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Objective To derive novel emergency weight estimation tables for New Zealand children aged 5-10 years using ethnicity and sex to increase accuracy and precision. Methods Using an existing dataset (collected in five New Zealand primary schools during July 2013; n = 376), body mass index and current emergency weight estimates were calculated. Stepwise regression with Akanke Information produced two best-fit models for predicting weight, one based on age and the other on height. Potential explanatory variables included ethnicity, sex and body habitus. Results The length-based model included height, ethnicity and body habitus. Accuracy (weight estimates within 10% of actual weight) for the length-based lookup table (74.8%) was similar to that for the Broselow-Luten tape (73.4%). The age-based model included age, sex, ethnicity and body habitus. Accuracy (51.3%) for the age-based lookup table was better than for existing formulae including Shann (45.7%), Advanced Paediatric Life Support (39.1%) and Theron (28.7%). Conclusion The most accurate method for weight estimation in Auckland children aged 5-10 years is either the novel length-based lookup table or the existing Broselow-Luten tape. When length-based methods are not possible, the age-based lookup tables incorporating age, sex, ethnicity and body habitus are more accurate than existing methods of weight estimation. [ABSTRACT FROM AUTHOR]

The purpose of this study was to quantify the inter-session reliability of force–velocity–power profiling and estimated maximal strength in youth. Thirty-six males (11–15 years old) performed a ballistic supine leg press test at five randomized loads (80%, 100%, 120%, 140%, and 160% body mass) on three separate occasions. Peak and mean force, power, velocity, and peak displacement were collected with a linear position transducer attached to the weight stack. Mean values at each load were used to calculate different regression lines and estimate maximal strength, force, velocity, and power. All variables were found reliable (change in the mean [CIM] = − 1 to 14%; coefficient of variation [CV] = 3–18%; intraclass correlation coefficient [ICC] = 0.74–0.99), but were likely to benefit from a familiarization, apart from the unreliable maximal force/velocity ratio (CIM = 0–3%; CV = 23–25%; ICC = 0.35–0.54) and load at maximal power (CIM = − 1 to 2%; CV = 10–13%; ICC = 0.26–0.61). Isoinertial force–velocity–power profiling and maximal strength in youth can be assessed after a familiarization session. Such profiling may provide valuable insight into neuromuscular capabilities during growth and maturation and may be used to monitor specific training adaptations. [ABSTRACT FROM PUBLISHER]

Summary: Background: There is a relationship between childhood obesity and area‐level deprivation. While the New Zealand Index of Deprivation (NZDep) has been used widely in research for the past 20 years, the Index of Multiple Deprivation (IMD) was released in 2017. This study aims to investigate the association between deprivation and childhood obesity in New Zealand and compare measures of deprivation. Methods: Data from 316 794 4‐year‐olds in New Zealand undertaking the B4 School Check in 2010 to 2016, a national health and development screen, were analysed. Multilevel logistic regression models assessed the relationship between area‐level deprivation and individual‐level child obesity. Models were adjusted for age, sex, immigration status, ethnicity, and year. Deprivation was measured using the census‐based NZDep2013 (deciles) and the administrative data‐based IMD (deciles). The seven domains of the IMD were also considered. Results: The relationship between deprivation and obesity was very similar for the IMD and NZDep2013, point estimates were near identical, and confidence intervals overlapped substantially. Higher levels of deprivation were associated with a higher prevalence of child obesity. The relationship between deprivation and child obesity varied considerably across IMD domains. The education domain had the strongest association with child obesity and had an association with child obesity independent of the other domains of deprivation. Conclusion: Overall, there was little difference between the NZDep and IMD. However, the IMD's domains and IMD‐1 approach reveal more nuanced understandings of the deprivation‐obesity gradient, including the importance of area‐level education deprivation for predicting child obesity rates. [ABSTRACT FROM AUTHOR]