Quality of Life Research

, Volume 25, Issue 3, pp 517–524 | Cite as

Use of weekly assessment data to enhance evaluation of a subjective wellbeing intervention

  • Lucia Colla
  • Matthew Fuller-Tyszkiewicz
  • Adrian J. Tomyn
  • Ben Richardson
  • Justin D. Tomyn
Special Section: PROs in Non-Standard Settings (by invitation only)



While intervention effects in target outcomes have typically been tested based on change from baseline to post-intervention, such approaches typically ignore individual differences in change, including time taken to see improvement. The present study demonstrates how weekly patient-reported data may be used to augment traditional pre–post intervention evaluations in order to gain greater insights into treatment efficacy.


Two hundred and fifty-two adolescent boys and girls (M age = 13.6 years, SD = 0.6 years) from four secondary schools in Victoria, Australia, were assigned by school into control (n = 88) or intervention (n = 164) groups. The intervention group participated in a 6-week course designed to improve subjective wellbeing (SWB) by fostering resilience, coping skills, and self-esteem. In addition to baseline, post-intervention, and 3-month follow-up assessments of SWB, intervention group participants also completed weekly summarise of affective experiences for the duration of the intervention phase.


While standard pre–post data showed significant improvement in SWB for the intervention group relative to controls, weekly data showed individual differences in the trajectory of change during this intervention phase; low SWB individuals experienced initial worsening of symptoms followed by improvement in the second half of the intervention phase, whereas high SWB individuals experienced initial gains, followed by a plateau from Week 4 onwards.


Addition of weekly data provided greater insights into intervention effects by: (1) contradicting the notion that early responsiveness to treatment is predictive of level of improvement by post-intervention, and (2) providing data-based insights into ways to enhance the intervention.


Subjective wellbeing School intervention Trajectories of change Patient-reported outcomes 


Compliance with ethical standards

Conflict of interest

Authors declare that they have no conflict of interest.

Ethical approval

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

Informed consent

Informed consent was obtained from all participants included in the study.


  1. 1.
    Merry, S., McDowell, H., Hetrick, S., Bir, J., & Muller, N. (2004). Psychological and/or educational interventions for the prevention of depression in children and adolescents. Cochrane Database of Systematic Reviews, 2. Google Scholar
  2. 2.
    Spence, S. H., & Shortt, A. L. (2007). Research review: Can we justify the widespread dissemination of universal school-based interventions for the prevention of depression among children and adolescents? Journal of Child Psychology and Psychiatry, 48(6), 526–542.CrossRefPubMedGoogle Scholar
  3. 3.
    Sterba, S. K., & Bauer, D. J. (2010). Matching method with theory in person-oriented developmental psychopathology research. Development and Psychopathology, 22, 239–254.CrossRefPubMedGoogle Scholar
  4. 4.
    Timmons, A. C., & Preacher, K. J. (2015). The importance of temporal design: How do measurement intervals affect the accuracy and efficiency of parameter estimates in longitudinal research? Multivariate Behavioral Research, 50, 41–55.CrossRefPubMedGoogle Scholar
  5. 5.
    Katon, W., Russo, J., Von Korff, M., Lin, E., Simon, G., Bush, T., et al. (2002). Long-term effects of a collaborative care intervention in persistently depressed primary care patients. Journal of General Internal Medicine, 17, 741–748.PubMedCentralCrossRefPubMedGoogle Scholar
  6. 6.
    Wilksch, S., & Wade, T. D. (2009). Reduction of shape and weight concern in young adolescents: A 30-month controlled evaluation of a media literacy program. Journal of the American Academy of Child and Adolescent Psychiatry, 48(6), 652–661.CrossRefPubMedGoogle Scholar
  7. 7.
    Withers, G. F., & Wertheim, E. H. (2004). Applying the elaboration likelihood model of persuasion to a videotape-based eating disorders primary prevention program for adolescent girls. Eating Disorders, 12, 103–124.CrossRefPubMedGoogle Scholar
  8. 8.
    Barge-Schaapveld, D. Q. C. M., & Nicolson, N. A. (2002). Effects of antidepressant treatment on the quality of daily life: An experience sampling study. Journal of Clinical Psychiatry, 63(6), 477–485.CrossRefPubMedGoogle Scholar
  9. 9.
    Voogt, C., Kuntsche, E., Kleinjan, M., Poelen, E., & Engels, R. (2014). Using ecological momentary assessment to test the effectiveness of a web-based brief alcohol intervention over time among heavy-drinking students: Randomized controlled trial. Journal of Medical Internet Research, 16(1), e5.PubMedCentralCrossRefPubMedGoogle Scholar
  10. 10.
    Shiyko, M. P., & Ram, N. (2011). Conceptualizing and estimating process speed in studies employing ecological momentary assessment designs: A multilevel variance decomposition approach. Multivariate Behavioral Research, 46(6), 875–899.PubMedCentralCrossRefPubMedGoogle Scholar
  11. 11.
    Fuller-Tyszkiewicz, M., Skouteris, H., Richardson, B., Blore, J., Holmes, M., & Mills, J. (2013). Does the burden of the experience sampling method undermine data quality in state based body image research? Body Image, 10(4), 607–613.CrossRefPubMedGoogle Scholar
  12. 12.
    Morren, M., van Dulmen, S., Ouwerkerk, J., & Bensing, J. (2009). Compliance with momentary pain measurement using electronic diaries: A systematic review. European Journal of Pain, 13, 354–365.CrossRefPubMedGoogle Scholar
  13. 13.
    Scholten, L., Willemen, A. M., Last, B. F., Maurice-Stam, H., van Dijk, E. M., Ensink, E., et al. (2013). Efficacy of psychosocial group intervention for children with chronic illness and their parents. Pediatrics, 131(4), e1196–e1203.CrossRefPubMedGoogle Scholar
  14. 14.
    Spence, S. H., Sheffield, J. K., & Donovan, C. L. (2005). Long-term outcome of a school-based, universal approach to prevention of depression in adolescents. Journal of Consulting and Clinical Psychology, 73(1), 160–167.CrossRefPubMedGoogle Scholar
  15. 15.
    Brown, E. C., Catalano, R. F., Fleming, C. B., & Haggerty, K. P. (2005). Adolescent substance use outcomes in the Raising Healthy Children Project: A two-part latent growth curve analysis. Journal of Consulting and Clinical Psychology, 73(4), 699–710.CrossRefPubMedGoogle Scholar
  16. 16.
    Malmberg, M., Kleinjan, M., Overbeek, G., Vermulst, A., Lammers, J., Monshouwer, K., et al. (2015). Substance use outcomes in the Healthy School and Drugs program: Results from a latent growth curve approach. Addictive Behaviors, 42, 194–202.CrossRefPubMedGoogle Scholar
  17. 17.
    Horner, R. H., Sugai, G., Smolkowski, K., Eber, L., Nakasato, J., Todd, A. W., & Esperanza, J. (2009). A randomized, wait-list controlled effectiveness trial assessing school-wide positive behaviour support in elementary schools. Journal of Positive Behavior Interventions, 11(3), 133–144.CrossRefGoogle Scholar
  18. 18.
    Titov, N., Andrews, G., Davies, M., McIntyre, K., Robinson, E., & Solley, K. (2010). Internet treatment for depression: A randomized controlled trial comparing clinician vs. technician assistance. PLoS One, 5(6), e10939.PubMedCentralCrossRefPubMedGoogle Scholar
  19. 19.
    Cummins, R. A., & Lau, A. L. D. (2005). Personal wellbeing index-school children (PWI-SC) (3rd ed.). Retrieved from Australian Centre on Quality of Life, School of Psychology, Deakin University, Melbourne.
  20. 20.
    Tomyn, A. J., & Cummins, R. A. (2011). Subjective wellbeing and homeostatically protected mood: Theory validation with adolescents. Journal of Happiness Studies, 12(5), 897–914.CrossRefGoogle Scholar
  21. 21.
    Blore, J. (2008). Subjective wellbeing: An assessment of competing theories. (Doctoral dissertation, Deakin University). Retrieved from
  22. 22.
    Russell, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology, 39(6), 1161–1178.CrossRefGoogle Scholar
  23. 23.
    Russell, J. A. (2003). Core affect and the psychological construction of emotion. Psychological Review, 110, 145–172.CrossRefPubMedGoogle Scholar
  24. 24.
    Colautti, L. A., Fuller-Tyszkiewicz, M., Skouteris, H., McCabe, M., Blackburn, S., & Wyett, E. (2011). Accounting for fluctuations in body dissatisfaction. Body Image, 8(4), 315–321.CrossRefPubMedGoogle Scholar
  25. 25.
    Karatzias, A., Chouliara, Z., Power, K., & Swanson, V. (2006). Predicting general well-being from self-esteem and affectivity: An exploratory study with Scottish adolescents. Quality of Life Research, 15(7), 1143–1151.CrossRefPubMedGoogle Scholar
  26. 26.
    Geldhof, G. J., Preacher, K. J., & Zyphur, M. J. (2013). Reliability estimation in a multilevel confirmatory factor analysis framework. Psychological Methods, 19(1), 72–91.CrossRefPubMedGoogle Scholar
  27. 27.
    Australian Bureau of Statistics. (2013). Census of population and housing: Socio-economic indexes for areas (SEIFA), Australia. Retrieved from
  28. 28.
    Marmot, M. G. (2005). Social determinants of health inequalities. Lancet, 365, 1099–1104.CrossRefPubMedGoogle Scholar
  29. 29.
    Muthén, L. K., & Muthén, B. O. (1998-2012). Mplus user’s guide (7th ed.). Los Angeles, CA: Muthén & Muthén.Google Scholar
  30. 30.
    Quené, H., & van den Bergh, H. (2004). On multi-level modeling of data from repeated measures designs: A tutorial. Speech Communication, 43, 103–121.CrossRefGoogle Scholar
  31. 31.
    Stull, D. E. (2008). Analyzing growth and change: Latent variable growth curve modeling with an application to clinical trials. Quality of Life Research, 17(1), 47–59.CrossRefPubMedGoogle Scholar
  32. 32.
    Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and interpreting interactions. Newbury Park, California: Sage Publications.Google Scholar
  33. 33.
    Mendlewicz, J. (2010). Time in depression. Medicographia, 32(2), 109–111.Google Scholar
  34. 34.
    Horowitz, J. L., Garber, J., Ciesla, J. A., Young, J. F., & Mufson, L. (2007). Prevention of depressive symptoms in adolescents: A randomized trial of cognitive-behavioral and interpersonal prevention programs. Journal of Consulting and Clinical Psychology, 75(5), 693–706.CrossRefPubMedGoogle Scholar
  35. 35.
    Puskar, K., Sereika, S., & Tusaie-Mumford, K. (2003). Effect of the Teaching Kids to Cope (TKC) program on outcomes of depression and coping among rural adolescents. Journal of Child and Adolescent Psychiatric Nursing, 16(2), 71–80.CrossRefPubMedGoogle Scholar
  36. 36.
    Roberts, C. M., Kane, R., Bishop, B., Cross, D., Fenton, J., & Hart, B. (2010). The prevention of anxiety and depression in children from disadvantaged schools. Behaviour Research and Therapy, 48, 68–73.CrossRefPubMedGoogle Scholar
  37. 37.
    Collins, L. M., Murphy, S. A., & Strecher, V. (2007). The multiphase optimization strategy (MOST) and the sequential multiple assignment randomized trial (SMART): New methods for more potent eHealth interventions. American Journal of Preventive Medicine, 32(5), 112–118.CrossRefGoogle Scholar
  38. 38.
    Almirall, D., Compton, S. N., Gunlicks-Stoessel, M., Duan, N., & Murphy, S. A. (2012). Designing a pilot sequential multiple assignment randomized trial for developing an adaptive treatment strategy. Statistics in Medicine, 31(17), 1887–1902.PubMedCentralCrossRefPubMedGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Lucia Colla
    • 1
  • Matthew Fuller-Tyszkiewicz
    • 1
  • Adrian J. Tomyn
    • 1
  • Ben Richardson
    • 1
  • Justin D. Tomyn
    • 1
  1. 1.School of PsychologyDeakin UniversityBurwoodAustralia

Personalised recommendations