Lifestyle behaviors predict adolescents bullying victimization in low and middle-income countries.

BACKGROUND: In this study we aimed to provide a global perspective of the association between different lifestyle behaviors and bullying in school adolescents and to ascertain whether or not the Human Development Index moderated those associations. METHODS: Data from the Global school-based Student Health Survey were analyzed in 273 121 from 82 countries. Logistic regression was applied to determine country-specific bullying victimization probability from meeting the recommended guidelines for physical activity, excessive sitting time, physical education attendance and active transport. Meta-analysis was then undertaken to ascertain pooled global effect estimates of the relationship between these behaviors and bullying victimization. Linear regression was used to study the relationship between odds of bullying from meeting the lifestyle guidelines and the Human Development Index. RESULTS: Our estimates indicate that excessive sitting time [1.38 (1.34,1.41)], attendance to physical education [0.87 (0.85,0.89)], and active transport [0.94 (0.91,0.97)] but not overall physical activity [1.01 (0.99,1.04)] were associated with bullying in the study sample. LIMITATIONS: The present study is limited by its cross-sectional nature. Also, only two countries were surveyed from the European region CONCLUSIONS: Active commuting and attendance to physical education play a protective role for bullying, while physical activity does not.

Joint physical-activity/screen-time trajectories during early childhood: socio-demographic predictors and consequences on health-related quality-of-life and socio-emotional outcomes.

BACKGROUND: Understanding the early roots of physical activity and sedentary behaviors is critical to developing intervention programs that promote healthy lifestyle habits in infants and children. There is, however, no evidence on how these behaviors cluster and develop together during early childhood. The aim of this study was to identify single and joint longitudinal trajectories in physical activity and screen time amongst children aged 0 to 9 years, their social-demographic predictors and their prospective health-related quality-of-life and socio-emotional outcomes. METHODS: Three waves of data from The Longitudinal Study of Australian Children, a national study tracking two cohorts every 2 years (B-cohort, 0-5 years, n = 4,164; K-cohort, 4-9 years, n = 3,974) were analysed. Growth mixture modelling was applied to longitudinal time-use diary data to identify joint trajectories in children's physical activity and screen time over Waves 1-3. Key socio-demographic variables measured at Wave 1 were used to predict membership in different trajectories. The prospective consequences (at Wave 3) of time-use trajectories on health-related quality-of-life and socio-emotional outcomes were assessed. RESULTS: Three physical-activity/screen-time trajectories were identified for both cohorts: Cluster-A-children who maintained low levels of physical activity and screen time (∽50% of the sample), Cluster-B-children who progressively increased physical activity and maintained low screen-time levels (∽25%), and Cluster-C-children who maintained low physical-activity levels and increased screen time (∽25%). Children in Cluster-B experienced the best health-related quality-of-life and socio-emotional outcomes, while those in Cluster-C experienced the worst. Children who were female, Indigenous, from non-English-speaking backgrounds, not living with two biological parents, in more affluent households and neighbourhoods, without siblings and with parents with poor mental health were at greater risk of falling into Cluster-A or Cluster-C. CONCLUSION: Our findings identified which children are most at-risk of falling into time-use trajectories that lead to poor health-related quality-of-life and socio-emotional outcomes later in life, increasing our ability to monitor, detect and prevent these suboptimal behaviours prior to their onset.

Simultaneous and proportional force estimation for multifunction myoelectric prostheses using mirrored bilateral training.

This study presents a novel method for associating features of the surface electromyogram (EMG) recorded from one upper limb to the force produced by the contralateral limb. Bilateral-mirrored contractions from ten able-bodied subjects were recorded along with isometric forces in multiple degrees of freedom (DOF) from the right wrist. An artificial neural network was trained to provide force estimation. Combinations of processing parameters were evaluated and an estimation algorithm allowing high accuracy from relatively short signal epochs (100 ms) was proposed. The estimation performance when using surface EMG from the contralateral limb was 0.90 ± 0.02 for the able-bodied subjects. In comparison, the estimation performance for one subject with congenital malformation of the left forearm was 0.72 which, albeit lower than for able-bodied subjects, is still comparable to or better than previously reported results. The proposed method requires only the measured forces from one limb, such as in the case of unilateral amputees and has thus the potential to be used in clinical settings for intuitive, simultaneous control of multiple DOFs in myoelectric prostheses.