Eur J Nutr DOI 10.1007/s00394-013-0612-9
ORIGINAL CONTRIBUTION
Changes in body anthropometry and composition in obese adolescents in a lifestyle intervention program Yi Ning • Shibing Yang • Ronald K. Evans • Marilyn Stern • Shumei Sun • Gary L. Francis Edmond P. Wickham III
•
Received: 20 June 2013 / Accepted: 21 October 2013 Ó Springer-Verlag Berlin Heidelberg 2013
Abstract Purpose Impact of lifestyle modification on obesity control during adolescence, a period of significant physical growth and development, is less quantitatively evaluated. Therefore, we investigated the impact of changes in reported energy intake and physical activity on anthropometrics and body composition in adolescents. Methods Participants were obese adolescents aged 11–18 years. All of them have a body mass index (BMI) C 95th percentile specific for age and gender according to the 2000 CDC Growth Charts. The intervention consists of supervised physical activity, structured nutrition education and Y. Ning (&) S. Yang Division of Epidemiology, Department of Family Medicine and Population Health, Virginia Commonwealth University School of Medicine, One Capitol Plaza, Suite 8020, 830 East Main Street, Richmond, VA 23298-0212, USA e-mail: [email protected] R. K. Evans Department of Health and Human Performance, Virginia Commonwealth University School of Medicine, Richmond, VA 23298, USA R. K. Evans M. Stern G. L. Francis E. P. Wickham III Department of Pediatrics, Virginia Commonwealth University School of Medicine, Richmond, VA 23298, USA M. Stern Department of Psychology, Virginia Commonwealth University School of Medicine, Richmond, VA 23298, USA
dietary modification, and behavioral support in 6 months. Hundred and forty-five obese adolescents completed the study. Results Compared to baseline, significant reductions in body weight (-1.4 kg, p \ 0.001) and BMI (-0.1 kg/m2, p \ 0.001) were observed at 6 months. When compared to expected growth trajectories on the 2000 CDC Growth Charts, body weight and BMI were reduced by 3.6 kg and 1.5 kg/m2, respectively, in boys and 5.6 kg and 1.9 kg/m2 in girls. Age was inversely associated with changes in weight (b = -1.48 kg, p \ 0.01) and BMI (b = -0.32 kg/m2, p = 0.03). There was a dose–response relationship between reduction in energy intake and weight loss. A decrease of 100 kcal/day was significantly associated with reductions in body weight 0.30 kg, BMI 0.09 kg/m2, and BMI Z score 0.01 (all p \ 0.01). Physical activity was not significantly associated with changes in anthropometrics or body composition. Conclusions Reduction in energy intake was a significant predictor of obesity reduction in these adolescents. A quantitative evaluation of adolescent weight loss programs should account for natural growth and development. Keywords Obesity Intervention Nutrition Physical activity Evaluation Abbreviations BIA Bioelectric impedance analysis BMI Body mass index CDC Centers for disease control and prevention DXA Dual X-ray absorptiometry
S. Sun Department of Biostatistics, Virginia Commonwealth University School of Medicine, Richmond, VA 23298, USA
Introduction
G. L. Francis E. P. Wickham III Department of Internal Medicine, Virginia Commonwealth University School of Medicine, Richmond, VA 23298, USA
In U.S., the prevalence of obesity among adolescents has more than tripled between 1976–1980 and 2007–2008 [1].
123
Eur J Nutr
Hyperlipidemia, hypertension, and abnormal glucose tolerance occur with increased frequency in obese children and adolescents [2, 3]. Moreover, obesity in childhood is associated with an increased risk of cardiovascular disease, diabetes, and cancer in their adulthood, highlighting the importance of effective treatment earlier in life. Lifestyle treatment for obesity, which emphasizes healthy eating, physical activity, and behavioral change, has been a primary focus of clinical and public practice [4]. However, literature on energy intake and physical activity and adolescent weight management offers conflicting findings about the independent impacts of dietary and physical activity interventions [5, 6]. Although both dietary intervention and physical activity are important for body weight control and general health, they may manifest their impacts differently through various biological pathways in adolescents. Moreover, few studies have prospectively and quantitatively [7] investigated caloric changes in energy intake or physical activity in weight management in adolescents. Adolescence represents a dynamic period of development with significant changes in somatic growth and pubertal development. The pubertal period is associated with a significant increase in growth velocity from childhood, including rapid changes in height, weight, and body mass index (BMI) among normal weight males and females [8]. Accounting for these ‘‘expected’’ changes may introduce an additional layer of complexity to evaluate the impact of lifestyle modification on body weight and adiposity among overweight and obese adolescents. Purpose of the present study was to simultaneously evaluate the association between changes in reported energy intake and physical activity and reductions in body weight, BMI, BMI Z scores, waist circumference, and body composition. In addition, we compared changes in anthropometrics following 6 months of program participation with projected changes based on the 2000 CDC Growth Charts [8] anticipated to occur over the same time period without intervention.
Methods Study population The Teaching, Encouragement, Exercise, Nutrition, and Support (T.E.E.N.S.) is an ongoing community-based adolescent weight management research program that was initiated in November 2003. The intervention consists of supervised physical activity, structured nutrition education and dietary modification, and behavioral support. Between December 2004 and December 2010, obese boys and girls between 11 and 18 years of age with a BMI C 95th
123
percentile specific for age and gender according to the 2000 CDC Growth Charts [8] were eligible for participation if they had at least one adult in the household who was committed to attend the program meetings. Exclusion criteria were underlying genetic, neurologic, endocrine, or metabolic conditions that preclude weight loss with conventional diet and exercise programs (Prader-Willi, Cushing’s syndrome, et al.); weight greater than 400 lbs (181.4 kg); pregnancy; inability to understand program instructions due to a language barrier or a cognitive disability; a physical disability that severely limits participation in the outlined exercise program; or primary residence outside of a 30 mile radius of the study center. At baseline, a questionnaire-based in-person interview was administered to participants and their parents to collect information on sociodemographic information and family history of obesity. A family history of obesity was considered to be positive when the adolescent had at least one parent with a self-reported BMI C 30 kg/m2. Dietary intake and physical activity were respectively assessed by a 48-h diet recall [9, 10] and a 7-day physical activity recall [11] at baseline and 6 months after the intervention. A pediatric specialist conducted a detailed medical history examination and determined Tanner staging according to external primary and secondary sex characteristics. All data were collected in our research center. The T.E.E.N.S. program procedures were approved by the Institutional Review Board of Virginia Commonwealth University (VCU). Parents provided written informed consent, and adolescents provided written assent prior to program participation. Lifestyle modification intervention Enrolled participants completed standardized anthropometric, nutrition, and physical activity assessments at baseline and following 6-months of participation in the T.E.E.N.S. intervention. In brief, the program consists of three major components: (1) a structured nutrition education program, (2) supervised physical activity, and (3) behavioral support. Over the initial 6 months, the nutrition component of the intervention consists of a series of twelve, 30-min, individualized education sessions conducted with an adolescent and parent(s) by a registered dietitian with expertise in pediatric weight management. The program’s dietary intervention did not focus on specific caloric goals, but encouraged the modification of highrisk eating behaviors. Behavior support sessions designed to promote sustained behavior change were conducted in either individual or group formats by graduate students in health psychology under the supervision of trained faculty. Education and behavioral support sessions were each held once every 2 weeks, on alternating weeks. The physical activity program consisted of 60 min sessions conducted at
Eur J Nutr
the program’s dedicated gym facility at least 3 times per week during the initial 12 weeks of the intervention, and a minimum of twice a week thereafter. Details of the intervention can be found in previous publication [12]. Nutrition assessment Two parent-assisted 48-h dietary recalls were conducted in person with participants by a trained registered dietitian at baseline and 6 months, respectively [9]. The 2 days covered in the dietary recalls did not overlap with the 12-h fasting period prior to anthropometric measurements. To improve quality of dietary recall, we used a multiple-pass technique to assess dietary intake [10]. The dietitian used two- and three-dimensional tools, including food models and empty food containers (e.g., potato chips, drinks, and other frequently consumed items) to aid in portion estimates. Memory prompts were used to assist the recall of frequently forgotten foods. Using data collected from the 48-h recall, total energy intake and dietary macronutrient composition were determined using Nutritionist Pro software (Axxya Systems, Stafford, TX). An average of the 48-h intake of total dietary energy was calculated [9]. Physical activity assessment A standardized seven-day physical activity recall [11] was administered by a trained interviewer to determine the adolescent’s engagement in daily activity at both baseline and 6 months after staring intervention. Energy expenditure of reported physical activity was calculated based on these activities. MET-hours (metabolic equivalent [MET] unit times duration of activity in one week) for each participant were summed according to time engaged in sleep (1 MET), light activities (1.5 METs), moderate activities (4 METs), vigorous activities (6 METs), and very vigorous activities (10 METs). The physical activity questionnaire was validated for use in adolescents [11]. Anthropometry and body composition Anthropometric measurements were performed by trained dietician following a 12-h fasting at baseline and 6 months. Weight was measured (to the nearest 0.1 kg) using a calibrated digital scale with the participants dressed in light indoor clothing and without shoes. Height was measured (to the nearest 0.1 cm) using a calibrated wallmounted stadiometer. BMI was calculated, and BMI percentiles/z scores were determined using Epi Info Software (Centers for Disease Control, Version 3.3). Waist circumference was measured (to the nearest 0.1 cm) at the level of the umbilicus with a Gullick II tape (Country Technology, Inc., Gays Mills, WI). Body composition was
assessed by bioelectric impedance analysis (BIA) as determined by the Quantum II Bioelectrical Body Composition Analyzer (RJL Systems, Clinton Township, MI). Results were entered into a computer program, and percentages of body fat and body water were estimated using commercially available software (Cyprus 2.7, RJL systems, Clinton Township, MI) based on equations derived by Kotler [13]. Starting in 2007, body composition was also estimated at baseline and 6 months using dual X-ray absorptiometry (DXA) in 45 participants. The DXA scanning was performed using a Hologic 4500a/Discovery scanner. The correlation coefficients between the two methods for fat mass and fat-free mass were 0.93–0.99 for boys and girls at baseline and 6 months, respectively. Although BIA has been suggested to underestimate body fat in comparison with DXA [14], BIA was expected to provide valid results for body composition in the present study population. Statistical analysis After confirming the normal distribution of energy intake, energy expenditure of reported physical activity, anthropometry, and body composition, mean differences of these variables between baseline and 6 months were compared using paired t tests assuming equal variance. We also estimated the expected gains in body weight and BMI in 6 months based on the 2000 CDC Growth Charts [8]. In particular, the constant 99th percentiles of body weight and BMI at both baseline and 6 months after baseline were calculated using the LMS approach (http://www.cdc.gov/ growthcharts/percentile_data_files.htm). Age- and genderspecific parameters (for L, M and S) were obtained directly from this CDC website. The differences in 99th percentiles of weights and BMIs at baseline and 6 months after baseline (i.e., expected gains) were added to baseline measures to estimate the expected body weight and BMI after 6 months without experiencing the intervention. The expected gains in weights and BMIs at the 99th percentiles were estimated separately for boys and girls. To evaluate predictors of weight loss at an individual level, we performed linear regression analyses to examine the association between potential determinants and changes in anthropometrics and body composition. Specifically, we considered age, gender, race, family history of obesity, and Tanner stage of puberty. Changes in energy intake and physical activity were included as independent variables. Including physical activity either as a continuous or categorical (tertiles) variable did not change the estimates for total energy intake appreciably and the estimates for physical activity were comparable and were not significant, so we used physical activity as a continuous variable to reduce degree of freedom. Change in energy intake was
123
Eur J Nutr Table 1 Baseline characteristics of obese adolescents enrolled in the T.E.E.N.S. program
Characteristics Number
All (n, %) 145
Female (n, %) 94
Male (n, %) 51
Age (years) 11–\13
59 (40.7)
37 (39.4)
13–14.9
54 (37.2)
37 (39.4)
22 (43.1) 17 (33.3)
15–\18
32 (22.1)
20 (21.3)
12 (23.5)
Race/ethnicitya Caucasian African American Other Family history of obesityb a
Race/ethnicity data were selfreported
b
Defined as at least one parent with a body mass index C30 kg/m2
13 (13.8)
16 (31.4)
76 (80.9)
32 (62.8)
8 (5.5)
5 (5.3)
3 (5.9)
85 (58.6)
59 (62.8)
26 (51.0)
Tanner stage I–II
10 (6.9)
3 (3.2)
7 (13.7)
III
54 (37.2)
26 (27.7)
28 (54.9)
IV–V
81 (45.9)
55 (69.1)
16 (31.4)
categorized into tertiles. Tests of trends were also conducted using the median value for each category of energy intake as a continuous variable in multivariate models. Potential interactions between gender and energy intake/ physical activity on study outcomes were tested and ruled out. All analyses were conducted using SAS version 9.2 (SAS institute Inc, Cary, NC). All statistical tests were twosided, and p values less than 0.05 were considered statistically significant.
Results Baseline demographic and clinical characteristics for the 145 adolescents included in these analyses are shown in Table 1. The majority of the participants were African American (75 %), and 59 % had a positive family history of parental obesity. The average age of participants is 13.1 years. The study population had a mean BMI of 37.2 kg/m2. There was a higher percentage of African Americans among the girls than among the boys (p \ 0.01). Table 2 contains anthropometrics, body composition, energy intake, and energy expenditure of enrolled adolescents at baseline and after 6 months of lifestyle modification. At baseline, the estimated energy expenditure was approximately 240.0 ± 28.6 MET-h/week; the average intake of total energy was 2,020 ± 675 kcal/day. At 6 months after intervention, the mean reported energy intake was reduced by 425.0 kcal/day. Self-reported physical activity increased by 12.0 MET-h/week, which corresponds to the addition of 17 min of vigorous activity per day. Compared with baseline, the adolescents’ overall body weight, BMI, BMI percentile, BMI Z scores, waist
123
29 (20.0) 108 (74.5)
circumference, percentages of body fat, and fat mass were significantly reduced, while fat-free mass was significantly increased following program participation. Figure 1a–d graphically compares measures of body adiposity of these participants at baseline and at 6 months of intervention and projected values at 6 months according to age and gender. Because adolescents normally experience significant physical growth, even over a period of 6 months, expected 6-month values were derived from 2000 CDC Growth Charts as if there were no intervention [8]. Observed BMI was reduced from baseline after the intervention across all the age groups, while body weight was reduced for boys aged C15 years and girls aged C14 years. The reduction in weight and BMI became more pronounced compared with the expected trajectories at 6 months without intervention, with a relative reduction of 3.6 kg in weight and 1.5 kg/m2 in BMI among boys and reductions of 5.6 kg and 1.9 kg/m2 in girls. Figure 1e, f highlights the observed reductions in body fat percentage from baseline to 6 months across age groups following intervention. In the univariate linear regression analyses (Table 3), age was inversely and significantly associated with changes in weight (p \ 0.01) and BMI (p = 0.01), but not significantly associated with change in percent body fat (p = 0.90). However, there were significant inverse associations between baseline age and changes in both fat mass (p \ 0.01) and fat-free mass (p \ 0.01). Self-reported reductions in energy intake were significantly associated with greater reductions in body weight, BMI, fat mass, and fat-free mass (all ptrend B 0.05). Results of multivariate analyses were similar to those in univariate analysis. In multivariate models, older age was associated with more reductions in weight (-1.48 kg/year, p \ 0.01) and BMI (-0.32 kg/m2/year, p = 0.03). Each reduction in energy
Eur J Nutr Table 2 Changes in energy intake, energy expenditure, anthropometry, and body composition in obese adolescents Variables (mean ± SD)
Baseline
Physical activity (MET-h/week)a Total calorie intake (kcal/day)
b
6 months
240.0 ± 28.6
251.8 ± 37.2
2,020.0 ± 674.6
1,595.3 ± 426.3
Weight (kg)
100.9 ± 21.1
99.5 ± 20.9
p value§ 0.003
N 125c
\0.001
117
0.005
145 145
Height (cm)
164.3 ± 8.6
165.8 ± 8.5
\0.001
BMI (kg/m2)
37.2 ± 6.0
36.1 ± 6.1
\0.001
145
BMI percentile
99.0 ± 0.9
98.6 ± 1.6
\0.001
145
2.4 ± 0.3
2.3 ± 0.3
\0.001
145
103.3 ± 12.0
102.6 ± 16.4
0.531
121
43.7 ± 11.6
42.3 ± 11.8
\0.001
140
BMI Z score Waist circumference (cm) Body fat percentage
BIA
(%)
44.8 ± 16.7
42.7 ± 16.5
\0.001
140
Fat-free mass BIA (kg) Body fat percentage DXA
56.3 ± 15.1 42.6 ± 4.7
56.8 ± 15.3 40.2 ± 5.7
0.052 \0.001
140 45d
Fat mass
41.9 ± 12.0
39.0 ± 12.2
\0.001
45d
55.7 ± 9.1
56.6 ± 8.3
0.021
45d
Fat mass
BIA
(kg)
DXA
(kg)
Fat-free mass
DXA
(kg)
BIA bioimpedance analysis, DXA dual-energy X-ray absorptiometry §
p values were obtained through paired t test
a
Assessed via 7-day physical activity recall
b
Assessed via dietitian-assisted 48-h dietary recall
c
Missing values on MET-hours at 6 months for six participants were imputed with information at 3 months
d
The information was collected after a change in research protocol
intake of 100 calories/day was significantly associated with change in weight loss (-0.30 kg, p = 0.004) and BMI (-0.1 kg/m2, p = 0.006) at 6 months. In contrast, increase in physical activity levels was not significantly associated with reductions in body fat percentage and fat mass and increase in fat-free mass in either univariate or multivariate regression models (p [ 0.05). No significant interaction with gender was identified for the association of age with changes in anthropometry or body composition. Considering change in BMI Z score as the dependent variable of interest, reduction in energy intake was moderate, but significantly associated with reduction in BMI Z scores in age- and gender-adjusted model (b = -0.02/100 kcal, p = 0.03). After further adjustment for race/ethnicity, Tanner stage and energy expenditure, the magnitudes of association were not changed appreciably even though the p values became non-significant. The results were not significantly different when comparing analyses using body composition data assessed by DXA and BIA among 45 adolescents who had body composition measured by two methods.
Discussion In the present study, beneficial changes in body weight and BMI from baseline values were observed following the lifestyle intervention. In fact, the potential impact of the
lifestyle modification intervention on body weight and BMI is even more apparent after accounting for the expected normal development in adolescents using trajectories based on 2000 CDC Growth Charts. Self-reported reductions in energy intake at 6 months were significantly associated with reductions in body weight, BMI, percent body fat, and fat mass as well as increased fat-free mass. No significant associations were observed between selfreported changes in physical activity and changes in anthropometrics or body composition. Two major limitations merit discussion and consideration. One limitation is that the study utilized data from an existing multidisciplinary weight management program, which was designed without an active comparison group. However, the serial measures of body anthropometry and composition provided us thorough information to investigate the association of changes in reported energy intake and physical activity prospectively with objective changes in weight status and body composition within a given adolescent. Our comprehensive analysis of weight, BMI, BMI Z scores, waist circumference, body fat percentage, fat mass, and fat-free mass showed consistent results. Another limitation is that both data regarding dietary intake and physical activity were self-reported. Seven-day physical activity was assessed at both baseline and six months after starting intervention. It is well established that participants often underreport their calorie intake, representing an inherent limitation of dietary recall [9]. We were
123
Eur J Nutr Fig. 1 Changes in body weight, body mass index, and body fat percentage by gender and age at baseline
concerned that energy intakes at baseline (2,020 kcal per day) and at 6 months (1,559 kcal per day) in this study were lower than actual intakes. However, the energy intake reported in this specific population was comparable with or greater than those in published studies [15–17] or dietary guidelines involving adolescents [18]. Previous clinical trials involving obese children reported energy intake of ‘‘1,437–1,898 kcal’’ [16], ‘‘averaged 1,926 kcal’’ [17], or ‘‘a desired intake of 1,200–1,800 kcal’’ [15]. Dietary guidelines for Americans also suggest daily energy intakes between 1,400 and 2,400 kcal per day for girls and 1,600–3,200 kcal for boys aged 9–18 years [18]. Most clinical trials used calorie restriction approach limiting energy intake within the range from 900 to 1,800 kcal per day [19]. Therefore, underreporting of dietary intake is not viewed as a significant source of bias in the interpretation of our results. Moreover, changes in daily energy intake, despite being self-reported, were significantly associated with changes in weight and BMI, objective measures supportive of actual reductions in energy intake. Generally, people tend to underreport energy intake and over-report levels of physical activity perhaps due to social
123
desirability. If the recall bias is non-differential, it usually biases the observed associations toward the null. Therefore, the true association would be stronger than the reported results if there was non-differential recall bias. In our separate manuscript (unpublished), 48-h dietary recall better predicted weight loss than 24-h dietary recall. It is possible that the 48-h dietary recall better captured the dayby-day variation in dietary intake; thus, recall bias was minimal in our study. We collected dietary information on Monday to Thursday, and thus, adolescents seen on Monday would have 2 weekend days, and kids seen on Tuesday would have 1 weekend day. It would have been reasonable to estimate that half of the recalls included at least one weekend day, and 25 % of the recalls included both weekend days. Findings from the present study among obese adolescents are in line with results from clinical trials that target lower energy intake, which support the significant impact of reduced energy intake on weight loss and reduction in fat mass [19–24]. A comprehensive review of 5 clinical trials by Luttikhuis et al. [5] concluded that combined behavioral lifestyle interventions can produce a significant
Eur J Nutr Table 3 Relation of changes in energy intake and physical activity to changes in anthropometric measures and body composition (n = 117) Anthropometric increase
Model 1 b (p value)
Model 2 b (p value)
Model 3 b (p value)
Weight (kg) Age
-1.29 (\0.01)
-1.29 (\0.01)
-1.48 (\0.01)
Female versus male
-1.96 (0.07)
-1.95 (0.06)
-0.99 (0.50)
Increase in physical activity (10 MET-h/week)
-0.04 (0.77)
-0.01 (0.95)
-0.08 (0.57)
Reduction in calorie intake (kcal/day) Tertile 1 (\119 kcal)
Reference
Reference
Reference
Tertile 2 (119–638 kcal)
-2.04 (0.14)
-1.14 (0.38)
-0.98 (0.50)
Tertile 3 (C638 kcal)
-3.82 (0.01)
-3.95 (\0.01)
-3.90 (0.01)
\0.01
\0.01
0.01
Age Female versus male
-0.25 (0.01) -0.10 (0.77)
-0.25 (0.01) -0.10 (0.77)
-0.32 (0.03) 0.07 (0.90)
Increase in physical activity (10 MET-h/week)
-0.01 (0.80)
-0.02 (0.97)
-0.01 (0.80)
p for trend 2
BMI (kg/m )
Reduction in calorie intake Tertile 1 (\119 kcal)
Reference
Reference
Reference
Tertile 2 (119–638 kcal)
-0.41 (0.35)
-0.27 (0.54)
-0.20 (0.69)
Tertile 3 (C638 kcal)
-1.10 (0.02)
-1.15 (0.01)
-1.10 (0.04)
\0.01
\0.01
0.03
Age
-0.51 (0.49)
-0.51 (0.49)
-0.69 (0.56)
Female versus male
0.05 (0.98)
0.20 (0.94)
3.33 (0.41)
Increase in physical activity (10 MET-h/week)
-0.20 (0.53)
-0.21 (0.53)
-0.26 (0.53)
p for trend Waist circumference (cm)
Reduction in calorie intake Tertile 1 (\119 kcal)
Reference
Reference
Reference
Tertile 2 (119–638 kcal)
-0.46 (0.89)
-0.28 (0.93)
0.76 (0.85)
Tertile 3 (C638 kcal)
3.08 (0.37)
2.96 (0.39)
4.77 (0.27)
0.32
0.35
0.25
p for trend Percent body fat (%)
BIA
Age
0.02 (0.90)
0.02 (0.90)
0.10 (0.66)
Female versus male
-0.25 (0.60)
-0.25 (0.60)
-0.29 (0.71)
Increase in physical activity (10 MET-h/week)
-0.03 (0.65)
-0.032 (0.61)
-0.02 (0.78)
Tertile 1 (\119 kcal)
Reference
Reference
Reference
Tertile 2 (119-638 kcal)
0.07 (0.91)
0.17 (0.80)
0.12 (0.87)
Tertile 3 (C638 kcal)
-0.50 (0.45)
-0.49 (0.47)
-0.39 (0.62)
0.40
0.41
0.59
Reduction in calorie intake
p for trend Fat mass
BIA
(kg)
Age
-0.64 (\0.01)
-0.64 (0.01)
-0.72 (0.05)
Female versus male
-1.15 (0.17)
-1.15 (0.16)
-0.81 (0.52)
Increase in physical activity (10 MET-h/week)
-0.06 (0.58)
-0.03 (0.74)
-0.06 (0.63)
Tertile 1 (\119 kcal) Tertile 2 (119–638 kcal)
Reference -0.94 (0.40)
Reference -0.30 (0.78)
Reference -0.39 (0.75)
Tertile 3 (C638 kcal)
-2.18 (0.05)
-2.22 (0.04)
-2.09 (0.11)
0.05
0.03
0.10
Reduction in calorie intake
p for trend Fat-free mass
BIA
(kg)
Age
-0.63 (\0.01)
-0.63 (\0.01)
-0.74 (\0.01)
Female versus male
-0.85 (0.12)
-0.85 (0.10)
-0.27 (0.70)
123
Eur J Nutr Table 3 continued Anthropometric increase Increase in physical activity (10 MET-h/week)
Model 1 b (p value)
Model 2 b (p value)
Model 3 b (p value)
0. 13 (0.32)
0.08 (0.53)
0.17 (0.15)
Reference
Reference
Reference
Reduction in calorie intake Tertile 1 (\119 kcal) Tertile 2 (119–638 kcal)
-1.20 (0.07)
-0.80 (0.21)
-0.68 (0.33)
Tertile 3 (C638 kcal)
-1.69 (0.01)
-1.76 (0.01)
-1.84 (0.01)
0.02
\0.01
0.01
Age
-0.01 (0.33)
-0.02 (0.33)
-0.003 (0.75)
Female versus male
-0.03 (0.20)
-0.03 (0.20)
-0.03 (0.33)
Increase in physical activity (10 MET-h/week)
0.004 (0.89)
0.0003 (0.93)
0.005 (0.99) Reference
p for trend BMI Z score
Reduction in calorie intake Tertile 1 (\119 kcal)
Reference
Reference
Tertile 2 (119–638 kcal)
-0.004 (0.89)
0.005 (0.87)
0.01 (0.78)
Tertile 3 (C638 kcal)
-0.06 (0.04)
-0.06 (0.04)
-0.05 (0.19)
0.03
0.03
0.16
p for trend
Model 1: univariate; Model 2: adjusted for age and gender; Model 3: adjusted for age, gender, race/ethnicity, Tanner Stage, energy intakes (tertiles), energy expenditure
and clinically meaningful reduction in overweight in children and adolescents. Another meta-analysis of 9 clinical trials by Ho et al. [19] provided support for the importance of dietary interventions as an essential component for managing childhood obesity. Among included trials, Ebbelling et al. [22] reported favorable effects on absolute BMI and fat mass reduction following a dietary intervention which resulted in overall reduced energy intake. Rolland-Cachera et al. [20] found that substantial weight loss was obtained with a moderate energy-restricted diet. In the present study, the increase in energy expenditure estimated by reported physical activity was not significantly associated with changes in anthropometrics and body fat. Such results are consistent with findings from Harris’ quantitative review of 18 randomized controlled trials of school-based physical activity intervention involving 18,141 children [7] and Ho’s systematic review of clinical trials comparing diet plus exercise and diet only interventions [19]. In Harris’ review, BMI was not significantly improved with school-based physical activity interventions (difference (95 % CI), -0.05 (-0.19 to 0.10) kg/m2) [7]. Ho’s review of 9 interventions with 519 participants showed no significant differences in BMI between the diet-only intervention and the diet plus aerobic training (p = 0.21) or plus a combination of aerobic and resistance training (p = 0.59). The diet-only group, however, showed a greater reduction than the diet plus resistance training group (p = 0.01) [19]. The present study found that increase in physical activity was
123
associated with reductions in body fat percentage and fat mass and increase in fat-free mass, but the associations were not statistically significant (ps [ 0.05). However, previous clinical trials showed that exercise reduced body fat percentage and improved lean body mass [19]. The non-significant association observed with our intervention may result from several reasons in this study. First, the reported mean increase in physical activity was only 12 MET-h/week, equivalent to less than 17 min of vigorous physical activity per day. The increase in activity may be smaller than necessary to produce significant effects on anthropometrics and body composition over 6 months. Second, physical activity is a complex and multifaceted behavior that is challenging to measure accurately, especially for adolescents [25]. However, the self-reported physical activity shows more validity in adolescents than in children [11]. Third, increasing physical activity may physiologically require more energy intake among adolescents, and energy intake may be impacted by energy expenditure [26]. Even though associations with changes in physical activity were not significant in our analyses, it has been established that physical activity offers other significant health benefits, such as improving physical fitness, metabolic profile and psychopathologic conditions, and preventing future obesity-related diseases [3, 24]. Review of clinical trials also indicated that addition of exercise to dietary intervention led to greater improvement in cholesterol, glucose, and insulin [19]. Adolescents should be encouraged to participate in physical activity.
Eur J Nutr
Conclusion Our results demonstrate that participation in a lifestyle modification program for 6 months was associated with reductions in body weight, BMI, BMI Z score, and fat mass among obese adolescents, and objective changes in BMI among participants were significantly associated with self-reported changes in energy intake. The observed improvement in anthropometric parameters was even more pronounced when we accounted for anticipated increases in body weight and BMI associated with expected weight gain during adolescence. The independent relationship between subject age and 6-month changes in body weight/BMI supports the importance of interpreting the specific results of lifestyle modification programs in children and adolescents in light of the anticipated changes in weight and BMI that normally occur with increasing age. In light of these findings, it is advisable that longitudinal studies addressing pediatric obesity report data on both absolute changes in BMI and age- and sex-controlled BMI Z scores. Acknowledgments The authors thank the TEENS program participants and their parents and gratefully acknowledge Ms. Janet Delorme for her role as Program Coordinator. This study was supported by funding from Virginia Premier Health Plan, Inc., the American Heart Association, Ronald McDonald House Charities and the YMCA of Greater Richmond. Additional Support was provided by the National Institutes of Health (NIH) through CTSA award No. UL1TR000058 from the National Center for Advancing Translational Sciences [to VCU] and K23HD053742 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development [to EPW]. Its contents are solely the responsibility of the authors and do not necessarily represent official views of the NIH. The study is registered with ClinicalTrials.Gov NCT00167830 and NCT00562263. Conflict of interest
None.
References 1. Ogden CL, Carroll MD, Curtin LR, Lamb MM, Flegal KM (2010) Prevalence of high body mass index in US children and adolescents, 2007–2008. JAMA 303(3):242–249 2. l’Allemand-Jander D (2010) Clinical diagnosis of metabolic and cardiovascular risks in overweight children: early development of chronic diseases in the obese child. Int J Obes (Lond) 34(Suppl 2):S32–S36 3. Dietz WH (1998) Health consequences of obesity in youth: childhood predictors of adult disease. Pediatrics 101(3 Pt 2): 518–525 4. Oude Luttikhuis H, Baur L, Jansen H, Shrewsbury VA, O’Malley C, Stolk RP, Summerbell CD (2009) Interventions for treating obesity in children. Cochrane Database Syst Rev 1:CD001872 5. Luttikhuis OH, Baur L, Jansen H, Shrewsbury VA, O’Malley C, Stolk RP, Summerbell CD (2009) Interventions for treating obesity in children. Cochrane Database Syst Rev 1:CD001872 6. Bleich SN, Ku R, Wang YC (2011) Relative contribution of energy intake and energy expenditure to childhood obesity: a
7.
8.
9. 10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
review of the literature and directions for future research. Int J Obes (Lond) 35(1):1–15 Harris KC, Kuramoto LK, Schulzer M, Retallack JE (2009) Effect of school-based physical activity interventions on body mass index in children: a meta-analysis. CMAJ 180(7):719–726 Centers for Disease Control and Prevention, National Center for Health Statistics (2000) CDC growth charts: United States. http:// www.cdc.gov/growthcharts/ Willett W (1990) Nutritional epidemiology. Oxford University Press, New York Johnson RK, Driscoll P, Goran MI (1996) Comparison of multiple-pass 24-hour recall estimates of energy intake with total energy expenditure determined by the doubly labeled water method in young children. J Am Diet Assoc 96(11):1140–1144 Chinapaw MJ, Mokkink LB, van Poppel MN, van Mechelen W, Terwee CB (2010) Physical activity questionnaires for youth: a systematic review of measurement properties. Sports Med 40(7):539–563 Bean MK, Mazzeo SE, Stern M, Evans RK, Bryan D, Ning Y, Wickham EP 3rd, Laver J (2011) Six-month dietary changes in ethnically diverse, obese adolescents participating in a multidisciplinary weight management program. Clin Pediatr (Phila) 50(5):408–416 Kotler DP, Burastero S, Wang J, Pierson RN Jr (1996) Prediction of body cell mass, fat-free mass, and total body water with bioelectrical impedance analysis: effects of race, sex, and disease. Am J Clin Nutr 64(3 Suppl):489S–497S Hosking J, Metcalf BS, Jeffery AN, Voss LD, Wilkin TJ (2006) Validation of foot-to-foot bioelectrical impedance analysis with dual-energy X-ray absorptiometry in the assessment of body composition in young children: the EarlyBird cohort. Br J Nutr 96(6):1163–1168 Kalarchian MA, Levine MD, Arslanian SA, Ewing LJ, Houck PR, Cheng Y, Ringham RM, Sheets CA, Marcus MD (2009) Family-based treatment of severe pediatric obesity: randomized, controlled trial. Pediatrics 124(4):1060–1068 Papadaki A, Linardakis M, Larsen TM, van Baak MA, Lindroos AK, Pfeiffer AF, Martinez JA, Handjieva-Darlenska T, Kunesova M, Holst C et al (2010) The effect of protein and glycemic index on children’s body composition: the DiOGenes randomized study. Pediatrics 126(5):e1143–e1152 Duckworth LC, Gately PJ, Radley D, Cooke CB, King RF, Hill AJ (2009) RCT of a high-protein diet on hunger motivation and weight-loss in obese children: an extension and replication. Obesity (Silver Spring) 17(9):1808–1810 U.S. Department of Agriculture and U.S. Department of Health and Human Services (2010) Dietary guidelines for Americans, 2010, 7th edn. U.S. Government Print Office, Washington, DC, December 2010 Ho M, Garnett SP, Baur LA, Burrows T, Stewart L, Neve M, Collins C (2013) Impact of dietary and exercise interventions on weight change and metabolic outcomes in obese children and adolescents: a systematic review and meta-analysis of randomized trials. JAMA Pediatr 167(8):759–768 Rolland-Cachera MF, Thibault H, Souberbielle JC, Soulie D, Carbonel P, Deheeger M, Roinsol D, Longueville E, Bellisle F, Serog P (2004) Massive obesity in adolescents: dietary interventions and behaviours associated with weight regain at 2 y follow-up. Int J Obes Relat Metab Disord 28(4):514–519 Gutin B, Barbeau P, Owens S, Lemmon CR, Bauman M, Allison J, Kang HS, Litaker MS (2002) Effects of exercise intensity on cardiovascular fitness, total body composition, and visceral adiposity of obese adolescents. Am J Clin Nutr 75(5):818–826 Ebbeling CB, Leidig MM, Sinclair KB, Hangen JP, Ludwig DS (2003) A reduced-glycemic load diet in the treatment of adolescent obesity. Arch Pediatr Adolesc Med 157(8):773–779
123
Eur J Nutr 23. Carels RA, Young KM, Coit C, Clayton AM, Spencer A, Wagner M (2008) Skipping meals and alcohol consumption. The regulation of energy intake and expenditure among weight loss participants. Appetite 51(3):538–545 24. Daley AJ, Copeland RJ, Wright NP, Roalfe A, Wales JK (2006) Exercise therapy as a treatment for psychopathologic conditions in obese and morbidly obese adolescents: a randomized, controlled trial. Pediatrics 118(5):2126–2134
123
25. Sazonov ES, Schuckers S (2010) The energetics of obesity: a review: monitoring energy intake and energy expenditure in humans. IEEE Eng Med Biol Mag 29(1):31–35 26. Thivel D, Aucouturier J, Doucet E, Saunders TJ, Chaput J-P (2013) Daily energy balance in children and adolescents. Does energy expenditure predict subsequent energy intake? Appetite 60(1):58–64
ORIGINAL CONTRIBUTION
Changes in body anthropometry and composition in obese adolescents in a lifestyle intervention program Yi Ning • Shibing Yang • Ronald K. Evans • Marilyn Stern • Shumei Sun • Gary L. Francis Edmond P. Wickham III
•
Received: 20 June 2013 / Accepted: 21 October 2013 Ó Springer-Verlag Berlin Heidelberg 2013
Abstract Purpose Impact of lifestyle modification on obesity control during adolescence, a period of significant physical growth and development, is less quantitatively evaluated. Therefore, we investigated the impact of changes in reported energy intake and physical activity on anthropometrics and body composition in adolescents. Methods Participants were obese adolescents aged 11–18 years. All of them have a body mass index (BMI) C 95th percentile specific for age and gender according to the 2000 CDC Growth Charts. The intervention consists of supervised physical activity, structured nutrition education and Y. Ning (&) S. Yang Division of Epidemiology, Department of Family Medicine and Population Health, Virginia Commonwealth University School of Medicine, One Capitol Plaza, Suite 8020, 830 East Main Street, Richmond, VA 23298-0212, USA e-mail: [email protected] R. K. Evans Department of Health and Human Performance, Virginia Commonwealth University School of Medicine, Richmond, VA 23298, USA R. K. Evans M. Stern G. L. Francis E. P. Wickham III Department of Pediatrics, Virginia Commonwealth University School of Medicine, Richmond, VA 23298, USA M. Stern Department of Psychology, Virginia Commonwealth University School of Medicine, Richmond, VA 23298, USA
dietary modification, and behavioral support in 6 months. Hundred and forty-five obese adolescents completed the study. Results Compared to baseline, significant reductions in body weight (-1.4 kg, p \ 0.001) and BMI (-0.1 kg/m2, p \ 0.001) were observed at 6 months. When compared to expected growth trajectories on the 2000 CDC Growth Charts, body weight and BMI were reduced by 3.6 kg and 1.5 kg/m2, respectively, in boys and 5.6 kg and 1.9 kg/m2 in girls. Age was inversely associated with changes in weight (b = -1.48 kg, p \ 0.01) and BMI (b = -0.32 kg/m2, p = 0.03). There was a dose–response relationship between reduction in energy intake and weight loss. A decrease of 100 kcal/day was significantly associated with reductions in body weight 0.30 kg, BMI 0.09 kg/m2, and BMI Z score 0.01 (all p \ 0.01). Physical activity was not significantly associated with changes in anthropometrics or body composition. Conclusions Reduction in energy intake was a significant predictor of obesity reduction in these adolescents. A quantitative evaluation of adolescent weight loss programs should account for natural growth and development. Keywords Obesity Intervention Nutrition Physical activity Evaluation Abbreviations BIA Bioelectric impedance analysis BMI Body mass index CDC Centers for disease control and prevention DXA Dual X-ray absorptiometry
S. Sun Department of Biostatistics, Virginia Commonwealth University School of Medicine, Richmond, VA 23298, USA
Introduction
G. L. Francis E. P. Wickham III Department of Internal Medicine, Virginia Commonwealth University School of Medicine, Richmond, VA 23298, USA
In U.S., the prevalence of obesity among adolescents has more than tripled between 1976–1980 and 2007–2008 [1].
123
Eur J Nutr
Hyperlipidemia, hypertension, and abnormal glucose tolerance occur with increased frequency in obese children and adolescents [2, 3]. Moreover, obesity in childhood is associated with an increased risk of cardiovascular disease, diabetes, and cancer in their adulthood, highlighting the importance of effective treatment earlier in life. Lifestyle treatment for obesity, which emphasizes healthy eating, physical activity, and behavioral change, has been a primary focus of clinical and public practice [4]. However, literature on energy intake and physical activity and adolescent weight management offers conflicting findings about the independent impacts of dietary and physical activity interventions [5, 6]. Although both dietary intervention and physical activity are important for body weight control and general health, they may manifest their impacts differently through various biological pathways in adolescents. Moreover, few studies have prospectively and quantitatively [7] investigated caloric changes in energy intake or physical activity in weight management in adolescents. Adolescence represents a dynamic period of development with significant changes in somatic growth and pubertal development. The pubertal period is associated with a significant increase in growth velocity from childhood, including rapid changes in height, weight, and body mass index (BMI) among normal weight males and females [8]. Accounting for these ‘‘expected’’ changes may introduce an additional layer of complexity to evaluate the impact of lifestyle modification on body weight and adiposity among overweight and obese adolescents. Purpose of the present study was to simultaneously evaluate the association between changes in reported energy intake and physical activity and reductions in body weight, BMI, BMI Z scores, waist circumference, and body composition. In addition, we compared changes in anthropometrics following 6 months of program participation with projected changes based on the 2000 CDC Growth Charts [8] anticipated to occur over the same time period without intervention.
Methods Study population The Teaching, Encouragement, Exercise, Nutrition, and Support (T.E.E.N.S.) is an ongoing community-based adolescent weight management research program that was initiated in November 2003. The intervention consists of supervised physical activity, structured nutrition education and dietary modification, and behavioral support. Between December 2004 and December 2010, obese boys and girls between 11 and 18 years of age with a BMI C 95th
123
percentile specific for age and gender according to the 2000 CDC Growth Charts [8] were eligible for participation if they had at least one adult in the household who was committed to attend the program meetings. Exclusion criteria were underlying genetic, neurologic, endocrine, or metabolic conditions that preclude weight loss with conventional diet and exercise programs (Prader-Willi, Cushing’s syndrome, et al.); weight greater than 400 lbs (181.4 kg); pregnancy; inability to understand program instructions due to a language barrier or a cognitive disability; a physical disability that severely limits participation in the outlined exercise program; or primary residence outside of a 30 mile radius of the study center. At baseline, a questionnaire-based in-person interview was administered to participants and their parents to collect information on sociodemographic information and family history of obesity. A family history of obesity was considered to be positive when the adolescent had at least one parent with a self-reported BMI C 30 kg/m2. Dietary intake and physical activity were respectively assessed by a 48-h diet recall [9, 10] and a 7-day physical activity recall [11] at baseline and 6 months after the intervention. A pediatric specialist conducted a detailed medical history examination and determined Tanner staging according to external primary and secondary sex characteristics. All data were collected in our research center. The T.E.E.N.S. program procedures were approved by the Institutional Review Board of Virginia Commonwealth University (VCU). Parents provided written informed consent, and adolescents provided written assent prior to program participation. Lifestyle modification intervention Enrolled participants completed standardized anthropometric, nutrition, and physical activity assessments at baseline and following 6-months of participation in the T.E.E.N.S. intervention. In brief, the program consists of three major components: (1) a structured nutrition education program, (2) supervised physical activity, and (3) behavioral support. Over the initial 6 months, the nutrition component of the intervention consists of a series of twelve, 30-min, individualized education sessions conducted with an adolescent and parent(s) by a registered dietitian with expertise in pediatric weight management. The program’s dietary intervention did not focus on specific caloric goals, but encouraged the modification of highrisk eating behaviors. Behavior support sessions designed to promote sustained behavior change were conducted in either individual or group formats by graduate students in health psychology under the supervision of trained faculty. Education and behavioral support sessions were each held once every 2 weeks, on alternating weeks. The physical activity program consisted of 60 min sessions conducted at
Eur J Nutr
the program’s dedicated gym facility at least 3 times per week during the initial 12 weeks of the intervention, and a minimum of twice a week thereafter. Details of the intervention can be found in previous publication [12]. Nutrition assessment Two parent-assisted 48-h dietary recalls were conducted in person with participants by a trained registered dietitian at baseline and 6 months, respectively [9]. The 2 days covered in the dietary recalls did not overlap with the 12-h fasting period prior to anthropometric measurements. To improve quality of dietary recall, we used a multiple-pass technique to assess dietary intake [10]. The dietitian used two- and three-dimensional tools, including food models and empty food containers (e.g., potato chips, drinks, and other frequently consumed items) to aid in portion estimates. Memory prompts were used to assist the recall of frequently forgotten foods. Using data collected from the 48-h recall, total energy intake and dietary macronutrient composition were determined using Nutritionist Pro software (Axxya Systems, Stafford, TX). An average of the 48-h intake of total dietary energy was calculated [9]. Physical activity assessment A standardized seven-day physical activity recall [11] was administered by a trained interviewer to determine the adolescent’s engagement in daily activity at both baseline and 6 months after staring intervention. Energy expenditure of reported physical activity was calculated based on these activities. MET-hours (metabolic equivalent [MET] unit times duration of activity in one week) for each participant were summed according to time engaged in sleep (1 MET), light activities (1.5 METs), moderate activities (4 METs), vigorous activities (6 METs), and very vigorous activities (10 METs). The physical activity questionnaire was validated for use in adolescents [11]. Anthropometry and body composition Anthropometric measurements were performed by trained dietician following a 12-h fasting at baseline and 6 months. Weight was measured (to the nearest 0.1 kg) using a calibrated digital scale with the participants dressed in light indoor clothing and without shoes. Height was measured (to the nearest 0.1 cm) using a calibrated wallmounted stadiometer. BMI was calculated, and BMI percentiles/z scores were determined using Epi Info Software (Centers for Disease Control, Version 3.3). Waist circumference was measured (to the nearest 0.1 cm) at the level of the umbilicus with a Gullick II tape (Country Technology, Inc., Gays Mills, WI). Body composition was
assessed by bioelectric impedance analysis (BIA) as determined by the Quantum II Bioelectrical Body Composition Analyzer (RJL Systems, Clinton Township, MI). Results were entered into a computer program, and percentages of body fat and body water were estimated using commercially available software (Cyprus 2.7, RJL systems, Clinton Township, MI) based on equations derived by Kotler [13]. Starting in 2007, body composition was also estimated at baseline and 6 months using dual X-ray absorptiometry (DXA) in 45 participants. The DXA scanning was performed using a Hologic 4500a/Discovery scanner. The correlation coefficients between the two methods for fat mass and fat-free mass were 0.93–0.99 for boys and girls at baseline and 6 months, respectively. Although BIA has been suggested to underestimate body fat in comparison with DXA [14], BIA was expected to provide valid results for body composition in the present study population. Statistical analysis After confirming the normal distribution of energy intake, energy expenditure of reported physical activity, anthropometry, and body composition, mean differences of these variables between baseline and 6 months were compared using paired t tests assuming equal variance. We also estimated the expected gains in body weight and BMI in 6 months based on the 2000 CDC Growth Charts [8]. In particular, the constant 99th percentiles of body weight and BMI at both baseline and 6 months after baseline were calculated using the LMS approach (http://www.cdc.gov/ growthcharts/percentile_data_files.htm). Age- and genderspecific parameters (for L, M and S) were obtained directly from this CDC website. The differences in 99th percentiles of weights and BMIs at baseline and 6 months after baseline (i.e., expected gains) were added to baseline measures to estimate the expected body weight and BMI after 6 months without experiencing the intervention. The expected gains in weights and BMIs at the 99th percentiles were estimated separately for boys and girls. To evaluate predictors of weight loss at an individual level, we performed linear regression analyses to examine the association between potential determinants and changes in anthropometrics and body composition. Specifically, we considered age, gender, race, family history of obesity, and Tanner stage of puberty. Changes in energy intake and physical activity were included as independent variables. Including physical activity either as a continuous or categorical (tertiles) variable did not change the estimates for total energy intake appreciably and the estimates for physical activity were comparable and were not significant, so we used physical activity as a continuous variable to reduce degree of freedom. Change in energy intake was
123
Eur J Nutr Table 1 Baseline characteristics of obese adolescents enrolled in the T.E.E.N.S. program
Characteristics Number
All (n, %) 145
Female (n, %) 94
Male (n, %) 51
Age (years) 11–\13
59 (40.7)
37 (39.4)
13–14.9
54 (37.2)
37 (39.4)
22 (43.1) 17 (33.3)
15–\18
32 (22.1)
20 (21.3)
12 (23.5)
Race/ethnicitya Caucasian African American Other Family history of obesityb a
Race/ethnicity data were selfreported
b
Defined as at least one parent with a body mass index C30 kg/m2
13 (13.8)
16 (31.4)
76 (80.9)
32 (62.8)
8 (5.5)
5 (5.3)
3 (5.9)
85 (58.6)
59 (62.8)
26 (51.0)
Tanner stage I–II
10 (6.9)
3 (3.2)
7 (13.7)
III
54 (37.2)
26 (27.7)
28 (54.9)
IV–V
81 (45.9)
55 (69.1)
16 (31.4)
categorized into tertiles. Tests of trends were also conducted using the median value for each category of energy intake as a continuous variable in multivariate models. Potential interactions between gender and energy intake/ physical activity on study outcomes were tested and ruled out. All analyses were conducted using SAS version 9.2 (SAS institute Inc, Cary, NC). All statistical tests were twosided, and p values less than 0.05 were considered statistically significant.
Results Baseline demographic and clinical characteristics for the 145 adolescents included in these analyses are shown in Table 1. The majority of the participants were African American (75 %), and 59 % had a positive family history of parental obesity. The average age of participants is 13.1 years. The study population had a mean BMI of 37.2 kg/m2. There was a higher percentage of African Americans among the girls than among the boys (p \ 0.01). Table 2 contains anthropometrics, body composition, energy intake, and energy expenditure of enrolled adolescents at baseline and after 6 months of lifestyle modification. At baseline, the estimated energy expenditure was approximately 240.0 ± 28.6 MET-h/week; the average intake of total energy was 2,020 ± 675 kcal/day. At 6 months after intervention, the mean reported energy intake was reduced by 425.0 kcal/day. Self-reported physical activity increased by 12.0 MET-h/week, which corresponds to the addition of 17 min of vigorous activity per day. Compared with baseline, the adolescents’ overall body weight, BMI, BMI percentile, BMI Z scores, waist
123
29 (20.0) 108 (74.5)
circumference, percentages of body fat, and fat mass were significantly reduced, while fat-free mass was significantly increased following program participation. Figure 1a–d graphically compares measures of body adiposity of these participants at baseline and at 6 months of intervention and projected values at 6 months according to age and gender. Because adolescents normally experience significant physical growth, even over a period of 6 months, expected 6-month values were derived from 2000 CDC Growth Charts as if there were no intervention [8]. Observed BMI was reduced from baseline after the intervention across all the age groups, while body weight was reduced for boys aged C15 years and girls aged C14 years. The reduction in weight and BMI became more pronounced compared with the expected trajectories at 6 months without intervention, with a relative reduction of 3.6 kg in weight and 1.5 kg/m2 in BMI among boys and reductions of 5.6 kg and 1.9 kg/m2 in girls. Figure 1e, f highlights the observed reductions in body fat percentage from baseline to 6 months across age groups following intervention. In the univariate linear regression analyses (Table 3), age was inversely and significantly associated with changes in weight (p \ 0.01) and BMI (p = 0.01), but not significantly associated with change in percent body fat (p = 0.90). However, there were significant inverse associations between baseline age and changes in both fat mass (p \ 0.01) and fat-free mass (p \ 0.01). Self-reported reductions in energy intake were significantly associated with greater reductions in body weight, BMI, fat mass, and fat-free mass (all ptrend B 0.05). Results of multivariate analyses were similar to those in univariate analysis. In multivariate models, older age was associated with more reductions in weight (-1.48 kg/year, p \ 0.01) and BMI (-0.32 kg/m2/year, p = 0.03). Each reduction in energy
Eur J Nutr Table 2 Changes in energy intake, energy expenditure, anthropometry, and body composition in obese adolescents Variables (mean ± SD)
Baseline
Physical activity (MET-h/week)a Total calorie intake (kcal/day)
b
6 months
240.0 ± 28.6
251.8 ± 37.2
2,020.0 ± 674.6
1,595.3 ± 426.3
Weight (kg)
100.9 ± 21.1
99.5 ± 20.9
p value§ 0.003
N 125c
\0.001
117
0.005
145 145
Height (cm)
164.3 ± 8.6
165.8 ± 8.5
\0.001
BMI (kg/m2)
37.2 ± 6.0
36.1 ± 6.1
\0.001
145
BMI percentile
99.0 ± 0.9
98.6 ± 1.6
\0.001
145
2.4 ± 0.3
2.3 ± 0.3
\0.001
145
103.3 ± 12.0
102.6 ± 16.4
0.531
121
43.7 ± 11.6
42.3 ± 11.8
\0.001
140
BMI Z score Waist circumference (cm) Body fat percentage
BIA
(%)
44.8 ± 16.7
42.7 ± 16.5
\0.001
140
Fat-free mass BIA (kg) Body fat percentage DXA
56.3 ± 15.1 42.6 ± 4.7
56.8 ± 15.3 40.2 ± 5.7
0.052 \0.001
140 45d
Fat mass
41.9 ± 12.0
39.0 ± 12.2
\0.001
45d
55.7 ± 9.1
56.6 ± 8.3
0.021
45d
Fat mass
BIA
(kg)
DXA
(kg)
Fat-free mass
DXA
(kg)
BIA bioimpedance analysis, DXA dual-energy X-ray absorptiometry §
p values were obtained through paired t test
a
Assessed via 7-day physical activity recall
b
Assessed via dietitian-assisted 48-h dietary recall
c
Missing values on MET-hours at 6 months for six participants were imputed with information at 3 months
d
The information was collected after a change in research protocol
intake of 100 calories/day was significantly associated with change in weight loss (-0.30 kg, p = 0.004) and BMI (-0.1 kg/m2, p = 0.006) at 6 months. In contrast, increase in physical activity levels was not significantly associated with reductions in body fat percentage and fat mass and increase in fat-free mass in either univariate or multivariate regression models (p [ 0.05). No significant interaction with gender was identified for the association of age with changes in anthropometry or body composition. Considering change in BMI Z score as the dependent variable of interest, reduction in energy intake was moderate, but significantly associated with reduction in BMI Z scores in age- and gender-adjusted model (b = -0.02/100 kcal, p = 0.03). After further adjustment for race/ethnicity, Tanner stage and energy expenditure, the magnitudes of association were not changed appreciably even though the p values became non-significant. The results were not significantly different when comparing analyses using body composition data assessed by DXA and BIA among 45 adolescents who had body composition measured by two methods.
Discussion In the present study, beneficial changes in body weight and BMI from baseline values were observed following the lifestyle intervention. In fact, the potential impact of the
lifestyle modification intervention on body weight and BMI is even more apparent after accounting for the expected normal development in adolescents using trajectories based on 2000 CDC Growth Charts. Self-reported reductions in energy intake at 6 months were significantly associated with reductions in body weight, BMI, percent body fat, and fat mass as well as increased fat-free mass. No significant associations were observed between selfreported changes in physical activity and changes in anthropometrics or body composition. Two major limitations merit discussion and consideration. One limitation is that the study utilized data from an existing multidisciplinary weight management program, which was designed without an active comparison group. However, the serial measures of body anthropometry and composition provided us thorough information to investigate the association of changes in reported energy intake and physical activity prospectively with objective changes in weight status and body composition within a given adolescent. Our comprehensive analysis of weight, BMI, BMI Z scores, waist circumference, body fat percentage, fat mass, and fat-free mass showed consistent results. Another limitation is that both data regarding dietary intake and physical activity were self-reported. Seven-day physical activity was assessed at both baseline and six months after starting intervention. It is well established that participants often underreport their calorie intake, representing an inherent limitation of dietary recall [9]. We were
123
Eur J Nutr Fig. 1 Changes in body weight, body mass index, and body fat percentage by gender and age at baseline
concerned that energy intakes at baseline (2,020 kcal per day) and at 6 months (1,559 kcal per day) in this study were lower than actual intakes. However, the energy intake reported in this specific population was comparable with or greater than those in published studies [15–17] or dietary guidelines involving adolescents [18]. Previous clinical trials involving obese children reported energy intake of ‘‘1,437–1,898 kcal’’ [16], ‘‘averaged 1,926 kcal’’ [17], or ‘‘a desired intake of 1,200–1,800 kcal’’ [15]. Dietary guidelines for Americans also suggest daily energy intakes between 1,400 and 2,400 kcal per day for girls and 1,600–3,200 kcal for boys aged 9–18 years [18]. Most clinical trials used calorie restriction approach limiting energy intake within the range from 900 to 1,800 kcal per day [19]. Therefore, underreporting of dietary intake is not viewed as a significant source of bias in the interpretation of our results. Moreover, changes in daily energy intake, despite being self-reported, were significantly associated with changes in weight and BMI, objective measures supportive of actual reductions in energy intake. Generally, people tend to underreport energy intake and over-report levels of physical activity perhaps due to social
123
desirability. If the recall bias is non-differential, it usually biases the observed associations toward the null. Therefore, the true association would be stronger than the reported results if there was non-differential recall bias. In our separate manuscript (unpublished), 48-h dietary recall better predicted weight loss than 24-h dietary recall. It is possible that the 48-h dietary recall better captured the dayby-day variation in dietary intake; thus, recall bias was minimal in our study. We collected dietary information on Monday to Thursday, and thus, adolescents seen on Monday would have 2 weekend days, and kids seen on Tuesday would have 1 weekend day. It would have been reasonable to estimate that half of the recalls included at least one weekend day, and 25 % of the recalls included both weekend days. Findings from the present study among obese adolescents are in line with results from clinical trials that target lower energy intake, which support the significant impact of reduced energy intake on weight loss and reduction in fat mass [19–24]. A comprehensive review of 5 clinical trials by Luttikhuis et al. [5] concluded that combined behavioral lifestyle interventions can produce a significant
Eur J Nutr Table 3 Relation of changes in energy intake and physical activity to changes in anthropometric measures and body composition (n = 117) Anthropometric increase
Model 1 b (p value)
Model 2 b (p value)
Model 3 b (p value)
Weight (kg) Age
-1.29 (\0.01)
-1.29 (\0.01)
-1.48 (\0.01)
Female versus male
-1.96 (0.07)
-1.95 (0.06)
-0.99 (0.50)
Increase in physical activity (10 MET-h/week)
-0.04 (0.77)
-0.01 (0.95)
-0.08 (0.57)
Reduction in calorie intake (kcal/day) Tertile 1 (\119 kcal)
Reference
Reference
Reference
Tertile 2 (119–638 kcal)
-2.04 (0.14)
-1.14 (0.38)
-0.98 (0.50)
Tertile 3 (C638 kcal)
-3.82 (0.01)
-3.95 (\0.01)
-3.90 (0.01)
\0.01
\0.01
0.01
Age Female versus male
-0.25 (0.01) -0.10 (0.77)
-0.25 (0.01) -0.10 (0.77)
-0.32 (0.03) 0.07 (0.90)
Increase in physical activity (10 MET-h/week)
-0.01 (0.80)
-0.02 (0.97)
-0.01 (0.80)
p for trend 2
BMI (kg/m )
Reduction in calorie intake Tertile 1 (\119 kcal)
Reference
Reference
Reference
Tertile 2 (119–638 kcal)
-0.41 (0.35)
-0.27 (0.54)
-0.20 (0.69)
Tertile 3 (C638 kcal)
-1.10 (0.02)
-1.15 (0.01)
-1.10 (0.04)
\0.01
\0.01
0.03
Age
-0.51 (0.49)
-0.51 (0.49)
-0.69 (0.56)
Female versus male
0.05 (0.98)
0.20 (0.94)
3.33 (0.41)
Increase in physical activity (10 MET-h/week)
-0.20 (0.53)
-0.21 (0.53)
-0.26 (0.53)
p for trend Waist circumference (cm)
Reduction in calorie intake Tertile 1 (\119 kcal)
Reference
Reference
Reference
Tertile 2 (119–638 kcal)
-0.46 (0.89)
-0.28 (0.93)
0.76 (0.85)
Tertile 3 (C638 kcal)
3.08 (0.37)
2.96 (0.39)
4.77 (0.27)
0.32
0.35
0.25
p for trend Percent body fat (%)
BIA
Age
0.02 (0.90)
0.02 (0.90)
0.10 (0.66)
Female versus male
-0.25 (0.60)
-0.25 (0.60)
-0.29 (0.71)
Increase in physical activity (10 MET-h/week)
-0.03 (0.65)
-0.032 (0.61)
-0.02 (0.78)
Tertile 1 (\119 kcal)
Reference
Reference
Reference
Tertile 2 (119-638 kcal)
0.07 (0.91)
0.17 (0.80)
0.12 (0.87)
Tertile 3 (C638 kcal)
-0.50 (0.45)
-0.49 (0.47)
-0.39 (0.62)
0.40
0.41
0.59
Reduction in calorie intake
p for trend Fat mass
BIA
(kg)
Age
-0.64 (\0.01)
-0.64 (0.01)
-0.72 (0.05)
Female versus male
-1.15 (0.17)
-1.15 (0.16)
-0.81 (0.52)
Increase in physical activity (10 MET-h/week)
-0.06 (0.58)
-0.03 (0.74)
-0.06 (0.63)
Tertile 1 (\119 kcal) Tertile 2 (119–638 kcal)
Reference -0.94 (0.40)
Reference -0.30 (0.78)
Reference -0.39 (0.75)
Tertile 3 (C638 kcal)
-2.18 (0.05)
-2.22 (0.04)
-2.09 (0.11)
0.05
0.03
0.10
Reduction in calorie intake
p for trend Fat-free mass
BIA
(kg)
Age
-0.63 (\0.01)
-0.63 (\0.01)
-0.74 (\0.01)
Female versus male
-0.85 (0.12)
-0.85 (0.10)
-0.27 (0.70)
123
Eur J Nutr Table 3 continued Anthropometric increase Increase in physical activity (10 MET-h/week)
Model 1 b (p value)
Model 2 b (p value)
Model 3 b (p value)
0. 13 (0.32)
0.08 (0.53)
0.17 (0.15)
Reference
Reference
Reference
Reduction in calorie intake Tertile 1 (\119 kcal) Tertile 2 (119–638 kcal)
-1.20 (0.07)
-0.80 (0.21)
-0.68 (0.33)
Tertile 3 (C638 kcal)
-1.69 (0.01)
-1.76 (0.01)
-1.84 (0.01)
0.02
\0.01
0.01
Age
-0.01 (0.33)
-0.02 (0.33)
-0.003 (0.75)
Female versus male
-0.03 (0.20)
-0.03 (0.20)
-0.03 (0.33)
Increase in physical activity (10 MET-h/week)
0.004 (0.89)
0.0003 (0.93)
0.005 (0.99) Reference
p for trend BMI Z score
Reduction in calorie intake Tertile 1 (\119 kcal)
Reference
Reference
Tertile 2 (119–638 kcal)
-0.004 (0.89)
0.005 (0.87)
0.01 (0.78)
Tertile 3 (C638 kcal)
-0.06 (0.04)
-0.06 (0.04)
-0.05 (0.19)
0.03
0.03
0.16
p for trend
Model 1: univariate; Model 2: adjusted for age and gender; Model 3: adjusted for age, gender, race/ethnicity, Tanner Stage, energy intakes (tertiles), energy expenditure
and clinically meaningful reduction in overweight in children and adolescents. Another meta-analysis of 9 clinical trials by Ho et al. [19] provided support for the importance of dietary interventions as an essential component for managing childhood obesity. Among included trials, Ebbelling et al. [22] reported favorable effects on absolute BMI and fat mass reduction following a dietary intervention which resulted in overall reduced energy intake. Rolland-Cachera et al. [20] found that substantial weight loss was obtained with a moderate energy-restricted diet. In the present study, the increase in energy expenditure estimated by reported physical activity was not significantly associated with changes in anthropometrics and body fat. Such results are consistent with findings from Harris’ quantitative review of 18 randomized controlled trials of school-based physical activity intervention involving 18,141 children [7] and Ho’s systematic review of clinical trials comparing diet plus exercise and diet only interventions [19]. In Harris’ review, BMI was not significantly improved with school-based physical activity interventions (difference (95 % CI), -0.05 (-0.19 to 0.10) kg/m2) [7]. Ho’s review of 9 interventions with 519 participants showed no significant differences in BMI between the diet-only intervention and the diet plus aerobic training (p = 0.21) or plus a combination of aerobic and resistance training (p = 0.59). The diet-only group, however, showed a greater reduction than the diet plus resistance training group (p = 0.01) [19]. The present study found that increase in physical activity was
123
associated with reductions in body fat percentage and fat mass and increase in fat-free mass, but the associations were not statistically significant (ps [ 0.05). However, previous clinical trials showed that exercise reduced body fat percentage and improved lean body mass [19]. The non-significant association observed with our intervention may result from several reasons in this study. First, the reported mean increase in physical activity was only 12 MET-h/week, equivalent to less than 17 min of vigorous physical activity per day. The increase in activity may be smaller than necessary to produce significant effects on anthropometrics and body composition over 6 months. Second, physical activity is a complex and multifaceted behavior that is challenging to measure accurately, especially for adolescents [25]. However, the self-reported physical activity shows more validity in adolescents than in children [11]. Third, increasing physical activity may physiologically require more energy intake among adolescents, and energy intake may be impacted by energy expenditure [26]. Even though associations with changes in physical activity were not significant in our analyses, it has been established that physical activity offers other significant health benefits, such as improving physical fitness, metabolic profile and psychopathologic conditions, and preventing future obesity-related diseases [3, 24]. Review of clinical trials also indicated that addition of exercise to dietary intervention led to greater improvement in cholesterol, glucose, and insulin [19]. Adolescents should be encouraged to participate in physical activity.
Eur J Nutr
Conclusion Our results demonstrate that participation in a lifestyle modification program for 6 months was associated with reductions in body weight, BMI, BMI Z score, and fat mass among obese adolescents, and objective changes in BMI among participants were significantly associated with self-reported changes in energy intake. The observed improvement in anthropometric parameters was even more pronounced when we accounted for anticipated increases in body weight and BMI associated with expected weight gain during adolescence. The independent relationship between subject age and 6-month changes in body weight/BMI supports the importance of interpreting the specific results of lifestyle modification programs in children and adolescents in light of the anticipated changes in weight and BMI that normally occur with increasing age. In light of these findings, it is advisable that longitudinal studies addressing pediatric obesity report data on both absolute changes in BMI and age- and sex-controlled BMI Z scores. Acknowledgments The authors thank the TEENS program participants and their parents and gratefully acknowledge Ms. Janet Delorme for her role as Program Coordinator. This study was supported by funding from Virginia Premier Health Plan, Inc., the American Heart Association, Ronald McDonald House Charities and the YMCA of Greater Richmond. Additional Support was provided by the National Institutes of Health (NIH) through CTSA award No. UL1TR000058 from the National Center for Advancing Translational Sciences [to VCU] and K23HD053742 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development [to EPW]. Its contents are solely the responsibility of the authors and do not necessarily represent official views of the NIH. The study is registered with ClinicalTrials.Gov NCT00167830 and NCT00562263. Conflict of interest
None.
References 1. Ogden CL, Carroll MD, Curtin LR, Lamb MM, Flegal KM (2010) Prevalence of high body mass index in US children and adolescents, 2007–2008. JAMA 303(3):242–249 2. l’Allemand-Jander D (2010) Clinical diagnosis of metabolic and cardiovascular risks in overweight children: early development of chronic diseases in the obese child. Int J Obes (Lond) 34(Suppl 2):S32–S36 3. Dietz WH (1998) Health consequences of obesity in youth: childhood predictors of adult disease. Pediatrics 101(3 Pt 2): 518–525 4. Oude Luttikhuis H, Baur L, Jansen H, Shrewsbury VA, O’Malley C, Stolk RP, Summerbell CD (2009) Interventions for treating obesity in children. Cochrane Database Syst Rev 1:CD001872 5. Luttikhuis OH, Baur L, Jansen H, Shrewsbury VA, O’Malley C, Stolk RP, Summerbell CD (2009) Interventions for treating obesity in children. Cochrane Database Syst Rev 1:CD001872 6. Bleich SN, Ku R, Wang YC (2011) Relative contribution of energy intake and energy expenditure to childhood obesity: a
7.
8.
9. 10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
review of the literature and directions for future research. Int J Obes (Lond) 35(1):1–15 Harris KC, Kuramoto LK, Schulzer M, Retallack JE (2009) Effect of school-based physical activity interventions on body mass index in children: a meta-analysis. CMAJ 180(7):719–726 Centers for Disease Control and Prevention, National Center for Health Statistics (2000) CDC growth charts: United States. http:// www.cdc.gov/growthcharts/ Willett W (1990) Nutritional epidemiology. Oxford University Press, New York Johnson RK, Driscoll P, Goran MI (1996) Comparison of multiple-pass 24-hour recall estimates of energy intake with total energy expenditure determined by the doubly labeled water method in young children. J Am Diet Assoc 96(11):1140–1144 Chinapaw MJ, Mokkink LB, van Poppel MN, van Mechelen W, Terwee CB (2010) Physical activity questionnaires for youth: a systematic review of measurement properties. Sports Med 40(7):539–563 Bean MK, Mazzeo SE, Stern M, Evans RK, Bryan D, Ning Y, Wickham EP 3rd, Laver J (2011) Six-month dietary changes in ethnically diverse, obese adolescents participating in a multidisciplinary weight management program. Clin Pediatr (Phila) 50(5):408–416 Kotler DP, Burastero S, Wang J, Pierson RN Jr (1996) Prediction of body cell mass, fat-free mass, and total body water with bioelectrical impedance analysis: effects of race, sex, and disease. Am J Clin Nutr 64(3 Suppl):489S–497S Hosking J, Metcalf BS, Jeffery AN, Voss LD, Wilkin TJ (2006) Validation of foot-to-foot bioelectrical impedance analysis with dual-energy X-ray absorptiometry in the assessment of body composition in young children: the EarlyBird cohort. Br J Nutr 96(6):1163–1168 Kalarchian MA, Levine MD, Arslanian SA, Ewing LJ, Houck PR, Cheng Y, Ringham RM, Sheets CA, Marcus MD (2009) Family-based treatment of severe pediatric obesity: randomized, controlled trial. Pediatrics 124(4):1060–1068 Papadaki A, Linardakis M, Larsen TM, van Baak MA, Lindroos AK, Pfeiffer AF, Martinez JA, Handjieva-Darlenska T, Kunesova M, Holst C et al (2010) The effect of protein and glycemic index on children’s body composition: the DiOGenes randomized study. Pediatrics 126(5):e1143–e1152 Duckworth LC, Gately PJ, Radley D, Cooke CB, King RF, Hill AJ (2009) RCT of a high-protein diet on hunger motivation and weight-loss in obese children: an extension and replication. Obesity (Silver Spring) 17(9):1808–1810 U.S. Department of Agriculture and U.S. Department of Health and Human Services (2010) Dietary guidelines for Americans, 2010, 7th edn. U.S. Government Print Office, Washington, DC, December 2010 Ho M, Garnett SP, Baur LA, Burrows T, Stewart L, Neve M, Collins C (2013) Impact of dietary and exercise interventions on weight change and metabolic outcomes in obese children and adolescents: a systematic review and meta-analysis of randomized trials. JAMA Pediatr 167(8):759–768 Rolland-Cachera MF, Thibault H, Souberbielle JC, Soulie D, Carbonel P, Deheeger M, Roinsol D, Longueville E, Bellisle F, Serog P (2004) Massive obesity in adolescents: dietary interventions and behaviours associated with weight regain at 2 y follow-up. Int J Obes Relat Metab Disord 28(4):514–519 Gutin B, Barbeau P, Owens S, Lemmon CR, Bauman M, Allison J, Kang HS, Litaker MS (2002) Effects of exercise intensity on cardiovascular fitness, total body composition, and visceral adiposity of obese adolescents. Am J Clin Nutr 75(5):818–826 Ebbeling CB, Leidig MM, Sinclair KB, Hangen JP, Ludwig DS (2003) A reduced-glycemic load diet in the treatment of adolescent obesity. Arch Pediatr Adolesc Med 157(8):773–779
123
Eur J Nutr 23. Carels RA, Young KM, Coit C, Clayton AM, Spencer A, Wagner M (2008) Skipping meals and alcohol consumption. The regulation of energy intake and expenditure among weight loss participants. Appetite 51(3):538–545 24. Daley AJ, Copeland RJ, Wright NP, Roalfe A, Wales JK (2006) Exercise therapy as a treatment for psychopathologic conditions in obese and morbidly obese adolescents: a randomized, controlled trial. Pediatrics 118(5):2126–2134
123
25. Sazonov ES, Schuckers S (2010) The energetics of obesity: a review: monitoring energy intake and energy expenditure in humans. IEEE Eng Med Biol Mag 29(1):31–35 26. Thivel D, Aucouturier J, Doucet E, Saunders TJ, Chaput J-P (2013) Daily energy balance in children and adolescents. Does energy expenditure predict subsequent energy intake? Appetite 60(1):58–64
