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Achilles Tendon Injury and Seasonal Variation: An Analysis Using Google Trends
Korean J Sports Med 2019;37:155-161
Published online December 1, 2019;  https://doi.org/10.5763/kjsm.2019.37.4.155
© 2019 The Korean Society of Sports Medicine.

Yun-Sik Cha, Seok-Min Hwang, Pei-Jiun Yang

Department of Orthopedic Surgery, Seoul Red Cross Hospital, Seoul, Korea
Correspondence to: Seok-Min Hwang
Department of Orthopedic Surgery, Seoul Red Cross Hospital, 9 Saemunan-ro, Jongno-gu, Seoul 03181, Korea
Tel: +82-2-2002-8000, Fax: +82-2-2002-8855 E-mail: gulpae@naver.com
Received August 23, 2019; Revised October 14, 2019; Accepted October 19, 2019.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
 Abstract
Purpose: Achilles tendon injury is one of the most common sports-related injuries. Several studies suggest that Achilles tendon injury is associated with seasonal variation. The purpose of this study is to determine the relationship between seasonal variations and Achilles tendon injury through Google Trends (GT) and to evaluate the correlation between GT and actual data.
Methods: We identified three articles through PubMed database as control group. The experimental group (GT group) was collected from GT by setting the same conditions as the control group. For GT group, we use the search terms related to the Achilles tendon injury. The exploration period was set from January 1, 2004 to December 31, 2018.
Results: There is approximately more than 90% (p<0.05) correlation between GT group and control group. The incidences of Ontario were the highest in the summer. Those of New York and Vancouver were higher in spring compared to those of Ontario.
Conclusion: Our study implies that there is significant seasonal variation for Achilles tendon injury. Most of these injuries seem to occur in spring and summer. Also, there is a significant relationship between GT data and actual data. If the data from GT can be analyzed properly, these approach methods will be useful for epidemiological research.
Keywords : Achilles tendon, Big data, Incidence, Seasons
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