Dataset Information

Protocol for an HTA report: Does therapeutic writing help people with long-term conditions? Systematic review, realist synthesis and economic modelling.

ABSTRACT:

Introduction

Long-term medical conditions (LTCs) cause reduced health-related quality of life and considerable health service expenditure. Writing therapy has potential to improve physical and mental health in people with LTCs, but its effectiveness is not established. This project aims to establish the clinical and cost-effectiveness of therapeutic writing in LTCs by systematic review and economic evaluation, and to evaluate context and mechanisms by which it might work, through realist synthesis.

Methods

Included are any comparative study of therapeutic writing compared with no writing, waiting list, attention control or placebo writing in patients with any diagnosed LTCs that report at least one of the following: relevant clinical outcomes; quality of life; health service use; psychological, behavioural or social functioning; adherence or adverse events. Searches will be conducted in the main medical databases including MEDLINE, EMBASE, PsycINFO, The Cochrane Library and Science Citation Index. For the realist review, further purposive and iterative searches through snowballing techniques will be undertaken. Inclusions, data extraction and quality assessment will be in duplicate with disagreements resolved through discussion. Quality assessment will include using Grading of Recommendations Assessment, Development and Evaluation (GRADE) criteria. Data synthesis will be narrative and tabular with meta-analysis where appropriate. De novo economic modelling will be attempted in one clinical area if sufficient evidence is available and performed according to the National Institute for Health and Care Excellence (NICE) reference case.

SUBMITTER: Meads C

PROVIDER: S-EPMC3932001 | biostudies-literature | 2014 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Protocol for an HTA report: Does therapeutic writing help people with long-term conditions? Systematic review, realist synthesis and economic modelling.

Meads C C Nyssen O P OP Wong G G Steed L L Bourke L L Ross C A CA Hayman S S Field V V Lord J J Greenhalgh T T Taylor S J C SJ

BMJ open 20140218 2

<h4>Introduction</h4>Long-term medical conditions (LTCs) cause reduced health-related quality of life and considerable health service expenditure. Writing therapy has potential to improve physical and mental health in people with LTCs, but its effectiveness is not established. This project aims to establish the clinical and cost-effectiveness of therapeutic writing in LTCs by systematic review and economic evaluation, and to evaluate context and mechanisms by which it might work, through realist ...[more]

PMID: 24549165

Similar Datasets

Project description:BACKGROUND:Expressive writing involves writing about stressful or traumatic experiences. Despite trials in people with advanced disease, no systematic review to date has critiqued the evidence on expressive writing in this population. To synthesise the evidence of the effects of expressive writing on pain, sleep, depression and anxiety in people with advanced disease. METHODS:A systematic review according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. CINAHL, CENTRAL, PsycINFO and PubMed were searched from January 1986 to March 2018. Other sources included clinical data registers and conference proceedings. Studies were included if they were randomised controlled trials that assessed the impact of an intervention involving expressive writing for adults with advanced disease and/or studies involving linguistic analysis on the expressive writing output. Methodological quality was assessed using the Cochrane risk of bias tool and the Mixed Methods Appraisal Tool. The Grading of Recommendations Assessment, Development and Evaluation tool was used to assess the level of evidence for the outcomes of interest. The protocol of this systematic review has been registered on PROSPERO (CRD42017058193). RESULTS:Six eligible studies with a total of 288 participants were identified, including four randomised controlled trials. All of the trials were in cancer and recruited predominantly women. None of the interventions were tailored to the population. Studies had methodological shortcomings and evidence was generally of low quality. Combined analysis of the four trials, involving 214 participants in total, showed no clear difference in the effect of expressive writing on sleep, anxiety or depression compared to an active control. Pain was not evaluated in the trials. In contrast, analysis of the four studies that included linguistic analysis alluded to linguistic mechanisms for potential effects. CONCLUSION:Although the trial results suggest there is no benefit in expressive writing for people with advanced disease, the current evidence is limited. There is a need for more rigorous trials. It would be of benefit first to undertake exploratory research in trial design including how best to measure impact and in tailoring of the intervention to address the specific needs of people with advanced disease. TRIAL REGISTRATION:The protocol of this systematic review has been registered on PROSPERO, which can be accessed here (registration number: CRD42017058193 ).

Project description:BackgroundRealist approaches seek to answer questions such as 'how?', 'why?', 'for whom?', 'in what circumstances?' and 'to what extent?' interventions 'work' using context-mechanism-outcome (CMO) configurations. Quantitative methods are not well-established in realist approaches, but structural equation modelling (SEM) may be useful to explore CMO configurations. Our aim was to assess the feasibility and appropriateness of SEM to explore CMO configurations and, if appropriate, make recommendations based on our access to primary care research. Our specific objectives were to map variables from two large population datasets to CMO configurations from our realist review looking at access to primary care, generate latent variables where needed, and use SEM to quantitatively test the CMO configurations.MethodsA linked dataset was created by merging individual patient data from the English Longitudinal Study of Ageing and practice data from the GP Patient Survey. Patients registered in rural practices and who were in the highest deprivation tertile were included. Three latent variables were defined using confirmatory factor analysis. SEM was used to explore the nine full CMOs. All models were estimated using robust maximum likelihoods and accounted for clustering at practice level. Ordinal variables were treated as continuous to ensure convergence.ResultsWe successfully explored our CMO configurations, but analysis was limited because of data availability. Two hundred seventy-six participants were included. We found a statistically significant direct (context to outcome) or indirect effect (context to outcome via mechanism) for two of nine CMOs. The strongest association was between 'ease of getting through to the surgery' and 'being able to get an appointment' with an indirect mediated effect through convenience (proportion of the indirect effect of the total was 21%). Healthcare experience was not directly associated with getting an appointment, but there was a statistically significant indirect effect through convenience (53% mediated effect). Model fit indices showed adequate fit.ConclusionsSEM allowed quantification of CMO configurations and could complement other qualitative and quantitative techniques in realist evaluations to support inferences about strengths of relationships. Future research exploring CMO configurations with SEM should aim to collect, preferably continuous, primary data.

Dataset Information

Protocol for an HTA report: Does therapeutic writing help people with long-term conditions? Systematic review, realist synthesis and economic modelling.

Introduction

Methods

Publications

Protocol for an HTA report: Does therapeutic writing help people with long-term conditions? Systematic review, realist synthesis and economic modelling.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets