Evaluation of body composition is an important part of assessing nutritional status and provides prognostically useful data and opportunity to monitor the effects of nutrition-related disease progression and nutritional intervention. The aim of this narrative review is to critically evaluate body composition methodology in adults, focusing on anthropometric variables. The variables considered include height, weight, body mass index and alternative indices, trunk measurements (waist and hip circumferences and sagittal abdominal diameter) and limb measurements (midupper arm and calf circumferences) and skinfold thickness. The importance of adhering to a defined measurement protocol, checking measurement error and the need to interpret measurements using appropriate population-specific cut-off values to identify health risks were identified. Selecting the optimum method of assessing body composition using anthropometry depends on the purpose, i.e. evaluating obesity or undernutrition, and requires practitioners to have a good understanding of both practical and theoretical limitations and to wisely interpret the results.
The accurate and valid assessment of body composition is essential for the diagnostic evaluation of nutritional status, identifying relevant outcome measures, and determining the effectiveness of current and future nutritional interventions. Developments in technology and our understanding of the influences of body composition on risk and outcome will provide practitioners with new opportunities to enhance current practice and to lead future improvements in practice. This is the second of a two-part narrative review that aims to critically evaluate body composition methodology in diverse adult populations, with a primary focus on its use in the assessment and monitoring of under-nutrition. Part one focused on anthropometric variables [Madden and Smith (2016) J Hum Nutr Diet 29: 7-25] and part two focuses on the use of imaging techniques, bioelectrical impedance analysis, markers of muscle strength and functional status, with particular reference to developments relevant to practice.
The recent Scottish Health Survey reported that 65.1 % of adults in Scotland were overweight or obese (1) . It is well established that the low levels of activity are associated with increased body weight (2) and the current guidelines for activity are not being met with only 33 % of woman meeting the target of 30 minutes of activity most days of the week (1) . The aim of this study was to increase activity levels using a simple, self-guided activity point system (3) and assess changes in body weight. Overweight and obese women (BMI > 25 kg/m 2 ) were recruited from the University. to participate in a 12 week intervention programme. Participants were instructed to increase activity using the point system and this was recorded using a daily self-reported activity diary. Participants were reviewed at baseline, 6 weeks and 12 weeks when body weight, body mass index and actual activity levels were measured using an activPAL accelerometer. Activity was categorised as sitting/lying, standing or stepping. Paired t-tests were used to compare the changes from baseline to 12 weeks.Seven women mean age 46 SD 6.7 years completed the intervention programme. These results indicate that following a simple activity point system is an effective method to reduce inactivity in an overweight and obese female population. Whilst the activity point system did result in lower levels of sitting/lying times it was not apparent that this translated into increased levels of activities such as stepping as the resultant effect was an increase in standing time only. However this model for reducing inactivity shows potential as a method of weight reduction, as despite being a pilot study, significant reductions in both body weight and BMI were evident. It remains to be elucidated whether these results can be generalised to the population as a whole and whether improvements in diet quality contribute to the weight loss seen.
Over 12 weeks, supervised physical activity (PA) interventions have demonstrated improvements in morphological and health parameters, whereas community walking programmes have not. The present study piloted a self-guided programme for promoting PA and reducing sedentary behaviour in overweight individuals and measured its effect on a range of health outcomes. Six male and 16 female sedentary adults aged 48.5 ± 5.5 years with body mass index (BMI) 33.4 ± 6.3 kg m(-2) were assessed for anthropometric variables, blood pressure, functional capacity, well-being and fatigue. After an exercise consultation, participants pursued their own activity and monitored PA points weekly. At baseline, mid-point and 12 weeks, eight participants wore activity monitors, and all participants undertook a 5-day food diary to monitor dietary intake. In 17 completers, mass, BMI, sit-to-stand, physical and general fatigue had improved by 6 weeks. By 12 weeks, waist, sagittal abdominal diameter (SAD), diastolic blood pressure, well-being and most fatigue dimensions had also improved. Throughout the intervention, PA was stable, energy intake and lying time decreased and standing time increased; thus, changes in both energy intake and expenditure explain the health-related outcomes. Observed changes in function, fatigue and quality of life are consistent with visceral fat loss and can occur at levels of weight loss which may not be considered clinically significant.
Equations developed by Powell-Tuck and Hennessy in 2003 (1) allow an estimate of body mass index (BMI) to be determined from mid upper arm circumference (MUAC). These equations are widely used in clinical practice when an individual's BMI cannot be calculated from measured height and weight. Anecdotal reports also suggest that estimated BMI derived from these equations is being used along with height to estimate the weight of some individuals who cannot be weighed, which in turn is used to calculate nutritional requirements. Secondary analysis of data for individuals who participated in the National Diet and Nutrition Survey (aged 65 years or over) suggests that there is a substantial variability in BMI predicted from an individual MUAC (2) . However there appears to be a lack of such data in younger adults. Therefore a pilot study aimed at assessing the accuracy of BMI estimated using the Powell-Tuck and Hennessy equations and the subsequent accuracy of estimated weight, within a younger healthy female adult population was undertaken.Subjects who volunteered had their age recorded, and actual height, weight and MUAC measured. From this data actual BMI, estimated BMI and estimated weight were calculated. The relationship between actual BMI and MUAC, actual BMI and estimated BMI were explored using pearsons correlation. The accuracy of estimated BMI with actual BMI and estimated weight with actual weight were determined using Bland-Altman limits of agreement.29 subjects volunteered with a mean age of 26.1 years (standard deviation (SD) 10.2 years) and a mean actual BMI of 23.1 Kg/m 2 (SD 4.3 Kg/m 2 ). A strong positive (r = 0.912), statistically significant (p < 0.001) correlation between actual BMI and MUAC was identified, as was a strong positive correlation between actual BMI and estimated BMI (r = 0.896). Bland-Altman analysis revealed a mean difference of -0.08 Kg/m 2 for estimated BMI versus actual BMI and a mean difference of -0.16 Kg for estimated weight versus actual weight. However the 95 % confidence intervals for estimated BMI versus actual BMI were -5.64 Kg/m 2 to 4.10 Kg/m 2 and -13.23 Kg to 12.91 Kg for estimated weight versus actual weight.The results demonstrate a strong positive relationship between BMI and MUAC, and between actual BMI and estimated BMI. However they also demonstrated that at an individual level both estimated BMI and estimated weight could be significantly over or under the actual value, to a level that could affect clinical practice by altering BMI classification and/or calculated nutritional requirements.Although further work is required to determine the accuracy of Powell-Tuck and Hennessy equations in other populations and to potentially derive more accurate equations, practitioners should be aware of the potential inaccuracies of using the Powell-Tuck and Hennessy equations to estimate an individual's BMI or to derive an individual estimation of weight.
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