Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
8
result(s) for
"Cameron, Noèel"
Sort by:
Methods in human growth research
This volume is a review of up-to-date methods used in human growth research.
Book
Methods in Human Growth Research
In order to gain an understanding of the dynamics of human individual and average growth patterns it is essential that the right methods are selected. There are a variety of methods available to analyse individual growth patterns, to estimate variation in different growth measures in populations and to relate genetic and environmental factors to individual and average growth. This 2004 volume provides an overview of modern techniques for the assessment and collection of growth data and methods of analysis for individual and population growth data. The book contains the basic mathematical and statistical tools required to understand the concepts of the methods under discussion and worked examples of analyses, but it is neither a mathematical treatise, nor a recipe book for growth data analysis. Aimed at junior and senior researchers involved in the analysis of human growth data, this book will be an essential reference for anthropologists, auxologists and paediatricians.
eBook
Calibration and validation of the ActiGraph GT3X+ in 2–3 year olds
To calibrate and validate the ActiGraph GT3X+ to measure sedentary behaviour and physical activity in 2–3 year olds, using 5-s epochs; and to compare the predictive validity of the resulting cut-points with that of NHANES’, Trost's, and Pate's 15-s cut-points.
Cross-sectional study.
Eighteen children (2.86±0.60 years) wore an ActiGraph GT3X+ during video-recorded semi-structured calibration activity sessions. Activity was coded following Children's Activity Rating Scale. Receiver Operating Characteristic analysis was used to derive Axis1 and vector magnitude cut-points for sedentary behaviour and moderate–to-vigorous physical activity at 5-s epochs. Agreement with Children's Activity Rating Scale was assessed with Cohen's kappa, Lin's concordance, and Bland–Altman plots. Predictive validity of all cut-points was assessed in an independent sample of 20 children (2.99±0.48 years) video-recorded during free-play, using the same procedures as the calibration phase.
During calibration, vector magnitude cut-points (sedentary behaviour≤96.12counts; moderate–to-vigorous physical activity≥361.94counts) showed slightly better classification agreement with Children's Activity Rating Scale than Axis1 cut-points (sedentary behaviour≤5counts; moderate–to-vigorous physical activity≥165counts), but the latter showed the lowest bias in estimated sedentary behaviour and moderate–to-vigorous physical activity time. In the validation sample, 5-s Axis1 cut-points showed the best predictive validity and lowest mean differences of all cut-points between predicted and observed sedentary behaviour (−2.31%), light physical activity (−24.40%), and total physical activity time (−0.95%). Moderate–to-vigorous physical activity time was significantly overestimated by all cut-points (128.33–184.17%).
Because moderate–to-vigorous physical activity was highly overestimated, using only the 5-s Axis1 sedentary behaviour cut-point to distinguish sedentary behaviour from total physical activity is advised. The high accuracy indicates that these cut-points are useful for epidemiological studies involving the sedentary behaviour and physical activity of 2–3 year olds.
Journal Article
GROWTH AND DEVELOPMENT AND ATHLETIC PERFORMANCE/RAST IN RAZVOJ TER USPESNOST V SPORTU
The pattern of linear growth experienced by all children is characterised by rapid growth during infancy, relatively constant growth during childhood, and then accelerated growth during the adolescent growth spurt prior to reaching adult maturity and the cessation of growth in length. Whilst this is a universal pattern it demonstrates a considerable degree of sexual dimorphism generally favouring early growth and maturation in girls and delay in boys. The pattern of linear growth is also not common in all tissues. In particular, soft tissues that are associated with fine and gross motor skills and coordination, cognitive ability, strength, endurance, and other aspects of athletic performance follow patterns that may be associated with but are less predictable than growth in length. The development of athletic performance during childhood and adolescence will thus be strongly influenced not only by growth in size and increasing complexity of the nervous system but also by rates of maturational change and sexual dimorphism.
Journal Article
Risk factors for stress fracture in female endurance athletes: a cross-sectional study
Objective To identify psychological and physiological correlates of stress fracture in female endurance athletes. Design A cross-sectional design was used with a history of stress fractures and potential risk factors assessed at one visit. Methods Female-endurance athletes (58 runners and 12 triathletes) aged 26.0±7.4 years completed questionnaires on stress fracture history, menstrual history, athletic training, eating psychopathology and exercise cognitions. Bone mineral density, body fat content and lower leg lean tissue mass (LLLTM) were assessed using dual-x-ray absorptiometry. Variables were compared between athletes with a history of stress fracture (SF) and those without (controls; C) using χ², analysis of variance and Mann-Whitney U tests. Results Nineteen (27%) athletes had previously been clinically diagnosed with SFs. The prevalence of current a/oligomenorrhoea and past amenorrhoea was higher in SF than C (p=0.008 and p=0.035, respectively). SF recorded higher global scores on the eating disorder examination questionnaire (p=0.049) and compulsive exercise test (p=0.006) and had higher LLLTM (p=0.029) compared to C. These findings persisted with weight and height as covariates. In multivariate logistic regression, compulsive exercise, amenorrhoea and LLLTM were significant independent predictors of SF history (p=0.006, 0.009 and 0.035, respectively). Conclusions Eating psychopathology was associated with increased risk of SF in endurance athletes, but this may be mediated by menstrual dysfunction and compulsive exercise. Compulsive exercise, as well as amenorrhoea, is independently related to SF risk.
Journal Article
The Impact of Height during Childhood on the National Prevalence Rates of Overweight: e85769
Background It is known that height and body mass index (BMI) are correlated in childhood. However, its impact on the (trend of) national prevalence rates of overweight and obesity has never been investigated. The aim of our study is to investigate the relation between height and national prevalence rates of overweight and obesity in childhood between 1980, 1997, and 2009, and to calculate which fixed value of p (2.0,2.1, ...,3.0) in kg/mp during childhood is most accurate in predicting adult overweight. Methods and findings Cross-sectional growth data of children from three Dutch nationwide surveys in 1980, 1997, and 2009, and longitudinal data from the Terneuzen Birth Cohort and the Harpenden Growth Study were used. Relative risks (RR) and 95% confidence intervals (CI) were calculated. Our study showed that tall (>1 standard deviation (SD)) girls aged 5.0-13.9 y were more often overweight (RR = 3.5,95%CI:2.8-4.4) and obese (RR = 3.9,95%CI:2.1-7.4) than short girls (<-1 SD). Similar results were found in boys aged 5.0-14.9 y (RR = 4.4,95%CI:3.4-5.7 and RR = 5.3,95%CI:2.6-11.0). No large differences were found in the other age groups and in comparison with children with an average stature. Tall boys aged 2.0-4.9 y had a significantly higher positive trend in overweight between 1980 and 1997 compared to short boys (RR = 4.0,95%CI:1.38-11.9). For other age groups and in girls, no significant trends were found. The optimal Area Under the Curve (AUC) to predict adult overweight was found for p = 2.0. Conclusions and significance Tall girls aged 5.0-13.9y and tall boys aged 5.0-14.9y have much higher prevalence rates of overweight and obesity than their shorter peers. We suggest taking into account the impact of height when evaluating trends and variations of BMI distributions in childhood, and to use BMI to predict adult overweight.
Journal Article
Developing Prediction Equations and a Mobile Phone Application to Identify Infants at Risk of Obesity. e71183
Background Advancements in knowledge of obesity aetiology and mobile phone technology have created the opportunity to develop an electronic tool to predict an infant's risk of childhood obesity. The study aims were to develop and validate equations for the prediction of childhood obesity and integrate them into a mobile phone application (App). Methods and Findings Anthropometry and childhood obesity risk data were obtained for 1868 UK-born White or South Asian infants in the Born in Bradford cohort. Logistic regression was used to develop prediction equations (at 6 plus or minus 1.5, 9 plus or minus 1.5 and 12 plus or minus 1.5 months) for risk of childhood obesity (BMI at 2 years >91st centile and weight gain from 0-2 years >1 centile band) incorporating sex, birth weight, and weight gain as predictors. The discrimination accuracy of the equations was assessed by the area under the curve (AUC); internal validity by comparing area under the curve to those obtained in bootstrapped samples; and external validity by applying the equations to an external sample. An App was built to incorporate six final equations (two at each age, one of which included maternal BMI). The equations had good discrimination (AUCs 86-91%), with the addition of maternal BMI marginally improving prediction. The AUCs in the bootstrapped and external validation samples were similar to those obtained in the development sample. The App is user-friendly, requires a minimum amount of information, and provides a risk assessment of low, medium, or high accompanied by advice and website links to government recommendations. Conclusions Prediction equations for risk of childhood obesity have been developed and incorporated into a novel App, thereby providing proof of concept that childhood obesity prediction research can be integrated with advancements in technology.
Journal Article