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Anthropometric prediction equations for estimating body mass composition: a scoping review protocol.
Kandiah Umapathysivam1, Catalin Tufanaru, Renuka Visvanathan
11The Joanna Briggs Institute, Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, Australia 2Aged and Extended Care Services, The Queen Elizabeth Hospital, Central Adelaide Local Health Network, South Australia, Australia 3Adelaide Geriatrics Training and Research with Aged Care (G-TRAC) Centre, School of Medicine, Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, Australia 4Adelaide Medical School, Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, Australia.
This review identifies the best body measurements for creating equations to estimate body composition. Understanding optimal anthropometric variables is key for accurate body mass estimations.
Area of Science:
- Anthropometry
- Body Composition Analysis
- Biostatistics
Background:
- Accurate estimation of body composition is crucial in various fields, including sports science, clinical nutrition, and public health.
- Existing predictive equations for body composition often vary in their chosen anthropometric variables, leading to inconsistencies in results.
- A systematic approach is needed to identify the most effective anthropometric parameters for developing reliable predictive models.
Purpose of the Study:
- To conduct a scoping review to identify and map the anthropometric variable parameters utilized in the development of predictive equations for body mass composition.
- To determine the optimal anthropometric variables that demonstrate the highest utility and accuracy in estimating body composition components.
- To provide a comprehensive overview of current methodologies and identify gaps in the literature regarding anthropometric predictors of body composition.
Main Methods:
- Systematic literature search across multiple databases (e.g., PubMed, Scopus, Web of Science) using predefined keywords related to anthropometry, body composition, and predictive equations.
- Screening of search results based on inclusion and exclusion criteria to identify relevant studies.
- Data extraction and synthesis of information regarding the anthropometric variables, populations studied, and methodologies employed in the development of predictive equations.
Main Results:
- The review will map the range of anthropometric variables (e.g., height, weight, skinfolds, circumferences) used in existing body composition prediction equations.
- Analysis will highlight which anthropometric variables are most frequently reported and associated with higher predictive accuracy across diverse populations.
- Identification of common methodologies and potential limitations in the development and application of these equations.
Conclusions:
- The findings will guide researchers and practitioners in selecting optimal anthropometric variables for developing more accurate and reliable body composition predictive equations.
- This review will contribute to standardizing methods for body composition assessment using anthropometry.
- Establishing optimal parameters will enhance the validity of body composition estimations in research and clinical practice.