J Community Health DOI 10.1007/s10900-015-0004-6
ORIGINAL PAPER
Assessment of Body Mass Index, Sugar Sweetened Beverage Intake and Time Spent in Physical Activity of American Indian Children in Oklahoma Michelle E. Dennison • Susan B. Sisson • Karina Lora • Lancer D. Stephens • Kenneth C. Copeland • Cynthia Caudillo
Ó Springer Science+Business Media New York 2015
Abstract American Indian (AI) children have a combined overweight and obesity prevalence of 53 %. Behaviors that contribute to obesity, such as sugar sweetened beverage (SSB) intake and time spent in physical activity (PA), have been poorly explored in this population. The purpose of this study is to report body mass index (BMI), SSB intake, and time spent in PA of 7-to-13-year-old AI children who reside in rural and urban areas in Oklahoma. Cross-sectional survey study. Self-reported SSB intake in the last month, and time spent in PA were collected via questionnaires. Height and weight were professionally measured. The sample included 124 7-to-13-year-old AI children who attended a diabetes prevention summer camp in 2013. BMI percentile, overweight and obesity prevalence, SSB intake, time spent in PA, and number of participants meeting the Physical Activity Guidelines for Americans. Descriptive characteristics for BMI percentile,
M. E. Dennison S. B. Sisson (&) K. Lora C. Caudillo Department of Nutritional Sciences, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA e-mail: [email protected] M. E. Dennison C. Caudillo Oklahoma City Indian Clinic, Oklahoma City, OK, USA L. D. Stephens Oklahoma Shared Clinical and Translational Resources, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA K. C. Copeland Department of Pediatrics, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA C. Caudillo Native Youth Preventing Diabetes Coalition, Oklahoma City, OK, USA
overweight and obesity, SSB intake, time spent in PA, and meeting PA recommendations were calculated using means, standard deviations, and frequencies. Independent t test and Chi square analyses were used to test for gender differences. Participants were 10.2 ± 1.5 years old and 57 % female. Sixty-three percent were overweight or obese. Children consumed 309 ± 309 kcal/day of SSB and spent 4.4 ± 3.8 h per week in moderate-to-vigorous PA. Approximately 32 % met the 2008 Physical Activity Guidelines for Americans. No gender differences were observed. The prevalence of overweight and obesity was higher than previously reported in a similar population, and higher than that of US children in the general population. SSB intake and physical activity levels were also found to be higher in this group than in the general population. Keywords SSB Exercise Pre-adolescent Native American BMI
Introduction Over the last 19 years, the rates of overweight [85–94th body mass index (BMI) percentile] and obesity (C95th BMI percentile) [1] for 6-to-11-year-old US children have increased to 15 % overweight and 18 % obese [2]. This is in despite of recent reports that pediatric overweight and obesity rates have stabilized in the general population [3]. American Indian (AI) children have higher rates of overweight (20 %) and obesity (25 %) [4] than the national average. Increases in obesity prevalence have been reported in some age groups [5]. This places AI children at elevated risk of obesity-related diseases, such as hypertension, dyslipidemia, and type 2 diabetes [6], and has a pronounced impact in states with large AI populations,
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such as Oklahoma. Oklahoma has the third highest number of AI residents per capita [7], making up 9 % of the state’s population [8]. Oklahoma is 44th in the nation for overall state health ranking [9]. The etiology of overweight and obesity is complex and includes genetics, environmental contributors, and personal behavioral choices [10]. Obesogenic behaviors are defined as behaviors that contribute to the development of overweight and obesity [11], and include excessive caloric intake and inadequate physical activity (PA). Reedy and Krebs-Smith report that sugar sweetened beverages (SSB) are the largest source of added sugars in the diets of US youth [12]. SSB have generally been defined as beverages sweetened with a caloric sweetener, like sugar or high fructose corn syrup [13]. Examples of SSB include sports drinks, energy drinks, regular soda, sweetened tea, and non-fruit juice drinks [13]. However, a review of the literature shows that the SSB included in a given study are not always consistent, thus introducing potential variability when comparing studies. Han and Powell defined SSB as any beverage containing added sugars, and found that 2-to-11-year-old US children and 12-to-19-year-old US adolescents consume 178 and 286 kcal of SSB per day, respectively [14]. Another study, in which SSB were limited to non-diet soda, sweetened fruit drinks, and sweetened iced tea, reported that schoolage children were 1.6 times more likely to be classified as obese for every additional 12 oz of SSB consumed/day [15]. LaRowe et al. [16] reported that overweight/obese 2-to-5-year-old AI children consumed 51 % more SSB than their normal weight AI counterparts. It is clear that SSB contribute to excessive caloric intake. However, energy intake is only part of the energy balance equation. A discussion of behaviors associated with obesity would be remiss if it did not address the role of energy expenditure. Human energy expenditure includes the energy required to sustain life and bodily functions, as well as the energy expended during PA, such as exercise and non-exercise activity thermogenesis [17]. The energy expended to maintain normal bodily functions is relatively fixed. Thus, PA accumulated through volitional exercise and non-exercise activity thermogenesis become target areas for increasing energy expenditure and preventing excessive weight gain [18]. The 2008 Physical Activity Guidelines for Americans state that children who engage in more than 60 min of moderate or vigorous PA per day have a greater chance of living a healthy life that will last into adulthood [19]. According to data from the 2009–2010 National Health and Nutrition Examination Survey, parents reported that 70 % of 6-to-11-year-old children met those recommendations [20]. However, a 2010 report examining PA of 5-to-19-year-old AI children living in the northern plains of the US reported that only 27 % of children met the same
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recommendations [21]. Few studies have examined obesogenic behaviors in AI children who reside in the US Southern Plains, including those who reside in Oklahoma. Overweight and obesity prevention is desperately needed in health disparate minority groups, given the high prevalence of these conditions in the children of this region. However, before we can create interventions that target AI youth, we need a greater understanding of their obesogenic behaviors, including SSB intake and time in PA. To our knowledge, no study has examined the levels of SSB intake and PA behaviors in healthy AI children living in Oklahoma. The purpose of this study is to report body mass index (BMI), SSB intake, and time spent in PA of 7-to-13-year-old AI children who reside in Oklahoma.
Methods Study Design and Participants This was a cross-sectional, field-based study that included 124 7-to-13-year-old AI children who attended the annual Native Youth Preventing Diabetes summer camp in June 2013. Participants resided in areas spanning across the state of Oklahoma, which has 38 federally-recognized tribes headquartered within its borders. This study was approved by the Institutional Review Board of the University of Oklahoma Health Sciences Center. The Native Youth Preventing Diabetes coalition, consisting of 15 different tribes, approved the project. Upon arrival at camp, children were invited to complete the study questionnaires and provided assent at this time. Trained camp staff were readily available to assist beginning readers and to answer any questions. Survey instruments included an abbreviated version of the previously validated Beverage Questionnaire (BEVQ-15) [22] to assess SSB intake and an abbreviated version of a survey used by Project EAT [23] to assess PA. Of the 141 participants, one was excluded for being outside the targeted age range, and 16 were excluded for providing incomplete data, leaving a final analytical sample size of 124.
Measures Anthropometric Measurements Anthropometric measurements were conducted by a registered nurse or registered dietitian nutritionist. On the day of measurements, participants wore light clothing (shorts and a t-shirt) and were asked to remove their shoes before being weighed using the Tanita TBF-310 Body Composition Analyzer (Tanita Corporation, Arlington Heights, Ill). Body weights were recorded in pounds. Height, without
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shoes, was immediately measured using a portable Seca stadiometer (Seca Corporation, Chino, CA). Heights were recorded in centimeters and converted to inches for BMI calculation. Participants’ BMIs (wt(lb)/(ht(in))2 9 703) and percentiles [24] were calculated for age in months and gender using the Shape Up America! childhood obesity assessment calculator [25]. Participants’ BMI percentiles were classified as underweight, healthy weight, overweight, or obese as defined by the Centers for Disease Control and Prevention [26], and collapsed for dichotomized analyses as under and healthy weight (\85th percentile), and overweight and obese (C85th percentile).
‘‘[6 h/week’’. Following the recommended procedures for this instrument [23], outcomes for each PA type were assigned 0.0 h for ‘‘none’’, 0.5 h for ‘‘\ h’’, 1.25 h for ‘‘– 2 h’’, 3.25 h for ‘‘2–4 h’’, 5.25 h for ‘‘4–6 h’’, and 7.25 h for ‘‘6? h’’. A moderate test–retest reliability has been reported (mild PA r = 0.54, moderate PA r = 0.53, and vigorous PA r = 0.72) [27]. In order to make comparisons with the 2008 Physical Activity Guidelines for Americans, a moderate-to-vigorous PA (MVPA) variable was calculated by combining time spent in moderate and vigorous PA. Sociodemographics
Questionnaires The BEVQ-15 survey was abbreviated from fifteen questions to five. These questions assessed intake of sweetened juice/beverages, regular soft drinks, diet soft drinks, sweetened teas, and energy/sports drinks. We shortened the survey tool due to the comprehension capabilities of the 7and 8-year-old participants and feasibility of completion in the field-based environment. Additional BEVQ-15 questions regarding alcoholic beverage intake were removed, as those questions were outside the scope of this study and not applicable to the population’s age range. The BEVQ-15 has been previously validated in children 9 years old and older [22], but not with an AI-specific population. The selected psychometric properties for these questions are r2 = 0.52–0.95 (p \ 0.001), which indicates a moderateto-strong test/retest reliability [22]. Assessment questions included frequency and volume of intake of selected SSBs. During instrument administration, visual displays consisting of five clear plastic bottles labeled and filled with 6, 8, 12, 16, and 20 oz of colored water were used to assist participants in determining accurate volume levels. Each of these options corresponded with intake volume on the survey instrument. Questionnaire responses for frequency of SSB intake included ‘‘never or \1 time/week’’, ‘‘1 time/ week’’, ‘‘2–3 times/week’’, ‘‘4–6 times/week’’, ‘‘1 time/day’’, ‘‘2 times/day’’, and ‘‘3? times/day’’ [22]. Daily kcal values for type and volume of SSB were calculated using the BEVQ-15 key, which assigns frequency to ounce and volume to kcal conversions to each variable [22]. Type and duration of PA behavior was evaluated using survey questions from Project EAT [23], which was designed for use with 12-to-18-year-old adolescents. We selected this instrument based on its previous testing in a culturally similar population and since it targeted PA behaviors. Questions included assessments for mild (e.g., slow walking), moderate (e.g., slow bicycling), and vigorous (e.g., running, fast bicycling) PA. Response options for each question included ‘‘none’’, ‘‘\ h/week’’, ‘‘ –2 h/week’’, ‘‘2–4 h/week’’, ‘‘4–6 h/week’’, and
Participants’ ages, dates of birth, and genders were reported by parents/guardians in the initial camp registration and consent to attend camp process. Per camp requirements, all participants were of AI heritage and sponsored by a federally-recognized tribe. Statistical Analysis Means, standard deviations, and medians were calculated for descriptive and outcome variables of interest, including age, BMI percentile, SSB kcal intake, and time spent in PA. Frequency percentages were calculated for gender, BMI classification, and meeting the 2008 Physical Activity Guidelines for Americans. To examine gender differences, independent t tests were calculated between males and females for the following descriptive and outcome variables: BMI percentile, SSB kcal intake, and time spent in each intensity level of PA. Chi square analysis was used to examine the differences between gender for overweight and obese classification and percentage meeting the 2008 Physical Activity Guidelines for Americans. Alpha was set at\0.05 for statistical significance. SPSSÓ version 20 was used for analyses.
Results The sample consisted of 124 children (age 10.2 ± 1.5 years), of whom 57 % were female and 63 % were classified as overweight or obese. Descriptive characteristics are presented in Table 1. Table 2 presents the means, standard deviations, and medians of kcal intake of individual and total SSB kcal intake, and time spent in PA. Because there were no statistically significant differences between genders, frequencies, means, and standard deviations are presented below for the entire sample. Average daily SSB intake for the overall sample was 309 ± 309 kcal. The total mean hours per week in MVPA was 4.4 ± 3.8 h. Almost one-third (32 %) of participants met the guidelines for MVPA [19].
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J Community Health Table 1 Descriptive characteristics of participating American Indian children (n = 124) Variables
Total sample
Female
Male
p value
Male
42.7 (53)
n/a
n/a
Female
57.3 (71)
n/a
n/a
Age (years) mean ± SD
10.2 ± 1.5
10.4 ± 1.5
9.9 ± 1.5
0.119
BMI percentile
80.5 ± 23.8
80.4 ± 24.5
80.5 ± 22.9
0.972
Overweight/obese % (n)
62.9 (78)
60.6 (43)
66.0 (35)
0.577§
Gender %
BMI percentile calculated for weight in pounds, height in inches, and age in months; overweight was considered 85th–94th percentile for BMI, obese was considered C95th percentile for BMI
Independent t test, non significant
§
Chi square analysis, non significant
Discussion Sixty-three percent of our sample was overweight or obese. This finding reinforces existing health concerns for this population. It is important to note that although the sample was taken from a group attending a diabetes prevention camp, attendees of the camp were not previously considered at-risk for diabetes, other than being of AI heritage. In other words, the tribal coalition that conducts the camp understands that all AI children are at risk for developing diabetes later in life, and thus sends all AI children interested in attending a summer camp to this camp. The coalition does not target those who may be overweight or obese. It is likely that high SSB intake (total 309 kcal/day) and low participation in MVPA (4.4 h/week) contributed to the high prevalence of overweight and obesity in this sample. The rate of overweight and obesity (63 %) in our sample of 7-to-13-year-old AI children is consistent with the two published reports of 5-to-22-year-old AI children and adolescents in the Northern Plains (57–75 %) [28, 29]. Our findings indicate a higher rate of obesity and overweight than was reported in a 2002 study of AI children residing in the Southern Plains (52 %) [28, 30]. This inconsistency likely reflects the progression of obesity in AI children. However, these findings support a higher risk for overweight and obesity than what has been previously reported in the general population [2], and an increase in overweight and obese AI children in the Southern Plains [30]. This is a cross-sectional study. Thus, future research is needed to further evaluate the current obesity rates and trends in this population. Similar to the findings from a study of 6-to-12-year-old Hopi children [31], there were no statistically significant differences in BMI between genders. These results also support nationally representative data indicating that obesity rates are similar in 6–11 year old children regardless of gender [2].
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As reported in other studies, SSB intake is associated with an increased risk for overweight and obesity [15]. AI children in the present study had higher SSB intake than was reported for children in the general population [14]. Compared to Han and Powell’s results, this sample had a lower intake of sweetened juices (115 kcal/day vs. 153 kcal/day), regular sodas (87 kcal/day vs. 129 kcal/day), and energy drinks (57 kcal/day vs. 84 kcal/day) than the general population [14]. However, the total SSB kcal intake for this sample is higher than what has been previously reported (309 kcal/day vs. 178 kcal/day) [14]. Overall SSB kcal intake is calculated by summing all individual SSB group intakes. Thus, this result appears to signify that while the general population tends to favor one type of SSB, the 7-to-13-year-old AI population in Oklahoma tends to consume many different types of SSB in any given day, which yields lower individual SSB intake but higher total SSB. However, it should be noted that Han and Powell’s study [14] did not include sweetened tea or diet sodas in their overall SSB intake totals. This may explain some of the difference in total SSB intake. Regardless, it is clear that a wide variety of SSB is readily available to AI youth in Oklahoma. Dietary intervention strategies targeting this population must address the access to and intake of many different types of SSB. The PA outcomes for the present study also clearly contrast with previously reported data. The prevalence of those who meet MVPA recommendations is lower in this sample than has been reported in the general population (32 vs. 70 %) [20]. However, the general population data was reported by proxy (parent report) and specifically categorized a subject as having met MVPA if they had C60 m of MVPA every day of the week. PA in the present study was reported by the child (i.e., self-report) and categorized a subject as having met MVPA if they had C60 m of MVPA for at least 5 days in a given week. Both of these differences in data collection contribute to the reported
J Community Health Table 2 Daily sugar sweetened beverage intake and weekly time spent in physical activity
Variable
Overall Mean ± SD (Min–max) [Median]
Females Mean ± SD (Min–max) [Median]
Males Mean ± SD (Min–max) [Median]
p value
115 ± 153
104 ± 153
129 ± 152
0.369
(0–858) [61]
(0–858) [41]
(0–858) [61]
SSB intake (kcal/day) Sweet juice
Diet soda
Regular soda
Sweet tea
Energy drink
Total SSB
1±2
1±2
1±3
(0–18)
(0–7.2)
(0–18)
[0]
[0]
[0]
87 ± 140
79 ± 130
98 ± 152
(0–798)
(0–798)
(0–798)
[38]
[38]
[57]
49 ± 92
45 ± 94
55 ± 90
(0–600)
(0–600)
(0–400)
[13]
[13]
[10]
57 ± 117
44 ± 117
74 ± 117
(0–840)
(0–840)
(0–560)
[15]
[0]
[8]
309 ± 309
273 ± 301
357 ± 314
(0–1769) [228]
(0–1768) [172]
(0–1370) [252]
2.4 ± 2.3
2.4 ± 2.2
2.4 ± 2.4
(0–7.25)
(0–7.25)
(0–7.25)
0.382
0.451
0.546
0.161
0.131
Physical activity (h/week) Vigorous
Moderate
Mild * Moderate ? vigorous physical activity was a combination of moderate physical activity variable and the vigorous physical activity variable as is consistent with federal physical activity recommendations
Moderate ? vigorous*
% Meeting PA
[1.3]
[1.3]
[1.3]
1.9 ± 2.1
2.1 ± 2.3
1.7 ± 2.0
(0–7.25)
(0–7.25)
(0–7.25)
[1.3]
[1.3]
[1.3]
1.2 ± 1.5
1.2 ± 1.6
1.3 ± 1.6
(0–7.25)
(0–7.25)
(0–7.25)
4.4 ± 3.8
4.5 ± 4.1
4.2 ± 3.4
(0–14.5)
(0–14.5)
(0–12.5)
[2.9]
[2.5]
[3.8]
31.5
29.5
33.9
0.933
0.327
0.776 0.613
0.697
Recommendations
differences of time spent in MVPA between AI children and the general population. It should also be noted that the percentage of AIs who met the PA guidelines in the present study was higher than previously reported with a similarly measured AI population in the Northern Plains (32 vs. 27 %) [21]. Although our findings show an increase in PA, the PA levels are still not optimal. Possible contributors for lower PA levels within AIs versus the general population have been previously reported and include lower household income, perceived unsafe neighborhoods, single family households, and higher likelihood of watching two or more hours of television per day [32]. Possible interventions that target AI children must address the socioeconomic barriers
to PA and focus on culturally and geographically specific obstacles, which may include limited availability of community resources, culture-specific healthy lifestyle perceptions, and relevancy of preventive lifestyle choices when basic needs are difficult to meet [33]. A discussion of this study’s strengths and limitations is warranted. The primary strengths of this study include the unique nature of the sample population. Few studies have focused solely on AI children and adolescents, partly due to this population’s continued distrust of researchers as a whole. However, given the long-standing relationship of the present researchers with this population, the present study was granted and supported, adding valuable insight
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into AI obesogenic behaviors to the existing body of literature. The inclusion of anthropometric data measured by trained clinicians is also a strength. This method is superior to relying on self-report data, which can introduce error and bias. An additional strength is the number of different types of SSB assessed. Many of the referenced studies included fewer types of beverages. The inclusion of a wide array of beverages may have provided for a more accurate estimation of the sample’s SSB intake. A limitation of the study is the self-reported SSB and PA behavior data. While participants completed the behavioral surveys under the supervision of trained staff, it is impossible to control for under- or over-reporting, especially within a young age group which may be subject to greater recall bias [34]. Furthermore, most of this sample was overweight or obese; children who are overweight and obese often under-report dietary intake [35]. This is especially troublesome in light of the sample’s high reports of kcal intake related to SSB intake, as the reported values may be underestimations of actual intake. To minimize these limitations, investigators identified validated tools used in similar populations, and trained personnel were on site to assist young readers. An additional limitation of the study is the lack of validation of the survey tools in the younger children of this sample. To address this limitation and identify possible differences between age groups, additional statistical analysis was performed. There were no significant differences in SSB kcal consumption or time spent in physical activity by age in this study, further building confidence in the data presented. Finally, the survey was conducted 2–3 weeks into the summer break. For most participants, behaviors are assumed to be different during break than those practiced when school is in session. Access to SSB could be higher in the summer than in the school year, and our data may reflect higher summer intake patterns. No studies examining the seasonality of beverage intake have been published. In addition, reported PA behaviors may be higher than those reported during non-summer months [36]. This suggests that the present study may overestimate PA throughout and, therefore, overestimate the overall percentage of participants who meet PA guidelines. Given the prevalence of chronic disease in the AI population and the role of overweight and obesity in the progression of chronic disease, continued assessment of weight management behaviors is needed. To further understand the mechanisms causing higher SSB intake and lower engagement in PA, researchers must pursue in-depth studies that specifically investigate whole family behavior patterns, food availability, neighborhood safety, and attitudes toward healthy lifestyles. The results from these studies may help us to identify triggers for unhealthy lifestyle decisions.
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Conclusions The present study showed that the prevalence of overweight and obesity in AI children in Oklahoma remains higher than the national average, despite plateauing national pediatric overweight and obesity rates [2, 37]. Our data reveal SSB kcal intake that is higher than average and PA levels that are lower than average, regardless of gender. These findings indicate that healthy lifestyle behaviors, like SSB intake and PA engagement, remain relevant concerns for the AI population in Oklahoma. In-depth and culturally appropriate studies that examine the underlying reasons that AI youth choose obesogenic behaviors, like high intake of SSB and inadequate levels of PA, are necessary to break the cycle of health disparities related to obesity in the AI community.
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25. Shape Up America!. (2013). Childhood Obesity Assessment Calculator. From www.shapeup.org/oap/entry.php. 26. US Centers for Disease Control. (2013). Body mass index: Considerations for practitioners. http://www.cdc.gov/obesity/ downloads/bmiforpactitioners.pdf (2014). 27. Larson, N., Neumark-Sztainer, D., Story, M., van den Berg, P., & Hannan, P. J. (2011). Identifying correlates of young adults’ weight behavior: Survey development. American Journal of Health Behavior, 35(6), 712–725. 28. Harris, K. M., Gordon-Larsen, P., Chantala, K., & Udry, J. R. (2006). Longitudinal trends in race/ethnic disparities in leading health indicators from adolescence to young adulthood. Archives of Pediatrics and Adolescent Medicine, 160(1), 74–81. doi:10. 1001/archpedi.160.1.74. 29. Zephier, E., Himes, J. H., Story, M., & Zhou, X. (2006). Increasing prevalences of overweight and obesity in Northern Plains American Indian children. Archives of Pediatrics and Adolescent Medicine, 160(1), 34–39. doi:10.1001/archpedi.160.1. 34. 30. Stoddart, M. L., Blevins, K. S., Lee, E. T., Wang, W., & Blackett, P. R. (2002). Association of acanthosis nigricans with hyperinsulinemia compared with other selected risk factors for type 2 diabetes in Cherokee Indians: The Cherokee diabetes study. Diabetes Care, 25(6), 1009–1014. 31. Eisenmann, J. C., Arnall, D. A., Kanuho, V., & McArel, H. (2003). Growth status and obesity of Hopi children. American Journal of Human Biology, 15(6), 741–745. doi:10.1002/ajhb. 10211. 32. Ness, M., Barradas, D. T., Irving, J., & Manning, S. E. (2012). Correlates of overweight and obesity among American Indian/ Alaska Native and non-Hispanic White children and adolescents: National survey of children’s health, 2007. Maternal and Child Health Journal, 16(Suppl 2), 268–277. doi:10.1007/s10995-0121191-8. 33. Adams, A. K., Harvey, H., & Brown, D. (2008). Constructs of health and environment inform child obesity prevention in American Indian communities. Obesity, 16(2), 311–317. doi:10. 1038/oby.2007.71. 34. Sallis, J. F., Buono, M. J., Roby, J. J., Micale, F. G., & Nelson, J. A. (1993). Seven-day recall and other physical activity self-reports in children and adolescents. Medicine and Science in Sports and Exercise, 25(1), 99–108. 35. Garcia-Dominic, O., Wray, L. A., Ledikwe, J. H., Mitchell, D. C., Ventura, A. K., Hernandez, A. E., & Ulbrecht, J. S. (2010). Accuracy of self-reported energy intakes in low-income urban 4th grade minority children. Obesity, 18(11), 2220–2226. doi:10. 1038/oby.2010.144. 36. Carson, V., & Spence, J. C. (2010). Seasonal variation in physical activity among children and adolescents: A review. Pediatric Exercise Science, 22(1), 81–92. 37. Ogden, C. L., Carroll, M. D., Kit, B. K., & Flegal, K. M. (2012). Prevalence of obesity in the United States, 2009–2010. NCHS Data Brief, 82, 1–8.
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ORIGINAL PAPER
Assessment of Body Mass Index, Sugar Sweetened Beverage Intake and Time Spent in Physical Activity of American Indian Children in Oklahoma Michelle E. Dennison • Susan B. Sisson • Karina Lora • Lancer D. Stephens • Kenneth C. Copeland • Cynthia Caudillo
Ó Springer Science+Business Media New York 2015
Abstract American Indian (AI) children have a combined overweight and obesity prevalence of 53 %. Behaviors that contribute to obesity, such as sugar sweetened beverage (SSB) intake and time spent in physical activity (PA), have been poorly explored in this population. The purpose of this study is to report body mass index (BMI), SSB intake, and time spent in PA of 7-to-13-year-old AI children who reside in rural and urban areas in Oklahoma. Cross-sectional survey study. Self-reported SSB intake in the last month, and time spent in PA were collected via questionnaires. Height and weight were professionally measured. The sample included 124 7-to-13-year-old AI children who attended a diabetes prevention summer camp in 2013. BMI percentile, overweight and obesity prevalence, SSB intake, time spent in PA, and number of participants meeting the Physical Activity Guidelines for Americans. Descriptive characteristics for BMI percentile,
M. E. Dennison S. B. Sisson (&) K. Lora C. Caudillo Department of Nutritional Sciences, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA e-mail: [email protected] M. E. Dennison C. Caudillo Oklahoma City Indian Clinic, Oklahoma City, OK, USA L. D. Stephens Oklahoma Shared Clinical and Translational Resources, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA K. C. Copeland Department of Pediatrics, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA C. Caudillo Native Youth Preventing Diabetes Coalition, Oklahoma City, OK, USA
overweight and obesity, SSB intake, time spent in PA, and meeting PA recommendations were calculated using means, standard deviations, and frequencies. Independent t test and Chi square analyses were used to test for gender differences. Participants were 10.2 ± 1.5 years old and 57 % female. Sixty-three percent were overweight or obese. Children consumed 309 ± 309 kcal/day of SSB and spent 4.4 ± 3.8 h per week in moderate-to-vigorous PA. Approximately 32 % met the 2008 Physical Activity Guidelines for Americans. No gender differences were observed. The prevalence of overweight and obesity was higher than previously reported in a similar population, and higher than that of US children in the general population. SSB intake and physical activity levels were also found to be higher in this group than in the general population. Keywords SSB Exercise Pre-adolescent Native American BMI
Introduction Over the last 19 years, the rates of overweight [85–94th body mass index (BMI) percentile] and obesity (C95th BMI percentile) [1] for 6-to-11-year-old US children have increased to 15 % overweight and 18 % obese [2]. This is in despite of recent reports that pediatric overweight and obesity rates have stabilized in the general population [3]. American Indian (AI) children have higher rates of overweight (20 %) and obesity (25 %) [4] than the national average. Increases in obesity prevalence have been reported in some age groups [5]. This places AI children at elevated risk of obesity-related diseases, such as hypertension, dyslipidemia, and type 2 diabetes [6], and has a pronounced impact in states with large AI populations,
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such as Oklahoma. Oklahoma has the third highest number of AI residents per capita [7], making up 9 % of the state’s population [8]. Oklahoma is 44th in the nation for overall state health ranking [9]. The etiology of overweight and obesity is complex and includes genetics, environmental contributors, and personal behavioral choices [10]. Obesogenic behaviors are defined as behaviors that contribute to the development of overweight and obesity [11], and include excessive caloric intake and inadequate physical activity (PA). Reedy and Krebs-Smith report that sugar sweetened beverages (SSB) are the largest source of added sugars in the diets of US youth [12]. SSB have generally been defined as beverages sweetened with a caloric sweetener, like sugar or high fructose corn syrup [13]. Examples of SSB include sports drinks, energy drinks, regular soda, sweetened tea, and non-fruit juice drinks [13]. However, a review of the literature shows that the SSB included in a given study are not always consistent, thus introducing potential variability when comparing studies. Han and Powell defined SSB as any beverage containing added sugars, and found that 2-to-11-year-old US children and 12-to-19-year-old US adolescents consume 178 and 286 kcal of SSB per day, respectively [14]. Another study, in which SSB were limited to non-diet soda, sweetened fruit drinks, and sweetened iced tea, reported that schoolage children were 1.6 times more likely to be classified as obese for every additional 12 oz of SSB consumed/day [15]. LaRowe et al. [16] reported that overweight/obese 2-to-5-year-old AI children consumed 51 % more SSB than their normal weight AI counterparts. It is clear that SSB contribute to excessive caloric intake. However, energy intake is only part of the energy balance equation. A discussion of behaviors associated with obesity would be remiss if it did not address the role of energy expenditure. Human energy expenditure includes the energy required to sustain life and bodily functions, as well as the energy expended during PA, such as exercise and non-exercise activity thermogenesis [17]. The energy expended to maintain normal bodily functions is relatively fixed. Thus, PA accumulated through volitional exercise and non-exercise activity thermogenesis become target areas for increasing energy expenditure and preventing excessive weight gain [18]. The 2008 Physical Activity Guidelines for Americans state that children who engage in more than 60 min of moderate or vigorous PA per day have a greater chance of living a healthy life that will last into adulthood [19]. According to data from the 2009–2010 National Health and Nutrition Examination Survey, parents reported that 70 % of 6-to-11-year-old children met those recommendations [20]. However, a 2010 report examining PA of 5-to-19-year-old AI children living in the northern plains of the US reported that only 27 % of children met the same
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recommendations [21]. Few studies have examined obesogenic behaviors in AI children who reside in the US Southern Plains, including those who reside in Oklahoma. Overweight and obesity prevention is desperately needed in health disparate minority groups, given the high prevalence of these conditions in the children of this region. However, before we can create interventions that target AI youth, we need a greater understanding of their obesogenic behaviors, including SSB intake and time in PA. To our knowledge, no study has examined the levels of SSB intake and PA behaviors in healthy AI children living in Oklahoma. The purpose of this study is to report body mass index (BMI), SSB intake, and time spent in PA of 7-to-13-year-old AI children who reside in Oklahoma.
Methods Study Design and Participants This was a cross-sectional, field-based study that included 124 7-to-13-year-old AI children who attended the annual Native Youth Preventing Diabetes summer camp in June 2013. Participants resided in areas spanning across the state of Oklahoma, which has 38 federally-recognized tribes headquartered within its borders. This study was approved by the Institutional Review Board of the University of Oklahoma Health Sciences Center. The Native Youth Preventing Diabetes coalition, consisting of 15 different tribes, approved the project. Upon arrival at camp, children were invited to complete the study questionnaires and provided assent at this time. Trained camp staff were readily available to assist beginning readers and to answer any questions. Survey instruments included an abbreviated version of the previously validated Beverage Questionnaire (BEVQ-15) [22] to assess SSB intake and an abbreviated version of a survey used by Project EAT [23] to assess PA. Of the 141 participants, one was excluded for being outside the targeted age range, and 16 were excluded for providing incomplete data, leaving a final analytical sample size of 124.
Measures Anthropometric Measurements Anthropometric measurements were conducted by a registered nurse or registered dietitian nutritionist. On the day of measurements, participants wore light clothing (shorts and a t-shirt) and were asked to remove their shoes before being weighed using the Tanita TBF-310 Body Composition Analyzer (Tanita Corporation, Arlington Heights, Ill). Body weights were recorded in pounds. Height, without
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shoes, was immediately measured using a portable Seca stadiometer (Seca Corporation, Chino, CA). Heights were recorded in centimeters and converted to inches for BMI calculation. Participants’ BMIs (wt(lb)/(ht(in))2 9 703) and percentiles [24] were calculated for age in months and gender using the Shape Up America! childhood obesity assessment calculator [25]. Participants’ BMI percentiles were classified as underweight, healthy weight, overweight, or obese as defined by the Centers for Disease Control and Prevention [26], and collapsed for dichotomized analyses as under and healthy weight (\85th percentile), and overweight and obese (C85th percentile).
‘‘[6 h/week’’. Following the recommended procedures for this instrument [23], outcomes for each PA type were assigned 0.0 h for ‘‘none’’, 0.5 h for ‘‘\ h’’, 1.25 h for ‘‘– 2 h’’, 3.25 h for ‘‘2–4 h’’, 5.25 h for ‘‘4–6 h’’, and 7.25 h for ‘‘6? h’’. A moderate test–retest reliability has been reported (mild PA r = 0.54, moderate PA r = 0.53, and vigorous PA r = 0.72) [27]. In order to make comparisons with the 2008 Physical Activity Guidelines for Americans, a moderate-to-vigorous PA (MVPA) variable was calculated by combining time spent in moderate and vigorous PA. Sociodemographics
Questionnaires The BEVQ-15 survey was abbreviated from fifteen questions to five. These questions assessed intake of sweetened juice/beverages, regular soft drinks, diet soft drinks, sweetened teas, and energy/sports drinks. We shortened the survey tool due to the comprehension capabilities of the 7and 8-year-old participants and feasibility of completion in the field-based environment. Additional BEVQ-15 questions regarding alcoholic beverage intake were removed, as those questions were outside the scope of this study and not applicable to the population’s age range. The BEVQ-15 has been previously validated in children 9 years old and older [22], but not with an AI-specific population. The selected psychometric properties for these questions are r2 = 0.52–0.95 (p \ 0.001), which indicates a moderateto-strong test/retest reliability [22]. Assessment questions included frequency and volume of intake of selected SSBs. During instrument administration, visual displays consisting of five clear plastic bottles labeled and filled with 6, 8, 12, 16, and 20 oz of colored water were used to assist participants in determining accurate volume levels. Each of these options corresponded with intake volume on the survey instrument. Questionnaire responses for frequency of SSB intake included ‘‘never or \1 time/week’’, ‘‘1 time/ week’’, ‘‘2–3 times/week’’, ‘‘4–6 times/week’’, ‘‘1 time/day’’, ‘‘2 times/day’’, and ‘‘3? times/day’’ [22]. Daily kcal values for type and volume of SSB were calculated using the BEVQ-15 key, which assigns frequency to ounce and volume to kcal conversions to each variable [22]. Type and duration of PA behavior was evaluated using survey questions from Project EAT [23], which was designed for use with 12-to-18-year-old adolescents. We selected this instrument based on its previous testing in a culturally similar population and since it targeted PA behaviors. Questions included assessments for mild (e.g., slow walking), moderate (e.g., slow bicycling), and vigorous (e.g., running, fast bicycling) PA. Response options for each question included ‘‘none’’, ‘‘\ h/week’’, ‘‘ –2 h/week’’, ‘‘2–4 h/week’’, ‘‘4–6 h/week’’, and
Participants’ ages, dates of birth, and genders were reported by parents/guardians in the initial camp registration and consent to attend camp process. Per camp requirements, all participants were of AI heritage and sponsored by a federally-recognized tribe. Statistical Analysis Means, standard deviations, and medians were calculated for descriptive and outcome variables of interest, including age, BMI percentile, SSB kcal intake, and time spent in PA. Frequency percentages were calculated for gender, BMI classification, and meeting the 2008 Physical Activity Guidelines for Americans. To examine gender differences, independent t tests were calculated between males and females for the following descriptive and outcome variables: BMI percentile, SSB kcal intake, and time spent in each intensity level of PA. Chi square analysis was used to examine the differences between gender for overweight and obese classification and percentage meeting the 2008 Physical Activity Guidelines for Americans. Alpha was set at\0.05 for statistical significance. SPSSÓ version 20 was used for analyses.
Results The sample consisted of 124 children (age 10.2 ± 1.5 years), of whom 57 % were female and 63 % were classified as overweight or obese. Descriptive characteristics are presented in Table 1. Table 2 presents the means, standard deviations, and medians of kcal intake of individual and total SSB kcal intake, and time spent in PA. Because there were no statistically significant differences between genders, frequencies, means, and standard deviations are presented below for the entire sample. Average daily SSB intake for the overall sample was 309 ± 309 kcal. The total mean hours per week in MVPA was 4.4 ± 3.8 h. Almost one-third (32 %) of participants met the guidelines for MVPA [19].
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J Community Health Table 1 Descriptive characteristics of participating American Indian children (n = 124) Variables
Total sample
Female
Male
p value
Male
42.7 (53)
n/a
n/a
Female
57.3 (71)
n/a
n/a
Age (years) mean ± SD
10.2 ± 1.5
10.4 ± 1.5
9.9 ± 1.5
0.119
BMI percentile
80.5 ± 23.8
80.4 ± 24.5
80.5 ± 22.9
0.972
Overweight/obese % (n)
62.9 (78)
60.6 (43)
66.0 (35)
0.577§
Gender %
BMI percentile calculated for weight in pounds, height in inches, and age in months; overweight was considered 85th–94th percentile for BMI, obese was considered C95th percentile for BMI
Independent t test, non significant
§
Chi square analysis, non significant
Discussion Sixty-three percent of our sample was overweight or obese. This finding reinforces existing health concerns for this population. It is important to note that although the sample was taken from a group attending a diabetes prevention camp, attendees of the camp were not previously considered at-risk for diabetes, other than being of AI heritage. In other words, the tribal coalition that conducts the camp understands that all AI children are at risk for developing diabetes later in life, and thus sends all AI children interested in attending a summer camp to this camp. The coalition does not target those who may be overweight or obese. It is likely that high SSB intake (total 309 kcal/day) and low participation in MVPA (4.4 h/week) contributed to the high prevalence of overweight and obesity in this sample. The rate of overweight and obesity (63 %) in our sample of 7-to-13-year-old AI children is consistent with the two published reports of 5-to-22-year-old AI children and adolescents in the Northern Plains (57–75 %) [28, 29]. Our findings indicate a higher rate of obesity and overweight than was reported in a 2002 study of AI children residing in the Southern Plains (52 %) [28, 30]. This inconsistency likely reflects the progression of obesity in AI children. However, these findings support a higher risk for overweight and obesity than what has been previously reported in the general population [2], and an increase in overweight and obese AI children in the Southern Plains [30]. This is a cross-sectional study. Thus, future research is needed to further evaluate the current obesity rates and trends in this population. Similar to the findings from a study of 6-to-12-year-old Hopi children [31], there were no statistically significant differences in BMI between genders. These results also support nationally representative data indicating that obesity rates are similar in 6–11 year old children regardless of gender [2].
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As reported in other studies, SSB intake is associated with an increased risk for overweight and obesity [15]. AI children in the present study had higher SSB intake than was reported for children in the general population [14]. Compared to Han and Powell’s results, this sample had a lower intake of sweetened juices (115 kcal/day vs. 153 kcal/day), regular sodas (87 kcal/day vs. 129 kcal/day), and energy drinks (57 kcal/day vs. 84 kcal/day) than the general population [14]. However, the total SSB kcal intake for this sample is higher than what has been previously reported (309 kcal/day vs. 178 kcal/day) [14]. Overall SSB kcal intake is calculated by summing all individual SSB group intakes. Thus, this result appears to signify that while the general population tends to favor one type of SSB, the 7-to-13-year-old AI population in Oklahoma tends to consume many different types of SSB in any given day, which yields lower individual SSB intake but higher total SSB. However, it should be noted that Han and Powell’s study [14] did not include sweetened tea or diet sodas in their overall SSB intake totals. This may explain some of the difference in total SSB intake. Regardless, it is clear that a wide variety of SSB is readily available to AI youth in Oklahoma. Dietary intervention strategies targeting this population must address the access to and intake of many different types of SSB. The PA outcomes for the present study also clearly contrast with previously reported data. The prevalence of those who meet MVPA recommendations is lower in this sample than has been reported in the general population (32 vs. 70 %) [20]. However, the general population data was reported by proxy (parent report) and specifically categorized a subject as having met MVPA if they had C60 m of MVPA every day of the week. PA in the present study was reported by the child (i.e., self-report) and categorized a subject as having met MVPA if they had C60 m of MVPA for at least 5 days in a given week. Both of these differences in data collection contribute to the reported
J Community Health Table 2 Daily sugar sweetened beverage intake and weekly time spent in physical activity
Variable
Overall Mean ± SD (Min–max) [Median]
Females Mean ± SD (Min–max) [Median]
Males Mean ± SD (Min–max) [Median]
p value
115 ± 153
104 ± 153
129 ± 152
0.369
(0–858) [61]
(0–858) [41]
(0–858) [61]
SSB intake (kcal/day) Sweet juice
Diet soda
Regular soda
Sweet tea
Energy drink
Total SSB
1±2
1±2
1±3
(0–18)
(0–7.2)
(0–18)
[0]
[0]
[0]
87 ± 140
79 ± 130
98 ± 152
(0–798)
(0–798)
(0–798)
[38]
[38]
[57]
49 ± 92
45 ± 94
55 ± 90
(0–600)
(0–600)
(0–400)
[13]
[13]
[10]
57 ± 117
44 ± 117
74 ± 117
(0–840)
(0–840)
(0–560)
[15]
[0]
[8]
309 ± 309
273 ± 301
357 ± 314
(0–1769) [228]
(0–1768) [172]
(0–1370) [252]
2.4 ± 2.3
2.4 ± 2.2
2.4 ± 2.4
(0–7.25)
(0–7.25)
(0–7.25)
0.382
0.451
0.546
0.161
0.131
Physical activity (h/week) Vigorous
Moderate
Mild * Moderate ? vigorous physical activity was a combination of moderate physical activity variable and the vigorous physical activity variable as is consistent with federal physical activity recommendations
Moderate ? vigorous*
% Meeting PA
[1.3]
[1.3]
[1.3]
1.9 ± 2.1
2.1 ± 2.3
1.7 ± 2.0
(0–7.25)
(0–7.25)
(0–7.25)
[1.3]
[1.3]
[1.3]
1.2 ± 1.5
1.2 ± 1.6
1.3 ± 1.6
(0–7.25)
(0–7.25)
(0–7.25)
4.4 ± 3.8
4.5 ± 4.1
4.2 ± 3.4
(0–14.5)
(0–14.5)
(0–12.5)
[2.9]
[2.5]
[3.8]
31.5
29.5
33.9
0.933
0.327
0.776 0.613
0.697
Recommendations
differences of time spent in MVPA between AI children and the general population. It should also be noted that the percentage of AIs who met the PA guidelines in the present study was higher than previously reported with a similarly measured AI population in the Northern Plains (32 vs. 27 %) [21]. Although our findings show an increase in PA, the PA levels are still not optimal. Possible contributors for lower PA levels within AIs versus the general population have been previously reported and include lower household income, perceived unsafe neighborhoods, single family households, and higher likelihood of watching two or more hours of television per day [32]. Possible interventions that target AI children must address the socioeconomic barriers
to PA and focus on culturally and geographically specific obstacles, which may include limited availability of community resources, culture-specific healthy lifestyle perceptions, and relevancy of preventive lifestyle choices when basic needs are difficult to meet [33]. A discussion of this study’s strengths and limitations is warranted. The primary strengths of this study include the unique nature of the sample population. Few studies have focused solely on AI children and adolescents, partly due to this population’s continued distrust of researchers as a whole. However, given the long-standing relationship of the present researchers with this population, the present study was granted and supported, adding valuable insight
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into AI obesogenic behaviors to the existing body of literature. The inclusion of anthropometric data measured by trained clinicians is also a strength. This method is superior to relying on self-report data, which can introduce error and bias. An additional strength is the number of different types of SSB assessed. Many of the referenced studies included fewer types of beverages. The inclusion of a wide array of beverages may have provided for a more accurate estimation of the sample’s SSB intake. A limitation of the study is the self-reported SSB and PA behavior data. While participants completed the behavioral surveys under the supervision of trained staff, it is impossible to control for under- or over-reporting, especially within a young age group which may be subject to greater recall bias [34]. Furthermore, most of this sample was overweight or obese; children who are overweight and obese often under-report dietary intake [35]. This is especially troublesome in light of the sample’s high reports of kcal intake related to SSB intake, as the reported values may be underestimations of actual intake. To minimize these limitations, investigators identified validated tools used in similar populations, and trained personnel were on site to assist young readers. An additional limitation of the study is the lack of validation of the survey tools in the younger children of this sample. To address this limitation and identify possible differences between age groups, additional statistical analysis was performed. There were no significant differences in SSB kcal consumption or time spent in physical activity by age in this study, further building confidence in the data presented. Finally, the survey was conducted 2–3 weeks into the summer break. For most participants, behaviors are assumed to be different during break than those practiced when school is in session. Access to SSB could be higher in the summer than in the school year, and our data may reflect higher summer intake patterns. No studies examining the seasonality of beverage intake have been published. In addition, reported PA behaviors may be higher than those reported during non-summer months [36]. This suggests that the present study may overestimate PA throughout and, therefore, overestimate the overall percentage of participants who meet PA guidelines. Given the prevalence of chronic disease in the AI population and the role of overweight and obesity in the progression of chronic disease, continued assessment of weight management behaviors is needed. To further understand the mechanisms causing higher SSB intake and lower engagement in PA, researchers must pursue in-depth studies that specifically investigate whole family behavior patterns, food availability, neighborhood safety, and attitudes toward healthy lifestyles. The results from these studies may help us to identify triggers for unhealthy lifestyle decisions.
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Conclusions The present study showed that the prevalence of overweight and obesity in AI children in Oklahoma remains higher than the national average, despite plateauing national pediatric overweight and obesity rates [2, 37]. Our data reveal SSB kcal intake that is higher than average and PA levels that are lower than average, regardless of gender. These findings indicate that healthy lifestyle behaviors, like SSB intake and PA engagement, remain relevant concerns for the AI population in Oklahoma. In-depth and culturally appropriate studies that examine the underlying reasons that AI youth choose obesogenic behaviors, like high intake of SSB and inadequate levels of PA, are necessary to break the cycle of health disparities related to obesity in the AI community.
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