Optimizing recovery to support multi-evening cycling competition performance

Night running and night cycling: a review of sociological drivers, health benefits, and their interaction with urban green spaces

This study investigates the rising trends, sociological drivers, health benefits, and the interaction of night running and night cycling with urban green spaces. Findings indicate that night running and night cycling are mainly driven by increased health awareness, changes in work patterns, social media promotion, and urban economic development. Physiologically, night running and cycling enhance cardiovascular function, alleviate stress, and improve sleep. However, air pollution, inadequate infrastructure, and safety concerns limit the sustainability of these activities. Urban green spaces play a crucial role in promoting nocturnal exercise, with improvements in lighting, safety, and recreational areas due to the growing demand for night fitness. Governments are encouraged to enhance infrastructure and optimize green space planning to foster healthier urban environments.

Multi-View Integrative Approach For Imputing Short-Chain Fatty Acids and Identifying Key factors predicting Blood SCFA

Short-chain fatty acids (SCFAs) are the main metabolites produced by bacterial fermentation of dietary fiber within gastrointestinal tract. SCFAs produced by gut microbiotas (GMs) are absorbed by host, reach bloodstream, and are distributed to different organs, thus influencing host physiology. However, due to the limited budget or the poor sensitivity of instruments, most studies on GMs have incomplete blood SCFA data, limiting our understanding of the metabolic processes within the host. To address this gap, we developed an innovative multi-task multi-view integrative approach (M2AE, Multi-task Multi-View Attentive Encoders), to impute blood SCFA levels using gut metagenomic sequencing (MGS) data, while taking into account the intricate interplay among the gut microbiome, dietary features, and host characteristics, as well as the nuanced nature of SCFA dynamics within the body. Here, each view represents a distinct type of data input (i.e., gut microbiome compositions, dietary features, or host characteristics). Our method jointly explores both view-specific representations and cross-view correlations for effective predictions of SCFAs. We applied M2AE to two in-house datasets, which both include MGS and blood SCFAs profiles, host characteristics, and dietary features from 964 subjects and 171 subjects, respectively. Results from both of two datasets demonstrated that M2AE outperforms traditional regression-based and neural-network based approaches in imputing blood SCFAs. Furthermore, a series of gut bacterial species (e.g., Bacteroides thetaiotaomicron and Clostridium asparagiforme), host characteristics (e.g., race, gender), as well as dietary features (e.g., intake of fruits, pickles) were shown to contribute greatly to imputation of blood SCFAs. These findings demonstrated that GMs, dietary features and host characteristics might contribute to the complex biological processes involved in blood SCFA productions. These might pave the way for a deeper and more nuanced comprehension of how these factors impact human health.

Sleep restriction between consecutive days of exercise impairs sprint and endurance cycling performance

The study aim was to determine the effect of sleep restriction (3 h) between consecutive days of exercise on sprint and endurance cycling performance, wellness, and mood. A total of 10 well-trained males performed 2 consecutive-day trials separated by a normal night sleep (control [CONT]; mean [SD] sleep duration 3.0 [0.2] h) or sleep restriction (RES; mean [SD] sleep duration 3.0 [0.2] h). Experimental trials included a 90-min fixed-paced cycling bout and the respective sleep conditions on Day 1, followed by two 6-s peak power (6-s PP) tests, a 4- and 20-min time trial (TT) on Day 2. Profile of Mood States (POMS) and wellness questionnaires were recorded on Day 1 and Day 2. Blood lactate and glucose, heart rate (HR), and rating of perceived exertion were recorded throughout Day 2. Power output (PO) was significantly reduced for RES in the 6-s PP trial (mean [SD] 1159 [127] W for RES versus 1250 [186] W for CONT; p = 0.04) and mean PO during the 20-min TT (mean [SD] 237 [59] W for RES versus 255 [58] W for CONT; p = 0.03). There were no differences for HR, lactate and glucose, or POMS between CONT and RES in all experimental trials (p = 0.05-0.89). Participants reported a reduction in overall wellness prior to exercise on Day 2 following RES (mean [SD] 14.5 [1.6] au) compared to CONT (mean [SD] 16 [3.0] au; p = 0.034). Sleep restriction and the associated reductions in wellness, reduce cycling performance during consecutive days of exercise in a range of cycling tests that are relevant to both track and road cyclists.

Consistency Is Key When Setting a New World Record for Running 10 Marathons in 10 Days

Background: We describe the requirements and physiological changes when running 10 consecutive marathons in 10 days at the same consistent pace by a female ultra-endurance athlete. Methods: Sharon Gayter (SG) 54 yrs, 162.5 cm, 49.3 kg maximal oxygen uptake (VO₂ max) 53 mL/kg⁻¹/min⁻¹. SG completed 42.195 km on a treadmill every day for 10 days. We measured heart rate (HR), Rating of Perceived Exertion (RPE), oxygen uptake (VO₂), weight, body composition, blood parameters, nutrition, and hydration. Results: SG broke the previous record by ~2.5 h, with a cumulative completion time of 43 h 51 min 39 s. Over the 10 days, weight decreased from 51 kg to 48.4 kg, bodyfat mass from 9.1 kg to 7.2 kg (17.9% to 14.8%), and muscle mass from 23.2 kg to 22.8 kg. For all marathons combined, exercise intensity was ~60% VO₂ max; VO₂ 1.6 ± 0.1 L.min⁻¹/32.3 ± 1.1 mL.kg⁻¹.min⁻¹, RER 0.8 ± 0, HR 143 ± 4 b.min⁻¹. Energy expenditure (EE) was 2030 ± 82 kcal/marathon, total EE for 10 days (including BMR) was 33,056 kcal, daily energy intake (EI) 2036 ± 418 kcal (20,356 kcal total), resulting an energy deficit (ED) of 12,700 kcal. Discussion: Performance and pacing were highly consistent across all 10 marathons without any substantial physiological decrements. Although overall EI did not match EE, leading to a significant ED, resulting in a 2.6 kg weight loss and decreases in bodyfat and skeletal muscle mass, this did not affect performance.

Inflammation, muscle damage and postrace physical activity following a mountain ultramarathon

BACKGROUND

The study aimed at exploring whether muscle membrane disruption, as a surrogate for muscle damage, and inflammation recovery following a mountain ultramarathon (MUM) was related with race performance and postrace physical activity.

METHODS

Blood samples were obtained from thirty-four athletes (29 men and 5 women) before a 118-km MUM, immediately after and three- and seven-days postrace. Creatine kinase (CK), lactate dehydrogenase (LDH) and C-reactive protein (CRP) were compared between faster (FR) and slower (SR) runners. Physical activity performed during the week following the MUM was objectively analyzed using accelerometers and compared between FR and SR.

RESULTS

CK was significantly higher in FR at 3 days postrace (P<0.012, d=1.17) and LDH was significantly higher in FR at 3- and 7-days postrace (P=0.005, d=1.01; P<0.015, d=1.05 respectively), as compared to SR. No significant differences were identified in postrace physical activity levels between FR and SR. Significant relationships were found between race time and CK and LDH concentrations at 3 days postrace (r<inf>s</inf>=-0.41, P=0.017; r<inf>s</inf>=-0.52, P=0.002 respectively) and 7 days postrace (r<inf>s</inf>=-0.36, P=0.039; r<inf>s</inf>=-0.46. P=0.007 respectively). However, postrace physical activity was not associated with muscle damage and inflammation recovery, except for light intensity and CRP at 3 days postrace (r<inf>s</inf>=-0.40, P=0.025).

CONCLUSIONS

Race time appeared to have a higher influence on muscle damage recovery than the intensity of physical activities performed in the week after running a MUM. Inflammatory activity takes longer to normalize than muscle damage following a MUM, it is not related with race time and lightly related with postrace physical activity.

External Responsiveness of the SuperOpTM Device to Assess Recovery After Exercise: A Pilot Study

Post-exercise recovery is a complex process involving a return of performance and a physiological or perceptual feeling close to pre-exercise status. The hypothesis of this study is that the device investigated here is effective in evaluating the recovery state of professional cyclists in order to plan effective training. Ten professional male cyclists belonging to the same team were enrolled in this study. Participants performed a 7-day exercise program [D1, D4, and D7: low-intensity training; D2 and D5: passive recovery; D3: maximum oxygen consumption (VO2Max) test (for maximum mechanical power assessment only); and D6: constant load test]. During the week of monitoring, each morning before getting up, the device assessed each participant's so-called Organic Readiness {OR [arbitrary unit (a.u.)]}, based on blood pressure (BP), heart rate (HR), features of past exercise session, and following self-perceived condition. Based on its readings and algorithm, the device graphically displayed four different colors/values, indicating general exercise recommendations: green/3 = "you can train hard," yellow/2 = "you can train averagely," orange/1 = "you can train lightly," or red/0 = "you should recover passively." During the week of research, morning OR values and Bonferroni post-hoc comparisons showed significant differences between days and, namely, values (1) D2 (after low intensity training) was higher than D4 (after VO2Max test; P = 0.033 and d = 1.296) and (2) D3 and D6 (after passive recovery) were higher than D4 (after VO2Max test; P = 0.006 and d = 2.519) and D5 (after low intensity training; P = 0.033 and d = 1.341). The receiver operating characteristic analysis area under curve (AUC) recorded a result of 0.727 and could differentiate between D3 and D4 with a sensitivity and a specificity of 80%. Preliminarily, the device investigated is a sufficiently effective and sensitive/specific device to assess the recovery state of athletes in order to plan effective training.