Top Articles (Article-Level Metrics)

 
 
Rank Article Tweets Tweets per MonthA
Tweets Influence FactorB
Twimpact Factor (tw7)C Twindex7D
 
171
An Analysis of Online Evaluations on a Physician Rating Website: Evidence From a German Public Reporting Instrument
Martin Emmert, Florian Meier
J Med Internet Res 2013;15(8):e157
(Aug 6, 2013)
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5 0.83 23.00 5 40
 
 
172
Using Text Messaging to Assess Adolescents' Health Information Needs: An Ecological Momentary Assessment
Rebecca Schnall, Anastasia Okoniewski, Victoria Tiase, Alexander Low, Martha Rodriguez, Steven Kaplan
J Med Internet Res 2013;15(3):e54
(Mar 6, 2013)
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5 0.83 0.00 18 75
 
 
173
Patient and Public Views on Electronic Health Records and Their Uses in the United Kingdom: Cross-Sectional Survey
Serena A Luchenski, Julie E Reed, Cicely Marston, Chrysanthi Papoutsi, Azeem Majeed, Derek Bell
J Med Internet Res 2013;15(8):e160
(Aug 23, 2013)
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5 0.83 0.00 28 85
 
 
174
New Media Use by Patients Who Are Homeless: The Potential of mHealth to Build Connectivity
Lori Ann Post, Federico E Vaca, Kelly M. Doran, Cali Luco, Matthew Naftilan, James Dziura, Cynthia Brandt, Steven Bernstein, Liudvikas Jagminas, Gail D'Onofrio
J Med Internet Res 2013;15(9):e195
(Sep 3, 2013)
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5 0.83 0.00 58 100
 
 
175
Online Mental Health Resources in Rural Australia: Clinician Perceptions of Acceptability
Craig Sinclair, Kristi Holloway, Geoffrey Riley, Kirsten Auret
J Med Internet Res 2013;15(9):e193
(Sep 5, 2013)
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5 0.83 0.00 8 55
 
 
176
Making Sense of Mobile- and Web-Based Wellness Information Technology: Cross-Generational Study
Daniel Kutz, Kalpana Shankar, Kay Connelly
J Med Internet Res 2013;15(5):e83
(May 14, 2013)
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5 0.83 8.00 6 25
 
 
177
Online Interventions for Social Marketing Health Behavior Change Campaigns: A Meta-Analysis of Psychological Architectures and Adherence Factors
Brian Cugelman, Mike Thelwall, Phil Dawes
J Med Internet Res 2011;13(1):e17
(Feb 14, 2011)
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5 0.83 0.00 21 65
 
 
178
Adolescents Just Do Not Know What They Want: A Qualitative Study to Describe Obese Adolescents’ Experiences of Text Messaging to Support Behavior Change Maintenance Post Intervention
Kyla L Smith, Deborah A Kerr, Ashley A Fenner, Leon M Straker
J Med Internet Res 2014;16(4):e103
(Apr 8, 2014)
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4 12.40 0.00 4 45
 
 
179
Animated Randomness, Avatars, Movement, and Personalization in Risk Graphics
Holly O Witteman, Andrea Fuhrel-Forbis, Harindra C Wijeysundera, Nicole Exe, Mark Dickson, Lisa Holtzman, Valerie C Kahn, Brian J Zikmund-Fisher
J Med Internet Res 2014;16(3):e80
(Mar 18, 2014)
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4 4.00 0.00 4 50
 
 
180
Do Email and Mobile Phone Prompts Stimulate Primary School Children to Reuse an Internet-Delivered Smoking Prevention Intervention?
Henricus-Paul Cremers, Liesbeth Mercken, Rik Crutzen, Paul Willems, Hein de Vries, Anke Oenema
J Med Internet Res 2014;16(3):e86
(Mar 18, 2014)
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4 4.00 0.00 4 55
 
 
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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.