{"id":6776,"date":"2019-12-27T06:30:00","date_gmt":"2019-12-27T12:30:00","guid":{"rendered":"http:\/\/thesportjournal.org\/?p=6776"},"modified":"2020-06-02T11:25:00","modified_gmt":"2020-06-02T16:25:00","slug":"the-role-of-organized-youth-sports-in-reducing-trends-in-childhood-obesity","status":"publish","type":"post","link":"https:\/\/thesportjournal.org\/article\/the-role-of-organized-youth-sports-in-reducing-trends-in-childhood-obesity\/","title":{"rendered":"The Role of Organized Youth Sports in Reducing Trends in Childhood Obesity"},"content":{"rendered":"\n

Authors:<\/strong> Alysia Cohen, Heidi Wegis, Darren Dutto, Viktor Bovbjerg<\/p>\n\n\n\n

Corresponding Author:
<\/strong>Alysia Cohen, PhD, ATC, CSCS
1435 Village Drive
Ogden, UT 84408
alysiacohen@weber.edu<\/a>
801-626-7115<\/p>\n\n\n\n

Alysia\nCohen is an Assistant Professor in the Department of Athletic Training at Weber\nState University.<\/p>\n\n\n\n

The Role of Organized Youth Sports in Reducing Trends in Childhood Obesity<\/strong><\/h3>\n\n\n\n

Abstract<\/strong><\/p>\n\n\n\n

Purpose: To examine physical activity (PA) levels of children playing youth sports and the relationship of recommended levels of PA to contextual factors of the organized youth sports environment that may boost fitness and health during childhood and adolescence.  Methods: Accelerometer-measured PA was obtained from 167 children (85 male, 82 female) aged 7-13 years. Sport contextual factors were recorded via direct observation of 29 coaches. PA levels were examined by age, gender, and between group variability. Direct observation intervals were analyzed by category using the Chi-square statistic for degree of association to moderate-to vigorous-intensity physical activity (MVPA).  Results: On average children spent 21.9 \u00b17.9 minutes in MVPA during sport practices (< 50% of practice time).  Proportion of practice time MVPA was lower among females (28.7 \u00b1 7.2%) than males (35.0 \u00b1 9.1%). Proportion of practice time MVPA was higher among children (male and female) aged 7-9 years (32.6 \u00b1 1.4%) compared to children aged 10-13 years (30.66 \u00b11.25%). Longer practice times were not shown to increase the proportion of time spent in MVPA. The most frequently observed sport activities were sports drills (51.6%), activities involving all players (37.8%), management\/general instruction (52.3%), and proximal positioning of the coach (99.5%). Management and general instruction coaching behavior was not significantly associated with MVPA but did consume a prominent proportion of practice time. Health-related fitness activities made up 1.7% of practice time.  Conclusions: In comparison with recommendations, youth sports appear active, however, a large portion of practice time is sedentary suggesting room for improvement.  Including fun non-specific or specific sport activities that promote participation from all players and increase heart rate. Fun play experiences during sport practices may encourage greater in active play within and outside of sport with behaviors persisting into adolescence and adulthood.  Applications in Sport: Training coaches to teach fun sport activities that engage all players would improve within practice active time and enjoyable experiences that may promote future participation in sport or activity outside of sport. <\/p>\n\n\n\n\n\n\n\n

Key words<\/strong>: youth sport, obesity, physical activity, children<\/p>\n\n\n\n

INTRODUCTION<\/strong><\/p>\n\n\n\n

Organized youth sports programs, including soccer, basketball, and football, are popular non-scholastic activities among youth. Sport participation during childhood and adolescence is associated with positive outcomes in physical fitness, body weight, mental health, academic performance, and social development (1,4,17,25,27-29). In particular, youth sport participation trends in the U.S. increased dramatically during the early 21st<\/sup> century (22), however, over the last 10 years nearly all sports experienced declines (26). Unfortunately, the downshift in sport participation occurred simultaneously with increased physical inactivity and prevalence of obesity among children and adolescents. Among youth aged 6 to 19 years, obesity prevalence increased from 16.8% (2007-2008) to 18.5% (2015-2016) (13). Physical inactivity is well known to be associated with being overweight or obese and with related noncommunicable diseases such as heart disease, type 2 diabetes, high blood pressure, and certain forms of cancer (15,21,27). Although many chronic diseases associated with physical inactivity do not present until adulthood, the precursors can begin to manifest as early as childhood (7,9). Recent health projections presented by the Global Obesity Prevention Center at John Hopkins University suggest an increase of 50% of youth that get and stay active until 18 years of age would live an average of 20.5 years longer and reduce their risk of becoming overweight or obese by 15.5 times compared to a child that does not maintain their activity level (3). In the moment, coaching physical activity behavior may seem like an inherent part of sport participation, but statistically, we see otherwise.  <\/p>\n\n\n\n

In the last decade a few studies have provided an overview of physical activity (PA) behavior within and outside of sport participation. In general, Troiano et al. (30) reported youth aged 6 to 19 years participate in less than 50% of the daily recommendation of 60 minutes of moderate-to vigorous-intensity physical activity (MVPA) (33). Among youth participating in sports, daily MVPA is higher with one-third to three-quarters occurring during sport practices.  For instance, Leek et al. (18) and Cohen et al. (5) reported two very different perspectives with 46.1% and 36.8%, respectively, of practice time spent in MVPA. Translated to minutes of MVPA, 20 to 45 minutes of practices was spent in MVPA. Although significant, many youth programs practice one to two days a week resulting in activity levels well below the recommendations on non-sport days (35). When combined with other MVPA opportunities, such as physical education or free play, sport practices serve as a primary source of MVPA. Addressing the more than 50% of time spent in sedentary physical activity during sport practices is reasonable to increasing children\u2019s daily MVPA and improving short-term and long-term health benefits.<\/p>\n\n\n\n

Second to school settings, youth\nsports engage with a high volume of children. Similar to education teachers,\nsport coaches serve as teachers and role models in their field. However, a\ndifference between teachers and many sport coaches is the presence of training\nin their respective field. According to recent survey data of youth coaches, 4\nin 10 coaches lack adequate and routine training in areas of basic life\nsupport, concussion management, general safety and injury prevention, physical\nconditioning, sport skills and tactics, and effective motivational techniques (26).\nWhen considering the emphasis given to youth sports in addressing a national\nhealth crisis involving physical inactivity and obesity among youth and adults,\na lack of training may explain the more than 50% of sedentary PA during sport\npractices.    <\/p>\n\n\n\n

Guagliano et al. (12) examined the effect of single 2-hour coach education training session on mediating factors of player MVPA (eg, coach MVPA, management, knowledge delivery, promoting PA, and demonstrating PA).  Coaches received training on 4 topics: 1) strategies to increase MVPA and decrease inactivity during practice, 2) self-monitoring, 3) goal-setting, and 4) recommended target step counts per minute. Although the training was shown to be effective at improving player MVPA, the effect was isolated to changes in the coaches\u2019 physical activity behavior during practice. For instance, following the intervention, coaches that were more physically active during practice also observed more active players (compared to pre-test measurements). Unfortunately, training on more effective management strategies, delivery of knowledge to players, promoting PA, and use of PA demonstration in coaching pedagogical strategies did not improve player MVPA, which contradicts results from Dudley et al. (6) that suggest training of physical educators reduced time spent in management and knowledge delivery alongside increases in MVPA during physical education classes. While both studies aimed to address factors of MVPA, the setting (school, youth sports) and source of instruction (teacher, coach) are quite different and are not always reflective of each other. The purpose of this study was to provide a greater understanding of the physical activity strengths and weaknesses of sport practices to better address coaching competencies and organizational strategies to enhance physical behavior within sport and known directly related health benefits.<\/p>\n\n\n\n

METHODS<\/strong><\/p>\n\n\n\n

Study\nParticipants and Setting <\/em><\/strong><\/strong><\/p>\n\n\n\n

The current study design was\nnonexperimental. Two organized youth soccer programs (American Youth Soccer\nOrganization and a Parks and Recreation Department) from two small Pacific\nNorthwest cities were recruited for participation in the study. A total of 30\nyouth soccer teams (15 male and 15 female) and 29 volunteer (unpaid) coaches (23\nmale, 6 female), aged 35 years and older, consented to participate. Consent was\nobtained from parents\/guardians of 167 children (85 male, 82 female) aged 7-13\nyears (mean age 8.97 years) that provided written and verbal assent to\nparticipate in the study. Participant characteristics are provided in Table 1. This\nstudy was approved by the Oregon State University Institutional Review Board (IRB)\nand both participating youth sports organizations. <\/p>\n\n\n\nTable 1:<\/strong> Participant Characteristics\n\n \n \n \n \n \n \n \n \n \n \n \n \n
Teams (n=30)<\/strong><\/td>\n Male<\/strong><\/td>\n Female<\/strong><\/td>\n Coaches (n=29)<\/strong><\/td>\n Male<\/strong><\/td>\n Female<\/strong><\/td>\n <\/tr>\n
 <\/td>\n 15<\/td>\n 15<\/td>\n  <\/td>\n 23<\/td>\n 6<\/td>\n <\/tr>\n
Children
\n <\/strong><\/td>\n 		85<\/td>\n 82<\/td>\n  <\/td>\n  <\/td>\n  <\/td>\n <\/tr>\n
(n=167)<\/strong><\/td>\n  <\/td>\n  <\/td>\n 35-39 years<\/td>\n 11
<\/td>\n 		2
<\/td>\n <\/tr>\n
7 years old<\/td>\n 23<\/td>\n 24<\/td>\n 40-44 years<\/td>\n 7
<\/td>\n 		2<\/td>\n <\/tr>\n
8 years old<\/td>\n 11
<\/td>\n 		19
<\/td>\n 		45-49 years<\/td>\n 2
<\/td>\n 		1<\/td>\n <\/tr>\n
9 years old<\/td>\n 16
<\/td>\n 		11
<\/td>\n 		> 50 years  <\/td>\n 3<\/td>\n 1<\/td>\n <\/tr>\n
10 years old<\/td>\n 16
<\/td>\n 		12
<\/td>\n 		 <\/td>\n  <\/td>\n  <\/td>\n <\/tr>\n
11 years old<\/td>\n 8
<\/td>\n 		10
<\/td>\n 		 <\/td>\n  <\/td>\n  <\/td>\n <\/tr>\n
12 years old<\/td>\n 8<\/td>\n 5
<\/td>\n 		 <\/td>\n  <\/td>\n  <\/td>\n <\/tr>\n
13 years old<\/td>\n 3<\/td>\n 1<\/td>\n  <\/td>\n  <\/td>\n  <\/td>\n <\/tr>\n <\/tbody>\n<\/table>\n\n\n\n

Measurement\nProcedures<\/em><\/strong><\/p>\n\n\n\n

Accelerometry<\/em><\/p>\n\n\n\n

Physical activity levels of\nchildren participating in the study were obtained using Actigraph (Actigraph\nCorporation, Pensacola, FL) GT1M and GT3X accelerometer-based motion sensors.\nThe GT1M and GT3X have been shown to be valid and reliable instruments for\nmeasuring children\u2019s physical activity levels (31-32) with no observed\ndifferences in physical activity measurement outcomes between the two models (24).\nPrior to each practice, the accelerometers were synchronized to a universal\ntime clock within the propriety computer software then each unit was\ninitialized to record counts via a 15-second epoch setting. A 15-second epoch\nsetting was selected based on protocol established by Evenson et al. (8) for\nmeasuring physical activity levels in children (19). A universal stopwatch was\nalso synched to the computer software time clock and used to report start and\nstop wear-time, practice time, and direct observation interval time.  Accelerometers positioned around the waist of\neach child on the right hip and held in place by an elastic waist belt. Data\nfrom each accelerometer was later downloaded to the Actigraph proprietary\nsoftware. Physical activity levels were interpreted as total counts using the\nintensity-based cut-points developed by Evenson et al. (8).<\/p>\n\n\n\n

Direct Observation<\/em><\/p>\n\n\n\n

Children\u2019s physical activity and contextual\nfactors of the practice environment were gathered via momentary-time sampling\nprocedures of the Observation System for Recording Activity in Children, Youth\nSports version (OSRAC:YS). The OSRAC:YS was selected based on its validity (r =\n0.73, P < .001) and reliability (Kappa coefficients of 0.67 to 0.93) among\nyouth soccer players for measuring PA levels and contextual factors of PA\nduring soccer practices (5). <\/p>\n\n\n\n

Team practices were observed once\nduring the first half of the programs\u2019 8-week soccer season. For each practice,\nthe researcher observed a single volunteer coach and player. For teams with\nmore than one coach (n=2), only the head coach was observed. Prior to the start\nof practice this information was verbally confirmed with the coaches. Player\nobservation order was determined randomly prior to a team\u2019s scheduled practice.\n Upon arriving at practice and\nidentifying players present, any child not in attendance was passed over in the\nobservation order. Identifying information (eg, color of socks, shorts) were\nrecorded on the observation worksheet allowing the observer to quickly identify\na focal child. A median of five children per practice were observed. A single\nobservation time period of 10 minutes was completed for each player in\nattendance, which included a total of 20 observation cycles consisting of a\n10-second observation followed by 20-second recording interval. Observation\nperiods less than 10 minutes occurred when (1) the focal-child was removed from\npractice for a restroom break, behavioral issue (instructed by the parent,\ncoach, or self-selected not to participate), or an injury, (2) practice ended\nprior to interval completion, or (3) the researcher was unable to complete the\nobservation interval (environmental factors such as loss of daylight; equipment\nmalfunction). Incomplete observation periods were included in the final data analysis.\nAt the conclusion of each observation period, approximately 2 minutes was\ndelegated to setting up for the next period, thus, a 60 minute practice included\napproximately five observation periods. In addition to use of the instrument\nfor purposes of the study, specific psychometric tests (i.e., Kappa coefficients\nand percent agreement) were included to address accuracy in direct observation\nof defined contexts of the practice setting. <\/p>\n\n\n\n

The OSRAC:YS consists of five\nobservational categories: 1) physical activity level, 2) practice context, 3)\nsocial context, 4) coach behavior, and 5) coach proximity, each with defined\nobservational codes (Table 2). When observing children\u2019s physical activity level,\ncodes were recorded based on the Children\u2019s Activity Rating Scale (CARS). The\nCARS instrument (23) includes five levels of activity that are described in\nTable 2. The CARS instrument was found to be a valid and reliable (84.1% agreement)\ntool for measuring children\u2019s physical activity levels. More recently, testing\nof the OSRAC:YS and CARS demonstrated reliability (Kappa coefficient 0.71) of\nthe instrument to measure children\u2019s physical activity levels during youth\nsport practices (5). <\/p>\n\n\n\nTable 2:<\/strong> Observational contexts with coding categories and descriptions of each code.\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Context<\/strong><\/td>\n Code<\/strong><\/td>\n Description<\/strong><\/td>\n <\/tr>\n
Physical Activity Level<\/td>\n (1) Stationary\/Motionless<\/td>\n Sedentary physical activity<\/td>\n <\/tr>\n
(2) Stationary\/movement of trunk or limbs<\/td>\n <\/tr>\n
 <\/td>\n (3) Slow\/easy movement<\/td>\n Low-intensity physical activity<\/td>\n <\/tr>\n
 <\/td>\n (4) Moderate movement<\/td>\n Moderate-intensity physical activity<\/td>\n <\/tr>\n
 <\/td>\n (5) Fast movement<\/td>\n Vigorous intensity physical activity<\/td>\n <\/tr>\n
Practice Context<\/td>\n (1) Warm-up<\/td>\n any activity performed at the start of practice (e.g. low- to moderate-intensity aerobic activity and\/or stretching)<\/td>\n <\/tr>\n
 <\/td>\n (2) Drills<\/td>\n activity with a set purpose to focus on a specific component of the activity\/sport<\/td>\n <\/tr>\n
 <\/td>\n (3) Tactic\/instruction<\/td>\n activity with a set purpose on learning the game rules or skill development<\/td>\n <\/tr>\n
 <\/td>\n (4) Fitness<\/td>\n activity for improving player cardiovascular fitness or muscular strength<\/td>\n <\/tr>\n
 <\/td>\n (5) Game<\/td>\n full team activity in which all players of the team are involved, either working together or opposing other players<\/td>\n <\/tr>\n
 <\/td>\n (6) Cooldown<\/td>\n end of practice activities with focus on reducing the intensity <\/td>\n <\/tr>\n
 <\/td>\n (7) Transition<\/td>\n change between two practice contexts that do not include activities associated with sport activity (e.g., water or restroom break, free time)<\/td>\n <\/tr>\n
Social Context<\/td>\n (1) Solitary<\/td>\n action or physical activity behavior is independent of the action of another player (e.g., personal skill development, periods of transition)<\/td>\n <\/tr>\n
 <\/td>\n (2) 1v1<\/td>\n focal child is paired with another player to complete a desired task or activity<\/td>\n <\/tr>\n
 <\/td>\n (3) Greater than 2, < full team<\/td>\n activity involving more than 2 players (the focal child and at least two other children\/teammates) but less than all players on the team<\/td>\n <\/tr>\n
 <\/td>\n (4) Full team<\/td>\n all players are involved in a single activity in which player participation and actions are dependent on each other<\/td>\n <\/tr>\n
Coach Behavior<\/td>\n (1) Watching with feedback<\/td>\n watching a player then providing feedback for the skill or task performed<\/td>\n <\/tr>\n
 <\/td>\n (2) Watching without feedback<\/td>\n watching the focal player or more than one player but not providing feedback<\/td>\n <\/tr>\n
 <\/td>\n (3) Demonstration<\/td>\n any skill demonstrated by the coach or participation of the coach in the practice activity (e.g., playing with the team during a scrimmage)<\/td>\n <\/tr>\n
 <\/td>\n (4) Management\/general instruction<\/td>\n engaged in practice set-up activities (i.e., setting up a drill), provides activity instructions to the team or general team or sport discussions with the players<\/td>\n <\/tr>\n
 <\/td>\n (5) Disengaged\/off-task<\/td>\n coach appearing to be removed or uninvolved in any form to the practice activities<\/td>\n <\/tr>\n
Coach Proximity<\/td>\n (1) Proximal<\/td>\n within the boundary of the activity<\/td>\n <\/tr>\n
 <\/td>\n (2) Distal<\/td>\n coach outside of the playing boundary<\/td>\n <\/tr>\n <\/tbody>\n<\/table>\n\n\n\n

During each 10-second observation\ninterval, the observer followed along, identifying physical activity levels of\nthe focal child and contextual factors from each category. Physical activity\nlevel is coded at the highest level observed for continuous 3 or more seconds\nof the 10-second observation interval. Practice context, social context, coach\nbehavior, and coach proximity followed the same 3-second protocol but did not\ninclude a rank. For each category, a single code is recorded based on its\noccurrence with a standard of 3 seconds established as the baseline. For\ninstance, if practice context was observed for 3 seconds as a drill followed by\n7 seconds in a transition, the interval would be coded as \u201ctransition\u201d. For\neach 10-second observation interval, the observer identified and recorded\nfindings for all 5 OSRAC:YS categories.<\/p>\n\n\n\n

Statistical\nAnalysis<\/em><\/strong><\/p>\n\n\n\n

Descriptive statistics (means and\nstandard deviations) were determined for physical activity levels obtained by accelerometry.\nPhysical activity intensity levels were examined across all participants, by\nteam, age, and gender. Total time (minutes) and percent practice time spent in\nsedentary, MVPA, and vigorous-intensity levels were assessed. Independent\nsamples t<\/em>-tests examined differences\nbetween groups (gender and age category). Coding frequencies were calculated\nfor each observation category via the OSRAC:YS as well as observations\nassociated with MVPA from direct observation (code of 4 or 5). Chi-square tests\nof independence were performed for intervals coded as MVPA and corresponding\ncodes from the OSRAC:YS contextual categories. Corresponding p-values were\ncalculated as well as a Pearson product-moment correlation for significant\nchi-square values. Psychometric properties of the OSRAC:YS were characterized via\nKappa coefficients and percent agreement of each contextual category. Kappa\ncoefficients were interpreted using the rating scale developed by Landis and\nKoch (16): 0 to 0.2 poor, 0.2 to 0.4 fair, 0.4 to 0.6 moderate, 0.6 to 0.8\nsubstantial, and 0.8 to 1.0 almost perfect agreement. An interrater correlation\ncoefficient (ICC) was obtained to estimate the agreement between observers to\nrate MVPA. All statistical analysis procedures were performed in SPSS version\n25 (SPSS Inc., Chicago, IL) with statistical significance established at P <\/em>< .05.<\/p>\n\n\n\n

RESULTS<\/strong><\/p>\n\n\n\n

Reliability of the OSRAC:YS<\/em><\/p>\n\n\n\n

Inter-observer percent agreement\nand Kappa coefficients for each of the five OSRAC:YS observational categories are\nreported in Table 3. Two observers completed 170 observations on two non-consecutive\ndays during the 8-week season and for two different teams. Kappa coefficients ranged\nfrom 0.74 to 0.98 (Table 3). Inter-observer percent agreement of 80% and higher\nwas determined for all five categories. An ICC of 0.85 suggests significant\nagreement between observers in classifying physical activity levels via direct\nobservation and percent agreement of coding MVPA.<\/p>\n\n\n\nTable 3:<\/strong> OSRAC:YS Reliability Coefficients\n\n \n \n \n \n \n \n \n
Contextual Category<\/strong><\/td>\n % Agreement<\/strong><\/td>\n Kappa Statistic<\/strong><\/td>\n 95% CI<\/strong><\/td>\n <\/tr>\n
Physical Activity Level<\/td>\n 87.7%<\/td>\n 0.83<\/td>\n 0.76 to 0.89<\/td>\n <\/tr>\n
Practice Context<\/td>\n 98.2%<\/td>\n 0.98<\/td>\n 0.94 to 1.00<\/td>\n <\/tr>\n
Social Context<\/td>\n 94.7%<\/td>\n 0.89<\/td>\n \u00a0 0.83 to 0.96<\/td>\n <\/tr>\n
Coach Behavior<\/td>\n 85.3%<\/td>\n 0.74<\/td>\n 0.64 to 0.83<\/td>\n <\/tr>\n
Coach Proximity<\/td>\n 100.0%<\/td>\n N\/A<\/td>\n N\/A<\/td>\n <\/tr>\n <\/tbody>\n<\/table>\n\n\n\n

Physical Activity Levels<\/em><\/p>\n\n\n\n

Across teams, practice ranged from\n60 to 90 minutes (mean 68.8, SD\u00b112. 8). On average, children aged 7-13 years spent the\ngreatest proportion of practice in sedentary activity, followed by MVPA and\nthen light intensity PA (Table 4). No significant differences were found\nbetween age groups for time spent in MVPA as a percent of practice time (df=28,\nP=0.54) or in minutes (df=28, P=0.47) or percent time in sedentary physical\nactivity (df=28, P=0.31). Significant differences were observed for percent\nMVPA by gender with boys more active than girls (35.0% \u00b1 9.1 vs 28.7% \u00b1 7.2, respectively, P=0.006). Although\nthe researchers did not observe a significant decline in PA from age 7 to 13\nfor all participants, increases in practice time across both genders and ages\nresulted in a percent decrease in overall practice time MVPA and increase in\ntime spent in sedentary activity.<\/p>\n\n\n\nTable 4:<\/strong>\u00a0 Physical Activity Levels by Age Group\n\n \n \n \n \n \n \n \n \n \n \n \n
Mean Activity
\n Level (SD)<\/strong><\/td>\n 		All Subjects
\n (n=167)<\/strong><\/td>\n 		95% CI<\/strong><\/td>\n Age 7-9 years
\n (n=104)<\/strong><\/td>\n 		95% CI<\/strong><\/td>\n Age 10-13 years
\n (n=63)<\/strong><\/td>\n 		95% CI<\/strong><\/td>\n <\/tr>\n
Sedentary<\/strong><\/td>\n  <\/td>\n  <\/td>\n  <\/td>\n  <\/td>\n  <\/td>\n  <\/td>\n <\/tr>\n
Minutes<\/td>\n 27.25 (\u00b19.13)<\/td>\n (25.86 to 28.63)<\/td>\n 25.51 (\u00b12.57)<\/td>\n (22.61 to 28.41)<\/td>\n 33.84 (\u00b15.50)<\/td>\n (28.45 to 39.23)<\/td>\n <\/tr>\n
Percentage<\/td>\n 39.93 (\u00b110.63)<\/td>\n (38.32 to 41.54) <\/td>\n 38.83 (\u00b11.89)<\/td>\n (36.72 to 40.93)<\/td>\n 41.89 (\u00b11.65)<\/td>\n (40.28 to 43.50)<\/td>\n <\/tr>\n
Vigorous<\/strong><\/td>\n  <\/td>\n  <\/td>\n  <\/td>\n  <\/td>\n  <\/td>\n  <\/td>\n <\/tr>\n
Minutes<\/td>\n 13.97 (\u00b15.77)<\/td>\n (13.09 to 14.84)<\/td>\n 13.76 (\u00b12.09)<\/td>\n (11.40 to 16.12)<\/td>\n 16.34 (\u00b12.99)<\/td>\n (13.41 to 19.27)<\/td>\n <\/tr>\n
Percentage<\/td>\n 20.45 (\u00b17.15)<\/td>\n (19.37 to 21.53) <\/td>\n 20.64 (\u00b11.01)<\/td>\n (19.50 to 21.78)<\/td>\n 20.19 (\u00b11.01)<\/td>\n (19.08 to 21.30)<\/td>\n <\/tr>\n
MVPA<\/strong><\/td>\n  <\/td>\n  <\/td>\n  <\/td>\n  <\/td>\n  <\/td>\n  <\/td>\n <\/tr>\n
Minutes<\/td>\n 21.88 (\u00b17.93)<\/td>\n (20.67 to 23.08)<\/td>\n 21.76 (\u00b13.12)<\/td>\n (18.23 to 25.29)<\/td>\n 24.89 (\u00b14.10)<\/td>\n (20.87 to 28.91)<\/td>\n <\/tr>\n
Percentage<\/td>\n 31.97 (\u00b18.79)<\/td>\n (30.63 to 33.30)<\/td>\n 32.59 (\u00b11.44)<\/td>\n (30.96 to 34.22)<\/td>\n 30.66 (\u00b11.25)<\/td>\n (29.44 to 31.88)<\/td>\n <\/tr>\n <\/tbody>\n<\/table>\n\n\n\n

OSRAC:YS \u2013 Contextual Factors of\nPhysical Activity<\/em><\/p>\n\n\n\n

The primary researcher observed a\ntotal of 2939 intervals. Results of contextual factor frequency to MVPA are\nreported in Table 5. For the four contextual categories \u2013 practice context,\nsocial context, coach behavior, and coach proximity \u2013 the frequency of codes\nrecorded as well as percent of which were recorded with a physical activity\nlevel code of 3 or 4 (moderate, fast), or MVPA, were determined. Within the practice\ncontext category, drill-like practice activities were observed most frequently (51.6%)\nfollowed by tactic\/instruction (16.9%), transition (15.2%), game (10.3%),\nwarm-up (4.1%), fitness (1.7%), and cool-down (0.3%). There was a significant\nassociation between the type of practice context and MVPA (X<\/em>2<\/sup>=299, df=6, P < .001). The proportion of time spent\nin MVPA for each code varied with greatest proportion occurring during\nfitness-related activities (75.5%). Because the proportion of MVPA determined\nfor each code is relative to the frequency of each code, the overall proportion\nof practice time spent in MVPA during fitness activities was relatively high despite\nlow occurrence of fitness-related activities. Not surprisingly, MVPA was not\nobserved during cool-down activities. The proportion of practice time spent in\neach social context code was greatest among full team activities (37.8%) followed\nby solitary (36.5%) , group (13.5%), and 1v1 activities (12.1%). No significant\nassociation was found between social context of practice activities and MVPA (X<\/em>2<\/sup>=4.63, df=3, P=0.20). The\nproportion of each social context code spent in MVPA varied relatively little, ranging\nfrom 28.4% for group activities to 23.3% for solitary activities. Over half (52.3%)\nof all coach behavior codes observed were recorded as management\/general\ninstruction with a low portion of such codes resulting in MVPA during practice\ntime (14.1%). There was a significant association between coaching behavior and\nMVPA (X<\/em>2<\/sup>=212, df=4, P <\n.001). Watching without giving verbal feedback was recorded nearly one-third of\nall coach behavior observations and was more associated with MVPA than other codes\nof coaching behavior. Watching and providing verbal feedback was less frequent\nbut displayed a higher proportion of time with MVPA (39.9%) compared to no\nfeedback given (36.9%). The remaining coach behavior codes, demonstration and\nappearing disengaged, made up a small portion of the observations. Despite the\nlow occurrence of demonstration (2.5% of practice time), almost half of all demonstration\ncodes were found to be associated with MVPA. Finally, MVPA was associated more\nfrequently with the disengaged\/off-task code than management\/general\ninstruction suggesting children moved more when the coach appeared withdrawn\nfrom practice activities. For the remaining contextual factor, coach proximity,\nthe vast majority of observations recorded were marked as proximal. There was\nno significant association between placement of the coach to players\u2019 MVPA (X<\/em>2<\/sup>=.55, df=1, P=0.46). <\/p>\n\n\n\nTable 5:<\/strong>\u00a0 OSRAC:YS Categorical Code Frequencies\n\n \n \n \n \n \n \n \n \n \n
 <\/td>\n Proportion of Intervals<\/td>\n Proportion of Intervals in MVPA<\/td>\n <\/tr>\n
Contextual Category<\/strong><\/td>\n All subjects<\/strong>
\n (2939
\n intervals)<\/td>\n

7-9 y
\n <\/strong>\n (2016
\n intervals)<\/p><\/td>\n

10-13 y<\/strong>
\n (923
\n intervals)<\/td>\n 		All
\n subjects<\/strong><\/td>\n 		7-9 y<\/strong><\/td>\n 10-13 y<\/strong><\/td>\n <\/tr>\n
Physical Activity Level
\n Stationary\/No Movement
\n Stationary Limb
\n Movement
\n Slow Easy
\n Moderate
\n Fast<\/td>\n
\n 30.5%
\n 16.3%
\n 28.2%
\n 11.4%
\n 13.6%
\n 25.1%<\/td>\n 		 
\n 29.6%
\n 15.9%
\n 28.7%
\n 12.3%
\n 13.5%
\n 25.8%<\/td>\n 		 
\n 32.4%
\n 17.1%
\n 27.0%
\n 9.5%
\n 13.9%
\n 23.4%<\/td>\n 		 <\/td>\n  <\/td>\n  <\/td>\n <\/tr>\n
Practice Context**
\n Warm-up
\n Drills
\n Tactic\/Instruction
\n Fitness
\n Game
\n Cooldown
\n Transition<\/td>\n 		 
\n 4.1%
\n 51.6%
\n 16.9%
\n 1.7%
\n 10.3%
\n 0.3%
\n 15.2%<\/td>\n 		 
\n 2.8%
\n 51.4%
\n 14.0%
\n 1.4%
\n 13.3%
\n 0.1%
\n 17.0%<\/td>\n 		 
\n 6.8%
\n 52.0%
\n 23.2%
\n 2.3%
\n 3.6%
\n 0.8%
\n 11.4%<\/td>\n 		 
\n 30.8%
\n 32.1%
\n 2.6%
\n 75.5%
\n 35.4%
\n 0.0%
\n 12.5%<\/td>\n 		 
\n 29.8%
\n 31.9%
\n 2.8%
\n 89.3%
\n 34.6%
\n 0.0%
\n 13.7%<\/td>\n 		 
\n 31.7%
\n 32.5%
\n 2.3%
\n 57.1%
\n 42.4%
\n 0.0%
\n 8.6%<\/td>\n <\/tr>\n
Social Context
\n Solitary
\n 1 v 1
\n Group
\n Full Team<\/td>\n 		 
\n 36.5%
\n 12.1%
\n 13.5%
\n 37.8%<\/td>\n 		 
\n 36.1%
\n 12.0%
\n 11.7%
\n 40.1%<\/td>\n 		 
\n 37.4%
\n 12.4%
\n 17.6%
\n 32.7%<\/td>\n 		 
\n 23.3%
\n 26.6%
\n 28.4%
\n 25.1%<\/td>\n 		 
\n 24.1%
\n 25.9%
\n 25.4%
\n 27.5%<\/td>\n 		 
\n 21.4%
\n 28.1%
\n 32.7%
\n 18.9%<\/td>\n <\/tr>\n
Coach Behavior**
\n Watching with Feedback
\n Watching w\/o Feedback
\n Demonstration
\n Management \/ Instruction
\n Disengaged\/Off-task<\/td>\n 		 
\n 12.7%
\n 31.7%
\n 2.5%
\n 52.3%
\n 0.9%<\/td>\n 		 
\n 14.5%
\n 29.8%
\n 2.5%
\n 52.1%
\n 1.0%<\/td>\n 		 
\n 8.6%
\n 35.6%
\n 2.4%
\n 52.9%
\n 0.5%<\/td>\n 		 
\n 39.5%
\n 36.2%
\n 41.1%
\n 14.1%
\n 24.0%<\/td>\n 		 
\n 39.9%
\n 36.6%
\n 41.2%
\n 15.2%
\n 15.0%<\/td>\n 		 
\n 38.0%
\n 35.6%
\n 40.9%
\n 11.7%
\n 60.0%<\/td>\n <\/tr>\n
Coach Proximity
\n Proximal
\n Distal<\/td>\n 		 
\n 99.5%
\n 0.5%<\/td>\n 		 
\n 99.4%
\n 0.6%<\/td>\n 		 
\n 99.7%
\n 0.3%<\/td>\n 		 
\n 25.0%
\n 33.3%<\/td>\n 		 
\n 25.8%
\n 25.0%<\/td>\n 		 
\n 23.3%
\n 66.7%<\/td>\n <\/tr>\n
** Significant at <.001 <\/td>\n <\/tr>\n <\/tbody>\n<\/table>\n\n\n\n

DISCUSSION<\/strong><\/p>\n\n\n\n

In this study, youth sports were\nfound to provide less than half of the recommended daily levels of MVPA. Unfortunately,\nlonger practices did not positively reflect an increase in MVPA. Rather,\nchildren were more sedentary. In general, practice context (eg, type of\nactivity occurring) and coaches behavior (eg, demonstrating a skill) were more\nlikely to be associated with MVPA among children participating in youth sports\npractices, while number of players involved in the practice activity (social\ncontext) and proximity of the coach to the players did not significantly influence\nplayer MVPA. Children were more likely to achieve health-related PA levels\nduring fitness, sport-specific drills, game activities, receiving feedback from\nthe coach, and during activities when the coach was active alongside players\n(demonstrating a skill or activity). Contextual factors of coach behavior that\nare not associated with MVPA but make up a significant proportion (>50%) of coaching\nbehavior, such as management and general instruction, during practice time. These\nare important considerations in the development of coach education training\nprograms that aim to improve children\u2019s time spent in health-related levels of\nPA. <\/p>\n\n\n\n

Despite substantial research on\nphysical activity levels during youth sport practices, very little remains\nunderstood about components of sport practices could be improved to further\nincrease time spent in recommended levels of PA, as well as enhance motivation\namong children to engage in such behaviors during practices and on their own\ntime. This study aimed to address this gap by examining the relationship between\ncontextual factors of the practice setting and levels of MVPA. By acknowledging\nthe dynamics of the youth sport environment, including its strengths and\nweaknesses, we learn more about the role of volunteer youth sport coaches in\nteaching and delivering physically active sports programs. <\/p>\n\n\n\n

In addition to this study, Guagliano\net al. (10) identified a few emerging themes among coaches in their perceived\nresponsibility toward PA outcomes of sport. According to interviews conducted\nwith youth sport coaches, many did not define themselves as a role model for PA\nand that being physically active during practice was emphasized as a\nperformance factor (eg, outperform an opponent during competition) over a whole\nbody health benefit. In addition, fitness activities were considered a part of\nearly season training but once sufficient, practice time emphasized teaching\nsport-related skills, which in the current study was found to be less likely to\nproduce MVPA opportunities. These perceptions are important as they may impose\na barrier to revising youth sport outcomes and programs to include PA behavior\nawareness and advocacy. <\/p>\n\n\n\n

Since interventions to decrease\ninstruction and practice management in youth sports are limited, claims of\neffectiveness of coach education training are derived from past research performed\nin the PE setting among trained PE teachers (McKenzie et al., 2010). Recent\nliterature has suggested positive effects on MVPA during PE following\neducational teacher training (20,34). In 2015, Guagliano et al. (11) replicated\nsimilar methods from interventions in the PE setting and found a significant\nincrease in MVPA among players of coaches that received the intervention\ncompared to players of coaches that were not given the training, a change of\n15.1% and 0.4% of practice time, respectively. The change in MVPA was reported in\na separate study, which was defined as an action of the coach to increase their\nown activity level during practices that resulted in more active players (12).\nThis result is consistent with our findings between player MVPA and a coach\nobserved demonstrating a sport behavior (eg, being physically active with a\nchild). An increase in coach PA behavior during practices may suggest coaches more\nskilled in movements of the sport and are physically capable of performing them\nwould positively influence player participation and PA levels during sport.\nThese findings reflect a content focus for coach education programs. However,\ncompared to the 2-year intervention performed by McKenzie et al. (20), the\ncoaching intervention was provided on 2 days over a 5 day period with\nmeasurement occurring on the first and fifth day. It is unclear whether or not\nobserved increases in MVPA would fatigue over time. More research is needed to\nexamine long-term effects of coach education training programs that emphasize\noutcomes associated with players\u2019 physical activity behavior within and outside\nof sport participation and retention of youth in sport. <\/p>\n\n\n\n

CONCLUSIONS<\/strong><\/p>\n\n\n\n

Physical inactivity is a growing\nconcern among youth and associated health-related outcomes in adolescence and adulthood.\nParticipation in organized youth sports are shown to be beneficial toward\nmeeting recommended levels of MVPA during the day, however, more than half of\npractice time is spent in sedentary PA levels with no effect observed for\nlonger practice time periods. Further, it appears that physical activity\nbehavior within sport does not transfer to time outside of sport, suggesting a\nlack of coaching emphasis on lifestyle physical activity skills and motivation\nof youth to live an active lifestyle, which may be fostered through a fun and\nenjoyable sport experiences. Volunteer coaches play an important role in the\ndevelopment of the physical activity profile of youth sport experiences (14).\nPractice design and delivery, and coaching behaviors reflect the likelihood\nthat children will engage in MVPA as well as continued participation in sport, all\nof which can be influenced through targeted coach education training on\nphysical activity and health. A majority of youth sport organizations do not\nrequire volunteer coaches to have specific training or knowledge of any aspect\nof coaching. By highlighting youth sports as a viable source of daily MVPA and\nfuture health of those children participating in sport, we recommend coach\neducation training in recommended areas of 1) philosophy and ethics, 2) safety\nand injury prevention, 3) physical conditioning, 4) growth and development, 5)\nteaching and communication, 6) sport skills and tactics, 7) organization and\nadministration, and 8) evaluation, as presented by SHAPE America, formerly\nknown as American Alliance for Health, Physical Education, Recreation and Dance\n(2). Promoting change from the administrative level of youth sport\norganizations can help drive positive change at the community level. <\/p>\n\n\n\n

APPLICATIONS IN SPORT<\/strong><\/p>\n\n\n\n

Youth\nsports are long existing community service programs. Continued access to youth\nsports programs remains a vital resource to communities, however, as most\ncommunities grow and their needs change, so too should programs to address gaps\nand unhealthy trends, such as that of physical inactivity, overweight and\nobesity. The pulse of a program is housed in its many adult parent volunteers. Children\nlook up to coaches and benefit from coaches educated and trained in the\nimportance and value of being physically active during and outside of sport\nparticipation. By incorporating physical activity-related concepts to coach\neducation training programs, public health practitioners will be better able to\nmeasure immediate and long-term outcomes associated with children\u2019s physical\nactivity behavior and attrition or continued participation in sport. <\/p>\n\n\n\n

ACKNOWLEDGMENTS<\/strong><\/p>\n\n\n\n

None<\/p>\n\n\n\n

REFERENCES<\/p>\n\n\n\n

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    Authors: Alysia Cohen, Heidi Wegis, Darren Dutto, Viktor Bovbjerg Corresponding […]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"jetpack_publicize_message":"","jetpack_is_tweetstorm":false,"jetpack_publicize_feature_enabled":true,"jetpack_social_options":[]},"categories":[994,898],"tags":[1539,273,1171,1423],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/p4btio-1Li","jetpack-related-posts":[{"id":8156,"url":"https:\/\/thesportjournal.org\/article\/relationships-between-bmi-and-self-perception-of-adequacy-in-and-enjoyment-of-physical-activity-in-youth-following-a-physical-literacy-intervention\/","url_meta":{"origin":6776,"position":0},"title":"Relationships Between BMI and Self-Perception of Adequacy in and Enjoyment of Physical Activity in Youth Following a Physical Literacy Intervention","date":"March 11, 2022","format":false,"excerpt":"Authors: Brandi M. Eveland-Sayers1, Andy R. Dotterweich1, Alyson J. Chroust2, Abigail D. Daugherty3, and Kara L. Boynewicz4 1Department of Sport, Exercise, Recreation & Kinesiology, East Tennessee State University, Johnson City, Tennessee2 Department of Psychology, East Tennessee State University, Johnson City, Tennessee3Department of Kinesiology, Recreation, and Sport Studies, University of Tennessee,\u2026","rel":"","context":"In "General"","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":7776,"url":"https:\/\/thesportjournal.org\/article\/an-evidence-based-sports-nutrition-curriculum-for-youth\/","url_meta":{"origin":6776,"position":1},"title":"An evidence-based sports nutrition curriculum for youth","date":"February 5, 2021","format":false,"excerpt":"Authors: Ronald L. Gibbs Jr.1, Tyler B. Becker1,2 1MSU Extension, Health and Nutrition Institute, Michigan State University, East Lansing, MI, USA2Department of Food Science and Human Nutrition, Michigan State University, East Lansing, MI, USA Corresponding Author:Ronald L. Gibbs Jr PhD, MCHES446 W. Circle Drive, Justin S. Morrill Hall of Agriculture,\u2026","rel":"","context":"In "Sport Education"","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":5584,"url":"https:\/\/thesportjournal.org\/article\/youth-fitness-testing-practices-global-trends-and-new-development\/","url_meta":{"origin":6776,"position":2},"title":"Youth Fitness Testing Practices: Global Trends and New Development","date":"March 1, 2018","format":false,"excerpt":"Authors: Xiaofen D. Keating, Ph.D. Institution: Department of Curriculum and Instruction, The University of Texas at Austin, US Peter Smolianov, Ph.D. Institution: Department of Sport and Movement Science, Salem State University, US Xiaolu Liu, M.A. Institution: Department of Curriculum and Instruction, The University of Texas at Austin, US Jose Castro-Pi\u00f1ero,\u2026","rel":"","context":"In "Sports Health & Fitness"","img":{"alt_text":"Table 1","src":"https:\/\/i0.wp.com\/thesportjournal.org\/wp-content\/uploads\/2018\/03\/Table1.png?resize=350%2C200","width":350,"height":200},"classes":[]},{"id":382,"url":"https:\/\/thesportjournal.org\/article\/preschool-childrens-level-of-proficiency-in-motor-skills-and-the-level-of-their-physical-fitness-as-adolescents\/","url_meta":{"origin":6776,"position":3},"title":"Preschool Children\u2019s Level of Proficiency in Motor Skills and the Level of their Physical Fitness as Adolescents","date":"July 9, 2010","format":false,"excerpt":"Michelle Reillo, Eric Vlahov, Judith Bohren, Margaret Leppo, and Diane Davis Full Title: A longitudinal study to determine and comprehend the relationship between preschool children\u2019s level of proficiency in motor skills and the level of their physical fitness as adolescents Abstract The epidemic of pediatric obesity and associated health-related issues\u2026","rel":"","context":"In "Contemporary Sports Issues"","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":7693,"url":"https:\/\/thesportjournal.org\/article\/recreational-sport-opportunities-for-youth-with-disabilities-perspectives-of-recreation-directors-in-new-england\/","url_meta":{"origin":6776,"position":4},"title":"Recreational sport opportunities for youth with disabilities: Perspectives of recreation directors in New England","date":"November 17, 2020","format":false,"excerpt":"Authors: James MacGregor1, Deb Risisky2, Kevin McGinniss1 1 Department of Recreation, Tourism and Sport Management, Southern Connecticut State University, New Haven, Connecticut, USA.2 Department of Public Health, Southern Connecticut State University, New Haven, Connecticut, USA. Corresponding Author:James MacGregor, EdDDepartment of Recreation, Tourism, and Sport ManagementSouthern Connecticut State University501 Crescent StreetNew\u2026","rel":"","context":"In "Research"","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":479,"url":"https:\/\/thesportjournal.org\/article\/physical-self-perception-profile-of-female-college-students-kinesiology-majors-vs-non-kinesiology-majors\/","url_meta":{"origin":6776,"position":5},"title":"Physical Self-Perception Profile of Female College Students: Kinesiology Majors vs. Non-Kinesiology Majors","date":"November 21, 2012","format":false,"excerpt":"Jay Thornton and Kim KatoABSTRACTThe purpose of this study was to compare college student\u2019s Physical Self-Perception Profile (PSPP) (18) scores in female kinesiology majors and non-kinesiology majors. Participants included 68 female kinesiology majors and 88 female non-majors in a mid-sized university. The mean age for the kinesiology majors was 20.8\u2026","rel":"","context":"In "Contemporary Sports Issues"","img":{"alt_text":"Table 1","src":"https:\/\/i0.wp.com\/thesportjournal.org\/wp-content\/uploads\/2012\/11\/Table1.jpg?resize=350%2C200","width":350,"height":200},"classes":[]}],"_links":{"self":[{"href":"https:\/\/thesportjournal.org\/wp-json\/wp\/v2\/posts\/6776"}],"collection":[{"href":"https:\/\/thesportjournal.org\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/thesportjournal.org\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/thesportjournal.org\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/thesportjournal.org\/wp-json\/wp\/v2\/comments?post=6776"}],"version-history":[{"count":15,"href":"https:\/\/thesportjournal.org\/wp-json\/wp\/v2\/posts\/6776\/revisions"}],"predecessor-version":[{"id":7248,"href":"https:\/\/thesportjournal.org\/wp-json\/wp\/v2\/posts\/6776\/revisions\/7248"}],"wp:attachment":[{"href":"https:\/\/thesportjournal.org\/wp-json\/wp\/v2\/media?parent=6776"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/thesportjournal.org\/wp-json\/wp\/v2\/categories?post=6776"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/thesportjournal.org\/wp-json\/wp\/v2\/tags?post=6776"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}