Social cognitive determinants of nutrition and physical activity among web-health users enrolling in an online intervention: the influence of social support, self-efficacy, outcome expectations, and self-regulation

Eileen Smith Anderson-Bill, Richard A Winett, Janet R Wojcik, Eileen Smith Anderson-Bill, Richard A Winett, Janet R Wojcik

Abstract

Background: The Internet is a trusted source of health information for growing majorities of Web users. The promise of online health interventions will be realized with the development of purely online theory-based programs for Web users that are evaluated for program effectiveness and the application of behavior change theory within the online environment. Little is known, however, about the demographic, behavioral, or psychosocial characteristics of Web-health users who represent potential participants in online health promotion research. Nor do we understand how Web users' psychosocial characteristics relate to their health behavior-information essential to the development of effective, theory-based online behavior change interventions.

Objective: This study examines the demographic, behavioral, and psychosocial characteristics of Web-health users recruited for an online social cognitive theory (SCT)-based nutrition, physical activity, and weight gain prevention intervention, the Web-based Guide to Health (WB-GTH).

Methods: Directed to the WB-GTH site by advertisements through online social and professional networks and through print and online media, participants were screened, consented, and assessed with demographic, physical activity, psychosocial, and food frequency questionnaires online (taking a total of about 1.25 hours); they also kept a 7-day log of daily steps and minutes walked.

Results: From 4700 visits to the site, 963 Web users consented to enroll in the study: 83% (803) were female, participants' mean age was 44.4 years (SD 11.03 years), 91% (873) were white, and 61% (589) were college graduates; participants' median annual household income was approximately US $85,000. Participants' daily step counts were in the low-active range (mean 6485.78, SD 2352.54) and overall dietary levels were poor (total fat g/day, mean 77.79, SD 41.96; percent kcal from fat, mean 36.51, SD 5.92; fiber g/day, mean 17.74, SD 7.35; and fruit and vegetable servings/day, mean 4.03, SD 2.33). The Web-health users had good self-efficacy and outcome expectations for health behavior change; however, they perceived little social support for making these changes and engaged in few self-regulatory behaviors. Consistent with SCT, theoretical models provided good fit to Web-users' data (root mean square error of the approximation [RMSEA] < .05). Perceived social support and use of self-regulatory behaviors were strong predictors of physical activity and nutrition behavior. Web users' self-efficacy was also a good predictor of healthier levels of physical activity and dietary fat but not of fiber, fruits, and vegetables. Social support and self-efficacy indirectly predicted behavior through self-regulation, and social support had indirect effects through self-efficacy.

Conclusions: Results suggest Web-health users visiting and ultimately participating in online health interventions may likely be middle-aged, well-educated, upper middle class women whose detrimental health behaviors put them at risk of obesity, heart disease, some cancers, and diabetes. The success of Internet physical activity and nutrition interventions may depend on the extent to which they lead users to develop self-efficacy for behavior change, but perhaps as important, the extent to which these interventions help them garner social-support for making changes. Success of these interventions may also depend on the extent to which they provide a platform for setting goals, planning, tracking, and providing feedback on targeted behaviors.

Trial registration: ClinicalTrials.gov NCT00128570.

Conflict of interest statement

None declared

Figures

Figure 1
Figure 1
Social cognitive model of health behavior
Figure 2
Figure 2
Social cognitive model of fiber, fruits and vegetables among web-health users. * P < .05, ** P < .01, *** P < .001
Figure 3
Figure 3
Social cognitive model of fat consumption among Web-health users where * signifies P < .05, ** signifies P < .01, and *** signifies P < .001
Figure 4
Figure 4
Social cognitive model of fiber, fruit, and vegetable consumption among Web-health users where * signifies P < .05, ** signifies P < .01, and *** signifies P < .001
Figure 5
Figure 5
Social cognitive model of physical activity among Web-health users where * signifies P < .05, ** signifies P < .01, and *** signifies P < .001

References

    1. Atkinson NL, Saperstein SL, Pleis J. Using the internet for health-related activities: findings from a national probability sample. J Med Internet Res. 2009;11(1):e4. doi: 10.2196/jmir.1035. v11i1e4
    1. Online activities, daily. Washington, DC: Pew Internet & American Life Project; 2010. [2011-01-23]. .
    1. Who's online: Demographics of Internet-users. Washington, DC: Pew Internet & American Life Project; 2010. [2011-01-23]. .
    1. Murray E, Khadjesari Z, White IR, Kalaitzaki E, Godfrey C, McCambridge J, Thompson SG, Wallace P. Methodological challenges in online trials. J Med Internet Res. 2009;11(2):e9. doi: 10.2196/jmir.1052. v11i2e9
    1. Bennett GG, Glasgow RE. The delivery of public health interventions via the Internet: actualizing their potential. Annu Rev Public Health. 2009 Apr 29;30:273–92. doi: 10.1146/annurev.publhealth.031308.100235.
    1. Eysenbach G. The law of attrition. J Med Internet Res. 2005;7(1):e11. doi: 10.2196/jmir.7.1.e11. v7e11
    1. Glasgow RE, Emmons KM. How can we increase translation of research into practice? Types of evidence needed. Annu Rev Public Health. 2007;28:413–33. doi: 10.1146/annurev.publhealth.28.021406.144145.
    1. Ogden J. Some problems with social cognition models: a pragmatic and conceptual analysis. Health Psychol. 2003 Jul;22(4):424–8.
    1. Rothman AJ. "Is there nothing more practical than a good theory?": Why innovations and advances in health behavior change will arise if interventions are used to test and refine theory. Int J Behav Nutr Phys Act. 2004 Jul 27;1(1):11. doi: 10.1186/1479-5868-1-11. 1479-5868-1-11
    1. Schnoll RA, Fang CY, Manne SL. The application of SEM to behavioral research in oncology: Past accomplishments and future opportunities. Structural Equation Modeling. 2004;11:583–614.
    1. Weinstein ND. Misleading tests of health behavior theories. Ann Behav Med. 2007 Feb;33(1):1–10. doi: 10.1207/s15324796abm3301_1.
    1. Webb TL, 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(1):e4. doi: 10.2196/jmir.1376. v12i1e4
    1. Winett RA, Tate DF, Anderson ES, Wojcik JR, Winett SG. Long-term weight gain prevention: a theoretically based Internet approach. Prev Med. 2005 Aug;41(2):629–41. doi: 10.1016/j.ypmed.2004.12.005.S0091-7435(05)00037-X
    1. van den Berg MH, Schoones JW, Vliet Vlieland TP. Internet-based physical activity interventions: a systematic review of the literature. J Med Internet Res. 2007;9(3):e26. doi: 10.2196/jmir.9.3.e26. v9i3e26
    1. Bandura A. Self-efficacy: The Exercise of Control. New York, NY: W.H. Freeman; 1997.
    1. Bandura A. The primacy of self-regulation in health promotion. Applied Psychology. 2005;54:245–254. doi: 10.1111/j.1464-0597.2005.00208.x.
    1. Anderson ES, Winett RA, Wojcik JR, Winett SG, Bowden T. A computerized social cognitive intervention for nutrition behavior: direct and mediated effects on fat, fiber, fruits, and vegetables, self-efficacy, and outcome expectations among food shoppers. Ann Behav Med. 2001;23(2):88–100.
    1. Anderson ES, Winett RA, Wojcik JR. Social-cognitive determinants of nutrition behavior among supermarket food shoppers: a structural equation analysis. Health Psychol. 2000 Sep;19(5):479–86.
    1. Anderson ES, Winett RA, Wojcik JR. Self-regulation, self-efficacy, outcome expectations, and social support: social cognitive theory and nutrition behavior. Ann Behav Med. 2007;34(3):304–12. doi: 10.1080/08836610701677659.
    1. Anderson ES, Winett RA, Wojcik JR, Williams DM. Social cognitive mediators of change in a group randomized nutrition and physical activity intervention: social support, self-efficacy, outcome expectations and self-regulation in the guide-to-health trial. J Health Psychol. 2010 Jan;15(1):21–32. doi: 10.1177/1359105309342297.15/1/21
    1. Luszczynska A, Gibbons X, Piko BF, Tekozel M. Self-regulatory cognitions, social comparison, and perceived peers' behaviors as predictors of nutrition and physical activity: A comparison among adolescents in Hungary, Poland, Turkey, and USA. Psychology and Health. 2004;19:577–593. doi: 10.1080/0887044042000205844.
    1. Anderson ES, Wojcik JR, Winett RA, Williams DM. Social-cognitive determinants of physical activity: the influence of social support, self-efficacy, outcome expectations, and self-regulation among participants in a church-based health promotion study. Health Psychol. 2006 Jul;25(4):510–20. doi: 10.1037/0278-6133.25.4.510.2006-08842-008
    1. McAuley E, Blissmer B. Self-efficacy determinants and consequences of physical activity. Exerc Sport Sci Rev. 2000 Apr;28(2):85–8.
    1. Courneya KS, McAuley E. Cognitive mediators of the social influence-exercise adherence relationship: a test of the theory of planned behavior. J Behav Med. 1995 Oct;18(5):499–515.
    1. Steptoe A, Perkins-Porras L, Rink E, Hilton S, Cappuccio FP. Psychological and social predictors of changes in fruit and vegetable consumption over 12 months following behavioral and nutrition education counseling. Health Psychol. 2004 Nov;23(6):574–81. doi: 10.1037/0278-6133.23.6.574.2004-20316-003
    1. Ford ES, Ahluwalia IB, Galuska DA. Social relationships and cardiovascular disease risk factors: findings from the third national health and nutrition examination survey. Prev Med. 2000 Feb;30(2):83–92. doi: 10.1006/pmed.1999.0606.S0091-7435(99)90606-0
    1. Williams DM, Anderson ES, Winett RA. A review of the outcome expectancy construct in physical activity research. Ann Behav Med. 2005 Feb;29(1):70–9. doi: 10.1207/s15324796abm2901_10.
    1. Maes S, Karoly P. Self-regulation assessment and intervention in physical health and illness: A review. Applied Psychology. 2005;54(2):267–299. doi: 10.1111/j.1464-0597.2005.00210.x.
    1. Ammerman AS, Lindquist CH, Lohr KN, Hersey J. The efficacy of behavioral interventions to modify dietary fat and fruit and vegetable intake: a review of the evidence. Prev Med. 2002 Jul;35(1):25–41.S0091743502910285
    1. Grossarth-Maticek R, Eysenck HJ. Self-regulation and mortality from cancer, coronary heart disease, and other causes: A prospective study. Personality and Individual Differences. 1995 Dec;19(6):781–795. doi: 10.1016/S0191-8869(95)00123-9.
    1. Pelletier LG, Dion SC, Slovinec-D'Angelo M, Reid R. Why do you regulate what you eat? Relationships between forms of regulation, eating behaviors, sustained dietary behavior change, and psychological adjustment. Motivation and Emotion. 2004;28(3):245–277. doi: 10.1023/B:MOEM.0000040154.40922.14.
    1. Vartanian LR, Herman CP, Polivy J. Does regulatory focus play a role in dietary restraint? Eat Behav. 2006 Nov;7(4):333–41. doi: 10.1016/j.eatbeh.2005.11.007.S1471-0153(05)00077-2
    1. Schnoll R, Zimmerman BJ. Self-regulation training enhances dietary self-efficacy and dietary fiber consumption. J Am Diet Assoc. 2001 Sep;101(9):1006–11. doi: 10.1016/S0002-8223(01)00249-8.S0002-8223(01)00249-8
    1. Cerin E, Vandelanotte C, Leslie E, Merom D. Recreational facilities and leisure-time physical activity: An analysis of moderators and self-efficacy as a mediator. Health Psychol. 2008 Mar;27(2 Suppl):S126–35. doi: 10.1037/0278-6133.27.2(Suppl.).S126.2008-03488-005
    1. American College of Sports Medicine . ACSM's guidelines for exercise testing and prescription. 8th edition. Baltimore, MD: Lippincott Williams & Wilkins; 2009.
    1. Block G, Thompson FE, Hartman AM, Larkin FA, Guire KE. Comparison of two dietary questionnaires validated against multiple dietary records collected during a 1-year period. J Am Diet Assoc. 1992 Jun;92(6):686–93.
    1. Joreskog K, Sorbom D. LISREL 8: User's Reference Guide. Chicago, IL: Scientific Software International; 1996.
    1. Tudor-Locke C, Bassett DR. How many steps/day are enough? Preliminary pedometer indices for public health. Sports Med. 2004;34(1):1–8.3411
    1. Pedhazur EJ . Multiple Regression in Behavioral Research: Explanation and Prediction. New York, NY: Holt, Rinehart and Winston; 1982.
    1. Centers for Disease Control and Prevention (CDC) Health disparities experienced by black or African Americans--United States. MMWR Morb Mortal Wkly Rep. 2005 Jan 14;54(1):1–3. mm5401a1
    1. Stopponi MA, Alexander GL, McClure JB, Carroll NM, Divine GW, Calvi JH, Rolnick SJ, Strecher VJ, Johnson CC, Ritzwoller DP. Recruitment to a randomized web-based nutritional intervention trial: characteristics of participants compared to non-participants. J Med Internet Res. 2009;11(3):e38. doi: 10.2196/jmir.1086. v11i3e38
    1. Polivy J, Herman CP. If at first you don't succeed. False hopes of self-change. Am Psychol. 2002 Sep;57(9):677–89.

Source: PubMed

Sponsoren und Mitarbeiter

Krankheiten

Drogeninterventionen

3
Abonnieren