IMPORTANCE Pediatric guidelines suggest that infants younger than 2 years avoid screen time altogether, while children aged 2 to 5 years receive no more than 1 hour per day. Although these guidelines have been adopted around the world, substantial variability exists in adherence to the guidelines, and precise estimates are needed to inform public health and policy initiatives.OBJECTIVE To derive the pooled prevalence via meta-analytic methods of children younger than 2 years and children aged 2 to 5 years who are meeting guidelines about screen time.DATA SOURCES Searches were conducted in MEDLINE, PsycINFO, and Embase up to March 2020.STUDY SELECTION Studies were included if participants were 5 years and younger and the prevalence of meeting (or exceeding) screen time guidelines was reported.DATA EXTRACTION AND SYNTHESIS Data extraction followed Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. Two independent reviewers extracted all relevant data. Random-effects meta-analyses were used to derive the mean prevalence rates. MAIN OUTCOMES AND MEASURES Prevalence of meeting screen time guidelines.RESULTS From 63 studies, 95 nonoverlapping samples with a total of 89 163 participants were included. For children younger than 2 years, the pooled prevalence of meeting the screen time guideline (0 h/d) was 24.7% (95% CI, 19.0%-31.5%). Moderator analyses revealed that prevalence of meeting screen time guidelines varied as a function of year of data collection (increased over time), measurement method (higher when questionnaires compared with interview), and type of device use (higher when a combination of screen use activities compared with television/movies only). For children aged 2 to 5 years, the mean prevalence of meeting the screen time guideline (1 h/d) was 35.6% (95% CI, 30.6%-40.9%). Moderator analyses revealed that the prevalence of meeting screen time guidelines varied as a function of type of device use (higher when screen time was television/movies only compared with a combination of screen use activities). CONCLUSIONS AND RELEVANCEThe findings of this meta-analysis indicate that only a minority of children 5 years and younger are meeting screen time guidelines. This highlights the need to provide support and resources to families to best fit evidence-based recommendations into their lives.

Objectives To (1) identify the wearable devices and associated metrics used to monitor workload and assess injury risk, (2) describe the situations in which workload was monitored using wearable technology (including sports, purpose of the analysis, location and duration of monitoring, and athlete characteristics), and (3) evaluate the quality of evidence that workload monitoring can inform injury prevention. Design Scoping review. Literature Search We searched the CINAHL, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, Embase, HealthSTAR, MEDLINE, PsycINFO, SPORTDiscus, and Web of Science databases. Study Selection Criteria We included all studies that used wearable devices (eg, heart rate monitor, inertial measurement units, global positioning system) to monitor athlete workload in a team sport setting. Data Synthesis We provided visualizations that represented the workload metrics reported, sensors used, sports investigated, athlete characteristics, and the duration of monitoring. Results The 407 included studies focused on team ball sports (67% soccer, rugby, or Australian football), male athletes (81% of studies), elite or professional level of competition (74% of studies), and young adults (69% of studies included athletes aged between 20 and 28 years). Thirty-six studies of 7 sports investigated the association between workload measured with wearable devices and injury. Conclusion Distance-based metrics derived from global positioning system units were common for monitoring workload and are frequently used to assess injury risk. Workload monitoring studies have focused on specific populations (eg, elite male soccer players in Europe and elite male rugby and Australian football players in Oceania). Different injury definitions and reported workload metrics and poor study quality impeded conclusions regarding the relationship between workload and injury. J Orthop Sports Phys Ther 2020;50(10):549–563. doi:10.2519/jospt.2020.9753

Training load has been identified as a risk factor for musculoskeletal injury in sport, but little is known about the effects of training load in dance. The purpose of this study was to describe adolescent dancers' internal training load (ITL) and compare objective and subjective measures of ITL using heart rate (HR) training impulse methods and session Rating of Perceived Exertion (sRPE), respectively. Fifteen female elite adolescent ballet dancers at a vocational dance school volunteered to participate in the study. Internal training load data using HR and sRPE were collected over 9 days of multiple technique classes at the midpoint of the dancers' training year. Heart rate data were quantified using Edwards' training impulse (ETRIMP) and Banister's training impulse (BTRIMP), and sRPE was estimated from the modified Borg 0 to 10 scale and class duration. Descriptive statistics (median [M], and interquartile range [IQR]) were determined in arbitrary units (AU), and were as follows for all classes combined: ETRIMP: M = 134 AU (IQR = 79 to 244 AU); BTRIMP: M = 67 AU (IQR = 38 to 109); sRPE: M = 407 AU (IQR = 287 to 836 AU). The association and agreement between objective and subjective ITL measures in ballet and pointe class was assessed using Spearman correlations (rs) and adjusted Bland-Altman 95% limits of agreement (LOA), respectively, with alpha set at 0.05. A significant moderate positive correlation was found between ETRIMP and BTRIMP in pointe class (rρ = 0.8000, p = 0.0031). The mean difference (LOA) between ETRIMP and BTRIMP was 121 AU (33 to 210 AU) in ballet and 43 AU (-3 to 88 AU) in pointe. It is concluded that some, but not all, measures of ITL in elite adolescent ballet dancers are comparable. Additional research is needed to examine the utilization of ITL measures for evaluating dance-related injury risk, as well as the application of ITL to inform the development of effective injury prevention strategies for this high-risk population.

Introduction: Wearable technology (WT) has become common place in sport. Increased affordability has allowed WT to reach the wrists and bodies of grassroots and community athletes. While WT is commonly used by sport populations to monitor training load, the use of WT among dancers and dance teachers is unknown. Therefore, the purpose of this study is to explore the perspectives of dancers, dance teachers, and dance parents on using WT in the dance studio environment. Methods: Dancers (aged 14+), dance teachers (aged 18+), and dance parents (with a child <18 years registered in a dance program) were recruited from local dance studios (including those offering vocational programs and/or professional training opportunities), and dance retail stores. Participants provided informed consent/assent and completed a one-time online survey about their attitudes, self-efficacy, motivations, barriers, and current practices of using WT in the studio. Results: Sixty-seven participants (19 dancers, 32 dance teachers, and 16 dance parents) completed the survey. Attitudes toward using WT were similar across all groups (mean score range = 34-38/45). Thirteen dancers (68%), 29 teachers (91%), and 7 dance parents reporting on behalf of their children (47%) were permitted to use WT in the studio. Smartwatches were the most common WT used in the studio by dancers (7/9) and teachers (13/17), while dance parents reported that their children primarily used wristband activity trackers (3/4). Among all groups, the primary reason for using WT was to track personalized training data, with calories, total duration, and heart rate being the most important perceived metrics for improving dancing. Conclusion: Across all groups, attitudes toward WT were modest. Prevalence of WT use in the dance studio varied, with wrist-based gadgets being the most common. As WT research continues in dance populations, it will be important for future studies to consider studio permissions as well as participants’ existing WT use practices.

Dance is a popular physical activity. Increased dance training has been associated with an increased risk of injury. Given the established association between training load (TL) and injury in sport, knowledge of how TL is currently being measured in dance is critical. The objective of this study is to summarise published literature examining TL monitoring in dance settings. Six prominent databases (CINAHL, EMBASE, Medline, ProQuest, Scopus, SportDiscus) were searched and nine dance-specific journals were handsearched up to May 2022. Selected studies met inclusion criteria, where original TL data were collected from at least one dancer in a class, rehearsal and/or performance. Studies were excluded if TL was not captured in a dance class, rehearsal or performance. Two reviewers independently assessed each record for inclusion at title, abstract and full-text screening stages. Study quality was assessed using Joanna Briggs Institute Critical Appraisal Tool checklists for each study design. The 199 included studies reported on female dancers (61%), ballet genre (55%) and the professional level (31%). Dance hours were the most common tool used to measure TL (90%), followed by heart rate (20%), and portable metabolic systems (9%). The most common metric for each tool was mean weekly hours (n=381; median=9.5 hours, range=0.2–48.7 hours), mean heart rate (n=143) and mean oxygen consumption (n=93). Further research on TL is needed in dance, including a consensus on what tools and metrics are best suited for TL monitoring in dance.