Abstract
Objective:
In the present study, we sought to determine if a comprehensive school physical activity program (CSPAP) delivered using the Be a Champion! (BAC) framework was effective in increasing moderate-to-vigorous physical activity (MVPA) and decreasing sedentary time in elementary school youth.
Methods:
We implemented a CSPAP in 3 elementary schools to determine its effectiveness to youth behaviors compared to 2 control schools. Youth physical activity was assessed via accelerometry in spring 2015 and spring 2016 during school hours on school days. Implementation of the BAC components and youth behavior was assessed through direct observation from fall 2015 through winter 2016.
Results:
In a multilevel, mixed model examining the effects of intervention, we found no statistically significant effect of the intervention on overall MVPA. However, a significant increase in MVPA was observed among girls (but not boys) in the intervention schools relative to controls. No differences in sedentary behaviors were observed by group.
Conclusion:
CSPAP implementation may be effective in reducing sedentary time and increasing MVPA in girls, but not boys. Research is necessary to increase implementation dose and fidelity to best practices in physical activity promotion.
Keywords: moderate-to-vigorous physical activity, sedentary, school health, elementary school
Children are insufficiently physically active, and schools have been targeted as a setting where opportunities can be provided for physical activity and strategies can be implemented to reduce time spent sedentary.1 To address this priority, comprehensive school physical activity programs (CSPAPs) have been developed.2 A CSPAP is a 5-component approach, which includes physical activity (PA) and sedentary break opportunities before, during, and after school.3,4 Specifically, the 5 CSPAP components target: (1) quality of physical education (PE) classes, (2) PA in academic classes and recess, (3) PA before and after school, (4) staff involvement, and (5) family and community engagement. Despite a wealth of promising CSPAP materials, few evidence-based, effective strategies for implementation of CSPAPs exist.5 Building upon our previous work, best practices, and implementation science, we developed the Be a Champion! (BAC) implementation and evaluation framework.6
BAC is a framework for the implementation and evaluation of a CSPAP, designed to streamline planning, delivery, and implementation for school practitioners and standardize evaluation for researchers.6 BAC is coordinated by a district-level liaison who provides training, resources, and technical assistance to local implementation teams led by PA Champions. The BAC liaison aids the school Champion in the school assessment, planning, implementation, and monitoring of the CSPAP, which is informed by existing CSPAP strategies to promote positive changes in moderate to vigorous PA (MVPA) and sedentary behavior among youth. The present study sought to determine if a CSPAP delivered using the BAC framework was effective in increasing MVPA and decreasing sedentary time in a sample of elementary school youth. We hypothesize that youth attending intervention schools would exhibit higher levels of moderate-to-vigorous physical activity (MVPA) following the intervention periods than youth attending control schools. Secondarily, we sought to determine if expected changes were observed in school activities and staff behaviors during implementation of the CSPAP at the school level.
METHODS
Setting and participants
Five elementary schools located in a rural school district serving youth in the 2nd-5th grades were recruited to participate from a larger population of 11 eligible elementary schools. Following a presentation to the principals, 6 of the 11 elementary schools expressed interest. Upon further discussion, one principal decided that school capacity (ie, a number of unfilled administrative positions) prohibited participation, leaving a sample of 5 schools. The participating schools served a racially diverse population (> 30% non-white), and a majority of the students were eligible for free and reduced lunch. Following baseline data collection, schools were randomized to intervention (N = 3) or control (N = 2) conditions using a random number generator. Parents of the participating children provided informed consent via an information packet sent home to all families, while children provided assent prior to providing data.
Intervention
Table 1 shows the phases and activities comprising the intervention. In fall of 2015, a Champion was identified by the principal at each of the 3 intervention schools. There were no restrictions placed upon who could be the school Champion. A half-day workshop was conducted by a research staff liaison with prior formal training in CSPAP implementation to introduce the Champion to the project, process, and expectations of their role. Once trained and given tools to assess the school and build their team, Champions compiled an implementation team comprised of 4 or 5 persons and completed a school assessment with the team. The assessment included a survey sent to all school staff (ie, administrative, clerical, faculty) and an environmental and policy assessment tool (the School Physical Activity Policy Assessment7). Assessment results were compiled by the liaison and a second half-day workshop was conducted by the same liaison with the teams (separately) to develop action plans, strategies, and match resources with needs. Over the following 4 months, the liaison worked with the teams to execute the plans. More information concerning the intervention can be found elsewhere.6
Table 1.
Phases of Be a Champion!
| In-service training I | • Skills training related to school needs assessment. • Provision of tools for school assessments • Education components related to: Comprehensive School Physical Activity Program (CSPAP). Existing evidence-based best practices for the promotion of PA. |
| School needs assessment | • Environmental audit of school grounds related to PA opportunities. • Policy audit related to PA opportunities (eg, PE, recess, in-class, before/after school) • Survey of school staff to assess knowledge, skills, and attitudes towards PA promotion. |
| In-service training II | • Skills training related to implementation of evidence-based best practices. • Education components related to: Policy development for school settings Implementation of programmatic elements. |
| Identification of best practices implementation | • Development of “menu” of potential evidence-based best practices. • Identification and prioritization of strategies for implementation. • Implementation of identified strategies with emphasis on selecting one strategy each at the policy/environmental and programmatic levels. |
Instrumentation
Process evaluation which included implementation monitoring was employed to capture changes at the organizational and individual levels that occur as part of a CSPAP across all segments of the school day (ie, before/after school, classroom, physical education, recess). Setting specific systematic observation tools were utilized including the System for Observing Play and Leisure Activities (SOPLAY),8 the System for Observing Staff Promotion of Activity and Nutrition (SOSPAN),9 and the System for Observing Student Movement in Academic Routines and Transitions (SOSMART).10 SOPLAY was used to capture youth PA by intensity in all settings, while SOSPAN recorded staff/teacher behaviors related to the promotion of PA before/after school, in PE, and in recess. SOSMART similarly captured staff/teacher behaviors related to the promotion of PA in the classroom setting. More information regarding the implementation monitoring and evaluation can be found elsewhere.11 In brief, a list of segments (eg, before school, recess, classroom by teacher) by school was compiled, and a schedule was developed where each school (randomly ordered) would be assessed each month. Segments within schools were observed in a randomly determined order (ie, the order that classrooms were assessed was determined using a random number table). Observations were conducted in all settings in all schools at least one in the fall and spring semester.
Youth physical activity was assessed via accelerometry in 2nd and 4th grade children (spring of 2015; prior to the intervention) and 2nd, 3rd, 4th, and 5th grade children (spring of 2016), with participating youth asked to wear accelerometers for 7 consecutive days. Fifty participants per school were selected using simple random sampling to wear the accelerometer each year. The primary outcome variables of interest were minutes of moderate-to-vigorous PA (MVPA) and minutes spent sedentary by students during time spent on school grounds. MVPA and sedentary time was obtained using ActiGraph GT3X+ Triaxial Activity Monitors. Data were collected during April and May of the spring semester each year. During each 7-day data collection period, these accelerometers were affixed to a belt and worn on the subject’s right hip. Participants were instructed to wear the monitor continuously over the next 7 days except during bathing and sleeping. The accelerometers were initialized and set to record beginning at 5:00 am the day following their dispersal (Monday-Thursday). The accelerometers were set to collect data in “raw” format and analyzed by converting the data to one-second epochs. Movement counts were converted using count thresholds established by Evenson et al12 to determine time spent in sedentary, light, moderate, and vigorous PA. Non-wear was determined by 60 minutes of continuous zero counts with a tolerance of 2 minutes of non-zeros per hour.
Data Analysis
Changes over time in the direct observation data that included percent of children engaged in MVPA, sedentary, and teacher behaviors were examined using multilevel mixed effects linear regression (ie, expressed as the mean percentage of children engaged in behaviors, and the percentage of scans teacher behaviors were observed). Models were run separately for scans completed during physical education, recess, afterschool, during class time, and for boys and girls. Separate multilevel models were used to examine our main outcomes of interest, minutes per school day of MVPA, and sedentary behavior among the students from year 1 (2015) to year 2 (2016). For these analyses, we restricted our data to weekdays (Monday-Friday) only, and to hours from 8:00am through 4:59pm. We then limited our data to valid days (days where 7 or more hours of activity were recorded with the accelerometer from 8:00am and 4:59 pm). Students had to have at least 2 valid weekdays of data to remain in our analyses. We computed minutes of moderate/vigorous activities per day as well as minutes of sedentary behavior per day. Our final data set for these analyses included 1487 valid weekdays pertaining to a total of 380 students (across the 5 schools and 2 years.) We modeled minutes of MVPA (and of sedentary behavior) as a function of intervention condition (intervention vs control), year (pre-intervention 2015 vs post-intervention 2016), age in years, sex, race (white, black, other), the first-order interaction between condition and year, and the second-order interactions between race, condition, and year and sex, condition, and year. Our hypothesis was that we would see more improvement from 2015 to 2016 among the students in the intervention condition (ie, we would see a significant intervention*year interaction term.) Models were estimated using PROC MIXED in SAS.
RESULTS
Across the 5 schools, approximately 25% of the students’ parents provided consent to participate. Participating students were 58% white, 30% black, and 12% other races in 2015, and 63% white, 27% black, and 10% other races in 2016. Participants were 53% girls in 2015 and 61% girls in 2016. Students included in the analytical sample were not significantly different demographically than those not providing data. Review of implementation teams and school action plans indicated considerable variability in the composition of the teams and the plan components by school. For example, one intervention school emphasized family fun nights and fun runs over changes to physical education curriculum, although some emphasis was placed on pre-packaged classroom activities (eg, activity videos). Another interention school focused on morning activities and improving activity at recess. The third intevention school focused on improving their activity offerings during physical education, integrating pre-recorded activity videos in the classroom, and increasing the time spent in recess. Little overlap in the action plans existed between schools. Accordingly, implementation monitoring results utilizing direct observation tools suggested non-significant differences by group that favored the intervention, but these differences did not increase over time (Table 2). Over the complete school day, a statistically non-significant difference in the percentage of boys and girls engaged in MVPA was observed between intervention and control schools, with higher levels in the control schools. These differences changed significantly over time by group, favoring the control schools. Similarly, fewer boys and girls were observed being sedentary in the control schools, but not significantly lower than seen in the intervention schools. These differences didn’t change significantly over time by group. No differences were seen in the percentage of children engaged in MVPA or sedentary behaviors in PE class between groups, nor did the instructional practices differ. A greater percentage of boys and girls in intervention schools were observed engaging in MVPA during recess and in PE class, but these differences in MVPA did not increase over time by group. During the after-school period, a greater percentage of boys and girls in the intervention schools were active than those observed in the control schools. However, the intervention schools displayed a considerable decline in the percentage of boys and girls who were active, indicative of a significant time by group interaction. Similarly, a smaller percentage of boys and girls in intervention schools were observed engaging in sedentary behaviors during recess, during the after-school period, and in the classroom. These differences did not change significantly over time by group. Few changes in teacher/staff behaviors were observed between intervention and control schools, and none achieve statistical significance (data not shown).
Table 2.
Percent of Children Observed in Sedentary and MVPA by Intervention Group and Setting
| Boys | 2015 | 2016 | Δ | Group-by-time Interaction | 95% CI | ||
| Sedentary | All Settings | Intervention | 15.8 | 11.5 | −4.3 | −0.2 | (−4.4, 4.1) |
| Control | 10.0 | 5.9 | −4.1 | ||||
| Class | Intervention | 91.3 | 86.4 | −4.9 | −1.7 | (−6.7, 3.2) | |
| Control | 93.6 | 90.4 | −3.2 | ||||
| Recess | Intervention | 19.1 | 17.7 | −1.4 | 13.5 | (−2.6, 29.6) | |
| Control | 37.3 | 22.3 | −15.0 | ||||
| PE | Intervention | 32.8 | 33.9 | 1.1 | −2.5 | (−14.9, 9.9) | |
| Control | 34.1 | 37.8 | 3.6 | ||||
| ASP | Intervention | 26.7 | 23.9 | −2.8 | 0.2 | (−22.0, 22.3) | |
| Control | 33.8 | 30.9 | −3.0 | ||||
| MVPA | All Settings | Intervention | 60.2 | 54.0 | −6.2 | −7.5 | (−14.1, −0.9) |
| Control | 69.5 | 70.9 | 1.3 | ||||
| Class | Intervention | 1.4 | 1.0 | −0.4 | −1.3 | (−3.2, 0.6) | |
| Control | 0.0 | 0.9 | 0.9 | ||||
| Recess | Intervention | 16.6 | 16.0 | −0.6 | −0.1 | (−10.9, 10.8) | |
| Control | 6.6 | 6.1 | −0.5 | ||||
| PE | Intervention | 29.9 | 21.4 | −8.5 | 2.2 | (−9.7, 14.1) | |
| Control | 27.5 | 16.8 | −10.7 | ||||
| ASP | Intervention | 41.1 | 9.5 | −31.6 | −33.6 | (−56.5, −10.8) | |
| Control | 11.0 | 13.1 | 2.1 | ||||
| Girls | 2015 | 2016 | Δ | Group-by-time Interaction | 95% CI | ||
| Sedentary | All Settings | Intervention | 14.7 | 11.2 | −3.5 | 0.6 | (−3.5, 4.8) |
| Control | 9.0 | 4.9 | −4.1 | ||||
| Class | Intervention | 91.1 | 85.2 | −5.9 | −3.6 | (−8.2, 1.1) | |
| Control | 94.9 | 92.5 | −2.4 | ||||
| Recess | Intervention | 34.2 | 42.1 | 7.9 | 8.7 | (−9.1, 26.5) | |
| Control | 47.1 | 46.3 | −0.8 | ||||
| PE | Intervention | 36.3 | 35.9 | −0.4 | −2.1 | (−14.7, 10.6) | |
| Control | 36.4 | 38.1 | 1.7 | ||||
| ASP | Intervention | 45.1 | 26.9 | −18.2 | 1.8 | (−26.1, 29.8) | |
| Control | 65.6 | 45.6 | −20.0 | ||||
| MVPA | All Settings | Intervention | 63.7 | 57.0 | −6.8 | −9.4 | (−15.6, −3.1) |
| Control | 73.1 | 75.7 | 2.6 | ||||
| Class | Intervention | 1.4 | 1.0 | −0.4 | −1.2 | (−3.0, 0.6) | |
| Control | 0.0 | 0.8 | 0.8 | ||||
| Recess | Intervention | 16.8 | 14.2 | −2.6 | −1.4 | (−12.8, 9.9) | |
| Control | 6.5 | 5.4 | −1.1 | ||||
| PE | Intervention | 29.7 | 22.1 | −7.6 | 5.8 | (−6.4, 17.9) | |
| Control | 26.6 | 13.2 | −13.4 | ||||
| ASP | Intervention | 29.8 | 6.3 | −23.6 | −24.7 | (−46.5, −2.8) | |
| Control | 8.0 | 9.1 | 1.1 | ||||
Note.
Bold text indicates significant interactions at p < .05. PE = Physical Education. ASP = After School Program
At least 2 school days of accelerometer data were available for 162 participants in 2015 and 218 participants in 2016. In a multilevel, mixed model examining the effects of intervention (yes/no) and year (pre vs post intervention) in the 5 schools, we found no significant overall effect of the intervention on MVPA (p-value for intervention group*year intervention = 0.27.) However, we found a statistically significant second-order interaction (p = .016) between condition, year, and sex. Table 3 shows the means illustrating this interaction. Among boys, the intervention and control groups both showed similarly higher levels of average minutes of daily MVPA in year 2 compared to year 1 (ie, no effect of intervention among boys). Among girls, however, there is evidence suggestive of a intervention effect: while both groups of girls, intervention and control, had higher average MVPA in year 2 compared to year 1, the increase among girls in the intervention group was larger. On average, across all conditions and years, boys were more active than girls, and black children were more active than white children and children of other races/ethnicities. Additionally, among our sample of students in grades 2–5, age was significantly negatively associated with levels of MVPA.
Table 3.
Least Squares Mean Estimates (SE) of Daily Average Moderate-to-Vigorous Physical Activity (MVPA) and Sedentary Time by Year and Sex
| Boys | Girls | ||||
|---|---|---|---|---|---|
| 2015 (N = 84) | 2016 (N = 78) | 2015 (N = 94) | 2016 (N = 124) | ||
| MVPA | |||||
| Intervention | 30.7 (3.4) | 40.4 (2.6) | 18.8 (3.3)* | 37.1 (2.7)* | |
| Control | 29.4 (3.6) | 42.3 (3.3) | 28.9 (3.8)* | 35.3 (3.5)* | |
| Sedentary | |||||
| Intervention | 401.1 (9.3) | 362.0 (7.2) | 422.8 (9.2) | 364.2 (7.6) | |
| Control | 400.3 (9.9) | 350.9 (9.1) | 401.3 (10.5) | 358.4 (9.6) | |
Note.
Time by group interactions are significant at p < .05.
Considering the outcome of average daily minutes of sedentary behavior (Table 3), we found a statistically significant (p = .007) main effect of year (both intervention and control groups were lower on average minutes of sedentary behavior in year 2 compared to year 1), of race (p = .018; black children had lower levels of sedentary behavior than the other 2 race/ethnicity groups across conditions and years), and age (p < .001); older age students were more sedentary than younger students). We did not find evidence of a intervention*time interaction (p-value for interaction term between intervention group and year = .80), nor were any second-order interaction terms with intervention and time that we examined statistically significant. Finally, we note that there is often more than one way to model repeated measures data. Our findings reported in Table 3 were robust to different modeling parameterizations (eg, accounting for school nested within intervention; including grade rather than age in the model).
DISCUSSION
Our results suggest that the BAC implementation framework shows promise for improving the effectiveness of CSPAP implementation relative to previous studies with similar CSPAP approaches.13 However, the intervention was only effective in increasing accelerometer derived MVPA in girls relative to controls. While boys did engage in greater amounts of MVPA at post-test relative to baseline, there were non-significant differences between the intervention and control groups. Whereas none of the school-based Champions indicated in their action planning that they were specifically targeting girls’ activity levels, the observed effects could be a function of the low baseline levels of MVPA observed among girls (eg, more room for improvement). Concurrently, the boys in the current study exhibited relatively high levels of MVPA compared to those observed in school-level interventions, resulting in a ceiling effect for the intervention. As such, the girls in the present study may have had a greater possibility of improving through the provision of more physical activity opportunities and encouragement. This result is promising in light of previous studies that have specifically targeted physical activity in girls with mixed results. For example, the Trial of Activity for Adolescent Girls (TAAG) was modestly successful in targeting physical activity in middle school girls (6th-8th grades), producing an increase of 1.6 minutes of daily MVPA in the 8th grade year.14 Similarly, the Lifestyle Education for Activity Program (LEAP) sought to elicit changes in physical activity among 9th grade girls.15 LEAP was successful in promoting more vigorous physical activity in girls attending schools that fully implemented and maintained the intervention. Importantly, both of these studies targeted older girls (6th-12th grade) at a time where physical activity is rapidly falling (middle school)16 or when other more sedentary leisure activities have been identified.17 The results of LEAP highlight the need to rigorously monitor the implementation of the intervention to discern differences by school, which was beyond the scope of the present study.
Despite increases in accelerometer derived MVPA, differences or changes in teacher/staff behaviors between intervention and control schools were not observed. This lack of effect is consistent with previous literature which suggests that formal classroom teacher training is necessary to increase teacher self-efficacy and classroom activity break implementation.18 Interestingly, there was incongruence between the direct observation data and those obtained via accelerometry. For example, the direct observation data suggests that the children in the intervention schools were more active and less sedentary in a number of settings (eg, recess, after-school), but these results were not reflected in the accelerometer data. This incongruence may be partially explained by the fact that the direct observation data were not collected at the same time as the accelerometer data. Accelerometer data were collected in spring preceding the intervention year and in spring of the intervention year, while direct observation data were collected fall to spring of the intervention year (but not during the accelerometer collection period). Another possibility is that direct observation may be more sensitive to movement below the MVPA threshold.
Despite having assessed the policy environment at the school during the first phase of the project, none of the intervention schools attempted to impact school policy. This lack of a policy-level approach is important because it has been reported that written district policies with stronger wording supports implementation of evidence-based practices in schools.19 Similarly, the Champions and their teams acted independently of study staff who simply provided training and technical assistance. Other interventions with more robust improvements in daily physical activity have employed more resource-intensive approaches, which may be required to elicit significant changes in physical activity behaviors.20
Our study has a number of strengths, including the utilization of validated direct observation tools, accelerometry, and a randomized design. However, the results should be considered in light of several limitations. For example, the schools were provided flexibility to choose strategies, tailor strategies, and implement strategies in a manner that best suited their school and available resources, and as such, we were unable to assess the fidelity of the intervention strategies. This flexibility necessitated that there were no required elements for the school plans to allow for autonomy in tailoring at the school level. The consequence of this flexibility was an inability to assess fidelity consistently across schools in the present study. In future research, it could be possible to design a school-specific fidelity assessment that would allow some comparability across sites and permit evaluators to explore links between fidelity and effectiveness. Unfortunately, that was beyond the scope of the resources allocated the current pilot project. Related, staffing limitations affected the number of days that each school and segment of the day could be observed. As such, the number of scans by segment of the day (eg, classroom, recess) were low relative to other studies using direct observation, which can affect the validity of the data. In addition, the observation period was relatively short (one year) to observe meaningful changes in school policies or staff practices. Relatedly, lack of compliance with accelerometer wear time recommendations necessitated expanding our analyses to those students with at least 2 days of data rather than the typical 3, which might provide less stable estimates of their behaviors. Related, the statistically significant second-order interactions should be interpreted with caution due to sample size limitations. Some of the objectives included in the school Champions’ action plans were ambitious and may require more time or resources to be implemented. For example, implementation of classroom activity breaks can be time intensive, which would explain our lack of an observed effect on staff or student behavior. Additionally, the flexibility of the BAC process may have introduced some ambiguity in the action-planning stage which could have limited effectiveness. However, we did observe an overall increase in MVPA among girls in our study, which is promising in the context of our previous research in this area.13 Finally, while policy and environmental assessments were conducted by the Champion teams, these data were not collected by the evaluation team, so those characteristics could not be compared across settings in the current study. Relatedly, the small number of schools in the study prevented us from statistically accounting for the clustering of students within schools. Future large-scale studies could control for these features in the analyses.
IMPLICATIONS FOR HEALTH BEHAVIOR OR POLICY
Our data suggest that BAC! can be effective in aiding the schools in the planning and implementation process but highlight a lack of effectiveness to change teacher/staff and youth behaviors. Our experience highlights the challenges in implementing a whole-of-school approach for physical activity promotion and the challenges of implementing a CSPAP in an elementary school setting. Despite these limitations, BAC! shows promise as a framework for the implementation and evaluation of a CSPAP, but refinements are necessary to increase the effectiveness for behavioral outcomes.
Future studies should better capture the intervention activities and fidelity of these activities relative to optimal implementation of the CSPAP model.
Teacher engagement should be a priority of future implementations of the CSPAP model in schools.
The connection between CSPAP policies, teacher/staff behaviors, and the impact on youth physical activity behaviors need to be enhanced if the Healthy People 2030 objective of increasing the proportion of children who meet the current aerobic physical activity guideline is to be met.
Acknowledgements
This research was funded by the National Heart, Lung, and Blood Institute of the National Institutes of Health (NIH) under award number R21HL121692. The content is solely the responsibility of the authors and does not represent the official views of the NIH. This study is registered in ClinicalTrials.gov under number NCT02465372. The authors would like to thank the administration, staff, teachers, students, and parents of the Kershaw County (SC, USA) School District for their participation.
Footnotes
Human Subjects Approval Statement
All procedures were approved by the institutional review boards of the Wake Forest School of Medicine and the University of South Carolina.
Conflict of Interest Disclosure Statement
All authors of this article declare they have no conflicts of interest.
Contributor Information
Justin B. Moore, Department of Implementation Science, Wake Forest School of Medicine, Winston-Salem, NC, United States.
R. Glenn Weaver, Department of Exercise Science, Arnold School of Public Health, University of South Carolina, Columbia, SC, United States.
Beverly J. Levine, Department of Social Sciences and Health Policy, Wake Forest School of Medicine, Winston-Salem, NC, United States.
Camelia R. Singletary, Department of Implementation Science, Wake Forest School of Medicine, Winston-Salem, NC, United States.
Russell L. Carson, Research and Health & Wellness Advisor, PlayCore, Chattanooga, TN, United States.
Michael W. Beets, Department of Exercise Science, Arnold School of Public Health, University of South Carolina, Columbia, SC, United States.
Darla M. Castelli, Department of Kinesiology & Health Education, University of Texas at Austin, Austin, TX, United States.
Aaron Beighle, Department of Kinesiology & Health Promotion, University of Kentucky, Lexington, KY, United States.
Russell R. Pate, Department of Exercise Science, Arnold School of Public Health, University of South Carolina, Columbia, SC, United States.
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