Effects of the COVID-19 Pandemic on Hand and Arm Dysfunction: A Google Trends Analysis
Bibliographic record
Abstract
Introduction The COVID-19 pandemic prompted individuals to make a number of lifestyle alterations. Few studies have examined the development of any hand and/or arm dysfunctions that may have resulted. The purpose of this study was to identify hand and/or arm overuse injuries that may have occurred as a result of the stay-at-home orders during the COVID-19 pandemic. Methods A Google Trends analysis of the terms "hand pain," "carpal tunnel syndrome," "cubital tunnel syndrome," "trigger finger," "de Quervain tenosynovitis," "elbow pain," "tennis elbow," "golfer's elbow," "thumb base arthritis," and "extensor carpi ulnaris tenosynovitis" in the United States, United Kingdom, Canada, and India was performed from June 2019 to January 2023. The noted timeframe was divided into quarters of 47 weeks, with the first quarter (June 2, 2019, through April 19, 2020) serving as a pre-pandemic baseline. The analysis compared initial results noted in the first quarter to individual results from the second, third, and fourth quarters. Results The most notable findings were the upward trends of the terms "hand pain," "carpal tunnel," and "trigger finger." Specifically, India showed a significant increase in the terms "hand pain" and "carpal tunnel syndrome" in the second, third, and fourth quarters. The United States additionally showed a significant upward trend in the terms "carpal tunnel syndrome" and "trigger finger" in the second, third, and fourth quarters. The United Kingdom also reported a significant upward trend in the term "trigger finger" in the second, third, and fourth quarters. Conclusion Numerous factors likely contributed to the increased interest in these terms, such as the increase in telework and associated mobile device usage due to lockdown during the COVID-19 pandemic. Movements associated with performing these tasks may have led to an increased prevalence of hand pain, thus prompting increased queries of these terms through an online search engine.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.020 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".