Top Articles (Article-Level Metrics)

 
 
Rank Article Tweets Tweets per MonthA
Tweets
Influence
Factor
B
Twimpact Factor (tw7)C Twindex7D
 
831
Can an Internet-Based Health Risk Assessment Highlight Problems of Heart Disease Risk Factor Awareness? A Cross-Sectional Analysis
Justin B Dickerson, Catherine J McNeal, Ginger Tsai, Cathleen M Rivera, Matthew Lee Smith, Robert L Ohsfeldt, Marcia G Ory
J Med Internet Res 2014;16(4):e106
(Apr 18, 2014)
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5 51.65 0.00 5 50
 
 
832
A Framework for Different Levels of Integration of Computational Models Into Web-Based Virtual Patients
Andrzej A Kononowicz, Andrew J Narracott, Simone Manini, Martin J Bayley, Patricia V Lawford, Keith McCormack, Nabil Zary
J Med Internet Res 2014;16(1):e23
(Jan 23, 2014)
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5 1.76 0.00 5 40
 
 
833
Correction: Development and Validation of Filters for the Retrieval of Studies of Clinical Examination From Medline
Robert Badgett, Nader Shaikh, Mini Pi, Nancy L Wilczynski, K. Ann McKibbon, Andrea M. Ketchum, R. Brian Haynes
J Med Internet Res 2012;14(4):e108
(Aug 3, 2012)
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5 0.25 0.00 5 35
 
 
834
Influence of Delivery Strategy on Message-Processing Mechanisms and Future Adherence to a Dutch Computer-Tailored Smoking Cessation Intervention
Nicola Esther Stanczyk, Rik Crutzen, Catherine Bolman, Jean Muris, Hein de Vries
J Med Internet Res 2013;15(2):e28
(Feb 6, 2013)
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5 0.35 0.00 5 40
 
 
835
An Innovative Smartphone-Based Otorhinoendoscope and Its Application in Mobile Health and Teleotolaryngology
Cheng-Jung Wu, Sheng-Yu Wu, Po-Chun Chen, Yaoh-Shiang Lin
J Med Internet Res 2014;16(3):e71
(Mar 3, 2014)
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5 3.16 0.00 0 0
 
 
836
The Effect of Program Design on Engagement With an Internet-Based Smoking Intervention: Randomized Factorial Trial
Jennifer B McClure, Susan M Shortreed, Andy Bogart, Holly Derry, Karin Riggs, Jackie St John, Vijay Nair, Larry An
J Med Internet Res 2013;15(3):e69
(Mar 25, 2013)
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5 0.40 0.00 4 10
 
 
837
Quit Now? Quit Soon? Quit When You’re Ready? Insights About Target Quit Dates for Smoking Cessation From an Online Quit Date Tool
Caroline O Cobb, Raymond S Niaura, Elisabeth A Donaldson, Amanda L Graham
J Med Internet Res 2014;16(2):e55
(Feb 17, 2014)
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5 2.46 0.00 5 55
 
 
838
Importance of Internet Surveillance in Public Health Emergency Control and Prevention: Evidence From a Digital Epidemiologic Study During Avian Influenza A H7N9 Outbreaks
Hua Gu, Bin Chen, Honghong Zhu, Tao Jiang, Xinyi Wang, Lei Chen, Zhenggang Jiang, Dawei Zheng, Jianmin Jiang
J Med Internet Res 2014;16(1):e20
(Jan 17, 2014)
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PDF ($22 pay-per-download, free for members [$4.92/mo.]
5 1.65 0.00 4 40
 
 
839
Keywords to Recruit Spanish- and English-Speaking Participants: Evidence From an Online Postpartum Depression Randomized Controlled Trial
Alinne Z Barrera, Alex R Kelman, Ricardo F Muñoz
J Med Internet Res 2014;16(1):e6
(Jan 9, 2014)
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5 1.52 0.00 5 45
 
 
840
An Assessment of Incentive Versus Survey Length Trade-offs in a Web Survey of Radiologists
Jeanette Y Ziegenfuss, Blake D Niederhauser, David Kallmes, Timothy J Beebe
J Med Internet Res 2013;15(3):e49
(Mar 20, 2013)
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5 0.39 0.00 3 0
 
 
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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.

 * Tweets Data Collection began January 2008