PREVALENCE OF MUSCULOSKELETAL PAIN AND DISCOMFORT DUE TO ONLINE TEACHING AND LEARNING METHODS DURING LOCKDOWN IN STUDENTS AND TEACHERS: OUTCOMES OF THE NEW NORMAL

Purpose: The sudden lockdown due to COVID-19 in India led to closure of schools and colleges. This resulted in an increased usage of online mode of study, with a more sedentary lifestyle. The survey study aimed to analyze the prevalence of any musculoskeletal problem in students and teachers due to the same. Methodology: A Google Form was distributed by a snowball sampling technique using various social media platforms. A total of 715 responses were received. Results: Maximum respondents were in the age range of 18-25 years. Eighty eight percent of participants in the survey were involved in the online mode of education, with 60.8% experiencing some form of musculoskeletal pain or discomfort;71% of people believed that the cause of pain was online working. Neck pain (51.3%) followed by low back pain (33.4%) and headaches (29.8%) were commonly reported. University teachers reported maximum pain followed by university students, school teachers and school students. Of all the respondents, 60.8% people admitted to adopting awkward postures while at work, whereas only 27.6% of them exercised to relieve pain and discomfort. Conclusion: There is an urgent need to develop appropriate intervention strategies for people involved in sedentary online work to prevent the occurrence of musculoskeletal pain and discomfort. Physical therapy can play a major role in managing this lifestyle hazard.

Are artificial intelligence and machine learning suitable to tackle the COVID-19 impacts? An agriculture supply chain perspective

Purpose: This article aims to model the challenges of implementing artificial intelligence and machine earning (AI-ML) for moderating the impacts of COVID-19, considering the agricultural supply chain (ASC) in the Indian context. Design/methodology/approach: 20 critical challenges were modeled based on a comprehensive literature review and consultation with experts. The hybrid approach of “Delphi interpretive structural modeling (ISM)-Fuzzy Matrice d' Impacts Croises Multiplication Applique'e à un Classement (MICMAC) − analytical network process (ANP)” was used. Findings: The study's outcome indicates that “lack of central and state regulations and rules” and “lack of data security and privacy” are the crucial challenges of AI-ML implementation in the ASC. Furthermore, AI-ML in the ASC is a powerful enabler of accurate prediction to minimize uncertainties. Research limitations/implications: This study will help stakeholders, policymakers, government and service providers understand and formulate appropriate strategies to enhance AI-ML implementation in ASCs. Also, it provides valuable insights into the COVID-19 impacts from an ASC perspective. Besides, as the study was conducted in India, decision-makers and practitioners from other geographies and economies must extrapolate the results with due care. Originality/value: This study is one of the first that investigates the potential of AI-ML in the ASC during COVID-19 by employing a hybrid approach using Delphi-ISM-Fuzzy-MICMAC-ANP. © 2021, Emerald Publishing Limited.