COMPARISON OF BODY COMPOSITION METHODS FOR ESTIMATING BODY FAT PERCENTAGE IN LOWER LIMB PROSTHESIS USERS
Bibliographic record
Abstract
BACKGROUND: There is a dearth of literature evaluating the accuracy of Air Displacement Plethysmography (ADP) compared to Dual-energy X-ray Absorptiometry (DXA) for assessing body composition in individuals with lower limb amputations. Validity of ADP in persons with lower limb amputations must be established. OBJECTIVE: The objective of this study was to compare body composition in persons with lower limb amputations using the BOD POD® and DXA. METHODOLOGY: Body composition was performed on eleven lower limb prosthesis users (age 53.2±14.3 years, weight 81.9±22.3kg) using ADP and DXA with and without prosthesis. FINDINGS: Repeated measures ANOVA indicated no significant difference in body composition among and between trials, F(3,8)= 3.36, p= 0.075. There were no significant differences in Body Fat (BF) percentage with and without prostheses on the BOD POD (28.5±15.7% and 33.7±12.1%, respectively) nor the DXA (32.9±10.6% and 32.0±9.9%, respectively). Association between the BOD POD and DXA were greatest when prostheses were not worn compared to when they were worn. Bland-Altman plots indicate agreement between BOD POD® and DXA was greatest while wearing the prosthesis. CONCLUSION: This study is a first to compare total body fat percent between the BOD POD® and DXA in lower limb prosthesis users. BOD POD® report valid indices of BF%. Future work will utilize the BOD POD® in intervention studies for monitoring body composition changes across the continuum of rehabilitation. Layman's Abstract Measurement of body composition is helpful in understanding the health of persons with lower limb prosthesis. The gold standard method of body composition assessment is through Dual-energy X-ray Absorptiometry (DXA). This method can be costly and is less economical than Air Displacement Plethysmography (ADP). The aim of this research was to explore the accuracy of the ADP using a BOD POD® instrument in lower limb prosthesis wearers. Body composition measurements using the BOD POD® and DXA were administered. Assessments were performed while wearing and not wearing the prosthesis. Results indicate that no differences between the two body composition assessment methods either with or without prosthesis. The less costly ADP technology may be utilized for body composition in lower limb prosthesis users. Article PDF Link: https://jps.library.utoronto.ca/index.php/cpoj/article/view/41605/32165 How To Cite: Smith JD, Guerra G, Symons TB, Kwon EH, Yoon EJ. Comparison of body composition methods for estimating body fat percentage in lower limb prosthesis users. Canadian Prosthetics & Orthotics Journal. 2023; Volume 6, Issue 1, No.2. https://doi.org/10.33137/cpoj.v6i1.41605 Corresponding Author: Gary Guerra, PhD Department of Exercise and Sport Science, St. Mary’s University, San Antonio, Texas, USA.E-Mail: gguerra5@stmarytx.eduORCID ID: https://orcid.org/0000-0002-0161-4616
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".