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Universities around the world are incorporating online learning, often relying on videos (asynchronous multimedia). We systematically reviewed the effects of video on learning in higher education. We searched five databases using 27 keywords to find randomized trials that measured the learning effects of video among college students. We conducted full-text screening, data extraction, and risk of bias in duplicate. We calculated pooled effect sizes using multilevel random-effects meta-analysis. Searches retrieved 9,677 unique records. After screening 329 full texts, 105 met inclusion criteria, with a pooled sample of 7,776 students. Swapping video for existing teaching methods led to small improvements in student learning (g = 0.28). Adding video to existing teaching led to strong learning benefits (g = 0.80). Although results may be subject to some experimental and publication biases, they suggest that videos are unlikely to be detrimental and usually improve student learning.

The internet has become the chosen medium for professional learning. Completing professional learning can improve work performance; however, many individuals who begin online courses do not complete them. It is not well understood which influences keep individuals engaged in onlin professional learning. We address these issues with a systematic review. Our review of 51 studies and 9,583 participants includes a narrative synthesis and a meta-analysis that examined influences on user engagemen in online professional learning. We found that course design and employers' provision of time to complete learning are key for engaging learners. Other important influences were learners' reasons for learning (e.g., intrinsic value and perceived usefulness), access to learning support, and opportunities for interaction during the learning experience.

Background Excessive engagement with digital screens is harmful to children’s health. However, new evidence suggests that exposure at moderate levels may not be harmful and may even provide benefit. Therefore, our objective was to determine if there are curvilinear relationships between different types of screen time and a diverse set of outcomes, including health and education. Methods We address our objective using a repeated measures design. Children ( N  = 4013), initially aged 10–11 were assessed every 2 years between 2010 and 2014. Children’s screen time behavior was measured using time-use diaries, and categorized into five types: social, passive, interactive, educational, or other. We used measures of children’s physical health, health-related quality of life, socio-emotional outcomes, and school achievement. The analysis plan was pre-registered. Models were adjusted for gender, socio-economic status, ethnicity, number of siblings, and housing factors. Results There were linear associations between total screen time and all outcomes, such that more screen time was associated with worse outcomes. However, there was variability when examined by screen time type. Passive screen time (e.g., TV) was associated with worse outcomes, educational screen time (e.g., computer for homework) was associated with positive educational outcomes and had no negative relations with other outcomes. Interactive screen time (e.g., video games) had positive associations with educational outcomes but negative associations with other outcomes. In all instances, these significant associations were small or very small, with standardised effects < 0.07. We found little evidence of curvilinear relationships. Conclusions The small effects of screen time on children’s outcomes appear to be moderated by the type of screen time. Policy makers, educators, and parents should consider the type of screen time when considering the benefits and harms of use.

Background Reliable estimates of habitual sleep, physical activity, and sedentary time are essential to investigate the associations between these behaviours and health outcomes. While the number of days needed and hours/day for estimates of physical activity and sedentary time are generally known, the criteria for sleep estimates are more uncertain. The objective of this study was to identify the number of nights needed to obtain reliable estimates of habitual sleep behaviour using the GENEActiv wrist worn accelerometer. The number of days to obtain reliable estimate of physical activity was also examined. Methods Data was used from a two-year longitudinal study. Children wore an accelerometer for up to 8 days 24 h/day across three timepoints. The sample included 2,745 children (51 % girls) between the ages of 7-12-years-old (mean = 9.8 years, SD = 1.1 year) with valid accelerometer data from any timepoint. Reliability estimates were calculated for sleep duration, sleep efficiency, sleep onset, wake time, time in bed, light physical activity, moderate physical activity, moderate-to-vigorous physical activity, vigorous physical activity, and sedentary time. Results Intraclass correlations and the Spearman Brown prophecy formula were used to determine the nights and days needed for reliable estimates. We found that between 3 and 5 nights were needed to achieve acceptable reliability (ICC = 0.7) in sleep outcomes, while physical activity and sedentary time outcomes required between 3 and 4 days. Conclusions To obtain reliable estimates, researchers should consider these minimum criteria when designing their studies and prepare strategies to ensure sufficient wear time compliance.

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.

Accurate, objective measurement is important for understanding adolescents' physical activity (PA) behaviour. When using accelerometry to objectively measure PA, a decision must be made regarding how frequently data is recorded (i.e., epoch length). The purpose of this study was to examine i) PA bout length, and ii) the effect of variations in accelerometer epoch length on PA estimates during physical education (PE) and leisure time in adolescent boys. Cross-sectional study. Year 9 boys (N = 133; mean age ±SD  = 14.36±0.48 years) wore accelerometers during two PE lessons, and for a period of seven consecutive days. Data were reintegrated from 1s into longer periods of 2, 5, 10, 30, and 60 seconds. ANOVAs were used to test for differences in PA estimates between epochs in leisure time and PE. The mean length of vigorous PA (VPA) bouts was 3.5±2.0 seconds for PE and 2.5±1.7 seconds for leisure time, and mean length of moderate PA (MPA) bouts was 2.3±0.5 seconds for PE and 2.9±0.5 seconds for leisure time. During PE, estimates of MVPA, MPA, and light PA (LPA) increased as epoch increased from 1 second to 60 seconds, while VPA and sedentary behaviour estimates decreased. During leisure time, estimates of all PA intensities decreased as epoch increased from 1 second to 60 seconds, with the exception of sedentary behaviour, which increased as epoch length increased. The context in which PA occurs can influence PA bout length measurement and the effect of variations in epoch length on PA estimates. Researchers measuring PA with accelerometry should be conscious of the possible influence of context on PA estimates.

Background Combinations of movement behaviors (i.e., physical activity, sedentary behavior, sleep) are associated with health and developmental outcomes in youth. Youth vary in how they accumulate these behaviors, both in volume and specific domains (e.g., sedentary time spent on recreational screen activities vs homework). The aim of this study was to examine how youth’s combined general and domain-specific movement trajectories differ by socioeconomic position. Methods We conducted a longitudinal, group-based multi-trajectory analysis to identify general and domain-specific movement trajectory profiles for 2457 youth from age 10 to 14 years from the Longitudinal Study of Australian Children from 2014–2018. We used multinomial logistic regression to test if socioeconomic position predicted profile membership. Results We identified three general movement trajectory profiles for both sexes, four domain-specific profiles for males, and five for females. For general movement trajectories, females from lower socioeconomic positions were more likely to be a combination of less active and more sedentary than females from higher socioeconomic positions. Males across socioeconomic positions spend similar amounts of time in physical activity, sedentary time, and sleep. For domain-specific movement trajectories, youth from lower socioeconomic positions were likely to spend a combination of less time in education-based sedentary behavior and more time in recreational screen activities than their higher socioeconomic position peers. Conclusion Our results indicate that socioeconomic position predicted in which domains youth accumulate their movements. Future observational research and interventions targeting different socioeconomic groups should therefore consider domain-specific movement trajectories.

Background Children living with disability have poorer health outcomes than their typically developing peers. They are less physically active and at increased risk of chronic disease. Teacher-led, whole-of-school physical activity interventions are promising levers for population-level change, but are seldom tested among children with disability. We aimed to evaluate the effect of a blended teacher-professional learning program (online and in-person) on fundamental movement skills (FMS) and physical activity among children with intellectual disability. Methods In this cluster randomized clinical trial, we randomized 20 government-funded primary schools, including 238 consenting students (Grades 2–5; aged 8–11 years at baseline). Ten schools received the blended teacher-professional learning intervention and 10 were assigned as waitlist controls. The professional learning was designed to support teachers as they implemented a whole-of-school intervention designed to enhance FMS and increase physical activity levels. The school-based intervention was mostly online learning, followed by lesson observations and site visits from project staff. Blinded assessors measured FMS competency using the Test of Gross Motor Development-3. Secondary outcomes were self-concept, enjoyment, wellbeing, 300-yard run time, and accelerometer-measured physical activity. Results We found no significant group-by-time effects for the primary outcome (FMS competency: b = 1.07 [95% CI -3.70, 5.84], p  =.658) or any secondary outcomes. Conclusions A school-based intervention did not improve FMS competency or physical activity in children with intellectual disability. Results may be attenuated by the COVID-19 pandemic. Alternatively, low intensity teacher-professional learning interventions may not be enough to improve FMS or physical activity among children with intellectual disability. Trial registration Australian New Zealand Clinical Trials Registry registration number: ACTRN12620000405910 , registered: 25/03/2020.

Background There is a recognised need for targeted community-wide mental health strategies and interventions aimed specifically at prevention and early intervention in promoting mental health. Young males are a high need group who hold particularly negative attitudes towards mental health services, and these views are detrimental for early intervention and help-seeking. Organised sports provide a promising context to deliver community-wide mental health strategies and interventions to adolescent males. The aim of the Ahead of the Game program is to test the effectiveness of a multi-component, community-sport based program targeting prevention, promotion and early intervention for mental health among adolescent males. Methods The Ahead of the Game program will be implemented within a sample drawn from community sporting clubs and evaluated using a sample drawn from a matched control community. Four programs are proposed, including two targeting adolescents, one for parents, and one for sports coaches. One adolescent program aims to increase mental health literacy, intentions to seek and/or provide help for mental health, and to decrease stigmatising attitudes. The second adolescent program aims to increase resilience. The goal of the parent program is to increase parental mental health literacy and confidence to provide help. The coach program is intended to increase coaches’ supportive behaviours (e.g., autonomy supportive behaviours), and in turn facilitate high-quality motivation and wellbeing among adolescents. Programs will be complemented by a messaging campaign aimed at adolescents to enhance mental health literacy. The effects of the program on adolescent males’ psychological distress and wellbeing will also be explored. Discussion Organised sports represent a potentially engaging avenue to promote mental health and prevent the onset of mental health problems among adolescent males. The community-based design, with samples drawn from an intervention and a matched control community, enables evaluation of adolescent males’ incremental mental health literacy, help-seeking intentions, stigmatising attitudes, motivation, and resilience impacts from the multi-level, multi-component Ahead of the Game program. Notable risks to the study include self-selection bias, the non-randomised design, and the translational nature of the program. However, strengths include extensive community input, as well as the multi-level and multi-component design. Trial registration Australian New Zealand Clinical Trials Registry ACTRN12617000709347 . Date registered 17 May 2017. Retrospectively registered.

Background Understanding the developmental trajectories of outdoor time, screen time and sleep is necessary to inform early interventions that promote healthy behaviours. This study aimed to describe concurrent trajectories of outdoor time, screen time and sleep across the early childhood period and their maternal predictors. Methods Data across five time points at child age 4, 9, 19, 42 and 60 months from the INFANT intervention were analysed. Mothers reported their child’s usual outdoor time, screen time and sleep duration, in addition to a range of maternal beliefs, attitudes, expectations and behaviours. Group-based multi-trajectory modelling was used to model concurrent trajectories of children’s behaviours. Multinomial logistic regression models determined the associations of maternal predictors with trajectory groups, adjusting for child sex and baseline age, intervention allocation, and clustering by recruitment. Results Of the 542 children recruited, 528 had data for outdoor time, screen time and sleep at one or more time points and were included in trajectory analyses Four trajectories were identified: ‘unstable sleep, increasing outdoor time, low screen’ (~ 22% of sample), ‘high outdoor time, low screen, high sleep’ (~ 24%), ‘high sleep, increasing outdoor time, low screen’ (~ 45%), ‘high screen, increasing outdoor time, high sleep’ (~ 10%). The ‘high sleep, increasing outdoor time, low screen’ group, comprising the largest percentage of the sample, demonstrated the healthiest behaviours. Predictors of group membership included: views of physically active children, screen time knowledge, screen time use, self-efficacy, physical activity optimism, future expectations for children’s physical activity and screen time, perceptions of floor play safety, and maternal physical activity, screen time, and sleep quality. Conclusions Four distinct trajectories of outdoor time, screen time and sleep were identified, with the most common (and healthiest) characterized by high levels of sleep. Maternal beliefs, attitudes, expectations and behaviours are important in the development of movement behaviour trajectories across early childhood. Future interventions and public policy may benefit from targeting these factors to support healthy movement behaviours from a young age.