Social Support Patterns of Middle-Aged and Older Adults Within a Physical Activity App: Secondary Mixed Method Analysis

Zakkoyya H Lewis, Maria C Swartz, Eloisa Martinez, Elizabeth J Lyons, Zakkoyya H Lewis, Maria C Swartz, Eloisa Martinez, Elizabeth J Lyons

Abstract

Background: Physical activity (PA) is critical for maintaining independence and delaying mobility disability in aging adults. However, 27 to 44% of older adults in the United States are meeting the recommended PA level. Activity trackers are proving to be a promising tool to promote PA adherence through activity tracking and enhanced social interaction features. Although social support has been known to be an influential behavior change technique to promote PA, how middle-aged and older adults use the social interaction feature of mobile apps to provide virtual support to promote PA engagement remains mostly underexplored.

Objective: This study aimed to describe the social support patterns of middle-aged and older adults using a mobile app as part of a behavioral PA intervention.

Methods: Data from 35 participants (mean age 61.66 [SD 6] years) in a 12-week, home-based activity intervention were used for this secondary mixed method analysis. Participants were provided with a Jawbone Up24 activity monitor and an Apple iPad Mini installed with the UP app to facilitate self-monitoring and social interaction. All participants were given an anonymous account and encouraged to interact with other participants using the app. Social support features included comments and likes. Thematic coding was used to identify the type of social support provided within the UP app and characterize the levels of engagement from users. Participants were categorized as superusers or contributors, and passive participants were categorized as lurkers based on the literature.

Results: Over the 12-week intervention, participants provided a total of 3153 likes and 1759 comments. Most participants (n=25) were contributors, with 4 categorized as superusers and 6 categorized as lurkers. Comments were coded as emotional support, informational support, instrumental support, self-talk, and other, with emotional support being the most prevalent type.

Conclusions: Our cohort of middle-aged and older adults was willing to use the social network feature in an activity app to communicate with anonymous peers. Most of our participants were contributors. In addition, the social support provided through the activity app followed social support constructs. In sum, PA apps are a promising tool for delivering virtual social support to enhance PA engagement and have the potential to make a widespread impact on PA promotion.

Trial registration: ClinicalTrials.gov NCT01869348; https://ichgcp.net/clinical-trials-registry/NCT01869348.

Keywords: aged; fitness tracker; middle aged; physical activity; social support; technology.

Conflict of interest statement

Conflicts of Interest: None declared.

©Zakkoyya H Lewis, Maria C Swartz, Eloisa Martinez, Elizabeth J Lyons. Originally published in JMIR Aging (http://aging.jmir.org), 23.08.2019.

Figures

https://www.ncbi.nlm.nih.gov/pmc/articles/instance/6744818/bin/aging_v0i0e0_fig1.jpg
Social support themes. The size of each box represents the prevalence of the different comment themes (not to scale). Study themes were developed based on the work of Heaney and Israel, Cavallo et al, and Cousins et al.

References

    1. Keadle SK, McKinnon R, Graubard BI, Troiano RP. Prevalence and trends in physical activity among older adults in the United States: a comparison across three national surveys. Prev Med. 2016 Aug;89:37–43. doi: 10.1016/j.ypmed.2016.05.009.
    1. Nyman SR, Victor CR. Older people's participation in and engagement with falls prevention interventions in community settings: an augment to the Cochrane systematic review. Age Ageing. 2012 Jan;41(1):16–23. doi: 10.1093/ageing/afr103.
    1. Tate DF, Lyons EJ, Valle CG. High-tech tools for exercise motivation: use and role of technologies such as the internet, mobile applications, social media, and video games. Diabetes Spectr. 2015 Jan;28(1):45–54. doi: 10.2337/diaspect.28.1.45.
    1. Lyons EJ, Lewis ZH, Mayrsohn BG, Rowland JL. Behavior change techniques implemented in electronic lifestyle activity monitors: a systematic content analysis. J Med Internet Res. 2014 Aug 15;16(8):e192. doi: 10.2196/jmir.3469.
    1. Eynon M, Foad J, Downey J, Bowmer Y, Mills H. Assessing the psychosocial factors associated with adherence to exercise referral schemes: a systematic review. Scand J Med Sci Sports. 2019 May;29(5):638–50. doi: 10.1111/sms.13403.
    1. Greaves CJ, Sheppard KE, Abraham C, Hardeman W, Roden M, Evans PH, Schwarz P, IMAGE Study Group Systematic review of reviews of intervention components associated with increased effectiveness in dietary and physical activity interventions. BMC Public Health. 2011 Feb 18;11:119. doi: 10.1186/1471-2458-11-119.
    1. Sarason IG, Levine HM, Basham RB, Sarason BR. Assessing social support: the social support questionnaire. J Pers Soc Psychol. 1983;44(1):127–39. doi: 10.1037/0022-3514.44.1.127.
    1. Ståhl T, Rütten A, Nutbeam D, Bauman A, Kannas L, Abel T, Lüschen G, Rodriquez DJ, Vinck J, van der Zee J. The importance of the social environment for physically active lifestyle--results from an international study. Soc Sci Med. 2001 Jan;52(1):1–10. doi: 10.1016/S0277-9536(00)00116-7.
    1. Bors P, Dessauer M, Bell R, Wilkerson R, Lee J, Strunk SL. The Active Living by Design national program: community initiatives and lessons learned. Am J Prev Med. 2009 Dec;37(6 Suppl 2):S313–21. doi: 10.1016/j.amepre.2009.09.027.
    1. McAuley E, Jerome GJ, Elavsky S, Marquez DX, Ramsey SN. Predicting long-term maintenance of physical activity in older adults. Prev Med. 2003 Aug;37(2):110–8. doi: 10.1016/S0091-7435(03)00089-6.
    1. Michie S, Richardson M, Johnston M, Abraham C, Francis J, Hardeman W, Eccles MP, Cane J, Wood CE. The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: building an international consensus for the reporting of behavior change interventions. Ann Behav Med. 2013 Aug;46(1):81–95. doi: 10.1007/s12160-013-9486-6.
    1. Swartz MC, Lewis ZH, Swartz MD, Martinez E, Lyons EJ. Brief report: active ingredients for adherence to a tracker-based physical activity intervention in older adults. J Appl Gerontol. 2019 Jul;38(7):1023–34. doi: 10.1177/0733464817739350.
    1. Deci EL, Ryan RM. The 'what' and 'why' of goal pursuits: human needs and the self-determination of behavior. Psychol Inq. 2000 Oct;11(4):227–68. doi: 10.1207/s15327965pli1104_01.
    1. Vallerand RJ. A hierarchical model of intrinsic and extrinsic motivation for sport and physical activity. In: Hagger MS, Chatzisarantis NL, editors. Intrinsic Motivation and Self-Determination in Exercise and Sport. Champaign, IL, US: Human Kinetics; 2007. pp. 356–63.
    1. George M, Eys MA, Oddson B, Roy-Charland A, Schinke RJ, Bruner MW. The role of self-determination in the relationship between social support and physical activity intentions. J Appl Soc Psychol. 2013 Apr 30;43(6):1333–41. doi: 10.1111/jasp.12142.
    1. Heaney CA, Israel BA. Social networks and social support. In: Glanz K, Rimer BK, Viswanath K, editors. Health Behavior and Health Education: Theory, Research, and Practice. San Francisco, CA, US: Jossey-Bass; 2008. pp. 189–219.
    1. Gibson AC, Foster C. The role of self-talk in the awareness of physiological state and physical performance. Sports Med. 2007;37(12):1029–44. doi: 10.2165/00007256-200737120-00003.
    1. Cousins SO, Gillis MM. 'Just do it… before you talk yourself out of it': the self-talk of adults thinking about physical activity. Psychol Sport Exerc. 2005 May;6(3):313–34. doi: 10.1016/j.psychsport.2004.03.001.
    1. Cavallo DN, Tate DF, Ries AV, Brown JD, DeVellis RF, Ammerman AS. A social media-based physical activity intervention: a randomized controlled trial. Am J Prev Med. 2012 Nov;43(5):527–32. doi: 10.1016/j.amepre.2012.07.019.
    1. de la Peña A, Quintanilla C. Share, like and achieve: the power of Facebook to reach health-related goals. Int J Consum Stud. 2015 Aug 24;39(5):495–505. doi: 10.1111/ijcs.12224.
    1. McNeill LH, Kreuter MW, Subramanian SV. Social environment and physical activity: a review of concepts and evidence. Soc Sci Med. 2006 Aug;63(4):1011–22. doi: 10.1016/j.socscimed.2006.03.012.
    1. Woolley P, Peterson M. Efficacy of a health-related Facebook social network site on health-seeking behaviors. Soc Mar Q. 2012 May 2;18(1):29–39. doi: 10.1177/1524500411435481.
    1. Nielsen J. Nielsen Norman Group. 2006. [2019-05-19]. Participation Inequality: The 90-9-1 Rule for Participation Inequality in Social Media and Online Communities
    1. van Mierlo T. The 1% rule in four digital health social networks: an observational study. J Med Internet Res. 2014 Feb 4;16(2):e33. doi: 10.2196/jmir.2966.
    1. Carron-Arthur B, Cunningham JA, Griffiths KM. Describing the distribution of engagement in an internet support group by post frequency: a comparison of the 90-9-1 principle and Zipf's law. Internet Interv. 2014 Oct;1(4):165–8. doi: 10.1016/j.invent.2014.09.003. doi: 10.1016/j.invent.2014.09.003.
    1. Ballantine PW, Stephenson RJ. Help me, I'm fat! Social support in online weight loss networks. J Consumer Behav. 2011 Dec 23;10(6):332–7. doi: 10.1002/cb.374.
    1. Edelmann N. Reviewing the definitions of 'lurkers' and some implications for online research. Cyberpsychol Behav Soc Netw. 2013 Sep;16(9):645–9. doi: 10.1089/cyber.2012.0362.
    1. Hwang KO, Ning J, Trickey AW, Sciamanna CN. Website usage and weight loss in a free commercial online weight loss program: retrospective cohort study. J Med Internet Res. 2013 Jan 15;15(1):e11. doi: 10.2196/jmir.2195.
    1. Schlosser AE. Posting versus lurking: communicating in a multiple audience context. J Consumer Res. 2005 Sep 1;32(2):260–5. doi: 10.1086/432235.
    1. Balatsoukas P, Kennedy CM, Buchan I, Powell J, Ainsworth J. The role of social network technologies in online health promotion: a narrative review of theoretical and empirical factors influencing intervention effectiveness. J Med Internet Res. 2015 Jun 11;17(6):e141. doi: 10.2196/jmir.3662.
    1. Kullgren JT, Harkins KA, Bellamy SL, Gonzales A, Tao Y, Zhu J, Volpp KG, Asch DA, Heisler M, Karlawish J. A mixed-methods randomized controlled trial of financial incentives and peer networks to promote walking among older adults. Health Educ Behav. 2014 Oct;41(1 Suppl):43S–50S. doi: 10.1177/1090198114540464.
    1. Immonen M, Sachinopoulou A, Kaartinen J, Konttila A. Using technology for improving the social and physical activity-level of the older adults. In: Wichert R, van Laerhoven K, Gelissen J, editors. Constructing Ambient Intelligence: Aml 2011 Workshops. Berlin, Heidelberg: Springer; 2012. pp. 201–5.
    1. Pew Research Center. 2018. [2018-05-20]. Social Media Fact Sheet
    1. Lyons EJ, Swartz MC, Lewis ZH, Martinez E, Jennings K. Feasibility and acceptability of a wearable technology physical activity intervention with telephone counseling for mid-aged and older adults: a randomized controlled pilot trial. JMIR Mhealth Uhealth. 2017 Mar 6;5(3):e28. doi: 10.2196/mhealth.6967.
    1. Warburton DE, Bredin SS, Jamnik VK, Gledhill N. Validation of the PAR-Q+ and ePARmed-X+ Health Fitness J Can. 2011;4(2):38–46. doi: 10.14288/hfjc.v4i2.151. doi: 10.14288/hfjc.v4i2.151.
    1. Lewis ZH, Ottenbacher KJ, Fisher SR, Jennings K, Brown AF, Swartz MC, Lyons EJ. Testing activity monitors' effect on health: study protocol for a randomized controlled trial among older primary care patients. JMIR Res Protoc. 2016 Apr 29;5(2):e59. doi: 10.2196/resprot.5454.
    1. Hsieh HF, Shannon SE. Three approaches to qualitative content analysis. Qual Health Res. 2005 Nov;15(9):1277–88. doi: 10.1177/1049732305276687.
    1. Arigo D. Promoting physical activity among women using wearable technology and online social connectivity: a feasibility study. Health Psychol Behav Med. 2015 Dec 31;3(1):391–409. doi: 10.1080/21642850.2015.1118350.
    1. Colón-Semenza C, Latham NK, Quintiliani LM, Ellis TD. Peer coaching through mhealth targeting physical activity in people with Parkinson disease: feasibility study. JMIR Mhealth Uhealth. 2018 Feb 15;6(2):e42. doi: 10.2196/mhealth.8074.
    1. Seeman TE, Berkman LF, Charpentier PA, Blazer DG, Albert MS, Tinetti ME. Behavioral and psychosocial predictors of physical performance: MacArthur studies of successful aging. J Gerontol A Biol Sci Med Sci. 1995 Jul;50(4):M177–83. doi: 10.1093/gerona/50a.4.m177.
    1. Sherwood NE, Jeffery RW. The behavioral determinants of exercise: implications for physical activity interventions. Annu Rev Nutr. 2000;20:21–44. doi: 10.1146/annurev.nutr.20.1.21.
    1. Lewis ZH, Lyons EJ, Jarvis JM, Baillargeon J. Using an electronic activity monitor system as an intervention modality: a systematic review. BMC Public Health. 2015 Jun 24;15:585. doi: 10.1186/s12889-015-1947-3.

Source: PubMed

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