The Journal of School Nursing
© The Author(s) 2020
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DOI: 10.1177/1059840520924453
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2022, Vol. 38(3) 241–248
The U.S. Department of Agriculture Final Rule on School Wellness Policy requires schools to self-evaluate wellness policies and environments. To understand the utility of this information, this study evaluates the validity of school-reported wellness information against directly observed data. Wellness leaders at 10 Midwestern elementary schools completed a questionnaire spanning nine school wellness settings. School-reported information was compared against a direct observation protocol. Percent agreement and к statistics were used to assess agreement between school reporters and direct observation. Overall percent agreement between reporters and direct observation was 77.1%. Agreement ranged from 67.3% (Lunchroom Environment) to 92.0% (School Wellness Policies) across the nine categories. к results showed that 65.7% of the items demonstrated fair or better reporter agreement. The results provide preliminary support for the utility of schools’ selfreported wellness information. Facilitation of independent reporting on wellness environments by school leaders will contribute to broader applications for school wellness programming.
school wellness policies, Final Rule, assessment, evaluation, school wellness environments, school nurse
The health of our nation’s youth remains an important public health concern. Many youth do not consume nutrientadequate diets (Banfield et al., 2016; Nielsen et al., 2014), are not physically active (Fakhouri et al., 2013), and are increasingly at risk of overweight and obesity (Ogden et al., 2012, 2014). Schools have been recommended as important settings for promoting healthy nutrition and physical activity (PA) to youth (Buscemi et al., 2014; Centers for Disease Control and Prevention, 2013; Hills et al., 2015; SHAPE America, 2015). The rationale for integrating wellness within school systems is sound, as most youth attend schools (*95%) and spend a significant amount of their waking time there, and the infrastructures for learning (teachers/classrooms), moving (playgrounds, gyms space, equipment), and eating (cafeterias, U.S. Department of Agriculture [USDA]-funded lunch and breakfast programs) are in place.
In 2016, the USDA published the Final Rule on School Wellness Policy (hereon, Final Rule), which expanded upon the school wellness policy requirements for schools participating in the National School Lunch Program set forth in the Healthy, Hunger-Free Kids Act of 2010 (HHFKA). At the local district/school level, the Final Rule strengthened the HHFKA by mandating the following new/additional requirements:
The Final Rule recognizes the importance of schools to promote and support healthy youth behaviors. Research has also demonstrated that schools have the potential to influence students’ health behaviors through multiple ecological levels, including wellness policies (Alaimo et al., 2013, 2015; Faulkner et al., 2014; Merlo et al., 2014; Morton et al., 2016), physical infrastructure (Ip et al., 2017; Lanningham-Foster et al., 2008; Larsen et al., 2009; Morton et al., 2016), and school culture (Faulkner et al., 2014; Kenney et al., 2017; Morton et al., 2016). However, many schools still do not act accordingly. For example, a study by Lucarelli et al. (2015) reported that most districts/schools had adopted “model” school wellness policy templates without editing the templates to address the needs and capacity of their specific school(s), potentially preventing adoption of wellness policies. Although the study was conducted prior to the passing of the Final Rule, the findings reported by Lucarelli et al. (2015) are concerning. However, this justifies the need for and heightens the importance of the Final Rule requiring schools to evaluate their compliance with and implementation of the school wellness policy/programming and to track progress over time.
Although studies have explored the degree to which districts/schools have representative school wellness policies (Lucarelli et al., 2015; Merlo et al., 2014; Vine et al., 2017), the accuracy of school reporting has not been established. The Final Rule requirements somewhat assume that school health leaders can (and will) accurately report aspects of their school wellness environments and policies. With this in mind, it is important to objectively evaluate the degree to which schools can self-report information about their wellness environments.
Previous work by Nathan et al. (2013) reported that principals in Australian primary schools reported information about nutrition and PA environments accurately 70% of the time compared to a direct observation protocol. However, because there are differences between the U.S. and Australian school systems, it is not clear if similar results would be observed in the United States. Further, in the U.S. school staff, such as school nurses (Blaine et al., 2017; Chen et al., 2018; Council on School Health, 2016; Pittman, 2018) and physical education teachers (Webster et al., 2015), play key roles in assessing student behavioral health and developing, implementing, and evaluating school wellness programming and policies. Therefore, the purpose of this study was to evaluate the degree to which school wellness leaders accurately report information about school wellness environments compared against direct observation.
Data for this cross-sectional, descriptive study were collected during spring 2017. Approval from the Institutional Review Board of Iowa State University was obtained prior to the start of the study (ID 17-041).
Schools were recruited to participate in the study through a monthly“HealthyStudents,Healthy Schools” newsletter sent to school nurses, wellness leaders, and food service representatives across the state. Although 22 schools inquired about the study, due to the feasibility constraints of conducting the direct observation protocol in schools, enrollment was limited to the first 10 elementary schools to sign up to participate in the study. Participating schools were required to have an official school wellness policy on file and serve all elementary grades (i.e., at least K–5), but could also serve other grades too (i.e., K–8), which is common in rural locations. Schools were provided with a US$500 stipend for participating in the study.
Participating schools were required to have a school wellness leader complete a 35-item survey of their school wellness environment and submit a copy of their official district/school wellness policy. Upon completing the assessment of the school wellness environment survey, a follow-up, fullday direct observation protocol was scheduled to take place within the 2 weeks of completing the assessment. Direct observation was conducted by the primary investigator, which entailed completing the 35-item wellness environment survey using direct observation.
Schools are complex systems with multiple physical settings (classrooms, physical education, playground, lunchroom, before/after school activities, etc.) that influence students’ behaviors. Therefore, we developed the School Wellness Environment survey items to capture relevant wellness information throughout the school day. To create a comprehensive list of questions to use in the study, we reviewed existing tools and considered “best practice” recommendations from national frameworks focused on nutrition and PA in schools. Frameworks and guidelines that informed the development of the items included the Comprehensive School Physical Activity Program (CSPAP; SHAPE America, 2015; Webster et al., 2015) National Framework, the CSPAP Policy Continuum (https://www. shapeamerica.org/uploads/pdfs/advocacy/CSPAP-Policy-Continuum-2-10-12final.pdf), and principles of the USDA Healthier U.S. School Challenge (HUSSC): smarter lunchrooms best practices for nutrition. The CSPAP National Framework provides information about relevant PA practices in five school domains: quality physical education, PA during school (recess, classroom), PA before and after school (programming, active transportation, etc.), staff involvement, and family and community engagement. The quality physical education, PA during school, and PA before and after school domains were most relevant for the current study. For nutrition, the HUSSC: Smarter Lunchrooms recommends the use of simple, cost-effective approaches to provide cafeterias with a framework to promote nutrition wellness to students. Table 1 provides an overview of the categories and number of questions on the survey for each category.
A draft survey was established and reviewed by a group of academic experts (n = 6). The expert feedback was applied to enhance the appropriateness, feasibility, and comprehension of the items used to assess school wellness environments in the present study. The final version of the assessment tool used in the study included 35 observable items each coded with simple no/yes response options. See the School Wellness Environment Survey in the Online Supplemental.
The direct observation protocol was standardized for consistency across schools. The direct observation protocol consisted of arriving to the school 1-hr prior to the start of the school day to check-in and begin observing the school environments. The observer was accompanied by a school staff member throughout the observation period/day. The observation protocol was initiated approximately 45-min prior to the school start time. During the period of time before the start of the school day, the observer evaluated the type(s) of before school programs/activities that were offered to students, what spaces they were able to use, and the presence of infrastructure to facilitate/support active transportation modalities (e.g., bike racks). This included observing indoor and outdoor spaces (scanning gymnasiums, outdoor facilities, and spaces where students were directed to wait for the school day to begin). Weather was not a factor on any of the 10 observation days, but the school staff accompanying the observer provided information about how any outdoor before/after school activities and recess were impacted on days with inclement weather. During the school day, observation was conducted in four key settings: physical education, recess, classroom time, and lunch. At least one physical education, recess, and lunch session were observed in full and two separate 30-min periods during classroom time to facilitate completion of the school wellness environment survey. The order/schedule of these observations for each school was scheduled to best accommodate each school’s schedule for the observation day. Near the end of each school day observation, a 30-min time block was scheduled to meet with staff from physical education, lunchroom, and classroom settings to allow for the observer to ask for clarification regarding any observation made. Finally, a 1-hr time period following the school day was used to observe after school programming offered to students, if any. After the direct observation protocol was conducted for a school, the school wellness policy was reviewed to evaluate schoolreports on wellness policy-related questions.
For schools, mean and standard deviation values for student enrollment, percent free and reduce priced lunch, percent male/female, and percent White were reported using publicly accessible data from the state’s Department of Education website. Descriptive characteristics for school reporters about role, years in current position, and percent of respondents that serve on the school wellness committee were self-reported.
Percent agreement between the school reporter and direct observation were calculated for each item. Prior research has utilized a threshold for acceptable percent agreement of ≥70% (Nathan et al., 2013) and was used to define reasonable agreement in the present study. Construct variables for each environment were created and mean percent agreement was calculated to evaluate if school wellness leaders reported more or less accurately depending on the domain of the school environment being observed. The construct variables established were Physical Education, Recess, Classroom, PA Before and After School, Active Transportation, Family and Community Engagement, Staff Involvement, School Food Environment, and School Wellness Policies.
Although percent agreement is a common and useful statistic (i.e., easy to interpret and compare across studies), it does not consider or correct for agreement beyond what is expected (Cunningham, 2009). Therefore, Cohen’s к statistic was employed to assess the degree of agreement between reporters. However, Cohen’s к statistic has limitations when the degree of agreement is above 75% or below 25% (Cunningham, 2009). In these cases, prevalence-adjusted biasadjusted к (PABAK) statistic was used to assess the degree of agreement between reporters in lieu of Cohen’s к statistic. Thresholds set by Landis and Koch (1977) were used to classify the degree of agreement from the к statistics: <.00 = poor, .00–.20 = slight, .21–.40 = fair, .41–.60 = moderate, .61–.80 = substantial, and >.80 = almost perfect. a for the current study was set at .05. To control for multiple tests for significance within the к analysis (i.e., n = 35 tests; one for each survey item), the α value necessary for statistical significance was adjusted to p < .0014 (i.e., .05/35).
Descriptive characteristics of the schools and school reporters are described in Tables 2 and 3, respectively. Six of the 10 schools were located in rural settings, 3 in towns, and 1 in a suburb as defined by the National Center for Education Statistics (https://nces.ed.gov/ccd/schoolsearch/). The relatively homogenous nature of the schools (7 of the 10 schools >89% White) is reflective of the demographics of the state. The U.S. Census Bureau reported that the population of Iowa was 91.4% White in the year 2016 (https://www.census.gov/quickfacts/IA). School reporters were most often school nurses (40%) or food service representatives (30%), had less than 10 years of experience in their current position (80%), and all were female.
The overall mean percent agreement between school reporters and direct observation was 77.1% with a range of 50%–100% across the 35 items. The school environments with the highest agreement between reporting methods were the Physical Education (86.7%), Active Transportation (86.7%), and School Wellness Policy environments (92.0%). The lowest degree of agreement was observed in the School Food Environment (62.7%). Twenty-seven of the 35 items had levels of agreement that reached or exceeded 70%. Figure 1 displays the percent agreement for each of the construct variables established to observe specific domains of the school environment.
The PABAK statistic was employed for 17 of the 35 items that exceeded 75% agreement between reporters; no items had less than 25% agreement. Overall, the results from the k/PABAK analysis revealed that 23 of the 35 items demonstrated fair to almost perfect agreement, 2 items demonstrated slight agreement, and 10 items demonstrated poor agreement. Generally, items relating to PA had a higher prevalence of fair or better agreement (79.2%) compared to the school food environment items (36.4%), which is consistent with the percent agreement results. Table 4 provides the mean percent agreement and k/PABAK statistics for each of the 35 items observed.
This study provides novel insights regarding the accuracy of school-reported PA and nutrition environment information. The results of the current study demonstrate that school health leaders in the study schools provide reasonably accurate information when reporting on their school PA and nutrition environments when using direct observation as the criterion measure. Considering greater emphasis is being placed on schools to evaluate the status and progress made over time on wellness policy/implementation through the USDA Final Rule, documentation, and evidence supporting the accuracy of school-reported wellness data provided herein is warranted.
The results from the current study are similar to those reported by Nathan et al. (2013) that explored the accuracy to which principals in Australian primary schools were able to self-report information on school nutrition and PA environments via a phone-based survey compared to direct observation. Both studies found that school reporters (school wellness leaders in the present study, principals in the latter) were able to report on school wellness environments with an acceptable degree of accuracy (i.e., ≥70%). In addition, both studies found that school reporters responded more accurately about PA environments compared to nutrition environments. Although the cafeteria settings in the United States and Australia possess fundamental differences, both serve or offer a wide variety of food that changes regularly/daily. This may influence the information recorded by observers/evaluators, particularly if lunchroom environments are evaluated on different days. The lower level of agreement among the nutrition items could also be indicative of an element of subjectivity within the nutrition items used in the present study. A study by Pikora et al. (2002) exploring neighborhood walking and cycling environments reported that items that were the most subjective had the lowest level of agreement. This highlights the importance of ensuring that objective items and clear instructions are used to evaluate school wellness environments.
Previous research has utilized survey or objective measures of school environments to examine their associations with student behaviors. One such study by Jones et al. (2010) developed and tested an objective audit tool to assess six domains of external school grounds and the corresponding association(s) to student PA (active commuting, activity at lunch) in UK primary schools. The findings indicated that students in schools within the highest quintile groups for walking and cycling environments were more active than those in the lowest quintile groups. Another study by Lanningham-Foster et al. (2008) reported that children were significantly more active in activity-permissive classroom environments (including standing desks, mobile whiteboards, and active lessons) compared to traditional classroom environments. In addition, a systematic mixedmethods review by Morton et al. (2016) reported that school policies, physical structures/facilities, and teacher practices are all factors in school environments that significantly influence student wellness behaviors in schools. Considering the impact of school environments on students’ lifestyle behaviors, it is important to continue to develop an understanding of how well school wellness leaders can report this information in evaluations. It would also be useful to establish a standardized assessment tool that is well-validated which schools could universally employ to evaluate their school wellness environments.
Despite meaningful findings, the current study is not without limitations. First, the study includes a relatively small sample of schools. Although a limitation, this made it feasible to employ direct observation, a criterion measure, to validate school wellness leaders’ abilities to report on their wellness environments accurately. Another limitation is that the sample of schools is homogenous and generalizing results to other areas outside of the Midwest should be conducted with caution. Although the student body was homogenous, this may be less impactful in the current study as this homogeneity may be less influential on a school’s infrastructure and a staff member’s ability to evaluate the infrastructure. In addition, the study was conducted only in elementary school (or “community school”) environments. Thus, separate studies in middle school and high school environments would be necessary to explore the accuracy of school reporters in those settings. However, the items developed for the current study were specifically intended to be used in elementary school settings. Next, the study was limited to the first 10 schools that enrolled using a mass communication recruitment strategy. It is possible that schools with the most motivation, best school wellness environment settings, or most active wellness team structures enrolled first. However, the participating schools were geographically distributed across the state and had comparable demographic characteristics compared to other schools in the state. Future studies using a more distributed enrollment process to capture a random sample of schools are necessary. Finally, the focus of the nutrition environment in the current study was limited to the lunchroom setting. Future evaluations of the accuracy of school-reported information should consider the school food environment more broadly (i.e., nutrition practices within the school outside of the lunchroom).
School nurses are looked to as key figures and role models for student (Walker, 2014) and staff (Wood et al., 2019) health and wellness and are in ideal positions to assist school leaders with facilitating evaluations of school wellness environments and policies. While factors associated with school nurses, such as their limited time and availability, have been identified as challenges to implementing school wellness policies and practices (Cygan et al., 2019), the results of this study provides schools with a concise set of 35 items that can be adopted and feasibly implemented to facilitate evaluation of the overall school wellness environments and policies with reasonable accuracy. Considering school nurses work within and across multiple sectors of the school environment, they can be key advocates for completing school-wide assessments of wellness environments and policies and doing so accurately. Further, school nurses are well equipped to contribute to the utilization of the wellness information collected in such assessments to identify and advocate for programming to address local school wellness-related needs.
This study identified that school wellness leaders self-report reasonably accurate estimates of their school wellness environments in practice. Given that the information reported by schools is used as a summative assessment to outline the current status of wellness and to comply with the federally mandated USDA Final Rule requirements, it is important that the information is reflective of the true school environment. In addition, the study provides novel insights into the utility of the school-reported information to be used as intended within the Final Rule as a formative assessment (e.g., needs assessment) to help guide school wellness planning and future goals. It may be beneficial to establish a tool to help facilitate school wellness leaders’ evaluation of the relevant factors for being compliant with the USDA Final Rule and that facilitates wellness planning and goal setting. Doing so can empower schools to take proactive steps to implement changes to their school wellness environment to improve students’ health behaviors, and to evaluate whether ongoing programming or interventions are having the desired effects.
This study was completed as part of a doctoral dissertation project at Iowa State University.
The authors would like to acknowledge and thank Dr. Lorraine Lanningham-Foster, Dr. Spyridoula Vazou, and Dr. Phillip Dixon for their guidance and contributions to this work. (Withdrew for blinding purposes during review process.)
Dr. Joey A. Lee was the/a primary contributor to the conceptualization, data curation, formal analysis, investigation, methodology, project administration, roles/writing—original draft, and writing—review and editing. Dr. Gabriella M. McLoughlin contributed to formal analysis, writing—review and editing. Dr. Greg J. Welk contributed to conceptualization, formal analysis, methodology, supervision, roles/writing—original draft, and writing—review and editing.
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
The author(s) received no financial support for the research, authorship, and/or publication of this article.
Joey A. Lee, PhD https://orcid.org/0000-0001-5890-6591
Supplemental material for this article is available online.
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Joey A. Lee, PhD, is an assistant professor in the Department of Health Sciences at the University of Colorado Colorado Springs in Colorado Springs, CO.
Gabriella M. McLoughlin, PhD, is a postdoctoral fellow in the Department of Kinesiology at Iowa State University in Ames, IA.
Gregory J. Welk, PhD, is a full professor in the Department of Kinesiology at Iowa State University in Ames, IA.
1 University of Colorado Colorado Springs, CO, USA
2 Iowa State University, Ames, IA, USA
Corresponding Author:Joey A. Lee, PhD, University of Colorado Colorado Springs, 1420 Austin Bluffs Pkwy., Colorado Springs, CO 80918, USA.Email: jlee29@uccs.edu
