Back

Obesity Intervention in Sub-Saharan Africa: A systematic review protocol

Agyemang, K.; Anokye, N.

2023-07-19 public and global health
10.1101/2023.07.18.23292826 medRxiv
Show abstract

IntroductionThe increasing prevalence of obesity in Sub-Saharan Africa (SSA) has made it a pressing public health issue that requires effective interventions tailored to the regions unique challenges. This protocol outlines a systematic review approach that aims to examine the existing evidence on obesity interventions in SSA, focusing on their effectiveness, implementation strategies, and potential barriers. Methods and analysisTo identify relevant studies in Sub-Saharan Africa (SSA), MEDLINE (PubMed), MEDLINE (EBSCOHost), Scopus, Web of Science, PsycINFO, Cochrane and EMBASE databases will be searched during the review. Studies on obesity interventions in SSA will be considered for inclusion criteria, encompassing various intervention types, target populations, and study designs. Abstracts and titles will be independently assessed by two reviewers, who are aware of the author and journal information. A third reviewer will be consulted for cases where agreement was not reached or when clarity will be needed. The methodological quality of the included research will be assessed using the "Effective Public Health Practise Project quality assessment tool for quantitative studies. For data synthesis and reporting, two independent reviewers will assign effect scores to each interventions outcome category (dietary behaviour, physical activity, or anthropometric outcomes) through a duplicate scoring exercise. Ethics and disseminationSince no original data will be collected as part of this review, ethical approval is not required. The completed review will be submitted for publication in a peer-reviewed journal and presented at conferences. PROSPERO registration numberCRD42023430503

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.