Submaximal heart rate seems inadequate to prescribe and monitor intensified training.

The aim of this study is to investigate whether the change in (sub)maximal heart rate after intensified training is associated with the change in performance. Thirty subjects were recruited who performed cardiopulmonary exercise tests to exhaustion 2 weeks before (pre), 1 week after (post) and 5 weeks after (follow-up) an 8-day non-competitive amateur cycling event (TFL). The exercise volume during the TFL was 7.7 fold the volume during the preparation period. Heart rate and cardiopulmonary parameters were obtained at standardised absolute submaximal workloads (low, medium and high intensity) and at peak level each test. Subjects were classified as functionally overreached (FOR) or acute fatigued (AF) based on the change in performance. No differences between FOR and AF were observed for heart rate (P = .51). On total group level (AF + FOR), post-TFL heart rate decreased significantly at low (-4.4 beats·min-1, 95% CI [-8.7, -0.1]) and medium (-5.5 beats·min-1 [-8.5, -2.4]), but not at high intensity. Peak heart rate decreased -3.4 beats·min-1 [-6.1, -0.7]. O2pulse was on average 0.49 ml O2·beat-1 [0.09, 0.89] higher at all intensities after intensified training. No changes in ⩒O2 (P = .44) or the ventilatory threshold (P = .21) were observed. Pearson's correlation coefficients revealed negative associations between heart rate and O2pulse at low (r = -.56, P < .01) and medium intensity (r = -.54, P < .01), but not with ⩒O2 or any other submaximal parameter. (Sub)maximal heart rate decreased after the TFL. However, this decrease is unrelated to the change in performance. Therefore, heart rate seems inadequate to prescribe and monitor intensified training.

Multi-dimensional flow cytometry analysis reveals increasing changes in the systemic neutrophil compartment during seven consecutive days of endurance exercise.

Endurance exercise is associated with a transient increase in neutrophil counts in the peripheral blood. Here we investigate the impact of intensified endurance exercise on the neutrophil compartment. We hypothesized that intensified endurance exercise leads to mobilization of neutrophil subsets, which are normally absent in the blood. Furthermore, we followed the potential build-up of neutrophil activation and the impact on overnight recovery of the neutrophil compartment during a seven-day cycling tour. The neutrophil compartment was studied in 28 healthy amateur cyclists participating in an eight-day strenuous cycling tour. Blood samples were taken at baseline, after 4 days and after 7 days of cycling. The neutrophil compartment was analyzed in terms of numbers and its phenotype by deep phenotyping of flow cytometry data with the multi-dimensional analysis method FLOOD. Repeated endurance exercise led to a gradual increase in total neutrophil counts over the days leading to a 1.26 fold-increase (95%CI 1.01-1.51 p = 0.0431) in the morning of day 8. Flow cytometric measurements revealed the appearance of 2 additional neutrophil subsets: CD16brightCD62Ldim and CD16dimCD62Lbright. A complex change in neutrophil phenotypes was present characterized by decreased expression of both CD11b and CD62L and marked increased expression of LAIR-1, VLA-4 and CBRM1/5. The changes in expression were found on all neutrophils present in the blood. Strikingly, in strong contrast to our findings during acute inflammation evoked by LPS challenge, these neutrophils did not upregulate classical degranulation markers. In fact, our FLOOD analysis revealed that the exercise induced neutrophil phenotype did not overlap with the neutrophil subsets arising upon acute inflammation. In conclusion, during multiple days of endurance exercise the neutrophil compartment does not regain homeostasis overnight. Thereby our study supports the concept of a build-up of inflammatory cues during repeated endurance exercise training, causing a prolonged change of the systemic neutrophil compartment.

Reduction in energy expenditure during weight loss is higher than predicted based on fat free mass and fat mass in older adults.

BACKGROUND & AIM: The aim of this study was to describe a decrease in resting energy expenditure during weight loss that is larger than expected based on changes in body composition, called adaptive thermogenesis (AT), in overweight and obese older adults. METHODS: Multiple studies were combined to assess AT in younger and older subjects. Body composition and resting energy expenditure (REE) were measured before and after weight loss. Baseline values were used to predict fat free mass and fat mass adjusted REE after weight loss. AT was defined as the difference between predicted and measured REE after weight loss. The median age of 55 y was used as a cutoff to compare older with younger subjects. The relation between AT and age was investigated using linear regression analysis. RESULTS: In this study 254 (M = 88, F = 166) overweight and obese subjects were included (BMI: 31.7 ± 4.4 kg/m2, age: 51 ± 14 y). The AT was only significant for older subjects (64 ± 185 kcal/d, 95% CI [32, 96]), but not for younger subjects (19 ± 152 kcal/d, 95% CI [-9, 46]). The size of the AT was significantly higher for older compared to younger adults (ß = 47, p = 0.048), independent of gender and type and duration of the weight loss program. CONCLUSIONS: We conclude that adaptive thermogenesis is present only in older subjects, which might have implications for weight management in older adults. A reduced energy intake is advised to counteract the adaptive thermogenesis.

Changes in Choice Reaction Time During and After 8 Days Exhaustive Cycling Are Not Related to Changes in Physical Performance.

PURPOSE: Reaction time has been proposed as a training monitoring tool, but to date, results are equivocal. Therefore, it was investigated whether reaction time can be used as a monitoring tool to establish overreaching. METHODS: The study included 30 subjects (11 females and 19 males, age: 40.8 [10.8] years, VO2max: 51.8 [6.3] mL/kg/min) who participated in an 8-day cycling event. The external exercise load increased approximately 900% compared with the preparation period. Performance was measured before and after the event using a maximal incremental cycling test. Subjects with decreased performance after the event were classified as functionally overreached (FOR) and others as acutely fatigued (AF). A choice reaction time test was performed 2 weeks before (pre), 1 week after (post), and 5 weeks after (follow-up), as well as at the start and end of the event. RESULTS: A total of 14 subjects were classified as AF and 14 as FOR (2 subjects were excluded). During the event, reaction time at the end was 68 ms (95% confidence interval, 46-89) faster than at the start. Reaction time post event was 41 ms (95% confidence interval, 12-71) faster than pre event and follow-up was 55 ms faster (95% confidence interval, 26-83). The time by class interaction was not significant during (P = .26) and after (P = .43) the event. Correlations between physical performance and reaction time were not significant (all Ps > .30). CONCLUSIONS: No differences in choice reaction time between AF and FOR subjects were observed. It is suggested that choice reaction time is not valid for early detection of overreaching in the field.

Prediction of Functional Overreaching From Subjective Fatigue and Readiness to Train After Only 3 Days of Cycling.

PURPOSE: To investigate whether monitoring of easily measurable stressors and symptoms can be used to distinguish early between acute fatigue (AF) and functional overreaching (FOR). METHODS: The study included 30 subjects (11 female, 19 male; age 40.8 ± 10.8 y, VO2max 51.8 ± 6.3 mL · kg-1 · min-1) who participated in an 8-d cycling event over 1300 km with 18,500 climbing meters. Performance was measured before and after the event using a maximal incremental test. Subjects with decreased performance after the event were classified as FOR, others as AF. Mental and physical well-being, internal training load, resting heart rate, temperature, and mood were measured daily during the event. Differences between AF and FOR were analyzed using mixed-model ANOVAs. Logistic regression was used to determine the best predictors of FOR after 3 and 6 d of cycling. RESULTS: Fifteen subjects were classified as FOR and 14 as AF (1 excluded). Although total group changes were observed during the event, no differences between AF and FOR were found for individual monitoring parameters. The combination of questionnaire-based changes in fatigue and readiness to train after 3 d cycling correctly predicted 78% of the subjects as AF or FOR (sensitivity = 79%, specificity = 77%). CONCLUSIONS: Monitoring changes in fatigue and readiness to train, using simple visual analog scales, can be used to identify subjects likely to become FOR after only 3 d of cycling. Hence, we encourage athlete support staff to monitor not only fatigue but also the subjective integrated mental and physical readiness to perform.

Resting energy expenditure prediction in recreational athletes of 18-35 years: confirmation of Cunningham equation and an improved weight-based alternative.

INTRODUCTION: Resting energy expenditure (REE) is expected to be higher in athletes because of their relatively high fat free mass (FFM). Therefore, REE predictive equation for recreational athletes may be required. The aim of this study was to validate existing REE predictive equations and to develop a new recreational athlete specific equation. METHODS: 90 (53 M, 37 F) adult athletes, exercising on average 9.1 ± 5.0 hours a week and 5.0 ± 1.8 times a week, were included. REE was measured using indirect calorimetry (Vmax Encore n29), FFM and FM were measured using air displacement plethysmography. Multiple linear regression analysis was used to develop a new FFM-based and weight-based REE predictive equation. The percentage accurate predictions (within 10% of measured REE), percentage bias, root mean square error and limits of agreement were calculated. Results: The Cunningham equation and the new weight-based equation REE(kJ / d) = 49.940* weight(kg) + 2459.053* height(m) - 34.014* age(y) + 799.257* sex(M = 1,F = 0) + 122.502 and the new FFM-based equation REE(kJ / d) = 95.272*FFM(kg) + 2026.161 performed equally well. De Lorenzo's equation predicted REE less accurate, but better than the other generally used REE predictive equations. Harris-Benedict, WHO, Schofield, Mifflin and Owen all showed less than 50% accuracy. CONCLUSION: For a population of (Dutch) recreational athletes, the REE can accurately be predicted with the existing Cunningham equation. Since body composition measurement is not always possible, and other generally used equations fail, the new weight-based equation is advised for use in sports nutrition.