Advertisement

Prevention Science

, Volume 18, Issue 8, pp 984–994 | Cite as

Efficacy of the Fun For Wellness Online Intervention to Promote Multidimensional Well-Being: a Randomized Controlled Trial

  • Nicholas D. MyersEmail author
  • Isaac Prilleltensky
  • Ora Prilleltensky
  • Adam McMahon
  • Samantha Dietz
  • Carolyn L. Rubenstein
Article

Abstract

Subjective well-being refers to people’s level of satisfaction with life as a whole and with multiple dimensions within it. Interventions that promote subjective well-being are important because there is evidence that physical health, mental health, substance use, and health care costs may be related to subjective well-being. Fun For Wellness (FFW) is a new online universal intervention designed to promote growth in multiple dimensions of subjective well-being. The purpose of this study was to provide an initial evaluation of the efficacy of FFW to increase subjective well-being in multiple dimensions in a universal sample. The study design was a prospective, double-blind, parallel group randomized controlled trial. Data were collected at baseline and 30 and 60 days-post baseline. A total of 479 adult employees at a major university in the southeast of the USA were enrolled. Recruitment, eligibility verification, and data collection were conducted online. Measures of interpersonal, community, occupational, physical, psychological, economic (i.e., I COPPE), and overall subjective well-being were constructed based on responses to the I COPPE Scale. A two-class linear regression model with complier average causal effect estimation was imposed for each dimension of subjective well-being. Participants who complied with the FFW intervention had significantly higher subjective well-being, as compared to potential compliers in the Usual Care group, in the following dimensions: interpersonal at 60 days, community at 30 and 60 days, psychological at 60 days, and economic at 30 and 60 days. Results from this study provide some initial evidence for both the efficacy of, and possible revisions to, the FFW intervention.

Keywords

Interpersonal well-being Community well-being Occupational well-being Physical well-being Psychological well-being Economic well-being 

Notes

Compliance with Ethical Standards

Funding

The project described was supported by funds from the Erwin and Barbara Mautner Endowed Chair in Community Well-Being at University of Miami School of Education and Human Development.

Conflict of Interest

Adam McMahon and Isaac Prilleltensky are partners in Wellnuts LLC, which may commercialize the FFW intervention described in this study.

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The institutional review board at the University of Miami provided necessary permission to conduct this study, IRB no. 20150237.

Informed Consent

Informed consent was obtained from all individual participants included in the study. More specifically, immediately after passing the inclusionary criteria, screened respondents were presented with the IRB-approved consent form to read and sign electronically. Those who clicked “decline to consent” were locked out of the remaining program activities.

Supplementary material

11121_2017_779_MOESM1_ESM.doc (84 kb)
ESM 1 (DOC 84 kb)
11121_2017_779_MOESM2_ESM.doc (100 kb)
ESM 1 (DOC 100 kb)

References

  1. Angrist, J., Imbens, G. W., & Rubin, D. B. (1996). Identification of causal effects using instrumental variables. Journal of the American Statistical Association, 91, 444–472. doi: 10.2307/2291629.CrossRefGoogle Scholar
  2. Bloom, H. S. (1984). Accounting for no-shows in experimental evaluation designs. Evaluation Review, 8, 225–246. doi: 10.1177/0193841X8400800205.CrossRefGoogle Scholar
  3. Chmiel, M., Brunner, M., Martin, R., & Schalke, D. (2012). Revisiting the structure of subjective well-being in middle-aged adults. Social Indicators Research, 106, 109–116. doi: 10.1007/s11205-011-9796-7.CrossRefGoogle Scholar
  4. Cobb, N. K., & Poirier, J. (2014). Effectiveness of a multimodal online well-being intervention. American Journal of Preventive Medicine, 46, 41–48. doi: 10.1016/j.amepre.2013.08.018.CrossRefPubMedGoogle Scholar
  5. Conley, C. S., Durlak, J. A., & Kirsch, A. C. (2015). A meta-analysis of universal mental health prevention programs for higher education students. Prevention Science, 16, 487–507. doi: 10.1007/s11121-015-0543-1.CrossRefPubMedGoogle Scholar
  6. Couper, M. P., Alexander, G. L., Maddy, N., Zhang, N., Nowak, M. A., McClure, J. B.,... Cole Johnson, C. (2010). Engagement and retention: Measuring breadth and depth of participant use of an online intervention. Journal of Medical Internet Research, 12, e52. doi: 10.2196/jmir.1430
  7. Dolan, P. (2014). Happiness by design. New York, NY: Penguin.Google Scholar
  8. González, M., Coenders, G., Saez, M., & Casas, F. (2010). Non-linearity, complexity and limited measurement in the relationship between satisfaction with specific life domains and satisfaction with life as a whole. Journal of Happiness Studies, 11, 335–352. doi: 10.1007/s10902-009-9143-8.CrossRefGoogle Scholar
  9. Griffin, K. W., Botvin, G. J., Scheier, L. M., Epstein, J. A., & Doyle, M. M. (2002). Personal competence skills, distress, and well-being as determinants of substance use in a predominantly minority urban adolescent sample. Prevention Science, 3, 23–33. doi: 10.1023/A:1014667209130.CrossRefPubMedGoogle Scholar
  10. Harrison, P. L., Pope, J. E., Coberley, C. R., & Rula, E. Y. (2012). Evaluation of the relationship between individual well-being and future health care utilization and cost. Population Health Management, 15, 325–330. doi: 10.1089/pop.2011.0089.CrossRefPubMedGoogle Scholar
  11. Hays, P. A. (2014). Creating well-being: Four steps to a happier, healthier life. Washington, DC: American Psychological Association.CrossRefGoogle Scholar
  12. Irvine, A. B., Gelatt, V. A., Hammond, M., & Seeley, J. R. (2015). A randomized study of internet parent training accessed from community technology centers. Prevention Science, 16, 597–608. doi: 10.1007/s11121-014-0521-z.CrossRefPubMedGoogle Scholar
  13. Jo, B. (2002a). Estimation of intervention effects with noncompliance: Alternative model specifications. Journal of Educational and Behavioral Statistics, 27, 385–409. doi: 10.3102/10769986027004385.CrossRefGoogle Scholar
  14. Jo, B. (2002b). Model misspecification sensitivity analysis in estimating causal effects of interventions with non-compliance. Statistics in Medicine, 21, 3161–3181. doi: 10.1002/sim.1267.CrossRefPubMedGoogle Scholar
  15. Jo, B., Ginexi, E. M., & Ialongo, N. S. (2010). Handling missing data in randomized experiments with noncompliance. Prevention Science, 11, 384–396. doi: 10.1007/s11121-010-0175-4.CrossRefPubMedPubMedCentralGoogle Scholar
  16. Keyes, C. L. M., & Simoes, E. J. (2012). To flourish or not: Positive mental health and all-cause mortality. American Journal of Public Health, 102, 2164–2172. doi: 10.2105/AJPH.2012.300918.CrossRefPubMedPubMedCentralGoogle Scholar
  17. McDonald, R. P. (1970). The theoretical foundations of common factor analysis, principal factor analysis, and alpha factor analysis. British Journal of Mathematical and Statistical Psychology, 23, 1–21. doi: 10.1111/j.2044-8317.1970.tb00432.x.CrossRefGoogle Scholar
  18. Moessner, M., Minarik, C., Ozer, F., & Bauer, S. (2016). Effectiveness and cost-effectiveness of school-based dissemination strategies of an internet-based program for the prevention and early intervention in eating disorders: A randomized trial. Prevention Science, 17, 306–313. doi: 10.1007/s11121-015-0619-y.CrossRefPubMedGoogle Scholar
  19. Muthén, L. K., & Muthén, B. O. (1998-2012). Mplus user’s guide (7th ed.). Los Angeles: Muthén & Muthén.Google Scholar
  20. Myers, N. D., Prilleltensky, I., Jin, Y., Dietz, S., Rubenstein, C. L., Prilleltensky, O., & McMahon, A. (2014). Empirical contributions of the past in assessing multidimensional well-being. Journal of Community Psychology, 42, 789–798. doi: 10.1002/jcop.21653.CrossRefGoogle Scholar
  21. Myers, N. D., Park, S. E., Lefevor, G. T., Dietz, S., Prilleltensky, I., & Prado, G. J. (2016). Measuring multidimensional well-being with the I COPPE Scale in a Hispanic sample. Measurement in Physical Education and Exercise Science, 20, 230–243. doi: 10.1080/1091367X.2016.1226836.CrossRefGoogle Scholar
  22. Norcross, J. C. (2012). Changeology: 5 steps to realizing your goals and resolutions. New York: Simon & Schuster.Google Scholar
  23. Pinker, S. (2014). The village effect: How face-to-face contact can make us healthier and happier. Toronto: Random House.Google Scholar
  24. Portnoy, D. B., Scott-Sheldon, L. A. J., Johnson, B. T., & Carey, M. P. (2008). Computer-delivered interventions for health promotion and behavioral risk reduction: A meta-analysis of 75 randomized controlled trials, 1988–2007. Preventive Medicine, 47, 3–16. doi: 10.1016/j.ypmed.2008.02.014.CrossRefPubMedPubMedCentralGoogle Scholar
  25. Prilleltensky, I., Dietz, S., Prilleltensky, O., Myers, N. D., Rubenstein, C. L., Jin, Y., & McMahon, A. (2015). Assessing multidimensional well-being: Development and validation of the I COPPE Scale. Journal of Community Psychology, 43, 199–226. doi: 10.1002/jcop.21674.CrossRefGoogle Scholar
  26. Primack, B. A., Carroll, M. V., McNamara, M., Klem, M. L., King, B., Rich, M., ... Nayak, S. (2012). Role of video games in improving health-related outcomes: A systematic review. American Journal of Preventive Medicine, 42, 630–638. doi: 10.1016/j.amepre.2012.02.023
  27. Prochaska, J. O., Evers, K. E., Castle, P. H., Johnson, J. L., Prochaska, J. M., Rula, E. Y.,. .. Pope, J. E. (2012). Enhancing multiple domains of well-being by decreasing multiple health risk behaviors: A randomized clinical trial. Population Health Management, 15, 276–286. doi: 10.1089/pop.2011.0060
  28. Proyer, R. T., Gander, F., Wellenzohn, S., & Ruch, W. (2014). Positive psychology interventions in people aged 50-79 years: Long-term effects of placebo-controlled online interventions on well-being and depression. Aging & Mental Health, 18, 997–1005. doi: 10.1080/13607863.2014.899978.CrossRefGoogle Scholar
  29. Rahmani, E., & Boren, S. A. (2012). Videogames and health improvement: A literature review of randomized controlled trials. Games for Health Journal, 1, 331–341. doi: 10.1089/g4h.2012.0031.CrossRefPubMedGoogle Scholar
  30. Rath, T., & Harter, J. (2010). Well-being: The five essential elements. New York: Gallup Press.Google Scholar
  31. Roepke, A. M., Jaffee, S. R., Riffle, O. M., McGonigal, J., Broome, R., & Maxwell, B. (2015). Randomized controlled trial of SuperBetter, a smartphone-based/internet-based self-help tool to reduce depressive symptoms. Games for Health Journal, 4, 235–246. doi: 10.1089/g4h.2014.0046.CrossRefPubMedGoogle Scholar
  32. Rubenstein, C. L., Duff, J., Prilleltensky, I., Jin, Y., Dietz, S., Myers, N. D., & Prilleltensky, O. (2016). Demographic group differences in domain-specific well-being. Journal of Community Psychology, 44, 499–515. doi: 10.1002/jcop.21784.CrossRefGoogle Scholar
  33. Rubin, D. B. (1978). Bayesian inference for causal effect: The role of randomization. Annals of Statistics, 6, 34–58. doi: 10.1214/aos/1176344064.CrossRefGoogle Scholar
  34. Schwinn, T. M., Schinke, S. P., & Di Noia, J. (2010). Preventing drug abuse among adolescent girls: Outcome data from an internet-based intervention. Prevention Science, 11, 24–32. doi: 10.1007/s11121-009-0146-9.CrossRefPubMedPubMedCentralGoogle Scholar
  35. Sears, L., Shi, Y., Coberley, C., & Pope, J. (2013). Overall well-being as a predictor of health care, productivity, and retention in a large employer. Population Health Management, 16, 397–405. doi: 10.1089/pop.2012.0114.CrossRefPubMedPubMedCentralGoogle Scholar
  36. Seligman, M. (2011). Flourish: A visionary new understanding of happiness and well-being. New York: Simon and Schuster.Google Scholar
  37. Stuart, E. A., Perry, D. F., Le, H.-N., & Ialongo. (2008). Estimating intervention effects of prevention programs: Accounting for noncompliance. Prevention Science, 9, 288–298. doi: 10.1007/s11121-008-0104-y.CrossRefPubMedPubMedCentralGoogle Scholar
  38. Watson, D. L., & Tharp, R. G. (2014). Self-directed behavior: Self-modification for personal adjustment (10th ed.). Belmont: Cengage Learning.Google Scholar
  39. Wong, C. F., Schrager, S. M., Holloway, I. W., Meyer, I. H., & Kipke, M. D. (2014). Minority stress experiences and psychological well-being: The impact of support from and connection to social networks within the Los Angeles House and Ball communities. Prevention Science, 15, 44–55. doi: 10.1007/s11121-012-0348-4.CrossRefPubMedPubMedCentralGoogle Scholar
  40. World Health Organization. (2009). Global health risks: Mortality and burden of disease attributable to selected major risks. Geneva: World Health Organization.Google Scholar

Copyright information

© Society for Prevention Research 2017

Authors and Affiliations

  • Nicholas D. Myers
    • 1
    Email author
  • Isaac Prilleltensky
    • 2
  • Ora Prilleltensky
    • 2
  • Adam McMahon
    • 2
  • Samantha Dietz
    • 2
  • Carolyn L. Rubenstein
    • 3
  1. 1.Department of Kinesiology, College of EducationMichigan State UniversityEast LansingUSA
  2. 2.School of Education and Human DevelopmentUniversity of MiamiMiamiUSA
  3. 3.Department of Educational and Psychological StudiesUniversity of MiamiMiamiUSA

Personalised recommendations