Kartalias Katina, Lavorgna Tessa R, Saraf Shreya M, Mulcahey Mary K, Tucker Christopher J
Department of Orthopaedic Surgery, Naval Medical Center San Diego, San Diego, California, U.S.A.
Tulane University School of Medicine, New Orleans, Louisiana, U.S.A.
Arthrosc Sports Med Rehabil. 2024 Mar 26;6(3):100931. doi: 10.1016/j.asmr.2024.100931. eCollection 2024 Jun.
To determine whether activity on Twitter was correlated with increasing impact factor (IF) among 6 orthopaedic sports medicine journals.
Twitonomy software was used to collect account activity for the ; ; ; ; and . Data from 2000 to 2020 were collected. Each journal's annual IF score was collected via scijournal.org. A multivariate regression model was used to predict the influence of different Twitter metrics on IF from 2012 to 2019. The journal name, number of tweets, and interaction of the two were used to predict IF. Additionally, Pearson correlation was used to assess correlations between Twitter account metrics and IF.
Over the study period, all IFs increased, with the exception of that for . The effect size between number of tweets and IF was not the same for each journal. For every additional tweet, increased its IF by 0.001 ( = .18). and increased their IF by 0.01 ( = .002) and 0.022 ( < .001), respectively. would expect a decrease in its IF by 0.004 ( = .55) and and would increase its IF by 0.002 ( = .71) and 0.001 ( = .99), but this was not significant. There was a statistically significant positive correlation between annual tweets and IF across all journals.
Markers of Twitter account activity, specifically the number of annual tweets, were predictive of an increase in IF among the orthopedic sports medicine journals included in this study.
The findings of this study may allow orthopaedic sports medicine journals to make more effective, targeted, and productive use of their social media accounts to reach a broader audience, increase their influence, and increase the IF of their journal.
确定6本骨科运动医学期刊在Twitter上的活跃度是否与影响因子（IF）的增加相关。
使用Twitonomy软件收集《 》《 》《 》《 》和《 》的账户活动数据。收集了2000年至2020年的数据。通过scijournal.org收集每本期刊的年度IF评分。使用多元回归模型预测2012年至2019年不同Twitter指标对IF的影响。期刊名称、推文数量以及两者的交互作用被用于预测IF。此外，使用Pearson相关性来评估Twitter账户指标与IF之间的相关性。
在研究期间，除《 》外，所有期刊的IF均有所增加。各期刊推文数量与IF之间的效应大小不尽相同。每增加一条推文，《 》的IF增加0.001（P = 0.18）。《 》和《 》的IF分别增加0.01（P = 0.002）和0.022（P < 0.001）。《 》预计其IF会降低0.004（P = 0.55），而《 》和《 》会使其IF分别增加0.002（P = 0.71）和0.001（P = 0.99），但这并不显著。所有期刊的年度推文与IF之间存在统计学上显著的正相关。
Twitter账户活跃度指标，特别是年度推文数量，可预测本研究中所纳入的骨科运动医学期刊的IF增加情况。
本研究结果可能使骨科运动医学期刊更有效地、有针对性地和高效地利用其社交媒体账户，以覆盖更广泛的受众、增加其影响力并提高其期刊的IF。
