scholarly journals The Effects of Flexibility and Relative Muscle Strength on Injury in Running

2021 ◽  
Vol 10 (3) ◽  
Author(s):  
James Hou ◽  
Alfred Renaud

When exercising, physical injury is almost inevitable. Although there is a multitude of practices to avoid injury, a large portion of luck is required to minimize injury proneness. In this paper, with the aid of a public dataset gait kinetics and kinematics, flexibility and strength are tested against the Boolean value of injury to conduct a linear binary regression model.

2020 ◽  
Vol 10 (4) ◽  
pp. 1657-1673
Author(s):  
Aliyah Glover ◽  
Lakshmi Pillai ◽  
Shannon Doerhoff ◽  
Tuhin Virmani

Background: Freezing of gait (FOG) is a debilitating feature of Parkinson’s disease (PD) for which treatments are limited. To develop neuroprotective strategies, determining whether disease progression is different in phenotypic variants of PD is essential. Objective: To determine if freezers have a faster decline in spatiotemporal gait parameters. Methods: Subjects were enrolled in a longitudinal study and assessed every 3– 6 months. Continuous gait in the levodopa ON-state was collected using a gait mat (Protokinetics). The slope of change/year in spatiotemporal gait parameters was calculated. Results: 26 freezers, 31 non-freezers, and 25 controls completed an average of 6 visits over 28 months. Freezers had a faster decline in mean stride-length, stride-velocity, swing-%, single-support-%, and variability in single-support-% compared to non-freezers (p < 0.05). Gait decline was not correlated with initial levodopa dose, duration of levodopa therapy, change in levodopa dose or change in Montreal Cognitive Assessment scores (p > 0.25). Gait progression parameters were required to obtain 95% accuracy in categorizing freezers and non-freezers groups in a forward step-wise binary regression model. Change in mean stride-length, mean stride-width, and swing-% variability along with initial foot-length variability, mean swing-% and apathy scores were significant variables in the model. Conclusion: Freezers had a faster temporal decline in objectively quantified gait, and inclusion of longitudinal gait changes in a binary regression model greatly increased categorization accuracy. Levodopa dosing, cognitive decline and disease severity were not significant in our model. Early detection of this differential decline may help define freezing prone groups for testing putative treatments.


2015 ◽  
Vol 1090 ◽  
pp. 93-95
Author(s):  
Yong Mei Qiao ◽  
Chao Gao

Based on the existing empirical formula, applied binary regression model, Lytag, as an example, established a new style of regression equation for lightweight aggregate, and compared with Existing empirical formula, proved the availability of the new formula.


2018 ◽  
Vol 110 ◽  
pp. e112-e118 ◽  
Author(s):  
José Alberto Escribano Mesa ◽  
Enrique Alonso Morillejo ◽  
Tesifón Parrón Carreño ◽  
Antonio Huete Allut ◽  
José María Narro Donate ◽  
...  

2004 ◽  
Vol 24 (2) ◽  
pp. 253-267 ◽  
Author(s):  
Aparecida D. P. Souza ◽  
Helio S. Migon

A Bayesian binary regression model is developed to predict death of patients after acute myocardial infarction (AMI). Markov Chain Monte Carlo (MCMC) methods are used to make inference and to evaluate Bayesian binary regression models. A model building strategy based on Bayes factor is proposed and aspects of model validation are extensively discussed in the paper, including the posterior distribution for the c-index and the analysis of residuals. Risk assessment, based on variables easily available within minutes of the patients' arrival at the hospital, is very important to decide the course of the treatment. The identified model reveals itself strongly reliable and accurate, with a rate of correct classification of 88% and a concordance index of 83%.


2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Ehsan Ayazi ◽  
Abdolreza Sheikholeslami

The aim of this study is to identify the important factors influencing overloading of commercial vehicles on Tehran’s urban roads. The weight information of commercial freight vehicles was collected using a pair of portable scales besides other information needed including driver information, vehicle features, load, and travel details by completing a questionnaire. The results showed that the highest probability of overloading is for construction loads. Further, the analysis of the results in the lorry type section shows that the least likely occurrence of overloading is among pickup truck drivers such that this likelihood within this group was one-third among Nissan and small truck drivers. Also, the results of modeling the type of route showed that the highest likelihood of overloading is for internal loads (origin and destination inside Tehran), and the least probability of overloading is for suburban trips (origin and destination outside of Tehran). Considering the type of load packing as a variable, the results of binary regression model analysis showed that the most probability of overloading occurs for packed (boxed) loads. Finally, it was concluded that drivers are 18 times more likely to commit overloading on weekends than on weekdays.


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