Combinations of Physical Activity, Sedentary Behavior, and Sleep Duration and Their Associations With Physical, Psychological, and Educational Outcomes in Children and Adolescents: A Systematic Review.

We conducted a systematic review to evaluate combinations of physical activity, sedentary behavior, and sleep duration (defined as "movement behaviors") and their associations with physical, psychological, and educational outcomes in children and adolescents. MEDLINE, CINAHL, PsychInfo, SPORTDiscus, PubMed, EMBASE, and ERIC were searched in June 2020. Included studies needed to 1) quantitatively analyze the association of 2 or more movement behaviors with an outcome, 2) analyze a population between 5 and 17 years of age, and 3) include at least an English abstract. We included 141 studies. Most studies included the combination of physical activity and sedentary behavior in their analyses. Sleep was studied less frequently. In combination, a high level of physical activity and a low level of sedentary behavior were associated with the best physical health, psychological health, and education-related outcomes. Sleep was often included in the combination that was associated with the most favorable outcomes. Sedentary behavior had a stronger influence in adolescents than in children and tended to be associated more negatively with outcomes when it was defined as screen time than when defined as overall time spent being sedentary. More initiatives and guidelines combining all 3 movement behaviors will provide benefit with regard to adiposity, cardiometabolic risk factors, cardiorespiratory fitness, muscular physical fitness, well-being, health-related quality of life, mental health, academic performance, and cognitive/executive function.

A Systematic Review of Machine Learning for Assessment and Feedback of Treatment Fidelity / Revisión sistemática del aprendizaje automático para la evaluación y feedback de la fidelidad al tratamiento

Many psychological treatments have been shown to be cost-effective and efficacious, as long as they are implemented faithfully. Assessing fidelity and providing feedback is expensive and time-consuming. Machine learning has been used to assess treatment fidelity, but the reliability and generalisability is unclear. We collated and critiqued all implementations of machine learning to assess the verbal behaviour of all helping professionals, with particular emphasis on treatment fidelity for therapists. We conducted searches using nine electronic databases for automated approaches of coding verbal behaviour in therapy and similar contexts. We completed screening, extraction, and quality assessment in duplicate. Fifty-two studies met our inclusion criteria (65.3% in psychotherapy). Automated coding methods performed better than chance, and some methods showed near human-level performance; performance tended to be better with larger data sets, a smaller number of codes, conceptually simple codes, and when predicting session-level ratings than utterance-level ones. Few studies adhered to best-practice machine learning guidelines. Machine learning demonstrated promising results, particularly where there are large, annotated datasets and a modest number of concrete features to code. These methods are novel, cost-effective, scalable ways of assessing fidelity and providing therapists with individualised, prompt, and objective feedback. (AU)

Se ha puesto de manifiesto que muchos tratamientos psicológicos tienen un coste efectivo y son eficaces siempre que se apliquen con fidelidad. La evaluación de esta y el feedback son caros y exigen mucho tiempo. El aprendizaje automático se ha utilizado para evaluar la fidelidad al tratamiento, aunque su fiabilidad y capacidad de generalización no estén claras. Recopilamos y analizamos todas las aplicaciones de aprendizaje automático con el fin de evaluar el comportamiento verbal de todos los profesionales de ayuda, con el acento particular en la fidelidad al tratamiento de los terapeutas. Llevamos a cabo búsquedas en nueve bases de datos electrónicas para enfoques automáticos de codificación de comportamiento verbal en terapia y contextos semejantes. Llevamos a cabo el cribado, la extracción y la evaluación de la calidad por duplicado. Cincuenta y dos estudios cumplían nuestros criterios de inclusión (el 65.3% en psicoterapia). Los métodos de codificación automática resultaban mejor que el azar y algunos de ellos mostraban un desempeño casi al nivel humano, que tendía a ser mejor con conjuntos más grandes de datos, un número de códigos menor, códigos conceptualmente simples y cuando predecían índices al nivel de sesión que los de tipo declaración. Escasos estudios cumplían las directrices de buena praxis en aprendizaje automático. Este presentó unos resultados alentadores, sobre todo donde había conjuntos de datos grandes y anotados y un escaso número de características concretas que codificar, modos expansibles de evaluar la fidelidad y facilitar a los terapeutas un feedback individualizado, rápido y objetivo. (AU)

Reliability of GENEActiv accelerometers to estimate sleep, physical activity, and sedentary time in children.

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.

Effect of a Scalable School-Based Intervention on Cardiorespiratory Fitness in Children: A Cluster Randomized Clinical Trial.

Importance: Cardiorespiratory fitness is an important marker of childhood health and low fitness levels are a risk factor for disease later in life. Levels of children's fitness have declined in recent decades. Whether school-based physical activity interventions can increase fitness at the population level remains unclear. Objective: To evaluate the effect of an internet-based intervention on children's cardiorespiratory fitness across a large number of schools. Design, Setting, and Participants: In this cluster randomized clinical trial, 22 government-funded elementary schools (from 137 providing consent) including 1188 students stratified from grades 3 and 4 in New South Wales, Australia, were randomized. The other schools received the intervention but were not included in the analysis. Eleven schools received the internet-based intervention and 11 received the control intervention. Recruitment and baseline testing began in 2016 and ended in 2017. Research assistants, blinded to treatment allocation, completed follow-up outcome assessments at 12 and 24 months. Data were analyzed from July to August 2020. Interventions: The internet-based intervention included standardized online learning for teachers and minimal in-person support from a project mentor (9-10 months). Main Outcomes and Measures: Multistage 20-m shuttle run test for cardiorespiratory fitness. Results: Of 1219 participants (49% girls; mean [SD] age, 8.85 [0.71] years) from 22 schools, 1188 students provided baseline primary outcome data. At 12 months, the number of 20-m shuttle runs increased by 3.32 laps (95% CI, 2.44-4.20 laps) in the intervention schools and 2.11 laps (95% CI, 1.38-2.85 laps) in the control schools (adjusted difference = 1.20 laps; 95% CI, 0.17-2.24 laps). By 24 months, the adjusted difference was 2.22 laps (95% CI, 0.89-3.55 laps). The cost per student was AUD33 (USD26). Conclusions and Relevance: In this study, a school-based intervention improved children's cardiorespiratory fitness when delivered in a large number of schools. The low cost and sustained effect over 24 months of the intervention suggests that it may have potential to be scaled at the population level. Trial Registration: http://anzctr.org.au Identifier: ACTRN12616000731493.

Day-to-day and longer-term longitudinal associations between physical activity, sedentary behavior, and sleep in children.

STUDY OBJECTIVES: To determine the day-to-day and longer-term longitudinal associations between daytime physical activity and night-time sleep. METHODS: We used data from a 2-year longitudinal study which included three time points (i.e. baseline, year 1, and year 2). Participants were recruited from primary schools and included 1059 children (50% girls) with a mean age of 8.81-years-old (SD = 0.72) at baseline. Sleep variables included sleep duration, sleep efficiency, time in bed, sleep onset, and wake time. Physical activity variables included light, moderate, moderate-to-vigorous, and vigorous physical activity as well as sedentary time. We objectively assessed physical activity and sleep behaviors using the GENEActiv wrist-worn accelerometer over an 8-day period at each timepoint for a potential 21 190 observed days. RESULTS: We used fixed-effects multilevel models and parallel latent growth curve modeling to examine day-to-day and longer-term associations, respectively. Day-to-day, physical activity, and sleep variables were significantly, positively, and bidirectionally associated, except for sleep efficiency, which showed little association with physical activity. Longer-term, we found little association between physical activity and sleep variables. CONCLUSIONS: Overall, our findings indicate that there is a day-to-day association between the amount of time spent being physically active and improved sleep. The lack of a longer-term association indicates that a focus on children's daily behavior may be most appropriate to help children improve sleep and increase physical activity.

The effects of the Australian bushfires on physical activity in children.

OBJECTIVES: To determine the impact of bushfires on children's physical activity. DESIGN: Natural experiment comparing device-measured physical activity and air quality index data for schools exposed and not exposed to the Australian bushfires. METHODS: Participants were drawn from 22 schools participating in a cluster randomised controlled trial of a school-based physical activity intervention that coincided with the 2019 Australian bushfires. Students in Years 3 and 4 (8-10 years old) provided data. We used propensity score matching to match 245 exposed and 344 control participants. MAIN OUTCOME MEASURES: Minutes of moderate and vigorous physical activity. RESULTS: The bushfires had minimal effect on children's average weekly physical activity. Analysis of acute effects showed children maintained their levels of physical activity up to an estimated turning point of air quality index of 737.08 (95% CI = 638.63, 835.53), beyond which daily physical activity levels dropped sharply. Similar results were found for girls and boys and for children from low-to-average and higher socio-economic backgrounds. CONCLUSIONS: Children's physical activity was not strongly influenced by the presence of smoke and targeted public health advice during the bushfires might not have had the intended effect of reducing children's outdoor physical activity. Only when air quality deteriorated to approximately 3.5 times the Air Quality index threshold (>200) deemed 'hazardous' by the Australian Department of Health did children's physical activity decline. Public health agencies should re-evaluate the effectiveness of health messages during bushfires and develop strategies to mitigate risks to children's health.

Physical activity and sleep are inconsistently related in healthy children: A systematic review and meta-analysis.

Physical activity is considered an effective method to improve sleep quality in adolescents and adults. However, there is mixed evidence among children. Our objectives were to investigate this association in children and to examine potential moderating variables. Eight databases were systematically searched, and we included all study designs with a sample of healthy children ages 3-13 years-old. We identified 47 studies for meta-analysis. Overall, we found little association between physical activity and sleep (r = 0.02, 95% confidence interval = -0.03 to 0.07). There was a high amount of heterogeneity in the overall model (I2 = 93%). However, none of the examined variables significantly moderated the overall effect, including age, gender, risk of bias, study quality, measurement methodology, study direction, and publication year. Exploratory analyses showed some weak, but statistically significant associations for vigorous physical activity with sleep (r = 0.09, 95% CI = 0.01 to 0.17, I2 = 66.3%), specifically sleep duration (r = 0.07, 95% CI = 0.00 to 0.14, I2 = 41.1%). High heterogeneity and the lack of experimental research suggest our findings should be interpreted with caution. The current evidence, however, shows little support for an association between physical activity and sleep in children.

A monitoring system to provide feedback on student physical activity during physical education lessons.

School-based physical education (PE) provides opportunities to accumulate moderate-to-vigorous physical activity (MVPA), but many students are insufficiently active during PE lessons. Providing teachers with feedback regarding their students' physical activity may increase the effectiveness of PE for achieving MVPA goals, but existing physical activity monitoring technologies have limitations in class environments. Therefore, the purpose of this study was to develop and validate a system capable of providing feedback on PE lesson MVPA. Equations for translating step counts to %MVPA were derived from measures in 492 students who concurrently wore an ActiGraph GT3X+ (ActiGraph) and Yamax pedometer (Yamax) during a PE lesson. To enhance feedback availability during PE lessons, we then developed a bespoke monitoring system using wireless tri-axial pedometers (HMM) and a smart device app. After developing and testing the monitoring system, we assessed its validity and reliability in 100 students during a PE lesson. There was a strong correlation of 0.896 between step counts and accelerometer-determined %MVPA and quantile regression equations showed good validity for translating step counts to %MVPA with a mean absolute difference of 5.3 (95% CI, 4.4-6.2). The physical activity monitoring system was effective at providing %MVPA during PE lessons with a mean difference of 1.6 ± 7.1 compared with accelerometer-determined %MVPA (7% difference between the two measurement methods). Teachers and students can use a smart device app and wireless pedometers to conveniently obtain feedback during PE lessons. Future studies should determine whether such technologies help teachers to increase physical activity during PE lessons.