This study compared the effects of two supervised concurrent training interventions in breast cancer survivors with cancer-related fatigue at baseline. Twenty-three female breast cancer survivors (50±8 years) were randomized to a high- (n=13) or a moderate-intensity (n=10) training program. Both interventions lasted 16 weeks and included the same resistance exercises, but the aerobic component was supervised and more intense in the former (i.e., rating of perceived exertion of 7–8 vs. 6 on a 1–10 scale for the high and moderate-intensity intervention, respectively). The primary endpoint was fatigue perception. Endpoints were assessed at baseline and after 16 weeks. The p-value for statistical significance was set at 0.004 after Bonferroni correction for multiple comparisons. The high-intensity training program increased lower-limb muscle strength significantly (p=0.002) and tended to improve fatigue perception (p=0.006), waist circumference (p=0.013), neutrophil-to-lymphocyte ratio (p=0.028) and some quality of life items (p=0.011). Although the moderate-intensity training program did not provide such benefits in general (i.e., higher p-values for pre vs post-intervention comparisons), no significant differences were found between interventions (all p>0.004). Further research is needed to elucidate if the benefits provided by high-intensity concurrent training are superior to those elicited by moderate-intensity training in breast cancer survivors.

Regular physical activity (PA) decreases mortality risk in survivors of breast and colorectal cancer. Such impacts of exercise have prompted initiatives designed both to promote and adequately monitor PA in cancer survivors. This study examines the validity of 2 widely used self-report methods for PA determination, the International Physical Activity Questionnaire short version (IPAQ-SF) and Global Physical Activity Questionnaire (GPAQ). Both instruments were compared with the triaxial accelerometry (Actigraph) method as an objective reference standard. Study participants were 204 cancer survivors (both sexes, aged 18?79 years). Compared with accelerometry, both questionnaires significantly overestimated PA levels (across all intensities) and underestimated physical inactivity levels. No differences were detected between the 2 questionnaires except for a shorter inactivity time estimated by GPAQ (p=0.001). The Bland and Altman method confirmed that both questionnaires overestimated all PA levels. Receiver operating characteristic (ROC) analysis classified IPAQ and GPAQ as fair and poor predictors, respectively, of the proportions of survivors fulfilling international PA recommendations (?150?min?week?1 of moderate-vigorous PA). IPAQ-SF showed a higher sensitivity but lower specificity than GPAQ. Our data do not support the use of IPAQ-SF or GPAQ to determine PA or inactivity levels in cancer survivors.

The aim of this study was to determine the physiological variables that predict competition performance during a CrossFit competition. Fifteen male amateur CrossFit athletes (age, 35 ± 9 years; CrossFit experience, 40 ± 27 months) performed a series of laboratory-based tests (incremental load test for deep full squat and bench press; squat, countermovement and drop jump tests; and incremental running and Wingate tests) that were studied as potential predictors of CrossFit performance. Thereafter, they performed the five Workouts of the Day (WODs) corresponding to the CrossFit Games Open 2019, and we assessed the relationship between the laboratory-based markers and CrossFit performance with regression analyses. Overall CrossFit performance (i.e., final ranking considering the sum of all WODs, as assessed by number of repetitions, time spent in exercises or weight lifted) was significantly related to jump ability, mean and peak power output during the Wingate test, relative maximum strength for the deep full squat and the bench press, and maximum oxygen uptake (VO2max) and speed during the incremental test (all p < 0.05, r = 0.58–0.75). However, the relationship between CrossFit Performance and most laboratory markers varied depending on the analyzed WOD. Multiple linear regression analysis indicated that measures of lower-body muscle power (particularly jump ability) and VO2max explained together most of the variance (R2 = 81%, p < 0.001) in overall CrossFit performance. CrossFit performance is therefore associated with different power-, strength-, and aerobic-related markers.

Purpose: To compare endurance, strength and body composition indicators between cyclists of three different competition age categories.Methods: Fifty-one male road cyclists classified as either junior (n = 13, age 16.4 ± 0.5 years), under-23 [(U23), n = 24, 19.2 ± 1.3 years] or professional (n = 14, 26.1 ± 4.8 years) were studied. Endurance (assessed through a maximal incremental test and an 8-minute time-trial), strength/power (assessed through incremental loading tests for the squat, lunge and hip thrust exercises) and body composition (assessed through dual energy X-ray absorptiometry) were determined on three different testing sessions.Results: U23 and, particularly professional, cyclists attained significantly (p &lt; 0.05) higher values than juniors for most of the analyzed endurance indicators [time-trial performance, maximum oxygen uptake (VO2max), peak power output (PPO), respiratory compensation point (RCP), and ventilatory threshold (VT)]. Significant differences (p &lt; 0.05) between U23 and professionals were also found for time-trial performance, PPO and VT, but not for other markers such as VO2max or RCP. Professional cyclists also showed significantly (p &lt; 0.05) lower relative fat mass and higher muscle mass levels than U23 and, particularly, juniors. No consistent differences between age categories were found for muscle strength/power indicators.Conclusion: Endurance (particularly time-trial performance, PPO and VT) and body composition (fat and muscle mass) appear as factors that best differentiate between cyclists of different age categories, whereas no consistent differences are found for muscle strength/power. These findings might help in performance prediction and/or talent identification and may aid in guiding coaches in the design of training programs focused on improving those variables that appear more determinant.