Skip to main content
SpringerLink
Log in

Interest in a Twitter-delivered weight loss program among women of childbearing age

  • Original Research
  • Published:
Translational Behavioral Medicine

Abstract

Weight management through the childbearing years is important, yet few women have access to efficacious weight loss programs. Online social network-delivered programs may increase reach and thus impact. The aim of this study was to gauge interest in a Twitter-based weight loss intervention among women of childbearing age and the feasibility of recruitment via Twitter. We recruited English-speaking women aged 18–45 years (N = 63) from Twitter to complete an anonymous online survey including open-ended questions about program advantages and concerns. Forty percent of participants were obese and 83 % were trying to lose weight. Eighty-one percent were interested in a Twitter-delivered weight loss program. Interest was high in all subgroups (62–100 %). Participants (59 %) cited program advantages, including convenience, support/accountability, and privacy. Concerns (59 %) included questions about privacy, support/accountability, engagement, efficacy, and technology barriers. Research is needed to develop and evaluate social media-delivered interventions, and to develop methods for recruiting participants directly from Twitter.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Ogden CL et al. Prevalence of childhood and adult obesity in the United States, 2011–2012. JAMA. 2014; 311(8): 806-814.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Burke GL et al. Differences in weight gain in relation to race, gender, age and education in young adults: the CARDIA Study. Coronary Artery Risk Development in Young Adults. Ethn Health. 1996; 1(4): 327-335.

    Article  CAS  PubMed  Google Scholar 

  3. Gunderson EP et al. Excess gains in weight and waist circumference associated with childbearing: the Coronary Artery Risk Development in Young Adults Study (CARDIA). Int J Obes Relat Metab Disord. 2004; 28(4): 525-535.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Abrams B et al. Parity and body mass index in US women: a prospective 25-year study. Obesity (Silver Spring). 2013; 21(8): 1514-1518.

    Article  Google Scholar 

  5. Pi-Sunyer FX. Medical hazards of obesity. Ann Intern Med. 1993; 119(7 Pt 2): 655-660.

    Article  CAS  PubMed  Google Scholar 

  6. Institute of Medicine (IOM) and National Research Council (NRC). Weight gain during pregnancy: reexamining the guidelines, K.M. Rasmussen and A.L. Yaktine, Eds. The National Academies Press; 2009.

  7. Arendas K, Qiu Q, Gruslin A. Obesity in pregnancy: pre-conceptional to postpartum consequences. J Obstet Gynaecol Can. 2008; 30(6): 477-488.

    Article  PubMed  Google Scholar 

  8. Herring SJ, Oken E. Obesity and diabetes in mothers and their children: can we stop the intergenerational cycle? Curr Diab Rep. 2011; 11(1): 20-27.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Johnson DB et al. Preventing obesity: a life cycle perspective. J Am Diet Assoc. 2006; 106(1): 97-102.

    Article  PubMed  Google Scholar 

  10. American College of Obstetricians and Gynecologists. ACOG committee opinion no. 549: obesity in pregnancy. Obstet Gynecol. 2013; 121(1): 213-217.

    Article  Google Scholar 

  11. NIH Obestiy Research Task Force. Strategic plan for NIH obesity research. Washington DC; 2011.

  12. Healthy People 2010 Topics & Objectives. Accessed 3 October 2014; Available from: http://www.healthypeople.gov/2020/topicsobjectives2020/default.

  13. Pagoto S. The current state of lifestyle intervention implementation research: where do we go next? Transl Behav Med. 2011; 1(3): 401-405.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Montgomery KS et al. Women’s challenges with postpartum weight loss. Matern Child Health J. 2011; 15(8): 1176-1184.

    Article  PubMed  Google Scholar 

  15. Ciao AC, Latner JD, Durso LE. Treatment seeking and barriers to weight loss treatments of different intensity levels among obese and overweight individuals. Eat Weight Disord. 2012; 17(1): e9-e16.

    Article  CAS  PubMed  Google Scholar 

  16. Womble LG et al. A randomized controlled trial of a commercial internet weight loss program. Obes Res. 2004; 12(6): 1011-1018.

    Article  PubMed  Google Scholar 

  17. Harvey-Berino J et al. Internet delivered behavioral obesity treatment. Prev Med. 2010; 51: 123-128.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Chang T et al. The role of social media in online weight management: systematic review. J Med Internet Res. 2013; 15(11), e262.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Ashrafian H et al. Social networking strategies that aim to reduce obesity have achieved significant although modest results. Health Aff (Millwood). 2014; 33(9): 1641-1647.

    Article  Google Scholar 

  20. Gold B et al. Weight loss on the web: a pilot study comparing a structured behavioral intervention to a commercial program. Obesity. 2007; 15(1): 155-164.

    Article  PubMed  Google Scholar 

  21. Webber KH, Tate DF, Bowling JM. A randomized comparison of two motivationally enhanced Internet behavioral weight loss programs. Behav Res Ther. 2008; 46(9): 1090-1095.

    Article  PubMed  Google Scholar 

  22. Turner-McGrievy G, Tate D. Weight loss social support in 140 characters or less: use of an online social network in a remotely delivered weight loss intervention. Transl Behav Med: Pract, Policy Res. 2013; 3(3): 287-294.

    Article  Google Scholar 

  23. Turner-McGrievy GM et al. Comparison of traditional versus mobile app self-monitoring of physical activity and dietary intake among overweight adults participating in an mHealth weight loss program. J Am Med Inform Assoc. 2013; 20(3): 513-518.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Number of monthly active Twitter users worldwide from 1st quarter 2010 to 3rd quarter 2015 (in millions) 2015; Available from: http://www.statista.com/statistics/282087/number-of-monthly-active-twitter-users/.

  25. Social Networking Fact Sheet. 2014; Available from: http://www.pewinternet.org/fact-sheets/social-networking-fact-sheet/.

  26. Brenner J, Smith A. 72% of Online adults are social networking site users. 2013; Available from: http://www.pewinternet.org/2013/08/05/72-of-online-adults-are-social-networking-site-users/.

  27. Harris PA et al. Research electronic data capture (REDCap)—a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009; 42(2): 377-381.

    Article  PubMed  Google Scholar 

  28. Jensen MD et al. 2013 AHA/ACC/TOS guideline for the management of overweight and obesity in adults: a report of the American College of Cardiology/American Heart Association task force on practice guidelines and the Obesity Society. Circulation. 2014; 129(25 Suppl 2): S102-S138.

    Article  PubMed  Google Scholar 

  29. Pagoto S et al. The adoption and spread of a physical activity challenge through an online social network. J Phys Act Health. 2014; 11(3): 648-653.

    Article  PubMed  Google Scholar 

  30. Hsieh HF, Shannon SE. Three approaches to qualitative content analysis. Qual Health Res. 2005; 15(9): 1277-1288.

    Article  PubMed  Google Scholar 

  31. Hallgren KA. Computing inter-rater reliability for observational data: an overview and tutorial. Tutor Quant Methods Psychol. 2012; 8(1): 23-34.

    PubMed  PubMed Central  Google Scholar 

  32. Mailey E et al. Physical activity barriers and facilitators among working mothers and fathers. BMC Public Health. 2014; 14(1): 657.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Hutchesson MJ, Hulst J, Collins CE. Weight management interventions targeting young women: a systematic review. J Acad Nutr Diet. 2013; 113(6): 795-802.

    Article  PubMed  Google Scholar 

  34. Pagoto SL et al. Twitter-delivered behavioral weight loss interventions: a pilot series. JMIR Res Proc. 2015; 4(4), e123.

    Article  Google Scholar 

  35. Tate DF, Jackvony EH, Wing RR. Effects of Internet behavioral counseling on weight loss in adults at risk for type 2 diabetes: a randomized trial. JAMA. 2003; 289(14): 1833-1836.

    Article  PubMed  Google Scholar 

  36. Tate DF, Wing RR, Winett RA. Using Internet technology to deliver a behavioral weight loss program. JAMA. 2001; 285(9): 1172-1177.

    Article  CAS  PubMed  Google Scholar 

  37. Krukowski RA et al. Internet-based weight control: the relationship between web features and weight loss. Telemed J E Health. 2008; 14(8): 775-782.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Krukowski RA et al. Patterns of success: online self-monitoring in a web-based behavioral weight control program. Health Psychol. 2013; 32(2): 164-170.

    Article  PubMed  Google Scholar 

  39. Bennett GG et al. Web-based weight loss in primary care: a randomized controlled trial. Obesity (Silver Spring). 2010; 18(2): 308-313.

    Article  Google Scholar 

  40. Funk KL et al. Associations of internet website use with weight change in a long-term weight loss maintenance program. J Med Internet Res. 2010; 12(3), e29.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Eysenbach G. The law of attrition. J Med Internet Res. 2005; 7(1), e11.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Rafaeli S, Ravid G., Soroka V. De-lurking in virtual communities: a social communication network approach to measuring the effects of social and cultural capital. in System Sciences, 2004. Proceedings of the 37th Annual Hawaii International Conference on. 2004.

  43. Hwang KO et al. Social support in an Internet weight loss community. Int J Med Inform. 2010; 79(1): 5-13.

    Article  PubMed  Google Scholar 

  44. Duggan ME, Nicole B, Lampe C, Lenhart A, Madden M. Social Media Update 2014. Accessed 22 January 2015; Available from: http://www.pewinternet.org/2015/01/09/social-media-update-2014/.

  45. Pagoto S et al. Tweeting it off: characteristics of adults who tweet about a weight loss attempt. J Am Med Inform Assoc. 2014; 21(6): 1032-1037.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Lampe C, Ellison N, Steinfield C. A Face(book) in the crowd: social searching vs. social browsing, in Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work. Banff: ACM; 2006: 167-170.

    Google Scholar 

  47. Leonard A et al. Recruitment and retention of young women into nutrition research studies: practical considerations. Trials. 2014; 15: 23.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Quach S et al. The good, bad, and ugly of online recruitment of parents for health-related focus groups: lessons learned. J Med Internet Res. 2013; 15(11), e250.

    Article  PubMed  PubMed Central  Google Scholar 

  49. O’Connor A et al. Can I get a retweet please? Health research recruitment and the Twittersphere. J Adv Nurs. 2014; 70(3): 599-609.

    Article  PubMed  Google Scholar 

Download references

Acknowledgments

Support for this study provided by NIH grants KL2TR000160 (MEW), UL1TR000161 (RSX), K23HL107391 (AMB), and K23HL109620 (MCW).

Author information

Authors and Affiliations

  1. Division of Epidemiology of Chronic Diseases and Vulnerable Populations, Department of Quantitative Health Sciences, University of Massachusetts Medical School, 368 Plantation Street, AS8-1077, Worcester, MA, 01605, USA

    Molly E. Waring PhD & Rui S. Xiao MD PhD

  2. Department of Obstetrics & Gynecology, University of Massachusetts Medical School, Worcester, MA, USA

    Molly E. Waring PhD & Tiffany A. Moore Simas MD MPH MEd

  3. Department of Psychology, Rosalind Franklin University of Medicine & Science, North Chicago, IL, USA

    Kristin L. Schneider PhD

  4. Department of Preventive Medicine, Rush University Medical Center, Chicago, IL, USA

    Bradley M. Appelhans PhD

  5. Department of Pediatrics, University of Massachusetts Medical School, Worcester, MA, USA

    Tiffany A. Moore Simas MD MPH MEd

  6. Department of Psychology, East Carolina University, Greenville, NC, USA

    Matthew C. Whited PhD

  7. Department of Psychiatry & Human Behavior, Alpert Medical School of Brown University, Providence, RI, USA

    Andrew M. Busch PhD

  8. Centers for Behavioral and Preventive Medicine, The Miriam Hospital, Providence, RI, USA

    Andrew M. Busch PhD

  9. Division of Behavioral and Preventive Medicine, Department of Medicine, University of Massachusetts Medical School, Worcester, MA, USA

    Martinus M. Evans MS & Sherry L. Pagoto PhD

Authors
  1. Molly E. Waring PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kristin L. Schneider PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bradley M. Appelhans PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tiffany A. Moore Simas MD MPH MEd
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Rui S. Xiao MD PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Matthew C. Whited PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Andrew M. Busch PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Martinus M. Evans MS
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Sherry L. Pagoto PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Molly E. Waring PhD.

Ethics declarations

Compliance with ethical standards

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration and its later amendments.

Conflict of interest

The authors declare that they have no competing interests.

Additional information

Implications

For practice: Social media may be a novel platform by which to deliver behavioral programming for weight loss to patients.

For policy: Reimbursement policies should cover technology-based treatments to the extent that evidence supports them and patients are interested in them.

For research: Given interest among women of childbearing age in receiving weight loss programming via Twitter, research is needed to evaluate the efficacy of this approach.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Waring, M.E., Schneider, K.L., Appelhans, B.M. et al. Interest in a Twitter-delivered weight loss program among women of childbearing age. Behav. Med. Pract. Policy Res. 6, 277–284 (2016). https://doi.org/10.1007/s13142-015-0382-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13142-015-0382-4

Keywords

Search

Navigation