Top Articles (Article-Level Metrics)

 
 
Rank Article Tweets Tweets per MonthA
Tweets Influence FactorB
Twimpact Factor (tw7)C Twindex7D
 
1
Social Network Sites as a Mode to Collect Health Data: A Systematic Review
Fahdah Alshaikh, Farzan Ramzan, Salman Rawaf, Azeem Majeed
J Med Internet Res 2014;16(7):e171
(Jul 14, 2014)
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53 182.57 0.00 53 100
 
 
2
Predictors of eHealth Usage: Insights on The Digital Divide From the Health Information National Trends Survey 2012
Emily Kontos, Kelly D Blake, Wen-Ying Sylvia Chou, Abby Prestin
J Med Internet Res 2014;16(7):e172
(Jul 16, 2014)
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38 168.29 0.00 38 95
 
 
3
Assessing the Applicability of E-Therapies for Depression, Anxiety, and Other Mood Disorders Among Lesbians and Gay Men: Analysis of 24 Web- and Mobile Phone-Based Self-Help Interventions
Tomas Rozbroj, Anthony Lyons, Marian Pitts, Anne Mitchell, Helen Christensen
J Med Internet Res 2014;16(7):e166
(Jul 3, 2014)
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36 55.80 0.00 18 95
 
 
4
Characterizing the Followers and Tweets of a Marijuana-Focused Twitter Handle
Patricia Cavazos-Rehg, Melissa Krauss, Richard Grucza, Laura Bierut
J Med Internet Res 2014;16(6):e157
(Jun 27, 2014)
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30 35.77 0.00 28 100
 
 
5
Consensus on Use of the Term “App” Versus “Application” for Reporting of mHealth Research
Thomas Lorchan Lewis, Matthew Alexander Boissaud-Cooke, Timothy Dy Aungst, Gunther Eysenbach
J Med Internet Res 2014;16(7):e174
(Jul 17, 2014)
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24 124.03 0.00 24 90
 
 
6
The Role of Facebook in Crush the Crave, a Mobile- and Social Media-Based Smoking Cessation Intervention: Qualitative Framework Analysis of Posts
Laura Louise Struik, Neill Bruce Baskerville
J Med Internet Res 2014;16(7):e170
(Jul 11, 2014)
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21 54.25 0.00 21 95
 
 
7
Click “Like” to Change Your Behavior: A Mixed Methods Study of College Students’ Exposure to and Engagement With Facebook Content Designed for Weight Loss
Gina Merchant, Nadir Weibel, Kevin Patrick, James H Fowler, Greg J Norman, Anjali Gupta, Christina Servetas, Karen Calfas, Ketaki Raste, Laura Pina, Mike Donohue, William G Griswold, Simon Marshall
J Med Internet Res 2014;16(6):e158
(Jun 24, 2014)
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19 20.31 0.00 16 90
 
 
8
Can Tweets Predict Citations? Metrics of Social Impact Based on Twitter and Correlation with Traditional Metrics of Scientific Impact
Gunther Eysenbach
J Med Internet Res 2011;13(4):e123
(Dec 16, 2011)
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18 18.00 0.00 529 100
 
 
9
The Use of Social Media by State Tobacco Control Programs to Promote Smoking Cessation: A Cross-Sectional Study
Jennifer C Duke, Heather Hansen, Annice E Kim, Laurel Curry, Jane Allen
J Med Internet Res 2014;16(7):e169
(Jul 10, 2014)
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16 38.15 0.00 16 90
 
 
10
Evaluation of an eHealth Intervention in Chronic Care for Frail Older People: Why Adherence is the First Target
Peter Makai, Marieke Perry, Sarah HM Robben, Henk J Schers, Maud M Heinen, Marcel GM Olde Rikkert, René F Melis
J Med Internet Res 2014;16(6):e156
(Jun 23, 2014)
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15 15.50 0.00 13 80
 
 
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Total Tweets
 
Tweets per Month
 
Tweets Influence Factor
 
Twimpact Factor (TWIF) tw7
 
Twindex7

A Tweets per month statistic may be artifically inflated for newly published articles

B Tweets Influence Factor (TIF): number of tweets x avg. influence of tweeters (influence is determined by how often all tweets of that person are retweeted by others)

C Twimpact Factor (TWIF) tw7: Number of mentionings in tweets (tweetations) within first 7 days after article publication (Eysenbach 2011)

D Twindex7 (Eysenbach 2011): Rank percentile of this article when its twimpact factor (tw7) is compared to 19 previously published articles. Range 0-100, with higher scores reflecting higher impact on Twitter. Articles with a Twindex greater than 75 have a 75% chance of being highly cited (top quartile by citation), articles with a Twindex less than 75 have a 7% chance to be highly cited.