Predicted VO2 Max Research Articles

PREDICTING VO2 MAX USING ACCELEROMETRY AND HEART RATE METRICS

Traditional physical activity metrics include reported time spent in various levels of exertion, from sedentary to vigorous. Previous research has shown an association between time spent in moderate or vigorous activities and maximal aerobic capacity (VO2 max) in older adults, but the association between accelerometry derived activity metrics and VO2 max remains undefined. We modeled the association between VO2 max and objectively measured physical activity and heart rate in 411 BLSA participants (53% male, mean age 67, range 31–88), using percentages of heart rate reserve to define intensity of daily activities. Although the association between time spent in vigorous activities and VO2 max was significant, a continuous measure of total daily activity provided stronger results and a better model fit (accounting for 12% vs. 8% of unexplained variance), suggesting that total daily activity is a better predictor of VO2 max with aging, likely due to reduced intensity of daily activities.

New Generalized Equation for Predicting Maximal Oxygen Uptake (from the Fitness Registry and the Importance of Exercise National Database)

Impaired cardiorespiratory fitness (CRF) is closely linked to chronic illness and associated with adverse events. The American College of Sports Medicine (ACSM) regression equations (ACSM equations) developed to estimate oxygen uptake have known limitations leading to well-documented overestimation of CRF, especially at higher work rates. Thus, there is a need to explore alternative equations to more accurately predict CRF. We assessed maximal oxygen uptake (VO2 max) obtained directly by open-circuit spirometry in 7,983 apparently healthy subjects who participated in the Fitness Registry and the Importance of Exercise National Database (FRIEND). We randomly sampled 70% of the participants from each of the following age categories: <40, 40 to 50, 50 to 70, and ≥70 and used the remaining 30% for validation. Multivariable linear regression analysis was applied to identify the most relevant variables and construct the best prediction model for VO2 max. Treadmill speed and treadmill speed× grade were considered in the final model as predictors of measured VO2 max and the following equation was generated: VO2 max in ml O2/kg/min= speed (m/min)× (0.17+ fractional grade× 0.79)+ 3.5. The FRIEND equation predicted VO2 max with an overall error >4 times lower than the error associated with the traditional ACSM equations (5.1 ± 18.3% vs 21.4 ± 24.9%, respectively). Overestimation associated with the ACSM equation was accentuated when different protocols were considered separately. In conclusion, The FRIEND equation predicts VO2 max more precisely than the traditional ACSM equations with an overall error >4 times lower than that associated with the ACSM equations.

A Pregnancy-Specific Equation for Predicting Cardiorespiratory Fitness

PURPOSE: The ability to measure VO2max is important for determining health status and prescribing exercise. However, directly measuring VO2max is not always feasible, particularly in special populations such as pregnant women. Therefore, the purpose of this study was to create pregnancy-specific equations to predict cardiorespiratory fitness using baseline and exercise data. METHODS: Nineteen pregnant women (Age: 29.8±3.1 years, Pre-pregnancy BMI: 23.8± 3.9 kg/m2), Gestation Age: 22.0±1.4 weeks) participated in the study. Each participant completed baseline measurements/anthropometrics and the Bruce protocol maximal treadmill test. Exercise data was obtained from the maximal test using heart rate from stages 1, 2, 3, and 4, and used to create regression equations calculated to predict measured VO2max levels. In addition, other variables such as body mass index, age, gestation age, and physical activity level were included in these in regression models based on their predictive ability. RESULTS: Stepwise multiple regression analysis (SPSS, version 24) was conducted to predict the VO2max in pregnant women based upon baseline measures and exercise data. The results of this analysis indicated that VO2max was significantly related to maximum heart rate (maxHR), F(1,11) = 22.38, p < .001, and demonstrated R2 = .670 and adjusted R2 = .640. The regression equation for this linear model is: VO2max = -179.74 + (1.25 x maxHR) The other baseline and exercise variables did not predict significantly in this sample. Based upon these results, maxHR appears to be the best predictor of VO2max in pregnant women. CONCLUSIONS: Data from the current study was used to create an equation that can be used to predict cardiorespiratory fitness in pregnant women. A logical next step is to further validate such equations in a larger cohort of pregnant women. Validated equations created will allow healthcare providers to safely and effectively predict their pregnant patients’ fitness level; thus, allowing them to best tailor their patients’ exercise prescriptions in order to maximize the benefits of exercise in both the mother and her offspring.

Predicting Maximal Oxygen Uptake in Adults Using a Walk Test

PURPOSE: Maximal oxygen consumption (VO2max) testing is not always feasible, practical, or safe. The purpose of this study was to find a good fitting VO2max prediction equation using a 9-minute walking test. METHODS: Healthy adults aged 18-79 years participated in this study. Resting heart rate (RHR) and a VO2max test to volitional fatigue were assessed. Participants were asked to complete a 9-minute, ramped-intensity, over-ground walking test, where they were instructed to walk for 3-minutes each at a self-selected slower than normal, normal, and faster than normal walking speed. VO2 and heart rate (HR) responses were collected using a portable metabolic device and averaged the last two minutes of each stage. These represent the main predictors of interest in this study. Other covariates were age, gender, body mass (kg) and change in HR from RHR (delta HR). A stepwise selection method with a p-value criterion of .05 for inclusion and exclusion was used to determine the predictive model. RESULTS: The final sample (N=123) was 57.7% female, average age 46.4±17.2 years, BMI 25.8±4.6 kg/m2, VO2max 34.7±10.5 ml/kg/min, and RHR 60.5±9.2 bpm. A null model with only age (p<0.001), gender (p<0.0001), and body mass (p<.0001) was able to account for 73.5% of the variance in VO2max (R2=.736). The estimated prediction model using measurements from the normal walking speed stage including V02 (p<0.0001), HR (p<0.0001) and delta HR (p=0.0051) along with covariates age (p<0.0001), gender (p<0.0001) and body mass (p<0.0001) was able to achieve an R2 of 83.3%. The addition of these variables increased the strength of the prediction model by ~10%. CONCLUSION: The results from this study indicate that a 3-minute over-ground walking test at a self-selected normal pace, where only VO2 and HR are recorded in conjunction with demographic variables, is able to provide a good fitting model to predict VO2max. Compared to other reported over-ground submaximal prediction equations, the model obtained in this study was able to achieve a similar fit in predicting VO2max.

Determine of heart rate response during official competition in junior girl basketball players&lt;p&gt;Yıldız kız basketbolcularda resmi müsabaka sırasında kalp atım hızı yanıtlarının belirlenmesi

Developing technological issues provided that measure complex structure of team sports physiological loading during competition and trainings. During the last decade, limited numbers of researches reported about loading intensity and physiological response of junior girl basketball players during official competition. In this context, The aim of this study is determine of heart rate (HR) response during official competition in junior girl basketball players. 12 junior girl basketball players (age: 14.75±0.55 years, height: 168.48±5.3 cm, weight: 62.6±9.52 kg, predicted VO²max: 49.76±3.64 ml/kg/min) participated to this study. The HR measurements were taken official regional clubs league championship. Two days after followed up competition players performed the Shuttle Run Test (SRT) for determine maxHR levels. All HR measurement was taken by Polar Team2 (Finland). Frequency analyses were performed for the obtained data. Wilcoxon Rank Test was used for determined HR difference between halves. Friedman’s test was used for detected HR differences between periods. According to the analysis results, the maxHR levels obtained during SRT of the players were determined as 196,33 ± 10,04. The meanHR was determined 176,03±10,36 (89.66% of maxHR) in all competition. First half meanHR was 177,16±13,19 (90.23% of maxHR), second half meanHR was 175,00±11,56 (89.13% of maxHR), first period meanHR was 177,16±13,94 (90.00% of maxHR), second period meanHR was 176,87±12,10 (88.88% of maxHR), third period meanHR was 176,88±12,10 (90.08% of maxHR), fourth period meanHR was 179,40±10,71 (91.73% of maxHR). There were no difference found between halves and periods (p&gt;0.05). As a result of this study, HR response showed that intensity of junior girl basketball competition is approximately 89,66% of maxHR. This results showed that competition intensity is very high during all halves and periods. The determination of the official competition intensity of junior girl basketball players can provide important knowledge to coaches that young players have improved their sporting performance. ÖzetMüsabaka ve antrenmanlar sırasında takım sporunun fizyolojik yükünün karmaşık yapısını ölçen teknolojik gelişmeler sağlanmıştır. Son on yılda, resmi müsabaka sırasında yıldız kız basketbolcuların fizyolojik tepkileri ve yüklenme yoğunluğu ile ilgili sınırlı sayıda araştırma rapor edilmiştir. Bu bağlamda, bu çalışmanın amacı, yıldız kız basketbolcuların resmi müsabaka sırasında kalp atım hızı (KAH) yanıtlarını belirlemektir. Çalışmaya 12 yıldız kız basketbolcu (yaş: 14,75±0,55, boy: 168,48±5,30, vücut ağırlığı: 62,6±9,52, tahmini VO²maks: 49,76±3,64 ml/kg/dk) katıldı. KAH ölçümleri resmi bölgesel klüpler lig şampiyonasında alındı. Maçtan iki gün sonra KAH seviyelerini belirlemek için 20 m. Mekik Koşusu Testi (20MKT) uygulandı. Basketbolcuların müsabaka ve 20MKT sırasında kalp atım hızları Polar Team2 (Finland) sistem ile ölçüldü. Elde edilen veriler için frekans analizi uygulandı. Yarılar arasındaki KAH farkı için Wilcoxon Rank Testi kullanıldı. Periodlar arasındaki KAH farkları Friedman’s Testi ile tespit edildi. Analiz sonuçlarına göre, oyuncuların 20MKT sırasında elde edilen maksKAH değerleri 196,33±10.04 olarak tespit edildi. Basketbolcuların tüm müsabaka ortKAH 175,93±10,19 (maks KAH’ın %89.66). İlk yarı ortKAH 177,16±13,19 (maksKAH'ın %90.23), ikinci yarı ortHR 175,00±11,56 (maksKAH'ın 89.13), ilk period ortHR 177,16±13,94 (maksKAH'ın %90.00), ikinci period ortHR 176,87 ± 12,10 (maksKAH'ın %88.88), üçüncü period ortHR 176,88±12,10 (maksKAH'ın %90.08), dördüncü period ortHR 179,40±10,71 (maksKAH'ın %91.73) olarak tespit edildi. Periyotlar ve yarılar arasında ortKAH bakımından anlamlı fark bulunmamıştır (p&gt;0.05). Bu çalışmanın bir sonucu olarak, yıldız kız basketbol müsabakasında KAH cevaplarının maxKAH'ın yaklaşık %89,66 şiddetinde olduğu görüldü. Bu sonuçlar, müsabaka yoğunluğunun tüm yarılar ve periyotlar için çok yüksek olduğunu göstermektedir. Yıldız oyuncuların resmi müsabaka yoğunluğunun belirlenmesi, genç oyuncuların spor performansını geliştirdiklerine dair antrenörlere önemli bilgi sağlayabilir.

New Regression Equations for Estimating the Maximal Oxygen Uptake of College of Physical Education and Sports Students in Turkey

Aerobic endurance describes the ability of the body’s cardio-respiratory system to perform physical activity for an extended period of time and resist fatigue. Standard tests to determine aerobic endurance involves measuring the maximum volume of oxygen (VO2max) an athlete uses up while exercising at maximal capacity. Given that the tests of direct measurement of VO2max needs expensive equipment, a great deal of time, and trained staff with expertise, many researchers have attempted to find indirect and simpler ways of predicting VO2max based on prediction equations. The aim of this study is to establish new prediction equations for estimating the VO2max from gender, age, height, weight, body mass index (BMI), maximal heart rate (HRmax) and test time (TT) for college-aged students in Turkey. Particularly, 18 students from the College of Physical Education and Sports at Gazi University volunteered for this study. Gender has been used as a common predictor variable in all prediction models. By using different combinations of the rest of predictor variables together with the common predictor variable, twelve VO2max prediction equations have been established with the help of Multiple Linear Regression (MLR). The performance of the prediction equations have been evaluated using two well-known metrics, namely standard error of estimate (SEE) and multiple correlation coefficient (R). The results reveal that the regression equation, VO2max = - (12.331 x gender) - (0.805 x age ) + (0.883 x height) - (1.167 x weight) - (0.052 x HRmax) - (0.158 x TT) + 6.473, gave the lowest SEE (i.e3.49 mL.kg-1.min-1) and the highest R (i.e. 0.88). Application of this VO2max regression equation on an independent validation group including 6 subjects yielded an SEE of 6.24 mL.kg-1.min-1.It can be concluded that in situations where it is difficult or even not possible to measure VO2max using exercise tests, coaches and trainers may use the given equation to predict VO2max of College of Physical Education and Sports students with acceptable error rates.

New Regression Equations for Estimating the Maximal Oxygen Uptake of College of Physical Education and Sports Students in Turkey

Aerobic endurance describes the ability of the body’s cardio-respiratory system to perform physical activity for an extended period of time and resist fatigue. Standard tests to determine aerobic endurance involves measuring the maximum volume of oxygen (VO2max) an athlete uses up while exercising at maximal capacity. Given that the tests of direct measurement of VO2max needs expensive equipment, a great deal of time, and trained staff with expertise, many researchers have attempted to find indirect and simpler ways of predicting VO2max based on prediction equations. The aim of this study is to establish new prediction equations for estimating the VO2max from gender, age, height, weight, body mass index (BMİ), maximal heart rate (HRmax) and test time (TT) for college-aged students in Turkey. Particularly, 18 students from the College of Physical Education and Sports at Gazi University volunteered for this study. Gender has been used as a common predictor variable in all prediction models. By using different combinations of the rest of predictor variables together with the common predictor variable, twelve VO2max prediction equations have been established with the help of Multiple Linear Regression (MLR). The performance of the prediction equations have been evaluated using two well-known metrics, namely standard error of estimate (SEE) and multiple correlation coefficient (R). The results reveal that the regression equation, VO2max = - (12.331 x gender) - (0.805 x age ) + (0.883 x height) - (1.167 x weight) - (0.052 x HRmax) - (0.158 x TT) + 6.473, gave the lowest SEE (i.e3.49 mL.kg-1.min-1) and the highest R (i.e. 0.88). Application of this VO2max regression equation on an independent validation group including 6 subjects yielded an SEE of 6.24 mL.kg-1.min-1.İt can be concluded that in situations where it is difficult or even not possible to measure VO2max using exercise tests, coaches and trainers may use the given equation to predict VO2max of College of Physical Education and Sports students with acceptable error rates.

Cardiorespiratory fitness and activity explains the obesity-deprivation relationship in children.

This study examined the association between obesity and deprivation in English children and whether cardiorespiratory fitness or physical activity (PA) can explain this association. Obesity was assessed using IOTF criteria in 8,398 10-16 year olds. Social deprivation was measured using the Index of Multiple Deprivation (IMD) (subdivided into 3 groups; high, mid and low deprivation). Obesity was analysed using binary logistic regression with stature, age and sex incorporated as confounding variables. Children's fitness levels were assessed using predicted VO2 max (20-metre shuttle run test) and PA was estimated using the PA Questionnaire for Adolescents or Children (PAQ). A strong association was found between obesity and deprivation. When fitness and PA were added to the logistic regression models, increasing levels in both were found to reduce the odds of obesity, although it was only by including fitness into the model that the association between obesity and deprivation disappeared. Including estimated PA into the model was found to be curvilinear. Initial increases in PA increase the odds of obesity. Only by increasing PA to exceed the 71st percentile (PAQ = 3.22) did the odds of being obese start to decline. In order to reduce deprivation inequalities in children's weight-status, health practitioners should focus on increasing cardiorespiratory fitness via physical activity levels in areas of greater deprivation.

Correlation of ACSM's walking equation and directly measured Vo2 max in Indian population: A pilot study

Purpose of study: American College of Sports and Medicine equation is applied in many cardiovascular centers and sports medicine laboratory. It is used in majority of the commercial software available for exercise testing ACSM has published several metabolic equations for the indirect estimation of VO2max. So the purpose of the study was to find the applicability of ACSM walking equation in Indian population. Aim and Objective: To find out the correlation between actual VO2max and predicted VO2max 18-25 years female in Nagpur. Methodology: All the participants were selected according to sample of convenience and given a thorough explanation of the procedure in the language they understand. Each participant signed written informed consent before participating in the study in the language they could comprehend. Subjects were screened on the basis of selection criteria and PAR-q & you questionnaire. Total 20 participants were selected and demographic and anthropometric data was recorded. In this VO2 max was calculated directly by gas analyzer using AD instrument and indirectly by ACSM's walking equation and data was analyzed. Result: The mean and standard deviation of directly measured VO2max (ml/kg/min) is 8.6354 and ±4.5557 this shows the range of VO2max for the studied population and VO2max predicted by ACSM walking equation is 38.1891 and ±5.4691. The pearson's correlation for direct and indirect measurement of VO2max suggested that there is no co-relations with the value (r=0.11, p=, 0.05). Conclusion: The present study concluded that there is no correlation between actual and predicted VO2 max in the studied population and need to develop new regression equation for studied population i.e;ACSM equation is not applicable to the studied population. KEY WORDS: American College of Sports and Medicine walking equation; VO2 max; Indian population.

Effect of BMI, Body Fat Percentage and Fat Free Mass on Maximal Oxygen Consumption in Healthy Young Adults.

Maximal oxygen consumption (VO2max) is an important measure of cardiorespiratory capacity of an individual at a given degree of fitness and oxygen availability. Risk of cardiovascular diseases increases with increasing degree of obesity and a low level of VO2max has been established as an independent risk factor for cardiovascular mortality. To determine VO2max in young adults and to find its correlation with Body Mass Index (BMI), Body Fat% and Fat Free Mass (FFM). Fifty four (male=30, female=24) healthy young adults of age group18-25 years after screening by Physical Activity Readiness Questionnaire (PAR-Q) participated in the study. Height was measured by stadiometer. Weight was measured by digital weighing scale with 0.1 kg sensitivity. Body fat% was measured by Bioelectrical Impedance Analysis (BIA) method. FFM was calculated by subtracting fat mass from the body weight. VO2max (mL.kg-1.min-1) was obtained by Submaximal Exercise Test (SET) by first two stages of Bruce Protocol with the basis of linear relationship between Heart Rate (HR) and oxygen consumption (VO2). Data were analysed statistically in GraphPad Prism software version 6.01 for windows. VO2max (mL.kg-1.min-1) of male (43.25±7.25) was significantly (p<0.001) higher than female (31.65±2.10). BMI showed weak negative correlation (r= -0.3232, p=0.0171) with VO2max but Body Fat% showed strong negative correlation (r= -0.7505, p<0.001) with VO2max. FFM positively correlated (r=0.3727, p=0.0055) with VO2max. Increased body fat is associated with decreased level of VO2max in young adults. Obesity in terms of Fat% is a better parameter than BMI for prediction of low VO2max.

Exercise Tolerance in Children With Early Onset Scoliosis: Growing Rod Treatment “Graduates”

Study DesignProspectively enrolled early-onset scoliosis (EOS) patients undergoing growing rod treatment, who have had no surgery for >1 year and/or have received definitive fusion (growing rod “graduates”). ObjectivesTo assess oxygen consumption during exercise and determine if a diminished conventional pulmonary function test (PFT) correlates with metabolic, pulmonary, and cardiovascular measures during exercise. Summary of Background DataBased on clinical impression and sequential PFT values, EOS patients who have undergone extensive treatment are thought to have limited capacity during exercise. The use of PFTs in this population has been a primary outcome measure of respiratory capacity; however, PFTs are dependent on effort, and thus subjective. This led us to find a new assessment of outcome, to better understand their pulmonary capacity. MethodsPatients underwent oxygen consumption (VO2) testing while walking at self-selected speed over-ground and during a graded exercise test. Maximal VO2 was predicted in those who completed the test to 85% of maximal heart rate (HR). Statistical analysis included Mann-Whitney U test and Spearman correlation coefficient (α = 0.05). Results12 patients participated. Over-ground walking showed that EOS graduates chose to walk at the same speed, but at a higher VO2 Cost (0.28 mL/kg/m) than controls (0.22 mL/kg/m; p < .001). Treadmill exercise testing showed 9 of 12 subjects able to complete the 85% of predicted maximum protocol. The EOS group had lower VO2 during the final stage (27.9 mL/kg/min) compared to controls (34.2 mL/kg/min; p = .021); however, their heart rate reached the same values. Subjects completing the protocol had lower predicted VO2 max (38.5 mL/kg/min) compared with controls (45.0 mL/kg/min), but this was not significant. ConclusionsAlthough PFT data suggest clinically relevant pulmonary compromise in EOS patients, the current study shows that these children are able to keep up with their peers in daily activities and also have the capacity to exercise. Level of EvidenceLevel II, therapeutic

Developing new VO2max prediction models from maximal, submaximal and questionnaire variables using support vector machines combined with feature selection

Maximal oxygen uptake (VO2max) is an essential part of health and physical fitness, and refers to the highest rate of oxygen consumption an individual can attain during exhaustive exercise. In this study, for the first time in the literature, we combine the triple of maximal, submaximal and questionnaire variables to propose new VO2max prediction models using Support Vector Machines (SVM’s) combined with the Relief-F feature selector to predict and reveal the distinct predictors of VO2max. For comparison purposes, hybrid models based on double combinations of maximal, submaximal and questionnaire variables have also been developed. By utilizing 10-fold cross-validation, the performance of the models has been calculated using multiple correlation coefficient (R) and root mean square error (RMSE). The results show that the best values of R and RMSE, with 0.94 and 2.92mLkg−1min−1 respectively, have been obtained by combining the triple of relevantly identified maximal, submaximal and questionnaire variables. Compared with the results of the rest of hybrid models in this study and the other prediction models in literature, the reported values of R and RMSE have been found to be considerably more accurate. The predictor variables gender, age, maximal heart rate (MX-HR), submaximal ending speed (SM-ES) of the treadmill and Perceived Functional Ability (Q-PFA) questionnaire have been found to be the most relevant variables in predicting VO2max. The results have also been compared with that of Multilayer Perceptron (MLP) and Tree Boost (TB), and it is seen that SVM significantly outperforms other regression methods for prediction of VO2max.

Reference values for and cross-validation of time to exhaustion on a modified Balke protocol in Norwegian men and women.

The aims of the present study were to provide reference values for time to exhaustion (TTE) on a modified Balke treadmill protocol, and to perform a cross-validation of TTE as a measure of maximal oxygen consumption (VO2max ), in Norwegian men and women 20-85years of age. Reference values for TTE were derived from a national sample of 765 subjects. An additional sample of 119 subjects was included in the cross-validation (total n=884), where prediction equations for VO2max was established. A decline in TTE was seen with increased age. Prediction of VO2max in an independent dataset (n=319) resulted in a R2 =0.78 and standard error of the estimate=4.55mL/kg/min. The observed-predicted bias was small (mean difference <1.24mL/kg/min), whereas random error was considerable (95% limits of agreement±7.11-9.70mL/kg/min) across age in both men and women. Despite limitations concerning the prediction of VO2max on an individual level, TTE from the Balke protocol is a good measure of aerobic fitness in adults across a range of settings, and could be evaluated according to the suggested reference values.

Comparison of VO2 Max Prediction Value, Physiological Response, and Borg Scale between 12-Minute and 3200-Meter Run Fitness Tests among Indonesian Army Soldiers

Objective: To compare the VO2 max and physiological responses between 12-minute and 3200-meter run fitness tests. Methods: The subjects were 40 soldiers aged 18–21 years old of the Infantry Battalion 303/SSM of Cikajang, Garut, West Java, Indonesia. The 12-minute and 3200-meter run fitness tests were conducted with one week resting period. The measurement of the VO2 max prediction was based on each of the formulations and the measurement of physiological response included blood pressure, heart rate, respiratory rate, Borg scale and oxygen saturation before and after the tests. This study used a cross-sectional method while the data were analyzed statistically using t-test (p=0.05). Results: The results showed that the VO2 max prediction in 12-minute run fitness test was 52.046±2.980 mL/KgBB/min and the 3200-meter run test was 55.323±3.238 mL/KgBB/min. The value was statistically significant (p=0.008). There was no significant difference in the physiological response in both tests, except the SpO2 parameter after the tests (p=0.021). Conclusions: There is a significant difference in VO2 max prediction between 12 minutes and 3200 meter run test. No significant difference was found in the physiological response in both tests, except the SpO2 parameter. Keywords: 12-minute run fitness test, 3200-meter run fitness test, physiological response, soldiers, VO2 max DOI: 10.15850/ijihs.v4n2.836

A randomized, controlled trial of a multi-modal exercise intervention in Huntington’s disease

IntroductionThis study aimed to evaluate the feasibility and benefit of a structured exercise intervention in people with Huntington’s Disease (HD). MethodsThis study was conducted at 6 sites, and participants were randomized into either exercise or control (usual care) groups, and were assessed at baseline, 13 and 26 weeks. The intervention was a 12 week, three times per week progressive exercise program, including aerobic (stationary cycling) and upper and lower body strengthening exercise with tapered 1:1 support for 20 of 36 sessions. Results314 adults were assessed for eligibility: 248 did not meet inclusion criteria, 34 declined, and 32 were recruited and randomized. Three individuals in the intervention group were withdrawn within the first month due to concomitant medical conditions, resulting in 14 participants in intervention and 15 in control groups. There were two AEs in the intervention group, both related to previous medical conditions, and there were two SAEs, both in the control group. The intervention group had better fitness (predicted VO2 max difference: 492.3 ml min−1, 95% CI: [97.1, 887.6]), lower UHDRS mMS (difference 2.9 points, 95% [−5.42, −0.32]) and lower weight at Week 13 (difference 2.25 kg, 95% CI: [−4.47, −0.03]). ConclusionThis study demonstrates that a short-term exercise intervention is safe and feasible. Individuals with HD may benefit from structured exercise, and intensity, monitoring and support may be key factors in optimizing response. Larger scale trials are now required to fully elucidate the extended clinical potential of exercise in HD.Trial registration: Current Controlled Trials ISRCTN11392629.

Maximum oxygen uptake and cardiovascular response of Professional male football and Basketball players to Chester step test.

The cardiovascular response to exercise has several variables but this study will be limited to the heart rate response, blood pressure response and predicted VO2 max. These variables are what this study will be utilizing to evaluate the fitness level of the participants. The purpose of this study was to estimate the maximum oxygen uptake (VO2 max) and compare the cardiovascular response of professional male football players and basketball players to Chester step bench test.A total of 100 athletes (50 football players and 50 basketball players) recruited through convenience sampling participated in the study. The research design was quasi experimental research design. The instruments used were heart rate monitor, electronic blood pressure monitor, 30cm Chester step, stop watch, mobile metronome version 1.2.4, height meter, bathroom scale. The V02max was estimated from HR recovery (HRrec) after 15seconds using the equation V02max=111.3-(0.42 xHRrec).Comparative analysis of the data was done using descriptive statistics of mean and standard deviation, and inferential statistics of independent t-test with alpha level of significance of 0.05. In the result, Significant differences were recorded between the VO2 max of football players 81.15kg/ml/min and basketball players 72.39kg/ml/min; heart rate 72.10bpm and 93.58bpm; systolic blood pressure 147.90mmHg and 142.46mmHg; while there was no statistically significant difference in their diastolic blood pressure 81.820mmHg and 81.020mmHg. The study showed that football players had significantly higher VO2 max (81.14±0.66 ml/kg/min), than the basketball players (72.38±0.78 ml/kg/min), indicating that football players had more aerobic capacity than basketball players. High VO2 max is the primary indicator of aerobic fitness, cardiovascular health, and endurance performance. Coaches of different football clubs and basketball clubs should take serious the matter of consistent training of their players to improve and avoid decline in their cardiorespiratory fitness. Individuals should cultivate the habit of exercising in order to improve their cardiorespiratory fitness and minimize the risk of some diseases associated with low cardiorespiratory fitness.

Prediction of VO2max in Children and Adolescents Using Exercise Testing and Physical Activity Questionnaire Data

Purpose: The purpose of this study was to evaluate the use of a treadmill walk-jog-run exercise test previously validated in adults and physical activity questionnaire data to estimate maximum oxygen consumption (VO2max) in boys (n = 62) and girls (n = 66) aged 12 to 17 years old. Methods: Data were collected from Physical Activity Rating (PA-R) and Perceived Functional Ability (PFA) questionnaires, a walk-jog-run submaximal treadmill exercise test, and a maximal graded exercise test. Results: Regression analysis resulted in the development of 2 models to predict VO2max. Submaximal exercise test data were used to build the following model (R2 = .73; SEE = 4.59 mL + kg− 1 + min− 1): VO2max (mL + kg− 1 + min− 1) = 26.890+(5.877 × Gender; 0 = female; 1 = male) − (0.782 × Body Mass Index [BMI])+(0.438 × PFA Score) +(2.712 × Treadmill Speed; mph) +(0.746 × Age) +(0.449 × PA-R Score). Maximal exercise test data were used to build the following model (R2 = .83; SEE = 3.63 mL + kg− 1 + min− 1): VO2max (mL + kg− 1 + min− 1) = 10.716+(1.334 × Maximal Treadmill Grade) +(5.203 × Treadmill Speed; mph) +(3.494 × Gender; 0 = female; 1 = male) − (0.413 × BMI) +(0.249 × PFA). Conclusions: The results of this study demonstrate, for the first time, that regression equations that use both exercise data and physical activity questionnaire data can accurately predict VO2max in youth. The submaximal and maximal exercise tests that use self-selected treadmill speeds can be used to assess cardiorespiratory fitness of youth with a wide range of fitness levels.

Innovative Assessments Help Elucidate Sustained Improvements in Fitness and Metabolic Health in Obese Children.

ObjectiveTreatment of childhood obesity is a medical challenge and limited data are available describing successful long term interventions. This study presents a multi-disciplinary intervention that resulted in sustained physiological improvement over a one-year period.MethodsThe criterion outcome variables include cardiovascular fitness (CVF) measured by a population-specific treadmill test to predict maximal oxygen uptake (predicted VO2 max) and the body composition (BC) variables of fat mass, non-bone lean mass and percent body fat from whole body dual energy x-ray absorptiometry (DXA) scans. Subjects were overweight and obese children (N=79) evaluated at baseline, 6 and 12 months at a University Hospital-based pediatric fitness clinic.ResultsStatistically significant improvements in non-bone lean body mass (+4.24 kg ± 5.0, p<0.0001) and predicted VO2 max (+0.14L/min ± 0.10, p<0.0001) were seen at 6 months. These significant improvements were sustained over 12 months: body fat percentage (−2.28 ± 3.49, p<0.0001), lean mass (+6.0 kg ± 4.0, p<0.0001) and predicted VO2 max (+0.22 L/min ± 0.19, p<0.0001). These results were observed despite increases in weight and body mass index (BMI) at 6-months (weight: +6.6 kg ± 6.93, p<0.0001; BMI: +0.37 ± 1.21, p=0.47) and 12-months (weight: +6.3 kg ± 5.8, p<0.0001; BMI: +0.91 ± 2.06, p=0.0002).ConclusionThese results reflect the sustained effect of a multidisciplinary approach, and the value of using valid and reliable assessment methods to measure sustained physiological changes in a sample of 79 overweight and obese children.

Modelling the association between weight status and social deprivation in English school children: Can physical activity and fitness affect the relationship?

Background: The association between being overweight/obese and deprivation is a serious concern in English schoolchildren.Aim: To model this association incorporating known confounders and to discover whether physical fitness and physical activity may reduce or eliminate this association.Subjects and methods: Cross-sectional data were collected between 2007–2009, from 8053 10–16 year old children from the East-of-England Healthy Heart Study. Weight status was assessed using waist circumference (cm) and body mass (kg). Deprivation was measured using the Index of Multiple Deprivation (IMD). Confounding variables used in the proportional, allometric models were hip circumference, stature, age and sex. Children’s fitness levels were assessed using predicted VO2 max (20-metre shuttle-run test) and physical activity was estimated using the Physical Activity Questionnaire for Adolescents or Children.Results: A strong association was found between both waist circumference and body mass and the IMD. These associations persisted after controlling for all confounding variables. When the children’s physical activity and fitness levels were added to the models, the association was either greatly reduced or, in the case of body mass, absent.Conclusions: To reduce deprivation inequalities in children’s weight-status, health practitioners should focus on increasing physical fitness via physical activity in areas of greater deprivation.

Machine learning and statistical methods for the prediction of maximal oxygen uptake: recent advances.

Maximal oxygen uptake (VO2max) indicates how many milliliters of oxygen the body can consume in a state of intense exercise per minute. VO2max plays an important role in both sport and medical sciences for different purposes, such as indicating the endurance capacity of athletes or serving as a metric in estimating the disease risk of a person. In general, the direct measurement of VO2max provides the most accurate assessment of aerobic power. However, despite a high level of accuracy, practical limitations associated with the direct measurement of VO2max, such as the requirement of expensive and sophisticated laboratory equipment or trained staff, have led to the development of various regression models for predicting VO2max. Consequently, a lot of studies have been conducted in the last years to predict VO2max of various target audiences, ranging from soccer athletes, nonexpert swimmers, cross-country skiers to healthy-fit adults, teenagers, and children. Numerous prediction models have been developed using different sets of predictor variables and a variety of machine learning and statistical methods, including support vector machine, multilayer perceptron, general regression neural network, and multiple linear regression. The purpose of this study is to give a detailed overview about the data-driven modeling studies for the prediction of VO2max conducted in recent years and to compare the performance of various VO2max prediction models reported in related literature in terms of two well-known metrics, namely, multiple correlation coefficient (R) and standard error of estimate. The survey results reveal that with respect to regression methods used to develop prediction models, support vector machine, in general, shows better performance than other methods, whereas multiple linear regression exhibits the worst performance.