Morris, KL, Widstrom, L, Goodrich, J, Poddar, S, Rueda, M, Holliday, M, San Millian, I, and Byrnes, WC. A retrospective analysis of collegiate athlete blood biomarkers at moderate altitude. J Strength Cond Res 33(11): 2913-2919, 2019-Blood biomarkers are used to assess overall health and determine positive/negative adaptations to training/environmental stimuli. This study aimed to describe the changes in blood biomarkers in collegiate football (FB) (n = 31) and cross-country (XC) (n = 29; 16 women [FXC], 13 men [MXC]) athletes across a competitive season while training and living at a moderate altitude (1,655 m). This study used a database of previously collected hematological (complete blood count and serum ferritin) and muscle damage (lactate dehydrogenase and creatine kinase) blood biomarkers. Data were analyzed both within and between groups using linear mixed-model and variance component analyses, alpha = 0.05. All 3 groups had significant but different patterns of change in the measured biomarkers. Hematological blood biomarkers increased at different time points but remained within the normal reference ranges with greater between-subject vs. within-subject variability, suggesting no significant decrements to oxygen-carrying capacity across the season for FB, MXC, or FXC. Muscle damage biomarkers increased over time and exceeded the normal reference ranges, indicating cell damage pathology. However, it is also possible that the demands of training and competition might alter baseline values in these athletes, although this cannot be confirmed with the current experimental design. The patterns of change in the hematological and muscle damage biomarkers varied by sport discipline, suggesting that the training/competitive environments of these athletes influence these changes. Further studies should assess how much training, altitude, and nutrition influence these changes by using a more comprehensive set of biomarkers and related performance parameters.

Background: In laboratory settings, cycling workstations improve cardiometabolic risk factors. Our purpose was to quantify risk factors following a cycling intervention in the workplace. Methods: Twenty-one office workers who sat at work >= 6 hours per day underwent baseline physiological measurements (resting blood pressure, blood lipid profile, maximum oxygen consumption [(V)over dotO(2)max], body composition, and 2-h oral glucose tolerance test). Participants were randomly assigned to a 4-week intervention only group (n = 12) or a delayed intervention group (n = 9) that involved a 4-week control condition before beginning the intervention. During the intervention, participants were instructed to use the cycling device a minimum of 15 minutes per hour, which would result in a total use of >= 2 hours per day during the workday. Following the intervention, physiological measurements were repeated. Results: Participants averaged 1.77 (0.48) hours per day of cycling during the intervention with no changes in actigraphy-monitored noncycling physical activity. Four weeks of the workplace intervention increased (V)over dotO(2)max (2.07 [0.44] to 2.17 [0.44] L.min(-1), P < .01); end of (V)over dotO(2)max test power output (166.3 [42.2] to 176.6 [46.1] W, P < .01); and high-density lipoprotein cholesterol (1.09 [0.17] to 1.17 [0.24] mmol.L-1, P = .04). Conclusions: A stationary cycling device incorporated into a sedentary workplace for 4 weeks improves some cardiometabolic risk factors with no compensatory decrease in noncycling physical activity.

In laboratory settings, replacing sitting with cycling improves cardiometabolic risk factors. However, changes to risk factors following a cycling intervention in the workplace have yet to be examined. PURPOSE: To quantify how a compact, stationary cycling device used in a sedentary workplace affects cardiometabolic risk factors. METHODS: Twenty-one inactive to recreationally active office workers who sat at work >6 h·d-1 visited the laboratory for baseline physiological measurements (resting blood pressure, blood lipid profile, VO2max, body composition, and 2-h oral glucose tolerance test). Participants were assigned to a 4-week intervention (n=12) or a 4-week control period (n=9). At the end of the control period, participants in the control group repeated the baseline physiological measurements and then began the workplace intervention. During the workplace intervention, participants were instructed to use the cycling device a minimum of 15 min·h-1 which would result in a total use of >2 h·d-1 during the workday. Following the 4-week intervention period, the physiological measurements were repeated. RESULTS: Participants averaged 1.73±0.47 h·d-1 of cycling during the intervention with no changes in actigraphy monitored non-cycling physical activity. Four weeks of the workplace intervention increased VO2max (2.07±0.44 to 2.17±0.44 L·min-1), end of VO2max test power output (166.3±42.2 to 176.6±46.1 W), and HDL cholesterol (1.09±0.17 to 1.17±0.24 mmol·L-1). CONCLUSIONS: A compact stationary cycling device incorporated into a sedentary workplace improves some cardiometabolic risk factors in 4 weeks with no compensatory decrease in non-cycling physical activity. Therefore, compact cycling devices are a feasible intervention for a sedentary workplace. Supported by NIH Grant UL1 TR000154, NIH Grant UL1 TR001082, the Rocky Mountain Chapter of the American College of Sports Medicine, and 3D Innovations LLC. This study was prospectively registered as a clinical trial (NCT02855541).

This study used a type of electric assist bicycle known as a pedelec. A pedelec is a bicycle equipped with a modest electric motor that provides assistance only when the rider is actively pedaling thus helping to overcome the common hurdles associated with active transportation (e.g. difficult hills and longer distances). PURPOSE: Our primary purpose was to quantify improvements in cardiometabolic risk factors associated with pedelec commuting for 4 weeks. Our secondary purpose was to quantify pedelec usage patterns (duration and intensity). METHODS: Twenty physically inactive participants (6 males, 14 females) visited the lab three times for baseline physiological measurements (body composition, VO2max test, mean arterial pressure (MAP) blood pressure, lipid profile, and 2-hour oral glucose tolerance test (OGTT)). During the following 4 weeks, participants commuted using a pedelec a minimum of 3 day/week for 40 min/day. While riding the pedelec, participants wore a heart rate monitor and used a GPS device. Heart rate data was used in conjunction with a regression equation developed from the VO2max test to estimate METS. After 4 weeks, participants repeated the physiological measurements. RESULTS: Commuting with a pedelec significantly improved 2-hr post OGTT glucose (5.45±1.18 to 5.02±0.91 mmol/L, p<0.05), VO2max (2.19±0.48 to 2.37±0.52 L/min, p<0.05), and power output at the end of the VO2max test (165.6±39.4 to 185.6±38.2 Watts, p<0.05). There was a trend for improvements in MAP (84.1±10.5 to 82.7±9.4 mmHg, p=0.15) and fat mass (28.3±11.3 to 27.8±11.4 kg, p=0.07). The average ride distance was 11.2±6.8 km with ride time averaging 0:32:56±0:14:41 (hr:min:sec). Average 4 week total distance and time were 317.9±113.7 km and 16:16:41±3:19:05, respectively. Estimated METS while riding were in the moderate intensity range (4.6±1.2 METS). CONCLUSIONS: Commuting with a pedelec for 4 weeks resulted in significant improvements in 2-hr post OGTT glucose, VO2max, and peak power output. Despite the electric assistance, riders self-selected an intensity that helped them meet the ACSM guidelines for physical activity. Pedelecs are an effective form of active transportation that can improve some cardiometabolic risk factors. Supported by NIH Grant UL1 TROOO154, the City of Boulder, and Skratch Labs

Pedelecs are bicycles that provide electric assistance only when a rider is pedaling and have become increasingly popular.

PURPOSE: This study assessed selected seasonal hematological changes in elite male and female collegiate cross-country (XC) runners residing at a moderate altitude (1655 m). METHODS: Previously collected de-identified data from 29 members of the University of Colorado’s XC team (12 males, 17 females) were analyzed for this project. The data was part of the regularly scheduled monitoring of these athletes through the CU Sports Medicine program. This program involves blood samples being taken following a rest day, after an overnight fast, at five time points across the year, (August, October, January, April, and August of the new season). Hematological parameters measured included red blood cell count (RBC), hemoglobin concentration (Hb), hematocrit (Hct), mean corpuscular volume (MCV), red cell distribution width (RDW) and serum ferritin. A linear mixed model was used to assess changes over time, significance set at p < .05. For variables that violated the assumptions of the linear mixed model (ferritin), non-parametric analysis was used. RESULTS: Males (M) and females (F) had significantly different baseline values for Hct (%) (M: 46.5 ± .8 versus F: 43.0 ± .6) and Hb (gm/dL) (M: 16.3 ± .3 versus F: 14.6 ± .2), although they exhibited the same pattern of change across the season. Overall, Hct increased from baseline at the October time point (+5.4%) before returning to near baseline levels for the remainder of the season. Hb had a similar trend, being higher at the October time point (+2.2%, p = 0.083) before returning to near baseline levels. MCV (Aug1- 90.4 ± .6, Aug2- 92.4 ± .6) and RDW (Aug1- 12.7 ± .1, Aug2- 12.5 ± .1) were the only two variables whose two August time points were significantly different. Serum ferritin (ng/mL) was stable over all five time points for males (average of all time points: 56.5), whereas females demonstrated significantly lower values in January (49.2), (average of all time points excluding January: 56.9). CONCLUSION: These results suggest seasonal hematological changes occur in elite collegiate XC runners. These changes could be the result of adaptations associated with alterations in training, nutrition and/or altitude exposure. Future studies should directly assess the contribution of these parameters to the observed changes and determine the impact of these changes on performance.