Rural children's afterschool environment and health behaviors.
|Abstract:||The objective of the current study was to determine the association of rural children's afterschool environment and healthy behaviors. The study employed a cross-sectional design recruiting youth (N=147) ages 9-18 enrolled in grades 4, 7 and 11 from two rural counties in Georgia. Analyses tested the association of afterschool setting and physical activity, television viewing, and tobacco use. Results indicated that youth who attended a supervised afterschool program or sports program reported more MVPA, less weekday television viewing, and were less likely to report having tried tobacco. The findings support previous studies that observed relationships between adult supervision and healthy behaviors and extend this finding to understudied rural areas.|
Health care disparities (Prevention)
Health behavior (Research)
Rural health (Research)
Domestic relations (Influence)
Domestic relations (Health aspects)
Teenagers (Health aspects)
Youth (Health aspects)
Moore, Justin B.
Shores, Kindal A.
Watts, Clifton E.
|Publication:||Name: American Journal of Health Studies Publisher: American Journal of Health Studies Audience: Professional Format: Magazine/Journal Subject: Health Copyright: COPYRIGHT 2012 American Journal of Health Studies ISSN: 1090-0500|
|Issue:||Date: Wntr, 2012 Source Volume: 27 Source Issue: 1|
|Topic:||Event Code: 310 Science & research Canadian Subject Form: Health behaviour|
|Product:||Product Code: E121930 Youth|
|Geographic:||Geographic Scope: United States Geographic Code: 1USA United States|
Since the publication of the Carnegie Council on Adolescent Development report titled: A Matter of Time--Risk and Opportunity in the Non-School Hours (1992), interventions in the afterschool hours have garnered much attention. This report demonstrated that youth were more likely to engage in risk behaviors (e.g., juvenile crime, substance use, etc.) between the hours of 2-7 pm. In subsequent years, research on afterschool programs investigated the ability of these interventions to mitigate risk behaviors and promote positive changes such as academic success and social skills. For example, research on afterschool programs links participation to improved mathematic and reading skills in at-risk students (Lauer et al., 2006), increased social skills, and reduced drug use and problem behaviors (Durlak & Weissberg, 2007). In the last decade, studies have also sought to document the potential beneficial health effects of afterschool programs.
An area of considerable research focuses on the ability of afterschool programs to promote physical activity (Moore et al., 2010). These studies describe outcomes associated with youth engagement in afterschool sport, structured afterschool programs and unstructured afterschool time (Beets, Beighle, Erwin, & Huberty, 2009; Mahoney, Eccles, & Larson, 2004; Scales, Benson, & Mannes, 2006) concluding that afterschool programs can be effective in promoting physical activity in youth. These are important findings that have application to afterschool settings, since a recent study reported high levels of overweight and obesity and low levels of physical fitness in a sample of low-income children attending afterschool programs (Huberty, Rosenkranz, Balluff, & High, 2010). While activity promotion in these settings is promising, much of the research on these interventions has been conducted in urban settings, thus little is known about these relationships in rural settings (Edwards, Miller, & Blackburn, 2011; Moore, Davis, Baxter, Lewis, & Yin, 2008). Additionally, while considerable research demonstrates that unsupervised afterschool time is related to increased risk for substance use (Richardson, Radziszewska, Dent, & Flay, 1993), few studies examine these risk behaviors concurrently with protective factors such as physical activity or undesirable non-substance related outcomes such as watching television.
The lack of research in rural populations is notable, since approximately 20% of residents of the United States live in rural areas ("Measuring rurality," 2010). Research indicates that physical inactivity and obesity are more prevalent among rural populations than urban populations (Bruner, Lawson, Pickett, Boyce, & Janssen, 2008; Davis et al., 2008; Joens-Matre et al., 2008). For example, Moreno and colleagues found that youth living in rural communities were less physically active and less physically fit than urban youth (Moreno et al., 2001). Concurrently, rural youth reportedly smoke at slightly higher rates than their urban counterparts (Cronk & Sarvela, 1997). While disparities are well documented between rural vs. urban populations with regard to general health and health care utilization (Hartley, 2004; McMurray, Harrell, Bangdiwala, & Deng, 1999), only in the last decade have researchers examined cardiovascular risk factors (e.g., smoking) and physical activity in rural settings.
The objective of this study was to determine the association of rural children's afterschool environment and their self-reported health behaviors. This study compares different afterschool settings (i.e., an afterschool sports program, an adult supervised afterschool program, an adult supervised home environment, or a non-supervised home environment) in relation to children's engagement in physical activity, hours of television viewing, and tobacco use.
This study used a cross-sectional design to understand health behaviors among elementary, middle and high school children in two rural counties in Georgia during the spring of 2004. As of the 2000 US Census, county #1 (population 8,575) was composed of 56 percent white and 41per cent African American residents. Of adults in county #1, 38 percent did not finish high school and 28 percent lived below the poverty line. In contrast, county #2 (population 35,902) was predominantly white (89 per cent) with only 5 percent of the population reporting African American race. Among adults in county #2, 29 percent did not finish high school and 12 percent lived below the poverty level in 2000. Data collection occurred at school sites with the cooperation of the County Board of Education of the two counties. Students who assented and whose parents provided consent were administered survey questionnaires by non-school research staff in the schools' dining hall or a vacant classroom. The research staff read aloud the instructions and questionnaire items to students and gave time for students to ask questions.
Schoolchildren in grades 4, 8, and 11 participated in the research study during spring 2004. The intent of this sample selection strategy was to diversify the age of participants and the anticipated correlates of physical activity. Of the 332 students enrolled in county #1 for these three grades, 123 provided parental consent and youth assent (37 percent consent rate). Of the 607 students enrolled in county #2 for these three grades, only 532 were available for recruitment due to scheduling conflicts in the middle school. From this remaining group, 125 students provided consent to participate in the study (24 percent consent rate). In each county, 75 students were randomly selected for participation (25 per grade), and 74 students per county completed all portions of the study (N = 148). Given the lower than desired consent rates, data were weighted to reflect the study population (see analysis).
The independent variable for the study is the child's afterschool environment. With dichotomous yes-no response sets, youth indicated whether their primary afterschool setting was a sports program, an adult supervised afterschool program, an adult supervised home environment, or a non-supervised home environment. Respondents were then categorized into mutually exclusive categories. Three youth identified more than one primary afterschool environment and were removed from the analysis. Self-reported health behaviors served as the dependent variables. Youth PA was measured two ways. Physical activity was objectively measured using CSA accelerometers. The CSA accelerometers collected data in 30s epochs across four days for each child. During each data collection period these monitors were affixed to a belt and worn on the subject's right hip during all waking hours except when bathing. Movement counts were converted to physical activity intensity using count thresholds established by Treuth and associates (Treuth et al., 2004) to determine time spent in sedentary, light, moderate, and vigorous PA. Minutes in moderate and vigorous physical activity serves as the dependent variable for analysis. Physical activity participation was also measured using the Physical Activity Questionnaire for Older Children (PAQ-C). The PAQ-C is a 9-item, 7-day PA recall designed for use with elementary and middle school children in a field setting. The PAQ-C has displayed desirable psychometric properties in children as young as 8 years (Moore et al., 2007). Television viewing has correlated with sedentary time and was the second dependent variable of interest. The measure for television viewing captured the number of hours that youth spent watching TV on weekdays and weekends. The third health behavior was tobacco use. Tobacco use was measured with two variables. One variable asked youth if they tried tobacco in the last 30 days, and the other asked if they used tobacco more than once in the last 30 days. Questions for the latter two dependent variables emanated from a questionnaire utilized previously with children of similar age (Moore et al., 2008).
A multivariate analysis of covariance (MANCOVA) tested for significant differences between afterschool groups in CSA-measured physical activity, subjective PAQ-C measures of physical activity, television viewing, and tobacco use according to the child's primary afterschool environment. Analyses included post stratification weighting for race-ethnicity and SES. First, respondents were post stratified to county population estimates of the race-ethnicity domain totals from Census data. The control total was obtained by averaging 2000 and 2010 undercount adjusted weights. Second, respondents were post stratified for family income. Our measure of SES, federal free and reduced lunch, is an indicator of socio-economic status. However, the proportion of students qualifying for free and reduced lunch is significantly greater than the proportion of families at or below the Census poverty threshold. This results because free and reduced lunch is available to students from families at the 185% of poverty threshold and below (130-185% qualify for reduced lunch; 130% and below qualify for free lunch). Thus, to post-stratify for SES, 2008 data on the qualifying households per school district (provided by 2008 Georgia Department of Education statistics) were used to determine the adjusted weights for the sample. Child's age, sex, and body mass index were entered as covariates in the model. Estimated marginal means were compared using the Tukey procedure to adjust for multiple comparisons. These findings describe differences in health outcomes according to afterschool setting.
There were 148 study participants ranging from 9-18 years of age, with the greatest number of participants clustered at ages 9 and 10 (30.8 percent), 13 and 14 (30.8 percent), and 16 and 17 (28.8 percent) to correspond to grades 4, 8 and 11. As shown in Table 1, the 148 study participants closely resembled the student population profiles of their respective county schools with one major exception. Respondents in county #1 had a notably higher rate of free and reduced lunch than non-participants (77 percent of respondents vs. 61 percent county-wide). This proxy of socio-economic status indicates that, for county #1, participants in the study were more likely to be from low SES households when compared to those students who chose not to enroll in the study.
Of the 148 participating students, 145 were classified into mutually exclusive afterschool settings. Forty-four students spent afterschool time at home without adult supervision while 36 youth were at home with adult supervision. Twenty-nine students stayed afterschool for an adult-supervised afterschool program and 36 students stayed afterschool to participate in a sports program.
Results from the MANCOVA indicated significant differences in children's PA achievement, hours of television during the week, whether they had ever tried tobacco, and whether they regularly used tobacco according to their afterschool program environment. Television viewing on weekends was not significantly different for youth in different afterschool environments. Sex and age were significant covariates although BMI was not.
As described in Table 2, estimated marginal means indicated that youth from supervised afterschool settings achieved considerably more moderate and vigorous physical activity each week. This result was consistent across subjective (PAQ-C) and objective (CSA accelerometer) measures. With respect to the CSA measured physical activity, youth who participated in an adult-supervised afterschool program registered significantly more minutes than youth engaged in the other three settings. When physical activity was measured using the PAQ-C, youth who listed their primary afterschool environment as a supervised afterschool program or afterschool sports program were most active.
With regard to television viewing, youth in a supervised activity program had significantly lower mean minutes of weekday television watching than children in all other groups. These students watched just over 90 minutes (1.6 hours) of television on weekdays compared to 3 hours or more television for youth in all other settings.
Finally, youth with any type of adult supervision (afterschool program, sports program, at home) were significantly less likely, on average, to report having tried tobacco or to use tobacco regularly. On average, youth who had no adult at home afterschool reported twice as many days of tobacco use in the last 30 days compared to youth who had adult supervision in sports, an afterschool program or at home.
With regard to covariates, youth in fourth grade had significantly higher levels of physical activity and lower tobacco experimentation and use rates than youth in 8th and 11th grades. Similarly, girls had lower reported rates of physical activity, tobacco experimentation, and tobacco use when compared to boys. There were no significant differences by sex or age with regard to television viewing on weekdays.
The present findings suggest that supervised afterschool settings are protective against tobacco experimentation and use, while structured afterschool programs are associated with higher levels of physical activity among rural youth. Surprisingly, only structured, adult supervised, non-sport programs were associated with lower weekday television viewing, despite what one would assume is a smaller window of time available to watch television. This is somewhat consistent with a previous report, which found similar associations between television time and adult supervision (Hohepa, Scragg, Schofield, Kolt, & Schaaf, 2009). Consistent with past research, this study found no differences in children's weekend television viewing relative to their afterschool care.
The findings extend the support of studies from urban to rural settings that find relationships between adult supervision and behavior that reflects positive youth development. Literature from the field of child development indicates that from childhood into adolescence, adults provide role models to which adolescents can connect and emulate. Parents and other adults provide caring relationships and expectations around the use of free time and behavior during this period (Constantine, Bernard, & Diaz, 1999). Supervised afterschool activities offer opportunities for physical recreation that might not exist at home (Mahoney, Larson, & Eccles, 2005) which is especially relevant for children who return home from school to rural areas where opportunities are limited (Shores, Moore, & Yin, 2010).
A number of studies also support the notion that children thrive in supervised activities and often struggle with the prospect of idle time (Mahoney et al., 2005). Supervised activities often offer challenging environments and support the internalization of values and behaviors touted by structured programs (Larson, 2000; Ryan & Deci, 2000). Opportunities for meaningful participation often emanate from those environments where supports autonomy, competence and relatedness (i.e., social connectedness) exist (Caldwell, 2005; Watts & Caldwell, 2008). These psychological supports often exist in structured activities, where adults can monitor, provide guidance, and allow youth to be self-determined in their behavior.
When considering unstructured settings, youth are often left to their own devices. Often, youth lack the requisite skills to derive meaningful engagement in unstructured settings. They are wont to utilize their time in ways that lack challenge and yield little in terms of beneficial, developmental outcomes (Caldwell & Baldwin, 2005). Scholars in adolescent development report that youth are prone to boredom within unstructured settings, and this often leads to low yield (e.g., television watching) and negative behaviors such as tobacco and substance use (Caldwell & Smith, 2006; Wegner & Flisher, 2009). This is particularly salient in rural areas, as structured experiences are often limited in their availability. Future research should focus on understanding why youth initiate structured activities and what supports their continued participation. Studies should examine how youth negotiate the structural constraints (e.g., lack of transportation/distance, availability of resources) to structured activity participation, as well as the social-ecological relationships that support participation.
There are a number of limitations of the current study. The cross-sectional nature makes the establishment of causation impossible, and there is a very real possibility that parents who promote healthy behaviors at home are more likely to provide structured afterschool care to their children. In addition, sports programs are by definition activity promoting environments, so one would expect that physical activity levels in children attending these programs would be higher than non-sport programs. However, adult supervised afterschool programs demonstrated similar associations with physical activity levels of their participants, suggesting the supervision might be driving the effect rather than the focus of the program. Finally, the student response rate was low in the study due to low profile recruitment effort and per school recruitment quota (25 students per grade per school). In addition, the communities were chosen based upon their relative diversity compared to each other, not to be representative of the state or the nation. Thus, the rural setting from which the participants were drawn may limit the generalizability of these findings to children in urban or suburban settings or other rural areas. However, based upon the heterogeneity of the sample, these findings may be relevant to the rural population of the United States.
Provided the observed relationship between lower tobacco use and any type of adult supervision can be substantiated, findings have positive implications for outreach to those youth who do not attend afterschool programs but go home to a parent following the school day. Research demonstrates that substance and tobacco use is generally lower in situations where adult monitoring is present (Mott, Crowe, Richardson, & Flay, 1999). In these situations, the behavior is not supported or desirable to adults. It is recommended that schools target parents and families to impact normative beliefs and behaviors related to television viewing and physical activity. These efforts would support what occurs in afterschool programs, and take advantage of a support that exists naturally in the home. Tobacco use and physical inactivity are the leading causes of preventable death in American adults. Understanding effective prevention strategies is critically important in improving the health conditions and reducing the health disparities of rural populations.
Linking social and ecological factors to physical inactivity and obesity in youths" funded by the Georgia Biomedical Initiatives via the Georgia Center for the Prevention of Obesity and Related Disorders (PI: Z. Yin).
Beets, M., Beighle, A., Erwin, H., & Huberty, J. (2009). After-school program impact on physical activity and fitness: A meta-analysis. American Journal of Preventive Medicine, 36(6), 527-537.
Bruner, M. W., Lawson, J., Pickett, W., Boyce, W., & Janssen, I. (2008). Rural Canadian adolescents are more likely to be obese compared with urban adolescents. International Journal of Pediatric Obesity, 3(4), 205-211. doi: doi:10.1080/17477160802158477
Caldwell, L. L. (2005). Recreation and youth development. In P. A. Witt & L. L. Caldwell (Eds.), Recreation and Youth Development (pp. 169-192). State College, PA: Venture Publishing.
Caldwell, L. L., & Baldwin, C. K. (2005). What constrains adolescents' leisure? A developmental approach. In E. Jackson (Ed.), Contraints to leisure (pp. 75-88). State College, PA: Venture.
Caldwell, L. L., & Smith, E. A. (2006). Leisure as a context for youth development and delinquency prevention. Australian & New Zealand Journal of Criminology, 39(3), 398-418. doi: 10.1375/acri.39.3.398
Carnegie Council on Adolescent Development: Task Force on Youth Development and Community Programs. (1992). A matter of time: Risk and opportunity in the nonschool hours. Report of the Task Force on Youth Development and Community Programs. Waldorf, MD: TASCO, Inc.
Constantine, N. A., Bernard, B., & Diaz, M. (1999). Measuring protective factors and resilience traits in youth: The healthy kids resilience assessment. Paper presented at the Seventh Annual Meeting of the Society for Prevention Research, New Orleans, LA.
Cronk, C. E., & Sarvela, P. D. (1997). Alcohol, tobacco, and other drug use among rural/small town and urban youth: A secondary analysis of the monitoring the future data set. American Journal of Public Health, 87(5), 760-764. doi: 10.2105/ajph.87.5.760
Davis, A. M., Boles, R. E., James, R. L., Sullivan, D. K., Donnelly, J. E., Swirczynski, D. L., & Goetz, J. (2008). Health behaviors and weight status among urban and rural children. Rural Remote Health, 8(2), 810. doi: 810 [pii]
Durlak, J. A., & Weissberg, R. P. (2007). The impact of after-school programs that promote personal and social skills. Chicago, IL: Collaborative for Academic, Social, and Emotional Learning.
Edwards, M. B., Miller, J. L., & Blackburn, L. (2011). After-school programs for health promotion in rural communities: Ashe County Middle School 4-H After-School Program. Journal of Public Health Management and Practice, 17(3), 283-287.
Hartley, D. (2004). Rural health disparities, population health, and rural culture. American Journal of Public Health, 94(10), 1675-1678.
Hohepa, M., Scragg, R., Schofield, G., Kolt, G. S., & Schaaf, D. (2009). Associations between after-school physical activity, television use, and parental strategies in a sample of new zealand adolescents. Journal of Physical Activity and Health, 6(3), 299-305.
Huberty, J., Rosenkranz, R., Balluff, M., & High, R. (2010). Describing weight status and fitness in a community sample of children attending after-school programming. Journal of Sports Medicine and Physical Fitness, 50(2), 217-228.
Joens-Matre, R. R., Welk, G. J., Calabro, M. A., Russell, D. W., Nicklay, E., & Hensley, L. D. (2008). Rural-urban differences in physical activity, physical fitness, and overweight prevalence of children. Journal of Rural Health, 24(1), 49-54.
Larson, R. W. (2000). Toward a psychology of positive youth development. American Psychologist, 55(1), 170-183.
Lauer, P. A., Akiba, M., Wilkerson, S. B., Apthorp, H. S., Snow, D., & Martin-Glenn, M. L. (2006). Out of-school-time programs: A meta-analysis of effects for at-risk students. Review of Educational Research, 76(2), 275-313.
Mahoney, J. L., Eccles, J. S., & Larson, R. W. (2004). Processes of adjustment in organized out-of-school activities: Opportunities and risks. New Directions for Youth Development, 101(115-144).
Mahoney, J. L., Larson, R. W., & Eccles, J. S. (Eds.). (2005). Organized activities as contexts of development extracurricular activities, after school and community programs Mahwah, NJ: Lawrence Erlbaum Associates.
McMurray, R., Harrell, J., Bangdiwala, S., & Deng, S. (1999). Cardiovascular disease risk factors and obesity of rural and urban elementary school children. Journal of Rural Health, 15(4), 365-374.
Measuring rurality. (2010). Briefing Rooms Retrieved January 29, 2010, from http://www.ers.usda.gov/ Briefing/Rurality/
Moore, J. B., Davis, C. L., Baxter, S. D., Lewis, R. D., & Yin, Z. (2008). Physical activity, metabolic syndrome, and overweight in rural youth. Journal of Rural Health, 24(2), 136-142.
Moore, J. B., Hanes, J. C., Barbeau, P., Gutin, B., Trevino, R., & Yin, Z. (2007). Validation of the physical activity questionnaire for older children in children of different races. Pediatric Exercise Science, 19(1), 6-19.
Moore, J. B., Schneider, L., Lazorick, S., Shores, K. A., Beighle, A., Jilcott, S. B., & Newkirk, J. (2010). Rationale and development of the move more north carolina: Recommended standards for after-school physical activity. Journal of Public Health Management and Practice, 16(4), 359-366.
Moreno, L. A., Sarria, A., Fleta, J., Rodriguez, G., Gonzalez, J. M., & Bueno, M. (2001). Sociodemographic factors and trends on overweight prevalence in children and adolescents in Aragon (Spain) from 1985 to 1995. Journal of Clinical Epidemiology, 54(9), 921-927.
Mott, J. A., Crowe, P. A., Richardson, J., & Flay, B. (1999). After-school supervision and adolescent cigarette smoking: Contributions of the setting and intensity of after-school self-care. Journal of Behavioral Medicine, 22(1), 35-58.
Richardson, J. L., Radziszewska, B., Dent, C. W., & Flay, B. R. (1993). Relationship between after-school care of adolescents and substance use, risk taking, depressed mood, and academic achievement. Pediatrics, 92(1), 32-38.
Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68-78.
Scales, P. C., Benson, P. L., & Mannes, M. (2006). The contribution to adolescent well-being made by nonfamily adults: An examination of developmental assets as contexts and processes. Journal of Community Psychology, 34(4), 401-413.
Shores, K. A., Moore, J. B., & Yin, Z. (2010). An examination of triple jeopardy in rural youth physical activity participation. The Journal of Rural Health, 26(4), 352-360.
Treuth, M. S., Schmitz, K., Catellier, D. J., McMurray, R. G., Murray, D. M., Almeida, M. J., ... Pate, R. (2004). Defining accelerometer thresholds for activity intensities in adolescent girls. Medicine and Science in Sports and Exercise, 36(7), 1259-1266.
Watts, C. E., & Caldwell, L. L. (2008). Self-determination and free time activity participation as predictors of initiative. Journal of Leisure Research, 40, 156-181.
Wegner, L., & Flisher, A. J. (2009). Leisure boredom and adolescent risk behaviour: A systematic literature review. Journal of Child & Adolescent Mental Health, 21(1), 1-28.
Justin B. Moore, PhD, MS, Department of Health Promotion, Education, & Behavior, Arnold School of Public Health, University of South Carolina, 800 Sumter Street--Room 216, Columbia, SC 29208, Office: 803.777.5887, Fax: 803.777.7096, Email: firstname.lastname@example.org. Kindal A. Shores, PhD, Department of Recreation and Leisure Studies, College of Health and Human Performance, East Carolina University, Carol G. Belk Building, Room 2404--MS# 540, Greenville, NC 27858-4353, Office: 252.328.5649, Fax: 252.328.4642, Email: email@example.com. Clifton E. Watts, PhD, Department of Recreation and Leisure Studies, College of Health and Human Performance, East Carolina University, Carol G. Belk Building, Room 1403--MS# 540, Greenville, NC 27858-4353, Office: 252.737.2426, Fax: 252.328.4642, Email: firstname.lastname@example.org. Zenong Yin, PhD, Department of Health and Kinesiology, College of Education and Human Development, University of Texas at San Antonio, 1 UTSA Circle, San Antonio, TX 78249, Main Building 3.432, Phone: 210.458.5650, Fax: 210.458.5873, Email: email@example.com
Table 1. Characteristics of the Population and Study Respondents (unweighted) County 1 County 2 All students Sample All students Sample Fiscal year Fiscal year 2003 2003 Number of 1679 74 5738 74 students Sex Male 48% 45% 52% 50.0% Female 52% 55% 48% 50.0% Grade 4 -- 34% -- 34% 8 -- 32% -- 34% 11 -- 34% -- 32% Race/ethnicity Black 53% 54% 2% 4% White (non- 44% 43% 81% 90% Hispanic) Hispanic 0% 0% 4% 3% Other 3% 3% 17% 3% Body Mass Index by age Normal weight -- 53% -- 55% Overweight -- 15% -- 20% Obese -- 32% -- 25% Academic performance Mostly As -- 31% -- 43% Mostly Bs -- 48% -- 41% Mostly Cs -- 20% -- 12% Mostly Ds/Fs -- 0% -- 1% Not sure 1% 3% Free/reduced 77% 61% 26% 28% lunch Table 2. Summary of observed differences in behaviors according to afterschool setting Sports Program Adult at Adult at (n=36) afterschool home (n=36) program (n=29) Group Estimated Marginal Mean (Standard error) Activity CSA 55.60 (42.6) b 89.5 (50.4) a 61.92 (40.4) b Activity PAQ-C 3.22 (.254) a 3.35 (.136) a 2.891 (.081) b TV hours weekday 2.95 (.630) a 1.60 (1.30) b 3.06 (.169) a TV hours weekend 3.55 (.679) a 3.68 (.985) a 3.69 (1.01) a Tried tobacco 0.50 (.112) b 0.53 (.122) b 0 .41 (.233) b Used tobacco <30 2.13 (.424) b 3.54 (1.32) b 2.69 (.756) b days ago No-adult at home (n=44) Group Estimated Marginal Mean (Standard error) F p Activity CSA 47.58 (36.9) b 3.138 .028 Activity PAQ-C 2.79 (.123) b 9.67 .004 TV hours weekday 2.99 (.421) a 2.98 .015 TV hours weekend 3.54 (.948) a 2.12 .144 Tried tobacco 1.02 (.564) a 13.60 .001 Used tobacco <30 6.72 (.239) a 6.96 .007 days ago Note: Means in the same row with different sub-scripts are significantly different as reported by the Tukey post-hoc procedure. "Activity CSA" means reflect respondents' minutes of moderate and vigorous physical activity per day. "Activity PAQ-C" reflects respondents' mean scores on the PAC-Q (8-items). Television viewing means reflect hours of viewing. Tobacco use means reflect days of use in the last 30 days.
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