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Learning Effects in Air Displacement Plethysmography.

ABSTRACT: Air displacement plethysmography (ADP) is a widespread technique for assessing global obesity in both health and disease. The reliability of ADP has been demonstrated by studies focused on duplicate trials. The present study was purported to evaluate learning effects on the reliability of body composition assessment using the BOD POD system, the sole commercially available ADP instrument. To this end, quadruplicate trials were performed on a group of 105 subjects (51 women and 54 men). We estimated measurement error from pairs of consecutive trials-(1,2), (2,3), and (3,4)-to test the hypothesis that early measurements are subject to larger errors. Indeed, statistical analysis revealed that measures of reliability inferred from the first two trials were inferior to those computed for the other pairs of contiguous trials: for percent body fat (%BF), the standard error of measurement (SEM) was 1.04% for pair (1,2), 0.71% for pair (2,3), and 0.66% for pair (3,4); the two-way random effects model intraclass correlation coefficient (ICC) was 0.991 for pair (1,2), and 0.996 for pairs (2,3) and (3,4). Our findings suggest that, at least for novice subjects, the first ADP test should be regarded as a practice trial. When the remaining trials were pooled together, the reliability indices of single ADP tests were the following: ICC = 0.996, SEM = 0.70%, and minimum detectable change (MDC) = 1.93% for %BF, and ICC = 0.999, SEM = 0.49 kg, and MDC = 1.35 kg for fat-free mass (FFM). Thus, the present study pleads for eliminating learning effects to further increase the reliability of ADP.

SUBMITTER: Muntean P

PROVIDER: S-EPMC10304159 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature

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Learning Effects in Air Displacement Plethysmography.

Muntean Paul P Popa Anca A Miclos-Balica Monica M Schick Falk F Munteanu Oana O Pupazan Vasile V Neagu Adrian A Neagu Monica M

Life (Basel, Switzerland) 20230602 6

Air displacement plethysmography (ADP) is a widespread technique for assessing global obesity in both health and disease. The reliability of ADP has been demonstrated by studies focused on duplicate trials. The present study was purported to evaluate learning effects on the reliability of body composition assessment using the BOD POD system, the sole commercially available ADP instrument. To this end, quadruplicate trials were performed on a group of 105 subjects (51 women and 54 men). We estima ...[more]

PMID: 37374098

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Project description:BackgroundWith the greatest burden of infant undernutrition and morbidity in low and middle income countries (LMICs), there is a need for suitable approaches to monitor infants in a simple, low-cost and effective manner. Anthropometry continues to play a major role in characterising growth and nutritional status.MethodsWe developed a range of models to aid in identifying neonates at risk of malnutrition. We first adopted a logistic regression approach to screen for a composite neonatal morbidity, low and high body fat (BF%) infants. We then developed linear regression models for the estimation of neonatal fat mass as an assessment of body composition and nutritional status.ResultsWe fitted logistic regression models combining up to four anthropometric variables to predict composite morbidity and low and high BF% neonates. The greatest area under receiver-operator characteristic curves (AUC with 95% confidence intervals (CI)) for identifying composite morbidity was 0.740 (0.63, 0.85), resulting from the combination of birthweight, length, chest and mid-thigh circumferences. The AUCs (95% CI) for identifying low and high BF% were 0.827 (0.78, 0.88) and 0.834 (0.79, 0.88), respectively. For identifying composite morbidity, BF% as measured via air displacement plethysmography showed strong predictive ability (AUC 0.786 (0.70, 0.88)), while birthweight percentiles had a lower AUC (0.695 (0.57, 0.82)). Birthweight percentiles could also identify low and high BF% neonates with AUCs of 0.792 (0.74, 0.85) and 0.834 (0.79, 0.88). We applied a sex-specific approach to anthropometric estimation of neonatal fat mass, demonstrating the influence of the testing sample size on the final model performance.ConclusionsThese models display potential for further development and evaluation in LMICs to detect infants in need of further nutritional management, especially where traditional methods of risk management such as birthweight for gestational age percentiles may be variable or non-existent, or unable to detect appropriately grown, low fat newborns.

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Learning Effects in Air Displacement Plethysmography.

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Learning Effects in Air Displacement Plethysmography.

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