REalist Synthesis Of non-pharmacologicaL interVEntions for antipsychotic-induced weight gain (RESOLVE) in people living with severe mental illness (SMI).


Journal

Systematic reviews
ISSN: 2046-4053
Titre abrégé: Syst Rev
Pays: England
ID NLM: 101580575

Informations de publication

Date de publication:
09 03 2022
Historique:
received: 24 09 2021
accepted: 01 03 2022
entrez: 10 3 2022
pubmed: 11 3 2022
medline: 3 5 2022
Statut: epublish

Résumé

People with severe mental illnesses (SMI) such as schizophrenia die on average 15 to 20 years earlier than everyone else. Two thirds of these deaths are from preventable physical illnesses such as hypertension, cardiovascular disease, and diabetes, which are worsened by weight gain. Antipsychotics are associated with significant weight gain. In REalist Synthesis Of non-pharmacologicaL interVEntions (RESOLVE), a realist synthesis, combining primary and secondary data, will be used to understand and explain how, why, for whom, and in what contexts non-pharmacological interventions can help service users to manage antipsychotic-induced weight gain. A five-step approach will be used to develop guidance: 1. Developing the initial programme theory An initial (candidate) programme theory, which sets out how and why outcomes occur within an intervention, will be developed. 2. Developing the search The initial programme theory will be refined using academic and grey literature. The proposed initial sampling frame are as follows: Context: people living with SMI, taking antipsychotics, different types of SMI. non-pharmacological interventions. triggered by the intervention. Outcomes e.g. weight, metabolic adverse events, quality of life, adherence, burden, economic. Searching for relevant documents will continue until sufficient data is found to conclude that the refined programme theory is coherent and plausible. Lived experience (service users) and stakeholder (practitioners) groups will provide feedback. 3. Selection, appraisal and data extraction Documents will be screened against inclusion and exclusion criteria. The text extracted from these documents will be coded as contexts, mechanisms and their relationships to outcomes. 4. Primary data collection Realist interviews with up to 30 service users and informal carers, and 20 practitioners will gather data to support, refute or refine the programme theory. 5. Data analysis A realist logic of analysis will be used to develop and refine the programme theory from secondary and primary data. The analysis will aim to identify practical intervention strategies to change contexts so that key mechanisms are triggered to produce desired outcomes. Guidance will be produced based on these strategies. This realist synthesis aims to develop guidance for service users and practitioners on the most appropriate interventional strategies to manage and limit antipsychotic weight gain. PROSPERO: CRD42021268697.

Sections du résumé

BACKGROUND
People with severe mental illnesses (SMI) such as schizophrenia die on average 15 to 20 years earlier than everyone else. Two thirds of these deaths are from preventable physical illnesses such as hypertension, cardiovascular disease, and diabetes, which are worsened by weight gain. Antipsychotics are associated with significant weight gain. In REalist Synthesis Of non-pharmacologicaL interVEntions (RESOLVE), a realist synthesis, combining primary and secondary data, will be used to understand and explain how, why, for whom, and in what contexts non-pharmacological interventions can help service users to manage antipsychotic-induced weight gain.
METHODS
A five-step approach will be used to develop guidance: 1. Developing the initial programme theory An initial (candidate) programme theory, which sets out how and why outcomes occur within an intervention, will be developed. 2. Developing the search The initial programme theory will be refined using academic and grey literature. The proposed initial sampling frame are as follows: Context: people living with SMI, taking antipsychotics, different types of SMI.
INTERVENTION
non-pharmacological interventions.
MECHANISMS
triggered by the intervention. Outcomes e.g. weight, metabolic adverse events, quality of life, adherence, burden, economic. Searching for relevant documents will continue until sufficient data is found to conclude that the refined programme theory is coherent and plausible. Lived experience (service users) and stakeholder (practitioners) groups will provide feedback. 3. Selection, appraisal and data extraction Documents will be screened against inclusion and exclusion criteria. The text extracted from these documents will be coded as contexts, mechanisms and their relationships to outcomes. 4. Primary data collection Realist interviews with up to 30 service users and informal carers, and 20 practitioners will gather data to support, refute or refine the programme theory. 5. Data analysis A realist logic of analysis will be used to develop and refine the programme theory from secondary and primary data. The analysis will aim to identify practical intervention strategies to change contexts so that key mechanisms are triggered to produce desired outcomes. Guidance will be produced based on these strategies.
DISCUSSION
This realist synthesis aims to develop guidance for service users and practitioners on the most appropriate interventional strategies to manage and limit antipsychotic weight gain.
SYSTEMATIC REVIEW REGISTRATION
PROSPERO: CRD42021268697.

Identifiants

pubmed: 35264212
doi: 10.1186/s13643-022-01912-9
pii: 10.1186/s13643-022-01912-9
pmc: PMC8906356
doi:

Substances chimiques

Antipsychotic Agents 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

42

Subventions

Organisme : Medical Research Council
ID : MC_UU_00006/6
Pays : United Kingdom
Organisme : Health Services and Delivery Research Programme
ID : 131871
Organisme : Department of Health
Pays : United Kingdom

Informations de copyright

© 2022. The Author(s).

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Auteurs

Ian D Maidment (ID)

College of Health and Life Sciences, Aston University, Birmingham, B4 7ET, UK. i.maidment@aston.ac.uk.

Geoffrey Wong (G)

Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK.

Claire Duddy (C)

Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK.

Rachel Upthegrove (R)

School of Psychology, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.

Sheri Oduola (S)

School of Health Sciences, University of East Anglia, Norwich Research Park, Norwich, NR4 7TJ, UK.

Dan Robotham (D)

The McPin Foundation, 7-14 Great Dover St, London, SE1 4YR, UK.

Suzanne Higgs (S)

School of Psychology, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.

Amy Ahern (A)

MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.

Gurkiran Birdi (G)

College of Health and Life Sciences, Aston University, Birmingham, B4 7ET, UK.

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Classifications MeSH