The behaviour change techniques used by Australian physiotherapists to promote non-treatment physical activity to patients with musculoskeletal conditions

Objectives

To determine: (i) the behaviour change techniques used by a sample of Australian physiotherapists to promote non-treatment physical activity; and (ii) whether those behaviour change techniques are different to the techniques used to encourage adherence to rehabilitation exercises.

The association among demographic factors,health behaviors and sleep quality in youth with Autism Spectrum Disorder

BackgroundA majority of youth with Autism Spectrum Disorder (ASD) have disrupted sleep patterns, but there has been limited research examining factors associated with sleep in this population. Objective: The objective of this study was to compare demographic and lifestyle behaviors with sleep quality in youth with ASD. Methods: A total of 49 children (12.44 years; 78% male) with ASD wore the Actigraph GT9X accelerometer over seven days and nights to assess moderate to vigorous physical activity (MVPA), sedentary behavior (SB), total sleep duration, and sleep efficiency. Parents reported their child’s weekly amount of screen time and demographic information. Participants were classified according to whether they met sleep criteria for duration and efficiency (8–9 h of sleep duration and ≥85% sleep efficiency). T-tests and ANOVA were used to compare demographic and lifestyle factors between the groups. Results: Participants who meet both sleep duration and efficiency criteria had greater minutes of MVPA per day (113.65 min/day) than participants who only met sleep efficiency criteria (40.27 min/day) and participants who did not meet either sleep criteria (67.5 min/day; p < 0.0001). Additionally, participants who met both sleep criteria had fewer minutes of SB compared to those who only met sleep efficiency criteria (384.79 vs 526.05 min/day; p = 0.02). Conclusions: Youth who had indicators of good sleep quality had greater amounts of MVPA and lower amounts of SB. Studies should further examine the relationship between sleep and health behaviors in youth with ASD to determine causal mechanisms, leading to more effective sleep interventions.     

Are primary care physicians ill equipped to evaluate and treat childhood physical inactivity?

ABSTRACT

Objective

To investigate primary care physician clinical practice patterns, barriers, and education surrounding pediatric physical activity (PA), and to compare practice patterns by discipline.     

Effects of exogenous factors on spatial accuracy in neurosurgery

The study aimed to assess the effect of exogenous factors such as surgeon posture, surgical instrument length, fatigue after a night shift, exercise and caffeine consumption on the spatial accuracy of neurosurgical manipulations. For the evaluation and simulation of neurosurgical manipulations, a testing device developed by the authors was used. The experimental results were compared using nonparametric analysis (Wilcoxon test) and multivariate analysis, which was performed using mixed models. The results were considered statistically significant at p < 0.05. The study included 11 first-year neurosurgery residents who met the inclusion criteria. Hand support in the sitting position (Wilcoxon test p value = 0.0033), caffeine consumption (p = 0.0058) and the length of the microsurgical instrument (p = 0.0032) had statistically significant influences on the spatial accuracy of surgical manipulations (univariate analysis). The spatial accuracy did not significantly depend on the type of standing position (Wilcoxon test p value = 0.2860), whether the surgeon was standing/sitting (p = 0.1029), fatigue following a night shift (p = 0.3281), or physical exertion prior to surgery (p = 0.2845).When conducting the multivariate analysis, the spatial accuracy significantly depended on the test subject (p < 0.0001), the use of support during the test (p = 0.0001), and the length of the microsurgical instrument (p = 0.0397). To increase the spatial accuracy of microsurgical manipulations, hand support and shorter tools should be used. Caffeine consumption in high doses should also be avoided prior to surgery.     

Machine learning models for Parkinson’s disease detection and stage classification based on spatial-temporal gait parameters

BackgroundParkinson’s disease (PD) is a chronic and progressive neurodegenerative disease with no cure, presenting a challenging diagnosis and management. However, despite a significant number of criteria and guidelines have been proposed to improve the diagnosis of PD and to determine the PD stage, the gold standard for diagnosis and symptoms monitoring of PD is still mainly based on clinical evaluation, which includes several subjective factors. The use of machine learning (ML) algorithms in spatial-temporal gait parameters is an interesting advance with easy interpretation and objective factors that may assist in PD diagnostic and follow up.Research questionThis article studies ML algorithms for: i) distinguish people with PD vs. matched-healthy individuals; and ii) to discriminate PD stages, based on selected spatial-temporal parameters, including variability and asymmetry.MethodsGait data acquired from 63 people with PD with different levels of PD motor symptoms severity, and 63 matched-control group individuals, during self-selected walking speed, was study in the experiments.ResultsIn the PD diagnosis, a classification accuracy of 84.6 %, with a precision of 0.923 and a recall of 0.800, was achieved by the Naïve Bayes algorithm. We found four significant gait features in PD diagnosis: step length, velocity and width, and step width variability. As to the PD stage identification, the Random Forest outperformed the other studied ML algorithms, by reaching an Area Under the ROC curve of 0.786. We found two relevant gait features in identifying the PD stage: stride width variability and step double support time variability.SignificanceThe results showed that the studied ML algorithms have potential both to PD diagnosis and stage identification by analysing gait parameters.