The spread of traffic jams in urban networks has long been viewed as a complex spatio-temporal phenomenon that often requires computationally intensive microscopic models for analysis purposes. In this study, we present a framework to describe the dynamics of congestion propagation and dissipation of traffic in cities using a simple contagion process, inspired by those used to model infectious disease spread in a population. We introduce two macroscopic characteristics for network traffic dynamics, namely congestion propagation rate β and congestion dissipation rate μ. We describe the dynamics of congestion spread using these new parameters embedded within a system of ordinary differential equations, similar to the well-known susceptible-infected-recovered (SIR) model. The proposed contagion-based dynamics are verified through an empirical multi-city analysis, and can be used to monitor, predict and control the fraction of congested links in the network over time.

Lifestyle risk behaviours typically emerge during adolescence, track into adulthood, and commonly co-occur. Interventions targeting multiple risk behaviours in adolescents have the potential to efficiently improve health outcomes, yet further evidence is required to determine their effect. We reviewed the effectiveness of eHealth school-based interventions targeting multiple lifestyle risk behaviours. In this systematic review and meta-analysis, we searched Ovid MEDLINE, Embase, PsycINFO, and the Cochrane Library databases between Jan 1, 2000, and March 14, 2019, with no language restrictions, for publications on school-based eHealth multiple health behaviour interventions in humans. We also screened the grey literature for unpublished data. Eligible studies were randomised controlled trials of eHealth (internet, computers, tablets, mobile technology, or tele-health) interventions targeting two or more of six behaviours of interest: alcohol use, smoking, diet, physical activity, sedentary behaviour, and sleep. Primary outcomes of interest were the prevention or reduction of unhealthy behaviours, or improvement in healthy behaviours of the six behaviours. Outcomes were summarised in a narrative synthesis and combined using random-effects meta-analysis. This systematic review is registered with PROSPERO, identifier CRD42017072163. Of 10 571 identified records, 22 publications assessing 16 interventions were included, comprising 18 873 students, of whom on average 56·2% were female, with a mean age of 13·41 years (SD 1·52). eHealth school-based multiple health behaviour change interventions significantly increased fruit and vegetable intake (standard mean difference 0·11, 95% CI 0·03 to 0·19; p=0·007) and both accelerometer-measured (0·33, 0·05 to 0·61; p=0·02) and self-reported (0·14, 0·05 to 0·23; p=0·003) physical activity, and reduced screen time (-0·09, -0·17 to -0·01; p=0·03) immediately after the intervention; however, these effects were not sustained at follow-up when data were available. No effect was seen for alcohol or smoking, fat or sugar-sweetened beverage or snack consumption. No studies examined sleep or used mobile health interventions. The risk of bias in masking of final outcome assessors and selective outcome reporting was high or unclear across studies and overall we deemd the quality of evidence to be low to very low. eHealth school-based interventions addressing multiple lifestyle risk behaviours can be effective in improving physical activity, screen time, and fruit and vegetable intake. However, effects were small and only evident immediately after the intervention. Further high quality, adolescent-informed research is needed to develop eHealth interventions that can modify multiple behaviours and sustain long-term effects. Paul Ramsay Foundation and Australian National Health and Medical Research Council.

Background:Dropout from organized youth sport has significant adverse health implications. Enjoyment and behavioral intentions have consistently been linked with participation and dropout; however, few studies have investigated these links using a prospective design. This study explored whether enjoyment and intentions to continue predicted dropout behavior at 1-year follow-up.Methods:Questionnaires were completed by 327 regular sport participants (mean age = 13.01 y at baseline). After 1 year, 247 individuals (75.5%) continued participating in their main sport and 26 individuals (8%) dropped out. A hierarchical logistic regression model estimated the probability of dropout. In step 1, the following covariates were included: age, sex, competition level, perceived competence, parental support, coach–athlete relationship, friendship quality, and peer acceptance. In step 2, enjoyment and intentions to continue were included.Results:Step 1 indicated that age, parental support, coach–athlete relationship quality, and peer acceptance were significantly associated with dropout. Step 2 explained further variance in dropout, with both enjoyment and intentions inversely associated with dropout. Peer acceptance was the only covariate to remain significantly associated with dropout in step 2.Conclusions:Findings support the use of enjoyment and behavioral intentions as indicators of sport participation/dropout behavior and may aid the development of interventions aimed at preventing future dropout.