Susan Fletcher1, Patty Chondros1, Konstancja Densley1, Elizabeth Murray2, Christopher Dowrick3, Amy Coe1, Kelsey Hegarty4, Sandra Davidson1, Caroline Wachtler5, Cathrine Mihalopoulos6, Yong Yi Lee7, Mary Lou Chatterton6, Victoria J Palmer1, Jane Gunn8. 1. Department of General Practice, Melbourne Medical School, University of Melbourne, Melbourne, Australia. 2. Department of General Practice, Melbourne Medical School, University of Melbourne, Melbourne, Australia; professor of eHealth and primary care, Research Department of Primary Care and Population Health, University College London, London, UK. 3. Department of General Practice, Melbourne Medical School, University of Melbourne, Melbourne, Australia; professor of primary medical care, Department of Health Services Research, University of Liverpool, Liverpool, UK. 4. Department of General Practice, Melbourne Medical School, University of Melbourne; director, Centre for Family Violence Prevention, The Royal Women's Hospital, Melbourne, Australia. 5. Department of General Practice, Melbourne Medical School, University of Melbourne, Melbourne, Australia; family medicine resident, Department of General Practice and Primary Care, Karolinska Institutet, Solna, Sweden. 6. Deakin Health Economics, Institute for Health Transformation, Deakin University, Geelong, Australia. 7. Deakin Health Economics, Institute for Health Transformation, Deakin University, Geelong; honorary fellow, School of Public Health, University of Queensland, Brisbane; health economist, Policy and Epidemiology Group, Queensland Centre for Mental Health Research, Brisbane, Australia. 8. Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne; chair of primary care research, Department of General Practice, Melbourne Medical School, University of Melbourne, Melbourne, Australia.
Abstract
BACKGROUND: Mental health treatment rates are increasing, but the burden of disease has not reduced. Tools to support efficient resource distribution are required. AIM: To investigate whether a person-centred e-health (Target-D) platform matching depression care to symptom severity prognosis can improve depressive symptoms relative to usual care. DESIGN AND SETTING: Stratified individually randomised controlled trial in 14 general practices in Melbourne, Australia, from April 2016 to February 2019. In total, 1868 participants aged 18-65 years who had current depressive symptoms; internet access; no recent change to antidepressant; no current antipsychotic medication; and no current psychological therapy were randomised (1:1) via computer-generated allocation to intervention or usual care. METHOD: The intervention was an e-health platform accessed in the GP waiting room, comprising symptom feedback, priority-setting, and prognosis-matched management options (online self-help, online guided psychological therapy, or nurse-led collaborative care). Management options were flexible, neither participants nor staff were blinded, and there were no substantive protocol deviations. The primary outcome was depressive symptom severity (9-item Patient Health Questionnaire [PHQ-9]) at 3 months. RESULTS: In intention to treat analysis, estimated between- arm difference in mean PHQ-9 scores at 3 months was -0.88 (95% confidence interval [CI] = -1.45 to -0.31) favouring the intervention, and -0.59 at 12 months (95% CI = -1.18 to 0.01); standardised effect sizes of -0.16 (95% CI = -0.26 to -0.05) and -0.10 (95% CI = -0.21 to 0.002), respectively. No serious adverse events were reported. CONCLUSION: Matching management to prognosis using a person-centred e-health platform improves depressive symptoms at 3 months compared to usual care and could feasibly be implemented at scale. Scope exists to enhance the uptake of management options.
BACKGROUND: Mental health treatment rates are increasing, but the burden of disease has not reduced. Tools to support efficient resource distribution are required. AIM: To investigate whether a person-centred e-health (Target-D) platform matching depression care to symptom severity prognosis can improve depressive symptoms relative to usual care. DESIGN AND SETTING: Stratified individually randomised controlled trial in 14 general practices in Melbourne, Australia, from April 2016 to February 2019. In total, 1868 participants aged 18-65 years who had current depressive symptoms; internet access; no recent change to antidepressant; no current antipsychotic medication; and no current psychological therapy were randomised (1:1) via computer-generated allocation to intervention or usual care. METHOD: The intervention was an e-health platform accessed in the GP waiting room, comprising symptom feedback, priority-setting, and prognosis-matched management options (online self-help, online guided psychological therapy, or nurse-led collaborative care). Management options were flexible, neither participants nor staff were blinded, and there were no substantive protocol deviations. The primary outcome was depressive symptom severity (9-item Patient Health Questionnaire [PHQ-9]) at 3 months. RESULTS: In intention to treat analysis, estimated between- arm difference in mean PHQ-9 scores at 3 months was -0.88 (95% confidence interval [CI] = -1.45 to -0.31) favouring the intervention, and -0.59 at 12 months (95% CI = -1.18 to 0.01); standardised effect sizes of -0.16 (95% CI = -0.26 to -0.05) and -0.10 (95% CI = -0.21 to 0.002), respectively. No serious adverse events were reported. CONCLUSION: Matching management to prognosis using a person-centred e-health platform improves depressive symptoms at 3 months compared to usual care and could feasibly be implemented at scale. Scope exists to enhance the uptake of management options.
Authors: David A Richards; Peter Bower; Carolyn Chew-Graham; Linda Gask; Karina Lovell; John Cape; Stephen Pilling; Ricardo Araya; David Kessler; Michael Barkham; J Martin Bland; Simon Gilbody; Colin Green; Glyn Lewis; Chris Manning; Evangelos Kontopantelis; Jacqueline J Hill; Adwoa Hughes-Morley; Abigail Russell Journal: Health Technol Assess Date: 2016-02 Impact factor: 4.014
Authors: Yong Yi Lee; Cathrine Mihalopoulos; Mary Lou Chatterton; Susan L Fletcher; Patty Chondros; Konstancja Densley; Elizabeth Murray; Christopher Dowrick; Amy Coe; Kelsey L Hegarty; Sandra K Davidson; Caroline Wachtler; Victoria J Palmer; Jane M Gunn Journal: PLoS One Date: 2022-05-25 Impact factor: 3.752
Authors: Gemma Skaczkowski; Shannen van der Kruk; Sophie Loxton; Donna Hughes-Barton; Cate Howell; Deborah Turnbull; Neil Jensen; Matthew Smout; Kate Gunn Journal: JMIR Ment Health Date: 2022-02-08