Developing a mental health index using a machine learning approach: assessing the impact of mobility and lockdown during the COVID-19 pandemic

Article


Nanath, K., Balasubramanian, S., Shukla, V., Islam, N. and Kaitheri, S. 2022. Developing a mental health index using a machine learning approach: assessing the impact of mobility and lockdown during the COVID-19 pandemic. Technological Forecasting and Social Change. 178. https://doi.org/10.1016/j.techfore.2022.121560
TypeArticle
TitleDeveloping a mental health index using a machine learning approach: assessing the impact of mobility and lockdown during the COVID-19 pandemic
AuthorsNanath, K., Balasubramanian, S., Shukla, V., Islam, N. and Kaitheri, S.
Abstract

Governments worldwide have implemented stringent restrictions to curtail the spread of the COVID-19 pandemic. Although beneficial to physical health, these preventive measures could have a profound detrimental effect on the mental health of the population. This study focuses on the impact of lockdowns and mobility restrictions on mental health during the COVID-19 pandemic. We first develop a novel mental health index based on the analysis of data from over three million global tweets using the Microsoft Azure machine learning approach. The computed mental health index scores are then regressed with the lockdown strictness index and Google mobility index using fixed-effects ordinary least squares (OLS) regression. The results reveal that the reduction in workplace mobility, reduction in retail and recreational mobility, and increase in residential mobility (confinement to the residence) have harmed mental health. However, restrictions on mobility to parks, grocery stores, and pharmacy outlets were found to have no significant impact. The proposed mental health index provides a path for theoretical and empirical mental health studies using social media. [Abstract copyright: © 2022 Elsevier Inc. All rights reserved.]

KeywordsMental health index; Mobility; Lockdown; Machine learning approach; Twitter; COVID-19 pandemic
PublisherElsevier
JournalTechnological Forecasting and Social Change
ISSN0040-1625
Electronic1873-5509
Publication dates
Online14 Feb 2022
Print01 May 2022
Publication process dates
Deposited07 Mar 2022
Accepted05 Feb 2022
Submitted03 May 2021
Output statusPublished
Publisher's version
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Copyright Statement

© 2022 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license https://creativecommons.org/licenses/by/4.0/

Digital Object Identifier (DOI)https://doi.org/10.1016/j.techfore.2022.121560
Web of Science identifierWOS:000778411300004
LanguageEnglish
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Shukla, V. and Naim, M. 2015. Rogue seasonality in supply chains: an investigation and a measurement approach. Journal of Manufacturing Technology Management. 26 (3), pp. 364-389. https://doi.org/10.1108/JMTM-09-2013-0118
Organization vision - Experimentation on its effective communication
Kaitheri, S. and Narayanamurthy, G. 2014. Organization vision - Experimentation on its effective communication. The Conference on Digital Experimentation (CODE@MIT). Massachusetts Institute of Technology, United States
Towards a model for user technology readiness in ICT4D initiatives
Supriya, K., Sebastian, M. and Nanath, K. 2014. Towards a model for user technology readiness in ICT4D initiatives. 25th Australasian Conference on Information Systems. Auckland, New Zealand 08 - 10 Dec 2014 ACIS.
Sustainability of environmental programs: A green IT perspective
Nanath, K., Radhakrishna Pillai, R. and Supriya, K. 2014. Sustainability of environmental programs: A green IT perspective. 25th Australasian Conference on Information Systems. Auckland, New Zealand 08 - 10 Dec 2014 ACIS.
E-governance readiness: Challenges for India
Sebastian, M. and Kaitheri, S. 2013. E-governance readiness: Challenges for India. IIM Kozhikode Society and Management Review. 2 (1), pp. 31-42. https://doi.org/10.1177/2277975213496511
Performance improvements seen through the lens of strategic trade-offs
Sarmiento, R., Shukla, V. and Izar-Landeta, J. 2013. Performance improvements seen through the lens of strategic trade-offs. International Journal of Production Research. 51 (15), pp. 4682-4694. https://doi.org/10.1080/00207543.2013.784417
Rogue seasonality detection in supply chains
Shukla, V., Naim, M. and Thornhill, N. 2012. Rogue seasonality detection in supply chains. International Journal of Production Economics. 138 (2), pp. 254-272. https://doi.org/10.1016/j.ijpe.2012.03.026
Systems thinking approach for transforming management education
Pillai, R. and Supriya, K. 2012. Systems thinking approach for transforming management education. Ninth AIMS International Conference on Management. Pune, India 01 - 04 Jan 2012 pp. 103-112
Zero-sum and frontier trade-offs: an investigation on compromises and compatibilities amongst manufacturing capabilities
Sarmiento, R. and Shukla, V. 2011. Zero-sum and frontier trade-offs: an investigation on compromises and compatibilities amongst manufacturing capabilities. International Journal of Production Research. 49 (7), pp. 2001-2017. https://doi.org/10.1080/00207540903555544
Bullwhip and backlash in supply pipelines.
Shukla, V., Naim, M. and Yaseen, E. 2009. Bullwhip and backlash in supply pipelines. International Journal of Production Research. 47 (23), pp. 6477-6497. https://doi.org/10.1080/00207540802270096
Data mining: a tool for detecting cyclical disturbances in supply networks
Afify, A., Dimov, S., Naim, M., Valeva, V. and Shukla, V. 2007. Data mining: a tool for detecting cyclical disturbances in supply networks. Proceedings of the Institution of Mechanical Engineers. Part B, Journal of Engineering Manufacture. 221 (12), pp. 1771-1785. https://doi.org/10.1243/09544054JEM879