Top Articles (Article-Level Metrics)

 
 
Rank Article Tweets Tweets per MonthA
Tweets Influence FactorB
Twimpact Factor (tw7)C Twindex7D
 
1
How Feedback Biases Give Ineffective Medical Treatments a Good Reputation
Mícheál de Barra, Kimmo Eriksson, Pontus Strimling
J Med Internet Res 2014;16(8):e193
(Aug 21, 2014)
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16 248.06 0.00 16 85
 
 
2
Massive Open Online Courses on Health and Medicine: Review
Tharindu Rekha Liyanagunawardena, Shirley Ann Williams
J Med Internet Res 2014;16(8):e191
(Aug 14, 2014)
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58 199.79 0.00 58 100
 
 
3
Representation of Health Conditions on Facebook: Content Analysis and Evaluation of User Engagement
Timothy M Hale, Akhilesh S Pathipati, Shiyi Zan, Kamal Jethwani
J Med Internet Res 2014;16(8):e182
(Aug 4, 2014)
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61 99.53 0.00 29 95
 
 
4
Numeracy and Literacy Independently Predict Patients’ Ability to Identify Out-of-Range Test Results
Brian J Zikmund-Fisher, Nicole L Exe, Holly O Witteman
J Med Internet Res 2014;16(8):e187
(Aug 8, 2014)
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40 82.66 0.00 25 95
 
 
5
Behavior Change Techniques Implemented in Electronic Lifestyle Activity Monitors: A Systematic Content Analysis
Elizabeth J Lyons, Zakkoyya H Lewis, Brian G Mayrsohn, Jennifer L Rowland
J Med Internet Res 2014;16(8):e192
(Aug 15, 2014)
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17 65.87 0.00 17 85
 
 
6
How an Online Intervention to Prevent Excessive Gestational Weight Gain Is Used and by Whom: A Randomized Controlled Process Evaluation
Margaret Mochon Demment, Meredith Leigh Graham, Christine Marie Olson
J Med Internet Res 2014;16(8):e194
(Aug 20, 2014)
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5 51.65 0.00 5 70
 
 
7
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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65 50.38 0.00 53 100
 
 
8
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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44 35.89 0.00 38 95
 
 
9
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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34 28.49 0.00 24 90
 
 
10
Is Biblioleaks Inevitable?
Adam G Dunn, Enrico Coiera, Kenneth D Mandl
J Med Internet Res 2014;16(4):e112
(Apr 22, 2014)
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112 28.23 0.00 91 100
 
 
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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.