Objectives: To investigate the utility of the Transtheoretical Model of Change in predicting exercise in young people.
Design: A prospective study: assessments were done at baseline and follow-up 6 months later.
Method: Using stratified random sampling 1055 Chinese high school pupils living in Hong Kong, 533 of who were followed up at 6 months, completed measures of stage of change (SCQ), self-efficacy (SEQ), perceptions of the pros and cons of exercising (DBQ) and processes of change (PCQ). Data were analysed using one-way ANOVA, repeated measures ANOVA and independent sample t tests.
Results: The utility of the TTM to predict exercise in this population is not strong; increases in self-efficacy and decisional balance discriminated between those remaining active at baseline and follow-up, but not in changing from an inactive (e.g., Precontemplation or Contemplation) to an active state (e.g., Maintenance) as one would anticipate given the staging algorithm of the TTM.
Conclusion: The TTM is a modest predictor of future stage of change for exercise in young Chinese people. Where there is evidence that TTM variables may shape movement over time, self-efficacy, pros and behavioural processes of change appear to be the strongest predictors.
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http://dx.doi.org/10.1016/j.ijnurstu.2009.06.013 | DOI Listing |
Sensors (Basel)
December 2024
Department of Mechanical Engineering, Brigham Young University, Provo, UT 84602, USA.
Flexible high-deflection strain gauges have been demonstrated to be cost-effective and accessible sensors for capturing human biomechanical deformations. However, the interpretation of these sensors is notably more complex compared to conventional strain gauges, particularly during dynamic motion. In addition to the non-linear viscoelastic behavior of the strain gauge material itself, the dynamic response of the sensors is even more difficult to capture due to spikes in the resistance during strain path changes.
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December 2024
Faculty of Medicine, Department of Kinesiology, Université Laval, Quebec City, QC G1V OA6, Canada.
Foot strike patterns influence vertical loading rates during running. Running retraining interventions often include switching to a new foot strike pattern. Sudden changes in the foot strike pattern may be uncomfortable and may lead to higher step-to-step variability.
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December 2024
Department of Computer Engineering, Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia.
In the field of muscle fatigue models (MFMs), the prior research has demonstrated success in fitting data in specific contexts, but it falls short in addressing the diverse efforts and rapid changes in exertion typical of soccer matches. This study builds upon the existing model, aiming to enhance its applicability and robustness to dynamic demand shifts. The objective is to encapsulate the complexities of soccer dynamics with a streamlined set of parameters.
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December 2024
Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, 40127 Bologna, Italy.
Temporal parameters are crucial for understanding running performance, especially in elite sports environments. Traditional measurement methods are often labor-intensive and not suitable for field conditions. This study seeks to provide greater clarity in parameter estimation using a single device by comparing it to the gold standard.
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December 2024
College of Physical Education and Health Engineering, Taiyuan University of Technology, Jinzhong 030600, China.
The application of dynamic data in biomechanics is crucial; traditional laboratory-level force measurement systems are precise, but they are costly and limited to fixed environments. To address these limitations, empirical evidence supports the widespread adoption of portable force-measuring platforms, with recommendations for their ongoing development and enhancement. Taiyuan University of Technology has collaborated with KunWei Sports Technology Co.
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