Advertisement

Past, Present, and Future of eHealth and mHealth Research to Improve Physical Activity and Dietary Behaviors

      Abstract

      Because physical inactivity and unhealthy diets are highly prevalent, there is a need for cost-effective interventions that can reach large populations. Electronic health (eHealth) and mobile health (mHealth) solutions have shown promising outcomes and have expanded rapidly in the past decade. The purpose of this report is to provide an overview of the state of the evidence for the use of eHealth and mHealth in improving physical activity and nutrition behaviors in general and special populations. The role of theory in eHealth and mHealth interventions is addressed, as are methodological issues. Key recommendations for future research in the field of eHealth and mHealth are provided.

      Key Words

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access

      SNEB Member Login

      SNEB Members, full access to the journal is a member benefit. Login via the SNEB Website to access all journal content and features.

      Subscribe:

      Subscribe to Journal of Nutrition Education and Behavior
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

      1. World Health Organization and Food and Agriculture Organization of the United Nations. Diet, nutrition, and the prevention of chronic diseases. Report of a joint WHO/FAO Expert consultation. Geneva, Switzerland: World Health Organization. Report 916; 2003. http://www.who.int/dietphysicalactivity/publications/trs916/en/. Accessed July 2, 2015.

      2. World Cancer Research Fund/American Institute for Cancer Research. Food, Nutrition, Physical Activity, and the Prevention of Cancer: a Global Perspective. Washington, DC: World Cancer Research Fund/American Institute for Cancer Research; 2007. http://www.dietandcancerreport.org/cancer_resource_center/downloads/Second_Expert_Report_full.pdf. Accessed July 15, 2015.

        • Lee I.M.
        • Shiroma E.J.
        • Lobelo F.
        • et al.
        Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy.
        Lancet. 2012; 380: 219-229
        • Lim S.S.
        • Vos T.
        • Flaxman A.D.
        • et al.
        A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010.
        Lancet. 2012; 380: 2224-2260
        • Pratt M.
        • Norris J.
        • Lobelo F.
        • Roux L.
        • Wang G.
        The cost of physical inactivity: moving into the 21st century.
        Br J Sports Med. 2014; 48: 171-173
        • Rayner M.
        • Scarborough P.
        The burden of food related ill health in the UK.
        J Epidemiol Commun Health. 2005; 59: 1054-1057
      3. World Health Organization. Global action plan for the prevention and control of non-communicable diseases 2013-2020. Geneva, Switzerland: World Health Organization; 2013. http://www.who.int/nmh/events/ncd_action_plan/en. Accessed July 5, 2015.

        • Sweet S.N.
        • Fortier M.S.
        Improving physical activity and dietary behaviours with single or multiple health behaviour interventions? A synthesis of meta-analyses and reviews.
        Int J Environ Res Public Health. 2010; 7: 1720-1743
      4. Cavill N, Ells L. Treating adult obesity through lifestyle change interventions. A briefing paper for commissioners. Oxford, UK: National Obesity Observatory; 2010. http://www.noo.org.uk/uploads/doc/vid_5189_Adult_weight_management_Final_220210.pdf. Accessed July 7, 2015.

      5. Vos T, Carter R, Barendregt J, et al. Assessing cost-effectiveness in prevention (ACE–prevention): Final report. Melbourne, Australia: University of Queensland, Brisbane and Deakin University; 2010. http://www.sph.uq.edu.au/docs/BODCE/ACE-P/ACE-Prevention_final_report.pdf. Accessed June 29, 2015.

      6. Centers for Disease Control and Prevention. Strategies to prevent obesity and other chronic diseases: the CDC guide to strategies to increase physical activity in the community. Atlanta, GA: Centers for Disease Control and Prevention; 2011. http://www.cdc.gov/obesity/downloads/PA_2011_WEB.pdf. Accessed June 29, 2015.

      7. World Health Organization: Global strategy on diet, physical activity and health. Geneva, Switzerland: World Health Organization; 2004. http://www.who.int/dietphysicalactivity/strategy/eb11344/strategy_english_web.pdf. Accessed July 1, 2015.

        • Strecher V.
        Internet methods for delivering behavioral and health-related interventions (eHealth).
        Annu Rev Clin Psychol. 2007; 3: 53-76
        • van Heerden A.
        • Tomlinson M.
        • Swartz L.
        Point of care in your pocket: a research agenda for the field of m-health.
        Bull World Health Org. 2012; 90: 393-394
        • Danaher B.G.
        • Brendryen H.
        • Seeley J.R.
        • Tyler M.S.
        • Woolley T.
        From black box to toolbox: outlining device functionality, engagement activities, and the pervasive information architecture of mHealth interventions.
        Internet Interv. 2015; 2: 91-101
      8. Eng TR. The eHealth landscape: a terrain map of emerging information and communication technologies in health and health care. Princeton, NJ: Robert Wood Johnson Foundation; 2001. http://www.hetinitiative.org/media/pdf/eHealth.pdf. Accessed June 30, 2015.

      9. World Health Organization: New horizons for health through mobile technologies: second global survey on eHealth. Geneva, Switzerland: World Health Organization; 2011. http://www.who.int/goe/publications/goe_mhealth_web.pdf. Accessed July 4, 2015.

      10. International Telecommunication Union: the world in 2014. ICT facts and figures. Geneva, Switzerland: International Telecommunication Union; 2014. http://www.itu.int/en/ITU-D/Statistics/Documents/facts/ICTFactsFigures2014-e.pdf. Accessed March 15, 2015.

        • Davies C.A.
        • Spence J.C.
        • Vandelanotte C.
        • Caperchione C.M.
        • Mummery W.K.
        Meta-analysis of internet-delivered interventions to increase physical activity levels.
        Int J Behav Nutr Phys Act. 2012; 9: 52https://doi.org/10.1186/1479-5868-9-52
        • Vandelanotte C.
        • Spathonis K.M.
        • Eakin E.G.
        • Owen N.
        Website-delivered physical activity interventions: a review of the literature.
        Am J Prev Med. 2007; 33: 54-64
        • Neve M.
        • Morgan P.J.
        • Jones P.R.
        • Collins C.E.
        Effectiveness of web-based interventions in achieving weight loss and weight loss maintenance in overweight and obese adults: a systematic review with meta-analysis.
        Obes Rev. 2010; 11: 306-321
        • Norman G.J.
        • Zabinski M.F.
        • Adams M.A.
        • Rosenberg D.E.
        • Yaroch A.L.
        • Atienza A.A.
        A review of eHealth interventions for physical activity and dietary behavior change.
        Am J Prev Med. 2007; 33: 336-345
        • Bort-Roig J.
        • Gilson N.D.
        • Puig-Ribera A.
        • Contreras R.S.
        • Trost S.G.
        Measuring and influencing physical activity with smartphone technology: a systematic review.
        Sports Med. 2014; 44: 671-686
        • Laranjo L.
        • Arguel A.
        • Neves A.L.
        • et al.
        The influence of social networking sites on health behavior change: a systematic review and meta-analysis.
        J Am Med Inform Assoc. 2014; 22: 243-256
        • Maher C.A.
        • Lewis L.K.
        • Ferrar K.
        • Marshall S.
        • de Bourdeaudhuij I.
        • Vandelanotte C.
        Are health behavior change interventions that use online social networks effective? A systematic review.
        J Med Internet Res. 2014; 16: e40
        • Harris J.
        • Felix L.
        • Miners A.
        • et al.
        Adaptive e-learning to improve dietary behaviour: a systematic review and cost-effectiveness analysis.
        Health Technol Assess. 2011; 15: 1-160
        • Tang J.
        • Abraham C.
        • Greaves C.
        • Yates T.
        Self-directed interventions to promote weight loss: a systematic review of reviews.
        J Med Internet Res. 2014; 16: e58
        • Lau P.W.C.
        • Lau E.Y.
        • Wong D.P.
        • Ransdell L.
        A systematic review of information and communication technology–based interventions for promoting physical activity behavior change in children and adolescents.
        J Med Internet Res. 2011; 13: e48
        • Hou S.
        • Charlery S.R.
        • Roberson K.
        Systematic literature review of Internet interventions across health behaviors.
        Health Psychol Behav Med. 2014; 2: 455-481
        • Cushing C.C.
        • Steele R.G.
        A meta-analytic review of eHealth interventions for pediatric health promoting and maintaining behaviors.
        J Pediatr Psychol. 2010; 35: 937-949
        • Kohl L.F.M.
        • Crutzen R.
        • de Vries N.K.
        Online prevention aimed at lifestyle behaviors: a systematic review of reviews.
        J Med Internet Res. 2013; 15: e146
        • Neville L.M.
        • O’Hara B.
        • Milat A.
        Computer-tailored physical activity behavior change interventions targeting adults: a systematic review.
        Int J Behav Nutr Phys Act. 2009; 6: 30https://doi.org/10.1186/1479-5868-6-30
        • Neville L.M.
        • O’Hara B.
        • Milat A.J.
        Computer-tailored dietary behaviour change interventions: a systematic review.
        Health Educ Res. 2009; 24: 699-720
        • Enwald H.P.K.
        • Huotari M.A.
        Preventing the obesity epidemic by second generation tailored health communication: an interdisciplinary review.
        J Med Internet Res. 2010; 12: e24
        • Krebs P.
        • Prochaska J.O.
        • Rossi J.S.
        A meta-analysis of computer-tailored interventions for health behavior change.
        Prev Med. 2010; 51: 214-221
        • Brouwer W.
        • Kroeze W.
        • Crutzen R.
        • et al.
        Which intervention characteristics are related to more exposure to internet-delivered healthy lifestyle promotion interventions? A systematic review.
        J Med Internet Res. 2011; 13: e2
        • Wantland D.J.
        • Portillo C.J.
        • Holzemer W.L.
        • Slaughter R.
        • McGhee E.M.
        The effectiveness of Web-based vs. non-Web-based interventions: a meta-analysis of behavioral change outcomes.
        J Med Internet Res. 2004; 6: e40
        • Webb T.
        • Joseph J.
        • Yardley L.
        • Michie S.
        Using the internet to promote health behavior change: a systematic review and meta-analysis of the impact of theoretical basis, use of behavior change techniques, and mode of delivery on efficacy.
        J Med Internet Res. 2010; 12: e4
        • Vandelanotte C.
        • Kirwan M.
        • Rebar A.
        • et al.
        Examining the use of evidence-based and social media supported tools in freely accessible physical activity intervention websites.
        Int J Behav Nutr Phys Act. 2014; 11: 105https://doi.org/10.1186/s12966-014-0105-0
        • Kumar S.
        • Nilsen W.J.
        • Abernethy A.
        • et al.
        Mobile health technology evaluation: the mHealth evidence workshop.
        Am J Prev Med. 2013; 45: 228-236
        • Fjeldsoe B.S.
        • Marshall A.L.
        • Miller Y.D.
        Behavior change interventions delivered by mobile telephone short-message service.
        Am J Prev Med. 2009; 36: 165-173
        • Fanning J.
        • Mullen S.P.
        • McAuley E.
        Increasing physical activity with mobile devices: a meta-analysis.
        J Med Internet Res. 2012; 14: e161
        • O’Reilly G.A.
        • Spruijt-Metz D.
        Current mHealth technologies for physical activity assessment and promotion.
        Am J Prev Med. 2013; 45: 501-507
        • Sharp D.B.
        • Allman-Farinelli M.
        Feasibility and validity of mobile phones to assess dietary intake.
        Nutrition. 2014; 30: 1257-1266
        • Head K.J.
        • Noar S.M.
        • Iannarino N.T.
        • Harrington N.G.
        Efficacy of text messaging-based interventions for health promotion: a meta-analysis.
        Soc Sci Med. 2013; 97: 41-48
        • Blackman K.C.
        • Zoellner J.
        • Berrey L.M.
        • et al.
        Assessing the internal and external validity of mobile health physical activity promotion interventions: a systematic literature review using the RE-AIM framework.
        J Med Internet Res. 2013; 15: e224
        • Weber Buchholz S.
        • Wilbur J.
        • Ingram D.
        • Fogg L.
        Physical activity text messaging interventions in adults: a systematic review.
        Worldviews Evid Based Nurs. 2013; 10: 163-173
      11. Muntaner A, Vidal-Conti J, Palou P. Increasing physical activity through mobile device interventions: a systematic review [published online ahead of print February 3, 2015]. Health Informatics J. http://dx.doi.org/10.1177/1460458214567004.

        • Lewis Z.H.
        • Lyons E.J.
        • Jarvis J.M.
        • Baillargeon J.
        Using an electronic activity monitor system as an intervention modality: a systematic review.
        BMC Public Health. 2015; 15: 585https://doi.org/10.1186/s12889-015-1947-3
        • Lyzwinski L.N.
        A systematic review and meta-analysis of mobile devices and weight loss with an intervention content analysis.
        J Pers Med. 2014; 4: 311-385
        • Siopis G.
        • Chey T.
        • Allman-Farinelli M.
        A systematic review and meta-analysis of interventions for weight management using text messaging.
        J Hum Nutr Diet. 2015; 28: 1-15
        • Gurman T.A.
        • Rubin S.E.
        • Roess A.A.
        Effectiveness of mHealth behavior change communication interventions in developing countries: a systematic review of the literature.
        J Health Commun. 2012; 17: 82-104
        • Conroy D.E.
        • Yang C.
        • Maher J.P.
        Behavior change techniques in top-ranked mobile apps for physical activity.
        Am J Prev Med. 2014; 46: 649-652
        • Cowan L.T.
        • Wagenen Van
        • Sarah A.
        • et al.
        Apps of steel: are exercise apps providing consumers with realistic expectations? A content analysis of exercise apps for presence of behavior change theory.
        Health Educ Behav. 2013; 40: 133-139
        • Direito A.
        • Dale L.P.
        • Shields E.
        • Dobson R.
        • Whittaker R.
        • Maddison R.
        Do physical activity and dietary smartphone applications incorporate evidence-based behaviour change techniques.
        BMC Public Health. 2014; 14: 646https://doi.org/10.1186/1471-2458-14-646
        • Lyons E.J.
        • Lewis Z.H.
        • Mayrsohn B.G.
        • Rowland J.L.
        Behavior change techniques implemented in electronic lifestyle activity monitors: a systematic content analysis.
        J Med Internet Res. 2014; 16: e192
        • Middelweerd A.
        • Mollee J.S.
        • van der Wal C.
        • Brug J.
        • Te Velde S.J.
        Apps to promote physical activity among adults: a review and content analysis.
        Int J Behav Nutr Phys Act. 2014; 11: 97https://doi.org/10.1186/s12966-014-0097-9
        • Abraham C.
        • Michie S.
        A taxonomy of behavior change techniques used in interventions.
        Health Psychol. 2008; 27: 379-387
        • Michie S.
        • Ashford S.
        • Sniehotta F.F.
        • Dombrowski S.U.
        • Bishop A.
        • French D.P.
        A refined taxonomy of behaviour change techniques to help people change their physical activity and healthy eating behaviours: the CALO-RE taxonomy.
        Psychol Health. 2011; 26: 1479-1498
        • Michie S.
        • Abraham C.
        • Whittington C.
        • McAteer J.
        • Gupta S.
        Effective techniques in healthy eating and physical activity interventions: a meta-regression.
        Health Psychol. 2009; 28: 690-701
        • Wong S.S.
        • Meng Y.
        • Hongu N.
        • Loprinzi P.
        Smart applications to track and record physical activity: Implications for obesity treatment.
        Smart Homecare Technology and TeleHealth. 2014; 2: 77-91
        • Hall C.S.
        • Fottrell E.
        • Wilkinson S.
        • Byass P.
        Assessing the impact of mHealth interventions in low-and middle-income countries—what has been shown to work.
        Global Health Action. 2014; 7: 25606
        • Kim B.H.
        • Glanz K.
        Text messaging to motivate walking in older African Americans: a randomized controlled trial.
        Am J Prev Med. 2013; 44: 71-75
        • Blumenthal D.
        • Mort E.
        • Edwards J.
        The efficacy of primary care for vulnerable population groups.
        Health Serv Res. 1995; 30: 253-273
        • Weitz T.A.
        • Freund K.M.
        • Wright L.
        Identifying and caring for underserved populations: experience of the National Centers of Excellence in Women's Health.
        J Womens Health Gend Based Med. 2001; 10: 937-952
        • Steinberg D.M.
        • Levine E.L.
        • Askew S.
        • Foley P.
        • Bennett G.G.
        Daily text messaging for weight control among racial and ethnic minority women: randomized controlled pilot study.
        J Med Internet Res. 2013; 15: e244
        • Nollen N.L.
        • Mayo M.S.
        • Carlson S.E.
        • Rapoff M.A.
        • Goggin K.J.
        • Ellerbeck E.F.
        Mobile technology for obesity prevention: a randomized pilot study in racial- and ethnic-minority girls.
        Am J Prev Med. 2014; 46: 404-408
        • Bennett G.G.
        • Steinberg M.
        • Stoute C.
        • et al.
        Electronic health (eHealth) interventions for weight management among racial/ethnic minority adults: a systematic review.
        Obes Rev. 2014; 15: 146-158
        • Müller A.M.
        • Khoo S.
        Non-face-to-face physical activity interventions in older adults: a systematic review.
        Int J Behav Nutr Phys Act. 2014; 11: 35
        • Geil N.M.
        • Rosenberg D.E.
        • Demiris G.
        • LaCroix A.Z.
        • Patel K.V.
        Patterns of technology use among older adults with and without disabilities.
        Gerontologist. 2015; 55: 412-421
        • Maher C.A.
        • Williams M.T.
        • Olds T.
        • Lane A.E.
        An internet-based physical activity intervention for adolescents with cerebral palsy: a randomized controlled trial.
        Dev Med Child Neurol. 2010; 52: 448-455
        • Maher D.
        • Ford N.
        • Unwin N.
        • Frontières M.S.
        Priorities for developing countries in the global response to non-communicable diseases.
        Global Health. 2012; 8: 14
        • Dalkou M.
        • Nikopoulou V.
        • Panagopoulou E.
        Why mHealth interventions are the new trend in health psychology? Effectiveness, applicability and critical points.
        Eur Health Psychol. 2015; 17: 129-136
        • Rotheram-Borus M.J.
        • Tomlinson M.
        • Gwegwe M.
        • Comulada W.S.
        • Kaufman N.
        • Keim M.
        Diabetes Buddies: Peer support through a mobile phone buddy system.
        Diabetes Educ. 2012; 38: 357-365
        • Ramachandran A.
        • Snehalatha C.
        • Ram J.
        • et al.
        Effectiveness of mobile phone messaging in prevention of type 2 diabetes by lifestyle modification in men in India: A prospective, parallel-group, randomised controlled trial.
        Lancet Diabetes Endocrinol. 2013; 1: 191-198
        • Viswanath K.
        • McCloud R.
        • Minsky S.
        • et al.
        Internet use, browsing, and the urban poor: implications for cancer control.
        J Natl Cancer Inst Monogr. 2013; 47: 199-205
        • Viswanath K.
        • Emmons K.M.
        Message effects and social determinants of health: its application to cancer disparities.
        J Commun. 2006; 56: 238-264
        • Nagler R.H.
        • Ramanadhan S.
        • Minsky S.
        • Viswanath K.
        Recruitment and retention for community-based eHealth interventions with populations of low socioeconomic position: strategies and challenges.
        J Commun. 2013; 63: 201-220
        • Prestwich A.
        • Sniehotta F.F.
        • Whittington C.
        • Dombrowski S.U.
        • Rogers L.
        • Michie S.
        Does theory influence the effectiveness of health behavior interventions? Meta-analysis.
        Health Psychol. 2014; 33: 465-474
        • Bartholomew L.K.
        • Parcel G.S.
        • Kok G.
        Intervention mapping: a process for developing theory and evidence-based health education programs.
        Health Educ Behav. 1998; 25: 545-563
        • Michie S.
        • Johnston M.
        • Francis J.
        • Hardeman W.
        • Eccles M.
        From theory to intervention: mapping theoretically derived behavioural determinants to behaviour change techniques.
        Appl Psychol. 2008; 57: 660-680
        • Glanz K.
        • Bishop D.B.
        The role of behavioral science theory in development and implementation of public health interventions.
        Annu Rev Publ Health. 2010; 31: 399-418
        • Brug J.
        • Oenema A.
        • Ferreira I.
        Theory, evidence and Intervention Mapping to improve behavior nutrition and physical activity interventions.
        Int J Behav Nutr Phys Act. 2005; 2: 2
        • Kelders S.M.
        • Kok R.N.
        • Ossebaard H.C.
        • van Gemert-Pijnen J.E.
        Persuasive system design does matter: a systematic review of adherence to web-based interventions.
        J Med Internet Res. 2012; 14: e152
        • Kuijpers W.
        • Groen W.G.
        • Aaronson N.K.
        • van Harten W.H.
        A systematic review of web-based interventions for patient empowerment and physical activity in chronic diseases: relevance for cancer survivors.
        J Med Internet Res. 2013; 15: e37
        • Petty R.E.
        • Wheeler S.C.
        • Tormala Z.L.
        Persuasion and attitude change.
        in: Mellon T. Learner M.J. Handbook of Psychology: Personality and Social Psychology. Wiley, Hoboken, NJ2003: 282-353
        • O’Brien H.L.
        • Toms E.G.
        What is user engagement? A conceptual framework for defining user engagement with technology.
        J Am Soc Inf Sci Technol. 2008; 59: 938-955
        • Petty R.E.
        • Barden J.
        • Wheeler S.C.
        The elaboration likelihood model of persuasion: health promotions that yield sustainable behavioral change.
        in: DiClemente R.J. Crosby R.A. Kegler M.C. Emerging Theories in Health Promotion Practice and Research. Josey-Bass, San Francisco, CA2009: 185-214
        • Ritterband L.M.
        • Thorndike F.P.
        • Cox D.J.
        • Kovatchev B.P.
        • Gonder-Frederick L.A.
        A behavior change model for internet interventions.
        Ann Behav Med. 2009; 38: 18-27
        • Short C.E.
        • Rebar A.L.
        • Plotnikoff R.C.
        • Vandelanotte C.
        Designing engaging online behaviour change interventions: a proposed model of user engagement.
        Eur Health Psychol. 2015; 17: 32-38
        • Crutzen R.
        • Ruiter R.A.C.
        • de Vries N.K.
        Can interest and enjoyment help to increase use of Internet-delivered interventions?.
        Psychol Health. 2014; 29: 1227-1244
        • Oinas-Kukkonen H.
        • Harjumaa M.
        Persuasive systems design: key issues, process model, and system features.
        Communications of the Association for Information Systems. 2009; 24: 485-500
        • Eysenbach G.
        • CONSORT-EHEATH Group
        CONSORT-EHEALTH: improving and standardizing evaluation reports of Web-based and mobile health interventions.
        J Med Internet Res. 2011; 13: e126
        • Lohtia R.
        • Donthu N.
        • Hershberger E.K.
        The impact of content and design elements on banner advertising click-through rates.
        J Advert Res. 2003; 43: 410-418
        • Douet L.
        • Milne R.
        • Anstee S.
        • Habens F.
        • Young A.
        • Wright S.
        The completeness of intervention descriptions in published National Institute of Health Research HTA-funded trials: a cross-sectional study.
        BMJ Open. 2014; 4: e003713
        • Zirger B.J.
        • Maidique M.A.
        A model of new product development: an empirical test.
        Manage Sci. 1990; 36: 867-883
      12. Center for Open Science: Open Science Framework. Project management with collaborators, project sharing with the public. https://osf.io/. Accessed July 5, 2015.

        • Vandelanotte C.
        • Maher C.A.
        Why we need more than just randomized controlled trials to establish the effectiveness of online social networks for health behaviour change.
        Am J Health Promot. 2015; 30: 74-76
        • Cobb N.K.
        • Poirier J.
        Effectiveness of a multimodal online well-being intervention: a randomized controlled trial.
        Am J Prev Med. 2014; 46: 41-48
        • Thompson F.E.
        • Subar A.F.
        • Loria C.M.
        • Baranowski T.
        Need for technological innovation in dietary assessment.
        J Am Diet Assoc. 2010; 110: 48-51
        • Free C.
        • Phillips G.
        • Galli L.
        • Watson L.
        • et al.
        The effectiveness of mobile-health technology-based health behaviour change or disease management interventions for health care consumers: a systematic review.
        PLoS Med. 2013; 10: e1001362
      13. Nelson-Field K. Viral Marketing: The Science of Sharing. 1st ed. Oxford University Press, South Melbourne, Australia2013