Despite the fact that health facilities in Ethiopia are being built closer to communities in all regions, the proportion of home deliveries remains high, and there are no studies being conducted to identify low birth weight (LBW) and premature newborn babies using simple, best, alternative, and appropriate anthropometric measurement in the study area. The objective of the present study was to find the simple, best, and alternative anthropometric measurement and identified its cut-off points for detecting LBW and premature newborn babies. A health facility-based cross-sectional study was conducted in the Dire Dawa city administration, Eastern Ethiopia. The study included 385 women who gave birth in health facility. To evaluate the overall accuracy of the anthropometric measurements, a non-parametric receiver operating characteristic curve was used. Chest circumference (AUC = 0⋅95) with 29⋅4 cm and mean upper arm circumference (AUC = 0⋅93) with 7⋅9 cm proved to be the best anthropometric diagnostic measure for LBW and gestational age, respectively. Also, both anthropometric measuring tools are achieved the highest correlation (r = 0⋅62) for LBW and gestational age. Foot length had a higher sensitivity (94⋅8 %) in detecting LBW than other measurements, with a higher negative predictive value (NPV) (98⋅4 %) and a higher positive predictive value (PPV) (54⋅8 %). Chest circumference and mid-upper arm circumference were found to be better surrogate measurements for identifying LBW and premature babies in need of special care. More research is needed to identify better diagnostic interventions in situations like the study area, which has limited resources and a high proportion of home deliveries.

Emotional and intuitive eating are associated with obesity. In the present study, it was aimed to evaluate the relationship between intuitive eating and emotional eating behaviours in adults with anthropometric measurements of obesity-related disease risk and gender. Body weight, body mass index (BMI), waist, hip and neck circumferences were taken. Emotional Eater Questionnaire and Intuitive Eating Scale-2 were used for the assessment of eating behaviour. A total of 3742 adult individuals (56⋅8 % (n 2125) female and (n 1617) male) were participated voluntarily. The total score and subscales of EEQ were higher in females than males (P < 0⋅001). The scores of the IES-2 subscales and the total score were higher in males than females (P < 0⋅05). In metabolic risk classification according to waist and neck circumference, EEQ scale scores (except type of food) were higher in the metabolic risk group, while IES-2 (except body-food congruence in neck circumference) scores were higher in the non-risk group (P < 0⋅05). While there was a positive correlation between EEQ and body weight, BMI, waist circumference, waist-height ratio, a negative correlation was found between age and waist-hip ratio. There was a negative correlation between IES-2 and body weight, BMI, waist-height ratio, waist-hip ratio. In addition, a negative correlation was found between IES-2 and EEQ. Intuitive eating and emotional eating differ by gender. Anthropometric measures and metabolic disease risk is associated with emotional eating and intuitive eating. Interventions to increase intuitive and decreasing emotional eating behaviour can be effective in preventing both obesity and obesity-related diseases.

No relevant studies have yet been conducted to explore which measurement can best predict the survival time of patients with cancer cachexia. This study aimed to identify an anthropometric measurement that could predict the 1-year survival of patients with cancer cachexia. We conducted a nested case–control study using data from a multicentre clinical investigation of cancer from 2013 to 2020. Cachexia was defined using the Fearon criteria. A total of 262 patients who survived less than 1 year and 262 patients who survived more than 1 year were included in this study. Six candidate variables were selected based on clinical experience and previous studies. Five variables, BMI, mid-arm circumference, mid-arm muscle circumference, calf circumference and triceps skin fold (TSF), were selected for inclusion in the multivariable model. In the conditional logistic regression analysis, TSF (P = 0·014) was identified as a significant independent protective factor. A similar result was observed in all patients with cancer cachexia (n 3084). In addition, a significantly stronger positive association between TSF and the 1-year survival of patients with cancer cachexia was observed in participants aged > 65 years (OR: 0·94; 95 % CI 0·89, 0·99) than in those aged ≤ 65 years (OR: 0·96; 95 % CI 0·93, 0·99; Pinteraction = 0·013) and in participants with no chronic disease (OR: 0·92; 95 % CI 0·87, 0·97) than in those with chronic disease (OR: 0·97; 95 % CI 0·94, 1·00; Pinteraction = 0·049). According to this study, TSF might be a good anthropometric measurement for predicting 1-year survival in patients with cancer cachexia.

To study the relationship between health and nutritional status in elderly populations, information about body composition is essential. To collect this information in large epidemiological studies, practical methods based on anthropometric data must be available. In the present study the relationship between body composition, determined by densitometry, and anthropometric data in 204 elderly men and women, aged 60–87 years, was analysed. Existing prediction equations described in the literature, and mainly based on young and middle-aged subjects, generally underestimated percentage body fat in the elderly study population. Therefore, new prediction equations were developed, based on sex and the sum of two (biceps and triceps) or four (biceps, triceps, suprailiaca and subscapula) skinfolds or the body mass index (BMI). Addition of age or body circumferences to the models did not improve the prediction of body density. Internal cross validation and external validation revealed that the formulas are valid for the estimation of body density in elderly subjects. The standard errors of estimate of the three models, expressed as percentage body fat, were 5.6, 5.4 and 4.8% respectively.