Explaining the Impact of Disabled Children’ Engagement with Physical Activity on Their Parents’ Smartphone Addiction Levels: A Sequential Explanatory Mixed Methods Research

Abstract

In this research, quantitative findings and qualitative follow-up themes were used to quantify, conceptualize and finally try to explain the impact of disabled children" engagement with physical activity on their parents" smartphone addiction levels. An initial phase of quantitative investigation was conducted with 116 parents. Analyses of statistical trends indicated that male parents use smartphones more often than female. Furthermore, quantitizing data towards parents" smartphone addiction showed that parents watching their disabled children during physical activity exhibit more smartphone-addicted behaviors than non-watching parents. Finally, data obtained from quantitative findings revealed that levels of smartphone addiction of parents whose disabled children participated in more than 60 minutes of physical activity were greater than the levels of smartphone addiction of parents whose disabled children participated in 60 minutes of physical activity. With no adequate theoretical explanation for these results, a follow-up explanations phase of qualitative investigation was conducted with 5 parents. Analyses of interview data emerged three themes: (a) Male dominance in smartphone use, (c) parents passive participation in physical activity with or without smartphones and (b) levels of smartphone addiction among parents in terms of duration of their children daily physical activity engagement.

Design of Sports Training Simulation System for Children Based on Improved Deep Neural Network

“…Previously, due to the fact that the convolution of the neural network output only considered the influence of one input without considering the influence of the other input, the convolution through neural network can be a success in the field of computer vision, but, in some situations related to the time order (such as video of the next frame document context and prediction), performance is unsatisfactory, and the neural network can develop cycles [ 16 ]. The recurrent neural network not only considers the input of the previous moment but also remembers the previous information and applies it to the calculation of the current output; that is, the nodes between the hidden layer are connected instead of unconnected, and the input of the hidden layer includes not only the output of the input layer but also the output of the hidden layer at the last moment.…”