Is there a difference in the pattern of muscle activity when performing neck exercises with a guild board versus a pulley?

Objective: Guild boards and pulleys are apparatus commonly used to train cervical muscle function for their purported benefit in facilitating activity of the deeper muscle layers, although this effect has not been substantiated. The objective of this study was to compare the activity of the different layers of cervical muscles when performing exercise with these 2 types of apparatus

Перспективы развития логистической деятельности мелких торговых организаций Беларуси

Дана оценка логистической деятельности мелких торговых организаций Беларуси и намечены пути ее совершенствования

Factors associated with work ability in patients with chronic whiplash-associated disorder grade II-III: a cross-sectional analysis

Objective: To investigate the factors related to self-perceived work ability in patients with chronic whiplash-associated disorder grades II-III. Design: Cross-sectional analysis. Patients: A total of 166 working age patients with chronic whiplash-associated disorder. Methods: A comprehensive survey collected data on work ability (using the Work Ability Index); demographic, psychosocial, personal, work- and condition-related factors. Forward, stepwise regression modelling was used to assess the factors related to work ability. Results: The proportion of patients in each work ability category were as follows: poor (12.7%); moderate (39.8%); good (38.5%); excellent (9%). Seven factors explained 65% (adjusted R2= 0.65, p < 0.01) of the variance in work ability. In descending order of strength of association, these factors are: greater neck disability due to pain; reduced self-rated health status and health-related quality of life; increased frequency of concentration problems; poor workplace satisfaction; lower self-efficacy for performing daily tasks; and greater work-related stress. Conclusion: Condition-specific and psychosocial factors are associated with self-perceived work ability of individuals with chronic whiplash-associated disorder

Investigating the Causal Mechanisms of Symptom Recovery in Chronic Whiplash-associated Disorders Using Bayesian Networks

Objectives: The present study’s objective was to understand the causal mechanisms underpinning the recovery of individuals with whiplash-associated disorders (WAD). We applied Bayesian Networks (BN) to answer 2 study aims: (1) to identify the causal mechanism(s) of recovery underpinning neck-specific exercise (NSE), and (2) quantify if the cyclical pathway of the fear-avoidance model (FAM) is supported by the present data. Materials and Methods: We analyzed a prospective cohort data set of 216 individuals with chronic WAD. Fifteen variables were used to build a BN model: treatment group (NSE with or without a behavioral approach, or general physical activity), muscle endurance, range of motion, hand strength, neck proprioception, pain catastrophizing, fear, anxiety, depression, self-efficacy, perceived work ability, disability, pain intensity, sex, and follow-up time. Results: The BN model showed that neck pain reduction rate was greater after NSE compared with physical activity prescription (β=0.59 points per month [P<0.001]) only in the presence of 2 mediators: global neck muscle endurance and perceived work ability. We also found the following pathway of variables that constituted the FAM: anxiety, followed by depressive symptoms, fear, catastrophizing, self-efficacy, and consequently pain. Conclusions: e uncovered 2 mediators that explained the mechanisms of effect behind NSE, and proposed an alternative FAM pathway. The present study is the first to apply BN modelling to understand the causal mechanisms of recovery in WAD. In doing so, it is anticipated that such analytical methods could increase the precision of treatment of individuals with chronic WAD

Clinical predictive modelling of post-surgical recovery in individuals with cervical radiculopathy: a machine learning approach

Prognostic models play an important role in the clinical management of cervical radiculopathy (CR). No study has compared the performance of modern machine learning techniques, against more traditional stepwise regression techniques, when developing prognostic models in individuals with CR. We analysed a prospective cohort dataset of 201 individuals with CR. Four modelling techniques (stepwise regression, least absolute shrinkage and selection operator [LASSO], boosting, and multivariate adaptive regression splines [MuARS]) were each used to form a prognostic model for each of four outcomes obtained at a 12 month follow-up (disability—neck disability index [NDI]), quality of life (EQ5D), present neck pain intensity, and present arm pain intensity). For all four outcomes, the differences in mean performance between all four models were small (difference of NDI &lt; 1 point; EQ5D &lt; 0.1 point; neck and arm pain &lt; 2 points). Given that the predictive accuracy of all four modelling methods were clinically similar, the optimal modelling method may be selected based on the parsimony of predictors. Some of the most parsimonious models were achieved using MuARS, a non-linear technique. Modern machine learning methods may be used to probe relationships along different regions of the predictor space