The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Relationship between adiposity and body size reveals limitations of BMI
Open accessAbstract
The aims of this study were to assess 1) whether the stature-adjusted body mass index (BMI) is a valid proxy for adiposity across both athletic and nonathletic populations, and 2) whether skinfold measurements increase in proportion to body size, thus obeying the principle of geometric similarity. The research design was cross-sectional, allowing the relationship between skinfold calliper readings (at eight sites and between specific athletic and nonathletic groups, n = 478) and body size (either mass, stature, or both) to be explored both collectively, using proportional allometric MANCOVA, and individually (for each site) with follow-up ANCOVAs. Skinfolds increase at a much greater rate relative to body mass than that assumed by geometric similarity, but taller subjects had less rather than more adiposity, calling into question the use of the traditional skinfold-stature adjustment, 170.18/stature. The best body-size index reflective of skinfold measurements was a stature-adjusted body mass index similar to the BMI. However, sporting differences in skinfold thickness persisted, having controlled for differences in body size (approximate BMI) and age, with male strength- and speed-trained athletes having significantly lower skinfolds (32% and 23%, respectively) compared with controls. Similarly, female strength athletes had 29% lower skinfold measurements compared to controls, having controlled for body size and age. These results cast serious doubts on the validity of BMI to represent adiposity accurately and its ability to differentiate between populations. These findings suggest a more valid (less biased) assessment of fatness will be obtained using surface anthropometry such as skinfolds taken by experienced practitioners following established procedures.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Progression Models in Resistance Training for Healthy Adults
(2002) · Medicine and Science in Sports and Exercise · doi:10.1097/00005768-200202000-00027 · Cited by 2,979
Cited by
Rico‐Martín, S., Calderón‐García, J. F., Sánchez‐Rey, P., Franco‐Antonio, C., Martínez Alvarez, M., & Sánchez Muñoz‐Torrero, J. F. (2020) · Obesity Reviews · doi:10.1111/obr.13023 · Cited by 355
Maessen, M. F. H., Eijsvogels, T. M. H., Verheggen, R. J. H. M., Hopman, M. T. E., Verbeek, A. L. M., & Vegt, F. D. (2014) · PLoS ONE · doi:10.1371/journal.pone.0107212 · Cited by 203
Eddolls, W. T. B., McNarry, M. A., Lester, L., Winn, C. O. N., Stratton, G., & Mackintosh, K. A. (2018) · Quality of Life Research · doi:10.1007/s11136-018-1915-3 · Cited by 124
Haghighatdoost, F., Sarrafzadegan, N., Mohammadifard, N., Asgary, S., Boshtam, M., & Azadbakht, L. (2014) · Nutrition · doi:10.1016/j.nut.2013.10.021 · Cited by 108
Yao, D., Chang, Q., Wu, Q.-J., Gao, S.-Y., Zhao, H., Liu, Y.-S., Jiang, Y.-T., & Zhao, Y.-H. (2020) · Journal of Diabetes Research · doi:10.1155/2020/6303820 · Cited by 106
Zhao, Q., Zhang, K., Li, Y., Zhen, Q., Shi, J., Yu, Y., Tao, Y., Cheng, Y., & Liu, Y. (2018) · Diabetic Medicine · doi:10.1111/dme.13787 · Cited by 105
Stergiakouli, E., Gaillard, R., Tavaré, J. M., Balthasar, N., Loos, R. J., Taal, H. R., Evans, D. M., Rivadeneira, F., St Pourcain, B., Uitterlinden, A. G., et al. (2014) · Obesity · doi:10.1002/oby.20840 · Cited by 90
Cai, S., Zhou, L., Zhang, Y., Cheng, B., Zhang, A., Sun, J., Li, M., Su, Y., Bao, Q., Zhang, Y., et al. (2022) · Frontiers in Nutrition · doi:10.3389/fnut.2022.894686 · Cited by 91
The History and Faults of the Body Mass Index and Where to Look Next: A Literature Review
Pray, R., & Riskin, S. (2023) · Cureus · doi:10.7759/cureus.48230 · Cited by 74
Applying novel technologies and methods to inform the ontology of self-regulation
Eisenberg, I. W., Bissett, P. G., Canning, J. R., Dallery, J., Enkavi, A. Z., Whitfield-Gabrieli, S., Gonzalez, O., Green, A. I., Greene, M. A., Kiernan, M., et al. (2018) · Behaviour Research and Therapy · doi:10.1016/j.brat.2017.09.014 · Cited by 61
Wang, J., Wu, M., Wu, S., & Tian, Y. (2022) · ESC Heart Failure · doi:10.1002/ehf2.13820 · Cited by 49
Anthropometric and metabolic indices in assessment of type and severity of dyslipidemia
Zaid, M., Ameer, F., Munir, R., Rashid, R., Farooq, N., Hasnain, S., & Zaidi, N. (2017) · Journal of Physiological Anthropology · doi:10.1186/s40101-017-0134-x · Cited by 40
Phenotypic differences between people varying in muscularity
Heymsfield, S. B., Smith, B., Chung, E. A., Watts, K. L., Gonzalez, M. C., Yang, S., Heo, M., Thomas, D. M., Turner, D., Bosy‐Westphal, A., et al. (2022) · Journal of Cachexia, Sarcopenia and Muscle · doi:10.1002/jcsm.12959 · Cited by 35
Eng, P. C., Teo, A. E. D., Leow, M. K. S., Tai, E. S., & Khoo, C. M. (2025) · Diabetes, Obesity and Metabolism · doi:10.1111/dom.16601 · Cited by 22
Metadata
- Authors
- Nevill, A. M., Stewart, A. D., Olds, T., & Holder, R.
- Venue
- American Journal of Physical Anthropology
- Publisher
- Wiley
- Published
- 2005
- Volume
- 129
- Issue
- 1
- Pages
- 151-156
- ISSN
- 0002-9483 (ISSN-L)
- 1096-8644
- OpenAlex
- W2045692224
- Cited by
- 388
- References
- 1
- FWCI
- 3.47
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
