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.

Construction of an adaptable and specific severity score for prehospital emergencies.

The aim of this study was to design a severity score specific to mobile emergency and resuscitation services (MERS). A prospective, multicentre cohort study including 17 868 patients was performed. The severity reference criterion was determined by multiple correspondence analysis. A multiple linear regression was used for the construction of the severity score. The score performances were analysed in terms of area under the receiver operating characteristics curves (AUC). Twelve variables were identified for the construction of the severity score. The multiple regression (r2 = 0.947; p<0.001) provided a severity score that took on values from 8 to 68. The score performs well in distinguishing the various patient outcomes in terms of AUC. This study develops the first adaptable and specific severity score of MERS activities.

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.