Can period-related symptoms predict menstrual manipulation among Australian female cyclists?

This study explored the extent of menstrual manipulation and its associated impact on period-related symptoms and training disruptions in Australian Female Cyclists. 205 female cyclists, from recreational to elite level, participated in an online "Female Cyclist Questionnaire (FCQ)". The FCQ utilised a series of validated questionnaires to obtain demographic information and menstrual function of the respondents, and to investigate their menstrual manipulation habits and perceptions on how their period-related symptoms affected their well-being, mood, energy and training tolerance. More than 80% of the cyclists reported that their period-related symptoms impacted upon training and 41% made training adjustments based on these symptoms. Two-thirds of respondents thought their training should be phase-controlled yet only half discussed their hormonal cycles with their coaches. Menstrual manipulation was predicted by reduced "workout tolerance" in these cyclists (odds ratio = 0.632). Half of the respondents reported compromised ability to tolerate high-intensity interval training with period-related symptoms. Period pain, increased irritability, lower energy levels and more sugar cravings were commonly reported but did not predict menstrual manipulation. The data indicated that period-related symptoms are present in Australian female cyclists across all levels of participation. However, the perceived impact to training and subsequent behavioural changes varied among individuals.

Reinforcement learning informs optimal treatment strategies to limit antibiotic resistance.

Antimicrobial resistance was estimated to be associated with 4.95 million deaths worldwide in 2019. It is possible to frame the antimicrobial resistance problem as a feedback-control problem. If we could optimize this feedback-control problem and translate our findings to the clinic, we could slow, prevent, or reverse the development of high-level drug resistance. Prior work on this topic has relied on systems where the exact dynamics and parameters were known a priori. In this study, we extend this work using a reinforcement learning (RL) approach capable of learning effective drug cycling policies in a system defined by empirically measured fitness landscapes. Crucially, we show that it is possible to learn effective drug cycling policies despite the problems of noisy, limited, or delayed measurement. Given access to a panel of 15 [Formula: see text]-lactam antibiotics with which to treat the simulated Escherichia coli population, we demonstrate that RL agents outperform two naive treatment paradigms at minimizing the population fitness over time. We also show that RL agents approach the performance of the optimal drug cycling policy. Even when stochastic noise is introduced to the measurements of population fitness, we show that RL agents are capable of maintaining evolving populations at lower growth rates compared to controls. We further tested our approach in arbitrary fitness landscapes of up to 1,024 genotypes. We show that minimization of population fitness using drug cycles is not limited by increasing genome size. Our work represents a proof-of-concept for using AI to control complex evolutionary processes.

Characterization and prevention of flame burns caused by electric bicycle battery chargers: results from a 7-year experience.

OBJECTIVE: With the recent exponential increase in express deliveries across China, the number of patients with flame burns caused by electric bicycle battery chargers (BEBBC) has markedly increased in burn units. In this study, we aimed to characterize BEBBC to systematically explore measures to prevent their occurrence. METHODS: We performed a retrospective chart review of patients with flame burns who visited the Burn Department of Rui Jin Hospital between January 2015 and December 2021. RESULTS: Sixty-three patients with BEBBC and 1412 with types of other flame burn were included in this study. Fifty-six of the 63 BEBBC cases occurred between 9 pm and 7 am. BEBBC incidents involved a higher incidence of group burn in which multiple individuals were affected. Non-local patients with BEBBC were significantly younger than their local counterparts. BEBBC had a higher mortality than types of other flame burn. CONCLUSIONS: The rising incidence of BEBBC calls for greater attention because of the associated high mortality and heavy burden on society. Enacting related legislation, disseminating information to the public, and improving treatment to control infection can help prevent BEBBC, increase its cure rate, and reduce patient mortality.

Associations between transport modes and site-specific cancers: a systematic review and meta-analysis.

BACKGROUND: Physical inactivity is a global public health problem. A practical solution would be to build physical activity into the daily routine by using active modes of transport. Choice of transport mode can influence cancer risk through their effects on levels of physical activity, sedentary time, and environmental pollution. This review synthesizes existing evidence on the associations of specific transport modes with risks of site-specific cancers. METHODS: Relevant literature was searched in PubMed, Embase, and Scopus from 1914 to 17th February 2023. For cancer sites with effect measures available for a specific transport mode from two or more studies, random effects meta-analyses were performed to pool relative risks (RR) comparing the highest vs. lowest activity group as well as per 10 Metabolic Equivalent of Task (MET) hour increment in transport-related physical activity per week (∼150 min of walking or 90 min of cycling). RESULTS: 27 eligible studies (11 cohort, 15 case-control, and 1 case-cohort) were identified, which reported the associations of transport modes with 10 site-specific cancers. In the meta-analysis, 10 MET hour increment in transport-related physical activity per week was associated with a reduction in risk for endometrial cancer (RR: 0.91, 95% CI: 0.83-0.997), colorectal cancer (RR: 0.95, 95% CI: 0.91-0.99) and breast cancer (RR: 0.99, 95% CI: 0.89-0.996). The highest level of walking only or walking and cycling combined modes, compared to the lowest level, were significantly associated with a 12% and 30% reduced risk of breast and endometrial cancers respectively. Cycling, compared to motorized modes, was associated with a lower risk of overall cancer incidence and mortality. CONCLUSION: Active transport appears to reduce cancer risk, but evidence for cancer sites other than colorectum, breast, and endometrium is currently limited.

Near-InfraRed Spectroscopy Provides a Reproducible Estimate of Muscle Aerobic Capacity, but Not Whole-Body Aerobic Power.

Near-infrared spectroscopy (NIRS) during repeated limb occlusions is a noninvasive tool for assessing muscle oxidative capacity. However, the method's reliability and validity remain under investigation. This study aimed to determine the reliability of the NIRS-derived mitochondrial power of the musculus vastus lateralis and its correlation with whole-body (cycling) aerobic power (V̇O2 peak). Eleven healthy active men (28 ± 10 y) twice (2 days apart) underwent repeated arterial occlusions to induce changes in muscle oxygen delivery after 15 s of electrical muscle stimulation. The muscle oxygen consumption (mV̇O2) recovery time and rate (k) constants were calculated from the NIRS O2Hb signal. We assessed the reliability (coefficient of variation and intraclass coefficient of correlation [ICC]) and equivalency (t-test) between visits. The results showed high reproducibility for the mV̇O2 recovery time constant (ICC = 0.859) and moderate reproducibility for the k value (ICC = 0.674), with no significant differences between visits (p > 0.05). NIRS-derived k did not correlate with the V̇O2 peak relative to body mass (r = 0.441, p = 0.17) or the absolute V̇O2 peak (r = 0.366, p = 0.26). In conclusion, NIRS provides a reproducible estimate of muscle mitochondrial power, which, however, was not correlated with whole-body aerobic capacity in the current study, suggesting that even if somewhat overlapping, not the same set of factors underpin these distinct indices of aerobic capacity at the different (peripheral and whole-body systemic) levels.

The perception of time is slowed in response to exercise, an effect not further compounded by competitors: behavioral implications for exercise and health.

INTRODUCTION: The theory of relativity postulates that time is relative to context and exercise seems such a situation. The purpose of this study was to examine whether situational factors such as perceived exertion and the introduction of an opponent influence competitors' perception of time. METHODS: Thirty-three recreationally active adults (F = 16; M = 17) performed three standardized 4-km cycling trials in a randomized order. Velotron 3D software was used to create a visual, virtual environment representing (1) a solo time trial (FAM and SO), (2) a time trial with a passive opponent avatar (PO), and (3) a time trial with an opponent avatar and participant instruction to actively finish the trial before the opponent (AO). Participants were asked to estimate a 30-s time period using a standardized protocol for reproducibility before exercise at 500 m, 1500 m, 2500 m, and post exercise. Rate of perceived exertion (RPE) was measured throughout the trials. RESULTS: Exercise trials revealed that time was perceived to run "slow" compared to chronological time during exercise compared to resting and post-exercise measurements (p < 0.001). There was no difference between exercise conditions (SO, PO, and AO) or time points (500 m, 1500 m, and 2500 m). RPE increased throughout the trials. CONCLUSION: The results of this study demonstrate for the first time that exercise both with and without the influence of opponents influences time perception. This finding has important implications for healthy exercise choices and also for optimal performance. Independent of RPE, time was perceived to move slower during exercise, underpinning inaccurate pacing and decision-making across physical activities.

Pro-cycling team cyclist assignment for an upcoming race.

Professional bicycle racing is a popular sport that has attracted significant attention in recent years. The evolution and ubiquitous use of sensors allow cyclists to measure many metrics including power, heart rate, speed, cadence, and more in training and racing. In this paper we explore for the first time assignment of a subset of a team's cyclists to an upcoming race. We introduce RaceFit, a model that recommends, based on recent workouts and past assignments, cyclists for participation in an upcoming race. RaceFit consists of binary classifiers that are trained on pairs of a cyclist and a race, described by their relevant properties (features) such as the cyclist's demographic properties, as well as features extracted from his workout data from recent weeks; as well additional properties of the race, such as its distance, elevation gain, and more. Two main approaches are introduced in recommending on each stage in a race and aggregate from it to the race, or on the entire race. The model training is based on binary label which represent participation of cyclist in a race (or in a stage) in past events. We evaluated RaceFit rigorously on a large dataset of three pro-cycling teams' cyclists and race data achieving up to 80% precision@i. The first experiment had shown that using TP or STRAVA data performs the same. Then the best-performing parameters of the framework are using 5 weeks time window, imputation was effective, and the CatBoost classifier performed best. However, the model with any of the parameters performed always better than the baselines, in which the cyclists are assigned based on their popularity in historical data. Additionally, we present the top-ranked predictive features.

FF-STGCN: A usage pattern similarity based dual-network for bike-sharing demand prediction.

Accurate bike-sharing demand prediction is crucial for bike allocation rebalancing and station planning. In bike-sharing systems, the bike borrowing and returning behavior exhibit strong spatio-temporal characteristics. Meanwhile, the bike-sharing demand is affected by the arbitrariness of user behavior, which makes the distribution of bikes unbalanced. These bring great challenges to bike-sharing demand prediction. In this study, a usage pattern similarity-based dual-network for bike-sharing demand prediction, called FF-STGCN, is proposed. Inter-station flow features and similar usage pattern features are fully considered. The model includes three modules: multi-scale spatio-temporal feature fusion module, bike usage pattern similarity learning module, and bike-sharing demand prediction module. In particular, we design a multi-scale spatio-temporal feature fusion module to address limitations in multi-scale spatio-temporal accuracy. Then, a bike usage pattern similarity learning module is constructed to capture the underlying correlated features among stations. Finally, we employ a dual network structure to integrate inter-station flow features and similar usage pattern features in the bike-sharing demand prediction module to realize the final prediction. Experiments on the Citi Bike dataset have demonstrated the effectiveness of our proposed model. The ablation experiments further confirm the indispensability of each module in the proposed model.

Power Profile during Cycling in World Triathlon Series and Olympic Games.

This study aimed to analyze the power profile (PP) during the cycling segment of international-level triathletes in the World Triathlon Series (WTS) and Olympics and to evaluate the influence of circuit type, race distance (Sprint or Olympic distance) and race dynamics on the development of the cycling leg and the final race position. Four male triathletes participated in the study. Twenty races were analyzed using geolocation technology and power-meter data to analyze PP, race dynamics, and course characteristics. Before the races, incremental tests of volitional exhaustion with gas analysis were performed to determine power intensity zones. Nonparametric Mann-Whitney U tests and correlation analyses were conducted to identify differences and relationships between various variables. A correlation between the time spent above maximal aerobic power (MAP) and dangerous curves per kilometer (r = 0.46; p < 0.05) and bike split result (BSR) (r = -0.50; p < 0.05) was observed. Also, moderate correlation was found between BSR and the final race position (r = 0.46; p < 0.01). No differences were found between sprint and Olympic distance races in any variable. Power output variability, influenced by technical circuit segments, remains the main characteristic in international short-distance races. The results of the present study suggest that the triathletes who are better adapted to intermittent high intensity efforts perform better cycling legs at international high-level races.

"Cyclist at 12 o'clock!": a systematic review of in-vehicle advanced driver assistance systems (ADAS) for preventing car-rider crashes.

Introduction: While Advanced Driver Assistance Systems (ADAS) have become a prominent topic in road safety research, there has been relatively little discussion about their effectiveness in preventing car collisions involving specific vulnerable road users, such as cyclists. Therefore, the primary objective of this systematic literature review is to analyze the available evidence regarding the effectiveness of in-vehicle ADAS in preventing vehicle collisions with cyclists. Methods: To achieve this goal, this systematic review analyzed a selection of original research papers that examined the effectiveness of ADAS systems in preventing car-cyclist collisions. The review followed the PRISMA protocol, which led to the extraction of 21 eligible studies from an initial pool of 289 sources indexed in the primary scientific literature databases. Additionally, word community-based content analyses were used to examine the research topics and their links within the current scientific literature on the matter. Results: Although the current number of studies available is still scarce (most sources focus on car-motorcyclist or car-pedestrian crashes), the overall quality of the available studies has been reasonably good, as determined by the selected evaluation methods. In terms of studies' outcomes, the literature supports the value of in-vehicle ADAS for preventing car-cyclist crashes. However, threatful side effects such as unrealistic expectations of these systems and users' overconfidence or desensitization are also highlighted, as well as the need to increase driver training and road user awareness. Conclusion: The results of this study suggest that Advanced Driver Assistance Systems have significant potential to contribute to the prevention of driving crashes involving cyclists. However, the literature emphasizes the importance of concurrently enhancing user-related skills in both ADAS use and road-user interaction through educational and training initiatives. Future research should also address emerging issues, such as ADAS-related behavioral ergonomics, and conduct long-term effectiveness assessments of ADAS in preventing car-cycling crashes and their subsequent injuries. Systematic review registration: PROSPERO, unique identifier CRD42024505492, https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=505492.

Post exercise hot water immersion and hot water immersion in isolation enhance vascular, blood marker, and perceptual responses when compared to exercise alone.

Exercise and passive heating induce some similar vascular hemodynamic, circulating blood marker, and perceptual responses. However, it remains unknown whether post exercise hot water immersion can synergise exercise derived responses and if they differ from hot water immersion alone. This study investigated the acute responses to post moderate-intensity exercise hot water immersion (EX+HWI) when compared to exercise (EX+REST) and hot water immersion (HWI+HWI) alone. Sixteen physically inactive middle-aged adults (nine males and seven females) completed a randomized cross-over counterbalanced design. Each condition consisted of two 30-min bouts separated by 10 min of rest. Cycling was set at a power output equivalent to 50% V̇o2 peak . Water temperature was controlled at 40°C up to the mid sternum with arms not submerged. Venous blood samples and artery ultrasound scans were assessed at 0 (baseline), 30 (immediately post stressor one), 70 (immediately post stressor two), and 100 min (recovery). Additional physiological and perceptual measures were assessed at 10-min intervals. Brachial and superficial femoral artery shear rates were higher after EX+HWI and HWI+HWI when compared with EX+REST (p < 0.001). Plasma nitrite was higher immediately following EX+HWI and HWI+HWI than EX+REST (p < 0.01). Serum interleukin-6 was higher immediately after EX+HWI compared to EX+REST (p = 0.046). Serum cortisol was lower at 30 min in the HWI+HWI condition in contrast to EX+REST (p = 0.026). EX+HWI and HWI+HWI were more enjoyable than EX+REST (p < 0.05). Irrespective of whether hot water immersion proceeded exercise or heating, hot water immersion enhanced vascular and blood marker responses, while also being more enjoyable than exercise alone.

The association between head injury and facial fracture treatment: an observational study of hospitalized bicyclists from a level 1 trauma centre.

PURPOSE: To compare the types of facial fractures and their treatment in bicyclists admitted to a level 1 trauma centre with major and minor-moderate head injury. METHODS: Retrospective analysis of data from bicycle-related injuries in the period 2005-2016 extracted from the Oslo University Hospital trauma registry. RESULTS: A total of 967 bicyclists with head injuries classified according to the Abbreviated Injury Scale (AIS) were included. The group suffering minor-moderate head injury (AIS Head 1-2) included 518 bicyclists, while 449 bicyclists had major head injury (AIS Head 3-6). The mean patient age was 40.2 years (range 3-91 years) and 701 patients (72%) were men. A total of 521 facial fractures were registered in 262 patients (on average 2 facial fractures per bicyclist). Bicyclists with major head injury exhibited increased odds for facial fractures compared to bicyclists with minor-moderate head injury (sex and age adjusted odds ratio (OR) 2.75, 95% confidence interval (CI) 2.03-3.72, p < 0.001. More specifically, there was increased odds for all midface fractures, but no difference for mandible fractures. There was also increased odds for orbital reconstruction in cyclist with major head injury compared to bicyclist with minor-moderate head injury (adjusted OR 3.34, 95% CI 1.30-8.60, p = 0.012). CONCLUSION: Bicyclists with more severe head injuries had increased odds for midface fractures and surgical correction of orbital fractures. During trauma triage, the head and the face should be considered as one unit.

Validation of LEOMO inertial measurement unit sensors with marker-based three-dimensional motion capture during maximum sprinting in track cyclists.

LEOMO™ is a commercial inertial measurement unit system that provides cycling-specific motion performance indicators (MPIs) and offers a mobile solution for monitoring cyclists. We aimed to validate the LEOMO sensors during sprint cycling using gold-standard marker-based three-dimensional (3D) motion technology (Qualisys, AB). Our secondary aim was to explore the relationship between peak power during sprints and MPIs. Seventeen elite track cyclists performed 3 × 15s seated start maximum efforts on a cycle ergometer. Based on intraclass correlation coefficient (ICC3,1), the MPIs derived from 3D and LEOMO showed moderate agreement (0.50 < 0.75) for the right foot angular range (FAR); left foot angular range first quadrant (FARQ1); right leg angular range (LAR); and mean angle of the pelvis in the sagittal plane. Agreement was poor (ICC < 0.50) between MPIs derived from 3D and LEOMO for the left FAR, right FARQ1, left LAR, and mean range of motion of the pelvis in the frontal and transverse planes. Only one LEOMO-derived (pelvic rotation) and two 3D-derived (right FARQ1 and FAR) MPIs showed large positive significant correlations with peak power. Caution is advised regarding use of the LEOMO for short maximal cycling efforts and derived MPIs to inform peak sprint cycling power production.

Unraveling the relation between cycling accidents and built environment typologies: Capturing spatial heterogeneity through a latent class discrete outcome model.

Today, cities seek to transition to more sustainable transportation modes. Cycling is critical in this shift, promoting a more beneficial lifestyle for most. However, cyclists are exposed to many hazardous circumstances or environments, resulting in accidents, injuries, and even death. Transport authorities must understand why accidents occur, to reduce the risk of those who cycle. This study applies a new modeling framework to analyze cycling accident severities. We employ a latent class discrete outcome model, where classes are derived from a Gaussian-Bernoulli mixture, applied to data from Berlin, and augmented with volunteered geographic information. We jointly estimate model components, combining machine learning and econometric approaches, allowing for more intricate and flexible representations while maintaining interpretability. Results show the potential of our approach. Risk factors are indexed depending on where accidents occurred and their contribution. We can discover complex relations between specific built environments and accident characteristics and uncover differences in the impact of certain accident factors on one environment typology but not others. Using multiple data sources also proves helpful as an additional layer of knowledge, providing unique value to understand and model cycling accidents. Another critical aspect of our approach is the potential for simulation, where locations can be examined through simulated accident features to understand the inherent risk of various locations. These findings highlight the ability to capture heterogeneity in accidents and their relation to the built environment. Capturing such relations allows for more direct countermeasures to risky situations or policies to be designed, simulated, and targeted.

Integrating visual factors in crash rate analysis at Intersections: An AutoML and SHAP approach towards cycling safety.

Cycling crashes constitute a significant and rising share of traffic accidents. Consequently, exploring factors affecting cycling safety has become a priority for both governmental bodies and scholars. However, most existing studies have neglected the vision factors capable of quantitatively describing the city-level cycling environment. Moreover, they have relied on limited models that lack interpretability and fail to capture the spatial variations in the contribution of factors. To address these gaps, this research proposed a framework that used origin-destination-based cycling flow and vision factors generated from Google Street View images to identify the leading factors. It also employed the comparative Automatic Machine Learning and interpretable SHAP value-based geospatial analysis to explain each factor's contribution to the cycling crash risk, with a particular focus on the spatial variations in the influence of vision factors. The effectiveness of this framework was validated by a case study in Manhattan, which examined the leading risk factors of cycling crash rates at intersections. The results showed that the LightGBM model, with selected subsets of factors, outperformed other models. Through SHAP explanations of global feature importance, the study identified the proportion of road barriers, the proportion of open sky, and the number of visible trucks as the leading visual risk factors. Additionally, using SHAP-based geospatial analysis, the study revealed the local variations in the effects of these three factors and identified eight areas with higher cycling crash rates. Based on these findings, the study provided practical measures for a safer cycling environment in Manhattan.

Acute effects of various doses of nitrate-rich beetroot juice on high-intensity interval exercise responses in women: a randomized, double-blinded, placebo-controlled, crossover trial.

BACKGROUND: This study investigated the acute effects of various doses of nitrate-rich beetroot juice on the responses to high-intensity interval exercise in women. METHODS: A double-blinded, randomized, placebo-controlled, crossover trial was conducted with 13 recreationally active young women (age = 23 ± 2 years). All participants performed interval exercise (8 × 1-min bouts of cycling at 85% of peak power output [PPO] interspersed with 1-min active recovery at 20% of PPO) 2.5 h after consumption of the randomly assigned beetroot juice containing 0 mmol (placebo), 6.45 mmol (single-dose), or 12.9 mmol (double-dose) NO3-. The heart rate (HR), blood pressure, blood lactate, blood glucose, oxygen saturation, rating of perceived exertion (RPE), and emotional arousal were assessed. RESULTS: Nitrate supplementation significantly altered the HR and RPE responses across the three trials. The mean HR was lower in the single- and double-dose groups than in the placebo control group during both work intervals and recovery periods, as well as across the overall protocol (all p < .05). The mean RPE was lower in the single- and double-dose groups than in the control group during recovery periods and across the overall protocol (all p < .001). However, there was no significant difference in either HR or RPE between the single- and double-dose groups at any time point. CONCLUSIONS: Acute nitrate ingestion led to significant decreases in the mean HR and RPE during high-intensity interval exercise, but no additional benefit was observed with higher nitrate content. These findings may assist practitioners in implementing more effective nitrate supplementation strategies during high-intensity interval exercise.

Effects of Stationary Bikes and Elliptical Machines on Knee Joint Kinematics during Exercise.

Background and Objectives: This study examined the influence of stationary bikes and elliptical machines on knee movement and joint load during exercise. Materials and Methods: Twelve healthy male participants engaged in pedaling exercises on stationary bikes and elliptical machines at speeds of 50 and 70 revolutions per minute (rpm). Knee movement and joint load were assessed using a motion analysis system. Results: The results indicated that elliptical machines induced higher knee joint torque compared to stationary bikes. Notably, peak torque occurred at different joint angles, with stationary bikes reaching an earlier peak at 70°-110° and elliptical machines showing a later peak at 135°-180°. Increased pedaling speed correlated with higher peak knee joint torque on both machines. With the elliptical machine, a higher pedaling frequency correlated with increased peak forces on the knee and ankle joints, as well as vertically. Interestingly, both types of equipment were associated with enhanced peak knee joint torques during high-speed pedaling. Conversely, constant pedaling on elliptical machines limited the ankle angle and could induce inward rotation. Conclusions: This study focused on knee joint torque variations during pedaling on indoor stationary bicycles and elliptical machines. Elliptical machines showed higher peak values of forces and torque, particularly during the propulsive and recovery phases, indicating potential challenges to the knee joint. Notably, peak pedal angles occurred earlier on indoor stationary bicycles, emphasizing the impact of equipment choice on joint kinetics.

Electrophysiological investigation of active-assisted vs recumbent cycling: A pilot study in healthy older adults.

There is a relationship between acute bouts of aerobic exercise and cognition in adults, yet the exact nature of this relationship remains unclear. The current pilot study aims to investigate how different modes of cycling (active-assisted cycling vs recumbent cycling) at different moderate-to-vigorous physical activity (MVPA) intensity levels (prescribed 65-70% Heart Rate Max and self-selected 12-13 Rate of Perceived Exertion) modulate neurocognitive, and behavioral markers of cognition in healthy older adults. A sample of 10 adults (aged 50-74years) participated in baseline (no exercise), active-assisted, and recumbent cycling interventions at different intensity levels. The P3 event-related potential (ERP), a neural index of executive functions, was recorded at baseline and following each exercise condition during an auditory odd-ball paradigm. Results revealed that greater amplitudes within the P3 ERP component were associated with post-exercise recumbent bike cycling compared to baseline and active-assisted cycling. Further, post-exercise behavioral cognitive measures (i.e., button press accuracy) were significantly greater than baseline for both active-assisted and recumbent bikes at both intensity levels. These findings suggest that exercise modulated both neurocognitive and behavioral measures of executive functions in older healthy adults, and that exercise modalities and intensity levels differentially modulate neurocognitive measures.

Torque-Cadence Profile and Maximal Dynamic Force in Cyclists: A Novel Approach.

We aimed to determine the feasibility, test-retest reliability and long-term stability of a novel method for assessing the force (torque)-velocity (cadence) profile and maximal dynamic force (MDF) during leg-pedaling using a friction-loaded isoinertial cycle ergometer and a high-precision power-meter device. Fifty-two trained male cyclists completed a progressive loading test up to the one-repetition maximum (1RM) on a cycle ergometer. The MDF was defined as the force attained at the cycle performed with the 1RM-load. To examine the test-retest reliability and long-term stability of torque-cadence values, the progressive test was repeated after 72 h and also after 10 weeks of aerobic and strength training. The participants' MDF averaged 13.4 ± 1.3 N·kg-1, which was attained with an average pedal cadence of 21 ± 3 rpm. Participants' highest power output value was attained with a cadence of 110 ± 16 rpm (52 ± 5% MDF). The relationship between the MDF and cadence proved to be very strong (R2 = 0.978) and independent of the cyclists' MDF (p = 0.66). Cadence values derived from this relationship revealed a very high test-retest repeatability (mean SEM = 4 rpm, 3.3%) and long-term stability (SEM = 3 rpm, 2.3%); despite increases in the MDF following the 10-week period. Our findings support the validity, reliability and long-term stability of this method for the assessment of the torque-cadence profile and MDF in cyclists.

Integrative Assessment of Cardiopulmonary Fitness Using Cardiopulmonary Exercise Test With Supine Bicycle Echocardiography in Patients Presenting Dyspnea.

BACKGROUND: Cardiopulmonary exercise test (CPET) with supine bicycle echocardiography (SBE) enables comprehensive physiologic assessment during exercise. We characterized cardiopulmonary fitness by integrating CPET-SBE parameters and evaluated its prognostic value in patients presenting with dyspnea. METHODS AND RESULTS: We retrospectively reviewed 473 consecutive patients who underwent CPET-SBE for dyspnea evaluation. A dimensionality reduction process was applied, transforming 24 clinical and CPET-SBE parameters into a 2-dimensional feature map, followed by patient clustering based on the data distribution. Clinical and exercise features were compared among the clusters in addition to the 5-year risk of clinical outcome (a composite of cardiovascular death and heart failure hospitalization). Maximum exercise effort (R >1) was achieved in 95% of cases. Through dimensionality reduction, 3 patient clusters were derived: Group 1 (n=157), 2 (n=104), and 3 (n=212). Median age and female proportion increased from Group 1 to 2, and 3, although resting echocardiography parameters showed no significant abnormalities among the groups. There was a worsening trend in the exercise response from Group 1 to 2 and 3, including left ventricular diastolic function, oxygen consumption, and ventilatory efficiency. During follow-up (median 6.0 [1.6-10.4] years), clinical outcome increased from Group 1 to 2 and 3 (5-year rate 3.7% versus 7.0% versus 13.0%, respectively; log-rank P=0.02), with higher risk in Group 2 (hazard ratio, 1.94 [95% CI, 0.52-7.22]) and Group 3 (3.92 [1.34-11.42]) compared with Group 1. CONCLUSIONS: Comprehensive evaluation using CPET-SBE can reveal distinct characteristics of cardiopulmonary fitness in patients presenting with dyspnea, potentially enhancing outcome prediction.