Body mass index and childhood obesity classification systems: A comparison of the French, International Obesity Task Force (IOTF) and World Health Organization (WHO) references.

AIM: This study aims to compare three body mass index (BMI)-based classification systems of childhood obesity: the French, the International Obesity Task Force (IOTF) and the World Health Organization (WHO) references. METHODS: The study involved 1382 schoolchildren, recruited from the Lille Academic District in France in May 2009 aged 8.4±1.7 years (4.0-12.0 years). Their mean height and body mass were 131.5±10.9cm and 30.7±9.2kg, respectively, resulting in a BMI of 17.4±3.2kg/m(2). The weight status was defined according to the three systems considered in this study. The agreement between these references was tested using the Cohen's kappa coefficient. RESULTS: The prevalence of overweight was higher with the WHO references (20.0%) in comparison with the French references (13.8%; P<0.0001) and the IOTF (16.2%; P≤0.01). A similar result was found with obesity (WHO: 11.6% vs. IOTF: 6.7%; or French references: 6.7%; P<0.0001). Agreement between the three references ranged from "moderate" to "perfect" (0.43≤κ≤1.00; P<0.0001). Kappa coefficients were higher when the three references were used to classify children as obese (0.63≤κ≤1.00; P<0.0001) as compared to classification in the overweight (obesity excluded) category (0.43≤κ≤0.94; P<0.0001). When sex and age categories (4-6 years vs. 7-12 years) were considered to define the overweight status, the lowest kappa coefficient was found between the French and WHO references in boys aged 7-12 years (κ=0.28; P<0.0001), and the highest one in girls aged 7-12 years between the French references and IOTF (κ=0.97; P<0.0001). As for obesity, agreement between the three references ranged from 0.60 to 1.00 (P<0.0001), with the lowest values obtained in the comparison of the WHO references against French references or IOTF among boys aged 7-12 years (κ=0.60; P<0.0001). CONCLUSION: Overall, the WHO references yield an overestimation in overweight and/or obesity within this sample of schoolchildren as compared to the French references and the IOTF. The magnitude of agreement coefficients between the three references depends on of both sex and age categories. The French references seem to be in rather close agreement with the IOTF in defining overweight, especially in 7-12-year-old children.

Does the body adiposity index (BAI) apply to paediatric populations?

OBJECTIVE: Validation of body adiposity index (BAI) in a paediatrics sample; and to develop, if necessary, a valid BAI for paediatrics (i.e. BAIp). METHODS: A total of 1615 children (52% boys) aged 5-12 years underwent anthropometry. Their body composition was assessed using a foot-to-foot bioimpedance. The validity of BAI = (Hip circumference/Height(1.5)) - 18 was tested by combining correlation and agreement statistics. Then, the sample was split into two sub-samples for the construction of BAIp. A regression was used to compute the prediction equation for BAIp-based percentage of body fat (%BF). RESULTS: The initial BAI over-estimated the %BF of children by 49% (29.6 ± 4.2% versus 19.8 ± 6.8%; p < 0.0001). The original methodology led to a BAIp = (Hip circumference/Height(0.8)) - 38 in children. When compared to BAI, BAIp showed both better correlation (r = 0.57; p < 0.01 versus r = 0.74; p < 0.0001) and agreement (ICC = 0.34; [95% CI = -0.19-0.65] versus ICC = 0.83; [95% CI = 0.81-0.84]). However, there were some systematic biases between the two values of %BF as exemplified by the large 95% limit of agreement [-9.1%; 8.8%] obtained. CONCLUSION: BAI over-estimates the %BF in children. In contrast, BAIp appears as a new index for children's body fatness, with acceptable accuracy. In its current form, this index is valid only for large-scale studies.

Does biological maturity actually confound gender-related differences in physical activity in preadolescence?

AIM: To examine: (i) if maturity-related gender differences in moderate-to-vigorous physical activity (MVPA) depend on how maturity status is defined and measured; and (ii) the influence of maturity level on compliance with PA recommendations. METHODS: The study involved 253 children (139 boys) aged 9.9 ± 0.9 years, with mean stature and weight of 1.39 ± 0.08 m and 35.8 ± 8.8 kg respectively. Their PA was evaluated using an Actigraph accelerometer (Model 7164). Maturity was assessed using the estimated age at peak height velocity (APHV) and a standardized APHV by gender (i.e. centred APHV). RESULTS: Boys engaged in significantly more MVPA than girls (P < 0.0001). There was a significant correlation between the centred APHV and MVPA in boys (r = 0.20; P = 0.016), but not in girls (r = 0.13; P = 0.155). An ancova controlling for the estimated APHV showed no significant interactions between gender and APHV, and the main effect of gender on MVPA was negated. Conversely, there was a significant main effect of APHV on MVPA (F 1,249 = 6.12; P = 0.014; η p (2) = 0.024). Only 9.1% of children met the PA recommendations, including 14.4% of boys and 2.6% of girls (P < 0.01). This observation also applies in both pre-APHV (12.7% of boys vs. 2.4% of girls, P < 0.001) and post-APHV children (23.8% of boys vs. 3.4% of girls, P < 0.0001). No differences in PA guidelines were observed between pre-APHV and post-APHV children. CONCLUSIONS: Among prepubescent children, the influence of biological maturity on gender differences in PA may be a function of how maturity status is determined. The most physically active prepubescent children were those who were on time according to APHV.

An architecture for online comparison and validation of processing methods and computerized guidelines in intensive care units.

Clinical decision support systems are a combination of software techniques to help the clinicians in their medical decision making process via functionalities ranging from basic signal analysis to therapeutic planning and computerized guidelines. The algorithms providing all these functionalities must be very carefully validated on real patient data and must be confronted to everyday clinical practice. One of the main problems when developing these techniques is the difficulty to obtain high-quality complete patient records, comprising data coming both from the biomedical equipment (high-frequency signals), and from numerous other sources (therapeutics, imagery, clinical actions, etc.). In this paper, we present an infrastructure for developing and testing such software algorithms. It is based on a bedside workstation where testing different algorithms simultaneously on real-time data is possible in the ward. It is completed by a collaborative portal enabling different teams to test their software algorithms on the same patient records, making comparisons and cross-validations more easily.