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Record W4399370613 · doi:10.1016/s2542-5196(24)00064-0

Investigating intervention components and their effectiveness in promoting environmentally sustainable diets: a systematic review

2024· review· en· W4399370613 on OpenAlexaboutno aff
Noor M Wadi, Kenza Cheikh, Yan Wah Keung, Rosemary Green

Bibliographic record

VenueThe Lancet Planetary Health · 2024
Typereview
Languageen
FieldEnvironmental Science
TopicAgriculture Sustainability and Environmental Impact
Canadian institutionsnot available
FundersWellcome Trust
KeywordsIntervention (counseling)Systematic reviewEnvironmentally friendlyBusinessMedicinePolitical scienceMEDLINEBiologyEcologyNursing

Abstract

fetched live from OpenAlex

Global food systems contribute 30% of global greenhouse gas emissions, threatening the global temperature targets of the Paris Agreement. Diets in high-income countries exceed the recommendations for animal-based foods, whereas consumption of fruits and vegetables is below recommendations. Shifting to a more plant-based diet can reduce up to 30% of greenhouse gas emissions from diet and also reduce risk of chronic disease. Interventions addressing sustainable dietary behaviour, defined by a shift in dietary patterns and food-waste practices, could therefore improve population and planetary health, but knowledge of the interventions that are likely to be most effective in changing sustainable dietary behaviour is so far limited. This systematic review aimed to investigate, classify, and assess the effectiveness of interventions that promote environmentally sustainable diets in high-income countries. We searched MEDLINE, Embase, PsycINFO, and Cumulative Index to Nursing and Allied Health Literature for randomised controlled trials and quasi-experimental trials published from inception until June 16, 2022, evaluating the effectiveness of any intervention promoting environmentally sustainable dietary behaviour. Studies were eligible for inclusion if they included adults and children from high-income countries (as defined by the World Bank classification) and used individual-level behaviour change interventions. Online choice experiments and studies reporting results on only change in fruit and vegetable consumption were excluded. Interventions were classified using the nine intervention functions of the behaviour change wheel. Data were extracted on number of participants, intervention characteristics, diet change (eg, meat consumption and fruit and vegetable intake), food waste, greenhouse gas emissions, and health outcomes. 13 studies were identified and included in the systematic review. Articles were from six different countries (ie, Canada, the USA, Germany, the UK, the Netherlands, Italy). Six of the nine intervention functions of the behaviour change wheel were used. Interventions using education had the most robust evidence base, whereas interventions using persuasion had the strongest effect on reducing meat consumption. Overall, interventions using education in combination with other factors were most successful. Five studies had high risk of bias, five had some concerns of bias, and three had low risk of bias. This systematic review provides insight into the effectiveness of behavioural interventions to meet health and climate change goals through promotion of environmentally sustainable diets. Evidence supports the use of multicomponent interventions through education, persuasion, and environmental restructuring to provide opportunity for change. Little high-quality research was available, and more robustly designed intervention studies are needed to inform future guidelines and policies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.036
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.030
GPT teacher head0.304
Teacher spread0.274 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations33
Published2024
Admission routes1
Has abstractyes

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