Impact of Adding Bedside Cycling to Intensive Care Unit Rehabilitation on Physical Function and Length of Stay After Liver Transplantation: A Randomized Controlled Trial.

OBJECTIVE: To assess the effectiveness of early bedside cycling training in enhancing recovery among patients following liver transplantation. METHODS: A total of 64 patients who underwent liver transplantation were randomly assigned to either an experimental group or a control group, with 32 patients in each group. Patients in the control group received routine rehabilitation training after postoperative consciousness recovery and tracheal extubation. The experimental group received supplemental lower limb bedside cycling training in addition to the routine rehabilitation. Differences in physical function, length of intensive care unit, and hospital stays were compared between the two groups. RESULTS: The experimental group showed significantly greater improvements in physical function, such as activities of daily living scores and Berg Balance Scale scores, compared to the control group (P < .05). While the experimental group also exhibited decreased length of intensive care unit and hospital stays compared to the control group, these differences lacked statistical significance. CONCLUSION: Early bedside cycling training contributes to enhanced physical function, such as balance function and activities of daily living, among patients undergoing liver transplantation.

Occupational and sex differences in active commuting among Canadian workers from 2006 to 2016.

Background: Active commuting (AC) to and from work is associated with numerous health benefits, through increased physical activity. This study examined whether occupation types and part-time work, by sex, were associated with AC in a population-based sample of Canadian workers. Data and methods: Cross-sectional public use microdata files from the 2006 (n=363,048), 2011 (n=370,672), and 2016 (n=362,310) Census of Population were examined. Multinomial logistic regression models were used to estimate the odds of cycling, walking, and using public transit, relative to using a private motorized vehicle, by occupation and sex. Time trends in mode share were also analyzed. Results: In 2016, commuting by private motorized vehicle and cycling were more common among males, while public transit and walking were more common among females. Occupations in art, culture, recreation, and sport were associated with the greatest odds of cycling (odds ratio [OR]=3.02, 99% confidence interval [CI]: 2.65 to 3.39), while those in trades, transportation, natural resources, and manufacturing had the lowest odds of cycling (OR=0.47, 99% CI: 0.44 to 0.51) and walking (OR=0.36, 99% CI: 0.33 to 0.38). Since 2006, relative declines of 1% and 8% in the proportion of workers commuting by driving and walking, respectively, were observed (absolute change of -1% each). Relative increases of 14% and 12% were observed for cycling and public transit, respectively (absolute changes of less than 1% and 1.5%, respectively). Interpretation: This study found that sex and occupation are important correlates of AC among Canadian workers. Further research aimed at understanding occupational barriers and facilitators may inform future AC interventions.

Understanding the interaction between cyclists and motorized vehicles at unsignalized intersections: Results from a cycling simulator study.

INTRODUCTION: With cycling gaining more popularity in urban areas, it is vital to obtain accurate knowledge of cyclists' behavior to develop behavioral models that can predict the cyclist's intent. Most conflicts between cyclists and vehicles happen at crossings where the road users share the path, especially at unsignalized intersections. However, few studies have investigated and modeled the interaction between cyclists and vehicles at unsignalized intersections. METHOD: A bike simulator experiment was conducted to scrutinize cyclists' response process as they interacted with a passenger car at an unsignalized intersection. An existing unsignalized intersection in Gothenburg was simulated for test participants. Two independent variables were varied across trials: the difference in time to arrival at the intersection (DTA) and intersection visibility (IV). Subjective and quantitative data were analyzed to model the cyclists' behavior. RESULTS: When approaching the intersection, cyclists showed a clear sequence of actions (pedaling, braking, and head turning). The distance from the intersection at which cyclists started braking was significantly affected by the two independent variables. It was also found that DTA, looking duration, and pedaling behavior significantly affected cyclists' decisions to yield. Finally, the questionnaire outputs show that participants missed eye contact or communication with the motorized vehicle. CONCLUSIONS: The kinematic interaction between cyclists and vehicles, along with the cyclist's response process (visual and kinematic), can be utilized to predict cyclists' yielding decision at intersections. From the infrastructural perspective, enhancing visibility at intersections has the potential to reduce the severity of interactions between cyclists and vehicles. The analysis of the questionnaire emphasizes the significance of visual communication between cyclists and drivers to support the cyclist's decision-making process when yielding. PRACTICAL APPLICATIONS: The models can be used in threat assessment algorithms so that active safety systems and automated vehicles can react safely to the presence of cyclists in conflict scenarios.

Europe has the fastest Ironman race courses and the fastest Ironman age group triathletes.

The majority of participants in Ironman triathlon races are age group athletes. We have extensive knowledge about recreational athletes' training and competition participation. Nonetheless, Ironman age group triathletes must achieve fast race times to qualify for the Ironman World Championship in Hawaii. They can, therefore, benefit from knowing where the fastest Ironman racecourses in the world are. The aim of the present study was to investigate where the fastest Ironman racecourses for age group triathletes are located in the world. Data from 677,702 Ironman age group finishers' records (544,963 from men and 132,739 from women) originating from 228 countries and participating in 444 events across 66 different Ironman race locations between 2002 and 2022 were analyzed. Data was analyzed through traditional descriptive statistics and with machine learning regression models. Four algorithms were tested (Random Forest Regressor, XG Boost Regressor, Cat Boot Regressor, and Decision Tree Regressor). The models used gender, age group, country of origin, environmental factors (average air and water temperatures), and the event location as independent variables to predict the final overall race time. Despite the majority of successful Ironman age group triathletes originating from the USA (274,553), followed by athletes from the United Kingdom (55,410) and Canada (38,264), these countries exhibited average overall race times that were significantly slower compared to the fastest countries. Most of the triathletes competed in Ironman Wisconsin (38,545), followed by Ironman Florida (38,157) and Ironman Lake Placid (34,341). The fastest overall race times were achieved in Ironman Copenhagen (11.68 ± 1.38 h), followed by Ironman Hawaii (11.72 ± 1.86 h), Ironman Barcelona (11.78 ± 1.43 h), Ironman Florianópolis (11.80 ± 1.52 h), Ironman Frankfurt (12.03 ± 1.38 h) and Ironman Kalmar (12.08 ± 1.47 h). The fastest athletes originated from Belgium (11.48 ± 1.47 h), followed by athletes from Denmark (11.59 ± 1.40 h), Switzerland (11.62 ± 1.49 h), Austria (11.68 ± 1.50), Finland (11.68 ± 1.40 h) and Germany (11.74 ± 15.1 h). Flat running and cycling courses were associated with faster overall race times. Three of the predictive models identified the 'country' and 'age group' variables as the most important predictors. Environmental characteristics showed the lowest influence regarding the other variables. The origin of the athlete was the most predictive variable whereas environmental characteristics showed the lowest influence. Flat cycling and flat running courses were associated with faster overall race times. The fastest overall race times were achieved mainly in European races such as Ironman Copenhagen, Ironman Hawaii, Ironman Barcelona, Ironman Florianópolis, Ironman Frankfurt and Ironman Kalmar. The fastest triathletes originated from European countries such as Belgium, Denmark, Switzerland, Austria, Finland, and Germany.

Validity and Reliability of Movesense HR+ ECG Measurements for High-Intensity Running and Cycling.

Low-cost, portable devices capable of accurate physiological measurements are attractive tools for coaches, athletes, and practitioners. The purpose of this study was primarily to establish the validity and reliability of Movesense HR+ ECG measurements compared to the criterion three-lead ECG, and secondarily, to test the industry leader Garmin HRM. Twenty-one healthy adults participated in running and cycling incremental test protocols to exhaustion, both with rest before and after. Movesense HR+ demonstrated consistent and accurate R-peak detection, with an overall sensitivity of 99.7% and precision of 99.6% compared to the criterion; Garmin HRM sensitivity and precision were 84.7% and 87.7%, respectively. Bland-Altman analysis compared to the criterion indicated mean differences (SD) in RR' intervals of 0.23 (22.3) ms for Movesense HR+ at rest and 0.38 (18.7) ms during the incremental test. The mean difference for Garmin HRM-Pro at rest was -8.5 (111.5) ms and 27.7 (128.7) ms for the incremental test. The incremental test correlation was very strong (r = 0.98) between Movesense HR+ and criterion, and moderate (r = 0.66) for Garmin HRM-Pro. This study developed a robust peak detection algorithm and data collection protocol for Movesense HR+ and established its validity and reliability for ECG measurement.

Understanding how and why travel mode changes: analysis of longitudinal qualitative interviews.

BACKGROUND: Encouraging alternatives to the car such as walking, cycling or public transport is a key cross-sector policy priority to promote population and planetary health. Individual travel choices are shaped by individual and environmental contexts, and changes in these contexts - triggered by key events - can translate to changes in travel mode. Understanding how and why these changes happen can help uncover more generalisable findings to inform future intervention research. This study aimed to identify the mechanisms and contexts facilitating changes in travel mode. METHODS: Prospective longitudinal qualitative cohort study utilising semi-structured interviews at baseline (in 2021), three- and six-month follow up. Participants were residents in a new town in Cambridgeshire, UK, where design principles to promote walking, cycling and public transport were used at the planning stage. At each interview, we followed a topic guide asking participants about previous and current travel patterns and future intentions. All interviews were audio recorded and transcribed. Data analysis used the framework approach based on realist evaluation principles identifying the context and mechanisms described by participants as leading to travel behaviour change. RESULTS: We conducted 42 interviews with 16 participants and identified six mechanisms for changes in travel mode. These entailed increasing or reducing access, reliability and financial cost, improving convenience, increasing confidence and raising awareness. Participants described that these led to changes in travel mode in contexts where their existing travel mode had been disrupted, particularly in terms of reducing access or reliability or increasing cost, and where there were suitable alternative travel modes for their journey. Experiences of the new travel mode played a role in future travel intentions. IMPLICATIONS: Applying realist evaluation principles to identify common mechanisms for changes in travel mode has the potential to inform future intervention strategies. Future interventions using mechanisms that reduce access to, reduce reliability of, or increase the financial cost of car use may facilitate modal shift to walking, cycling and public transport when implemented in contexts where alternative travel modes are available and acceptable.

The effect of technology-based service characteristics on relationship quality in metaverse exercise services: a questionnaire survey of bicycle simulator users in Korea.

BACKGROUND: As non-face-to-face contact has become a daily routine owing to the development of science and technology and impact of the coronavirus disease (COVID-19) pandemic, online technology-based services (TBSSs) have been expanding. Consequently, in virtual metaverse exercise spaces, the number of participants using TBSS is increasing. This study aimed to examine the effects of TBSS characteristics on the quality of the relationship between service providers and users of metaverse exercise services. METHODS: The participants were metaverse exercise service users in Korea, who were selected through purposive sampling (n = 254, 194 men, 61 women). A questionnaire survey was conducted to measure the participants' TBSS characteristics (enjoyment, stability, usefulness, ease of use, and reliability) and relationship quality (satisfaction, trust, and commitment). We analyzed the frequency, validity, reliability, and descriptive statistics of the collected data. Moreover, correlation and multivariate multiple regression analyses were conducted. RESULTS: Enjoyment, stability, usefulness, and reliability of TBSS had positive effects on satisfaction; however, ease of use did not have a significant effect. In addition, enjoyment, usefulness, ease of use, and reliability of TBSS had positive effects on trust; however, stability did not have a significant effect. Furthermore, enjoyment and reliability of TBSS had positive effects on commitment; however, usefulness, ease of use, and stability had no significant effects. CONCLUSIONS: This study provides metaverse exercise service providers with management strategies for attracting and retaining members. The results of this study could help develop effective TBSS and aid metaverse service companies facing a fiercely competitive environment.

Effect of high-dose ß-Alanine supplementation on uphill cycling performance in World Tour cyclists: A randomised controlled trial.

Scientists and coaches seek effective ergogenic aids for performance improvement. Cyclists commonly use ß-Alanine, which may enhance post-exercise recovery and physical performance. High-dose ß-Alanine supplementation's impact on World Tour cyclists during a 7-day camp remains unstudied. This study aimed to analyse the effect of a high dose of ß-alanine in World Tour cyclist during a 7-day camp. A double-blinded, randomised controlled trial was conducted. 11 cyclists were included in the final analysis: ß-alanine supplementation (n = 5; VO2max: 67.6±1.6 ml/kg/min) and a placebo group (n = 6; VO2max: 68.0±2.4 ml/kg/min). The duration of the supplementation protocol was seven days with four daily intakes. The subjects commenced supplementation after the physical tests (immediately following the snack) and consumed the final intake after breakfast on the day of the final test (a total of 7 days and 3 additional doses, 31 servings in total; 5g per dosage; 155g the total cumulative amount). Before and after seven days of supplementation, the cyclists performed an uphill time trial. Blood lactate, heart rate and rating of perceived exertion were measured during test. ß-alanine supplementation improved the relative mean power attained during the time-trial compared with the control group (Z = -2.008; p = 0.045; Δ = 0.060), as well as the time needed to complete this trial (Z = -2.373; p = 0.018). As for physiological and metabolic variables, no significant change was found. In conclusion, the present study supports the effectiveness of one-week high dose of ß-alanine during a cycling training in World Tour cyclists to improve their uphill time-trial performance. In addition, it is important to highlight the potential role of ß-alanine in improving recovery power. This aspect is particularly relevant in the context of a training camp, where fatigue levels can increase alongside training intensity. Trial registration: This study was registered in ClinicalTrials.gov: (identifier: NCT04427319).

Walking Bike as an Effective Tool to Reduce Plantar Peak Pressure in Diabetes Mellitus.

BACKGROUND: Foot ulcers and infections are a major and costly problem in patients with diabetes and a major cause of amputations. Plantar peak pressure plays an essential role in plantar ulceration. Off-loading is a common tool to reduce plantar peak pressure and risk of ulceration. The goal of this study was to determine whether reduction of plantar peak pressure can be achieved using a walking bike (a bike without pedals) compared with walking. METHODS: The study starts with a PubMed literature review. In a blinded prospective protocol, 14 healthy individuals (seven men, seven women; mean ± SD age, 39.5 ± 11.3 years) are included. In-shoe pedobarography sensors were attached between the skin and the standardized shoes, then participants walked 10 m three times and then moved over the same distance using a walking bike without removal of the sensor (three times) in a gait laboratory (84 measurements). RESULTS: In this single-blinded prospective study, mean ± SD plantar peak pressure was significantly reduced from 49.4 ± 12.9 N/cm2 with walking to 35.2 ± 14.6 N/cm2 using a walking bike (P = .003). Mean ± SD step length increased significantly from 0.68 ± 0.13 m to 0.91 ± 0.19 m (P < .001) due to a significantly reduced number of steps (from 7.7 ± 1.4 steps per 10 m of walking to 5.7 ± 1.1 steps per 10 m of using a walking bike; P < .001). CONCLUSIONS: Plantar peak pressure is a risk factor for ulceration in diabetes. Herein, a significant reduction of plantar peak pressure was seen using a walking bike compared with walking (P = .003). Walking bikes may be a tool for off-loading for diabetic patients, especially if both feet are ulcerated. Additional studies to validate these findings in patient care are planned.

Composite activity type and stride-specific energy expenditure estimation model for thigh-worn accelerometry.

BACKGROUND: Accurately measuring energy expenditure during physical activity outside of the laboratory is challenging, especially on a large scale. Thigh-worn accelerometers have gained popularity due to the possibility to accurately detect physical activity types. The use of machine learning techniques for activity classification and energy expenditure prediction may improve accuracy over current methods. Here, we developed a novel composite energy expenditure estimation model by combining an activity classification model with a stride specific energy expenditure model for walking, running, and cycling. METHODS: We first trained a supervised deep learning activity classification model using pooled data from available adult accelerometer datasets. The composite energy expenditure model was then developed and validated using additional data based on a sample of 69 healthy adult participants (49% female; age = 25.2 ± 5.8 years) who completed a standardised activity protocol with indirect calorimetry as the reference measure. RESULTS: The activity classification model showed an overall accuracy of 99.7% across all five activity types during validation. The composite model for estimating energy expenditure achieved a mean absolute percentage error of 10.9%. For running, walking, and cycling, the composite model achieved a mean absolute percentage error of 6.6%, 7.9% and 16.1%, respectively. CONCLUSIONS: The integration of thigh-worn accelerometers with machine learning models provides a highly accurate method for classifying physical activity types and estimating energy expenditure. Our novel composite model approach improves the accuracy of energy expenditure measurements and supports better monitoring and assessment methods in non-laboratory settings.

The Influence of Acute Oral Lactate Supplementation on Responses to Cycle Ergometer Exercise: A Randomized, Crossover Pilot Clinical Trial.

The purpose of this study was to investigate the potential ergogenic effects of an oral lactate supplement. For this double-blind, randomized, placebo-controlled crossover design, fifteen recreational exercisers (nine males, six females) ingested a placebo or a commercially available lactate supplement prior to cycle ergometer exercise. Primary outcomes included peak oxygen uptake (VO2peak; via indirect calorimetry), VO2 at the ventilatory threshold, and work rate at the lactate threshold (arterialized venous blood from a heated hand) determined during incremental exercise to fatigue, and power output during a 20-min cycling time trial. Compared with placebo, the oral lactate supplement (19 ± 1 mg/kg body mass) did not influence VO2peak (placebo: 44.3 ± 7.8 vs. oral lactate: 44.3 ± 7.1 mL/kg/min (mean ± SD); p = 0.87), VO2 at the ventilatory threshold (placebo: 1.63 ± 0.25 vs. oral lactate: 1.65 ± 0.23 L/min; p = 0.82), or work rate at the lactate threshold (placebo: 179 ± 69 vs. oral lactate: 179 ± 59 W; p = 0.41). Throughout the 20-min time trial, the work rate was slightly greater (4%) with oral lactate (204 ± 41 W) compared with placebo (197 ± 41 W; main effect of treatment p = 0.02). Collectively, these data suggest that this commercially available lactate supplement did not acutely influence the physiological responses to incremental cycle ergometer exercise but elicited a modest ergogenic effect during the short-duration time trial.

The validity and reliability of quadriceps twitch force as a measure of skeletal muscle fatigue while cycling.

The measurement of skeletal muscle fatigue in response to cycling exercise is commonly done in isometric conditions, potentially limiting its ecological validity, and creating challenges in monitoring the time course of muscle fatigue across an exercise bout. This study aimed to determine if muscle fatigue could be reliably assessed by measuring quadriceps twitch force evoked while pedaling, using instrumented pedals. Nine participants completed three laboratory visits: a step incremental test to determine power output at lactate threshold, and on separate occasions, two constant-intensity bouts at a power output 10% above lactate threshold. Femoral nerve electrical stimulation was applied to elicit quadriceps twitch force both while pedaling (dynamic) and at rest (isometric). The test-retest reliability of the dynamic twitch forces and the agreement between the dynamic and isometric twitch forces were evaluated. Dynamic twitch force was found to have excellent reliability in an unfatigued state (intraclass correlation coefficient (ICC) = 0.920 and mean coefficient of variation (CV) = 7.5%), and maintained good reliability at task failure (ICC = 0.846 and mean CV = 11.5%). When comparing dynamic to isometric twitch forces across the task, there was a greater relative decline in the dynamic condition (P = 0.001). However, when data were normalized to the 5 min timepoint when potentiation between conditions was presumed to be more similar, this difference disappeared (P = 0.207). The reliability of this method was shown to be commensurate with the gold standard method utilizing seated isometric dynamometers and offers a new avenue to monitor the kinetics of muscle fatigue during cycling in real time.

Effects of Dietary Nitrate Supplementation on High-Intensity Cycling Sprint Performance in Recreationally Active Adults: A Systematic Review and Meta-Analysis.

This systematic review and meta-analysis investigated the influence of dietary nitrate supplementation on performance metrics during cycling sprint exercise according to the PRISMA guidelines. Searches were conducted on MEDLINE, PubMed, ScienceDirect, Scopus, and SPORTDiscus databases up to September 2023. Inclusion criteria were healthy recreationally active men and women who consumed nitrate-rich and nitrate-deficient beetroot juice to assess performance outcomes of mean power, peak power, time-to-peak power, and minimum power during 30-s cycling sprints. Risk of bias was assessed using the Cochrane Risk of Bias 2 and TESTEX tools and funnel plots. A random effects model was performed on six studies and showed that dietary nitrate had significant effects on time-to-peak power (SMD: -0.66, 95% CI: -1.127 to -0.192, p = 0.006) but not on mean power, peak power, or minimum power. Subgroup analysis revealed that an acute low nitrate dose improved time-to-peak power (SMD: -0.977, 95% CI: -1.524 to -0.430, p < 0.001) but not after a multiday moderate nitrate dose (SMD: -0.177, 95% CI: -0.619 to -0.264, p = 0.431). These data suggest that acute nitrate supplementation can benefit time-to-peak power during 30-s cycling sprints, but due to the limited availability of data and heterogeneity in methodology, these results should be interpreted with caution. There was insufficient data on women to analyze sex-based differences. Future studies are required to provide insight on how supplementation regimen and population impact the effects of dietary nitrate for enhancing cycling sprint performance.

Identifying factors related to pedestrian and cyclist crashes in ACT, Australia with an extended crash dataset.

As vulnerable road users, pedestrians and cyclists are facing a growing number of injuries and fatalities, which has raised increasing safety concerns globally. Based on the crash records collected in the Australian Capital Territory (ACT) in Australia from 2012 to 2021, this research firstly establishes an extended crash dataset by integrating road network features, land use features, and other features. With the extended dataset, we further explore pedestrian and cyclist crashes at macro- and micro-levels. At the macro-level, random parameters negative binomial (RPNB) model is applied to evaluate the effects of Suburbs and Localities Zones (SLZs) based variables on the frequency of pedestrian and cyclist crashes. At the micro-level, binary logit model is adopted to evaluate the effects of event-based variables on the severity of pedestrian and cyclist crashes. The research findings show that multiple factors are associated with high frequency of pedestrian total crashes and fatal/injury crashes, including high population density, high percentage of urban arterial road, low on-road cycleway density, high number of traffic signals and high number of schools. Meanwhile, many factors have positive relations with high frequency of cyclist total crashes and fatal/injury crashes, including high population density, high percentage of residents cycling to work, high median household income, high percentage of households with no motor vehicle, high percentage of urban arterial road and rural road, high number of bus stops and high number of schools. Additionally, it is found that more severe pedestrian crashes occur: (i) at non-signal intersections, (ii) in suburb areas, (iii) in early morning, and (iv) on weekdays. More severe cyclist crashes are observed when the crash type is overturned or struck object/pedestrian/animal; when more than one cyclist is involved; and when crash occurs at park/green space/nature reserve areas.

A novel framework for crash frequency prediction: Geographic support vector regression based on agent-based activity models in Greater Melbourne.

The field of spatial analysis in traffic crash studies can often enhance predictive performance by addressing the inherent spatial dependence and heterogeneity in crash data. This research introduces the Geographical Support Vector Regression (GSVR) framework, which incorporates generated distance matrices, to assess spatial variations and evaluate the influence of a wide range of factors, including traffic, infrastructure, socio-demographic, travel demand, and land use, on the incidence of total and fatal-or-serious injury (FSI) crashes across Greater Melbourne's zones. Utilizing data from the Melbourne Activity-Based Model (MABM), the study examines 50 indicators related to peak hour traffic and various commuting modes, offering a detailed analysis of the multifaceted factors affecting road safety. The study shows that active transportation modes such as walking and cycling emerge as significant indicators, reflecting a disparity in safety that heightens the vulnerability of these road users. In contrast, car commuting, while a consistent factor in crash risks, has a comparatively lower impact, pointing to an inherent imbalance in the road environment. This could be interpreted as an unequal distribution of risk and safety measures among different types of road users, where the infrastructure and policies may not adequately address the needs and vulnerabilities of pedestrians and cyclists compared to those of car drivers. Public transportation generally offers safer travel, yet associated risks near train stations and tram stops in city center areas cannot be overlooked. Tram stops profoundly affect total crashes in these areas, while intersection counts more significantly impact FSI crashes in the broader metropolitan area. The study also uncovers the contrasting roles of land use mix in influencing FSI versus total crashes. The proposed framework presents an approach for dynamically extracting distance matrices of varying sizes tailored to the specific dataset, providing a fresh method to incorporate spatial impacts into the development of machine learning models. Additionally, the framework extends a feature selection technique to enhance machine learning models that typically lack comprehensive feature selection capabilities.

Quantifying the heterogeneity impact of risk factors on regional bicycle crash frequency: A hybrid approach of clustering and random parameter model.

The existence of internal and external heterogeneity has been established by numerous studies across various fields, including transportation and safety analysis. The findings from these studies underscore the complexity of crash data and the multifaceted nature of risk factors involved in accidents. However, most studies consider the effects of unobserved heterogeneity from one perspective -- either within clusters (internal) or between clusters (external) -- and do not investigate the biases from both simultaneously on crash frequency analysis. To fill this gap, this study proposes a hybrid approach combining latent class cluster analysis with the random parameter negative binomial regression model (LCA-RPNB) to explore the association between risk factors and bicycle crash frequency. First, the bicycle crash data is categorized into three clusters using LCA based on crash features such as gender, trip purposes, weather, and light conditions. Then, the separated crash frequency models for different clusters and the overall model are developed based on RPNB using regional factors of crash locations as independent variables and the crash frequency of different clusters respectively as dependent variables. The hybrid approach enables a comprehensive examination of internal and external heterogeneities among bicycle crash frequency factors simultaneously. Results suggest that the proposed hybrid approach exhibits superior fitting and predictive performance compared to the model only considers the effects of unobserved heterogeneity from one perspective with the lower values of Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). This approach can help policymakers and urban planners to design more effective safety interventions by understanding the distinct needs of different bicyclist clusters and the specific factors that contribute to crash risk in each group.

Utilizing Entropy of Cadence to Optimize Cycling Rehabilitation in Individuals With Parkinson's Disease.

BACKGROUND: Previous studies have established that increased Sample Entropy (SampEn) of cadence, a measure of non-linear variability, during dynamic cycling leads to greater improvements in motor function for individuals with Parkinson's disease (PD). However, there is significant variability in responses among individuals with PD due to symptoms and disease progression. OBJECTIVES: The aim of this study was to develop and test a paradigm for adapting a cycling exercise intervention using SampEn of cadence and rider effort to improve motor function. METHODS: Twenty-two participants were randomized into either patient-specific adaptive dynamic cycling (PSADC) or non-adaptive (NA) group. SampEn of cadence was calculated after each of the 12 sessions, and motor function was evaluated using the Kinesia test. Pearson's correlation coefficient was used to analyze the relationship between SampEn of cadence and motor function improvement. Multiple linear regression (MLR) was used to identify the strongest predictors of motor function improvement. RESULTS: Pearson's correlation coefficient revealed a significant correlation between SampEn of cadence and motor function improvements (R2 = -.545, P = .009), suggesting that higher SampEn of cadence led to greater motor function improvement. MLR demonstrated that SampEn of cadence was the strongest predictor of motor function improvement (ß = -8.923, t = -2.632, P = .018) over the BMI, Levodopa equivalent daily dose, and effort. CONCLUSIONS: The findings show that PSADC paradigm promoted a greater improvement in motor function than NA dynamic cycling. These data will be used to develop a predictive model to optimize motor function improvement after cycling in individuals with PD.

Passive bicycle training stimulates epiphyseal bone formation and restores bone integrity independent of locomotor recovery in a rat spinal cord injury model.

It is unknown whether activity-based physical therapy (ABPT) modalities that mobilize the paralyzed limbs improve bone integrity at the highly fracture-prone epiphyseal regions of the distal femur and proximal tibia following severe spinal cord injury (SCI). In this study, 4-mo-old skeletally mature littermate-matched male Sprague-Dawley rats received either SHAM surgery or severe contusion SCI. At 1 wk postsurgery, SCI rats were stratified to undergo no-ABPT, two 20-min bouts/day of quadrupedal bodyweight-supported treadmill training (qBWSTT), or hindlimb passive isokinetic bicycle (cycle) training, 5 days/wk for another 3 wk. We assessed locomotor recovery and plantar flexor muscle mass, tracked cancellous and cortical bone microstructure at the distal femoral and proximal tibial epiphyses using in vivo microcomputed tomography (microCT), and evaluated bone turnover at the tibial epiphysis with histomorphometry. All SCI animals displayed persistent hindlimb paralysis and pervasive muscle atrophy. Over the initial 2 wk, which included 1 wk of no exercise and 1 wk of ABPT acclimation, a similar magnitude of bone loss developed in all SCI groups. Thereafter, cancellous bone loss and cortical bone decrements increased in the SCI no-ABPT group. qBWSTT attenuated this trabecular bone loss but did not prevent the ongoing cortical bone deficits. In comparison, twice-daily cycle training increased the number and activity of osteoblasts versus other SCI groups and restored all bone microstructural parameters to SHAM levels at both epiphyseal sites. These data indicate that a novel passive isokinetic cycle training regimen reversed cancellous and cortical bone deterioration at key epiphyseal sites after experimental SCI via osteoblast-mediated bone anabolic mechanisms, independent of locomotor recovery or increased muscle mass.NEW & NOTEWORTHY This study was the first to assess how quadrupedal bodyweight-supported treadmill training or passive isokinetic bicycle (cycle) training impacts bone recovery at the distal femoral and proximal tibial epiphyses in a rat model of severe contusion spinal cord injury. Our results demonstrate that passive isokinetic cycle training completely restored cancellous and cortical bone microstructural parameters at these sites via osteoblast-mediated bone anabolic actions, independent of locomotor recovery or increased plantar flexor muscle mass.

Interactive effects analysis of road, traffic, and weather characteristics on shared e-bike speeding risk: A data-driven approach.

As electric bikes (e-bikes) rapidly develop in China, their traffic safety issues are becoming increasingly prominent. Accurately detecting risky riding behaviors and conducting mechanism analysis on the multiple risk factors are crucial in formulating and implementing precise management policies. The emergence of shared e-bikes and the advancements in interpretable machine learning present new opportunities for accurately analyzing the determinants of risky riding behaviors. The primary objective of this study is to examine and analyze the risk factors related to speeding behavior to aid urban management agencies in crafting necessary management policies. This study utilizes a large-scale dataset of shared e-bike trajectory data to establish a framework for detecting speeding behavior. Subsequently, the extreme gradient boosting (XGBoost) model is employed to identify the level of speeding risk by leveraging its excellent identification ability. Moreover, based on measuring the degree of interaction among road, traffic, and weather characteristics, the investigation of the complex interactive effects of these risk factors on high-risk speeding is conducted using bivariate partial dependence plots (PDP) by its superior parsing ability. Feature importance analysis results indicate that the top five ranked variables that significantly affect the identified results of speed risk levels are land use density, rainfall, road level, curbside parking density, and bike lane width. The interaction analysis results indicate that higher levels of road and bike lane width correspond to an increased possibility of high-risk speeding among riders. Land use density, curbside parking density, and rainfall display a nonlinear effect on high-risk speeding. Introducing road level, bike lane width, and time interval could change the patterns of nonlinear effects in land use density, curbside parking density, and rainfall. Finally, several policy recommendations are proposed to improve e-bike traffic safety by utilizing the extracted feature values associated with a higher probability of high-risk speeding.

Optimizing vehicle Front-End structure for e-bike rider Safety: An advanced Multi-Objective approach using injury prediction models.

A multi-objective optimization method based on an injury prediction model is proposed to address the increasingly prominent safety issues for e-bike riders in Chinese road traffic. This method aims to enhance the protective effect of vehicle front-end for e-bike riders by encompassing a broader range of test scenarios. Initially, large-scale rider injury response data were collected using automated Madymo simulations. A machine learning model was then trained to accurately predict the risk of rider injury under varied crash conditions. Subsequently, this model was integrated into a multi-objective optimization framework, combined with multi-criteria decision analysis, to effectively evaluate and rank various design alternatives on the Pareto frontier. This process entailed a comparative analysis of the design in a baseline scenario before and after optimization, focusing on both kinematic and injury responses of riders. Through detailed injury mechanism analysis, key design variables such as the height of the hood front and the width of the bumper were identified. This led to the proposal of specific optimization strategies for these structural parameters. The results from this study demonstrate that the proposed optimization method not only guides the design process accurately and efficiently but also balances the injury risks across different body parts. This approach significantly reduces the injury risk for riders in car-to-e-bike collisions and provides actionable insights for vehicle design enhancements.