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      Prevalence of computer vision syndrome during the COVID-19 pandemic: a systematic review and meta-analysis

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          Abstract

          Background

          Computer vision syndrome has become a significant public health problem, especially in developing countries. Therefore, this study aims to identify the prevalence of computer vision syndrome during the COVID-19 pandemic.

          Methods

          A systematic review and meta-analysis of the literature was conducted using the databases PubMed, Scopus, Web of Science, and Embase up to February 22, 2023, using the search terms "Computer Vision Syndrome" and "COVID-19". Three authors independently performed study selection, quality assessment, and data extraction, and the Joanna Briggs Institute Meta-Analysis of Statistics Assessment and Review Instrument was used to evaluate study quality. Heterogeneity was assessed using the statistical test I 2 , and the R version 4.2.3 program was used for statistical analysis.

          Results

          A total of 192 studies were retrieved, of which 18 were included in the final meta-analysis. The total sample included 10,337 participants from 12 countries. The combined prevalence of computer vision syndrome was 74% (95% CI: 66, 81). Subgroup analysis based on country revealed a higher prevalence of computer vision syndrome in Pakistan (99%, 95% CI: 97, 100) and a lower prevalence in Turkey (48%, 95% CI: 44, 52). In addition, subgroup analysis based on study subjects showed a prevalence of 82% (95% CI: 74, 89) for computer vision syndrome in non-students and 70% (95% CI: 60, 80) among students.

          Conclusion

          According to the study, 74% of the participants experienced computer vision syndrome during the COVID-19 pandemic. Given this finding, it is essential to implement preventive and therapeutic measures to reduce the risk of developing computer vision syndrome and improve the quality of life of those affected.

          Trial registration

          The protocol for this systematic review and meta-analysis was registered in the international registry of systematic reviews, the International Prospective Register of Systematic Reviews (PROSPERO), with registration number CRD42022345965.

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          Worldwide Effect of COVID-19 on Physical Activity: A Descriptive Study

          Background: On 11 March 2020, the World Health Organization declared coronavirus disease 2019 (COVID-19) to be a global pandemic (1). To curb the spread of the disease, various regional and national governments advocated for social distancing measures with varying degrees of enforcement, ranging from unenforced recommendations to quarantine and business closures. Physical activity is an important determinant of health (2) and is likely affected by social distancing measures. Daily step count, a proxy for physical activity, has been associated with all-cause mortality (3). Beyond physical activity, regional step count trends may also provide a proxy for adherence to social distancing, providing real-time insights to inform public policy decisions. Because prolonged social distancing is considered to contain infection, it will be important to gauge adherence to these measures and their effect on other aspects of health, such as physical activity. Objective: To examine worldwide changes in step count before and after the announcement of COVID-19 as a global pandemic. Methods and Findings: In this descriptive study, we used deidentified, individual-level data from 19 January to 1 June 2020 that were collected from a convenience sample of users of the free, popular health and wellness smartphone app Argus (Azumio). Daily step counts were determined using smartphone accelerometers and Apple or Android algorithms for step counting (4). User location was determined by smartphone IP address. The COVID-19 pandemic declaration date used was 11 March 2020. Regional mean steps were calculated daily, and percentage of change in steps was calculated daily as a percentage of the regional mean from 19 January to 11 March 2020. Displayed figure regions were selected to achieve half less-affected and half more-affected regions with regard to both COVID-19 and social distancing and greater than 1000 and 700 users at the country and city levels, respectively. This study was exempted by the University of California, San Francisco Institutional Review Board. A total of 19 144 639 daily step count measurements were provided by 455 404 unique users from 187 unique countries during the study period; 92% of smartphones were Apple, and 8% were Android. Worldwide, within 10 days of the pandemic declaration, there was a 5.5% decrease in mean steps (287 steps), and within 30 days, there was a 27.3% decrease in mean steps (1432 steps). There was wide regional variation in average step count change and in the timing and rate of that change (Figures 1 and 2). For example, Italy declared a nationwide lockdown on 9 March 2020 and exhibited a 48.7% maximal decrease, whereas Sweden, to date, has primarily advocated for social distancing and limitations on gatherings and showed a 6.9% maximal decrease. Samples from countries such as Italy and Iran, which had earlier regional COVID-19 outbreaks, exhibited earlier step count decreases from their relative baselines. Samples from different countries varied widely in the number of days after pandemic declaration that a 15% step count decrease was seen: Italy (5 days), Spain (9 days), France (12 days), India (14 days), the United States (15 days), the United Kingdom (17 days), Australia (19 days), and Japan (24 days). Step count trends in samples from U.S. cities exhibited similarities, although there was wide international variability (Figure 2). Figure 1. Mean daily steps and percentage of change from step count at baseline by country. Top. Mean daily steps by country. Bottom. Percentage of change in steps from the prepandemic baseline by country. * Prepandemic baseline steps by country were calculated as the mean daily steps from 19 January to 11 March 2020 for that country. All values are plotted by region over a rolling 10-d average window for smoothness. Region sample sizes show total number of users who contributed data during the study period. Diamonds denote initiation dates and squares denote lifting dates of regional social distancing orders, if available. Specific regional orders were assembled from publicly available sources as accurately as possible. Brazil, South Korea, Sweden, Taiwan, and the United States: no national orders. France: stay-at-home orders, only essential businesses open (17 March to 10 May 2020). Iran: lockdown orders, only essential businesses open (14 March to 20 April 2020). Italy: lockdown orders, only essential businesses open (9 March to 18 May 2020). Japan: state of emergency for all prefectures and nonmandatory business closure request (16 April to 25 May 2020). United Kingdom: ongoing stay-at-home orders, only essential businesses open (23 March 2020 to present). Figure 1. Mean daily steps and percentage of change from step count at baseline by country. Top. Mean daily steps by country. Bottom. Percentage of change in steps from the prepandemic baseline by country. * Prepandemic baseline steps by country were calculated as the mean daily steps from 19 January to 11 March 2020 for that country. All values are plotted by region over a rolling 10-d average window for smoothness. Region sample sizes show total number of users who contributed data during the study period. Diamonds denote initiation dates and squares denote lifting dates of regional social distancing orders, if available. Specific regional orders were assembled from publicly available sources as accurately as possible. Brazil, South Korea, Sweden, Taiwan, and the United States: no national orders. France: stay-at-home orders, only essential businesses open (17 March to 10 May 2020). Iran: lockdown orders, only essential businesses open (14 March to 20 April 2020). Italy: lockdown orders, only essential businesses open (9 March to 18 May 2020). Japan: state of emergency for all prefectures and nonmandatory business closure request (16 April to 25 May 2020). United Kingdom: ongoing stay-at-home orders, only essential businesses open (23 March 2020 to present). Figure 2. Mean daily steps and percentage of change from step count at baseline by city. A. Mean daily steps by U.S. city. B. Percentage of change in steps from the prepandemic baseline by U.S. city. C. Mean daily steps in a sample of cities worldwide. D. Percentage of change in steps from the prepandemic baseline in a sample of cities worldwide. * Prepandemic baseline steps by city were calculated as the mean daily steps from 19 January to 11 March 2020 for that city. All values are plotted by region over a rolling 10-d average window for smoothness. Region sample sizes show the total number of users who contributed data during the study period. Diamonds denote initiation dates and squares denote lifting dates of regional social distancing orders, if available. Specific regional orders were assembled from publicly available sources as accurately as possible. Chicago: stay-at-home order, only essential businesses open (21 March to 3 June 2020). Dallas: shelter-in-place order, only essential businesses open (24 March to 30 April 2020). Houston: stay-at-home order, only essential businesses open (24 March to 30 April 2020). Los Angeles: ongoing stay-at-home order, only essential businesses open (19 March 2020 to present). New York City: ongoing shelter-in-place order, only essential businesses open (22 March 2020 to present). Philadelphia: stay-at-home order, only essential businesses open (23 March to 5 June 2020). Phoenix: stay-at-home order, phased reopening (31 March to 15 May 2020). San Antonio: stay-at-home order, only essential businesses open (24 March to 30 April 2020). San Diego: ongoing stay-at-home order, only essential businesses open (19 March 2020 to present). San Jose: ongoing stay-at-home order, only essential businesses open (17 March 2020 to present). Ho Chi Minh City: nationwide isolation, only essential activities allowed (1 April to 22 April 2020). London: ongoing stay-at-home orders, only essential businesses open (23 March 2020 to present). New York City: ongoing shelter-in-place order, only essential businesses open (22 March 2020 to present). Paris: stay-at-home order, only essential businesses open (17 March to 10 May 2020). Rome: lockdown orders, only essential businesses open (9 March to 17 May 2020). Sao Paulo: ongoing statewide quarantine, only essential businesses open (24 March 2020 to present). Seoul: no regional orders, citizens asked to remain indoors for 2 weeks starting 29 February 2020. Singapore: stay-at-home order, limits on social gatherings (7 April to 1 June 2020). Stockholm: no regional orders. Tokyo: state of emergency for Tokyo, nonmandatory business closure request (7 April to 25 May 2020). Figure 2. Mean daily steps and percentage of change from step count at baseline by city. A. Mean daily steps by U.S. city. B. Percentage of change in steps from the prepandemic baseline by U.S. city. C. Mean daily steps in a sample of cities worldwide. D. Percentage of change in steps from the prepandemic baseline in a sample of cities worldwide. * Prepandemic baseline steps by city were calculated as the mean daily steps from 19 January to 11 March 2020 for that city. All values are plotted by region over a rolling 10-d average window for smoothness. Region sample sizes show the total number of users who contributed data during the study period. Diamonds denote initiation dates and squares denote lifting dates of regional social distancing orders, if available. Specific regional orders were assembled from publicly available sources as accurately as possible. Chicago: stay-at-home order, only essential businesses open (21 March to 3 June 2020). Dallas: shelter-in-place order, only essential businesses open (24 March to 30 April 2020). Houston: stay-at-home order, only essential businesses open (24 March to 30 April 2020). Los Angeles: ongoing stay-at-home order, only essential businesses open (19 March 2020 to present). New York City: ongoing shelter-in-place order, only essential businesses open (22 March 2020 to present). Philadelphia: stay-at-home order, only essential businesses open (23 March to 5 June 2020). Phoenix: stay-at-home order, phased reopening (31 March to 15 May 2020). San Antonio: stay-at-home order, only essential businesses open (24 March to 30 April 2020). San Diego: ongoing stay-at-home order, only essential businesses open (19 March 2020 to present). San Jose: ongoing stay-at-home order, only essential businesses open (17 March 2020 to present). Ho Chi Minh City: nationwide isolation, only essential activities allowed (1 April to 22 April 2020). London: ongoing stay-at-home orders, only essential businesses open (23 March 2020 to present). New York City: ongoing shelter-in-place order, only essential businesses open (22 March 2020 to present). Paris: stay-at-home order, only essential businesses open (17 March to 10 May 2020). Rome: lockdown orders, only essential businesses open (9 March to 17 May 2020). Sao Paulo: ongoing statewide quarantine, only essential businesses open (24 March 2020 to present). Seoul: no regional orders, citizens asked to remain indoors for 2 weeks starting 29 February 2020. Singapore: stay-at-home order, limits on social gatherings (7 April to 1 June 2020). Stockholm: no regional orders. Tokyo: state of emergency for Tokyo, nonmandatory business closure request (7 April to 25 May 2020). Discussion: Step counts decreased worldwide in the period after COVID-19 was declared a global pandemic. Differences were seen between regions, likely reflecting regional variation in COVID-19 timing, regional enforcement, and behavior change. Countries that, to date, have had relatively low COVID-19 infection rates and have therefore not instituted lockdowns, such as South Korea, Taiwan, and Japan, have still exhibited decreases in overall step count. Within-region step count trends likely reflect a combination of changes to physical activity (for example, walking and exercising) and activities of daily living (for example, commuting and shopping) due to social distancing efforts. Assuming no regulatory changes that affect engaging in physical activity within a region, we suspect that sustained population-level trends over time may reflect changes to social distancing adherence (for example, many regions showed increases from their regional step count nadir before orders were lifted). Observed variation in step counts is also likely influenced by socioeconomic inequalities among regions and disparities in the ability to engage in or access to recreational physical activity within a region (4). Limitations of this study include sampling bias due to the reliance on smartphone and app ownership, measurement error from smartphone-measured step counts, variability in smartphone carry and use habits, no assessment of activity intensity, and inability to capture nonstepping exercise (5). Our data set is a nonrepresentative convenience sample with a variable number of contributing daily users. It also lacks participant characteristics beyond IP address, limiting comparisons among regions. Rapid worldwide step count decreases have been seen during the COVID-19 pandemic, with regional variability. Within-region step count trends may reflect social distancing measures and changes to social distancing adherence; however, more formal analytic studies are required. The effect of social distancing measures on overall physical activity, an important determinant of health, should be considered, particularly if prolonged social distancing is required.
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            A Literature Review on Impact of COVID-19 Pandemic on Teaching and Learning

            The COVID-19 pandemic has created the largest disruption of education systems in human history, affecting nearly 1.6 billion learners in more than 200 countries. Closures of schools, institutions and other learning spaces have impacted more than 94% of the world’s student population. This has brought far-reaching changes in all aspects of our lives. Social distancing and restrictive movement policies have significantly disturbed traditional educational practices. Reopening of schools after relaxation of restriction is another challenge with many new standard operating procedures put in place. Within a short span of the COVID-19 pandemic, many researchers have shared their works on teaching and learning in different ways. Several schools, colleges and universities have discontinued face-to-face teachings. There is a fear of losing 2020 academic year or even more in the coming future. The need of the hour is to innovate and implement alternative educational system and assessment strategies. The COVID-19 pandemic has provided us with an opportunity to pave the way for introducing digital learning. This article aims to provide a comprehensive report on the impact of the COVID-19 pandemic on online teaching and learning of various papers and indicate the way forward.
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              Obesity, eating behavior and physical activity during COVID-19 lockdown: A study of UK adults

              Eating, physical activity and other weight-related lifestyle behaviors may have been impacted by the COVID-19 crisis and people with obesity may be disproportionately affected. We examined weight-related behaviors and weight management barriers among UK adults during the COVID-19 social lockdown. During April–May of the 2020 COVID-19 social lockdown, UK adults (N = 2002) completed an online survey including measures relating to physical activity, diet quality, overeating and how mental/physical health had been affected by lockdown. Participants also reported on perceived changes in weight-related behaviors and whether they had experienced barriers to weight management, compared to before the lockdown. A large number of participants reported negative changes in eating and physical activity behavior (e.g. 56% reported snacking more frequently) and experiencing barriers to weight management (e.g. problems with motivation and control around food) compared to before lockdown. These trends were particularly pronounced among participants with higher BMI. During lockdown, higher BMI was associated with lower levels of physical activity and diet quality, and a greater reported frequency of overeating. Reporting a decline in mental health because of the COVID-19 crisis was not associated with higher BMI, but was predictive of greater overeating and lower physical activity in lockdown. The COVID-19 crisis may have had a disproportionately large and negative influence on weight-related behaviors among adults with higher BMI.
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                Author and article information

                Contributors
                mvalladares@continental.edu.pe
                Journal
                BMC Public Health
                BMC Public Health
                BMC Public Health
                BioMed Central (London )
                1471-2458
                29 February 2024
                29 February 2024
                2024
                : 24
                : 640
                Affiliations
                [1 ]Facultad de Medicina Humana, Universidad de San Martín de Porres, ( https://ror.org/03deqdj72) Chiclayo, 15011 Peru
                [2 ]Centro de Investigación en Atención Primaria en Salud, Universidad Peruana Cayetano Heredia, ( https://ror.org/03yczjf25) Lima, 15102 Peru
                [3 ]Unidad de Revisiones Sistemáticas y Meta-Análisis, Universidad San Ignacio de Loyola, ( https://ror.org/03vgk3f90) Juan del Corral 937. El Bosque, Trujillo, Lima Peru
                [4 ]Faculty of Pharmacy, Mansoura University, ( https://ror.org/01k8vtd75) Mansoura, 35516 Egypt
                [5 ]Department of Microbiology, Institute of Medicine, Tribhuvan University Teaching Hospital, ( https://ror.org/02me73n88) Kathmandu, 44600 Nepal
                [6 ]GRID grid.464654.1, ISNI 0000 0004 1764 8110, Department of Microbiology, Dr. D. Y. Patil Medical College, Hospital and Research Centre, ; Dr. D. Y. Patil Vidyapeeth, Pune, 411018 Maharashtra India
                [7 ]Department of Public Health Dentistry, Dr. D.Y. Patil Dental College and Hospital, ( https://ror.org/05watjs66) Dr. D.Y. Patil Vidyapeeth, Pune, 411018 Maharashtra India
                [8 ]Universidad Continental, ( https://ror.org/05rcf8d17) Lima, 15046 Peru
                [9 ]Oficina de Epidemiología, Hospital Regional Lambayeque, Chiclayo, 14012 Peru
                [10 ]Manipal College of Medical Sciences, ( https://ror.org/00qctrq52) Pokhara, Nepal
                [11 ]Research Scientist, Global Consortium for Public Health and Research, Datta Meghe Institute of Higher Education and Research, Jawaharlal Nehru Medical College, ( https://ror.org/00hdf8e67) Wardha, 442001 India
                [12 ]SR Sanjeevani Hospital, Kalyanpur-10, Siraha, Nepal
                [13 ]Master of Clinical Epidemiology and Biostatistics, Universidad Cientifica del Sur, ( https://ror.org/04xr5we72) Lima, 15067 Peru
                [14 ]Gilbert and Rose-Marie Chagoury School of Medicine, Lebanese American University, ( https://ror.org/00hqkan37) Beirut, 1102 Lebanon
                Article
                17636
                10.1186/s12889-024-17636-5
                10902934
                38424562
                f6e8583d-b400-4f48-81ab-0c417ac62cbe
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 1 June 2023
                : 1 January 2024
                Categories
                Research
                Custom metadata
                © BioMed Central Ltd., part of Springer Nature 2024

                Public health
                computer vision syndrome,covid-19,prevalence,systematic review,and pandemic
                Public health
                computer vision syndrome, covid-19, prevalence, systematic review, and pandemic

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