work schedule of the subjects was: work shift – 24 hours, rest period – 72 hours. The indicators recorded before and after work (n=46), between adjacent shifts after rest (n=23) were assessed, as well as at different periods of the year (n=16). HRV monitoring was used as a method for objective analysis of fatigue. The choice of methodology was determined by the specifics of the work of mobile ambulance teams during the COVID-19 pandemic, which was characterized by a large number of people in need of emergency medical care with a shortage of time and personnel. Research results and their analysis. The predominance of the activity of the parasympathetic department before work, the increased activity of sympathetic influences and the predominance of central regulatory mechanisms over autonomous ones after work were established; the greater severity of negative changes at the end of work in doctors compared with paramedics; negative dynamics of off-season indicators, confirming the persistence of negative changes in the functional state of employees.
... Advancements in wearable technology have enabled healthcare professionals and researchers to conveniently evaluate diverse health and fitness parameters. Portable devices, specifically designed for convenient heart rate variability recordings, have been developed, see [12]. Measurements of heart rate variability obtained through portable devices show a minimal level of absolute error, as indicated by [13]. ...
In this work, we propose an algorithm for the detection, measurement and classification of discontinuities in signals captured with noise. Our approach is based on the Harten’s subcell-resolution approximation adapted to the presence of noise. This technique has several advantages over other algorithms. The first is that there is a theory that allows us to ensure that discontinuities will be detected as long as we choose a sufficiently small discretization parameter size. The second is that we can consider different types of discretizations such as point values or cell-averages. In this work, we will consider the latter, as it is better adapted to functions with small oscillations, such as those caused by noise, and also allows us to find not only the discontinuities of the function, jumps in functions or edges in images, but also those of the derivative, corners. This also constitutes an advantage over classical procedures that only focus on jumps or edges. We present an application related to heart rate measurements used in sport as a physical indicator. With our algorithm, we are able to identify the different phases of exercise (rest, activation, effort and recovery) based on heart rate measurements. This information can be used to determine the rotation timing of players during a game, identifying when they are in a rest phase. Moreover, over time, we can obtain information to monitor the athlete’s physical progression based on the slope size between zones. Finally, we should mention that regions where heart rate measurements are abnormal indicate a possible cardiac anomaly.
... Research indicates that heart rate and HRV can objectively indicate fatigue. For instance, Dobbs [13] used a portable device to conveniently record HRV, showing a small absolute error compared with electrocardiography. Monitoring changes in heart rate and HRV is crucial for determining driver fatigue. ...
Existing vision-based fatigue detection methods commonly utilize RGB cameras to extract facial and physiological features for monitoring driver fatigue. These features often include single indicators such as eyelid movement, yawning frequency, and heart rate. However, the accuracy of RGB cameras can be affected by factors like varying lighting conditions and motion. To address these challenges, we propose a non-invasive method for multi-modal fusion fatigue detection called RPPMT-CNN-BiLSTM. This method incorporates a feature extraction enhancement module based on the improved Pan–Tompkins algorithm and 1D-MTCNN. This enhances the accuracy of heart rate signal extraction and eyelid features. Furthermore, we use one-dimensional neural networks to construct two models based on heart rate and PERCLOS values, forming a fatigue detection model. To enhance the robustness and accuracy of fatigue detection, the trained model data results are input into the BiLSTM network. This generates a time-fitting relationship between the data extracted from the CNN, allowing for effective dynamic modeling and achieving multi-modal fusion fatigue detection. Numerous experiments validate the effectiveness of the proposed method, achieving an accuracy of 98.2% on the self-made MDAD (Multi-Modal Driver Alertness Dataset). This underscores the feasibility of the algorithm. In comparison with traditional methods, our approach demonstrates higher accuracy and positively contributes to maintaining traffic safety, thereby advancing the field of smart transportation.
... 6 However, for data collection, storage, analysis, and export, it has been usual practise to employ mobile systems (e.g., apps, wearable devices) and chest straps, which offer improved practicability in terms of cost, convenience of use, portability, and interpretation. 7,8,9 However, in order to verify the reliability and validity of the RR interval data for subsequently HRV interpretation, these applications and sensor devices must be evaluated in varied populations. ...
Introduction: Heart rate variability (HRV) analysis has given a non-invasive way for assessing cardiac autonomic control. Reduced HRV is an independent indicator of poor prognosis in both heart disease patients and the general population. Aim/Objective: To establish reliability and validity for Kubios HRV smart phone application. Materials and methods: Fifty (n =50) office workers volunteered for the study, with 10 females and 40 males ranging in age from 30 to 50 years (Mean age =38.4±5.6). To evaluate Intra and Inter-rater reliability and validity the of HRV parameter, R-R intervals comparing simultaneous recording from ECG and Kubios HRV app, at different three times (day1,day2 &day3) one day apart and by two different trained examiners in the same participants. Each participants rested in supine for 10 minutes prior to the assessment and was instructed to remain relaxed, breathe properly, and refrain from talking and sleeping during the measurement. The RR intervals (R-R) were recorded during a 5 min period in supine position using ECG and polar H10 monitor with Kubios HRV smartphone application. Results: Kubios HRV smart phone application shows excellent reliability, Intra-rater (α =0.868) and Inter-rater (α =0.890) and Validity (r = 0.94) with R-R intervals calculated from ECG. Conclusion: The Kubios HRV smart phone application provides an accurate and reliable alternative to the ECG for acquiring inter-beat interval time series data. The Kubios HRV app gives an R-R interval that can be used for short term HRV analysis in office workers from steady resting conditions, supporting the selection of this approach of evaluation in research and clinical practise.
... Each domain was evaluated as "low", "high", or "unclear" regarding RoB and concerns for applicability. The HRV-speci c version of the original Standard for Reporting Diagnostic Accuracy Studies (STARD HRV ) was used to assess the methodological quality of HRV methodology [73,74]. It includes 25 parameters with a maximum of 25 points. ...
Background
This systematic review with meta-analyses aims to assess the overall validity of the first and second heart rate variability - derived threshold (HRVT1 and HRVT2, respectively) by computing global effect sizes for agreement and correlation between HRVTs and reference – lactate and ventilatory (LT-VTs) – thresholds. Furthermore, this review aims to assess the impact of subjects’ characteristics, HRV methods, and study protocols on the agreement and correlation between LT-VTs and HRVTs.
Methods
Systematic computerised searches for studies determining HRVTs during incremental exercise in humans were conducted between March and August 2023 using electronic databases (Cochrane Library, EBSCO, Embase.com, Google Scholar, Ovid, ProQuest, PubMed, Scopus, SportDiscus, Virtual Health Library and Web of science). The agreements and correlations meta-analyses were conducted using a random-effect model. Causes of heterogeneity were explored by subgroup analysis and meta-regression with subjects’ characteristics, incremental exercise protocols and HRV methods variables and compared using statistical tests for interaction. The methodological quality was assessed using QUADAS-2 and STARDHRV tools. The risk of bias was assessed by funnel plots, fail-safe N test, Egger's test of the intercept and the Begg and Mazumdar rank correlation test.
Results
Fifty included studies (1’160 subjects) assessed 314 agreements (95 for HRVT1, 219 for HRVT2) and 246 correlations (82 for HRVT1, 164 for HRVT2) between LT-VTs and HRVTs. The standardized mean differences were trivial between HRVT1 and LT1-VT1 (SMD = 0.08, 95% CI -0.04–0.19, n = 22) and between HRVT2 and LT2-VT2 (SMD = -0.06, 95% CI -0.15–0.03, n = 42). The correlations were very strong between HRVT1 and LT1-VT1 (r = 0.85, 95% CI 0.75–0.91, n = 22), and between HRVT2 and LT2-VT2 (r = 0.85, 95% CI 0.80–0.89, n = 41). Moderator analyses showed that HRVT1 better agreed with LT1 and HRVT2 with VT2. Moreover, subjects’ characteristics, type of ergometer, or initial and incremental workload had no impact on HRVTs determination. Simple visual HRVT determinations were reliable, as well as both frequency and non-linear HRV indices. Finally, short increment yielded better HRVT2 determination.
Conclusion
HRVTs showed trivial differences and very strong correlations with LT-VTs and might thus serve as surrogate thresholds and, consequently for the determination of the intensity zones. However, heterogeneity across study results and differences in agreement when comparing separately LTs and VTs to HRVTs were observed, underscoring the need for further research. These results emphasize the usefulness of HRVTs as promising, accessible, and cost-effective means for exercise and clinical prescription purposes
... Many studies have compared HRV measurements obtained by a wearable device with those taken by a clinical ECG system [53][54][55][56][57]. A meta-analysis that included 23 studies of HRV measurements from wearable devices showed that the HRV readings had a small absolute error when compared to readings using a clinical ECG; however, this error was considered acceptable, given the practicality and cost-effectiveness of acquiring HRV through wearable devices [58]. Among the parameters used in the measurements, SDNN had the greatest amount of error, whereas RMSSD and high-frequency bands did not significantly differ in the error rates between methodologies [58]. ...
... A meta-analysis that included 23 studies of HRV measurements from wearable devices showed that the HRV readings had a small absolute error when compared to readings using a clinical ECG; however, this error was considered acceptable, given the practicality and cost-effectiveness of acquiring HRV through wearable devices [58]. Among the parameters used in the measurements, SDNN had the greatest amount of error, whereas RMSSD and high-frequency bands did not significantly differ in the error rates between methodologies [58]. Other verification studies have been conducted for brands such as Oura [59], Whoop [60], and Apple Watch [38], and for assessing the use of different apps to acquire camera-based HRV, including Welltory [61] and HRV4 training [62]. ...
... Because HRV is unique to each person, the accuracy of baseline HRV values is fundamental to ensuring confidence in subsequent measurements. Dobbs et al. [58] concluded that meaningful interpretations of longitudinal HRV data are improved by using weekly averages of consecutive day-to-day recordings, which are superior to snapshot measures of HRV [121]. Unfortunately, there is currently no longitudinal study using wearable devices to record HRV continuously. ...
Heart rate variability (HRV) is a measurement of the fluctuation of time between each heartbeat and reflects the function of the autonomic nervous system. HRV is an important indicator for both physical and mental status and for broad-scope diseases. In this review, we discuss how wearable devices can be used to monitor HRV, and we compare the HRV monitoring function among different devices. In addition, we have reviewed the recent progress in HRV tracking with wearable devices and its value in health monitoring and disease diagnosis. Although many challenges remain, we believe HRV tracking with wearable devices is a promising tool that can be used to improve personal health.
... In contrast, PPG offers more flexibility and practicality for field settings as it measures HRV using light signals that are common in commercial portable devices, such as finger sensors, smartwatches, smartphone apps and chest bands (Allen, 2007). Additionally, PPG shows similar accuracy compared to ECG for measuring HRV in healthy individuals at rest (Dobbs et al., 2019). ...
Short-term heart rate variability (HRV) is increasingly used to assess autonomic nervous system activity and found to be useful for monitoring and providing care due to its quick measurement. With evidence of low HRV associated with chronic diseases, mental disorders, and an increased risk of cardiovascular disease, having normative data of HRV across the age spectrum would be useful for monitoring health and well-being of a population. This study examines HRV of healthy Singapore sample, with ages ranging from 10 to 89 years. Short-term HRV of five minutes was measured from 2,143 participants. 974 males and 1,169 females, and overall HRV was found to be 42.4ms (RMSSD) and 52.0 ms (SDNN) with a further breakdown of HRV by age and gender. Overall HRV declined with age and gender, although gender differences dissipated in the 60s age range onwards, with the 50s age range having the sharpest decline in HRV. Short-term HRV norms were similar to Nunan et al.’s (2010) systematic review in various populations and less similar to Choi et al.’s (2020) study on Koreans.
