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ORIGINAL ARTICLE
Year : 2019  |  Volume : 150  |  Issue : 3  |  Page : 297-305

Development & cross-validation of anthropometric predictive models to estimate the appendicular skeletal muscle mass in middle-aged women in Sri Lanka


1 Department of Nursing, Faculty of Allied Health Sciences, Galle, Sri Lanka
2 Department of Anatomy, Faculty of Medicine, University of Ruhuna, Galle, Sri Lanka
3 Department of Physiology, Faculty of Medicine, University of Ruhuna, Galle, Sri Lanka
4 Department of Medicine, Faculty of Medicine, University of Ruhuna, Galle, Sri Lanka

Correspondence Address:
Dr Nirmala Rathnayake
Department of Nursing, Faculty of Allied Health Sciences, University of Ruhuna, Galle
Sri Lanka
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ijmr.IJMR_1961_17

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Background & objectives: Attempts have been made to estimate appendicular skeletal muscle mass (ASMM) using anthropometric indices and most of these are country specific. This study was designed to develop and cross-validate simple predictive models to estimate the ASMM based on anthropometry in a group of healthy middle-aged women in Sri Lanka. Methods: The study was conducted on a randomly selected group of community-dwelling women aged 30-60 years. ASMM (kg) quantified with dual-energy X-ray absorptiometry (DXA) (ASMMDXA) was used as the reference standard. Anthropometric measurements such as body weight (kg), height (m), limb circumferences (cm) and skinfold thickness (mm) which showed significant correlations with ASMMDXA, were used to develop the models. The models were developed using a group of 165 women (aged 30-60 yr) and were cross-validated using a separate sample of women (n=167) (mean age: 48.9±8.56 yr), selected randomly. Results: Nine anthropometry-based models were developed using weight, height, skinfold thicknesses, circumferences, body mass index, menopausal status (MS) and age as independent variables. Four models which were based on height, weight, triceps skinfold thickness (TSFT), age and MS met all the validation criteria with high correlations (ranged 0.89-0.92) and high predictive values explaining high variance (80-84%) with low standard error of estimate (1.10-1.24 kg). Interpretation & conclusions: The four models (ASMM 1-ASMM 4) developed based on height, weight, TSFT, age and MS showed a high accuracy in estimating the ASMM in middle-aged women.


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