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Lookup NU author(s): Susan LennieORCiD, Dr Gina NguyenORCiD, Dr Anna Boath, Professor Luke ValeORCiD, Professor Dawn Teare, Professor Nicola HeslehurstORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Body fat distribution is a key indicator of obesity-related disease risk, often assessed through objective anthropometric measurements. However, objective implementation at scale is limited by measurement variability, cost, and anthropometrist skill. Subjective methods, widely applied in body image research, may offer an alternative but are less explored for determining obesity- and disease-related risk. This scoping review aimed to identify the availability and characteristics of subjective body shape assessment tools for assessing regional body fat distribution in adult females. A search across five databases (inception to September 8, 2023), using terms for body shape and assessment tools, limited to females, yielded 13,646 unique records; 177 studies were included, reporting 80 tools (13 were variations of 7 originals). Studies utilized tools for varied purposes: body image/shape attractiveness, satisfaction, or distortion (73.4%); health/disease risk (18.1%); tool development/validation (13.0%); clothing/fashion (5.6%); or other (4.0%). Tools types included: figural (38.8%); photographic (21.3%); silhouette (16.3%); figural/scanned image with shape overlay (6.3%); computer generated image (6.3%); inanimate shape (3.8%); somatograph (1.3%); and unclassified (6.3%). Some tools were culturally adapted (e.g., modifying skin tone, clothing, or shape to the population), but most (17.6% of 51 applicable tools) depicted White ethnicity, limiting inclusivity. Among applicable tools, 56.3% included facial features, and 25.4% nakedness. This review reveals a variety of subjective tools, but limited application for disease-related risk assessment. Further research should refine and culturally adapt subjective tools to ensure conceptual suitability, and validate their use for assessing obesity-related disease risk.
Author(s): Lennie SC, Hall A, Nguyen G, Boath B, Vale L, Teare MD, Heslehurst N
Publication type: Article
Publication status: Published
Journal: Obesity Reviews
Year: 2025
Pages: e70068
Online publication date: 16/12/2025
Acceptance date: 28/11/2025
Date deposited: 07/01/2026
ISSN (print): 1467-7881
ISSN (electronic): 1467-789X
Publisher: Wiley
URL: https://doi.org/10.1111/obr.70068
DOI: 10.1111/obr.70068
Data Access Statement: Data sharing not applicable to this article as no datasets were generated or analysed during the current study
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