ReviewBMC public health2022
Systematic realist synthesis of health-related and lifestyle interventions designed to decrease overweight, obesity and unemployment in adults.
Review in BMC public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed, 5 citations in OpenAlex.
- Improving behavioural interventions in healthcare to address the needs of disadvantaged groups: A policy-focused evidence brief.Public health in practice (Oxford, England) · 2026Article
- Article
- Understanding overweight and obesity subgroups: a cluster analysis of data from the UK Yorkshire Health Study.BMC public health · 2025Article
- Employment-dependent associations of serum biomarkers with short- and long-term antidepressant treatment outcomes.Frontiers in psychiatry · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundObesity and unemployment are complex social and health issues with underlying causes that are interconnected. While a clear link has been established, there is lack of evidence on the underlying causal pathways and how health-related interventions could reduce obesity and unemployment using a holistic approach.
objectivesThe aim of this realist synthesis was to identify the common strategies used by health-related interventions to reduce obesity, overweight and unemployment and to determine for whom and under what circumstances these interventions were successful or unsuccessful and why.
methodsA realist synthesis approach was used. Systematic literature searches were conducted in Cochrane library, Medline, SocIndex, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Scopus, and PsychInfo. The evidence from included studies were synthesised into Context-Mechanism-Outcome configurations (CMOcs) to better understand when and how programmes work, for which participants and to refine the final programme theory.
resultsA total of 83 articles met the inclusion criteria. 8 CMOcs elucidating the contexts of the health-related interventions, underlying mechanisms and outcomes were identified. Interventions that were tailored to the target population using multiple strategies, addressing different aspects of individual and external environments led to positive outcomes for reemployment and reduction of obesity.
conclusionThis realist synthesis presents a broad array of contexts, mechanisms underlying the success of health-related interventions to reduce obesity and unemployment. It provides novel insights and key factors that influence the success of such interventions and highlights a need for participatory and holistic approaches to maximise the effectiveness of programmes designed to reduce obesity and unemployment.
trial registrationPROSPERO 2020 CRD42020219897 .
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.