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      A Combined Digital and Biomarker Diagnostic Aid for Mood Disorders (the Delta Trial): Protocol for an Observational Study

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          Abstract

          Background

          Mood disorders affect hundreds of millions of people worldwide, imposing a substantial medical and economic burden. Existing diagnostic methods for mood disorders often result in a delay until accurate diagnosis, exacerbating the challenges of these disorders. Advances in digital tools for psychiatry and understanding the biological basis of mood disorders offer the potential for novel diagnostic methods that facilitate early and accurate diagnosis of patients.

          Objective

          The Delta Trial was launched to develop an algorithm-based diagnostic aid combining symptom data and proteomic biomarkers to reduce the misdiagnosis of bipolar disorder (BD) as a major depressive disorder (MDD) and achieve more accurate and earlier MDD diagnosis.

          Methods

          Participants for this ethically approved trial were recruited through the internet, mainly through Facebook advertising. Participants were then screened for eligibility, consented to participate, and completed an adaptive digital questionnaire that was designed and created for the trial on a purpose-built digital platform. A subset of these participants was selected to provide dried blood spot (DBS) samples and undertake a World Health Organization World Mental Health Composite International Diagnostic Interview (CIDI). Inclusion and exclusion criteria were chosen to maximize the safety of a trial population that was both relevant to the trial objectives and generalizable. To provide statistical power and validation sets for the primary and secondary objectives, 840 participants were required to complete the digital questionnaire, submit DBS samples, and undertake a CIDI.

          Results

          The Delta Trial is now complete. More than 3200 participants completed the digital questionnaire, 924 of whom also submitted DBS samples and a CIDI, whereas a total of 1780 participants completed a 6-month follow-up questionnaire and 1542 completed a 12-month follow-up questionnaire. The analysis of the trial data is now underway.

          Conclusions

          If a diagnostic aid is able to improve the diagnosis of BD and MDD, it may enable earlier treatment for patients with mood disorders.

          International Registered Report Identifier (IRRID)

          DERR1-10.2196/18453

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          Most cited references69

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          The World Mental Health (WMH) Survey Initiative version of the World Health Organization (WHO) Composite International Diagnostic Interview (CIDI)

          Ronald C Kessler, T Üstün (2004)
          This paper presents an overview of the World Mental Health (WMH) Survey Initiative version of the World Health Organization (WHO) Composite International Diagnostic Interview (CIDI) and a discussion of the methodological research on which the development of the instrument was based. The WMH‐CIDI includes a screening module and 40 sections that focus on diagnoses (22 sections), functioning (four sections), treatment (two sections), risk factors (four sections), socio‐demographic correlates (seven sections), and methodological factors (two sections). Innovations compared to earlier versions of the CIDI include expansion of the diagnostic sections, a focus on 12‐month as well as lifetime disorders in the same interview, detailed assessment of clinical severity, and inclusion of information on treatment, risk factors, and consequences. A computer‐assisted version of the interview is available along with a direct data entry software system that can be used to keypunch responses to the paper‐and‐pencil version of the interview. Computer programs that generate diagnoses are also available based on both ICD‐10 and DSM‐IV criteria. Elaborate CD‐ROM‐based training materials are available to teach interviewers how to administer the interview as well as to teach supervisors how to monitor the quality of data collection. Copyright © 2004 Whurr Publishers Ltd.
          Article 0 comments Cited 816 times – based on 0 reviews     Review now
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            What influences recruitment to randomised controlled trials? A review of trials funded by two UK funding agencies

            Alison McDonald, Rosemary Knight, Marion K Campbell … (2006)
            Background A commonly reported problem with the conduct of multicentre randomised controlled trials (RCTs) is that recruitment is often slower or more difficult than expected, with many trials failing to reach their planned sample size within the timescale and funding originally envisaged. The aim of this study was to explore factors that may have been associated with good and poor recruitment in a cohort of multicentre trials funded by two public bodies: the UK Medical Research Council (MRC) and the Health Technology Assessment (HTA) Programme. Methods The cohort of trials was identified from the administrative databases held by the two funding bodies. 114 trials that recruited participants between 1994 and 2002 met the inclusion criteria. The full scientific applications and subsequent trial reports submitted by the trial teams to the funders provided the principal data sources. Associations between trial characteristics and recruitment success were tested using the Chi-squared test, or Fisher's exact test where appropriate. Results Less than a third (31%) of the trials achieved their original recruitment target and half (53%) were awarded an extension. The proportion achieving targets did not appear to improve over time. The overall start to recruitment was delayed in 47 (41%) trials and early recruitment problems were identified in 77 (63%) trials. The inter-relationship between trial features and recruitment success was complex. A variety of strategies were employed to try to increase recruitment, but their success could not be assessed. Conclusion Recruitment problems are complex and challenging. Many of the trials in the cohort experienced recruitment difficulties. Trials often required extended recruitment periods (sometimes supported by additional funds). While this is of continuing concern, success in addressing the trial question may be more important than recruitment alone.
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              The efficacy of smartphone-based mental health interventions for depressive symptoms: a meta-analysis of randomized controlled trials.

              Joseph Firth, John Torous, Jennifer Lynn Nicholas … (2017)
              The rapid advances and adoption of smartphone technology presents a novel opportunity for delivering mental health interventions on a population scale. Despite multi-sector investment along with wide-scale advertising and availability to the general population, the evidence supporting the use of smartphone apps in the treatment of depression has not been empirically evaluated. Thus, we conducted the first meta-analysis of smartphone apps for depressive symptoms. An electronic database search in May 2017 identified 18 eligible randomized controlled trials of 22 smartphone apps, with outcome data from 3,414 participants. Depressive symptoms were reduced significantly more from smartphone apps than control conditions (g=0.38, 95% CI: 0.24-0.52, p<0.001), with no evidence of publication bias. Smartphone interventions had a moderate positive effect in comparison to inactive controls (g=0.56, 95% CI: 0.38-0.74), but only a small effect in comparison to active control conditions (g=0.22, 95% CI: 0.10-0.33). Effects from smartphone-only interventions were greater than from interventions which incorporated other human/computerized aspects along the smartphone component, although the difference was not statistically significant. The studies of cognitive training apps had a significantly smaller effect size on depression outcomes (p=0.004) than those of apps focusing on mental health. The use of mood monitoring softwares, or interventions based on cognitive behavioral therapy, or apps incorporating aspects of mindfulness training, did not affect significantly study effect sizes. Overall, these results indicate that smartphone devices are a promising self-management tool for depression. Future research should aim to distil which aspects of these technologies produce beneficial effects, and for which populations.
              Article 0 comments Cited 371 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Sabine Bahn:
                ORCID: https://orcid.org/0000-0003-4690-6302
                Department of Chemical Engineering and BiotechnologyUniversity of CambridgePhilippa Fawcett DrCambridge, CB3 0ASUnited Kingdom44 1223 334151sb209@cam.ac.uk
                Journal
                Journal ID (nlm-ta): JMIR Res Protoc
                Journal ID (iso-abbrev): JMIR Res Protoc
                Journal ID (publisher-id): ResProt
                Title: JMIR Research Protocols
                Publisher: JMIR Publications (Toronto, Canada )
                ISSN (Electronic): 1929-0748
                Publication date Collection: August 2020
                Publication date (Electronic): 10 August 2020
                Volume: 9
                Issue: 8
                Electronic Location Identifier: e18453
                Affiliations
                [1 ] Department of Chemical Engineering and Biotechnology University of Cambridge Cambridge United Kingdom
                [2 ] Psyomics Ltd Cambridge United Kingdom
                Author notes
                Corresponding Author: Sabine Bahn sb209@ 123456cam.ac.uk
                Author information
                https://orcid.org/0000-0003-4641-6442
                https://orcid.org/0000-0002-2459-5286
                https://orcid.org/0000-0002-1459-2516
                https://orcid.org/0000-0002-7552-1295
                https://orcid.org/0000-0002-5045-5308
                https://orcid.org/0000-0003-2037-3093
                https://orcid.org/0000-0003-4340-7401
                https://orcid.org/0000-0002-9371-3696
                https://orcid.org/0000-0002-4381-1271
                https://orcid.org/0000-0001-8022-0182
                https://orcid.org/0000-0002-0592-3934
                https://orcid.org/0000-0002-2127-4487
                https://orcid.org/0000-0001-6061-3544
                https://orcid.org/0000-0003-4690-6302
                Article
                Publisher ID: v9i8e18453
                DOI: 10.2196/18453
                PMC ID: 7445599
                PubMed ID: 32773373
                SO-VID: e7e22d25-3e41-4275-b8ad-b3f25f5ce0cd
                Copyright © ©Tony Olmert, Jason D Cooper, Sung Yeon Sarah Han, Giles Barton-Owen, Lynn Farrag, Emily Bell, Lauren V Friend, Sureyya Ozcan, Nitin Rustogi, Rhian L Preece, Pawel Eljasz, Jakub Tomasik, Daniel Cowell, Sabine Bahn. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 10.08.2020.
                License:

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Research Protocols, is properly cited. The complete bibliographic information, a link to the original publication on http://www.researchprotocols.org, as well as this copyright and license information must be included.

                History
                Date received : 28 February 2020
                Date revision requested : 11 April 2020
                Date revision received : 24 April 2020
                Date accepted : 26 April 2020
                Categories
                Subject: Protocol
                Subject: Protocol

                Keywords: proteomics,early diagnosis,mood disorders,bipolar disorder,major depressive disorders
                Data availability:
                Keywords: proteomics, early diagnosis, mood disorders, bipolar disorder, major depressive disorders

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