2021
DOI: 10.3390/ijerph18094543
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A Study of the Effects of the COVID-19 Pandemic on the Experience of Back Pain Reported on Twitter® in the United States: A Natural Language Processing Approach

Abstract: The COVID-19 pandemic has changed our lifestyles, habits, and daily routine. Some of the impacts of COVID-19 have been widely reported already. However, many effects of the COVID-19 pandemic are still to be discovered. The main objective of this study was to assess the changes in the frequency of reported physical back pain complaints reported during the COVID-19 pandemic. In contrast to other published studies, we target the general population using Twitter as a data source. Specifically, we aim to investigat… Show more

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Cited by 19 publications
(14 citation statements)
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“… 13 , 17 , 18 Moreover, a cross sectional study was showed that PA and exercise characteristics for both before and after the COVID-19 pandemic, that The PA self-perception in the Brazilian and Italian samples was considerably influenced by the COVID-19 lockdown by significant decrease in PA. 19 Recent studies demonstrating a decrease in all physical activity levels during house confinement in Brazil and other nations are consistent with this. 20 …”
Section: Discussionmentioning
confidence: 99%
“… 13 , 17 , 18 Moreover, a cross sectional study was showed that PA and exercise characteristics for both before and after the COVID-19 pandemic, that The PA self-perception in the Brazilian and Italian samples was considerably influenced by the COVID-19 lockdown by significant decrease in PA. 19 Recent studies demonstrating a decrease in all physical activity levels during house confinement in Brazil and other nations are consistent with this. 20 …”
Section: Discussionmentioning
confidence: 99%
“…Quantitative language features from semi-structured interviews have been used to quantify placebo response in chronic pain patients ( 110 ), and large-scale text-mining of electronic medical records has been utilized to detect pain disparities in underserved communities ( 111 ). Similarly, social media posts have been analyzed to longitudinally track patients and identify new pain phenotypes ( 112 ), geospatially monitor and characterize opioid use ( 113 ), conceptualize how pain is socially communicated ( 114 , 115 ), track local and federal pain treatment policies ( 116 ), and discover population-level increases in pain conditions and symptoms ( 117 ).…”
Section: State-of-the-art In Pain Methodsmentioning
confidence: 99%
“…CNN can identify the image of an object by using convolutions within its architecture; including convolutional layers that have parameters to create a feature map; pooling layers that reduce the number of features for computational efficiency; dropout layers that help avoid overfitting by randomly turning off perceptrons; and a output layer that map the learned features into the final decision, such as classification [ 135 , 136 ]. The recent emergence of the CNN algorithm has enabled outstanding performance in several application such as image processing, natural language processing, and classification of EEG recordings, particularly for MI tasks [ 137 , 138 , 139 , 140 ]. However, CNN performance is highly dependent on hyperparameters such as the number of convolution layers, and the size and number of kernels and pooling windows [ 137 ].…”
Section: Theoretical Backgroundmentioning
confidence: 99%