Prolonged and chronic sedentary behavior contributes to negative metabolic health conditions. Recent research has shown that a single bout of combined arm and leg cycling (CALC) can improve acute glucose metabolism in non-diabetic college aged adults. If a short bout of CALC can improve glucose metabolism in individuals that are non-diabetic then one could hypothesize that a short bout of CALC may improve glucose metabolism in individuals that are pre-diabetic. We hypothesized that short bouts of CALC would result in a greater decrease in the 60-min total (tAUC) area under the curve in pre-diabetic compared to non-diabetic males. PURPOSE: To compare acute glucose responses following a short bout of CALC in non and pre-diabetic males. METHODS: College age males were recruited for this trial. Diabetes status was based upon a fasting blood glucose measurement taken after an overnight 12 hour fast. Participants completed three randomized experimental trials. Two of the experimental trials consisted of a 1-min (1M) or 5-min (5M) bout of CALC at a self-selected pace. The third trial was a non-exercise control (CON) trial. Immediately following both exercise trials and during the CON trial, participants completed a 60-min oral glucose tolerance test. A 2-way (diabetes status vs trial) repeated measures ANOVA was done to determine if there were any significant differences between the two groups and the three trials for the tAUC during the 60-min OGTT. RESULTS: A total of 18 college age males completed this study. Fasting blood glucose was 94.0 ± 3.7 and 108.1 ± 4.6 mg/dl for the non (n=8, 21.4 ± 2.4 yrs) and pre-diabetics (n = 10, 22.6 ± 3.9 yrs) participants respectively. There was a significant interaction between trials and diabetes status (p = 0.04) for tAUC. Follow-up analysis showed that within the pre-diabetic group, tAUC was significantly lower in the 5-min trial compared to the CON trial (113.7 ± 22.5 mg/dl, p > 0.001). Within the pre-diabetic group, the tAUC for the 1M trial was not significantly different from the CON or 5M trials. Within the non-diabetic group, there were no significant differences in tAUC between the three trials. CONCLUSIONS: The results of this study indicate that a 5M bout of CALC done at a self-selected pace may have the ability to acutely improve glucose metabolism in college age pre-diabetic males when compared to non-diabetic males. Supported by a SIUE Graduate School Research Grant for Graduate Students, the School of Education, Health, and Human Behavior Dean's Grant, and the Undergraduate Research and Creative Activities Program. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

INTRODUCTION: Dual X-Ray Absorptiometry (DXA) scans are expensive and difficult for many college athletes to access. Because of this, there is an increased need for an affordable and widely accessible, yet accurate method of estimating percent body fat (%BF) in this population. The handheld device, Skulpt which uses electrical impedance myography (EIM) to estimate %BF has shown promise in normal weight individuals but has not been evaluated in college athletes. PURPOSE: The purpose of this study is to compare %BF measurements between the Skulpt and a DXA scan in Division I college soccer athletes. We hypothesized that there would be no difference between %BF values obtained from a DXA or the Skulpt scan. METHODS: Male and female college soccer athletes were recruited for this project. All athletes had their %BF measured by DXA. They also completed a 24-site full body (FB) scan and a 3-site quick scan (QS) using the Skulpt. A 2-way ANOVA (gender by method) was used to determine if there were significant differences in %BF between the three methods. RESULTS: A total of 45 athletes agreed to participate (male = 24, female = 21) in this study. For males, there was no significant difference (p = 0.629) in %BF between the three methods (DXA = 15.1 ± 3.1%, FB = 16.1 ± 3.8%, QS =15.6 ± 3.6%). In males, the average %BF was overestimated by 1.0 ± 1.0% with the FB method and by 0.5 ± 1.0% with the QS method. For females, there was no significant difference (p = 0.324) in %BF between the three methods (DXA = 25.9 ± 3.7%, FB = 28.1 ± 4.9%, QS =27.3 ± 5.2%). In females, the average %BF was overestimated by 2.2 ± 1.5% with the FB method and by 1.4 ± 1.4% with the QS method. CONCLUSION: The results of this study indicate that both the Skulpt’s FB and QS results in %BF that are not significantly different from DXA scans in male and female college soccer athletes. Despite no significant difference in methods in females, coaches may want to use the %BF value obtained from the Skulpt with caution.

Cardiac adipose tissue (CAT) has become an important target for the reduction of disease risk. Supervised exercise programs have shown potential to "significantly" reduce CAT; however, the impact of different exercise modalities is not clear, and the relationships between CAT, physical activity (PA) levels and fitness (PFit) remain unknown. Therefore, the purpose of this study was to analyze the relationships between CAT, PA and PFit, and to explore the effects of different exercise modalities in a group of women with obesity. A total of 26 women (age: 23.41 ± 5.78 years-old) were enrolled in the cross-sectional study. PA, cardiorespiratory fitness, muscular strength, body composition and CAT were evaluated. The pilot intervention included 16 women randomized to a control (CON, n=5), high intensity interval training (HIIT, n = 5) and high-intensity circuit training (HICT, n=6) groups. Statistical analysis showed negative correlations between CAT and vigorous PA (VPA) ( r s =-0.41, p =0.037); and between percent body fat (%BF), fat mass (FM), and all PA levels ( r s =-0.41– -0.68, p <0.05); while muscle mass was positively associated with moderate-to-vigorous PA, and upper-body lean mass with all PA levels ( r s =0.40–0.53, p <0.05). The HICT intervention showed significant improvements ( p <0.05) in %BF, FM, fat free mass, and whole-body and lower extremities lean mass and strength after three weeks; however, only leg strength and upper extremities’ FM improved significantly compared to CON and HICT. In conclusion, although all types of PA showed a positive influence on body fat content, only VPA significantly impacted on CAT volume. Moreover, three weeks of HICT induced positive changes in PFit in women with obesity. Further research is needed to explore VPA levels and high-intensity exercise interventions for short- and long-term CAT management.

INTRODUCTION: With obesity rates at an all-time high and still on the rise, it is becoming increasingly important for individuals to have access to accurate and inexpensive means of monitoring their body composition. Unfortunately, reliable, and accurate methods, such as dual x-ray absorptiometry (DXA) scans, can be expensive and are not widely available to the general population. The handheld device Skulpt uses electrical impedance myography to estimate percent body fat (%BF) and may offer an affordable solution. This device has shown promise in normal weight individuals but may be less accurate in overweight and obese individuals. PURPOSE: The purpose of this study is to compare %BF measurements between the Skulpt and a DXA scan in normal weight, overweight, and obese subjects. We hypothesized that as body mass index (BMI) increases, the difference between %BF estimates from the Skulpt and a DXA scan will increase, with the Skulpt underestimating %BF. METHODS: Participants ages (18-59) were grouped into one of three BMI weight categories: normal weight, overweight, and obese. All participants had their %BF measured by DXA. They also completed a 24-site full body (FB) scan and a 3-site quick scan (QS) using the Skulpt. A 3-way mixed ANOVA was used to determine if there were significant differences between method, gender, and BMI category. RESULTS: In men, regardless of BMI category, there were no significant differences in %BF between the three methods. In females, there were significant differences between QS and DXA (p < 0.001) as well as FB and DXA (p < 0.001) with Skulpt underestimating %BF by 3.74% for the QS and 2.76% for FB. Regardless of gender, there was no significant difference among scan type for the normal weight or overweight category. In the obese category, both QS (p < 0.001) and FB (p = 0.019) significantly differed from DXA with the QS underestimating %BF by 4.41% and the FB underestimating by 2.76%. There was no significant difference between the QS and FB measurements in the obese category (p = 0.058). CONCLUSION: The results of this study indicate that compared to DXA, the Skulpt underestimates %BF in females. These results also indicate that in obese individuals regardless of gender, the Skulpt underestimates %BF.

Short bouts of exercise at self-selected intensity have been shown to improve indices of metabolic health. Self-selected intensity is associated with a more positive affective response and adherence to exercise. Therefore, the affective response to short bouts of exercise at self-selected intensity has important implications for exercise adherence. PURPOSE: To examine the effects of 1-minute (1 M) and 5-minute (5 M) bouts of exercise at self-selected intensity on affective valence. METHODS: Thirty healthy male (n = 18) and female (n = 12) participants completed a VO2max test at baseline and two randomized exercise trials consisting of either a 1 M or 5 M bout of combined arm and leg cycling at a self-selected pace. Heart rate (HR) and pulmonary gas exchange data were collected during exercise. The Feeling Scale was used to measure acute affective valence (pleasure/displeasure) before, during, and following each bout of exercise using the following verbal anchors: -5 = very bad; -3 = bad; -1 = fairly bad; 0 = neutral; +1 fairly good; +3 = good; and + 5 = very good. Paired-samples t-tests were used to determine whether there were significant mean differences between bouts for HR, average VO2, % VO2max, and affect. RESULTS: Affective valence was positive before each exercise session for both the 1 M (M = 2.13 ± 1.96) and 5 M bout of exercise (M = 2.07 ± 2.32). The 5 M bout elicited a significant increase in HR (t(29) = 3.163, p = .004, d = 19.9), average VO2 (t(29) = 4.314, p < .001, d = 4.36), and % VO2max (t(29) = 4.461, p < .001, d = 10.3) compared to the 1 M bout. There were no significant differences in means (p < .05) for affect before, during, or after the 1 M bout compared to the 5 M bout. CONCLUSION: Physiological markers of exercise intensity were significantly higher in the 5 M bout compared to the 1 M bout. However, there were no significant differences in the affective response to exercise between the bouts. Additionally, mean affect remained positive during and after exercise, regardless of bout duration. These findings suggest self-selected exercise intensity may be more important than bout duration for managing the affective response to exercise. Further research is needed to examine the effects of various durations of exercise at imposed versus self-selected intensity on the affective response to exercise.

PURPOSE: The aim of this study was to compare different biomechanical models to identify the take-off velocity (TOV) of a vertical jump. METHODS:Fourteen young adults (age = 24 ± 4 yrs) participated in this study. Participants did five maximal vertical jumps while swinging their arms during the jump. Kinematic data was recorded using a 17-marker whole body set and a 10 camera motion capture system. Kinetic data was recorded using a force platform. The data was then analyzed using two kinetic and two kinematic models for phase identification. Both kinetic models (K1 and K2) required the double integration of the ground reaction force to estimate the vertical movement of the center of mass (COM). One kinematic model used 14 markers to create a segmental model (S1) and the other model used 3 markers to create a sacral model (S2) to estimate the movement of the COM. All models defined the start of the eccentric phase as the point where the COM starts to move downward. Three of the models (K1, S1, & S2) defined end of the eccentric phase/start of concentric phase as when the COM starts to move upward. The 4th model (K2) defined the end of the eccentric phase when the velocity of the COM is zero and the start of the eccentric phase when velocity of COM becomes positive. The concentric phase ended when both feet had left the ground for all models. TOV was defined as the velocity of the COM at the end of the eccentric phase. A 1-way ANOVA was used to identify differences in TOV between the 4 different models. RESULTS: There were significant differences between TOV for the 4 models (p < 0.001). Post-hoc comparisons show that the estimated TOV for S1 (3.21 ± 0.43 m/s) was significantly greater than K1, K2, and S2 (2.79 ± 0.43, 2.79 ± 0.43, and 2.67 ± 0.52 m/s respectively). The estimated TOV for S2 (2.67 ± 0.52 m/s) was significantly less than K1 and K2 (2.79 ± 0.43 and 2.79 ± 0.43 respectively). CONCLUSIONS: These results indicate that a whole body kinematic model (S1) results in a greater estimated TOV when compared to kinematic model that doesn’t account for whole body movement (S2). A whole body kinematic model also results in greater estimated TOV when compared to kinetic models.

Abstract BACKGROUND Prolonged and chronic sedentary behavior (SB) contributes to negative health problems including cardiovascular and metabolic diseases. Research has shown that short bouts of exercise throughout the day can minimize the negative cardiovascular and metabolic effects resulting from SB. The purpose of this study was to determine the effects of combined arm and leg cycling (CALC) on glucose metabolism. We hypothesized that short bouts of CALC would decrease the 60-min blood glucose (BG) area under the curve (AUC) and decrease BG at specific time points in a dose-dependent manner, when compared to a control (CON) trial. METHODS A repeated measures design was used with participants (n = 30) completing baseline assessments and three experimental trials: CON, a 1-min exercise bout (1M) and a 5-min exercise bout (5M). During the exercise trials, participants performed CALC at a self-selected speed on an Assault™ Air bike. Participants completed a 60-min oral glucose tolerance test (OGTT) immediately postexercise. Moreover, blood was sampled pre-exercise and every 15-min post-exercise. The CON trial followed the same experimental procedure; however, participants did not engage in any exercise. The analyses were a one-way repeated measures MANOVA to identify differences in BG at the individual time points between the trials and a two-way mixed ANOVA to identify differences in the AUC during the 60-min OGTT between trials by gender. RESULTS There were significant differences in the BG values when comparing the 5M and CON at 0-min (p = 0.034), 15-min (p < 0.001) and 30-min (p < 0.001) and when the 1M bout is compared to CON at 30-min (p = 0.017). The 5M and 1M bouts were significantly different at 0-min only (p = 0.045). There were also significant differences in the total area under the curve (tAUC) between CON and the 5M bout (p = 0.001). There were no significant differences in incremental area under the curve (iAUC) between the three trials. CONCLUSION Short bouts of CALC significantly reduced BG values up to 30-min post-exercise and reduced the tAUC, compared to performing no exercise at all. This mode and duration of exercise may aid healthy, able-bodied individuals in achieving benefits to glucose metabolism.

Although runners are at high risk of back and lower extremity injuries, available tools detect only current injury. Here, a model was developed to analyze kinetic and kinematic running gait data collected by an optical motion capture system to predict future injuries based on an individual's running gait pattern. The two key points, when the joints are most vulnerable because internal forces are the greatest, in the continuous running gait cycle were used to extract average parameter values to create predictive models: the heel strike, and when one leg supports the body weight. Three different prediction models—logistic regression, random forest, and boosting—were built using 10 significant parameters identified in a two-step feature selection approach. All collected metric data were normalized before building the predictive models to avoid outlier values and redundancy. The three models were tested to determine whether they could predict that a participant would incur chronic running injuries in the future based on their current running gait pattern. The logistic regression model had the highest prediction accuracy: the area under the curve was 0.9016 [95% confidence interval (CI) 0.8808–0.9369] for logistic regression, 0.8892 (95% CI 0.8463–0.9152) for the random forest, and 0.8732 (95% CI 0.8401–0.9178) for boosting. Further model development may not only enable clinicians to integrate injury intervention into running programs but also lead to predictive models that recognize patterns associated with neurological disorders, such as Parkinson's disease, autism, and multiple sclerosis, in which gait and balance deficiencies may be symptoms or even predictors of disease.

PURPOSE: The purpose of this study was to determine the short-term effects of resistance training (RT) on arterial compliance and physical fitness in obese women with normal blood pressure. METHODS: A total of 16 participants (10 control/6 intervention) were included in the analyses (age: 23.5±4.1 years; body mass index: 33.6±2.9 m/kg2). Pre- and post-intervention assessments included cardiorespiratory tests, arterial stiffness assessments, and leg press (LP) and bench press (BP) one repetition maximum tests (1RM). Trainings consisted of seven strength exercises performed at an intensity of 80% 1RM until 550 calories have been expended. RESULTS: Analysis of variance (ANOVA) showed significant interaction effects (time x group) in LP (p=0.001) and BP (p=0.001) tests. Further, pairwise comparisons showed significant increases in LP (p<0.001) and BP (p<0.001) total weight lifted in the RT group after the intervention (20.55±12.22 kg and 6.1±4.54 kg respectively), but not in the control group (-2.26±8.96 kg and 0.58 ±2.26 kg respectively). There were no statistically significant changes found for arterial compliance. CONCLUSIONS: Short-term high intensity RT had positive effects on muscle strength in obese women with normal blood pressure with no negative effects on arterial compliance.

There are a number of fitness watches currently on the market that can predict VO2max based upon resting heart (HR) values. Traditionally these watches have measured HR using a wireless chest transmitter but the Polar M430 uses optical technology which is built into the watch to measure HR. There is evidence that suggests this optical technology will accurately measure resting HR but there is limited information that suggests this new technology will accurately estimate VO2max PURPOSE: The purpose of this study was to compare predicted VO2max values obtained from the Polar M430 watch (M) and actual VO2max values (A) obtained from indirect calorimetry. METHODS: Seven females (age = 24.0 ± 4.5 y, BMI = 26.3 ± 5.9 kg/m2) and fourteen males (age = 24.9 ± 4.5 y, BMI = 28.1 ± 5.2 kg/m2) reported to the lab, provided their informed consent, and then were instructed to lie in a supine position to rest for 10 minutes. During this time, their information (age, height, weight, gender, self-reported training hours) was entered into the watch. Following the rest, the M430 was then fitted to the participant according to the manufacturer instructions and the resting fitness test was started in order to obtain (Pmax). A treadmill ramp protocol using indirect calorimetry was used to obtain Amax. RESULTS: There were no significant differences between Mmax and Amax (48.2±13.5 and 45.3±9.4 ml/kg/min, respectively). In males, there were no significant differences between Mmax and Amax (52.5±13.6 and 50.4±5.8 ml/kg/min, respectively). In females, there were no significant differences between Mmax and Amax (41.8±10.4 and 38.1±10.5 ml/kg/min, respectively). CONCLUSIONS: This evidence suggests that the optical technology used in the M430 provides an estimate of VO2max based upon resting HR that is comparable to a VO2max obtained via indirect calorimetry. The ability to accurately estimate VO2max under resting conditions removes many of the barriers that are associated with a true VO2max test. Removing barriers of a true VO2max test will allow individuals to quantify and make their training more efficient.

INTRODUCTION: The Polar M430 (M430) uses optical technology to measure heart rate (HR) from a sensor that is built into the back of the watch. The Polar V800 (V800) uses a wireless chest transmitter that is held in place by a chest strap. Both of these watches estimate exercise energy expenditure (ExEE) for numerous types of exercise. Although there is evidence that suggests that the wireless transmitters provide accurate estimates of ExEE, there is little information that shows that watches equipped with optical sensors provide accurate measurements of ExEE. PURPOSE: The purpose of this study was to compare the ExEE values obtained from the M430 and the V800 to ExEE values measured using indirect calorimetry during different bouts of exercise. METHODS: Two females (age = 20 ± 1 y, BMI = 24.2 ± 2.0 kg/m2) and ten males (age = 22.8 ± 1.0 y, BMI = 26.1 ± 1.3 kg/m2) reporting to the lab and were fitted with a chest strap HR transmitter (Polar H7) to measure and transmit HR data to the V800. The M430 was fitted on the participant’s wrist according to the manufacturer’s instructions. Participant’s then completed four, 5-min bouts of exercise which consisted of the following; walking 3.5 mph at 0% grade, walking 3.5 at 5% grade, running at 5.5 mph at 0% grade, and running at 5.5 mph at 5% grade. Indirect calorimetry was used to measure actual ExEE. RESULTS: There were no significant differences between the three methods when walking at 0% or 5%. When running at 0% there was a significant difference between methods (p=0.044), with the M430 underestimating ExEE when compared to indirect calorimetry (5.8±2.0 kcal, p=0.045). When running at 5% there was a significant difference between methods (p=0.001). The M430 underestimated ExEE when compared to the V800 (7.5±2.1 kcal, p=0.018) and when compared to indirect calorimetry (14.5±3.7 kcal, p=0.008). CONCLUSIONS: The V800 provided accurate estimates of ExEE during each bout of exercise. The M430 provide accurate estimates of ExEE only when walking. When running, the M430 consistently underestimated ExEE and the underestimation increased with exercise intensity. At the highest exercise intensity, the M430 underestimated ExEE when compared to both the V800 and indirect calorimetry. Caution should be taken when using the ExEE values obtained from the M430 when running.

Currently, there are heart rate (HR) monitors manufactured by Polar® that estimate VO2max from resting conditions. A majority of these monitors measure HR via a wireless chest transmitter but they also have monitors that measure HR via optical sensors that are built directly into the watch. Although the optical sensors have the ability to accurately measure resting HR when compared to a wireless chest transmitter, it is not known if the VO2max estimates from optical sensors are comparable to the values obtained from a wireless chest transmitter. PURPOSE: The purpose of this study was to compare VO2max estimates obtained from the Polar M430 which utilizes optical sensors (Omax) to measure HR to values obtained from the Polar V800 which utilizes a wireless chest transmitter (CTmax) to measure HR. METHODS: Seven females (BMI = 26.3 ± 5.9 kg/m2, age = 24.0 ± 4.6 yrs) and 14 males (BMI = 28.1 ± 5.2 kg/m2, age = 24.9 ± 4.5 yrs) reported to the lab and then were instructed to lie in a supine position to rest for 10-min. Following the 10-min rest, participants were fitted with a wireless chest transmitter which was held in place with a chest strap. This transmitter sent HR information to the V800. The M430 was then fitted to the participant’s wrist according to the manufacturer’s instructions. Once HR values were being displayed on all watches, the resting fitness test was started in order to obtain VO2max values from each watch. At the end of the test, the VO2max values were recorded from the watch. RESULTS: There were no significant differences between Omax and CTmax (48.2±13.5 and 48.3±12.9 ml/kg/min, respectively). In males, there were no significant differences between Omax and CTmax (52.0±14.2 and 51.9±13.8 ml/kg/min, respectively). In females, there were no significant differences between Omax and CTmax (41.8±10.4 and 42.2±9.2 ml/kg/min, respectively). CONCLUSIONS: This data shows that there are no significant differences between the VO2max estimates based upon resting HR values obtained from optical sensors (M430) or from a wireless chest transmitter (V800). Although both technologies produce similar estimates of VO2max, this project does not examine the accuracy of these estimates when compared to an individual’s actual VO2max.

The recent trend in activity tracking has increased the demand for smart watches that can provide estimates of exercise energy expenditure (ExEE) during different types of exercise based upon the heart rate response to that exercise. The Garmin Forerunner 230 (230) and Forerunner 235 (235) are similar watches with the only difference being how heart rate (HR) is measured. The 230 uses a wireless chest transmitter that is held in place with a chest strap while the 235 uses an optical sensor that is built directly into the watch. PURPOSE: The purpose of this study was to compare the ExEE values obtained from the 230 and the 235 to ExEE values measured using indirect calorimetry during different bouts of exercise. METHODS: Two females (BMI=24.2 ± 2.8 kg/m2, age=20 ± 1.4 y) and ten males (BMI=26.1 ± 4.1 kg/m2, age=22.8 ± 3.3y) reported to the Exercise Physiology Lab at Southern Illinois University Edwardsville where they were fitted with a wireless chest transmitter and chest strap for the 230. The 235 was fitted on the participant’s wrist according to the manufacturer’s instructions. The exercise consisted of two, 5-min walking intervals (3.5 mph + 0% incline and 3.5 mph + 5% incline) and two, 5-min running intervals (5.5 mph + 0% incline and 5.5 mph + 5% incline) with 3-min of rest between each exercise bout. Indirect calorimetry was used to measure actual ExEE. RESULTS: There were no significant differences between the three methods during the 5% walk or the 0% run. During the 0% walk, there was a significant difference between methods (p=0.048) with the 235 overestimating ExEE when compared to the 230 (7.2±3.2 kcal, p=0.044). During the 5% run, there was a significant difference between methods (p=0.002). Both the 230 and 235 underestimated ExEE when compared to indirect calorimetry (12.3±3.6 kcal, p=0.024 and 10.0±3.3 kcal, p=0.043, respectively). CONCLUSION: The 230, which uses a wireless chest transmitter, provides accurate estimates of ExEE in all but the most intense exercise bout. The 235, which uses an optical sensor, varies in its ability to estimate ExEE in that it overestimates at the lower exercise intensities and underestimates at the highest exercise intensity. Caution should be taken when using ExEE values from both the 230 and 235 for weight management or exercise prescription purposes.

CONCLUSIONS: The accelerometers successfully tracked subjects’ PA and SB for sedentary, standing and walking indicating that activPAL accelerometry is a feasible approach for assessing PA and SB. Although the results should be interpreted cautiously due to the small sample size, which precluded adequate statistical power, the study demonstrated feasibility of testing methods for a large scale gardening study.

Currently, there are affordable heart rate monitors on the market that estimate VO2max from resting conditions. Although these heart rate monitors are safer, less expensive, and more accessible than an actual VO2max test, there is limited evidence to show these VO2max estimates are accurate. PURPOSE: The purpose of this study was to compare actual VO2max values (AMax) to predicted VO2max values (PMax) obtained from the V800, M400, and FT60 Polar heart rate monitors. These monitors predict VO2max based upon gender, age, height, weight, resting heart rate variability, and self-reported training range (hours trained per week). METHODS: Seventeen females (BMI = 22.1 ± 2.4, age = 21.2 ± 1.2 yrs) and 18 males (BMI = 24.4 ± 3.2, age = 21.4 ± 2.1 yrs) reported to the lab and were fitted with heart rate monitor straps upon completion of a DXA scan. PMax values were obtained from the Polar monitors for the six different training ranges that can be programmed into the watches. These training ranges are based upon the number of self-reported training hours per week; Low (0-1hrs/wk), Moderate (1-3 hrs/wk), Frequent (3-5 hrs/wk), Heavy (5-8 hrs/wk), Semi-Pro (8-12 hrs/wk), and Pro (12+ hrs/wk). After the PMax values were obtained, participants then completed a Modified Bruce Treadmill VO2max test. PMax was defined as the VO2max estimate that matched their self-reported training range. Due to potential errors associated with self-reported training ranges, AMax also was compared to the PMax estimate that matched the training range that was one category higher (PMax+1) and one category lower (PMax-1) than self-reported training range. RESULTS: Overall, AMax was significantly correlated with PMax based upon self-reported training range (r=0.718, p<0.01). In females, AMax was not correlated with PMax (r=0.403, p=0.122) but was significantly correlated with the PMax+1 (r=0.569, p=0.027). In males, AMax was significantly correlated with PMax (r=0.560, p=0.019) and with the PMax-1 (r=0.565, p=0.035). CONCLUSION: The PMax value based upon self-reported training range obtained from the Polar V800, M400, and FT60 provide a good estimate of AMax in males. In females, the PMax value associated with the next higher training range (PMax+1) provides a better estimate of AMax than the PMax based upon their actual self-reported training range.