Matching depression management to severity prognosis in primary care: results of the Target-D randomised controlled trial

Susan Fletcher; Patty Chondros; Konstancja Densley; Elizabeth Murray; Christopher Dowrick; Amy Coe; Kelsey Hegarty; Sandra Davidson; Caroline Wachtler; Cathrine Mihalopoulos; Yong Yi Lee; Mary Lou Chatterton; Victoria J Palmer; Jane Gunn

doi:10.3399/BJGP.2020.0783

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.

INTRODUCTION

Despite significant investment in improving access to care,¹ depression remains a leading contributor to the burden of disease and constitutes a significant public health issue.² This in part reflects suboptimal targeting of care, with both over- and under-treatment occurring.¹^–³ Treatment guidelines and policy initiatives have aimed to address this mismatch by encouraging provision of the least intensive intervention that is likely to be effective for an individual (an approach known as stepped care).⁴^,⁵ However, there is currently no consensus as to how the appropriate level of intervention intensity is identified. Therefore, building the evidence base to support the implementation of stepped care is key to reducing the time and resources currently required to identify an individual’s mental needs and match them to care accordingly.⁶^–⁸

Such evidence is particularly important for primary care, where the majority of depression care is delivered.⁹^,¹⁰ Currently, GPs rely mostly on clinical judgement when allocating depression care, which can be a time-consuming and inconsistent process.⁸ This is in contrast to other areas of medicine where a range of clinical prediction tools (CPTs) are available to streamline systematic decision making,¹¹^–¹⁵ although there is increasing recognition that such tools must be user-friendly and action-oriented in order to be successfully translated in routine practice.¹⁶ To address this gap the authors developed a CPT that uses self-reported biopsychosocial data to classify individuals into one of three prognostic groups based on the predicted severity of their depressive symptoms in 3 months’ time (minimal/mild, moderate, or severe).¹⁷ The CPT was then embedded into an e-health platform¹⁸ (henceforth referred to as the Target-D platform), which was designed using the principles of motivational interviewing¹⁹ and psychologically-driven goal modelling²⁰ to deliver a person-centred approach to depression care.¹⁸ The Target-D platform provides patients with feedback on their responses, an opportunity to reflect on their mental health priorities and motivation to change, and a management option matched to their severity prognosis.

Depression is a leading contributor to the global burden of disease and a significant problem in primary care, where it is typically identified and managed. Stepped care approaches are recommended but difficult to implement in routine care, due in part to a lack of effective tools to guide GPs in matching intervention intensity to patient need. Therefore, a clinical prediction tool was developed, which was embedded into a person-centred e-health platform, that matches depression management options to symptom severity prognosis. This randomised controlled trial showed using this platform results in greater improvement in depressive symptoms at 3 months compared to usual care. This approach could be implemented in routine care to support more efficient and effective depression care without adding to GPs’ workload.

How this fits in

In the Target-D randomised controlled trial (RCT), the primary aim was to investigate whether a complex intervention comprising the Target-D platform and matched management options for primary care patients with depressive symptoms improved depressive symptoms at 3-month follow-up, relative to usual care plus attention control (UC+). Secondary aims were to test for an intervention effect at 12 months overall, and within prognostic groups at both 3 and 12 months.

METHOD

Study design

This is a stratified individual RCT, enrolling primary care patients who screened positive for depressive symptoms (see protocol for details²¹). The intervention period lasted 3 months. No substantive changes to the published protocol were made.

Participants

Research assistants (RAs) recruited participants from the waiting rooms of 14 general practices in metropolitan Melbourne, Australia (Supplementary Appendix S1 describes practice characteristics). Adults aged 18–65 years were invited to complete an eligibility survey on an iPad, and were eligible if they reported: current depressive symptoms (≥2 on the 2-item version of the Patient Health Questionnaire [PHQ-2]²²); no self-reported change to antidepressant medication in the past month; had access to the internet; and sufficient written English to follow an internet-based cognitive behavioural therapy (iCBT) programme. Patients reporting current use of antipsychotic medication or receipt of psychological therapy (online or face-to-face) were ineligible.

Randomisation occurred after participants provided informed consent and completed baseline measures (including items required for the CPT), all integrated with the Target-D platform on a purpose-built website accessible on any internet-enabled device.

Interventions

All participants received an automated email encouraging them to speak with their GP if they had concerns about their mental health and providing contact details for community-based services (for example, crisis support lines).

Intervention arm

Individuals received CPT feedback, set priorities, and received a management option matched to the prognostic group (see Supplementary Appendix S2). Briefly:

Minimal/mild: myCompass online programme,²³ a CBT-based self-help resource where participants could choose from 15 modules (such as, ‘Tackling Unhelpful Thinking’ and ‘Communicating Clearly’).
Moderate: Worry and Sadness course of the This Way Up iCBT programme,²⁴ which required participants to work through six lessons in sequence.
Severe: nurse-led collaborative care including up to eight contacts (over telephone or in person) with a trained research nurse to develop and implement a tailored depression management plan in conjunction with their GP.²⁵^–²⁹

Control arm

Individuals received UC+ telephone call from an RA about trial involvement and to seek views about research participation.

Randomisation and blinding

Participants were randomly assigned to a trial arm (1:1, stratified by practice and prognostic group) using a computer- generated biased-coin algorithm with an imbalance intolerance of three embedded in the purpose-built website (see Supplementary Appendix S3).

Due to the nature of the intervention, participants could not be blinded to their allocated management option. Staff involved in intervention delivery (for example, RAs discussing management options or nurses delivering collaborative care) were also unblinded. GPs were notified only of participants allocated to collaborative care, with no emergency unblinding required. RAs responsible for contacting participants at follow-up were blinded to trial arm and prognostic group. All analyses were conducted and discussed while statisticians and study investigators remained blind to trial arm allocation.

Outcomes

Data were collected primarily via online survey at baseline and at 3- and 12-months post-randomisation. At each time-point, non-responders received phone, text, and/or email reminders and were offered alternative options for completion (such as via hard copy or phone). At trial enrolment, participants provided information on demographic (age, sex, education, and employment) and clinical characteristics relevant to trial exclusion criteria.

The primary outcome was depressive symptom scores at 3 months, assessed using the 9-item Patient Health Questionnaire (PHQ–9).³⁰

Secondary outcomes included: depressive symptom severity at 12 months; anxiety symptom severity assessed using the 7-item Generalised Anxiety Disorder scale (GAD-7);³¹ mental health self-efficacy measured using the Mental Health Self- Efficacy Scale (MHSES);³² and quality of life using the Assessment of Quality of Life Instrument (AQoL-8D)³³ at 3 and 12 months.

Sample size

For the primary hypothesis, 1320 participants (n = 660 per arm) provided 90% power at 5% significance two-tailed alpha to detect a standardised mean difference (SMD) of 0.2 in depressive symptoms at 3 months, assuming 20% attrition over 12 months of the follow-up period. For secondary hypotheses, the authors had 80% power to detect a between-arm SMD of 0.2 in depressive symptoms in the minimal/mild group and 0.5 within the moderate and severe groups, respectively. Calculations assumed 70% (n = 924) of participants would be in the minimal\mild group and 15% (n = 198) in each of the moderate and severe groups.

Statistical methods

All analyses were pre-defined in the statistical analysis plan³⁴ and conducted using Stata (version 15). Main analyses employed an intention to treat (ITT) approach, where all participants were analysed in the trial arm to which they were allocated. Differences in mean outcomes between trial arms (intervention effect) were estimated with linear mixed- effects models, using restricted maximum likelihood with random intercepts for individuals. Stratification factors (practice or prognostic group) and time (baseline, 3, and 12 months) were included as fixed effects, with a two-way interaction between arm and time, except at baseline where trial-arm means were constrained to be equal. Similar mixed effects analysis was conducted for each prognostic group. Sensitivity analyses included random-effects for nurse in the severe group and assessed the robustness of the missing data assumption (see Supplementary Appendix S4). Complier average causal effect (CACE) analysis³⁵ used a two stage-least squares instrumental regression with trial arm used as the instrumental variable for adherence to treatment.³⁴

RESULTS

Figure 1 shows the trial profile. There were 1868 participants in total (n = 1270 female; n = 590 male; n = 8 other; mean age 35.5 [standard deviation = 12.1] years). The CPT classified 1357 (72.6%) participants to the minimal/mild group, 288 (15.4%) to the moderate group and 223 (11.9%) to the severe group. Some differential attrition was evident, with retention higher in the control arm overall (and within the minimal/mild and moderate prognostic groups). In the severe group, retention was higher in the intervention arm. Participants in the two trial arms were similar, overall and within prognostic groups (see Table 1 and Supplementary Appendix S1).

Figure 1.

Participant flow through the trial. Denominators used to calculate the percentage with follow-up at 3- and 12-months are the total number of participants randomised overall and within each prognostic group. None of the withdrawals requested that their data be withdrawn prior to the statistical analysis. CPT = clinical prediction tool. ITT = intention to treat.

View this table:

Table 1.

Baseline characteristics of participant according to trial arm, in total and stratified by prognostic group, N = 1868

Primary outcome

The estimated difference in mean depressive scores at 3 months was −0.88, favouring the intervention arm (95% confidence interval [CI] = −1.45 to −0.31) (see Table 2 and Figure 2), equivalent to a SMD of −0.16 (95% CI = −0.26 to −0.05). Findings were robust to different missing data assumptions (see Supplementary Appendix S4).

Figure 2.

Estimated mean depressive symptom severity (9-item Patient Health Questionnaire) with 95% confidence intervals for each trial arm, in total and by prognostic group and time-point.

View this table:

Table 2.

Depressive symptom severity (PHQ-9) score according to trial arm, in total and stratified by prognostic group

Secondary outcomes

At 12 months, weak evidence supported a smaller intervention effect on depressive symptoms at 12 months overall (see Table 2 and Figure 2), but no evidence for a difference in mean anxiety symptom severity between trial arms (see Table 3 and Supplementary Appendix S4). At 3 months mean mental health self-efficacy was 1.39 points higher in the intervention arm compared to the control arm (95% CI = 0.31 to 2.46). There was no evidence of an overall difference in mean quality of life between trial arms, although within the moderate group, mean scores were 0.05 points higher in the intervention arm (95% CI = 0.01 to 0.09) compared to control arm.

View this table:

Table 3.

Estimated difference in mean between trial arms for secondary outcomes, in total and stratified by prognostic group

Adherence-adjusted analyses

Five (0.7%) of 679 intervention participants in the minimal/mild group completed at least one myCompass module, and eight (5.6%) of 143 participants in the moderate group completed the Worry and Sadness course in full (see Supplementary Appendix S4). Given the few completers in these groups, no further planned analyses were conducted.

In the severe group, 64 participants (57.7%) attended at least one collaborative care appointment and 30 (27.0%) of 111 participants attended all eight (see Supplementary Appendix S4). Participants who completed all eight collaborative care appointments had a 5.2-point greater reduction in mean PHQ-9 score at 3 months (95% CI = −10.9 to 0.44) compared to their control arm counterparts, equivalent to an SMD of −1.4 (95% CI = −3.0 to 0.12) (data not shown).

DISCUSSION

Summary

To the authors’ knowledge, this is the first RCT of a person-centred e-health platform supporting prognosis-based allocation of depression management in primary care. For the primary outcome, results favoured the intervention overall, although the effect size was small. Pre-specified adherence-adjusted analysis identified greater improvements associated with completion of collaborative care in the severe group. The intervention effect on depressive symptoms had diminished by 12 months, and few differences were observed on secondary outcomes.

Strengths and limitations

Strengths include: individual randomisation and a primary outcome measure that allows comparison with international studies; successful recruitment to target with a balance of baseline characteristics between trial arms; primary outcome completion rates comparable to previous stepped depression care trials in primary care;³⁶^–⁴¹ and a pragmatic design that tested a model of care designed for scalability. However, low uptake of online management options limited the ability to complete planned analyses, and the wide availability of depression care in Australian primary care⁴² (see Supplementary Appendix S5) may have reduced the potential for the intervention to improve on usual care. The focus on depression may reduce generalisability to other mental health conditions and the authors did not assess symptom duration at enrolment, although a low threshold for eligibility was set and interventions were not disorder specific. Greater attrition than anticipated was observed and finally, the approach was limited to initial allocation only and intervention intensity was not adjusted according to participant response.

Comparison with existing literature

Development of revolutionary new treatments for depression is considered unlikely.⁴³ Instead, efforts to reduce the burden of disease have focused on better tailoring of existing interventions, leading to the development and testing of a range of stepped care approaches. While overall effect size at 3 months (−0.16) was lower than that reported in a stepped care meta-analysis (−0.34),⁴⁴ CIs included the clinically relevant value of −0.24 proposed by Cuijpers and colleagues.⁴⁵ Importantly, effect in the present study was achieved through delivery of a minimally time- and resource-intensive intervention in a routine setting across a large number of practices. Further, the meta-analysis by Cuijpers and colleagues⁴⁵ assessed the effectiveness of stepped care in people meeting diagnostic criteria for depression whereas effect in the present study was observed in a heterogenous sample reflecting real-life primary care.⁴⁶

Within prognostic groups, observed effect was smaller than that reported in previous RCTs of myCompass³² and This Way Up⁴⁷^,⁴⁸ but similar to iCBT effect sizes in primary care settings.⁴⁹^,⁵⁰ This is further compounded by the low rates of programme completion, although even if all participants completed their recommended programme, they had limited room for symptom improvement. Within the severe group, estimated effect size was consistent with previous trials of collaborative care,²⁵^,⁵¹^–⁵⁴ contributing to the growing literature showing that nurse-delivered collaborative care is both effective and acceptable in the management of depression.⁵⁵ Participants who completed the full course of collaborative care reported substantial improvements, the effect size comparing favourably to that associated with antidepressants.⁵⁶ Analysis of the characteristics of completers and their tailored management plans is underway to refine the intervention to enhance uptake and completion (manuscript in preparation).

This novel, theory-driven approach provides not only assessment but a prompt to reflect on priorities and motivation, aiming to empower patients to take ownership of their mental health care. Patients are triaged to care according to their predicted severity of depressive symptoms in 3 months’ time, rather than severity when first assessed. This approach incorporates broader determinants of poor mental health (for example, financial and physical health difficulties), which are critical to delivering comprehensive primary care, and recognises that mild and transitory depressive symptoms are prevalent and will often resolve without formal intervention. Current policy aims to redirect the minimal/mild group away from face-to-face services and towards lower intensity (including online) interventions, in line with clinical guidelines.⁴^,⁵ However, present study findings suggest simply recommending these interventions, even when designed to activate patients towards uptake,¹⁸ was insufficient to encourage their use. This experience is not unique; research and policy interest in online interventions has not yet translated into their widespread use (and the multifaceted reasons for this are discussed elsewhere).⁵⁷^–⁶¹ This is a rapidly evolving field and emerging health, social, and economic levers may improve acceptability of online interventions, and thus the potential for an approach like Target-D to serve as an effective conduit. For instance, public health crises such as the COVID-19 pandemic may necessitate greater engagement with online programmes⁶² due to overburdened health systems and a pressing need to efficiently triage people to mental health care without lengthy consultation in general practice.

Implications for research and practice

Mental health remains the predominant issue managed in primary care,⁶³ despite substantial and sustained investment. Worldwide, health systems face the challenge of ensuring that investments are well targeted to optimise patient outcomes and experiences of care. It is likely that improvements in mental health care will be incremental and gained by ongoing optimisation of promising approaches. The Target-D person-centred, e-health platform, which can quickly and easily triage and tailor depression care to severity prognosis, is a promising component of stepped mental health care. The authors present the cost-effectiveness of this approach elsewhere.⁶⁴ While the trial was not set up to test the effectiveness of the Target-D platform and matched management options in preventing disorder onset, this may be an avenue for future research. Alternatively, an option for implementation may be to offer the Target-D platform to all patients but provide matched management options only to the moderate and severe groups where the potential for improvement is greater. Findings also support further research into how to optimise uptake, particularly of low intensity services.

Acknowledgments

The authors thank all the patients, GPs, and general practices who took part in Target-D, and the many research assistants who assisted with data collection. The data used to develop the clinical prediction tool were collected as a part of the diamond project, which was funded by the NHMRC (references: 299869, 454463, 566511, 1002908). The authors acknowledge the 30 dedicated GPs, their patients, and general practice staff for making the diamond study possible. The authors also acknowledge staff and students at the School of Computing and Information Systems at the University of Melbourne for early work that informed the presentation of the e-health platform as well as the focus group participants that provided feedback on early versions of the Target-D materials. Finally, the authors thank staff at the former Melbourne Networked Society Institute, who were funded to build the Target-D website.

Notes

Funding

Target-D was funded by a grant from the National Health and Medical Research Council (NHMRC) (reference: 1059863, 2014–2018). The funding body had no role in the design and conduct of the study; collection, management analysis, and interpretation of the data; preparation, review or approval of the manuscript; or decision to submit the manuscript for publication.

Ethical approval

Ethical approval was provided by the Human Research Ethics Committee at the University of Melbourne (reference: 1543648)

Competing interests

The authors have declared no competing interests.

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Received August 20, 2020.
Revision requested November 18, 2020.
Accepted December 11, 2020.

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REFERENCES

1.↵
1. Jorm AF,
2. Patten SB,
3. Brugha TS,
4. Mojtabai R
(2017) Has increased provision of treatment reduced the prevalence of common mental disorders? Review of the evidence from four countries. World Psychiatry 16, 1, 90–99.
OpenUrl CrossRef PubMed
2.↵
1. Ferrari AJ,
2. Charlson FJ,
3. Norman RE,
4. et al.
(2013) Burden of depressive disorders by country, sex, age, and year: findings from the Global Burden of Disease Study 2010. PLoS Med 10, 11, e1001547.
OpenUrl CrossRef PubMed
3.↵
1. Dowrick C,
2. Frances A
(2013) Medicalising unhappiness: new classification of depression risks more patients being put on drug treatment from which they will not benefit. BMJ 347, f7140.
OpenUrl FREE Full Text
4.↵
1. National Institute for Health and Care Excellence
(2009) Depression in adults (update) Depression: the treatment and management of depression in adults, https://www.nice.org.uk/guidance/cg90/documents/depression-in-adults-update-full-guideline-prepublication2 (accessed 22 Dec 2020).
5.↵
1. Australian Government Department of Health
(2015) Australian government response to Contributing Lives, Thriving Communities — review of mental health programmes and services (Australian Government Department of Health, Canberra).
6.↵
1. Silverstone PH,
2. Rittenbach K,
3. Suen VYM,
4. et al.
(2017) Depression outcomes in adults attending family practice were not improved by screening, stepped-care, or online CBT during a 12-week study when compared to controls in a randomized trial. Front Psychiatry 8, 32.
OpenUrl
7.
1. Franx G,
2. Oud M,
3. de Lange J,
4. et al.
(2012) Implementing a stepped-care approach in primary care: results of a qualitative study. Implement Sci 7, 8.
OpenUrl CrossRef PubMed
8.↵
1. Hermens MLM,
2. Muntingh A,
3. Franx G,
4. et al.
(2014) Stepped care for depression is easy to recommend, but harder to implement: results of an explorative study within primary care in the Netherlands. BMC Fam Pract 15, 5.
OpenUrl CrossRef PubMed
9.↵
1. Parslow R,
2. Jorm A
(2000) Who uses mental health services in Australia? An analysis of data from the National Survey of Mental Health and Wellbeing. Aust N Z J Psychiatry 34, 6, 997–1008.
OpenUrl CrossRef PubMed
10.↵
1. Kessler RC,
2. Stafford D
1. Kessler RC,
2. Stafford D
(2008) Primary care is the de facto mental health system. in Collaborative medicine case studies: evidence in practice, eds Kessler RC, Stafford D (Springer, New York, NY).
11.↵
1. Apgar V,
2. Holaday DA,
3. James LS,
4. et al.
(1958) Evaluation of the newborn infant; second report. JAMA 168, 15, 1985–1988.
OpenUrl CrossRef PubMed
12.
1. Moons KGM,
2. Royston P,
3. Vergouwe Y,
4. et al.
(2009) Prognosis and prognostic research: what, why, and how? BMJ 338, b375.
OpenUrl FREE Full Text
13.
1. Kannel WB,
2. Mcgee D,
3. Gordon T
(1976) A general cardiovascular risk profile: the Framingham study. Am J Cardiol 38, 1, 46–51.
OpenUrl CrossRef PubMed
14.
1. Knaus WA,
2. Wagner DP,
3. Draper EA,
4. et al.
(1991) The APACHE III prognostic system. Risk prediction of hospital mortality for critically ill hospitalized adults. Chest 100, 6, 1619–1636.
OpenUrl CrossRef PubMed
15.↵
1. Yeh RW,
2. Secemsky EA,
3. Kereiakes DJ,
4. et al.
(2016) Development and validation of a prediction rule for benefit and harm of dual antiplatelet therapy beyond 1 year after percutaneous coronary intervention. JAMA 315, 16, 1735–1749.
OpenUrl CrossRef PubMed
16.↵
1. Pencina MJ,
2. Peterson ED
(2016) Moving from clinical trials to precision medicine: the role for predictive modeling. JAMA 315, 16, 1713–1714.
OpenUrl
17.↵
1. Chondros P,
2. Davidson S,
3. Wolfe R,
4. et al.
(2018) Development of a prognostic model for predicting depression severity in adult primary patients with depressive symptoms using the diamond longitudinal study. J Affect Disord 227, 854–860.
OpenUrl
18.↵
1. Wachtler C,
2. Coe A,
3. Davidson S,
4. et al.
(2018) Development of a mobile clinical prediction tool to estimate future depression severity and guide treatment in primary care: user-centred design. JMIR Mhealth Uhealth 6, 4, e95.
OpenUrl
19.↵
1. Miller WR,
2. Rollnick S
(2012) Motivational interviewing: helping people change (Guilford Press, New York, NY).
20.↵
1. Alatawi E,
2. Mendoza A,
3. Miller T
(2018) Psychologically-driven requirements engineering: a case study in depression care, doi:10.1109/ASWEC.2018.00014. 25th Australasian Software Engineering Conference (ASWEC). Adelaide.
21.↵
1. Gunn J,
2. Wachtler C,
3. Fletcher S,
4. et al.
(2017) Target-D: a stratified individually randomized controlled trial of the diamond clinical prediction tool to triage and target treatment for depressive symptoms in general practice: study protocol for a randomized controlled trial. Trials 18, 342.
OpenUrl
22.↵
1. Arroll B,
2. Goodyear-Smith F,
3. Crengle S,
4. et al.
(2010) Validation of PHQ-2 and PHQ-9 to screen for major depression in the primary care population. Ann Fam Med 8, 4, 348–353.
OpenUrl Abstract/FREE Full Text
23.↵
1. Proudfoot J,
2. Goldberg D,
3. Mann A,
4. et al.
(2003) Computerized, interactive, multimedia cognitive-behavioural program for anxiety and depression in general practice. Psychol Med 33, 2, 217–227.
OpenUrl CrossRef PubMed
24.↵
1. Newby JM,
2. MacKenzie A,
3. Williams AD,
4. et al.
(2013) Internet cognitive behavioural therapy for mixed anxiety and depression: a randomized controlled trial and evidence of effectiveness in primary care. Psychol Med 43, 12, 2635–2648.
OpenUrl CrossRef PubMed
25.↵
1. Archer J,
2. Bower P,
3. Gilbody S,
4. et al.
(2012) Collaborative care for depression and anxiety problems. Cochrane Database Syst Rev 10, CD006525.
OpenUrl PubMed
26.
1. Bower P,
2. Gilbody S,
3. Richards D,
4. et al.
(2006) Collaborative care for depression in primary care. Making sense of a complex intervention: systematic review and meta-regression. Br J Psychiatry 189, 484–493.
OpenUrl Abstract/FREE Full Text
27.
1. Thota AB,
2. Sipe TA,
3. Byard GJ,
4. et al.
(2012) Collaborative care to improve the management of depressive disorders: a community guide systematic review and meta-analysis. Am J Prev Med 42, 5, 525–538.
OpenUrl CrossRef PubMed
28.
1. Miller CJ,
2. Grogan-Kaylor A,
3. Perron BE,
4. et al.
(2013) Collaborative chronic care models for mental health conditions: cumulative meta-analysis and metaregression to guide future research and implementation. Med Care 51, 10, 922–930.
OpenUrl CrossRef PubMed
29.↵
1. Katon W,
2. Von Korff M,
3. Lin E,
4. et al.
(1999) Stepped collaborative care for primary care patients with persistent symptoms of depression: a randomized trial. Arch Gen Psychiatry 56, 12, 1109–1115.
OpenUrl CrossRef PubMed
30.↵
1. Kroenke K,
2. Spitzer RL,
3. Williams JB
(2001) The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med 16, 9, 606–613.
OpenUrl CrossRef PubMed
31.↵
1. Spitzer RL,
2. Kroenke K,
3. Williams JB,
4. Löwe B
(2006) A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med 166, 10, 1092–1097.
OpenUrl CrossRef PubMed
32.↵
1. Clarke J,
2. Proudfoot J,
3. Birch M,
4. et al.
(2014) Effects of mental health self-efficacy on outcomes of a mobile phone and web intervention for mild-to-moderate depression, anxiety and stress: secondary analysis of a randomised controlled trial. BMC Psychiatry 14, 272.
OpenUrl
33.↵
1. Richardson J,
2. Iezzi A,
3. Khan MA,
4. Maxwell A
(2014) Validity and reliability of the Assessment of Quality of Life (AQoL)-8D multi-attribute utility instrument. Patient 7, 1, 85–96.
OpenUrl CrossRef PubMed
34.↵
1. Chondros P,
2. Fletcher S,
3. Mihalopoulos C,
4. et al.
(2019) Update on Target-D: statistical analysis plan for the stratified individually randomised controlled trial of the diamond clinical prediction tool to triage and target treatment for depressive symptoms in general practice. http://anzctr.org.au/Steps11and12/367152-(Uploaded-26-03-2019-11-12-43)-Study-related%20document.pdf (accessed 4 Jan 2021).
35.↵
1. Dunn G,
2. Maracy M,
3. Tomenson B
(2005) Estimating treatment effects from randomized clinical trials with noncompliance and loss to follow-up: the role of instrumental variable methods. Stat Methods Med Res 14, 4, 369–395.
OpenUrl CrossRef PubMed
36.↵
1. Härter M,
2. Watzke B,
3. Daubmann A,
4. et al.
(2018) Guideline-based stepped and collaborative care for patients with depression in a cluster-randomised trial. Sci Rep 8, 1, 9389.
OpenUrl PubMed
37.
1. Gureje O,
2. Oladeji BD,
3. Araya R,
4. Montgomery AA
(2015) A cluster randomized clinical trial of a stepped care intervention for depression in primary care (STEPCARE) — study protocol. BMC Psychiatry 15, 148.
OpenUrl
38.
1. Pols AD,
2. van Dijk SE,
3. Bosmans JE,
4. et al.
(2017) Effectiveness of a stepped-care intervention to prevent major depression in patients with type 2 diabetes mellitus and/or coronary heart disease and subthreshold depression: a pragmatic cluster randomized controlled trial. PLoS One 12, 8, e0181023.
OpenUrl
39.
1. Stoop CH,
2. Nefs G,
3. Pommer AM,
4. et al.
(2015) Effectiveness of a stepped care intervention for anxiety and depression in people with diabetes, asthma or COPD in primary care: a randomized controlled trial. J Affect Disord 184, 269–276.
OpenUrl
40.
1. Zhang DX,
2. Lewis G,
3. Araya R,
4. et al.
(2014) Prevention of anxiety and depression in Chinese: a randomized clinical trial testing the effectiveness of a stepped care program in primary care. J Affect Disord 169, 212–220.
OpenUrl
41.↵
1. Seekles W,
2. van Straten A,
3. Beekman A,
4. et al.
(2011) Stepped care treatment for depression and anxiety in primary care: a randomized controlled trial. Trials 12, 171.
OpenUrl CrossRef PubMed
42.↵
1. Australian Institute of Health and Welfare
(2020) Mental health services in Australia. https://www.aihw.gov.au/reports/mental-health-services/mental-health-services-in-australia/report-contents/summary-of-mental-health-services-in-australia (accessed 22 Dec 2020).
43.↵
1. Cuijpers P
(2018) The challenges of improving treatments for depression. JAMA 320, 24, 2529–2530.
OpenUrl
44.↵
1. van Straten A,
2. Hill J,
3. Richards DA,
4. Cuijpers P
(2015) Stepped care treatment delivery for depression: a systematic review and meta-analysis. Psychol Med 45, 2, 231–246.
OpenUrl CrossRef PubMed
45.↵
1. Cuijpers P,
2. Turner EH,
3. Koole SL,
4. et al.
(2014) What is the threshold for a clinically relevant effect? The case of major depressive disorders. Depress Anxiety 31, 5, 374–378.
OpenUrl CrossRef PubMed
46.↵
1. Raine R,
2. Haines A,
3. Sensky T,
4. et al.
(2002) Systematic review of mental health interventions for patients with common somatic symptoms: can research evidence from secondary care be extrapolated to primary care? BMJ 325, 7372, 1082.
OpenUrl Abstract/FREE Full Text
47.↵
1. Titov N,
2. Andrews G,
3. Davies M,
4. et al.
(2010) Internet treatment for depression: a randomized controlled trial comparing clinician vs. technician assistance. PLoS One 5, 6, e10939.
OpenUrl CrossRef PubMed
48.↵
1. Perini S,
2. Titov N,
3. Andrews G
(2009) Clinician-assisted internet-based treatment is effective for depression: randomized controlled trial. Aust N Z J Psychiatry 43, 6, 571–578.
OpenUrl CrossRef PubMed
49.↵
1. Gilbody S,
2. Littlewood E,
3. Hewitt C,
4. et al.
(2015) Computerised cognitive behaviour therapy (cCBT) as treatment for depression in primary care (REEACT trial): large scale pragmatic randomised controlled trial. BMJ 351, h5627.
OpenUrl Abstract/FREE Full Text
50.↵
1. Newby JM,
2. Mewton L,
3. Williams AD,
4. Andrews G
(2014) Effectiveness of transdiagnostic internet cognitive behavioural treatment for mixed anxiety and depression in primary care. J Affect Disord 165, 45–52.
OpenUrl CrossRef PubMed
51.↵
1. Richards DA,
2. Bower P,
3. Chew-Graham C,
4. et al.
(2016) Clinical effectiveness and cost- effectiveness of collaborative care for depression in UK primary care (CADET): a cluster randomised controlled trial. Health Technol Asses 20, 14, 1–192.
OpenUrl
52.
1. Coventry P,
2. Lovell K,
3. Dickens C,
4. et al.
(2015) Integrated primary care for patients with mental and physical multimorbidity: cluster randomised controlled trial of collaborative care for patients with depression comorbid with diabetes or cardiovascular disease. BMJ 350, h638.
OpenUrl Abstract/FREE Full Text
53.
1. Grochtdreis T,
2. Brettschneider C,
3. Wegener A,
4. et al.
(2015) Cost-effectiveness of collaborative care for the treatment of depressive disorders in primary care: a systematic review. PLoS One 10, 5, e0123078.
OpenUrl CrossRef PubMed
54.↵
1. van Steenbergen-Weijenburg KM,
2. van der Feltz-Cornelis CM,
3. Horn EK,
4. et al.
(2010) Cost-effectiveness of collaborative care for the treatment of major depressive disorder in primary care. a systematic review. BMC Health Serv Res 10, 19.
OpenUrl CrossRef PubMed
55.↵
1. Halcomb EJ,
2. McInnes S,
3. Patterson C,
4. Moxham L
(2019) Nurse-delivered interventions for mental health in primary care: a systematic review of randomized controlled trials. Fam Pract 36, 1, 64–71.
OpenUrl
56.↵
1. Cipriani A,
2. Furukawa TA,
3. Salanti G,
4. et al.
(2018) Comparative efficacy and acceptability of 21 antidepressant drugs for the acute treatment of adults with major depressive disorder: a systematic review and network meta-analysis. Lancet 391, 10128, 1357–1366.
OpenUrl PubMed
57.↵
1. Meurk C,
2. Leung J,
3. Hall W,
4. et al.
(2016) Establishing and governing e-mental health care in Australia: a systematic review of challenges and a call for policy- focussed research. J Med Internet Res 18, 1, e10.
OpenUrl CrossRef PubMed
58.
1. Apolinário-Hagen J,
2. Kemper J,
3. Stürmer C
(2017) Public acceptability of e-mental health treatment services for psychological problems: a scoping review. JMIR Ment Health 4, 2, e10.
OpenUrl
59.
1. Wozney L,
2. Newton AS,
3. Gehring ND,
4. et al.
(2017) Implementation of emental health care: viewpoints from key informants from organizations and agencies with ehealth mandates. BMC Med Inform Decis Mak 17, 1, 78.
OpenUrl
60.
1. Vis C,
2. Mol M,
3. Kleiboer A,
4. et al.
(2018) Improving implementation of emental health for mood disorders in routine practice: systematic review of barriers and facilitating factors. JMIR Ment Health 5, 1, e20.
OpenUrl
61.↵
1. Mohr DC,
2. Riper H,
3. Schueller SM
(2018) A solution-focused research approach to achieve an implementable revolution in digital mental health. JAMA Psychiatry 75, 2, 113–114.
OpenUrl
62.↵
1. Wind TR,
2. Rijkeboer M,
3. Andersson G,
4. Riper H
(2020) The COVID-19 pandemic: the ‘black swan’ for mental health care and a turning point for e-health. Internet Interv 20, 100317.
OpenUrl
63.↵
1. Royal Australian College of General Practitioners
(2019) General practice: health of the nation 2019, https://www.racgp.org.au/getmedia/bacc0983-cc7d-4810-b34a-25e12043a53e/Health-of-the-Nation-2019-report.pdf.aspx (accessed 22 Dec 2020).
64.↵
1. Mihalopoulos C,
2. Lee Y,
3. Chatterton M,
4. et al.
Economic evaluation of the Target-D platform to match depression management to severity prognosis in primary care: a within-trial cost utility analysis. Br J Gen Pract.

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[1] 1.↵
Jorm AF,
Patten SB,
Brugha TS,
Mojtabai R
(2017) Has increased provision of treatment reduced the prevalence of common mental disorders? Review of the evidence from four countries. World Psychiatry 16, 1, 90–99.
OpenUrl CrossRef PubMed

[2] Jorm AF,

[3] Patten SB,

[4] Brugha TS,

[5] Mojtabai R

[6] 2.↵
Ferrari AJ,
Charlson FJ,
Norman RE,
et al.
(2013) Burden of depressive disorders by country, sex, age, and year: findings from the Global Burden of Disease Study 2010. PLoS Med 10, 11, e1001547.
OpenUrl CrossRef PubMed

[7] Ferrari AJ,

[8] Charlson FJ,

[9] Norman RE,

[10] et al.

[11] 3.↵
Dowrick C,
Frances A
(2013) Medicalising unhappiness: new classification of depression risks more patients being put on drug treatment from which they will not benefit. BMJ 347, f7140.
OpenUrl FREE Full Text

[12] Dowrick C,

[13] Frances A

[14] 4.↵
National Institute for Health and Care Excellence
(2009) Depression in adults (update) Depression: the treatment and management of depression in adults, https://www.nice.org.uk/guidance/cg90/documents/depression-in-adults-update-full-guideline-prepublication2 (accessed 22 Dec 2020).

[15] National Institute for Health and Care Excellence

[16] 5.↵
Australian Government Department of Health
(2015) Australian government response to Contributing Lives, Thriving Communities — review of mental health programmes and services (Australian Government Department of Health, Canberra).

[17] Australian Government Department of Health

[18] 6.↵
Silverstone PH,
Rittenbach K,
Suen VYM,
et al.
(2017) Depression outcomes in adults attending family practice were not improved by screening, stepped-care, or online CBT during a 12-week study when compared to controls in a randomized trial. Front Psychiatry 8, 32.
OpenUrl

[19] Silverstone PH,

[20] Rittenbach K,

[21] Suen VYM,

[22] et al.

[23] 7.
Franx G,
Oud M,
de Lange J,
et al.
(2012) Implementing a stepped-care approach in primary care: results of a qualitative study. Implement Sci 7, 8.
OpenUrl CrossRef PubMed

[24] Franx G,

[25] Oud M,

[26] de Lange J,

[27] et al.

[28] 8.↵
Hermens MLM,
Muntingh A,
Franx G,
et al.
(2014) Stepped care for depression is easy to recommend, but harder to implement: results of an explorative study within primary care in the Netherlands. BMC Fam Pract 15, 5.
OpenUrl CrossRef PubMed

[29] Hermens MLM,

[30] Muntingh A,

[31] Franx G,

[32] et al.

[33] 9.↵
Parslow R,
Jorm A
(2000) Who uses mental health services in Australia? An analysis of data from the National Survey of Mental Health and Wellbeing. Aust N Z J Psychiatry 34, 6, 997–1008.
OpenUrl CrossRef PubMed

[34] Parslow R,

[35] Jorm A

[36] 10.↵
Kessler RC,
Stafford D
Kessler RC,
Stafford D
(2008) Primary care is the de facto mental health system. in Collaborative medicine case studies: evidence in practice, eds Kessler RC, Stafford D (Springer, New York, NY).

[37] Kessler RC,

[38] Stafford D

[39] Kessler RC,

[40] Stafford D

[41] 11.↵
Apgar V,
Holaday DA,
James LS,
et al.
(1958) Evaluation of the newborn infant; second report. JAMA 168, 15, 1985–1988.
OpenUrl CrossRef PubMed

[42] Apgar V,

[43] Holaday DA,

[44] James LS,

[45] et al.

[46] 12.
Moons KGM,
Royston P,
Vergouwe Y,
et al.
(2009) Prognosis and prognostic research: what, why, and how? BMJ 338, b375.
OpenUrl FREE Full Text

[47] Moons KGM,

[48] Royston P,

[49] Vergouwe Y,

[50] et al.

[51] 13.
Kannel WB,
Mcgee D,
Gordon T
(1976) A general cardiovascular risk profile: the Framingham study. Am J Cardiol 38, 1, 46–51.
OpenUrl CrossRef PubMed

[52] Kannel WB,

[53] Mcgee D,

[54] Gordon T

[55] 14.
Knaus WA,
Wagner DP,
Draper EA,
et al.
(1991) The APACHE III prognostic system. Risk prediction of hospital mortality for critically ill hospitalized adults. Chest 100, 6, 1619–1636.
OpenUrl CrossRef PubMed

[56] Knaus WA,

[57] Wagner DP,

[58] Draper EA,

[59] et al.

[60] 15.↵
Yeh RW,
Secemsky EA,
Kereiakes DJ,
et al.
(2016) Development and validation of a prediction rule for benefit and harm of dual antiplatelet therapy beyond 1 year after percutaneous coronary intervention. JAMA 315, 16, 1735–1749.
OpenUrl CrossRef PubMed

[61] Yeh RW,

[62] Secemsky EA,

[63] Kereiakes DJ,

[64] et al.

[65] 16.↵
Pencina MJ,
Peterson ED
(2016) Moving from clinical trials to precision medicine: the role for predictive modeling. JAMA 315, 16, 1713–1714.
OpenUrl

[66] Pencina MJ,

[67] Peterson ED

[68] 17.↵
Chondros P,
Davidson S,
Wolfe R,
et al.
(2018) Development of a prognostic model for predicting depression severity in adult primary patients with depressive symptoms using the diamond longitudinal study. J Affect Disord 227, 854–860.
OpenUrl

[69] Chondros P,

[70] Davidson S,

[71] Wolfe R,

[72] et al.

[73] 18.↵
Wachtler C,
Coe A,
Davidson S,
et al.
(2018) Development of a mobile clinical prediction tool to estimate future depression severity and guide treatment in primary care: user-centred design. JMIR Mhealth Uhealth 6, 4, e95.
OpenUrl

[74] Wachtler C,

[75] Coe A,

[76] Davidson S,

[77] et al.

[78] 19.↵
Miller WR,
Rollnick S
(2012) Motivational interviewing: helping people change (Guilford Press, New York, NY).

[79] Miller WR,

[80] Rollnick S

[81] 20.↵
Alatawi E,
Mendoza A,
Miller T
(2018) Psychologically-driven requirements engineering: a case study in depression care, doi:10.1109/ASWEC.2018.00014. 25th Australasian Software Engineering Conference (ASWEC). Adelaide.

[82] Alatawi E,

[83] Mendoza A,

[84] Miller T

[85] 21.↵
Gunn J,
Wachtler C,
Fletcher S,
et al.
(2017) Target-D: a stratified individually randomized controlled trial of the diamond clinical prediction tool to triage and target treatment for depressive symptoms in general practice: study protocol for a randomized controlled trial. Trials 18, 342.
OpenUrl

[86] Gunn J,

[87] Wachtler C,

[88] Fletcher S,

[89] et al.

[90] 22.↵
Arroll B,
Goodyear-Smith F,
Crengle S,
et al.
(2010) Validation of PHQ-2 and PHQ-9 to screen for major depression in the primary care population. Ann Fam Med 8, 4, 348–353.
OpenUrl Abstract/FREE Full Text

[91] Arroll B,

[92] Goodyear-Smith F,

[93] Crengle S,

[94] et al.

[95] 23.↵
Proudfoot J,
Goldberg D,
Mann A,
et al.
(2003) Computerized, interactive, multimedia cognitive-behavioural program for anxiety and depression in general practice. Psychol Med 33, 2, 217–227.
OpenUrl CrossRef PubMed

[96] Proudfoot J,

[97] Goldberg D,

[98] Mann A,

[99] et al.

[100] 24.↵
Newby JM,
MacKenzie A,
Williams AD,
et al.
(2013) Internet cognitive behavioural therapy for mixed anxiety and depression: a randomized controlled trial and evidence of effectiveness in primary care. Psychol Med 43, 12, 2635–2648.
OpenUrl CrossRef PubMed

[101] Newby JM,

[102] MacKenzie A,

[103] Williams AD,

[104] et al.

[105] 25.↵
Archer J,
Bower P,
Gilbody S,
et al.
(2012) Collaborative care for depression and anxiety problems. Cochrane Database Syst Rev 10, CD006525.
OpenUrl PubMed

[106] Archer J,

[107] Bower P,

[108] Gilbody S,

[109] et al.

[110] 26.
Bower P,
Gilbody S,
Richards D,
et al.
(2006) Collaborative care for depression in primary care. Making sense of a complex intervention: systematic review and meta-regression. Br J Psychiatry 189, 484–493.
OpenUrl Abstract/FREE Full Text

[111] Bower P,

[112] Gilbody S,

[113] Richards D,

[114] et al.

[115] 27.
Thota AB,
Sipe TA,
Byard GJ,
et al.
(2012) Collaborative care to improve the management of depressive disorders: a community guide systematic review and meta-analysis. Am J Prev Med 42, 5, 525–538.
OpenUrl CrossRef PubMed

[116] Thota AB,

[117] Sipe TA,

[118] Byard GJ,

[119] et al.

[120] 28.
Miller CJ,
Grogan-Kaylor A,
Perron BE,
et al.
(2013) Collaborative chronic care models for mental health conditions: cumulative meta-analysis and metaregression to guide future research and implementation. Med Care 51, 10, 922–930.
OpenUrl CrossRef PubMed

[121] Miller CJ,

[122] Grogan-Kaylor A,

[123] Perron BE,

[124] et al.

[125] 29.↵
Katon W,
Von Korff M,
Lin E,
et al.
(1999) Stepped collaborative care for primary care patients with persistent symptoms of depression: a randomized trial. Arch Gen Psychiatry 56, 12, 1109–1115.
OpenUrl CrossRef PubMed

[126] Katon W,

[127] Von Korff M,

[128] Lin E,

[129] et al.

[130] 30.↵
Kroenke K,
Spitzer RL,
Williams JB
(2001) The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med 16, 9, 606–613.
OpenUrl CrossRef PubMed

[131] Kroenke K,

[132] Spitzer RL,

[133] Williams JB

[134] 31.↵
Spitzer RL,
Kroenke K,
Williams JB,
Löwe B
(2006) A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med 166, 10, 1092–1097.
OpenUrl CrossRef PubMed

[135] Spitzer RL,

[136] Kroenke K,

[137] Williams JB,

[138] Löwe B

[139] 32.↵
Clarke J,
Proudfoot J,
Birch M,
et al.
(2014) Effects of mental health self-efficacy on outcomes of a mobile phone and web intervention for mild-to-moderate depression, anxiety and stress: secondary analysis of a randomised controlled trial. BMC Psychiatry 14, 272.
OpenUrl

[140] Clarke J,

[141] Proudfoot J,

[142] Birch M,

[143] et al.

[144] 33.↵
Richardson J,
Iezzi A,
Khan MA,
Maxwell A
(2014) Validity and reliability of the Assessment of Quality of Life (AQoL)-8D multi-attribute utility instrument. Patient 7, 1, 85–96.
OpenUrl CrossRef PubMed

[145] Richardson J,

[146] Iezzi A,

[147] Khan MA,

[148] Maxwell A

[149] 34.↵
Chondros P,
Fletcher S,
Mihalopoulos C,
et al.
(2019) Update on Target-D: statistical analysis plan for the stratified individually randomised controlled trial of the diamond clinical prediction tool to triage and target treatment for depressive symptoms in general practice. http://anzctr.org.au/Steps11and12/367152-(Uploaded-26-03-2019-11-12-43)-Study-related%20document.pdf (accessed 4 Jan 2021).

[150] Chondros P,

[151] Fletcher S,

[152] Mihalopoulos C,

[153] et al.

[154] 35.↵
Dunn G,
Maracy M,
Tomenson B
(2005) Estimating treatment effects from randomized clinical trials with noncompliance and loss to follow-up: the role of instrumental variable methods. Stat Methods Med Res 14, 4, 369–395.
OpenUrl CrossRef PubMed

[155] Dunn G,

[156] Maracy M,

[157] Tomenson B

[158] 36.↵
Härter M,
Watzke B,
Daubmann A,
et al.
(2018) Guideline-based stepped and collaborative care for patients with depression in a cluster-randomised trial. Sci Rep 8, 1, 9389.
OpenUrl PubMed

[159] Härter M,

[160] Watzke B,

[161] Daubmann A,

[162] et al.

[163] 37.
Gureje O,
Oladeji BD,
Araya R,
Montgomery AA
(2015) A cluster randomized clinical trial of a stepped care intervention for depression in primary care (STEPCARE) — study protocol. BMC Psychiatry 15, 148.
OpenUrl

[164] Gureje O,

[165] Oladeji BD,

[166] Araya R,

[167] Montgomery AA

[168] 38.
Pols AD,
van Dijk SE,
Bosmans JE,
et al.
(2017) Effectiveness of a stepped-care intervention to prevent major depression in patients with type 2 diabetes mellitus and/or coronary heart disease and subthreshold depression: a pragmatic cluster randomized controlled trial. PLoS One 12, 8, e0181023.
OpenUrl

[169] Pols AD,

[170] van Dijk SE,

[171] Bosmans JE,

[172] et al.

[173] 39.
Stoop CH,
Nefs G,
Pommer AM,
et al.
(2015) Effectiveness of a stepped care intervention for anxiety and depression in people with diabetes, asthma or COPD in primary care: a randomized controlled trial. J Affect Disord 184, 269–276.
OpenUrl

[174] Stoop CH,

[175] Nefs G,

[176] Pommer AM,

[177] et al.

[178] 40.
Zhang DX,
Lewis G,
Araya R,
et al.
(2014) Prevention of anxiety and depression in Chinese: a randomized clinical trial testing the effectiveness of a stepped care program in primary care. J Affect Disord 169, 212–220.
OpenUrl

[179] Zhang DX,

[180] Lewis G,

[181] Araya R,

[182] et al.

[183] 41.↵
Seekles W,
van Straten A,
Beekman A,
et al.
(2011) Stepped care treatment for depression and anxiety in primary care: a randomized controlled trial. Trials 12, 171.
OpenUrl CrossRef PubMed

[184] Seekles W,

[185] van Straten A,

[186] Beekman A,

[187] et al.

[188] 42.↵
Australian Institute of Health and Welfare
(2020) Mental health services in Australia. https://www.aihw.gov.au/reports/mental-health-services/mental-health-services-in-australia/report-contents/summary-of-mental-health-services-in-australia (accessed 22 Dec 2020).

[189] Australian Institute of Health and Welfare

[190] 43.↵
Cuijpers P
(2018) The challenges of improving treatments for depression. JAMA 320, 24, 2529–2530.
OpenUrl

[191] Cuijpers P

[192] 44.↵
van Straten A,
Hill J,
Richards DA,
Cuijpers P
(2015) Stepped care treatment delivery for depression: a systematic review and meta-analysis. Psychol Med 45, 2, 231–246.
OpenUrl CrossRef PubMed

[193] van Straten A,

[194] Hill J,

[195] Richards DA,

[196] Cuijpers P

[197] 45.↵
Cuijpers P,
Turner EH,
Koole SL,
et al.
(2014) What is the threshold for a clinically relevant effect? The case of major depressive disorders. Depress Anxiety 31, 5, 374–378.
OpenUrl CrossRef PubMed

[198] Cuijpers P,

[199] Turner EH,

[200] Koole SL,

[201] et al.

[202] 46.↵
Raine R,
Haines A,
Sensky T,
et al.
(2002) Systematic review of mental health interventions for patients with common somatic symptoms: can research evidence from secondary care be extrapolated to primary care? BMJ 325, 7372, 1082.
OpenUrl Abstract/FREE Full Text

[203] Raine R,

[204] Haines A,

[205] Sensky T,

[206] et al.

[207] 47.↵
Titov N,
Andrews G,
Davies M,
et al.
(2010) Internet treatment for depression: a randomized controlled trial comparing clinician vs. technician assistance. PLoS One 5, 6, e10939.
OpenUrl CrossRef PubMed

[208] Titov N,

[209] Andrews G,

[210] Davies M,

[211] et al.

[212] 48.↵
Perini S,
Titov N,
Andrews G
(2009) Clinician-assisted internet-based treatment is effective for depression: randomized controlled trial. Aust N Z J Psychiatry 43, 6, 571–578.
OpenUrl CrossRef PubMed

[213] Perini S,

[214] Titov N,

[215] Andrews G

[216] 49.↵
Gilbody S,
Littlewood E,
Hewitt C,
et al.
(2015) Computerised cognitive behaviour therapy (cCBT) as treatment for depression in primary care (REEACT trial): large scale pragmatic randomised controlled trial. BMJ 351, h5627.
OpenUrl Abstract/FREE Full Text

[217] Gilbody S,

[218] Littlewood E,

[219] Hewitt C,

[220] et al.

[221] 50.↵
Newby JM,
Mewton L,
Williams AD,
Andrews G
(2014) Effectiveness of transdiagnostic internet cognitive behavioural treatment for mixed anxiety and depression in primary care. J Affect Disord 165, 45–52.
OpenUrl CrossRef PubMed

[222] Newby JM,

[223] Mewton L,

[224] Williams AD,

[225] Andrews G

[226] 51.↵
Richards DA,
Bower P,
Chew-Graham C,
et al.
(2016) Clinical effectiveness and cost- effectiveness of collaborative care for depression in UK primary care (CADET): a cluster randomised controlled trial. Health Technol Asses 20, 14, 1–192.
OpenUrl

[227] Richards DA,

[228] Bower P,

[229] Chew-Graham C,

[230] et al.

[231] 52.
Coventry P,
Lovell K,
Dickens C,
et al.
(2015) Integrated primary care for patients with mental and physical multimorbidity: cluster randomised controlled trial of collaborative care for patients with depression comorbid with diabetes or cardiovascular disease. BMJ 350, h638.
OpenUrl Abstract/FREE Full Text

[232] Coventry P,

[233] Lovell K,

[234] Dickens C,

[235] et al.

[236] 53.
Grochtdreis T,
Brettschneider C,
Wegener A,
et al.
(2015) Cost-effectiveness of collaborative care for the treatment of depressive disorders in primary care: a systematic review. PLoS One 10, 5, e0123078.
OpenUrl CrossRef PubMed

[237] Grochtdreis T,

[238] Brettschneider C,

[239] Wegener A,

[240] et al.

[241] 54.↵
van Steenbergen-Weijenburg KM,
van der Feltz-Cornelis CM,
Horn EK,
et al.
(2010) Cost-effectiveness of collaborative care for the treatment of major depressive disorder in primary care. a systematic review. BMC Health Serv Res 10, 19.
OpenUrl CrossRef PubMed

[242] van Steenbergen-Weijenburg KM,

[243] van der Feltz-Cornelis CM,

[244] Horn EK,

[245] et al.

[246] 55.↵
Halcomb EJ,
McInnes S,
Patterson C,
Moxham L
(2019) Nurse-delivered interventions for mental health in primary care: a systematic review of randomized controlled trials. Fam Pract 36, 1, 64–71.
OpenUrl

[247] Halcomb EJ,

[248] McInnes S,

[249] Patterson C,

[250] Moxham L

[251] 56.↵
Cipriani A,
Furukawa TA,
Salanti G,
et al.
(2018) Comparative efficacy and acceptability of 21 antidepressant drugs for the acute treatment of adults with major depressive disorder: a systematic review and network meta-analysis. Lancet 391, 10128, 1357–1366.
OpenUrl PubMed

[252] Cipriani A,

[253] Furukawa TA,

[254] Salanti G,

[255] et al.

[256] 57.↵
Meurk C,
Leung J,
Hall W,
et al.
(2016) Establishing and governing e-mental health care in Australia: a systematic review of challenges and a call for policy- focussed research. J Med Internet Res 18, 1, e10.
OpenUrl CrossRef PubMed

[257] Meurk C,

[258] Leung J,

[259] Hall W,

[260] et al.

[261] 58.
Apolinário-Hagen J,
Kemper J,
Stürmer C
(2017) Public acceptability of e-mental health treatment services for psychological problems: a scoping review. JMIR Ment Health 4, 2, e10.
OpenUrl

[262] Apolinário-Hagen J,

[263] Kemper J,

[264] Stürmer C

[265] 59.
Wozney L,
Newton AS,
Gehring ND,
et al.
(2017) Implementation of emental health care: viewpoints from key informants from organizations and agencies with ehealth mandates. BMC Med Inform Decis Mak 17, 1, 78.
OpenUrl

[266] Wozney L,

[267] Newton AS,

[268] Gehring ND,

[269] et al.

[270] 60.
Vis C,
Mol M,
Kleiboer A,
et al.
(2018) Improving implementation of emental health for mood disorders in routine practice: systematic review of barriers and facilitating factors. JMIR Ment Health 5, 1, e20.
OpenUrl

[271] Vis C,

[272] Mol M,

[273] Kleiboer A,

[274] et al.

[275] 61.↵
Mohr DC,
Riper H,
Schueller SM
(2018) A solution-focused research approach to achieve an implementable revolution in digital mental health. JAMA Psychiatry 75, 2, 113–114.
OpenUrl

[276] Mohr DC,

[277] Riper H,

[278] Schueller SM

[279] 62.↵
Wind TR,
Rijkeboer M,
Andersson G,
Riper H
(2020) The COVID-19 pandemic: the ‘black swan’ for mental health care and a turning point for e-health. Internet Interv 20, 100317.
OpenUrl

[280] Wind TR,

[281] Rijkeboer M,

[282] Andersson G,

[283] Riper H

[284] 63.↵
Royal Australian College of General Practitioners
(2019) General practice: health of the nation 2019, https://www.racgp.org.au/getmedia/bacc0983-cc7d-4810-b34a-25e12043a53e/Health-of-the-Nation-2019-report.pdf.aspx (accessed 22 Dec 2020).

[285] Royal Australian College of General Practitioners

[286] 64.↵
Mihalopoulos C,
Lee Y,
Chatterton M,
et al.
Economic evaluation of the Target-D platform to match depression management to severity prognosis in primary care: a within-trial cost utility analysis. Br J Gen Pract.

[287] Mihalopoulos C,

[288] Lee Y,

[289] Chatterton M,

[290] et al.

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Matching depression management to severity prognosis in primary care: results of the Target-D randomised controlled trial

Abstract

INTRODUCTION

METHOD

Study design

Participants

Interventions

Intervention arm

Control arm

Randomisation and blinding

Outcomes

Sample size

Statistical methods

RESULTS

Primary outcome

Secondary outcomes

Adherence-adjusted analyses

DISCUSSION

Summary

Strengths and limitations

Comparison with existing literature

Implications for research and practice

Acknowledgments

Notes

Funding

Ethical approval

Competing interests

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