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Depression is a common condition and is the leading cause of disability worldwide. Preventing or delaying the onset of depression is an important way to reduce the burden of depression. Some research suggests online methods may be effective in preventing depression, but to date, few studies have looked at the application of these methods in the UK.
This study aims to assess the effects of an online self-help intervention (Moodbuster) on preventing depression in a primary care population, who are experiencing mild-moderate symptoms of depression, but do not meet the threshold for diagnosis.
A randomised control design with a six-month and nine-month follow up will be used to compare Moodbuster to a wait-listed control group. Then, a qualitative process evaluation will be used to understand the barriers and facilitators of implementing the intervention.
Eligible participants in Greater Manchester (individuals with mild to moderate symptoms of depression, who do not have a diagnosis of major depressive disorder and have access to the internet) will take part in a 6-week online self-help programme, accompanied by three telephone calls with a trained researcher to support them in their use of the programme. Researchers will follow-up with participants six and nine months after starting the programme to measure depression, anxiety, quality of life, and use of services. The process evaluation will involve qualitative interviews with participants and focus groups with practitioners who referred individuals to the study.
This study will assess the effects of Moodbuster on preventing depression and barriers and facilitators of implementing such an intervention in a UK primary care population. It is hypothesised that the intervention group will display reduced depression symptoms and incidence, reduced service use, and improved quality of life, and the intervention will be acceptable to a UK primary care population.
Depression is a common, often recurring, condition and the leading cause of disability worldwide . In the UK, the one-week prevalence of depression is estimated at 3.3% , and by 2026, the cost of depression to the UK economy is estimated to reach £12.15 billion in lost earnings and service use costs.
As Buntrock and colleagues (2017) highlight, there are two main strategies to reduce the burden of depression, treatment and prevention. Treatment for depression is, even with full coverage of services to all those who need it, not enough.to avert the total burden of disease. Indeed, whist treatment is crucial, even if all people with depression received evidence-based treatment, it is likely that only 35% of the burden (years lived with disabilities) would be averted .
Prevention strategies are an integral part of reducing the burden of depression through fully preventing, or otherwise delaying, its onset . One way to improve the reach of prevention interventions and to deliver such approaches at scale is to offer interventions via digital technologies such as websites, computer programmes, or apps. This allows for a larger reach and it may help reach communities which are currently under-served by traditional mental health services.
Whilst digital tools have been shown to be effective and acceptable in primary care for the treatment of depression , less research has been conducted on its effectiveness or barriers and facilitators to implementation for prevention. A 2017 meta-analysis identified ten trials on the effectiveness of digital interventions for the prevention of anxiety and depression. The review found promising evidence for the use of digital interventions for prevention, finding a small but positive effect on depression symptoms in the short-term (0.25, [0.09-0.41], p = 0.003) . However, the majority of these studies were conducted outside of the UK, in Germany , Australia , USA [12-14], Japan , Norway , and the Netherlands . The trials also represented a mix of universal interventions, and indicated/selective efforts targeted to university students [13,14,16,18], older adults , and young adults . Of the two studies identified that were conducted in the UK, both had a short-term follow up, one of 6 weeks , and the other 12 weeks  and did not include diagnostic outcome assessments.
This suggests there is still more to learn about the application of digital technologies in the UK for the prevention of depression, and the medium to longer term effects of such interventions.
1. Can an online self-help tool reduce depression symptoms and help support the prevention of depression?
2. What are the effects of Moodbuster on the development of depression, anxiety symptoms, quality of life, and service use, and are there any negative unintended effects?
3. What are the barriers and facilitators to implementing an online tool for prevention in primary care and community settings?
It is hypothesised that the intervention group will display reduced depression symptoms and incidence, reduced service use, and improved quality of life, and the intervention will be acceptable to a UK primary care population.
Participants will be recruited via three routes:
1. Direct referral from GPs in participating practices, who feel the study would be appropriate for their patients
2. Through a software tool (FARSITE) that allows for the secure searching of GP records for potential study participants at participating GP practices
3. Self-referral by individuals in response to recruitment campaign launched both in-print and online by the Mental Health Foundation (MHF).
Participants who express an interest will be invited to take part in an online screening questionnaire, where they will be assessed for eligibility against the inclusion and exclusion criteria.
Should the participant be eligible, they will be invited for an interview where they will be assessed for depression symptoms. Participants found to meet diagnostic criteria for depression during the interview will be excluded from the study at this point. The final sample of participants accepted for the trial will be randomly allocated to either the intervention arm or the Treatment as Usual (TAU) arm by an independent statistician based at the University of Manchester.
Those assigned to the intervention will take part in a six-week online self-help programme called Moodbuster. Use of the programme will be supported by three telephone calls from a trained researcher at baseline, three weeks, and six weeks. The calls will serve to support participants with their use of Moodbuster and resolve any technical issues.
Treatment as usual during the study period will involve participants carrying on as they ordinarily would and seeking support as and when they feel it is needed using the (clinical) resources and services available in their area. Those assigned to the TAU arm will receive the stand-alone online intervention for free after the nine-month study period and will have access for an additional 9 months.
Baseline data will be collected via online questionnaires, and an interview, which assess: depression symptoms, depression incidence, anxiety symptoms, quality of life, and service use.
Participants in the intervention arm will be asked to complete a second set of online questionnaires upon completing the six-week self-help programme. This will involve the same set of online questionnaires completed at baseline with additional items related to acceptability of the self-help programme.
Participants in both groups will be contacted again at six months and nine months for follow-up data collection, which will involve the same set of online questionnaires that were completed at baseline. At nine months, participants will also be asked to participate in a second diagnostic telephone interview with trained researchers.
Sub-groups of participants taking part in the intervention arm of the study will be selected for inclusion in the qualitative process evaluation. The aim is to recruit 20-25 participants for interview.
In depth semi-structured interviews will be conducted with participants one month after the end of the intervention. Participants will be asked questions related to barriers and facilitators encountered in the use of Moodbuster, the perceived impact of Moodbuster on their daily lives, and their motivations for taking part in the study.
Interviews will last approximately 30 minutes and will be conducted by study team researchers over the phone.
Additional focus groups will be conducted with primary care professionals involved in the recruitment and referral to the intervention. Purposive sampling will be used to select staff to reflect different areas of expertise. Focus groups will be conducted one month after the end of the intervention and will explore professionals' perspectives on the implementation of the intervention. This will include the perceived impact of the tool and barriers and facilitators to use.
Sample size: Intervention For our design, which assesses baseline and two follow-up measurements, we anticipate an effect size of 0.30 at nine-month. This estimate is based on prior meta-analyses on depression prevention .
An effect size of 0.30 indicates a sample size of N = 116 per arm, or N = 232 for the full trial. Accounting for 20% drop-out, we would require a sample size of N = 290 participants. This is based on the assumption that the correlation between baseline and follow-up is 0.5.
Sub-Sample Size: Process evaluation For the qualitative component, we will aim to recruit up to 25 participants for interviews, and a further 15 professionals for focus groups. This number ensures sufficient data to answer the research questions and is achievable within the timescales of the project. It is consistent with previous research applying thematic analysis to interview data in mental health. The exact number of participants recruited will depend on the richness of the interview data and whether new themes continue to emerge. Recruitment and data collection will continue until collection of new data does not shed any further light on the issue under investigation and saturation has been reached.
Depression and Anxiety Symptoms Severity of anxiety and depression symptoms will be summarised descriptively for both groups at each assessment time point. The analysis of effectiveness of Moodbuster compared to care as usual will follow an intention to treat approach, i.e. i.e. the participants will be analysed according to their randomized allocation, regardless of the actual treatment and time in the study after baseline.
The effect of treatment will be estimated using linear mixed models for fixed (for treatment) and random effects (for patient) with each repeated continuous outcome measure as the dependent variable. A mixed model can effectively handle the dependency of measures taken from the same subject and can use all data available in case of missing values. In these models, a treatment*time independent variable is included to estimate the effectiveness of the intervention. In addition, we will test the addition of a treatment* quadratic time variable to investigate the possibility of treatment effect on non-linear change with time.
Depression Onset The onset of depression (as measured by the SCID-IV) will be considered as a survival outcome. Patients who did not develop depression will be censored at the end of the study, or at the time of last-known follow-up. This outcome will be graphically analysed using the Kaplan-Meier method and the curves will be compared between groups using the logrank test. If the proportional hazards assumption holds, we will use Cox regression to estimate a hazard ratio for treatment (independent variable) and onset of depression vs censoring as the dependent variable.
Both the mixed models and the Cox regression models allow to control for baseline differences in variables that are predictive of outcome, in case they occur despite randomization.
Differences in effect of 'Moodbuster' between subgroups will be statistically evaluated by testing treatment by subgroup interaction terms. In each analysis the significance level alpha will be set at 0.05, two-sided.
Service Use and Quality of Life Service use and quality of life will be summarised descriptively for both groups and compared both within and between groups using a repeated-measures ANCOVA. Changes in service use and quality of life will be used to estimate the potential for Moodbuster to offer good value for money through incremental health gains and costs.
Unintended side effects Negative side effects will be reported descriptively.
Quantitative data on recruitment, retention, and user data will be analysed descriptively. Overall response and retention rate will be calculated to provide an estimate to inform future full-scale RCTs. Summaries of user data will be calculated to provide a sense of overall user engagement with Moodbuster by the intervention group.
Qualitative We will analyse qualitative data using a thematic analysis. Codes based on the conceptual model for diffusion of innovations in device organisations will be developed . The model is the result of an extensive review of primary health care evidence on diffusion of innovation, the individual adopting the intervention, and the system in which he intervention and in which the intervention is embedded. The model looks at the strategies used to inform individuals about the innovation and the factors external to the organisation, all of which impact on the initial adoption and sustainability of innovation in health care settings.
To define our first order themes, we will use a comparative approach to data sorting. Two researchers will conduct the analysis. The researchers will read all the transcripts for data immersion, and then will start coding 20% of the transcripts individually. A data workshop will be held, and the two researchers will develop an initial coding framework that will be used to analyse the remaining transcripts individually. Emerging -codes will be discussed and agreed amongst the researchers in subsequent meetings. The data will be presented in the form a summary of key themes evidenced with illustrative quotes.
University of Manchester
Mental Health Foundation, London
Published on BioPortfolio: 2019-10-30T14:03:43-0400
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