HEALTH EDUCATION RESEARCH
Vol.30 no.4 2015 Pages 647–659
Results of a 3-year, nutrition and physical activity intervention for children in rural, low-socioeconomic status elementary schools Kristi McClary King1* and Jiying Ling2 1
Department of Health and Sport Sciences, University of Louisville, 110b Crawford, Louisville, KY 40292, USA and 2College of Nursing, Michigan State University, 1355 Bogue Street, East Lansing, MI 48824, USA Received on May 30, 2014; accepted on June 18, 2015
Abstract Improving children’s nutrition and physical activity have become priorities in the United States. This quasi-experimental study evaluated the longitudinal effects of a 3-year, school-based, health promotion intervention (i.e. nutrition and physical education, classroom physical activity, professional development and health promotion for teachers and families, and strengthening wellness policies and family/community partnerships) on children’s health behaviors in four, rural, low-socioeconomic status elementary schools. A total of 999 kindergarten to thirdgrade children participated in data collection consisting of 4-day pedometer tracking and previous-day fruits and vegetables consumption recall from baseline in January, 2011 through 12 follow-up assessments ending May, 2013. The mixed-effects regression models showed that children’s nutrition and physical activity behaviors significantly improved over the 3-year intervention. The percentages of children who met the nutrition recommendation increased from 11 to 23% for girls and 12 to 23% for boys, while the percent who met the physical activity recommendation increased from 1 to 16% for girls and 3 to 7% for boys. Further, children’s age and their school impacted certain intervention effects. This school-based intervention could be disseminated to promote healthy behaviors among rural disadvantaged children. Engaging parents and
community partnerships is recommended to expand the traditional, children-focused education interventions.
Introduction Improving children’s nutrition and physical activity (PA) have become priorities in the United States due to the fact that 34% of US children (>24 million) are overweight or obese [1]. Initiatives launched by the US Presidential Commission of the Task Force on Childhood Obesity [2] and promoted through the US First Lady Michelle Obama’s Let’s Move! Campaign [3] has sparked collaborative efforts to decrease childhood obesity. Federal legislation such as the Healthy Hunger Free Kids Act of 2010 [4] has allowed the US Department of Agriculture (USDA) to revamp the nutritional standards thus setting calorie limits and a minimal number of vegetables served for the National School Lunch and Breakfast Programs. These health-promoting initiatives, legislation, and corresponding policies have solidified the foundation for potential long-lasting improvements in children’s PA and nutrition behaviors. Although nutrition and PA improvements can help control childhood obesity, the majority of US children are not meeting the US nutrition and PA recommendations [5–7]. It is recommended that children accumulate at least 60 min of moderateto vigorous-intensity PA [8], limit sedentary screen-time to no >2 h [9], and consume two or more fruits and three or more vegetables every day
ß The Author 2015. Published by Oxford University Press. All rights reserved. For permissions, please email: [email protected]
doi:10.1093/her/cyv029
Downloaded from http://her.oxfordjournals.org/ at Monash University on November 14, 2015
*Correspondence to: K. M. King. E-mail: [email protected]
K. M. King and J. Ling
648
cardiovascular disease, hypertension and type II diabetes [30]. Therefore, integrating a school-based, comprehensive nutrition and PA intervention with an emphasis on partnerships and policies into rural children’s daily school life would allow researchers to better understand the efficacy of comprehensive school-based healthy lifestyle interventions on rural children’s health behaviors. The purpose of this study was to evaluate the longitudinal effects of a comprehensive, 3-year, school-based nutrition and PA intervention on nutrition and PA among rural elementary school children.
Methods Study design A quasi-experimental design (intervention group only) was used to evaluate a school-based nutrition and PA intervention among four rural elementary schools in the southern United States The intervention, grounded in the CDC’s ecological model-based Coordinated School Health Program [20], was delivered by three full-time, trained Healthy Lifestyle Coaches (HLCs) from 2011 to 2013. Children’s nutrition and PA behaviors were measured at baseline (January 2011, T0), and 12 follow-up assessments were conducted from February 2011 (T1) to April 2013 (T12).
Participants and recruitment University Institutional Review Board and school administrators approved all protocol prior to study implementation. All children from kindergarten to fifth grade in four rural elementary schools participated in the intervention. The four rural elementary schools were selected based on their low School Health Index scores [31] and willingness to participate in the intervention, and all schools met the criteria for rural districts described by the rural–urban commuting areas [32, 33]. The average driving time from the four schools to the closest metropolitan area is about 45 min. The population in the four schools included 48.7% female (min–max: 47.4–50%), 8.8% Hispanic (min–max: 3.8–25%), 2.7% Black (min–max: 0.4–9.9%) and 84.1%
Downloaded from http://her.oxfordjournals.org/ at Monash University on November 14, 2015
[10]. However, only 38% of children meet PA and screen-time recommendations [5, 6], and about 29% consume two or more fruits, and only 7% consume three or more vegetables daily [7]. To attenuate the obesity epidemic, multidisciplinary, collaborative approaches to promoting healthy behaviors among children are recommended [11–13]. Children’s behaviors are influenced by their peers, families, schools and communities, and since children spend most of their daytime in schools, schools play an integral role for educating and promoting healthy behaviors among children. Though school-based nutrition and PA interventions are effective ways to promote healthy living among children, previous school-based interventions have some limitations: (i) small sample size [14], (ii) lack of a school policy component [15, 16], (iii) lack of integration into children’s daily school routine [14, 17], (iv) lack of parental support or community partnerships [18] and (v) low intensity of intervention [19]. The Centers for Disease Control and Prevention (CDC) has recommended an ecological model-based, Coordinated School Health program as an effective strategy to improve children’s health, prevent or reduce risk behaviors and support lifelong learning [20]; and it has been shown to be an effective strategy in promoting healthy behaviors among children [21–23]. Although federal, foundation and local initiatives and dollars promoting children’s health have been allocated in hopes of reversing childhood obesity trends [24]; southern, rural areas exhibit a higher prevalence of obesity than suburban or urban geographic regions due to a higher prevalence of sedentary lifestyles [25]. Rural culture along with dependence upon transportation and long commutes has been shown to play roles in prolonging the obesity crisis [26, 27]. Of concern in rural communities is not only the increasing prevalence of childhood obesity but also the negative impact of obesity on children’s health and subsequent adult health [28]. Obesity-related diseases in adults cost $147 billion annually [29]. This statistic can be especially troublesome in rural communities that are medically underserved and where residents have a higher risk of suffering from lifestyle diseases, including
Nutrition and PA intervention
Intervention Because children’s nutrition and PA behaviors are influenced by personal, family and social factors, an ecological approach [35, 36] incorporating five of the eight components (nutrition education (NE), nutrition services, physical education (PE), health promotion for staff and family and community involvement; excluding health services, counseling, psychological and social services, and a healthy and safe environment) of the CDC’s Coordinated School Health Program [20] served as the framework. The intervention, funded by a 2010–2013 Carol M. White PE Program (PEP) grant to the school district, focused on improving children’s nutrition and PA by implementing NE, health education, the SPARK PE curriculum [37], and classroom PA, as well as strengthening school wellness policies, providing professional development and health promotion for teachers and families, and promoting family involvement and community partnerships. The culminating goal for each school was to achieve ‘bronze’ or higher status of the HealthierUS School Challenge [38]. Preliminary findings of the first year intervention exhibited improvements in healthy behaviors among children [39]. Table II describes the components of the intervention across intervention years. Three, full-time HLCs, hired from their local communities, worked daily within the four elementary schools (one in School 1, one in Schools 2 and 3 and one in School 4) implementing interventions, collecting data and building positive relationships with children, parents, teachers, administrators and community members; while also serving as advocates for health promotion at their school(s) and
Table I. Demographics of participants Variable Age Sex (female) Grade Kindergarten Grade 1 Grade 2 Grade 3 Grade 4 Grade 5 School School 1 School 2 School 3 School 4
Year 1 (n ¼ 999)
Year 2 (n ¼ 906)
Year 3 (n ¼ 815)
7.30 (1.36) 447 (45.2%)
8.14 (1.27) 374 (44.9%)
8.56 (1.24) 378 (46.4%)
237 232 264 264 — —
(23.8%) (23.3%) (26.5%) (26.5%)
3 (0.4%) 196 (23.6%) 202 (24.3%) 215 (25.9%) 214 (25.8%) —
— 4 (0.6%) 172 (23.8%) 179 (24.8%) 190 (26.3%) 178 (24.6%)
317 159 229 293
(31.8%) (15.9%) (22.9%) (29.4%)
272 138 174 249
261 127 187 239
(32.7%) (16.6%) (20.9%) (29.9%)
(32.0%) (15.6%) (23.0%) (29.3%)
within their communities. A part-time project director communicated and met regularly with each HLC, school administrators, part-time contractual employees, as well as those affiliated with local hospitals, clinics, universities and businesses, to build and strengthen community capacity and partnerships. Contractual employees, including two faculty from a local university, were hired for PE consultation and external evaluation services, and the school districts’ nutrition director provided input on nutrition programming.
Measures Nutrition Children’s nutrition behaviors, the number of fruits and vegetables (starchy, orange, green and others) consumed, were measured through five items selected from the School PA and Nutrition (SPAN) questionnaire [40]. Evidence has supported that the SPAN questionnaire has relatively good test-retest reliability and validity among children [41]. At baseline in this study, the Cronbach’s alpha coefficient was 0.75, with item-total correlation coefficients ranging from 0.45 to 0.58, indicating adequate reliability. The Cronbach’s alpha coefficients for the 12 follow-up assessments were 0.80, 0.82, 0.83, 0.87, 0.81, 0.82, 0.81, 0.76, 0.76, 0.77, 0.77 and 0.76, respectively. 649
Downloaded from http://her.oxfordjournals.org/ at Monash University on November 14, 2015
White (min–max: 57.1–92.8%). On average, 65% (min–max: 57.5–70.2%) were enrolled in free- or reduced-price school lunch programs [34]. If parents/guardians wished for their child(ren) to not participate in data collection, they could choose for their child to ‘opt out’. For this article, only children participating in the intervention for 3 years were included, resulting in 999 children at baseline. Table I presents the demographics of participants across intervention year.
K. M. King and J. Ling Table II. Components of the intervention across intervention years Intervention
Participants
NE
HLCs, Classroom Teachers, Children, Universities, Health Departments
Implementation description .
. .
PE Teachers, Children
PE
.
. .
Take10! activity once per dayb,c
Project Director, External Evaluator, HLCs, Nutrition Director, Nutrition Personnel, PE Consultant, PE Teachers, Universities
.
Intervention implementation and evaluation meetings: data collection training, process evaluation, year reportsa,b,c 2-day SPARK PEP Traininga,b,c 2-day Local and State PE Conferences each yeara,b,c 2-day Cognitive Coaching Workshopb
. .
. .
.
.
School Wellness Policies
Project Director, HLCs, PE Consultant, School Site-Based Decision Making Councils (principals, teachers, parents)
.
.
.
Family/Community Involvement and Partnerships
Project Director, HLCs, Nutrition Director, Food Service Personnel, PE Consultant, PE Teachers, Principals, Classroom Teachers, Parents, Children, Community Members (Universities, Hospitals, Health Departments, Grocery Stores, et al)
.
.
.
Health Promotion for HLCs, Principals, Classroom Teachers, Parents Teachers and Families
. .
. a
b
6-lesson HealthierUS School Challenge Nutrition Workshopa Annual School Nutrition Association Conferencea,b,c Nutrition Network Meetings monthlya,b,c Aligned the school’s wellness policy with national and state PE standards and HealthierUS School Challenge criteriac Implemented ‘no food as reward’ policy to limit unhealthy snacks and cupcake partiesb,c Implemented ‘no exercise as punishment’ to limit negative associations of PA and punishmentc Implemented a variety of before- and after-school PA and wellness programs such as cross country running club, Sport and PA Day Camp, Jump Rope for Heart, Archery Club, walking clubsa,b,c Organized Family Fitness Fun Nights once a yeara,b,c Built community partnershipsa,b,c Developed a monthly e-newsletterb,c Implemented a 8-week weight loss and healthy eating/exercise program for teachersb,c Offered Zumba to parents and teachersb,c
c
Note. Implemented during Year 1; Implemented during Year 2; Implemented during Year 3.
Physical activity The number of steps recorded by pedometers was used to assess PA, with more step counts indicating a higher level of PA [42]. All children were asked to 650
wear a pedometer for 4 consecutive days at each data collection window, and the average steps of the 4 days were used to describe PA levels among children. A sex-specific graduated step index has
Downloaded from http://her.oxfordjournals.org/ at Monash University on November 14, 2015
Professional Development
Classroom Teachers, Children
Provided ‘one-shot’ health education workshops for childrena,b,c Implemented SPARK PE curriculuma,b,c 45 min PE once per weeka,b,c Implemented Take 10!b,c
.
Classroom PA
Integrated NE into classroom instructions using Thinkfinityb,c Taught one NE lesson per week to childrenb,c
Nutrition and PA intervention
Data collection procedures Prior to the intervention, the three HLCs and the project director were trained by the external evaluator on proper data collection and data entry procedures. All nutrition and PA data were collected by the HLCs. On Friday, the HLCs gave each child a pedometer and a pedometer log with standard instructions in his/her homeroom classroom. Children and parents were instructed to attach the pedometer to children’s waistband of the right hip at midline of thigh each morning in accordance to the manufacturer’s direction—starting on the upcoming Sunday through Wednesday—record each day’s step counts, then reset the pedometer to zero each night before bed, and take off pedometer when water immersion may occur (e.g. swimming, shower) with assistance of their parents. On the next Thursday, the HLCs collected the pedometers and logs, and asked children to complete the previous-day (e.g. Wednesday) five-item nutrition questionnaire, selected from the SPAN questionnaire [40]. The same procedure was used for the rest 12 data collection windows.
Data analysis The SAS 9.3 for Windows (SAS Institute Inc, Cary, NC) was used to analyse data. Daily step counts 30 000 were truncated in this study due to evidence suggesting that these counts should be considered outliers [45, 46]. At baseline, the percentage of outliers was 12.6%; this number decreased to 5.0% in Year 1 (T1–T4), 2.5% in Year 2 (T5–T8) and 1.2% in Year 3 (T9–T12). Time was coded as a continuous variable: month 0 (baseline),
and follow-up assessments (months 1, 2, 3, 4, 20, 22, 24, 26, 32, 34, 37 and 39). Mixed-effects regression models were applied to assess the group main effects, time main effects, and interaction effects between group and time on children’s nutrition and PA. To decrease the limitation due to the absence of a control group, each participant served as his/her own control through considering baseline data as covariates for data analysis. Three commonly used covariance structures (compound symmetry, unstructured and first-order autoregressive) were compared to identify the best covariance structure for the current data. After comparing the values for Akaike’s Information Criterion (AIC), Bayesian information criterion (BIC) and second-order AIC (AICc), first-order autoregressive covariance structure was chosen for analysis. If interaction effects were significant, simple effect tests (contrasts) were applied. Multiple models for each behavior were developed and compared based on the values of AIC, BIC and AICc; one final model with smallest values of AIC, BIC and AICc was selected for nutrition and PA, respectively [47].
Results At baseline, children in School 4 were older (Mage ¼ 8.28) than those in School 1 (Mage ¼ 7.24), followed by School 2 (Mage ¼ 6.61) and 3 (Mage ¼ 6.65), and no school differences were found on grade or sex distributions. Figure 1 shows a flow diagram of this study including 13 data collection windows and the number of participants at each time point who provided data on each outcome measure. At baseline, about 91.0% of the children provided data on nutrition, while only 46.5% provided data on PA. The percentages of children providing PA data did not change significantly over time (P ¼ 0.420), while the percentages of children providing nutrition data decreased significantly (P < 0.001); 93 children dropped out of this study in Year 2, and 91 in Year 3 due to moving, health problems, or parent/guardian requesting for their child(ren) to ‘opt out’ of data collection. There were no significant differences on 651
Downloaded from http://her.oxfordjournals.org/ at Monash University on November 14, 2015
been developed for children aged 6–12 years. Values for boys were:
Vol.30 no.4 2015 Pages 647–659
Results of a 3-year, nutrition and physical activity intervention for children in rural, low-socioeconomic status elementary schools Kristi McClary King1* and Jiying Ling2 1
Department of Health and Sport Sciences, University of Louisville, 110b Crawford, Louisville, KY 40292, USA and 2College of Nursing, Michigan State University, 1355 Bogue Street, East Lansing, MI 48824, USA Received on May 30, 2014; accepted on June 18, 2015
Abstract Improving children’s nutrition and physical activity have become priorities in the United States. This quasi-experimental study evaluated the longitudinal effects of a 3-year, school-based, health promotion intervention (i.e. nutrition and physical education, classroom physical activity, professional development and health promotion for teachers and families, and strengthening wellness policies and family/community partnerships) on children’s health behaviors in four, rural, low-socioeconomic status elementary schools. A total of 999 kindergarten to thirdgrade children participated in data collection consisting of 4-day pedometer tracking and previous-day fruits and vegetables consumption recall from baseline in January, 2011 through 12 follow-up assessments ending May, 2013. The mixed-effects regression models showed that children’s nutrition and physical activity behaviors significantly improved over the 3-year intervention. The percentages of children who met the nutrition recommendation increased from 11 to 23% for girls and 12 to 23% for boys, while the percent who met the physical activity recommendation increased from 1 to 16% for girls and 3 to 7% for boys. Further, children’s age and their school impacted certain intervention effects. This school-based intervention could be disseminated to promote healthy behaviors among rural disadvantaged children. Engaging parents and
community partnerships is recommended to expand the traditional, children-focused education interventions.
Introduction Improving children’s nutrition and physical activity (PA) have become priorities in the United States due to the fact that 34% of US children (>24 million) are overweight or obese [1]. Initiatives launched by the US Presidential Commission of the Task Force on Childhood Obesity [2] and promoted through the US First Lady Michelle Obama’s Let’s Move! Campaign [3] has sparked collaborative efforts to decrease childhood obesity. Federal legislation such as the Healthy Hunger Free Kids Act of 2010 [4] has allowed the US Department of Agriculture (USDA) to revamp the nutritional standards thus setting calorie limits and a minimal number of vegetables served for the National School Lunch and Breakfast Programs. These health-promoting initiatives, legislation, and corresponding policies have solidified the foundation for potential long-lasting improvements in children’s PA and nutrition behaviors. Although nutrition and PA improvements can help control childhood obesity, the majority of US children are not meeting the US nutrition and PA recommendations [5–7]. It is recommended that children accumulate at least 60 min of moderateto vigorous-intensity PA [8], limit sedentary screen-time to no >2 h [9], and consume two or more fruits and three or more vegetables every day
ß The Author 2015. Published by Oxford University Press. All rights reserved. For permissions, please email: [email protected]
doi:10.1093/her/cyv029
Downloaded from http://her.oxfordjournals.org/ at Monash University on November 14, 2015
*Correspondence to: K. M. King. E-mail: [email protected]
K. M. King and J. Ling
648
cardiovascular disease, hypertension and type II diabetes [30]. Therefore, integrating a school-based, comprehensive nutrition and PA intervention with an emphasis on partnerships and policies into rural children’s daily school life would allow researchers to better understand the efficacy of comprehensive school-based healthy lifestyle interventions on rural children’s health behaviors. The purpose of this study was to evaluate the longitudinal effects of a comprehensive, 3-year, school-based nutrition and PA intervention on nutrition and PA among rural elementary school children.
Methods Study design A quasi-experimental design (intervention group only) was used to evaluate a school-based nutrition and PA intervention among four rural elementary schools in the southern United States The intervention, grounded in the CDC’s ecological model-based Coordinated School Health Program [20], was delivered by three full-time, trained Healthy Lifestyle Coaches (HLCs) from 2011 to 2013. Children’s nutrition and PA behaviors were measured at baseline (January 2011, T0), and 12 follow-up assessments were conducted from February 2011 (T1) to April 2013 (T12).
Participants and recruitment University Institutional Review Board and school administrators approved all protocol prior to study implementation. All children from kindergarten to fifth grade in four rural elementary schools participated in the intervention. The four rural elementary schools were selected based on their low School Health Index scores [31] and willingness to participate in the intervention, and all schools met the criteria for rural districts described by the rural–urban commuting areas [32, 33]. The average driving time from the four schools to the closest metropolitan area is about 45 min. The population in the four schools included 48.7% female (min–max: 47.4–50%), 8.8% Hispanic (min–max: 3.8–25%), 2.7% Black (min–max: 0.4–9.9%) and 84.1%
Downloaded from http://her.oxfordjournals.org/ at Monash University on November 14, 2015
[10]. However, only 38% of children meet PA and screen-time recommendations [5, 6], and about 29% consume two or more fruits, and only 7% consume three or more vegetables daily [7]. To attenuate the obesity epidemic, multidisciplinary, collaborative approaches to promoting healthy behaviors among children are recommended [11–13]. Children’s behaviors are influenced by their peers, families, schools and communities, and since children spend most of their daytime in schools, schools play an integral role for educating and promoting healthy behaviors among children. Though school-based nutrition and PA interventions are effective ways to promote healthy living among children, previous school-based interventions have some limitations: (i) small sample size [14], (ii) lack of a school policy component [15, 16], (iii) lack of integration into children’s daily school routine [14, 17], (iv) lack of parental support or community partnerships [18] and (v) low intensity of intervention [19]. The Centers for Disease Control and Prevention (CDC) has recommended an ecological model-based, Coordinated School Health program as an effective strategy to improve children’s health, prevent or reduce risk behaviors and support lifelong learning [20]; and it has been shown to be an effective strategy in promoting healthy behaviors among children [21–23]. Although federal, foundation and local initiatives and dollars promoting children’s health have been allocated in hopes of reversing childhood obesity trends [24]; southern, rural areas exhibit a higher prevalence of obesity than suburban or urban geographic regions due to a higher prevalence of sedentary lifestyles [25]. Rural culture along with dependence upon transportation and long commutes has been shown to play roles in prolonging the obesity crisis [26, 27]. Of concern in rural communities is not only the increasing prevalence of childhood obesity but also the negative impact of obesity on children’s health and subsequent adult health [28]. Obesity-related diseases in adults cost $147 billion annually [29]. This statistic can be especially troublesome in rural communities that are medically underserved and where residents have a higher risk of suffering from lifestyle diseases, including
Nutrition and PA intervention
Intervention Because children’s nutrition and PA behaviors are influenced by personal, family and social factors, an ecological approach [35, 36] incorporating five of the eight components (nutrition education (NE), nutrition services, physical education (PE), health promotion for staff and family and community involvement; excluding health services, counseling, psychological and social services, and a healthy and safe environment) of the CDC’s Coordinated School Health Program [20] served as the framework. The intervention, funded by a 2010–2013 Carol M. White PE Program (PEP) grant to the school district, focused on improving children’s nutrition and PA by implementing NE, health education, the SPARK PE curriculum [37], and classroom PA, as well as strengthening school wellness policies, providing professional development and health promotion for teachers and families, and promoting family involvement and community partnerships. The culminating goal for each school was to achieve ‘bronze’ or higher status of the HealthierUS School Challenge [38]. Preliminary findings of the first year intervention exhibited improvements in healthy behaviors among children [39]. Table II describes the components of the intervention across intervention years. Three, full-time HLCs, hired from their local communities, worked daily within the four elementary schools (one in School 1, one in Schools 2 and 3 and one in School 4) implementing interventions, collecting data and building positive relationships with children, parents, teachers, administrators and community members; while also serving as advocates for health promotion at their school(s) and
Table I. Demographics of participants Variable Age Sex (female) Grade Kindergarten Grade 1 Grade 2 Grade 3 Grade 4 Grade 5 School School 1 School 2 School 3 School 4
Year 1 (n ¼ 999)
Year 2 (n ¼ 906)
Year 3 (n ¼ 815)
7.30 (1.36) 447 (45.2%)
8.14 (1.27) 374 (44.9%)
8.56 (1.24) 378 (46.4%)
237 232 264 264 — —
(23.8%) (23.3%) (26.5%) (26.5%)
3 (0.4%) 196 (23.6%) 202 (24.3%) 215 (25.9%) 214 (25.8%) —
— 4 (0.6%) 172 (23.8%) 179 (24.8%) 190 (26.3%) 178 (24.6%)
317 159 229 293
(31.8%) (15.9%) (22.9%) (29.4%)
272 138 174 249
261 127 187 239
(32.7%) (16.6%) (20.9%) (29.9%)
(32.0%) (15.6%) (23.0%) (29.3%)
within their communities. A part-time project director communicated and met regularly with each HLC, school administrators, part-time contractual employees, as well as those affiliated with local hospitals, clinics, universities and businesses, to build and strengthen community capacity and partnerships. Contractual employees, including two faculty from a local university, were hired for PE consultation and external evaluation services, and the school districts’ nutrition director provided input on nutrition programming.
Measures Nutrition Children’s nutrition behaviors, the number of fruits and vegetables (starchy, orange, green and others) consumed, were measured through five items selected from the School PA and Nutrition (SPAN) questionnaire [40]. Evidence has supported that the SPAN questionnaire has relatively good test-retest reliability and validity among children [41]. At baseline in this study, the Cronbach’s alpha coefficient was 0.75, with item-total correlation coefficients ranging from 0.45 to 0.58, indicating adequate reliability. The Cronbach’s alpha coefficients for the 12 follow-up assessments were 0.80, 0.82, 0.83, 0.87, 0.81, 0.82, 0.81, 0.76, 0.76, 0.77, 0.77 and 0.76, respectively. 649
Downloaded from http://her.oxfordjournals.org/ at Monash University on November 14, 2015
White (min–max: 57.1–92.8%). On average, 65% (min–max: 57.5–70.2%) were enrolled in free- or reduced-price school lunch programs [34]. If parents/guardians wished for their child(ren) to not participate in data collection, they could choose for their child to ‘opt out’. For this article, only children participating in the intervention for 3 years were included, resulting in 999 children at baseline. Table I presents the demographics of participants across intervention year.
K. M. King and J. Ling Table II. Components of the intervention across intervention years Intervention
Participants
NE
HLCs, Classroom Teachers, Children, Universities, Health Departments
Implementation description .
. .
PE Teachers, Children
PE
.
. .
Take10! activity once per dayb,c
Project Director, External Evaluator, HLCs, Nutrition Director, Nutrition Personnel, PE Consultant, PE Teachers, Universities
.
Intervention implementation and evaluation meetings: data collection training, process evaluation, year reportsa,b,c 2-day SPARK PEP Traininga,b,c 2-day Local and State PE Conferences each yeara,b,c 2-day Cognitive Coaching Workshopb
. .
. .
.
.
School Wellness Policies
Project Director, HLCs, PE Consultant, School Site-Based Decision Making Councils (principals, teachers, parents)
.
.
.
Family/Community Involvement and Partnerships
Project Director, HLCs, Nutrition Director, Food Service Personnel, PE Consultant, PE Teachers, Principals, Classroom Teachers, Parents, Children, Community Members (Universities, Hospitals, Health Departments, Grocery Stores, et al)
.
.
.
Health Promotion for HLCs, Principals, Classroom Teachers, Parents Teachers and Families
. .
. a
b
6-lesson HealthierUS School Challenge Nutrition Workshopa Annual School Nutrition Association Conferencea,b,c Nutrition Network Meetings monthlya,b,c Aligned the school’s wellness policy with national and state PE standards and HealthierUS School Challenge criteriac Implemented ‘no food as reward’ policy to limit unhealthy snacks and cupcake partiesb,c Implemented ‘no exercise as punishment’ to limit negative associations of PA and punishmentc Implemented a variety of before- and after-school PA and wellness programs such as cross country running club, Sport and PA Day Camp, Jump Rope for Heart, Archery Club, walking clubsa,b,c Organized Family Fitness Fun Nights once a yeara,b,c Built community partnershipsa,b,c Developed a monthly e-newsletterb,c Implemented a 8-week weight loss and healthy eating/exercise program for teachersb,c Offered Zumba to parents and teachersb,c
c
Note. Implemented during Year 1; Implemented during Year 2; Implemented during Year 3.
Physical activity The number of steps recorded by pedometers was used to assess PA, with more step counts indicating a higher level of PA [42]. All children were asked to 650
wear a pedometer for 4 consecutive days at each data collection window, and the average steps of the 4 days were used to describe PA levels among children. A sex-specific graduated step index has
Downloaded from http://her.oxfordjournals.org/ at Monash University on November 14, 2015
Professional Development
Classroom Teachers, Children
Provided ‘one-shot’ health education workshops for childrena,b,c Implemented SPARK PE curriculuma,b,c 45 min PE once per weeka,b,c Implemented Take 10!b,c
.
Classroom PA
Integrated NE into classroom instructions using Thinkfinityb,c Taught one NE lesson per week to childrenb,c
Nutrition and PA intervention
Data collection procedures Prior to the intervention, the three HLCs and the project director were trained by the external evaluator on proper data collection and data entry procedures. All nutrition and PA data were collected by the HLCs. On Friday, the HLCs gave each child a pedometer and a pedometer log with standard instructions in his/her homeroom classroom. Children and parents were instructed to attach the pedometer to children’s waistband of the right hip at midline of thigh each morning in accordance to the manufacturer’s direction—starting on the upcoming Sunday through Wednesday—record each day’s step counts, then reset the pedometer to zero each night before bed, and take off pedometer when water immersion may occur (e.g. swimming, shower) with assistance of their parents. On the next Thursday, the HLCs collected the pedometers and logs, and asked children to complete the previous-day (e.g. Wednesday) five-item nutrition questionnaire, selected from the SPAN questionnaire [40]. The same procedure was used for the rest 12 data collection windows.
Data analysis The SAS 9.3 for Windows (SAS Institute Inc, Cary, NC) was used to analyse data. Daily step counts 30 000 were truncated in this study due to evidence suggesting that these counts should be considered outliers [45, 46]. At baseline, the percentage of outliers was 12.6%; this number decreased to 5.0% in Year 1 (T1–T4), 2.5% in Year 2 (T5–T8) and 1.2% in Year 3 (T9–T12). Time was coded as a continuous variable: month 0 (baseline),
and follow-up assessments (months 1, 2, 3, 4, 20, 22, 24, 26, 32, 34, 37 and 39). Mixed-effects regression models were applied to assess the group main effects, time main effects, and interaction effects between group and time on children’s nutrition and PA. To decrease the limitation due to the absence of a control group, each participant served as his/her own control through considering baseline data as covariates for data analysis. Three commonly used covariance structures (compound symmetry, unstructured and first-order autoregressive) were compared to identify the best covariance structure for the current data. After comparing the values for Akaike’s Information Criterion (AIC), Bayesian information criterion (BIC) and second-order AIC (AICc), first-order autoregressive covariance structure was chosen for analysis. If interaction effects were significant, simple effect tests (contrasts) were applied. Multiple models for each behavior were developed and compared based on the values of AIC, BIC and AICc; one final model with smallest values of AIC, BIC and AICc was selected for nutrition and PA, respectively [47].
Results At baseline, children in School 4 were older (Mage ¼ 8.28) than those in School 1 (Mage ¼ 7.24), followed by School 2 (Mage ¼ 6.61) and 3 (Mage ¼ 6.65), and no school differences were found on grade or sex distributions. Figure 1 shows a flow diagram of this study including 13 data collection windows and the number of participants at each time point who provided data on each outcome measure. At baseline, about 91.0% of the children provided data on nutrition, while only 46.5% provided data on PA. The percentages of children providing PA data did not change significantly over time (P ¼ 0.420), while the percentages of children providing nutrition data decreased significantly (P < 0.001); 93 children dropped out of this study in Year 2, and 91 in Year 3 due to moving, health problems, or parent/guardian requesting for their child(ren) to ‘opt out’ of data collection. There were no significant differences on 651
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been developed for children aged 6–12 years. Values for boys were:
