Validation study on a prediction formula to estimate the weight of children & adolescents with special needs aged 2-18 years old

J Health Popul Nutr. 2023 Nov 20;42(1):129. doi: 10.1186/s41043-023-00464-5.


Background: This study aims to validate two predictive formulas of weight estimating strategies in children with special needs, namely the Cattermole formula and the Mercy formula.

Methodology: A cross-sectional study with a universal sampling of children and adolescents with special needs aged 2-18 years old, diagnosed with cerebral palsy, down syndrome, autism and attention-deficit/hyperactivity disorder was conducted at Community-Based Rehabilitation in Central Zone Malaysia. Socio-demographic data were obtained from files, and medical reports and anthropometric measurements (body weight, height, humeral length, and mid-upper arm circumference) were collected using standard procedures. Data were analysed using IBM SPSS version 26. The accuracy of the formula was determined by intraclass correlation, prediction at 20% of actual body weight, residual error (RE) and root mean square error (RMSE).

Result: A total of 502 children with a median age of 7 (6) years were enrolled in this study. The results showed that the Mercy formula demonstrated a smaller degree of bias than the Cattermole formula (PE = 1.97 ± 15.99% and 21.13 ± 27.76%, respectively). The Mercy formula showed the highest intraclass correlation coefficient (0.936 vs. 0.858) and predicted weight within 20% of the actual value in the largest proportion of participants (84% vs. 48%). The Mercy formula also demonstrated lower RE (0.3 vs. 3.6) and RMSE (3.84 vs. 6.56) compared to the Cattermole formula. Mercy offered the best option for weight estimation in children with special needs in our study population.

Keywords: Body weight estimation; Body weight formula; Children with special needs; MUAC.

MeSH terms

  • Adolescent
  • Anthropometry / methods
  • Body Height*
  • Body Weight
  • Child
  • Child, Preschool
  • Cross-Sectional Studies
  • Humans
  • Malaysia