Weight estimation in people aged 65 years and over admitted to hospitalisations units

Joaquín Moncho^1,2María-Isabel Orts-Cortés^2,3,4,5,6Patricia Navarro-Llopes⁷Cristina Carretero-Randez^10,8,9*Rafaela Camacho-Bejarano^11,12,4María-Trinidad Castillo-García^13,14Eva María Trescastro-López^15,16,2,3NUTRIFAG Group¹⁷

• ¹Research Unit for the Analysis of Mortality and Health Statistics, Department of Community Nursing, Preventive Medicine, Public Health and History of Science, University of Alicante, Spain, Alicante, Spain
• ²Alicante Institute for Health and Biomedical Research (Group 23, ISABIAL), Alicante, Spain., Alicante, Spain
• ³Department of Nursing. University of Alicante. Spain, Alicante, Spain
• ⁴Nursing and Healthcare Research Unit (Investén-isciii), Instituto de Salud Carlos III, Madrid, Spain, Madrid, Spain
• ⁵CIBER of Frailty and Healthy Ageing (CIBERFES), Instituto de Salud Carlos III, Madrid, , Spain., Madrid, Spain
• ⁶Balmis Research Group in History of Science, Health Care and Food, University of Alicante, Alicante, Spain, Alicante, Spain
• ⁷Faculty of Health Sciences. University of Alicante (BALMIS), Alicante, Spain, Alicante, Spain
• ⁸Nursing in the Burns Unit. University and Polytechnic Hospital La Fe, Valencia, Spain, Valencia, Spain
• ⁹PhD Student. Doctorate in Health Sciences. Community Health and History of Science Team. Nursing department. University of Alicante, Alicante, Spain, Alicante, Spain
• ¹⁰Nursing Research Group: Infection, Inflammation and Chronicity (IIS La Fe). Hospital Universitario y Politécnico La Fe, Valencia, Spain., Valencia, Spain
• ¹¹Department of Nursing. Nursing Faculty. University of Huelva, Huelva, Spain, Huelva, Spain
• ¹²Research Network on Chronicity, Primary Care and Health Promotion (RICAPPS), Spain, Madrid, Spain
• ¹³Department of Nursing. University of Alicante. Spain., Alicante, Spain
• ¹⁴Dr. Balmis General University Hospital, Alicante Institute for Health and Biomedical Research (Group 20, ISABIAL), Alicante, Spain, Alicante, Spain
• ¹⁵Balmis Research Group in History of Science, Health Care and Food, University of Alicante, Alicante, Spain., Alicante, Spain
• ¹⁶Research Group on Applied Dietetics, Nutrition and Body Composition, University of Alicante, Alicante, Spain, Alicante, Spain
• ¹⁷NUTRIFAG Group, Alicante, Spain

Introduction. Malnutrition remains highly prevalent among hospitalised older adults, yet obtaining an accurate body weight, an essential parameter for nutritional assessment. In patients with reduced mobility, direct weight measurement is frequently unfeasible. This study aimed to evaluate the validity and accuracy of three commonly used body-weight estimation equations and to develop and internally validate a new predictive model specifically designed for hospitalised adults aged ≥65 years. Materials and Methods. A multicentre, cross-sectional study was conducted in nine Spanish hospitals using data from the NUTRIFAG project, that was carried out between October 2022 and February 2024. Measured weight, mid-arm circumference (BC), mid-calf circumference (CC), and heel–knee length (KH) were obtained following standardized procedures. Agreement between measured and estimated weight from three published equations was analyzed using intraclass correlation coefficients (ICC), linear regression, and Bland–Altman plots. A new predictive equation was developed through multiple linear regression in a derivation sample (60%) and validated in an independent subsample (40%). The impact of weight estimation on BMI and BMI-based nutritional according to GLIM criteria was also examined. Results. A total of 1,196 patients were included (54.7% men, 45.3% women; mean age 78.27 ± 7.95 years). All three reference equations systematically underestimated measured weight. Equation 1 yielded the highest consistency ICC (men = 0.769; women = 0.834) but lower absolute agreement. The new two-variable model (BC + CC) demonstrated good predictive capacity (R² = 0.639 in men; R² = 0.704 in women) and minimal mean bias (< 1 kg). In the validation sample, ICC was 0.764 (95% CI 0.710–0.810) for men and 0.824 (95% CI 0.777–0.862) for women, confirming excellent reliability without systematic bias. Previously published equations markedly underestimated BMI, resulting in a substantial overestimation of low BMI prevalence according to GLIM criteria, whereas the new equation showed minimal bias and preserved BMI classification comparable to measured weight. Conclusions. The proposed equation offers a simple, accurate, and clinically applicable method for estimating body weight in hospitalized older adults with reduced mobility. By reducing BMI classification errors, its use may contribute to more appropriate nutritional assessment and clinical decision-making. External validation in other settings is recommended.