ABSTRACT
Objective: The objectives of the study were: (1) To assess life style changes among children of <=15 years of age during COVID-19 pandemic and (2) to find out the effect of the life style changes on health of children of <=15 years of age. Method(s): The cross-sectional comparative study conducted at department of pediatrics, Vivekananda Polyclinic and Institute of Medical Sciences, Lucknow for duration of 1 year and sample size found to be 276 on calculation by applying the formula. Result(s): Out of 278 children, about 39% (108) were female children. Most of children were studying in primary level classes (52.51%) and most of enrolled children had joint family (66.18%). Level of physical activity reduced significantly due to closure of school and restriction on outdoor activities. Weight of children increased significantly during COVID-19 pandemic seems to be due to decreased in physical activities and consumption of more fast food/fried food (high calorie intake) and sedentary life style. Conclusion(s): During COVID-19 pandemic due to closure of schools and restricted outdoor activities results in decrease level of physical activities, increased consumption of high calorie food and sedentary behavior lead to increase in weight of children and changes in sleeping pattern of children.Copyright © 2023 The Authors. Published by Innovare Academic Sciences Pvt Ltd.
ABSTRACT
Objective: The objective of this study was to compare the screen time (ST) in pre-COVID and COVID era in children aged 5-15 years and to analyse the ST effect in pre-COVID and COVID era in the children. Method(s): The study was done at Vivekananda Polyclinic and Institute of Medical Sciences, Lucknow. Two hundred and seventy-six children aged between 5 and 15 years, attending outpatient department or inpatient department were enrolled in the study. Result(s): It was observed that the ST was significantly increased in post-COVID as compared to pre-COVID time and the difference was statistically significant (p<0.0001*). It was also observed that the screening time was significantly increased in post-COVID as compared to pre-COVID time and the difference was statistically significant (p<0.0001*). Conclusion(s): The present study found that when screening duration was analysed, the screening time during COVID-19 was significantly longer than the screening time before COVID-19 which may be associated with the various health problems reported among children during COVID-19 pandemic.Copyright © 2023 The Authors. Published by Innovare Academic Sciences Pvt Ltd.
ABSTRACT
COVID-19 has induced the need of exercises and yoga among people. Yoga is now becoming a habit of everyone for staying fit and healthy through body and mind. Since, it was the period of complete lockdown, people started preferring online modes for the same. There are many different apps on the play store specifically for doing yoga. With the advent of artificial intelligence in various industries, it is also winning the hearts of the yoga enthusiasts too. The integration of AI in the fitness industry (AI enabled fitness trainers, smart wearables, AI based gym equipment to name a few) is gaining huge momentum among the health conscious. This paper walks through different yoga mobile applications that use the techniques of artificial intelligence to motivate their customers with personalized experience and positive feedback and introduces a new concept of AI based Yoga Trainer who reminds, instructs, guides, and motivates a user to do yoga or while doing yoga. © 2022 IEEE.
ABSTRACT
This Ongoing COVID-19 epidemic situation, which has resulted in the loss of lives and economics. In this scenario, social distancing is the only way to prevent ourselves. In such a scenario to boost the economy, a globally large number of industries and businesses have shifted their system to cloud-like education, shipping, training and many more globally. To support this transition cloud services are the only solution to provide reliable and secure services to the user to sustain their business. Due to this, the load over the existing cloud infrastructure has drastically increased. So it is the responsibility of the cloud to manage the load over the existing infrastructure to maintain reliability and serve high-quality services to the user. Task allocation in the cloud is one of the key features to optimize the performance of cloud infrastructure. In this work, we have proposed a prediction-based technique using a pre-trained neural network to find a reliable resource for a task based on previous training and history of cloud and its performance to optimize the performance under the overloaded and under loaded situation. The main aim of this work is to reduce the fault and provide high performance by reducing scheduling time, execution time and network load. The proposed model uses the Big Bang Big Crunch algorithm to generated huge datasets for training our neural model. The accuracy of the BB-BC-ANN model is improved with 98% accuracy. © 2022 - IOS Press. All rights reserved.