Estimation of body weight from selected body circumferences in the hospital setting

Summary: Background and Aims: In hospital settings, body weight (BW) measurement can only sometimes be done, even though it is indispensable to justify nutritional and pharmacologic interventions. To be able to monitor the BW and define the dynamic of hospital malnutrition, it is pertinent to pursue an estimate of BW using the accessible body circumferences (BCs) variables, as described in the current study. Methods: Four hundred seventy-seven patients (aged 17–76) were recruited. Only those who could stand up for measuring direct body weight (BW), height (H), and selected BCs were eligible for the study. Thirty-seven patients were excluded from the statistical analyses: 18 with significant edema, 16 with BW > 110 kg (considered outliers), and three without BW data. A total of 440 patients (155 men and 285 women) were included in the final analyses. BW was measured using bioelectrical impedance SECA type 514, and BCs, namely mid-upper arm circumference (MUAC), abdominal circumference (AC), and calf circumference (CC), were measured using a SECA 201 non-elastic tape (SECA 201). We used hierarchical analyses to estimate BW (eBW) with gender and the existence of disease as control variables and BCs as predicted variables. Results: After controlling for gender and disease, the regression model could predict 94.1% of BW variability (R2= 0.942) using a combination of 3 BCs as predicted variables, 89.0–93.3% (R2=0.891–0.933) of BW variability using a combination of 2 BCs; and 81.2–84.6% of BW variability (R2 = 0.814–0.847) with single BC as predicted variables. Conclusions: The best-fit model to estimate patients' BW used a combination of 3 BCs as predicted variables. Nevertheless, other models with the predictability of BW variability of at least 80% could be considered alternatives in developing countries and Asian people with diverse hospital capacities. Further study is needed to validate these BW prediction formulas in clinical practices and describe their variations against the actual BW values.

Authors

Publication Details

Journal
Clinical Nutrition Open Science
Published
2025-03-17
DOI
https://doi.org/10.1016/j.nutos.2025.03.003
Primary Topic
Nutrition and Health in Aging
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Estimation of body weight from selected body circumferences in the hospital setting

V. Wijaya, Lindawati Wibowo, Widjaja Lukito, M.J.V. Parasvita et al.
Clinical Nutrition Open Science
Nutrition and Health in Aging
article

Estimation of body weight from selected body circumferences in the hospital setting

V. Wijaya, Lindawati Wibowo, Widjaja Lukito, M.J.V. Parasvita, N. Budiman
article en

Abstract

Summary: Background and Aims: In hospital settings, body weight (BW) measurement can only sometimes be done, even though it is indispensable to justify nutritional and pharmacologic interventions. To be able to monitor the BW and define the dynamic of hospital malnutrition, it is pertinent to pursue an estimate of BW using the accessible body circumferences (BCs) variables, as described in the current study. Methods: Four hundred seventy-seven patients (aged 17–76) were recruited. Only those who could stand up for measuring direct body weight (BW), height (H), and selected BCs were eligible for the study. Thirty-seven patients were excluded from the statistical analyses: 18 with significant edema, 16 with BW > 110 kg (considered outliers), and three without BW data. A total of 440 patients (155 men and 285 women) were included in the final analyses. BW was measured using bioelectrical impedance SECA type 514, and BCs, namely mid-upper arm circumference (MUAC), abdominal circumference (AC), and calf circumference (CC), were measured using a SECA 201 non-elastic tape (SECA 201). We used hierarchical analyses to estimate BW (eBW) with gender and the existence of disease as control variables and BCs as predicted variables. Results: After controlling for gender and disease, the regression model could predict 94.1% of BW variability (R2= 0.942) using a combination of 3 BCs as predicted variables, 89.0–93.3% (R2=0.891–0.933) of BW variability using a combination of 2 BCs; and 81.2–84.6% of BW variability (R2 = 0.814–0.847) with single BC as predicted variables. Conclusions: The best-fit model to estimate patients' BW used a combination of 3 BCs as predicted variables. Nevertheless, other models with the predictability of BW variability of at least 80% could be considered alternatives in developing countries and Asian people with diverse hospital capacities. Further study is needed to validate these BW prediction formulas in clinical practices and describe their variations against the actual BW values.

Clinical Nutrition Open ScienceVol. 61
Openalex Percentile: Top 94%
Nutrition and Health in Aging
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Estimation of body weight from selected body circumferences in the hospital setting — V. Wijaya, Lindawati Wibowo, et al. · Clinical Nutrition Open Science (2025) | TGRS Research Map | TGRS