Measured maximal heart rates compared to commonly used age-based prediction equations in the Heritage Family Study

Advice to people with Parkinson's in my clinic: Get a cardiopulmonary exercise test

Aerobic (cardiopulmonary) exercise training improves fitness and limits the progression of motor signs in Parkinson's disease. Individualized development of exercise prescriptions relies upon accurately measured peak heart rate (HR). Cardiopulmonary exercise testing (CPET) is the only methodology to objectively confirm an individual's peak HR. This test is important in individuals who may have autonomic dysfunction, which may manifest as chronotropic incompetence. Without CPET's confirmation of maximal effort, there is no way of knowing whether exercise prescription in those with chronotropic incompetence is accurate. CPET also provides information regarding cardiorespiratory fitness, which can motivate patients to exercise to prolong health.

Assessment of Maximum Heart Rate Prediction Equations in Adults at Low and High Risk of Cardiovascular Disease

PURPOSE

Maximum heart rate (HRmax) is commonly used to estimate exercise intensity. Since direct measurement of HRmax is not always practical, prediction equations were developed. However, most equations have not been properly validated in adults at low and high risk of cardiovascular disease (CVD). We sought to: 1) assess the accuracy of commonly used equations to predict HRmax among adults at low and high CVD risk and, 2) determine if SuperLearner (SL) modeling combining base machine algorithms could improve HRmax prediction.

METHODS

A total of 1208 participants (61.6 ± 7.3 yr; 62.7% male) were included. HRmax was measured during a maximal cardiorespiratory exercise test. Predicted HRmax was estimated using the following published equations: Fox, Åstrand, Tanaka, Gelish and Gulati, and a SL model. Bland-Altman analyses as well as performance indicators such as root mean squared error (RMSE) and Lin's Concordance Correlation Coefficient were performed.

RESULTS

All predicted HRmax-derived equations were positively associated with measured HRmax (women: r = 0.31; men: r = 0.46, P ≤ 0.001) but to a greater extent using a SL model (women: r = 0.47; men: r = 0.59, P ≤ 0.001). Overall, all equations tended to overestimate measured HRmax, with a RMSE which varied between 10.4 and 12.3 bpm. Although the SL model outperformed other equations, with no significant difference between measured and predicted HRmax, RMSE remained high (11.3 bpm). Lack of accuracy was mainly observed among adults with low aerobic fitness and with CVD risk factors, such as obesity, diabetes, and hypertension.

CONCLUSIONS

We showed that commonly used equations and the SL model have insufficient accuracy to predict HRmax among adults. The performance of the prediction equations varied considerably according to the population clinical characteristics such as the presence of CVD risk factors or a low aerobic fitness.

Criterion validity of a newly developed Apple Watch app ('MVPA') compared to the native Apple Watch 'activity' app for measuring criterion moderate intensity physical activity

Introduction

Many people fail to meet physical activity guidelines. One possible solution is wearable technology. Yet it is unclear if popular devices such as the Apple Watch can accurately measure intensity, which is a cornerstone of physical activity guidelines. We developed a bespoke Apple Watch app based on the use of relative intensity (%HRR), with the aim to determine if the bespoke app measures 'moderate' intensity more accurately compared to the native Apple Watch Activity app, using % oxygen consumption reserve (%VO2R) as the criterion.

Methods

Seventy-four participants (18-65 years) attended the laboratory twice. During Visit 1, they completed medical screening, and measurements of resting heart rate, maximal oxygen consumption, and maximal heart rate on a motorised treadmill. During Visit 2, participants completed 5-minute treadmill bouts starting at 3.5 km.h-1, increasing by 0.5 km.h-1 until the bespoke app recorded 3 minutes at ≥40%HRR, and the native app recorded 5 minutes of 'exercise'. Oxygen consumption and heart rate were recorded. Bayesian posterior distributions were used to compare the two apps.

Results

At the walking speed that the native app recorded exercise, the mean (95% HDI) %VO2R was 33 (31-36)%, which is below moderate relative intensity (40%HRR). This is compared to a mean (95% HDI) of 43 (40-44)% for our bespoke app.

Conclusion

The bespoke app measured relative moderate intensity more accurately compared to the native app when compared to the %VO2R criterion. Exercise guidelines and wearable devices should incorporate relative measures of physical activity to better individualise monitoring and prescription.

Target Heart Rate Formulas for Exercise Stress Testing: What Is the Evidence?

Exercise stress testing (EST) is commonly used to evaluate chest pain, with some labs using 85% of age-predicted maximum heart rate (APMHR) as an endpoint for EST. The APMHR is often calculated using the formula 220-age. However, the accuracy of this formula and 85% APMHR as an endpoint may be questioned. Moreover, failing to reach 85% APMHR (known as chronotropic insufficiency) may also indicate poor cardiovascular prognosis, but measurements, such as percentage heart rate reserve (%HRR), maximum rate pressure product (MRPP), and the maximum metabolic equivalent of tasks (METs) reached during EST may provide better prediction of cardiovascular outcomes than not reaching 85% of APMHR. There is a need to incorporate comprehensive measurements to improve the diagnostic and prognostic capabilities of EST.

Cardiorespiratory Fitness as a Predictor of Non-Cardiovascular Disease and Non-Cancer Mortality in Men

OBJECTIVE

To evaluate the association of 5 major cause-specific non-cardiovascular disease (CVD) and non-cancer deaths with cardiorespiratory fitness (CRF).

METHODS

Patients were 36,645 men (43.3±9.3 years) free of known CVD and cancer at baseline who completed a maximal treadmill graded exercise test during a preventive examination at the Cooper Clinic (Dallas, Texas) between 1971 and 2003. CRF was quantified as maximal treadmill exercise test duration and grouped as low (referent), moderate, and high. Cause-specific non-CVD non-cancer deaths were (1) diabetes or kidney disease, (2) chronic respiratory disease, (3) acute respiratory and infectious disease, (4) injuries, and (5) other non-CVD non-cancer deaths.

RESULTS

A total of 694 non-CVD non-cancer deaths occurred during an average of 17 years of follow-up. After adjustment for covariates, hazard ratios (95% confidence intervals) for moderate and high CRF, respectively, were 0.57 (0.47 to 0.69) and 0.43 (0.34 to 0.54) for overall non-CVD non-cancer deaths (P<.0001); 0.39 (0.28 to 0.54) and 0.17 (0.10 to 0.28) for diabetes or kidney disease (P<.001); 0.36 (0.22 to 0.59) and 0.09 (0.04 to 0.20) for chronic respiratory diseases (P<.001 for all); 0.74 (0.47 to 1.16) and 0.34 (0.19 to 0.61) for acute respiratory and infectious diseases (P<.01 for both); and 0.48 (0.35 to 0.66) and 0.38 (0.26 to 0.55) for any other non-CVD non-cancer deaths (P<.0001 for both).

CONCLUSION

Higher levels of CRF were significantly associated with lower risk of mortality from the 5 major non-CVD non-cancer causes. These results suggest that improvement in CRF may reduce non-CVD non-cancer deaths, which account for a significant proportion of adult mortality.

Identifying limitations to exercise with incremental cardiopulmonary exercise testing: a scoping review

Cardiopulmonary exercise testing (CPET) is a comprehensive and invaluable assessment used to identify the mechanisms that limit exercise capacity. However, its interpretation remains poorly standardised. This scoping review aims to investigate which limitations to exercise are differentiated by the use of incremental CPET in literature and which criteria are used to identify them. We performed a systematic, electronic literature search of PubMed, Embase, Cochrane CENTRAL, Web of Science and Scopus. All types of publications that reported identification criteria for at least one limitation to exercise based on clinical parameters and CPET variables were eligible for inclusion. 86 publications were included, of which 57 were primary literature and 29 were secondary literature. In general, at the level of the cardiovascular system, a distinction was often made between a normal physiological limitation and a pathological one. Within the respiratory system, ventilatory limitation, commonly identified by a low breathing reserve, and gas exchange limitation, mostly identified by a high minute ventilation/carbon dioxide production slope and/or oxygen desaturation, were often described. Multiple terms were used to describe a limitation in the peripheral muscle, but all variables used to identify this limitation lacked specificity. Deconditioning was a frequently mentioned exercise limiting factor, but there was no consensus on how to identify it through CPET. There is large heterogeneity in the terminology, the classification and the identification criteria of limitations to exercise that are distinguished using incremental CPET. Standardising the interpretation of CPET is essential to establish an objective and consistent framework.

The Effect of Wearable-Based Real-Time Feedback on Running Injuries and Running Performance: A Randomized Controlled Trial

BACKGROUND

Running technique and running speed are considered important risk factors for running injuries. Real-time feedback on running technique and running speed by wearables may help reduce injury risk.

PURPOSE

To investigate whether real-time feedback on spatiotemporal metrics and relative speed by commercially available pressure-sensitive insoles would reduce running injuries and improve running performance compared with no real-time feedback.

STUDY DESIGN

Randomized controlled trial; Level of evidence, 1.

METHODS

A total of 220 recreational runners were randomly assigned into the intervention and control groups. Both groups received pressure-sensitive insoles, but only the intervention group received real-time feedback on spatiotemporal metrics and relative speed. The feedback aimed to reduce loading on the joint/segment estimated to exhibit the highest load. Injury rates were compared between the groups using Cox regressions. Secondary outcomes compared included injury severity, the proportion of runners with multiple injuries, changes in self-reported personal best times and motivation (Behavioral Regulation in Exercise Questionnaire-2), and interest in continuing wearable use after study completion.

RESULTS

A total of 160 participants (73%) were included in analyses of the primary outcome. Intention-to-treat analysis showed no significant difference in injury rate between the groups (Hazard ratio [HR], 1.11; P = .70). This was expected, as 53 of 160 (33%) participants ended up in the unassigned group because they used incorrect wearable settings, nullifying any interventional effects. As-treated analysis showed a significantly lower injury rate among participants receiving real-time feedback (HR, 0.53; P = .03). Similarly, the first-time injury severity was significantly lower (-0.43; P = .042). Per-protocol analysis showed no significant differences in injury rates, but the direction favored the intervention group (HR, 0.67; P = .30). There were no significant differences in the proportion of patients with multiple injuries (HR, 0.82; P = .40) or changes in running performance (3.07%; P = .26) and motivation. Also, ~60% of the participants who completed the study showed interest in continuing wearable use.

CONCLUSION

Real-time feedback on spatiotemporal metrics and relative speed provided by commercially available instrumented insoles may reduce the rate and severity of injuries in recreational runners. Feedback did not influence running performance and exercise motivation.

REGISTRATION

NL8472 (Dutch Trial Register).

Normative data in resting and maximum heart rates and a prediction equation for young Tunisian soccer players: a cross-sectional study

Heart rate (HR) is an important indicator of work intensity during physical activity. Maximum heart rate (MHR) is a physiological measure that is frequently used as a benchmark for maximal exercise intensity. The aim of this study was to establish reference curves for maximum heart rate (MHR) and resting heart rate (RHR) and to develop an estimated equation for Tunisian adolescent footballers. The study involved 801 adolescent players, aged 11 to 18, who belonged to five Tunisian first-division soccer teams. The LMS method was used for smoothing the curves and the multivariate linear regression to develop a prediction equation of MHR. Our results showed that MHR and RHR reference curves decrease with age. The values of the median curves of MHR and RHR ranged from 208.64 bpm (11 years) to 196.93 (18 years) and 73.86 (11 years) to 63.64 (18 years), respectively. The prediction equation obtained from the model was MHR= 225.08 - 1.55 X Age (years) (R2 = 0.317; P < 0.001; standard error of the estimate (SEE) = 5.22). The comparisons between the estimated values and the measured values have found that our model (- 0.004 ±5.22 bpm) was to be more accurate than two other widely known models. BOX's equation underestimates the measured MHR values by -3.17 ± 5.37 bpm and TANAKA's equation overestimates by + 4.33 ±5.5 bpm. The reference curves can be used by coaches and physical trainers to classify the resting heart rate (RHR) and maximum heart rate (MHR) of each adolescent player, track their evolution over time, and design tailored training programs with specific intensities for Tunisian soccer players.

Body exposure and vocal analysis: validation of fundamental frequency as a correlate of emotional arousal and valence

Introduction

Vocal analysis of fundamental frequency (f0) represents a suitable index to assess emotional activation. However, although f0 has often been used as an indicator of emotional arousal and different affective states, its psychometric properties are unclear. Specifically, there is uncertainty regarding the validity of the indices of f0_mean and f0_{variabilitymeasures} (f0_dispersion, f0_range, and f0_SD) and whether higher or lower f0 indices are associated with higher arousal in stressful situations. The present study therefore aimed to validate f0 as a marker of vocally encoded emotional arousal, valence, and body-related distress during body exposure as a psychological stressor.

Methods

N = 73 female participants first underwent a 3-min, non-activating neutral reference condition, followed by a 7-min activating body exposure condition. Participants completed questionnaires on affect (i.e., arousal, valence, body-related distress), and their voice data and heart rate (HR) were recorded continuously. Vocal analyses were performed using Praat, a program for extracting paralinguistic measures from spoken audio.

Results

The results revealed no effects for f0 and state body dissatisfaction or general affect. F0_mean correlated positively with self-reported arousal and negatively with valence, but was not correlated with HR_mean/maximum. No correlations with any measure were found for any f0_{variabililtymeasures}.

Discussion

Given the promising findings regarding f0_mean for arousal and valence and the inconclusive findings regarding f0 as a marker of general affect and body-related distress, it may be assumed that f0_mean represents a valid global marker of emotional arousal and valence rather than of concrete body-related distress. In view of the present findings regarding the validity of f0, it may be suggested that f0_mean, but not f0_{variabilitymeasures}, can be used to assess emotional arousal and valence in addition to self-report measures, which is less intrusive than conventional psychophysiological measures.

Estimation methods to detect changes in cardiorespiratory fitness due to exercise training and subsequent detraining

PURPOSE

To determine whether estimated maximal oxygen consumption ([Formula: see text]) can detect cardiorespiratory fitness (CRF) changes by behavioral modifications. This study compared changes in measured [Formula: see text]O_2max (m[Formula: see text]O_2max) through exercise intervention with e[Formula: see text]O_2max using a multiple regression model (MRM) and linear extrapolation method (LEM).

METHODS

A cross-sectional analysis involving 173 adults was conducted to establish an MRM by including age, sex, body mass index, questionnaire score, heart rate (HR) from step test, and m[Formula: see text]O_2max. Subsequently, 15 men participated in an intervention experiment comprising an 8-week, high-intensity interval training, followed by 8-week detraining, and completed anthropometric measurements, questionnaires, step tests, and m[Formula: see text]O_2max tests. m[Formula: see text]O_2max changes throughout the intervention were compared to e[Formula: see text]O_2max changes calculated using the MRM and LEM. The LEM used the HR during the step test with constant values (predetermined [Formula: see text]O₂), such as the Chester step test.

RESULTS

Inclusion of the step test HR in a questionnaire-based MRM improved the estimation power, although the MRM underestimated higher m[Formula: see text]O_2max values. In the intervention, m[Formula: see text]O_2max increased by 20.0 ± 14.1% (P < 0.01) and subsequently decreased by 9.5 ± 6.6% (P < 0.01) after exercise training and detraining, respectively. Significant method × time interactions were observed between m[Formula: see text]O_2max and e[Formula: see text]O_2max in the MRM but not in the LEM, i.e., an apparent systematic error (underestimation of high values) of the MRM was absent in the LEM, although the correlation between m[Formula: see text]O_2max and e[Formula: see text]O_2max using the LEM was moderate.

CONCLUSION

e[Formula: see text]O_2max, particularly using the MRM with HR as an explanatory factor, is not an appropriate method for detecting CRF changes along with behavioral modifications.

CLINICAL TRIAL REGISTRATION

Registered number, UMIN000041031; Registered date, 2020/07/08; URL, https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000046855.

Maximum Heart Rate- and Lactate Threshold-Based Low-Volume High-Intensity Interval Training Prescriptions Provide Similar Health Benefits in Metabolic Syndrome Patients

Exercise is an integral part of metabolic syndrome (MetS) treatment. Recently, low-volume high-intensity interval training (LOW-HIIT) has emerged as a time-efficient approach to improving cardiometabolic health. Intensity prescriptions for LOW-HIIT are typically based on maximum heart rate (HR_max) percentages. However, HR_max determination requires maximal effort during exercise testing, which may not always be feasible/safe for MetS patients. This trial compared the effects of a 12-week LOW-HIIT program based on: (a) HR_max (HIIT-HR), or (b) submaximal lactate threshold (HIIT-LT), on cardiometabolic health and quality of life (QoL) in MetS patients. Seventy-five patients were randomized to HIIT-HR (5 × 1 min at 80-95% HR_max), HIIT-LT (5 × 1 min at 95-105% LT) groups, both performed twice weekly on cycle ergometers, or a control group (CON). All patients received nutritional weight loss consultation. All groups reduced their body weight (HIIT-HR: -3.9 kg, p < 0.001; HTT-LT: -5.6 kg, p < 0.001; CON: -2.6 kg, p = 0.003). The HIIT-HR and HIIT-LT groups similarly, improved their maximal oxygen uptake (+3.6 and +3.7 mL/kg/min, p < 0.001), glycohemoglobin (-0.2%, p = 0.005, and -0.3%, p < 0.001), homeostasis model assessment index (-1.3 units, p = 0.005, and -1.0 units, p = 0.014), MetS z-score (-1.9 and -2.5 units, p < 0.001) and QoL (+10 points, p = 0.029, and +11 points, p = 0.002), while the CON did not experience changes in these variables. We conclude that HIIT-LT is a viable alternative to HIIT-HR for patients who are not able/willing to undergo maximal exercise testing.

An Investigation of Physiological System Impairments in Individuals 4 Weeks to 6 Months Following Mild Traumatic Brain Injury

OBJECTIVE

The Buffalo Concussion Treadmill Test (BCTT) was developed to identify potential physiological system impairment (PSI) underlying persistent symptoms post-mild traumatic brain injury (mTBI). This study evaluates PSI in individuals 4 weeks to 6 months post-mTBI using the BCTT "failure" criteria, and additional exploratory measures of test duration and heart rate (HR) response.

SETTING

Tertiary hospital and university.

PARTICIPANTS

Participants included 73 individuals 4 weeks to 6 months post-mTBI and a comparison group of 39 healthy controls (HCs). The mTBI group was further subgrouped at screening into those considering themselves asymptomatic (Asymp mTBI) ( n = 35) or symptomatic (Symp mTBI) ( n = 36).

DESIGN

Observational cohort study.

MAIN MEASURES

BCTT; failure rate (%), test duration (minutes), HR responses.

RESULTS

: Thirty percent of the mTBI group (including 50% of the Symp and 9% of the Asymp subgroups) failed the BCTT. BCTT duration and associated overall HR change was significantly lower in the mTBI group and Symp subgroup compared with HCs. Compared with HCs maximal HR percentage was higher for the first 4 minutes of the test in the mTBI group, and for the first 2 minutes of the test for the Symp subgroup.

CONCLUSIONS

Individuals post-mTBI demonstrated PSI impairment subacutely. In some individuals this was despite an initially reported absence of symptoms. The study also showed some preliminary evidence that BCTT duration and HR responses may be additionally informative post-mTBI.

Improved VO2max Estimation by Combining a Multiple Regression Model and Linear Extrapolation Method

Maximal oxygen consumption (VO_2max) is an important health indicator that is often estimated using a multiple regression model (MRM) or linear extrapolation method (LEM) with the heart rate (HR) during a step test. Nonetheless, both methods have inherent problems. This study investigated a VO_2max estimation method that mitigates the weaknesses of these two methods. A total of 128 adults completed anthropometric measurements, a physical activity questionnaire, a step test with HR measurements, and a VO_2max treadmill test. The MRM included step-test HR, age, sex, body mass index, and questionnaire scores, whereas the LEM included step-test HR, predetermined constant VO₂ values, and age-predicted maximal HR. Systematic differences between estimated and measured VO_2max values were detected using Bland-Altman plots. The standard errors of the estimates of the MRM and LEM were 4.15 and 5.08 mL·kg^-1·min^-1, respectively. The range of 95% limits of agreement for the LEM was wider than that for the MRM. Fixed biases were not significant for both methods, and a significant proportional bias was observed only in the MRM. MRM bias was eliminated using the LEM application when the MRM-estimated VO_2max was ≥45 mL·kg^-1·min^-1. In conclusion, substantial proportional bias in the MRM may be mitigated using the LEM within a limited range.

Re-Visiting Maximal Heart Rate Prediction Using Cross-Validation in Population Aged 7-55 Years

The primary purpose of the present study was to re-visit HRmax prediction by two commonly used equations (i.e., Fox's and Tanaka's equation) compared to the direct measured HRmax using the large sample size of Asians. The second aim of the study was to focus on suggesting new equations for the Asian population by separating gender and specific age groups. A total of 672 participants aged from 7 to 55 years were recruited for the study (male: 280 and female: 392), and the maximal graded exercise test with Bruce protocol was used to measure HRmax. All data obtained from the study were analyzed by SPSS 25.0. Additionally, three statistical analysis methods (i.e., Mean Absolute Percent Errors (MAPE), Bland-Altman plots, and equivalence testing) were utilized to confirm the consistency between the measured HRmax and the two prediction equations. The main finding was that two equations showed significant differences in predicting the HRmax of Korean aged from 7 to 55 years. The outcome of children aged from 7 to 14 was a different fit in the agreement compared to other age groups. Fox's equation had the best fit in the average of the difference closer to zero and completely included within the equivalence zone, but females over 15 years old revealed higher errors than males in the values calculated by the two equations compared to the direct measured HRmax. Consequently, the study demonstrated that both equations tended to overestimate the HRmax for males and females over 15 years old, and the two universal equations were not suitable to predict the HRmax of Koreans except for children aged from 7 to 14 years. The new HRmax prediction equations suggested in this study will more accurately predict the HRmax of Asians, and additional analyses should be examined the cross-validity of the developed HRmax equation by age and gender in the future study.

The HERITAGE Family Study: A Review of the Effects of Exercise Training on Cardiometabolic Health, with Insights into Molecular Transducers

The aim of the HERITAGE Family Study was to investigate individual differences in response to a standardized endurance exercise program, the role of familial aggregation, and the genetics of response levels of cardiorespiratory fitness and cardiovascular disease and diabetes risk factors. Here we summarize the findings and their potential implications for cardiometabolic health and cardiorespiratory fitness. It begins with overviews of background and planning, recruitment, testing and exercise program protocol, quality control measures, and other relevant organizational issues. A summary of findings is then provided on cardiorespiratory fitness, exercise hemodynamics, insulin and glucose metabolism, lipid and lipoprotein profiles, adiposity and abdominal visceral fat, blood levels of steroids and other hormones, markers of oxidative stress, skeletal muscle morphology and metabolic indicators, and resting metabolic rate. These summaries document the extent of the individual differences in response to a standardized and fully monitored endurance exercise program and document the importance of familial aggregation and heritability level for exercise response traits. Findings from genomic markers, muscle gene expression studies, and proteomic and metabolomics explorations are reviewed, along with lessons learned from a bioinformatics-driven analysis pipeline. The new opportunities being pursued in integrative -omics and physiology have extended considerably the expected life of HERITAGE and are being discussed in relation to the original conceptual model of the study.

Individual heart rate assessment and bout analysis of vigorous physical activity in children

Children should engage in 1 hr/day of moderate‐to‐vigorous physical activity (MVPA) that results in increased heart rates (HRs) (CDC, 2022). However, precise individualized HR criteria for MVPA are not provided, and it is unclear whether observed behaviors classified as MVPA are associated with elevated HRs indicative of MVPA. The current study replicated an individualized heart rate assessment (IHRA) for identifying MVPA HR zones in children (Van Camp et al., 2021). We then evaluated whether engaging in vigorous PA (VPA) for half of the session resulted in HRs indicative of VPA for at least half of the session when children engaged in running for 30, 60, 90, and 120 s bouts. Individual differences were observed during the IHRA. During the bout analysis, HRs were not within VPA zones for 50% of the session. However, HRs were within moderate PA (MPA) zones, with 30 s bouts producing the highest percentages of MPA.

A Non-Exercise Model for Predicting Cardiovascular Risks among Apparently Healthy Male Office Workers-Cross-Sectional Analysis: A Pilot Study

The health condition of working-age males in Poland remains largely associated with long-lasting sick leaves, one of the main reasons of which being cardiovascular diseases (CVD). The aim of this work was to develop a prediction model for FIT Treadmill Score (“FIT” refers to Henry Ford ExercIse Testing (FIT) Project) that only depends on easily accessible somatic data and smoking without the need to perform the exercise test anymore. The study comprised 146 men with a negative cardiological history, aged 26–60, with desk-jobs. By means of regression analysis it was tested to what degree obesity-related indices as well as smoking cigarettes allow for determining the measure level of mortality risk, without the necessity of performing an exercise test. The following independent variables were entered into the linear regression model: age, BMI, Fat%, waist circumference (WC), waist to height ratio (WHtR) as well as smoking. Statistically significant factors were singled out from among them. The obtained model accounts for a significant part (over 87%) of the variability of the mortality risk measure among the tested population. Based on the value of the standardised regression coefficient β, it can be stated that age is the factor that mostly determines the mortality risk measure, followed by the WHtR and smoking. The simplicity of the worked-out model and, resulting from it, the possibility of its common application should enable better health monitoring of working-age men with regard to cardiovascular disease occurrence and, related to it, mortality risk, thereby improving the quality of public health management.

Assessment of Peak Oxygen Uptake with a Smartwatch and its Usefulness for Training of Runners

Peak oxygen uptake (˙VO _2peak ) is an important factor contributing to running performance. Wearable technology may allow the assessment of ˙VO _2peak more frequently and on a larger scale. We aim to i) validate the ˙VO _2peak assessed by a smartwatch (Garmin Forerunner 245), and ii) discuss how this parameter may assist to evaluate and guide training procedures. A total of 23 runners (12 female, 11 male; ˙VO _2peak : 48.6±6.8 ml∙min ⁻¹ ∙kg ⁻¹ ) visited the laboratory twice to determine their ˙VO _2peak during a treadmill ramp test. Between laboratory visits, participants wore a smartwatch and performed three outdoor runs to obtain ˙VO _2peak values provided by the smartwatch. The ˙VO _2peak obtained by the criterion measure ranged from 38 to 61 ml∙min ⁻¹ ∙kg ⁻¹ . The mean absolute percentage error (MAPE) between the smartwatch and the criterion ˙VO _2peak was 5.7%. The criterion measure revealed a coefficient of variation of 4.0% over the VO2peak range from 38–61 ml∙min ⁻¹ ∙kg ⁻¹ . MAPE between the smartwatch and criterion measure was 7.1, 4.1 and −6.2% when analyzing ˙VO _2peak ranging from 39–45 ml∙min ⁻¹ ∙kg ⁻¹ , 45–55 ml∙min ⁻¹ ∙kg ⁻¹ or 55–61 ml∙min ⁻¹ ∙kg ⁻¹ , respectively.

Accuracy of wrist-worn wearable devices for determining exercise intensity

Objective

As an indicator of exercise intensity, heart rate can be measured in a timely manner using wrist-worn devices. No study has attempted to estimate a target exercise intensity using wearable devices. The objective of the study was to evaluate the validity of prescribing exercise intensity using wrist-worn devices.

Methods

Thirty healthy subjects completed a maximal cardiopulmonary exercise test. Their heart rates were recorded using an electrocardiogram and two devices—Apple Watch Series 6 and Garmin Forerunner 945. Exercise intensity with the target heart rate was defined as resting heart rate + (maximal heart rate − resting heart rate) * n% ( n%: 40–60% for moderate-intensity exercise and 60–89% for vigorous-intensity exercise). Heart rate was analyzed at the lower and upper limits of each exercise intensity (HR40, HR60, and HR89). The mean absolute percentage error and concordance correlation coefficient were calculated, and Bland–Altman plots and scatterplots were constructed.

Results

Both devices showed a low mean absolute error (1.16–1.48 bpm for Apple and 1.35–2.25 for Garmin) and mean absolute percentage error (<1% for Apple and 1.16–1.39% for Garmin) in all intensities. A substantial correlation with electrocardiogram-measured heart rate was observed for moderate to vigorous intensity with concordance correlation coefficient > 0.95 for both devices, except that Garmin showed moderate correlation at the upper limit of vigorous activity with concordance correlation coefficient = 0.936. Moreover, Bland–Altman plots and scatterplots demonstrated a strong correlation without systematic error when the values obtained via the two devices were compared with electrocardiogram measurements.

Conclusions

Our findings indicate the high validity of exercise prescriptions based on the heart rate measured by the two devices. Additional research should explore other populations to confirm these findings.

Monitoring Physical Activity Intensity During Pregnancy

For apparently healthy pregnant women, regular physical activity is recommended. The American College of Obstetricians and Gynecologists (ACOG) created recommendations for physical activity and exercise during pregnancy in 1985. At that time, pregnant women were advised to not exceed a heart rate of 140 beats per minute with physical activity. The heart rate recommendation was subsequently removed with the recommendations published in 1994, 2002, and 2015. In 2020, the ACOG updated its recommendations on physical activity for pregnant and postpartum women. The recommendation included exercising at a "fairly light to somewhat hard" perceived intensity and at less than 60-80% of age-predicted maximum heart rate, usually not exceeding a heart rate of 140 beats per minute. Women often seek advice from healthcare providers on physical activity during pregnancy, yet providers report concern about giving appropriate physical activity guidance. This paper summarizes the key scientific literature on monitoring absolute and relative exercise intensity in relation to the current ACOG recommendations, providing background on intensity-related concepts used in the recommendation. This paper also provides practical guidance to assist healthcare providers in relaying this information to pregnant women.

Agreement between heart rate at first ventilatory threshold on treadmill and at 6-min walk test in coronary artery disease patients on β-blockers treatment

The purpose of this study was to verify the accuracy of the agreement between heart rate at the first ventilatory threshold (HR_VT1) and heart rate at the end of the 6-min walk test (HR_6MWT) in coronary artery disease (CAD) patients on β-blockers treatment. This was a cross-sectional study with stable CAD patients, which performed a cardiopulmonary exercise test (CPET) on a treadmill and a 6-min walk test (6MWT) on nonconsecutive days. The accuracy of agreement between HR_VT1 and HR_6MWT was evaluated by Bland–Altman analysis and Lin’s concordance correlation coefficient (r_c), mean absolute percentage error (MAPE), and standard error of estimate (SEE). Seventeen stable CAD patients on β-blockers treatment (male, 64.7%; age, 61±10 years) were included in data analysis. The Bland–Altman analysis revealed a negative bias of −0.41±6.4 bpm (95% limits of agreements, −13 to 12.2 bpm) between HR_VT1 and HR_6MWT. There was acceptable agreement between HR_VT1 and HR_6MWT (r_c=0.84; 95% confidence interval, 0.63 to 0.93; study power analysis=0.79). The MAPE of the HR_6MWT was 5.1% and SEE was 6.6 bpm. The ratio HR_VT1/HR_peak and HR_6MWT/HR_peak from CPET were not significantly different (81%±5% vs. 81%±6%, P=0.85); respectively. There was a high correlation between HR_VT1 and HR_6MWT (r=0.85, P<0.0001). Finally, the results of the present study demonstrate that there was an acceptable agreement between HR_VT1 and HR_6MWT in CAD patients on β-blockers treatment and suggest that HR_6MWT may be useful to prescribe and control aerobic exercise intensity in cardiac rehabilitation programs.

Impact of equine assisted therapy on the cardiovascular parameters of the elderly

Introduction: Physical activity programs in the elderly aim to prevent and/or reduce the functional decline resulting from the senescence process. Several studies provide evidence that Equine Assisted Therapy (EAT) is an effective means of improving health. However, studies addressing the effects of EATon the cardiovascular system are scarce. Objective: To evaluate the effect of Equine Assisted Therapyon the cardiovascular responses of participants divided into two groups: normotensive and hypertensive. Methods: Twenty individuals participated, aged between 60 and 79 years, divided into three groups: Normotensive Group (n=14), Hypertensive Group (n=6) and all participants, Total Group (n=20). The Omron® HEM 742 blood pressure monitor was used to measure blood pressure and the Gerathem® portable finger oximeter to measure heart rate and blood oxygen saturation. Ten visits were made once a week, lasting 30 minutes. Results: Between the first and tenth interventions, there was a reduction in blood pressure and heart rate, with statistical significance of systolic blood pressure for the Hypertensive Group (p=0.0478), the Total Group (p=0.0201) and diastolic pressure for the Total Group (p=0.0421). There was also a statistically significant difference in systolic blood pressure and heart rate during some visits. Blood oxygen saturation increased after the intervention, but without statistical significance. Conclusion: Equine Assisted Therapy can promote a reduction in blood pressure in the elderly, especially hypertensive individuals.

Estimation of Heart Rate Using Regression Models and Artificial Neural Network in Middle-Aged Adults

Purpose: Heart rate is the most commonly used indicator in clinical medicine to assess the functionality of the cardiovascular system. Most studies have focused on age-based equations to estimate the maximal heart rate, neglecting multiple factors that affect the accuracy of the prediction. Methods: We studied 121 middle-aged adults at an average age of 57.2years with an average body mass index (BMI) of 25.9. The participants performed on a power bike with a starting wattage of 0W that was increased by 25W every 3min until the experiment terminated. Ambulatory blood pressure and electrocardiography were monitored through gas metabolic analyzers for safety concerns. Six descriptive characteristics of participants were observed, which were further analyzed using a multivariate regression model and an artificial neural network (ANN). Results: The input variables for the multivariate regression model and ANN were selected by correlation for the reduction of dimension. The accuracy of estimation by multivariate regression model and ANN was 9.74 and 9.42%, respectively, which outperformed the traditional age-based model (with an accuracy of 10.31%). Conclusion: This study provides comprehensive approaches to estimate the maximal heart rate using multiple indicators, revealing that both the multivariate regression model and ANN incorporated with age, resting heart rate (RHR), and second-order heart rate (SOHR) are more accurate than univariate models.

Retrospective Analysis of Training and Its Response in Marathon Finishers Based on Fitness App Data

Objective: Finishing a marathon requires to prepare for a 42.2 km run. Current literature describes which training characteristics are related to marathon performance. However, which training is most effective in terms of a performance improvement remains unclear.

Methods: We conducted a retrospective analysis of training responses during a 16 weeks training period prior to an absolved marathon. The analysis was performed on unsupervised fitness app data (Runtastic) from 6,771 marathon finishers. Differences in training volume and intensity between three response and three marathon performance groups were analyzed. Training response was quantified by the improvement of the velocity of 10 km runs Δv₁₀ between the first and last 4 weeks of the training period. Response and marathon performance groups were classified by the 33.3rd and 66.6th percentile of Δv₁₀ and the marathon performance time, respectively.

Results: Subjects allocated in the faster marathon performance group showed systematically higher training volume and higher shares of training at low intensities. Only subjects in the moderate and high response group increased their training velocity continuously along the 16 weeks of training.

Conclusion: We demonstrate that a combination of maximized training volumes at low intensities, a continuous increase in average running speed up to the aimed marathon velocity and high intensity runs ≤ 5 % of the overall training volume was accompanied by an improved 10 km performance which likely benefited the marathon performance as well. The study at hand proves that unsupervised workouts recorded with fitness apps can be a valuable data source for future studies in sport science.

Psychophysiological Benefits of Real-Time Heart Rate Feedback in Physical Education

School physical education (PE) has the potential to contribute to public-health promotion and well-being, but oftentimes students' lack of motivation toward PE or physical activity in general, especially during adolescence, diminishes, or eradicates the positive effects associated with PE. Therefore, practical approaches are required that help teachers to increase or awake students intrinsic motivation toward PE, for which self-determination theory may provide the conceptual framework. In that regard, the purpose of the present study was to examine whether the use of real-time, heart rate feedback (as a method to support students' need for autonomy and competence) during regular PE lessons has the potential to increase students' autonomous motivation and physical effort. To achieve this, we had forty healthy adolescents between 16 and 17 years of age run for 30 min either with (experimental group, EG) or without (control group, CG) real-time, individualized heart rate feedback during a regular PE class and compared physical and perceived exertion as well as joy of running between the two groups. Participants were randomly assigned to the groups. Our data revealed that participants in the EG enjoyed running more than participants in the CG (joy of running was 3.20 in the EG vs. 2.63 in the CG, p = 0.03) despite a higher physical (163 to 178 in EG vs. 141 to 156 beats per minute in the CG, p < 0.001) and perceived exertion (rating of perceived exertion of 13.22 in the EG vs. 10.59 in the CG, p = 0.02). That means, running with real-time, individualized heart rate feedback apparently increased participants' motivation to run and to enjoy running at higher levels of exertion. In that regard, real-time, individualized activity feedback should be implemented in regular PE classes systematically and repeatedly to create a controllable and attainable situation that allows students to actively adjust their own behavior to achieve appealing and realistic goals.

Demarcating Exercise Intensity Domains in Freestyle Swimming: Is There an Alternative to Incremental Step Test and Beats Below HRmax Method?

Piatrikova, E, Sousa, AC, Willsmer, NJ, Gonzalez, JT, and Williams, S. Demarcating exercise intensity domains in freestyle swimming: is there an alternative to incremental step test and beats below HRmax method? J Strength Cond Res 34(9): 2575-2584, 2020-Critical power derived from the 3-minute all-out test (3MT) was recently used to estimate the exercise intensity boundaries in competitive cyclists. Considering that physiological testing is challenging in swimming, the purpose of this study was to examine whether critical speed (CS) derived from the 3MT could be used for the same purpose in swimming. The second aim was to assess the accuracy of the 50-40 and 30-20 beats below the maximal (BBM) heart rate method, currently used by swimming coaches to demarcate boundaries between moderate-heavy and heavy-severe exercise, respectively. Thirteen swimmers completed an incremental step test (IST) and 3MT in freestyle to establish speeds at: lactate threshold (LT), lactate turnpoint (LTP), maximum aerobic speed (Smax), and CS. Using linear regression through origin, speeds at LT, LTP, and Smax were predicted at 89, 98, and 104% of CS derived from the 3MT, respectively. There were no significant differences between threshold speeds derived from the IST and 3MT (p > 0.05), and nearly perfect correlations at LT (1.21 ± 0.06; 1.21 ± 0.06 m·s; r = 0.92) and LTP (1.33 ± 0.07; 1.33 ± 0.07 m·s; r = 0.90), and very large correlations at Smax (1.40 ± 0.06; 1.40 ± 0.07 m·s; r = 0.88; all p < 0.0001). Speeds estimated at 50 (1.11 ± 0.08 m·s) and 40 BBM (1.17 ± 0.07 m·s) were lower compared with LT, and speeds estimated at 30 (1.23 ± 0.07 m·s) and 20 BBM (1.29 ± 0.07 m·s) were lower compared with LTP and CS (all p < 0.02). The 3MT can therefore be used as an alternative to the IST to estimate exercise intensity boundaries, in practical settings where resources or time might be limited. However, the BBM significantly underestimates speeds at LT, LTP, and CS.

Accuracy of Commonly Used Age-Predicted Maximal Heart Rate Equations

Age-predicted maximal heart rate (APMHR) is an essential measure for healthcare professionals in determining cardiovascular response to exercise testing, exertion, and prescription. Although multiple APMHR prediction equations have been validated for specific populations, the accuracy of each within a general population requires testing. We aimed to determine which APMHR equation (Fox, Gellish, Gulati, Tanaka, Arena, Astrand, Nes, Fairbarn) most accurately predicts max heart rate (HRmax) in a general population. HRmax from 99 graded treadmill exercise tests (GXT) were measured. GXTs ended upon volitional fatigue and were only included for analysis if RER > 1.10. Individual paired t-test were performed to determine if significant differences existed between measured and predicted HRmax, along with root mean square errors for each equation. Bland-Altman plots were constructed to determine agreement between equations and measured HRmax. Significant differences between measured and predicted HRmax were found for the Gulati, Astrand, Nes, and Fairbarn (male) equations (p < 0.05). Bland-Altman plots revealed wide limits of agreement for all nine APMHR equations, suggesting poor agreement between measured and predicted HRmax. Proportional bias indicates that prediction equations under and overestimated HRmax in individuals with lower and higher measured HRmax, respectively, with the exception of the Fox equation. All equations used in this study show poor agreement between measured HRmax and APMHR. The Fox equation may represent the best option for a general population as it is less likely to under or overestimate based on individual HRmax. Individuals should use data from GXTs to determine HRmax when applicable to ensure accuracy.

Simplified indices of exercise tolerance in patients with multiple sclerosis and healthy subjects: A case-control study

Among patients with multiple sclerosis (MS), the impairment of exercise tolerance is closely related to disability. Maximal oxygen uptake (VO_2max ) is the gold standard to assess exercise tolerance in healthy subjects (HS). Among patients with MS, the accuracy of VO_2max measurement is often impaired because the patients are unable to reach the maximal exercise intensity due to interdependent factors linked to the disease (such as pathological fatigue, pain, lack of exercise habit, and lack of mobility). This study assesses the accuracy of simplified indices for assessing exercise tolerance, which are more suitable in patients with MS. They are simple in the way they are either measurable during submaximal exercise (oxygen uptake efficiency slopes (OUES), physical working capacity at 75% of maximal heart rate (PWC_75% ), oxygen consumption at a respiratory exchange ratio of 1 (VO₂ @RER1)) or not based on gas exchange analysis (peak work rate (PWR)-based predictive equation and PWC_75% ). All indices were significantly lower in the MS group compared to the HS group (P < .001). OUES appeared highly correlated (r > .70, P < .001) with VO_2peak , in both groups, without difference between groups. PWR-based prediction of VO_2peak showed a standard error of the estimate of 315 mL min^-1 in HS and 176 mL min^-1 in MS. PWC_75% did not correlate to VO_2peak in neither group. These findings suggest an impairment of exercise tolerance functions in mildly disabled persons with MS, independently from other factors. Submaximal indices involving gas exchange analysis or peakWR-based estimation of VO_2peak are usable to accurately assess exercise tolerance.

Criteria for the determination of maximal oxygen uptake in patients newly diagnosed with cancer: Baseline data from the randomized controlled trial of physical training and cancer (Phys-Can)

Introduction

Maximal oxygen uptake (V˙O2max) is a measure of cardiorespiratory fitness often used to monitor changes in fitness during and after treatment in cancer patients. There is, however, limited knowledge in how criteria verifying V˙O2max work for patients newly diagnosed with cancer. Therefore, the aim of this study was to describe the prevalence of fulfillment of typical criteria verifying V˙O2max and to investigate the associations between the criteria and the test leader’s evaluation whether a test was performed “to exhaustion”. An additional aim was to establish new cut-points within the associated criteria.

Methods

From the Phys-Can randomized controlled trial, 535 patients (59 ±12 years) newly diagnosed with breast (79%), prostate (17%) or colorectal cancer (4%) performed an incremental V˙O2max test on a treadmill. The test was performed before starting (neo-)adjuvant treatment and an exercise intervention. Fulfillment of different cut-points within typical criteria verifying V˙O2max was described. The dependent key variables included in the initial bivariate analysis were achievement of a V˙O2 plateau, peak values for maximal heart rate, respiratory exchange ratio (RER), the patients’ rating of perceived exertion on Borg’s scale_6-20 and peak breathing frequency (f_R). A receiver operating characteristic analysis was performed to establish cut-points for variables associated with the test leader’s evaluation. Last, a cross-validation of the cut-points found in the receiver operating characteristic analysis was performed on a comparable sample of cancer patients (n = 80).

Results

The criteria RERpeak (<0.001), Borg’s RPE (<0.001) and f_R peak (p = 0.018) were associated with the test leader’s evaluation of whether a test was defined as “to exhaustion”. The cut-points that best predicted the test leader’s evaluation were RER ≥ 1.14, RPE ≥ 18 and f_R ≥ 40. Maximal heart rate and V˙O2 plateau was not associated with the test leader’s evaluation.

Conclusion

We recommend a focus on RER (in the range between ≥1.1 and ≥1.15) and RPE (≥17 or ≥18) in addition to the test leader’s evaluation. Additionally, a f_R peak of ≥40 breaths/min may be a cut-point to help the test leader evaluate the degree of exhaustion. However, more research is needed to verify our findings, and to investigate how these criteria will work within a population that are undergoing or finished with cancer treatment.

The Contribution of Functional Magnetic Resonance Imaging to the Understanding of the Effects of Acute Physical Exercise on Cognition

The fact that a single bout of acute physical exercise has a positive impact on cognition is well-established in the literature, but the neural correlates that underlie these cognitive improvements are not well understood. Here, the use of neuroimaging techniques, such as functional magnetic resonance imaging (fMRI), offers great potential, which is just starting to be recognized. This review aims at providing an overview of those studies that used fMRI to investigate the effects of acute physical exercises on cerebral hemodynamics and cognition. To this end, a systematic literature survey was conducted by two independent reviewers across five electronic databases. The search returned 668 studies, of which 14 studies met the inclusion criteria and were analyzed in this systematic review. Although the findings of the reviewed studies suggest that acute physical exercise (e.g., cycling) leads to profound changes in functional brain activation, the small number of available studies and the great variability in the study protocols limits the conclusions that can be drawn with certainty. In order to overcome these limitations, new, more well-designed trials are needed that (i) use a more rigorous study design, (ii) apply more sophisticated filter methods in fMRI data analysis, (iii) describe the applied processing steps of fMRI data analysis in more detail, and (iv) provide a more precise exercise prescription.

Heart Rhythm Analyzed via Shapelets Distinguishes Sleep From Awake

Automatically determining when a person falls asleep from easily available vital signals is important, not just for medical applications but also for practical ones, such as traffic safety or smart homes. Heart dynamics and respiration cycle couple differently during sleep and awake. Specifically, respiratory modulation of heart rhythm or respiratory sinus arrhythmia (RSA) is more prominent during sleep, as both sleep and RSA are connected to strong vagal activity. The onset of sleep can be recognized or even predicted as the increase of cardio-respiratory coupling. Here, we employ this empirical fact to design a method for detecting the change of consciousness status (sleep/awake) based only on heart rate variability (HRV) data. Our method relies on quantifying the (self)similarity among shapelets - short chunks of HRV time series - whose "shapes" are related to the respiration cycle. To test our method, we examine the HRV data of 75 healthy individuals recorded with microsecond precision. We find distinctive patterns stable across age and sex, that are not only indicative of sleep and awake, but allow to pinpoint the change from awake to sleep almost immediately. More systematic analysis along these lines could lead to a reliable prediction of sleep.

Validity of Prediction Equations of Maximal Heart Rate in Physically Active Female Adolescents and the Role of Maturation

Background and objectives: Maximal heart rate (HR_max) is an important training and testing tool, especially in the context of evaluating intensity in exercise prescription; however, few studies have examined the validity of prediction equations of HR_max in physically active female adolescents and the role of maturation level. Therefore, the aim of the present study was to examine the differences between measured and predicted HR_max in a sample of physically active female adolescents. Materials and Methods: Seventy-one selected volleyball players (age 13.3 ± 0.7 years, body mass 62.0 ± 7.2 kg, height 1.72 ± 0.06 m) performed a 20 m shuttle run endurance test, and the actual HR_max was compared with Tanaka HR_max (‘208 − 0.7 × age’) and Fox HR_max (‘220 − age’). Results: A large main effect of assessment method on HR_max was found (p < 0.001, η² = 0.486) with Fox overestimating actual HR_max by 6.8 bpm (95% confidence intervals, CI; 4.2, 9.3) and Tanaka underestimating actual HR_max by −2.6 bpm (95% CI; −5.1, −0.1). The more matured participants had similar actual HR_max (mean difference −2.4 bpm; 95% CI; −6.5, 1.7; p = 0.242, d = −0.28), difference Fox − actual HR_max (1.5 bpm; 95% CI; −2.6, 5.6, p = 0.466, d = 0.17), and difference Tanaka − actual HR_max (1.7 bpm; 95% CI; −2.4, 5.8; p = 0.414, d = 0.19) to the less matured participants. Conclusions: These findings suggest that age-based prediction equations of HR_max developed in adult populations should be applied with caution in physically active female adolescents, and Tanaka should be preferred instead of the Fox equation.

Systematic Balance Exercises Influence Cortical Activation and Serum BDNF Levels in Older Adults

We sought to investigate whether systematic balance training modulates brain area activity responsible for postural control and influence brain-derived neurotrophic factor (BDNF) mRNA protein expression. Seventy-four older adults were randomly divided into three groups (mean age 65.34 ± 3.79 years, 30 females): Classic balance exercises (CBT), virtual reality balance exercises (VBT), and control (CON). Neuroimaging studies were performed at inclusion and after completion of the training or 12 weeks later (CON). Blood samples were obtained to measure BDNF expression. The study revealed significant interaction of sessions and groups: In the motor imagery (MI) condition for supplementary motor area (SMA) activity (F_{at peak} = 5.25, p < 0.05); in the action observation (AO) condition for left and right supramarginal gyrus/posterior insula (left: F_{at peak} = 6.48, p < 0.05; right: F_{at peak} = 6.92, p < 0.05); in the action observation together with motor imagery (AOMI) condition for the middle occipital gyrus (laterally)/area V5 (left: F_{at peak} = 6.26, p < 0.05; right: F_{at peak} = 8.37, p < 0.05), and in the cerebellum–inferior semilunar lobule/tonsil (F_{at peak} = 5.47, p < 0.05). After the training serum BDNF level has increased in CBT (p < 0.001) and in CBT compared to CON (p < 0.05). Systematic balance training may reverse the age-related cortical over-activations and appear to be a factor mediating neuroplasticity in older adults.

Dose-Response Matters! - A Perspective on the Exercise Prescription in Exercise-Cognition Research

In general, it is well recognized that both acute physical exercises and regular physical training influence brain plasticity and cognitive functions positively. However, growing evidence shows that the same physical exercises induce very heterogeneous outcomes across individuals. In an attempt to better understand this interindividual heterogeneity in response to acute and regular physical exercising, most research, so far, has focused on non-modifiable factors such as sex and different genotypes, while relatively little attention has been paid to exercise prescription as a modifiable factor. With an adapted exercise prescription, dosage can be made comparable across individuals, a procedure that is necessary to better understand the dose-response relationship in exercise-cognition research. This improved understanding of dose-response relationships could help to design more efficient physical training approaches against, for instance, cognitive decline.

Evaluation of Wearable Digital Devices in a Phase I Clinical Trial

We assessed the performance of two US Food and Drug Administration (FDA) 510(k)‐cleared wearable digital devices and the operational feasibility of deploying them to augment data collection in a 10‐day residential phase I clinical trial. The Phillips Actiwatch Spectrum Pro (Actiwatch) was used to assess mobility and sleep, and the Vitalconnect HealthPatch MD (HealthPatch) was used for monitoring heart rate (HR), respiratory rate (RR), and surface skin temperature (ST). We measured data collection rates, compared device readouts with anticipated readings and conventional in‐clinic measures, investigated data limitations, and assessed user acceptability. Six of nine study participants consented; completeness of data collection was adequate (> 90% for four of six subjects). A good correlation was observed between the HealthPatch device derived and in‐clinic measures for HR (Pearson r = 0.71; P = 2.2e‐16) but this was poor for RR (r = 0.08; P = 0.44) and ST (r = 0.14; P = 0.14). Manual review of electrocardiogram strips recorded during reported episodes of tachycardia > 180 beats/min showed that these were artefacts. The HealthPatch was judged to be not fit‐for‐purpose because of artefacts and the need for time‐consuming manual review. The Actiwatch device was suitable for monitoring mobility, collecting derived sleep data, and facilitating the interpretation of vital sign data. These results suggest the need for fit‐for‐purpose evaluation of wearable devices prior to their deployment in drug development studies.

High-intensity interval training in people with Parkinson's disease: a randomized, controlled feasibility trial

OBJECTIVES:

To investigate whether people with Parkinson's disease can exercise at a high-intensity across a 12-week intervention and to assess the impact of the intervention on cardiorespiratory fitness.

DESIGN:

This is a randomized, controlled, feasibility study with waiting list control. Assessors were blinded to group allocation.

SETTING:

The intervention took place at an exercise centre and assessments at a district general hospital.

SUBJECTS:

This study included 20 people with idiopathic Parkinson's disease.

INTERVENTION:

A total of 36 exercise sessions over 12 weeks, with each session lasting ~45 minutes, were conducted.

MAIN MEASURES:

The main measures were maximal heart rates achieved during exercise, recruitment rate, attendance, drop-out, change in peak oxygen consumption, cardiac output, cognitive function and quality of life. The study was considered technically feasible if participants achieved ⩾85% of maximal heart rate during exercise.

RESULTS:

There were 12 male and 8 female participants; they had a mean age of 68.5 years (standard deviation 6.825). Two participants were of Hoehn and Yahr stage I, 11 stage II and 7 stage III. In all, 17 participants completed the intervention. The median (interquartile range) proportion of repetitions delivered across the intervention which met our high-intensity criterion was 80% (67% to 84%). Mean peak heart rate was 88.8% of maximal. Peak oxygen consumption increased by 2.8 mL kg^-1 min^-1 in the intervention group and 1.5 mL kg^-1 min^-1 in the control group after 12 weeks of exercise. We estimate that a fully powered randomized controlled trial would require 30 participants per group.

CONCLUSION:

High-intensity interval exercise is feasible in people with Parkinson's disease. Improvements in cardiorespiratory function are promising.

Age-Predicted Maximal Heart Rate in Recreational Marathon Runners: A Cross-Sectional Study on Fox's and Tanaka's Equations

Age-based prediction equations of maximal heart rate (HR_max), such as the popular formulas Fox's 220-age, or Tanaka's 208-0.7 × age, have been widely used in various populations. Surprisingly, so far these equations have not been validated in marathon runners, despite the importance of the role of HR_max for training purposes in endurance running. The aim of the present study was to examine the validity of Fox and Tanaka equations in a large sample of women and men recreational marathon runners. Participants (n = 180, age 43.2 ± 8.5 years, VO_2max 46.8 mL/min/kg, finishers in at least one marathon during the last year) performed a graded exercise test on a treadmill, where HR_max was measured. Measured HR_max correlated largely with age in the total sample (r = −0.50, p < 0.001), women (r = −0.60, p < 0.001) and men (r = −0.53, p < 0.001). In women, a large main effect of method on HR_max (p = 0.001, η² = 0.294) was shown with measured HR_max lower than Fox-HR_max (−4.8 bpm; −8.4, −1.3) and Tanaka-HR_max (−4.9 bpm; −8.1, −1.8). In men, a moderate effect of assessment method on HR_max was found (p = 0.001, η² = 0.066) with measured HR_max higher than Fox-HR_max (+2.8; 1.0, 4.6), Tanaka-HR_max higher than Fox-HR_max (+1.2; 0.7, 1.7). Based on these findings, it was concluded that Fox and Tanaka' formulas overestimated HR_max by ~5 bpm in women, whereas Fox underestimated HR_max in men by ~3 bpm. Thus, we recommend the further use of Tanaka's formula in men marathon runners. In addition, exercise physiologists and sport scientists should consider the observed differences among various assessment methods when performing exercise testing or prescribing training program relying on HR.

Cross-validation of Peak Oxygen Consumption Prediction Models From OMNI Perceived Exertion

This study cross-validated statistical models for prediction of peak oxygen consumption using ratings of perceived exertion from the Adult OMNI Cycle Scale of Perceived Exertion. 74 participants (men: n=36; women: n=38) completed a graded cycle exercise test. Ratings of perceived exertion for the overall body, legs, and chest/breathing were recorded each test stage and entered into previously developed 3-stage peak oxygen consumption prediction models. There were no significant differences (p>0.05) between measured and predicted peak oxygen consumption from ratings of perceived exertion for the overall body, legs, and chest/breathing within men (mean±standard deviation: 3.16±0.52 vs. 2.92±0.33 vs. 2.90±0.29 vs. 2.90±0.26 L·min(-1)) and women (2.17±0.29 vs. 2.02±0.22 vs. 2.03±0.19 vs. 2.01±0.19 L·min(-1)) participants. Previously developed statistical models for prediction of peak oxygen consumption based on subpeak OMNI ratings of perceived exertion responses were similar to measured peak oxygen consumption in a separate group of participants. These findings provide practical implications for the use of the original statistical models in standard health-fitness settings.

Maximum Heart Rate during exercise: Reliability of the 220-age and Tanaka formulas in healthy young people at a moderate altitude

<p>Background. The formulas to predict maximum heart rate have been used for many years in different populations.</p><p><br />Objective. To verify the significance and the association<br />of formulas of Tanaka and 220-age when compared to real<br />maximum heart rate.</p><p><br />Materials and methods. 30 subjects –22 men, 8 women–<br />between 18 and 30 years of age were evaluated on a cycle<br />ergometer and their real MHR values were statistically compared with the values of formulas currently used to predict MHR.<br />Results. The results demonstrate that both Tanaka p=0.0026 and 220-age p=0.000003 do not predict real MHR, nor does a linear association exist between them.</p><p><br />Conclusions. Due to the overestimation with respect to real<br />MHR value that these formulas make, we suggest a correction<br />of 6 bpm to the final result. This value represents the median<br />of the difference between the Tanaka value and the real MHR.<br />Both Tanaka (r=0.272) and 220-age (r=0.276) are not adequate predictors of MHR during exercise at the elevation of Bogotá in subjects of 18 to 30 years of age, although more study with a larger sample size is suggested.</p>

Age-predicted vs. measured maximal heart rate in young team sport athletes

BACKGROUND

Although maximal heart rate (HR)max is used widely to assess exercise intensity in sport training and particularly in various team sports, there are limited data with regards to the use of age-based prediction equations of HRmax in sport populations. The aim of this study was to compare the measured-HRmax with three prediction equations (Fox-HRmax = 220-age and Tanaka-HRmax = 208-0.7×age and Nikolaidis-HRmax = 223-1.44×age) in young team sport athletes.

MATERIALS AND METHODS

Athletes of soccer, futsal, basketball and water polo, classified into three age groups (u-12, 9-12 years, n = 50; u-15, 12-15 years, n = 40; u-18, 15-18 years, n = 57), all members of competitive clubs, voluntarily performed a graded exercise field test (20 m shuttle run endurance test) to assess HRmax.

RESULTS

Fox-HRmax and Nikolaidis-HRmax overestimated measured-HRmax, while Tanaka-HRmax underestimated it (P < 0.001). However, this trend was not consistent when examining each group separately; measured-HRmax was similar with Tanaka-HRmax in u-12 and u-15, while it was similar with Nikolaidis-HRmax in u-18.

CONCLUSION

The results of this study failed to validate two widely used and one recently developed prediction equations in a large sample of young athletes, indicating the need for specific equation in different age groups. Therefore, coaches and fitness trainers should prefer Tanaka-HRmax when desiring to avoid overtraining, while Fox-HRmax and Nikolaidis-HRmax should be their choice in order to ensure adequate exercise intensity.

A study of the 200-metre fast walk test as a possible new assessment tool to predict maximal heart rate and define target heart rate for exercise training of coronary heart disease patients

Objective:

To develop a new predictive model of maximal heart rate based on two walking tests at different speeds (comfortable and brisk walking) as an alternative to a cardiopulmonary exercise test during cardiac rehabilitation.

Design:

Evaluation of a clinical assessment tool.

Setting:

A Cardiac Rehabilitation Department in France.

Subjects:

A total of 148 patients (133 men), mean age of 59 ±9 years, at the end of an outpatient cardiac rehabilitation programme.

Main measures:

Patients successively performed a 6-minute walk test, a 200 m fast-walk test (200mFWT), and a cardiopulmonary exercise test, with measure of heart rate at the end of each test. An all-possible regression procedure was used to determine the best predictive regression models of maximal heart rate. The best model was compared with the Fox equation in term of predictive error of maximal heart rate using the paired t-test.

Results:

Results of the two walking tests correlated significantly with maximal heart rate determined during the cardiopulmonary exercise test, whereas anthropometric parameters and resting heart rate did not. The simplified predictive model with the most acceptable mean error was: maximal heart rate = 130 − 0.6 × age + 0.3 × HR200mFWT (R2 = 0.24). This model was superior to the Fox formula (R2 = 0.138). The relationship between training target heart rate calculated from measured reserve heart rate and that established using this predictive model was statistically significant ( r = 0.528, p < 10−6).

Conclusions:

A formula combining heart rate measured during a safe simple fast walk test and age is more efficient than an equation only including age to predict maximal heart rate and training target heart rate.