Literature DB >> 26029278

Tweet for health: using an online social network to examine temporal trends in weight loss-related posts.

Gabrielle M Turner-McGrievy1, Michael W Beets2.   

Abstract

Few studies have used social networking sites to track temporal trends in health-related posts, particularly around weight loss. To examine the temporal relationship of Twitter messages about weight loss over 1 year (2012). Temporal trends in #weightloss mentions and #fitness, #diet, and #health tweets which also had the word "weight" in them were examined using three a priori time periods: (1) holidays: pre-winter holidays, holidays, and post-holidays; (2) Season: winter and summer; and (3) New Year's: pre-New Year's and post-New Year's. Regarding #weightloss, there were 145 (95 % CI 79, 211) more posts/day during holidays and 143 (95 % CI 76, 209) more posts/day after holidays as compared to 480 pre-holiday posts/day; 232 (95 % CI 178, 286) more posts/day during the winter versus summer (441 posts/day); there was no difference in posts around New Year's. Examining social networks for trends in health-related posts may aid in timing interventions when individuals are more likely to be discussing weight loss.

Entities:  

Keywords:  Exercise; Informatics; Social networks; Social support; Weight loss

Year:  2015        PMID: 26029278      PMCID: PMC4444704          DOI: 10.1007/s13142-015-0308-1

Source DB:  PubMed          Journal:  Transl Behav Med        ISSN: 1613-9860            Impact factor:   3.046


  25 in total

1.  Relation between holiday weight gain and total energy expenditure among 40- to 69-y-old men and women (OPEN study).

Authors:  Chad M Cook; Amy F Subar; Richard P Troiano; Dale A Schoeller
Journal:  Am J Clin Nutr       Date:  2012-02-01       Impact factor: 7.045

2.  Twitter classification model: the ABC of two million fitness tweets.

Authors:  Theodore A Vickey; Kathleen Martin Ginis; John G Breslin; Maciej Dabrowski
Journal:  Transl Behav Med       Date:  2013-09       Impact factor: 3.046

3.  Varying social media post types differentially impacts engagement in a behavioral weight loss intervention.

Authors:  Sarah B Hales; Charis Davidson; Gabrielle M Turner-McGrievy
Journal:  Transl Behav Med       Date:  2014-12       Impact factor: 3.046

4.  Seasonal variation in household, occupational, and leisure time physical activity: longitudinal analyses from the seasonal variation of blood cholesterol study.

Authors:  C E Matthews; P S Freedson; J R Hebert; E J Stanek; P A Merriam; M C Rosal; C B Ebbeling; I S Ockene
Journal:  Am J Epidemiol       Date:  2001-01-15       Impact factor: 4.897

5.  Decoding twitter: Surveillance and trends for cardiac arrest and resuscitation communication.

Authors:  Justin C Bosley; Nina W Zhao; Shawndra Hill; Frances S Shofer; David A Asch; Lance B Becker; Raina M Merchant
Journal:  Resuscitation       Date:  2012-10-27       Impact factor: 5.262

Review 6.  Fungal infections associated with contaminated methylprednisolone injections.

Authors:  Carol A Kauffman; Peter G Pappas; Thomas F Patterson
Journal:  N Engl J Med       Date:  2012-10-19       Impact factor: 91.245

7.  Weight loss social support in 140 characters or less: use of an online social network in a remotely delivered weight loss intervention.

Authors:  Gabrielle M Turner-McGrievy; Deborah F Tate
Journal:  Transl Behav Med       Date:  2013-09       Impact factor: 3.046

8.  Social support in an Internet weight loss community.

Authors:  Kevin O Hwang; Allison J Ottenbacher; Angela P Green; M Roseann Cannon-Diehl; Oneka Richardson; Elmer V Bernstam; Eric J Thomas
Journal:  Int J Med Inform       Date:  2009-11-27       Impact factor: 4.046

9.  Communication about childhood obesity on Twitter.

Authors:  Jenine K Harris; Sarah Moreland-Russell; Rachel G Tabak; Lindsay R Ruhr; Ryan C Maier
Journal:  Am J Public Health       Date:  2014-05-15       Impact factor: 9.308

10.  The use of Twitter to track levels of disease activity and public concern in the U.S. during the influenza A H1N1 pandemic.

Authors:  Alessio Signorini; Alberto Maria Segre; Philip M Polgreen
Journal:  PLoS One       Date:  2011-05-04       Impact factor: 3.240

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  4 in total

1.  How Do You #relax When You're #stressed? A Content Analysis and Infodemiology Study of Stress-Related Tweets.

Authors:  Son Doan; Amanda Ritchart; Nicholas Perry; Juan D Chaparro; Mike Conway
Journal:  JMIR Public Health Surveill       Date:  2017-06-13

2.  Tweets Related to Motivation and Physical Activity for Obesity-Related Behavior Change: Descriptive Analysis.

Authors:  Albert Park
Journal:  J Med Internet Res       Date:  2022-07-20       Impact factor: 7.076

3.  The Information Sharing Behaviors of Dietitians and Twitter Users in the Nutrition and COVID-19 Infodemic: Content Analysis Study of Tweets.

Authors:  Esther Charbonneau; Sehl Mellouli; Arbi Chouikh; Laurie-Jane Couture; Sophie Desroches
Journal:  JMIR Infodemiology       Date:  2022-09-16

4.  Sorting the Healthy Diet Signal from the Social Media Expert Noise: Preliminary Evidence from the Healthy Diet Discourse on Twitter.

Authors:  Theo Lynn; Pierangelo Rosati; Guto Leoni Santos; Patricia Takako Endo
Journal:  Int J Environ Res Public Health       Date:  2020-11-18       Impact factor: 3.390

  4 in total

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