Hostname: page-component-745bb68f8f-v2bm5 Total loading time: 0 Render date: 2025-01-26T21:06:42.007Z Has data issue: false hasContentIssue false
  • English
  • Français

Evaluating the effectiveness of a universal eHealth school-based prevention programme for depression and anxiety, and the moderating role of friendship network characteristics

Published online by Cambridge University Press:  15 July 2022

Jack L. Andrews Open the ORCID record for Jack L. Andrews [Opens in a new window] ,
Louise Birrell ,
Cath Chapman ,
Maree Teesson ,
Nicola Newton ,
Steve Allsop ,
Nyanda McBride ,
Leanne Hides ,
Gavin Andrews  and
Nick Olsen
...Show all authors
Jack L. Andrews*
Affiliation:
University of New South Wales, Sydney, NSW, Australia
Louise Birrell
Affiliation:
The Matilda Centre for Research in Mental Health and Substance Use, University of Sydney, Sydney, NSW, Australia
Cath Chapman
Affiliation:
The Matilda Centre for Research in Mental Health and Substance Use, University of Sydney, Sydney, NSW, Australia
Maree Teesson
Affiliation:
The Matilda Centre for Research in Mental Health and Substance Use, University of Sydney, Sydney, NSW, Australia
Nicola Newton
Affiliation:
The Matilda Centre for Research in Mental Health and Substance Use, University of Sydney, Sydney, NSW, Australia
Steve Allsop
Affiliation:
National Drug Research Institute, Curtin University, Perth, WA, Australia
Nyanda McBride
Affiliation:
National Drug Research Institute, Curtin University, Perth, WA, Australia
Leanne Hides
Affiliation:
University of Queensland, Brisbane, QLD, Australia
Gavin Andrews
Affiliation:
University of New South Wales, Sydney, NSW, Australia
Nick Olsen
Affiliation:
The Matilda Centre for Research in Mental Health and Substance Use, University of Sydney, Sydney, NSW, Australia
Louise Mewton
Affiliation:
University of New South Wales, Sydney, NSW, Australia
Tim Slade
Affiliation:
The Matilda Centre for Research in Mental Health and Substance Use, University of Sydney, Sydney, NSW, Australia
*
Author for correspondence: Jack L. Andrews, E-mail: jack.andrews@unsw.edu.au
Get access Rights & Permissions [Opens in a new window]

Abstract

Background

Lifetime trajectories of mental ill-health are often established during adolescence. Effective interventions to prevent the emergence of mental health problems are needed. In the current study we assessed the efficacy of the cognitive behavioural therapy (CBT)-informed Climate Schools universal eHealth preventive mental health programme, relative to a control. We also explored whether the intervention had differential effects on students with varying degrees of social connectedness.

Method

We evaluated the efficacy of the Climate Schools mental health programme (19 participating schools; average age at baseline was 13.6) v. a control group (18 participating schools; average age at baseline was 13.5) which formed part of a large cluster randomised controlled trial in Australian schools. Measures of internalising problems, depression and anxiety were collected at baseline, immediately following the intervention and at 6-, 12- and 18-months post intervention. Immediately following the intervention, 2539 students provided data on at least one outcome of interest (2065 students at 18 months post intervention).

Results

Compared to controls, we found evidence that the standalone mental health intervention improved knowledge of mental health, however there was no evidence that the intervention improved other mental health outcomes, relative to a control. Student's social connectedness did not influence intervention outcomes.

Conclusion

These results are consistent with recent findings that universal school-based, CBT-informed, preventive interventions for mental health have limited efficacy in improving symptoms of anxiety and depression when delivered alone. We highlight the potential for combined intervention approaches, and more targeted interventions, to better improve mental health outcomes.

Keywords

Adolescencemental healthpreventionrandomised controlled trialschool interventionsocial networks
Type
Original Article
Information
Psychological Medicine , Volume 53 , Issue 11 , August 2023 , pp. 5042 - 5051
Check for updates
Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

*

Joint senior authors.

References

Andrews, J. L., Ahmed, S. P., & Blakemore, S.-J. (2021). Navigating the social environment in adolescence: The role of social brain development. Biological Psychiatry, 89(2), 109–118. https://doi.org/10.1016/j.biopsych.2020.09.012.CrossRefGoogle ScholarPubMed
Bastounis, A., Callaghan, P., Banerjee, A., & Michail, M. (2016). The effectiveness of the Penn Resiliency Programme (PRP) and its adapted versions in reducing depression and anxiety and improving explanatory style: A systematic review and meta-analysis. Journal of Adolescence, 52, 37–48. https://doi.org/10.1016/j.adolescence.2016.07.004.CrossRefGoogle Scholar
Birrell, L., Furneaux-Bate, A., Chapman, C., & Newton, N. C. (2021). A mobile peer intervention for preventing mental health and substance use problems in adolescents: Protocol for a randomized controlled trial (The Mind Your Mate Study). JMIR Research Protocols, 10(7), e26796. https://doi.org/10.2196/26796.CrossRefGoogle ScholarPubMed
Caldwell, D., Davies, S., Hetrick, S., Palmer, J., Caro, P., López-López, J., … Welton, N. (2019). School-based interventions to prevent anxiety and depression in children and young people: A systematic review and network meta-analysis. The Lancet Psychiatry, 6(12), 1011–1020. https://doi.org/10.1016/S2215-0366(19)30403-1.CrossRefGoogle Scholar
Cuijpers, P., Smit, F., & Furukawa, T. A. (2021). Most at-risk individuals will not develop a mental disorder: The limited predictive strength of risk factors. World Psychiatry, 20(2), 224–225. https://doi.org/10.1002/wps.20852.CrossRefGoogle Scholar
Dray, J., Bowman, J., Campbell, E., Freund, M., Wolfenden, L., Hodder, R. K., … Wiggers, J. (2017). Systematic review of universal resilience-focused interventions targeting child and adolescent mental health in the school setting. Journal of the American Academy of Child & Adolescent Psychiatry, 56(10), 813–824. https://doi.org/10.1016/j.jaac.2017.07.780.CrossRefGoogle ScholarPubMed
Elmer, T., & Stadtfeld, C. (2020). Depressive symptoms are associated with social isolation in face-to-face interaction networks. Scientific Reports, 10(1), 1444. https://doi.org/10.1038/s41598-020-58297-9.CrossRefGoogle ScholarPubMed
Fazel, M., & Kohrt, B. A. (2019). Prevention versus intervention in school mental health. The Lancet. Psychiatry, 6(12), 969–971. https://doi.org/10.1016/S2215-0366(19)30440-7.CrossRefGoogle ScholarPubMed
Garrido, S., Millington, C., Cheers, D., Boydell, K., Schubert, E., Meade, T., … Nguyen, Q. V. (2019). What works and what doesn't work? A systematic review of digital mental health interventions for depression and anxiety in young people. Frontiers in Psychiatry, 10, 759. Retrieved from https://www.frontiersin.org/article/10.3389/fpsyt.2019.00759.CrossRefGoogle ScholarPubMed
Goodman, A., Lamping, D. L., & Ploubidis, G. B. (2010). When to use broader internalising and externalising subscales instead of the hypothesised five subscales on the strengths and difficulties questionnaire (SDQ): Data from British parents, teachers and children. Journal of Abnormal Child Psychology, 38(8), 1179–1191. https://doi.org/10.1007/s10802-010-9434-x.CrossRefGoogle ScholarPubMed
Hu, T. (2006). Perspectives: An international review of the national cost estimates of mental illness, 1990–2003. The Journal of Mental Health Policy and Economics, 9(1), 3–13.Google ScholarPubMed
Johnson, J. G., Harris, E. S., Spitzer, R. L., & Williams, J. B. W. (2002). The patient health questionnaire for adolescents: Validation of an instrument for the assessment of mental disorders among adolescent primary care patients. The Journal of Adolescent Health, 30(3), 196–204. https://doi.org/10.1016/s1054-139x(01)00333-0.CrossRefGoogle ScholarPubMed
Kessler, R. C., Angermeyer, M., Anthony, J. C., De Graaf, R., Demyttenaere, K., Gasquet, I., … Üstün, T. B. (2007). Lifetime prevalence and age-of-onset distributions of mental disorders in the world health organization's world mental health survey initiative. World Psychiatry, 6(3), 168–176.Google ScholarPubMed
Kessler, R. C., Barker, P. R., Colpe, L. J., Epstein, J. F., Gfroerer, J. C., Hiripi, E., … Zaslavsky, A. M. (2003). Screening for serious mental illness in the general population. Archives of General Psychiatry, 60(2), 184–189. https://doi.org/10.1001/archpsyc.60.2.184.CrossRefGoogle ScholarPubMed
Lawrence, D., Johnson, S., Hafekost, J., de Haan, K. B., Sawyer, M., Ainley, J., … Zubrick, S. (2015). The mental health of children and adolescents: Report on the second Australian child and adolescent survey of mental health and wellbeing. Commonwealth of Australia (pp. 23–39). Retrieved from https://research.acer.edu.au/well_being/1.Google ScholarPubMed
Levis, B., Benedetti, A., & Thombs, B. D. (2019). Accuracy of Patient Health Questionnaire-9 (PHQ-9) for screening to detect major depression: Individual participant data meta-analysis. BMJ, 365, l1476. https://doi.org/10.1136/bmj.l1476.CrossRefGoogle ScholarPubMed
Lewis, F., Butler, A., & Gilbert, L. (2011). A unified approach to model selection using the likelihood ratio test. Methods in Ecology and Evolution, 2(2), 155–162. https://doi.org/10.1111/j.2041-210X.2010.00063.x.CrossRefGoogle Scholar
MacArthur, G. J., Harrison, S., Caldwell, D. M., Hickman, M., & Campbell, R. (2016). Peer-led interventions to prevent tobacco, alcohol and/or drug use among young people aged 11–21 years: A systematic review and meta-analysis. Addiction, 111(3), 391–407. https://doi.org/10.1111/add.13224.CrossRefGoogle Scholar
Merikangas, K. R., He, J., Burstein, M., Swanson, S. A., Avenevoli, S., Cui, L., … Swendsen, J. (2010). Lifetime prevalence of mental disorders in US adolescents: Results from the national comorbidity study-adolescent supplement (NCS-A). Journal of the American Academy of Child and Adolescent Psychiatry, 49(10), 980–989. https://doi.org/10.1016/j.jaac.2010.05.017.CrossRefGoogle Scholar
Newton, N. C., Chapman, C., Slade, T., Birrell, L., Healy, A., Mather, M., … Teesson, M. (2020). A national effectiveness trial of an eHealth program to prevent alcohol and cannabis misuse: Responding to the replication crisis. Psychological Medicine, 52(2), 274–282. https://doi.org/10.1017/S0033291720001919.CrossRefGoogle Scholar
Okamoto, J., Johnson, C. A., Leventhal, A., Milam, J., Pentz, M. A., Schwartz, D., & Valente, T. W. (2011). Social network status and depression among adolescents: An examination of social network influences and depressive symptoms in a Chinese sample. Research in Human Development, 8(1), 67–88. https://doi.org/10.1080/15427609.2011.549711.CrossRefGoogle Scholar
Paluck, E. L., Shepherd, H., & Aronow, P. M. (2016). Changing climates of conflict: A social network experiment in 56 schools. Proceedings of the National Academy of Sciences, 113(3), 566–571. https://doi.org/10.1073/pnas.1514483113.CrossRefGoogle ScholarPubMed
Patton, G. C., Coffey, C., Romaniuk, H., Mackinnon, A., Carlin, J. B., Degenhardt, L., … Moran, P. (2014). The prognosis of common mental disorders in adolescents: A 14-year prospective cohort study. The Lancet, 383(9926), 1404–1411. https://doi.org/10.1016/S0140-6736(13)62116-9.CrossRefGoogle ScholarPubMed
Schielzeth, H., Dingemanse, N. J., Nakagawa, S., Westneat, D. F., Allegue, H., Teplitsky, C., … Araya-Ajoy, Y. G. (2020). Robustness of linear mixed-effects models to violations of distributional assumptions. Methods in Ecology and Evolution, 11(9), 1141–1152. https://doi.org/10.1111/2041-210X.13434.CrossRefGoogle Scholar
Spitzer, R. L., Kroenke, K., Williams, J. B. W., & Löwe, B. (2006). A brief measure for assessing generalized anxiety disorder: The GAD-7. Archives of Internal Medicine, 166(10), 1092–1097. https://doi.org/10.1001/archinte.166.10.1092.CrossRefGoogle ScholarPubMed
Tanner-Smith, E. E., & Grant, S. (2018). Meta-analysis of complex interventions. Annual Review of Public Health, 39(1), 135–151. https://doi.org/10.1146/annurev-publhealth-040617-014112.CrossRefGoogle ScholarPubMed
Teesson, M., Newton, N. C., Slade, T., Chapman, C., Allsop, S., Hides, L., … Andrews, G. (2014). The CLIMATE schools combined study: A cluster randomised controlled trial of a universal internet-based prevention program for youth substance misuse, depression and anxiety. BMC Psychiatry, 14(1), 32. https://doi.org/10.1186/1471-244X-14-32.CrossRefGoogle ScholarPubMed
Teesson, M., Newton, N. C., Slade, T., Chapman, C., Birrell, L., Mewton, L., … Andrews, G. (2020). Combined prevention for substance use, depression, and anxiety in adolescence: A cluster-randomised controlled trial of a digital online intervention. The Lancet Digital Health, 2(2), e74–e84. https://doi.org/10.1016/S2589-7500(19)30213-4.CrossRefGoogle ScholarPubMed
Tomova, L., Andrews, J. L., & Blakemore, S.-J. (2021). The importance of belonging and the avoidance of social risk taking in adolescence. Developmental Review, 61, 100981. https://doi.org/10.1016/j.dr.2021.100981.CrossRefGoogle Scholar
Ueno, K. (2005). The effects of friendship networks on adolescent depressive symptoms. Social Science Research, 34, 484–510. https://doi.org/10.1016/j.ssresearch.2004.03.002.CrossRefGoogle Scholar
Valente, T. W., Ritt-Olson, A., Stacy, A., Unger, J. B., Okamoto, J., & Sussman, S. (2007). Peer acceleration: Effects of a social network tailored substance abuse prevention program among high-risk adolescents. Addiction (Abingdon, England), 102(11), 1804–1815. https://doi.org/10.1111/j.1360-0443.2007.01992.x.CrossRefGoogle ScholarPubMed
Werner-Seidler, A., Perry, Y., Calear, A. L., Newby, J. M., & Christensen, H. (2017). School-based depression and anxiety prevention programs for young people: A systematic review and meta-analysis. Clinical Psychology Review, 51, 30–47. https://doi.org/10.1016/j.cpr.2016.10.005.CrossRefGoogle ScholarPubMed
Werner-Seidler, A., Spanos, S., Calear, A. L., Perry, Y., Torok, M., O'Dea, B., … Newby, J. M. (2021). School-based depression and anxiety prevention programs: An updated systematic review and meta-analysis. Clinical Psychology Review, 89, 102079. https://doi.org/10.1016/j.cpr.2021.102079.CrossRefGoogle ScholarPubMed
Whiteford, H. A., Degenhardt, L., Rehm, J., Baxter, A. J., Ferrari, A. J., Erskine, H. E., … Vos, T. (2013). Global burden of disease attributable to mental and substance use disorders: Findings from the global burden of disease study 2010. The Lancet, 382(9904), 1575–1586. https://doi.org/10.1016/S0140-6736(13)61611-6.CrossRefGoogle ScholarPubMed
Wyman, P. A., Pickering, T. A., Pisani, A. R., Rulison, K., Schmeelk-Cone, K., Hartley, C., … Valente, T. W. (2019). Peer-adult network structure and suicide attempts in 38 high schools: Implications for network-informed suicide prevention. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 60(10), 1065–1075. https://doi.org/10.1111/jcpp.13102.CrossRefGoogle ScholarPubMed
Yeager, D. S., Dahl, R. E., & Dweck, C. S. (2018). Why interventions to influence adolescent behavior often fail but could succeed. Perspectives on Psychological Science: A Journal of the Association for Psychological Science, 13(1), 101–122. https://doi.org/10.1177/1745691617722620.CrossRefGoogle ScholarPubMed
Supplementary material: File

Andrews et al. supplementary material

Andrews et al. supplementary material

Download Andrews et al. supplementary material(File)
File 512.6 KB