- Email: [email protected]
Live Video Diet and Exercise Intervention in Overweight and Obese Youth: Adherence and Cardiovascular Health
Live Video Diet and Exercise Intervention in Overweight and Obese Youth: Adherence and Cardiovascular Health Susan E. Nourse, BS1, Inger Olson, MD1, Rita A. Popat, PhD2, Katie J. Stauffer, RDCS1, Chau N. Vu, BA1, Samuel Berry, MS1, Jeffrey Kazmucha, MS, CES, CSCS1, Olga Ogareva, RDCS1, Sarah C. Couch, PhD3, Elaine M. Urbina, MD4, and Elif Seda Selamet Tierney, MD1 Objective To evaluate adherence of overweight and obese adolescents to a live video lifestyle intervention. The impact on vascular and functional health was also assessed. Study design Twenty adolescents 14.5 2.1 years of age with body mass index z-score 1.94 0.43 were enrolled. The 12-week intervention included 3-times-weekly videoconference sessions with a trainer and weekly diet consultations. Adherence was evaluated by completion rate and percentage of sessions attended. Vascular health indices and traditional cardiovascular risk factors were obtained at baseline and study end. Results Seventeen participants (85%) completed the intervention. The participants attended 93 11% of scheduled sessions. Reasons for absences included illness/injury (23%), school activities (21%), holidays (18%), forgetting the appointment (8%), Internet connectivity issues (7%), and family emergency (7%). Significant changes were observed in waist-hip ratio (0.87 0.08 vs 0.84 0.08, P = .03), total (159 27 vs 147 23 mg/dL, P = .004) and low-density lipoprotein cholesterol levels (91 20 vs 81 18 mg/dL, P = .004), volume of inspired oxygen per heartbeat at peak exercise (69 16 vs 72 15%, P = .01), and functional movement score (13 2 vs 17 1, P < .001). Participants with abnormal vascular function at baseline showed improvement in endothelial function and arterial stiffness indices (P = .01 and P = .04, respectively). Conclusions A 12-week live video intervention promotes adherence among overweight and obese adolescents and shows promise for improving vascular and functional health. Integrating telehealth into preventive care has the potential to improve cardiovascular health in the youth at risk. (J Pediatr 2015;167:533-9).
A
therosclerosis is associated with several risk factors that have been increasing in prevalence among children and adolescents.1 There are now validated tools for noninvasive measurement of early atherosclerotic disease in pediatrics including endothelial function testing, arterial stiffness testing, and ultrasound imaging of the carotid wall. These modalities are useful for the study of treatment efficacy in children and adolescents because this population does not reach hard cardiovascular endpoints before adulthood.2 Lifestyle interventions are reported to improve cardiovascular risk profile3 as well as measures of endothelial function4,5 and vascular stiffness6 in children and adolescents. However, the success of these interventions in practice is constrained by limited feasibility and low adherence. A survey of pediatric obesity clinics found that caregiver work hours, transportation, and having to miss school were among the most commonly reported barriers to follow-up.7 Telehealth is a promising strategy for engaging adolescents in health-related interventions because the approach may eliminate many of the barriers to clinic programs, but there are limited data on its effectiveness in the pediatric population. Live videoconferencing-based interventions have been used to improve health and modify behavior in children and adolescents with diabetes8 and asthma.9 Several studies have evaluated the effects of web-based weight management programs on children and adolescents,10 but no studies incorporated live training. The purpose of this study was to evaluate the adherence of overweight and obese youth to a live video diet and exercise intervention and the effect of the program on measures of cardiovascular health. We hypothesized that subjects would attend greater than 80% of diet and exercise sessions.
AIx75bpm BMI HRmax PWV RHI VO2max VO2pulse
Aortic augmentation index adjusted to a standard heart rate of 75 beats per minute Body mass index Maximum heart rate Pulse wave velocity Reactive hyperemia index Maximum volume of oxygen consumed Volume of oxygen consumed per heartbeat
From the 1Division of Cardiology, Department of Pediatrics, 2Division of Epidemiology, Department of Health Research and Policy, Stanford School of Medicine, Palo Alto, CA; 3Department of Nutritional Sciences, and 4Division of Cardiology, Department of Pediatrics, University of Cincinnati, Cincinnati, OH Supported by the Child Health Research Institute, Lucile Packard Foundation for Children’s Health, Stanford Clinical and Translational Science Award (UL1 RR025744), and American Council on Exercise (San Diego, CA). The authors declare no conflicts of interest. 0022-3476/$ - see front matter. Copyright ª 2015 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.jpeds.2015.06.015
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Methods Overweight and obese youth were recruited from a pediatric weight program and online community listing (www. craigslist.org) from June 2012 to November 2013. Subjects were eligible for the intervention if they met the following criteria: (1) age: 10-19 years; (2) body mass index (BMI) $85th percentile; (3) Internet access; and (4) presence of an adult at home during exercise for participants <14 years of age. Subjects were excluded if they had: (1) latex allergy; (2) acute illness; and (3) ongoing treatment for a significant medical condition, including hypertension and diabetes. Consent was obtained from subjects $18 years of age or a parent of subjects <18 years of age, and assent was obtained from subjects <18 years of age. The protocol was approved by the Stanford University Institutional Research Board. Assessment visits were completed at baseline and at 12 weeks in the mornings. Subjects were instructed to fast overnight. Operators were blinded to measurements made on prior visits. Sex, race, ethnicity, medical history, recent exposure to tobacco smoke, consumption of alcohol or caffeinated drinks, and use of medication and supplementation was recorded. Subjects were asked to recall food consumed in the previous 24 hours, and portions were queried by modeling standard measurement sizes. The subject’s weight and height were measured (ScaleTronix electronic scale 5221; Scale-Tronix, White Plains, New York; wall-mounted Seca stadiometer 225; Seca, Columbia, Maryland). BMI was calculated as mass in kilograms divided by height in meters squared, and BMI percentile and z-score were calculated.11 Waist and hip circumferences were measured with nonelastic measuring tape at the area of smallest circumference and at the widest portion of the buttocks. Four sets of resting brachial blood pressures were measured while seated with the oscillometric method (Dinamap; General Electric, Waukesha, Wisconsin), and the average of the last 3 measurements was used. Blood pressure z-scores were calculated (Boston Children’s Hospital normative database, courtesy of Steven D. Colan, MD). Endothelial Pulse Amplitude Test (Endo-PAT, Itamar Medical Ltd., Caesarea, Israel) was used to assess endothelial function by measuring the increase in peripheral blood flow after temporary ischemia. It has been validated in adults and studied in several pediatric groups.12,13 The testing protocol has been described previously.12 The data were analyzed with the software package and the reactive hyperemia index (RHI) was calculated (a lower RHI indicates worse endothelial function). Pulse wave velocity (PWV) and aortic augmentation index adjusted to a standard heart rate of 75 beats per minute (AIx75bpm) were measured in triplicate using arterial tonometry (SphygmoCor; Atcor-Medical, Sydney, Australia). The testing protocol has been described previously.14 A higher value of PWV or AIx75bpm indicates stiffer arteries. 534
Vol. 167, No. 3 Carotid ultrasound examinations with electrocardiography tracings were performed. The right and left common carotid arteries were examined in the transverse plane using high-resolution B-mode gray-scale ultrasonography (MTurbo Ultrasound System; Sonosite, Inc, Bothell, Washington).15 Cross-sectional dimensions of the common carotid arteries in systole and diastole were measured in triplicate and averaged to calculate the arterial pressure-strain elastic modulus16 and stiffness index.17 Fasting plasma lipid profiles were measured with standardized methods from the National Heart Lung and Blood Institute. Low-density lipoprotein cholesterol concentration was calculated with the Friedewald equation. C-reactive protein was measured with a high-sensitivity enzyme-linked immunoabsorbent assay. The dietary recall was analyzed using the Food Processor software (v 10.1, Elizabeth Stewart Hands and Associates, Salem, Oregon) to assess intake of selected nutrients. Subjects underwent cardiopulmonary exercise testing as described previously.18 Maximum volume of oxygen consumed (VO2max) was taken as the peak volume of oxygen consumed in milliliters per minute. Maximum heart rate (HRmax) was recorded as beats per minute. The maximum volume of oxygen consumed per heartbeat (VO2pulse) was calculated as VO2max divided by HRmax. VO2max, HRmax, and VO2pulse were compared with predicted values from empirically derived formulas19 and reported as percentages of the predicted values. Maximum volume oxygen consumed indexed by body mass was calculated as the VO2max divided by the subject’s weight in kilograms. Functional movement screening is an assessment tool that evaluates 7 movement patterns for evidence of functional limitations.20 Each movement was rated on a scale from 03, with higher scores indicating greater ability. Intervention Exercise training sessions were led by a trained research assistant or a professional trainer using a videoconferencing platform (Skype Communications; Microsoft Corporation, Luxembourg). Participants were given a weighted medicine ball, jump rope, and resistance tubing with door attachment. Sessions lasted 60 minutes and were scheduled 3 times weekly. The program combined aerobic and resistance training in a circuit format. Exercise intensity was selfreported on a scale of 1-10, and the program was adjusted to maintain an intensity of 8. Subjects were not reminded of upcoming sessions but were contacted if they did not appear online for their appointment. Attendance and active participation were recorded. If a subject missed a previously scheduled appointment or could not participate (eg, if they appeared online but could not conduct exercises because of illness), the appointment was recorded as nonattendance and the reason for nonattendance was documented. Subjects were allowed to make up missed sessions by extending Nourse et al
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September 2015 the intervention until 36 total sessions were completed. Adherence was evaluated as the percentage of participants who completed 12 weeks and the percentage of onschedule sessions attended by each participant. Participants were mailed a $15 gift card after every 2 weeks regardless of attendance. The dietary intervention employed the Dietary Approaches to Stop Hypertension diet developed for adolescents, slightly modified from the original version for adults.21 The format was based on the efficacy of this approach for changing dietary behaviors and blood pressures among adolescents with hypertension.22 A manual was provided and reviewed by a nutritionist after the baseline assessment. Weekly counseling conversations were used to set dietary goals. Participants reported daily servings of fruits and vegetables, low-fat dairy, and foods high in fat or sodium. Dietary goals included: (1) keeping a food record on paper for 5 out of 7 days; (2) increasing intake of fruits and vegetables up to 8 servings per day; (3) increasing intake of low-fat dairy foods up to 3 servings per day; and (4) lowering intake of foods high in fat and sodium to less than 15 servings per week. Adherence to the dietary protocol was evaluated by the percentage of dietary sessions attended, achievement of the dietary goals, and change in number of servings of food groups. Statistical Analyses Data are reported as mean SD, median (range), or frequency (percent). Sample size was calculated based on mean values of Endothelial Pulse Amplitude Testing RHI (1.78 0.51 in data of 30 normal subjects).12 Assuming a SD of 0.5, a sample size of 20 gives 80% power to detect a 20% change in Endothelial Pulse Amplitude Testing RHI at alpha = 0.05. Measurements at baseline and postintervention were compared using paired t test or Wilcoxon signed rank test as appropriate. Post-hoc secondary analysis was performed to explore if subjects with abnormal vascular function at baseline showed improvement. Measures of vascular stiffness, including AIx75bpm and PWV, have been reported to increase significantly with age throughout adolescence.23 Therefore, cut-off values for abnormal vascular function were chosen based on mean values for nonobese youth stratified by age into groups of 10-14 and 15-19 years.14,23 Paired analysis was conducted on the participants who completed the intervention, and baseline measures and demographics were compared between subjects who completed the intervention and subjects who started the intervention but did not complete, and subjects who did not participate after baseline assessment using independent t test or 1-way ANOVA as appropriate. A 2-sided P value of #.05 was considered statistically significant. All analysis was performed using SPSS Statistics, v 21 (IBM Corporation, Armonk, New York). Study data were collected and managed using Research Electronic Data Capture electronic data capture tools hosted at Stanford University.24
Results Twenty-three participants attended the baseline assessment (Figure 1; available at www.jpeds.com). Three subjects withdrew prior to beginning the intervention; reasons included lack of time (n = 2) and subject anxiety (n = 1). The remaining 20 subjects 14.5 2.1 years of age with a BMI percentile of 96 4% were included in the analysis (Table I). One subject (5%) reported a history of migraines, and 4 (20%) reported a history of asthma. No other significant medical history was reported. There were no significant baseline differences between subjects who completed the intervention and subjects who started the intervention but did not complete, and subjects who did not partake after baseline assessment. Seventeen (85%) of the 20 subjects completed the intervention. Three subjects (15%) did not complete the intervention; one withdrew after 7 weeks because of increased time commitments, one was lost to follow-up after 3 weeks, and one was lost to follow-up after 11 weeks because of a personal life event. There were no significant baseline differences between the 2 groups. Exercise Intervention The 17 subjects who completed the intervention attended 93 11% of scheduled exercise sessions. A median of 2 (015) sessions were missed by each participant, a total of 61 sessions out of 685 scheduled sessions (8.9%). Subjects made up 41/61 (67%) of sessions that were not attended on schedule later in the intervention period. Reasons given for missing sessions included illness or injury (unrelated to the intervention) (14/61, 23%), unanticipated school activities (13/61, 21%), scheduled holidays (11/61, 18%), forgetting the appointment (5/61, 8%), problems with Internet connectivity (4/61, 7%), and family emergency (4/61, 7%). No reason Table I. Subject demographics (n = 20) Sex (male/female) Age Race White Asian Ethnicity Hispanic Non-Hispanic Socioeconomic status Neighborhood income designation* Low income Nonlow income Geographic location† Urban Rural Health insurance coverage Publicz Private Unknown
11 (55%)/9 (45%) 14.3 2.1 15 (75%) 5 (25%) 12 (60%) 8 (40%) 7 (35%) 13 (75%) 16 (80%) 4 (20%) 10 (50%) 8 (40%) 2 (10%)
*Based upon the 2014 Federal Housing Finance Agency low-income area designations. †Based upon classifications from the US Census Bureau, 2010 Census Urban and Rural Classification. zIncludes Medi-Cal and Health Plan of San Mateo, which base eligibility on family income.
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was reported for 10/61 (16%) of absences. There were no significant differences in adherence based on sex, age, race, ethnicity, or socioeconomic status. The 17 subjects who completed the intervention attended 99 4% of their scheduled nutrition check-ins. By the end of the intervention subjects were eating 5.7 1.6 servings of fruits and vegetables per day, 2.0 0.9 servings of low fat dairy per day, and 6.1 7.4 servings of foods high in fat or sodium per week (Figure 2, A). Two subjects (2/20, 10%) raised daily servings of fruits and vegetables to the target, 4/ 20 (20%) raised daily servings of low fat dairy to the target, and 17/20 (85%) reduced weekly servings of food high in fat or sodium to the target. Three out of 20 subjects (15%) kept a food log for the duration of the study. There were no significant differences in adherence based on sex, age, race, ethnicity, or socioeconomic status. Subjects showed a significant reduction in dietary cholesterol and increase in
Vol. 167, No. 3 b-carotene. No significant differences were observed in consumption of total calories, protein, carbohydrates, fat, folate, calcium, potassium, or sodium. Results are summarized in Table II. Significant differences between the 2 visits were observed in waist-hip ratio, total cholesterol, non-high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol levels, percent of predicted VO2pulse, and functional movement screening total score (Figure 2, B). RHI, AIx75bpm, PWV, and carotid artery stiffness measures did not show significant changes between visits upon primary analysis. Secondary analysis was conducted to explore if the subset of individuals with measures indicating abnormal vascular function at baseline showed improvement (Figure 2, C). Subjects with an RHI #1.9112 (n = 12), abnormal AIx75bpm for their age (n = 7, subjects 10-14 years old AIx75bpm >2%; subjects 15- to 19-years old AIx75bpm
Figure 2. A, Dietary profile and B, lipid profile and fitness indices in all patients at baseline and at 12 weeks (n = 17). C, Patients with abnormal vascular function at baseline; vascular indices at baseline and at 12 weeks. Error bars display the SE of the mean. LDL, low-density lipoprotein. 536
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Table II. Baseline and postintervention measures for subjects completing the intervention* Anthropometrics Weight (kg) BMI (kg/m2) BMI z-score Waist-hip ratio BP profile Systolic BP (mm Hg) Systolic BP Z-score Diastolic BP (mm Hg) Diastolic BP Z-score Mean BP (mm Hg) Mean BP z-score Vascular measures RHI AIx75bpm (%) PWV (m/s) Right CCA Ep (mm Hg) bindex Left CCA Ep (mm Hg) bindex Laboratory analysis Total cholesterol (mg/dL) Non-HDL cholesterol (mg/dL) Triglycerides (mg/dL) HDL cholesterol (mg/dL) LDL cholesterol (mg/dL) C-reactive protein (mg/dL) Functional capacity VO2max (mL/min) VO2max/kg (mL/min/kg) Percent predicted VO2max (%) Percent predicted VO2pulse (%) FMS total score
n
Visit 1
Visit 2
Difference between visits
P†
17 17 17 17
80.4 8.2 29.9 5.3 1.9 0.5 0.87 0.08
81.0 18.2 30.1 5.6 1.9 0.55 0.84 0.08
0.4 [4.9 to 13.8] 0.2 [1.3 to 3.1] 0.0 (0.1 to 0.0) 0.03 (0.05 to 0.00)
.81z .76z .32 .03
17 17 17 17 17 17
115 12 0.54 0.97 69 6 1.13 0.61 79 7 0.30 0.68
115 12 0.53 0.98 69 7 1.13 0.69 81 7 0.43 0.69
0 (4 to 5) 0.00 (0.37 to 0.37) 0 (3 to 3) 0.00 (0.32 to 0.33) 1 (3 to 5) 0.13 (0.27 to 0.52)
.93 .99 .94 .99 .45 .50
17 17 16
1.80 0.56 4.5 11.3 4.8 1.1
1.96 0.54 5.4 9.2 4.7 0.9
0.16 (0.25 to 0.57) 0.9 (7.5 to 5.8) 0.1 (0.7 to 0.5)
.42 .78 .77
17 17
365 159 4.0 1.6
301 67 3.4 0.7
64 (152 to 24) 0.7 (1.6 to 0.3)
.14 .14
17 17
294 85 3.3 1.0
284 78 3.2 0.9
10 (52 to 33) 0.1 (0.6 to 0.4)
.64 .61
16 16 16 16 16 16
159 27 110 24 94 44 49 12 91 20 0.3 [<0.2-1.4]
147 23 101 21 99 54 46 15 81 18 <0.2 [<0.2-1.1]
12 (19 to 4) 9 (15 to 3) 5 (15 to 25) 3 (7 to 2) 10 (16 to 4) 0.0 [1.4 to 0.8]
.004 .005 .60 .22 .004 .54z
15 15 15 15 16
2128 577 27 6 66 14 69 16 13 2
2133 575 27 6 66 13 72 15 17 1
5 (83 to 93) 0 (1 to 1) 0 (3 to 3) 3 (1-6) 4 (3-5)
.90 1.00 .98 .01 <.001
bindex, stiffness index; BP, blood pressure; CCA, common carotid artery; Ep, pressure-strain elastic modulus; FMS, functional movement screening; HDL, high-density lipoprotein; LDL, low-density lipoprotein; VO2max/kg, maximum volume oxygen consumed indexed by body mass. Data are reported as frequency (percent), mean SD, median [range], and mean difference (95% CI). Bold values indicate P # .05. *Measurements not included in analysis: 1 PWV measurement excluded because of poor signal quality, 1 blood test excluded because of nonadherence to fast, 1 cardiopulmonary exercise test excluded because of subject difficulty with equipment, and 1 cardiopulmonary exercise test and FMS assessment not completed because of subject injury unrelated to intervention. †Paired t test, unless otherwise specified. zWilcoxon signed-rank test.
>5%)14 or abnormal PWV for their age (n = 7, subjects 10- to 14-years old PWV >4.5 m/s; subjects 15- to 19-years old PWV >5.5 m/s)23 showed a significant improvement (P = .01, .04, and .04, respectively).
Discussion Our results suggest that this telehealth intervention can promote high adherence to a structured program in an adolescent population with overweight and obesity. Attrition in clinic-based pediatric weight programs is reported to be high. Skelton et al25 reviewed 10 retrospective studies of pediatric outpatient weight programs and reported attrition rates that ranged from 27%-73%. The most recent Cochrane review of obesity interventions in children reported attrition rates ranging from 0%-43% in clinical trials that incorporate exercise training.26 The 15% attrition rate observed within the current study is low, compared with clinic programs. Attendance rates are less well reported, but Hampl et al7 surveyed clinic-based pediatric weight management programs
and reported initial nonattendance rates of 28%, and 7 of 8 programs reported that the majority of patients attended fewer than 50% of scheduled visits. In the current study, attendance rates were high, with the majority of participants attending 95% or more of scheduled sessions. The population recruited in this study was predominantly of Hispanic ethnicity. Multiple studies in the pediatric population have reported a significant difference in adherence to physical activity between Hispanic and non-Hispanic white children27 and adolescents.28 Fakhouri et al27 reported that 65.7 4.4% of Hispanic children met physical activity recommendations, and non-Hispanic white children and non-Hispanic black children had higher adherence (73.4 2.6% and 72.5 3.8%, respectively). In our study, both Hispanic and non-Hispanic white participants showed high adherence (attendance at 95 7% and 90 15% of sessions, respectively). The level of dietary change seen in our results is consistent with other studies of Dietary Approaches to Stop Hypertension-type interventions in adolescents. A study in
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adolescents with hypertension reported similar overall changes in number of servings of low fat dairy and fruits and vegetables, and a smaller change in servings of foods high in fat and sodium.22 A similar intervention using telephone reminders in adolescents with metabolic syndrome reported subjects consuming an average of 6 servings of fruits and vegetables per day, a similar level to the present study; however, total servings of low fat dairy and foods high in fat or sodium were not reported.29 The greater change in lipid levels observed in the present study may be due to an increased level of engagement (3 times weekly for diet and exercise sessions) with the intervention team. Significant improvements in vascular health were observed in participants who showed abnormal measures of vascular function at baseline. Endothelial function was impaired in 12/17 (71%) of the study participants based on mean values for a healthy lean adolescent population.12 Adults with impaired flow mediated dilation because of chronic heart failure30 and type-2 diabetes mellitus31 showed improvements after the intervention but healthy individuals showed no improvement,32 similar to our study. Arterial stiffness was impaired in 7/17 (41%) of our subjects based on median values for healthy lean adolescents.14 A study investigating a similar diet and exercise intervention showed no effect on PWV in obese adolescents33 but did not perform subgroup analysis on subjects with increased arterial stiffness. Thus, our observation that vascular function improves in adolescents with abnormal arteries requires confirmation in future studies. This study should be viewed in light of its limitations. First, it was structured as a pilot without a control group, and thus results should be considered preliminary. Second, secondary subgroup analyses of vascular measures were conducted post-hoc. In addition, defining subgroups with a baseline cutoff can be vulnerable to type 1 error because of regression to the mean, but this analysis was based upon objective criteria published previously. Self-selecting participants may be more highly motivated to change their health. The use of incentives to encourage return for the follow-up visit may have artificially improved the attrition rate. However, because the incentives were sent regardless of adherence, the attendance rate is less likely to be influenced. Finally, the intervention was conducted over a short term and the sustainability of changes in health measures over the long term is unknown. Longer periods of follow-up are necessary to determine if health effects can be maintained as the adolescent approaches adulthood. This program shows promise for improving the health of overweight and obese adolescents over 12 weeks. Integrating telehealth into clinical preventive care has the potential to improve cardiovascular health in the youth at risk. n We acknowledge the contributions of Laurie Steinberg, MS, RD, who ran the nutritional analysis on the 24-hour dietary recalls. Submitted for publication Feb 20, 2015; last revision received Apr 27, 2015; accepted Jun 4, 2015.
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Vol. 167, No. 3 Reprint requests: Elif Seda Selamet Tierney, MD, Division of Cardiology, Department of Pediatrics, Stanford School of Medicine, 750 Welch Rd, Suite 350, Palo Alto, CA 94304. E-mail: [email protected]
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September 2015 20. Cook G, Burton L, Hoogenboom B. Pre-participation screening: the use of fundamental movements as an assessment of function - part 1. N Am J Sports Phys Ther 2006;1:62-72. 21. Appel LJ, Moore TJ, Obarzanek E, Vollmer WM, Svetkey LP, Sacks FM, et al. A clinical trial of the effects of dietary patterns on blood pressure. DASH Collaborative Research Group. N Engl J Med 1997;336:1117-24. 22. Couch SC, Saelens BE, Levin L, Dart K, Falciglia G, Daniels SR. The efficacy of a clinic-based behavioral nutrition intervention emphasizing a DASH-type diet for adolescents with elevated blood pressure. J Pediatr 2008;152:494-501. 23. Sarkola T, Manlhiot C, Slorach C, Bradley TJ, Hui W, Mertens L, et al. Evolution of the arterial structure and function from infancy to adolescence is related to anthropometric and blood pressure changes. Arterioscler Thromb Vasc Biol 2012;32:2516-24. 24. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)–a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform 2009;42:377-81. 25. Skelton JA, Beech BM. Attrition in paediatric weight management: a review of the literature and new directions. Obes Rev 2011;12:e273-81. 26. Oude Luttikhuis H, Baur L, Jansen H, Shrewsbury VA, O’Malley C, Stolk RP, et al. Interventions for treating obesity in children. Cochrane Database Syst Rev 2009;1:CD001872.
ORIGINAL ARTICLES 27. Fakhouri TH, Hughes JP, Brody DJ, Kit BK, Ogden CL. Physical activity and screen-time viewing among elementary school-aged children in the United States from 2009 to 2010. JAMA Pediatr 2013;167:223-9. 28. Iannotti RJ, Wang J. Trends in physical activity, sedentary behavior, diet, and BMI among US adolescents, 2001-2009. Pediatrics 2013;132:606-14. 29. Saneei P, Hashemipour M, Kelishadi R, Rajaei S, Esmaillzadeh A. Effects of recommendations to follow the Dietary Approaches to Stop Hypertension (DASH) diet v. usual dietary advice on childhood metabolic syndrome: a randomised cross-over clinical trial. Br J Nutr 2013;110:2250-9. 30. Maiorana A, O’Driscoll G, Dembo L, Cheetham C, Goodman C, Taylor R, et al. Effect of aerobic and resistance exercise training on vascular function in heart failure. Am J Physiol Heart Circ Physiol 2000;279:H1999-2005. 31. Maiorana A, O’Driscoll G, Cheetham C, Dembo L, Stanton K, Goodman C, et al. The effect of combined aerobic and resistance exercise training on vascular function in type 2 diabetes. J Am Coll Cardiol 2001;38:860-6. 32. Maiorana A, O’Driscoll G, Dembo L, Goodman C, Taylor R, Green D. Exercise training, vascular function, and functional capacity in middle-aged subjects. Med Sci Sports Exerc 2001;33:2022-8. 33. Khadilkar VV, Pandit DS, Khadilkar AV, Chiplonkar SA, Kinare AS. Diet and exercise intervention, with special reference to micronutrients, reduces cardiometabolic risk in overweight children. Indian J Endocrinol Metab 2012;16:124-33.
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Figure 1. Study enrollment.
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therosclerosis is associated with several risk factors that have been increasing in prevalence among children and adolescents.1 There are now validated tools for noninvasive measurement of early atherosclerotic disease in pediatrics including endothelial function testing, arterial stiffness testing, and ultrasound imaging of the carotid wall. These modalities are useful for the study of treatment efficacy in children and adolescents because this population does not reach hard cardiovascular endpoints before adulthood.2 Lifestyle interventions are reported to improve cardiovascular risk profile3 as well as measures of endothelial function4,5 and vascular stiffness6 in children and adolescents. However, the success of these interventions in practice is constrained by limited feasibility and low adherence. A survey of pediatric obesity clinics found that caregiver work hours, transportation, and having to miss school were among the most commonly reported barriers to follow-up.7 Telehealth is a promising strategy for engaging adolescents in health-related interventions because the approach may eliminate many of the barriers to clinic programs, but there are limited data on its effectiveness in the pediatric population. Live videoconferencing-based interventions have been used to improve health and modify behavior in children and adolescents with diabetes8 and asthma.9 Several studies have evaluated the effects of web-based weight management programs on children and adolescents,10 but no studies incorporated live training. The purpose of this study was to evaluate the adherence of overweight and obese youth to a live video diet and exercise intervention and the effect of the program on measures of cardiovascular health. We hypothesized that subjects would attend greater than 80% of diet and exercise sessions.
AIx75bpm BMI HRmax PWV RHI VO2max VO2pulse
Aortic augmentation index adjusted to a standard heart rate of 75 beats per minute Body mass index Maximum heart rate Pulse wave velocity Reactive hyperemia index Maximum volume of oxygen consumed Volume of oxygen consumed per heartbeat
From the 1Division of Cardiology, Department of Pediatrics, 2Division of Epidemiology, Department of Health Research and Policy, Stanford School of Medicine, Palo Alto, CA; 3Department of Nutritional Sciences, and 4Division of Cardiology, Department of Pediatrics, University of Cincinnati, Cincinnati, OH Supported by the Child Health Research Institute, Lucile Packard Foundation for Children’s Health, Stanford Clinical and Translational Science Award (UL1 RR025744), and American Council on Exercise (San Diego, CA). The authors declare no conflicts of interest. 0022-3476/$ - see front matter. Copyright ª 2015 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.jpeds.2015.06.015
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Methods Overweight and obese youth were recruited from a pediatric weight program and online community listing (www. craigslist.org) from June 2012 to November 2013. Subjects were eligible for the intervention if they met the following criteria: (1) age: 10-19 years; (2) body mass index (BMI) $85th percentile; (3) Internet access; and (4) presence of an adult at home during exercise for participants <14 years of age. Subjects were excluded if they had: (1) latex allergy; (2) acute illness; and (3) ongoing treatment for a significant medical condition, including hypertension and diabetes. Consent was obtained from subjects $18 years of age or a parent of subjects <18 years of age, and assent was obtained from subjects <18 years of age. The protocol was approved by the Stanford University Institutional Research Board. Assessment visits were completed at baseline and at 12 weeks in the mornings. Subjects were instructed to fast overnight. Operators were blinded to measurements made on prior visits. Sex, race, ethnicity, medical history, recent exposure to tobacco smoke, consumption of alcohol or caffeinated drinks, and use of medication and supplementation was recorded. Subjects were asked to recall food consumed in the previous 24 hours, and portions were queried by modeling standard measurement sizes. The subject’s weight and height were measured (ScaleTronix electronic scale 5221; Scale-Tronix, White Plains, New York; wall-mounted Seca stadiometer 225; Seca, Columbia, Maryland). BMI was calculated as mass in kilograms divided by height in meters squared, and BMI percentile and z-score were calculated.11 Waist and hip circumferences were measured with nonelastic measuring tape at the area of smallest circumference and at the widest portion of the buttocks. Four sets of resting brachial blood pressures were measured while seated with the oscillometric method (Dinamap; General Electric, Waukesha, Wisconsin), and the average of the last 3 measurements was used. Blood pressure z-scores were calculated (Boston Children’s Hospital normative database, courtesy of Steven D. Colan, MD). Endothelial Pulse Amplitude Test (Endo-PAT, Itamar Medical Ltd., Caesarea, Israel) was used to assess endothelial function by measuring the increase in peripheral blood flow after temporary ischemia. It has been validated in adults and studied in several pediatric groups.12,13 The testing protocol has been described previously.12 The data were analyzed with the software package and the reactive hyperemia index (RHI) was calculated (a lower RHI indicates worse endothelial function). Pulse wave velocity (PWV) and aortic augmentation index adjusted to a standard heart rate of 75 beats per minute (AIx75bpm) were measured in triplicate using arterial tonometry (SphygmoCor; Atcor-Medical, Sydney, Australia). The testing protocol has been described previously.14 A higher value of PWV or AIx75bpm indicates stiffer arteries. 534
Vol. 167, No. 3 Carotid ultrasound examinations with electrocardiography tracings were performed. The right and left common carotid arteries were examined in the transverse plane using high-resolution B-mode gray-scale ultrasonography (MTurbo Ultrasound System; Sonosite, Inc, Bothell, Washington).15 Cross-sectional dimensions of the common carotid arteries in systole and diastole were measured in triplicate and averaged to calculate the arterial pressure-strain elastic modulus16 and stiffness index.17 Fasting plasma lipid profiles were measured with standardized methods from the National Heart Lung and Blood Institute. Low-density lipoprotein cholesterol concentration was calculated with the Friedewald equation. C-reactive protein was measured with a high-sensitivity enzyme-linked immunoabsorbent assay. The dietary recall was analyzed using the Food Processor software (v 10.1, Elizabeth Stewart Hands and Associates, Salem, Oregon) to assess intake of selected nutrients. Subjects underwent cardiopulmonary exercise testing as described previously.18 Maximum volume of oxygen consumed (VO2max) was taken as the peak volume of oxygen consumed in milliliters per minute. Maximum heart rate (HRmax) was recorded as beats per minute. The maximum volume of oxygen consumed per heartbeat (VO2pulse) was calculated as VO2max divided by HRmax. VO2max, HRmax, and VO2pulse were compared with predicted values from empirically derived formulas19 and reported as percentages of the predicted values. Maximum volume oxygen consumed indexed by body mass was calculated as the VO2max divided by the subject’s weight in kilograms. Functional movement screening is an assessment tool that evaluates 7 movement patterns for evidence of functional limitations.20 Each movement was rated on a scale from 03, with higher scores indicating greater ability. Intervention Exercise training sessions were led by a trained research assistant or a professional trainer using a videoconferencing platform (Skype Communications; Microsoft Corporation, Luxembourg). Participants were given a weighted medicine ball, jump rope, and resistance tubing with door attachment. Sessions lasted 60 minutes and were scheduled 3 times weekly. The program combined aerobic and resistance training in a circuit format. Exercise intensity was selfreported on a scale of 1-10, and the program was adjusted to maintain an intensity of 8. Subjects were not reminded of upcoming sessions but were contacted if they did not appear online for their appointment. Attendance and active participation were recorded. If a subject missed a previously scheduled appointment or could not participate (eg, if they appeared online but could not conduct exercises because of illness), the appointment was recorded as nonattendance and the reason for nonattendance was documented. Subjects were allowed to make up missed sessions by extending Nourse et al
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September 2015 the intervention until 36 total sessions were completed. Adherence was evaluated as the percentage of participants who completed 12 weeks and the percentage of onschedule sessions attended by each participant. Participants were mailed a $15 gift card after every 2 weeks regardless of attendance. The dietary intervention employed the Dietary Approaches to Stop Hypertension diet developed for adolescents, slightly modified from the original version for adults.21 The format was based on the efficacy of this approach for changing dietary behaviors and blood pressures among adolescents with hypertension.22 A manual was provided and reviewed by a nutritionist after the baseline assessment. Weekly counseling conversations were used to set dietary goals. Participants reported daily servings of fruits and vegetables, low-fat dairy, and foods high in fat or sodium. Dietary goals included: (1) keeping a food record on paper for 5 out of 7 days; (2) increasing intake of fruits and vegetables up to 8 servings per day; (3) increasing intake of low-fat dairy foods up to 3 servings per day; and (4) lowering intake of foods high in fat and sodium to less than 15 servings per week. Adherence to the dietary protocol was evaluated by the percentage of dietary sessions attended, achievement of the dietary goals, and change in number of servings of food groups. Statistical Analyses Data are reported as mean SD, median (range), or frequency (percent). Sample size was calculated based on mean values of Endothelial Pulse Amplitude Testing RHI (1.78 0.51 in data of 30 normal subjects).12 Assuming a SD of 0.5, a sample size of 20 gives 80% power to detect a 20% change in Endothelial Pulse Amplitude Testing RHI at alpha = 0.05. Measurements at baseline and postintervention were compared using paired t test or Wilcoxon signed rank test as appropriate. Post-hoc secondary analysis was performed to explore if subjects with abnormal vascular function at baseline showed improvement. Measures of vascular stiffness, including AIx75bpm and PWV, have been reported to increase significantly with age throughout adolescence.23 Therefore, cut-off values for abnormal vascular function were chosen based on mean values for nonobese youth stratified by age into groups of 10-14 and 15-19 years.14,23 Paired analysis was conducted on the participants who completed the intervention, and baseline measures and demographics were compared between subjects who completed the intervention and subjects who started the intervention but did not complete, and subjects who did not participate after baseline assessment using independent t test or 1-way ANOVA as appropriate. A 2-sided P value of #.05 was considered statistically significant. All analysis was performed using SPSS Statistics, v 21 (IBM Corporation, Armonk, New York). Study data were collected and managed using Research Electronic Data Capture electronic data capture tools hosted at Stanford University.24
Results Twenty-three participants attended the baseline assessment (Figure 1; available at www.jpeds.com). Three subjects withdrew prior to beginning the intervention; reasons included lack of time (n = 2) and subject anxiety (n = 1). The remaining 20 subjects 14.5 2.1 years of age with a BMI percentile of 96 4% were included in the analysis (Table I). One subject (5%) reported a history of migraines, and 4 (20%) reported a history of asthma. No other significant medical history was reported. There were no significant baseline differences between subjects who completed the intervention and subjects who started the intervention but did not complete, and subjects who did not partake after baseline assessment. Seventeen (85%) of the 20 subjects completed the intervention. Three subjects (15%) did not complete the intervention; one withdrew after 7 weeks because of increased time commitments, one was lost to follow-up after 3 weeks, and one was lost to follow-up after 11 weeks because of a personal life event. There were no significant baseline differences between the 2 groups. Exercise Intervention The 17 subjects who completed the intervention attended 93 11% of scheduled exercise sessions. A median of 2 (015) sessions were missed by each participant, a total of 61 sessions out of 685 scheduled sessions (8.9%). Subjects made up 41/61 (67%) of sessions that were not attended on schedule later in the intervention period. Reasons given for missing sessions included illness or injury (unrelated to the intervention) (14/61, 23%), unanticipated school activities (13/61, 21%), scheduled holidays (11/61, 18%), forgetting the appointment (5/61, 8%), problems with Internet connectivity (4/61, 7%), and family emergency (4/61, 7%). No reason Table I. Subject demographics (n = 20) Sex (male/female) Age Race White Asian Ethnicity Hispanic Non-Hispanic Socioeconomic status Neighborhood income designation* Low income Nonlow income Geographic location† Urban Rural Health insurance coverage Publicz Private Unknown
11 (55%)/9 (45%) 14.3 2.1 15 (75%) 5 (25%) 12 (60%) 8 (40%) 7 (35%) 13 (75%) 16 (80%) 4 (20%) 10 (50%) 8 (40%) 2 (10%)
*Based upon the 2014 Federal Housing Finance Agency low-income area designations. †Based upon classifications from the US Census Bureau, 2010 Census Urban and Rural Classification. zIncludes Medi-Cal and Health Plan of San Mateo, which base eligibility on family income.
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was reported for 10/61 (16%) of absences. There were no significant differences in adherence based on sex, age, race, ethnicity, or socioeconomic status. The 17 subjects who completed the intervention attended 99 4% of their scheduled nutrition check-ins. By the end of the intervention subjects were eating 5.7 1.6 servings of fruits and vegetables per day, 2.0 0.9 servings of low fat dairy per day, and 6.1 7.4 servings of foods high in fat or sodium per week (Figure 2, A). Two subjects (2/20, 10%) raised daily servings of fruits and vegetables to the target, 4/ 20 (20%) raised daily servings of low fat dairy to the target, and 17/20 (85%) reduced weekly servings of food high in fat or sodium to the target. Three out of 20 subjects (15%) kept a food log for the duration of the study. There were no significant differences in adherence based on sex, age, race, ethnicity, or socioeconomic status. Subjects showed a significant reduction in dietary cholesterol and increase in
Vol. 167, No. 3 b-carotene. No significant differences were observed in consumption of total calories, protein, carbohydrates, fat, folate, calcium, potassium, or sodium. Results are summarized in Table II. Significant differences between the 2 visits were observed in waist-hip ratio, total cholesterol, non-high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol levels, percent of predicted VO2pulse, and functional movement screening total score (Figure 2, B). RHI, AIx75bpm, PWV, and carotid artery stiffness measures did not show significant changes between visits upon primary analysis. Secondary analysis was conducted to explore if the subset of individuals with measures indicating abnormal vascular function at baseline showed improvement (Figure 2, C). Subjects with an RHI #1.9112 (n = 12), abnormal AIx75bpm for their age (n = 7, subjects 10-14 years old AIx75bpm >2%; subjects 15- to 19-years old AIx75bpm
Figure 2. A, Dietary profile and B, lipid profile and fitness indices in all patients at baseline and at 12 weeks (n = 17). C, Patients with abnormal vascular function at baseline; vascular indices at baseline and at 12 weeks. Error bars display the SE of the mean. LDL, low-density lipoprotein. 536
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Table II. Baseline and postintervention measures for subjects completing the intervention* Anthropometrics Weight (kg) BMI (kg/m2) BMI z-score Waist-hip ratio BP profile Systolic BP (mm Hg) Systolic BP Z-score Diastolic BP (mm Hg) Diastolic BP Z-score Mean BP (mm Hg) Mean BP z-score Vascular measures RHI AIx75bpm (%) PWV (m/s) Right CCA Ep (mm Hg) bindex Left CCA Ep (mm Hg) bindex Laboratory analysis Total cholesterol (mg/dL) Non-HDL cholesterol (mg/dL) Triglycerides (mg/dL) HDL cholesterol (mg/dL) LDL cholesterol (mg/dL) C-reactive protein (mg/dL) Functional capacity VO2max (mL/min) VO2max/kg (mL/min/kg) Percent predicted VO2max (%) Percent predicted VO2pulse (%) FMS total score
n
Visit 1
Visit 2
Difference between visits
P†
17 17 17 17
80.4 8.2 29.9 5.3 1.9 0.5 0.87 0.08
81.0 18.2 30.1 5.6 1.9 0.55 0.84 0.08
0.4 [4.9 to 13.8] 0.2 [1.3 to 3.1] 0.0 (0.1 to 0.0) 0.03 (0.05 to 0.00)
.81z .76z .32 .03
17 17 17 17 17 17
115 12 0.54 0.97 69 6 1.13 0.61 79 7 0.30 0.68
115 12 0.53 0.98 69 7 1.13 0.69 81 7 0.43 0.69
0 (4 to 5) 0.00 (0.37 to 0.37) 0 (3 to 3) 0.00 (0.32 to 0.33) 1 (3 to 5) 0.13 (0.27 to 0.52)
.93 .99 .94 .99 .45 .50
17 17 16
1.80 0.56 4.5 11.3 4.8 1.1
1.96 0.54 5.4 9.2 4.7 0.9
0.16 (0.25 to 0.57) 0.9 (7.5 to 5.8) 0.1 (0.7 to 0.5)
.42 .78 .77
17 17
365 159 4.0 1.6
301 67 3.4 0.7
64 (152 to 24) 0.7 (1.6 to 0.3)
.14 .14
17 17
294 85 3.3 1.0
284 78 3.2 0.9
10 (52 to 33) 0.1 (0.6 to 0.4)
.64 .61
16 16 16 16 16 16
159 27 110 24 94 44 49 12 91 20 0.3 [<0.2-1.4]
147 23 101 21 99 54 46 15 81 18 <0.2 [<0.2-1.1]
12 (19 to 4) 9 (15 to 3) 5 (15 to 25) 3 (7 to 2) 10 (16 to 4) 0.0 [1.4 to 0.8]
.004 .005 .60 .22 .004 .54z
15 15 15 15 16
2128 577 27 6 66 14 69 16 13 2
2133 575 27 6 66 13 72 15 17 1
5 (83 to 93) 0 (1 to 1) 0 (3 to 3) 3 (1-6) 4 (3-5)
.90 1.00 .98 .01 <.001
bindex, stiffness index; BP, blood pressure; CCA, common carotid artery; Ep, pressure-strain elastic modulus; FMS, functional movement screening; HDL, high-density lipoprotein; LDL, low-density lipoprotein; VO2max/kg, maximum volume oxygen consumed indexed by body mass. Data are reported as frequency (percent), mean SD, median [range], and mean difference (95% CI). Bold values indicate P # .05. *Measurements not included in analysis: 1 PWV measurement excluded because of poor signal quality, 1 blood test excluded because of nonadherence to fast, 1 cardiopulmonary exercise test excluded because of subject difficulty with equipment, and 1 cardiopulmonary exercise test and FMS assessment not completed because of subject injury unrelated to intervention. †Paired t test, unless otherwise specified. zWilcoxon signed-rank test.
>5%)14 or abnormal PWV for their age (n = 7, subjects 10- to 14-years old PWV >4.5 m/s; subjects 15- to 19-years old PWV >5.5 m/s)23 showed a significant improvement (P = .01, .04, and .04, respectively).
Discussion Our results suggest that this telehealth intervention can promote high adherence to a structured program in an adolescent population with overweight and obesity. Attrition in clinic-based pediatric weight programs is reported to be high. Skelton et al25 reviewed 10 retrospective studies of pediatric outpatient weight programs and reported attrition rates that ranged from 27%-73%. The most recent Cochrane review of obesity interventions in children reported attrition rates ranging from 0%-43% in clinical trials that incorporate exercise training.26 The 15% attrition rate observed within the current study is low, compared with clinic programs. Attendance rates are less well reported, but Hampl et al7 surveyed clinic-based pediatric weight management programs
and reported initial nonattendance rates of 28%, and 7 of 8 programs reported that the majority of patients attended fewer than 50% of scheduled visits. In the current study, attendance rates were high, with the majority of participants attending 95% or more of scheduled sessions. The population recruited in this study was predominantly of Hispanic ethnicity. Multiple studies in the pediatric population have reported a significant difference in adherence to physical activity between Hispanic and non-Hispanic white children27 and adolescents.28 Fakhouri et al27 reported that 65.7 4.4% of Hispanic children met physical activity recommendations, and non-Hispanic white children and non-Hispanic black children had higher adherence (73.4 2.6% and 72.5 3.8%, respectively). In our study, both Hispanic and non-Hispanic white participants showed high adherence (attendance at 95 7% and 90 15% of sessions, respectively). The level of dietary change seen in our results is consistent with other studies of Dietary Approaches to Stop Hypertension-type interventions in adolescents. A study in
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adolescents with hypertension reported similar overall changes in number of servings of low fat dairy and fruits and vegetables, and a smaller change in servings of foods high in fat and sodium.22 A similar intervention using telephone reminders in adolescents with metabolic syndrome reported subjects consuming an average of 6 servings of fruits and vegetables per day, a similar level to the present study; however, total servings of low fat dairy and foods high in fat or sodium were not reported.29 The greater change in lipid levels observed in the present study may be due to an increased level of engagement (3 times weekly for diet and exercise sessions) with the intervention team. Significant improvements in vascular health were observed in participants who showed abnormal measures of vascular function at baseline. Endothelial function was impaired in 12/17 (71%) of the study participants based on mean values for a healthy lean adolescent population.12 Adults with impaired flow mediated dilation because of chronic heart failure30 and type-2 diabetes mellitus31 showed improvements after the intervention but healthy individuals showed no improvement,32 similar to our study. Arterial stiffness was impaired in 7/17 (41%) of our subjects based on median values for healthy lean adolescents.14 A study investigating a similar diet and exercise intervention showed no effect on PWV in obese adolescents33 but did not perform subgroup analysis on subjects with increased arterial stiffness. Thus, our observation that vascular function improves in adolescents with abnormal arteries requires confirmation in future studies. This study should be viewed in light of its limitations. First, it was structured as a pilot without a control group, and thus results should be considered preliminary. Second, secondary subgroup analyses of vascular measures were conducted post-hoc. In addition, defining subgroups with a baseline cutoff can be vulnerable to type 1 error because of regression to the mean, but this analysis was based upon objective criteria published previously. Self-selecting participants may be more highly motivated to change their health. The use of incentives to encourage return for the follow-up visit may have artificially improved the attrition rate. However, because the incentives were sent regardless of adherence, the attendance rate is less likely to be influenced. Finally, the intervention was conducted over a short term and the sustainability of changes in health measures over the long term is unknown. Longer periods of follow-up are necessary to determine if health effects can be maintained as the adolescent approaches adulthood. This program shows promise for improving the health of overweight and obese adolescents over 12 weeks. Integrating telehealth into clinical preventive care has the potential to improve cardiovascular health in the youth at risk. n We acknowledge the contributions of Laurie Steinberg, MS, RD, who ran the nutritional analysis on the 24-hour dietary recalls. Submitted for publication Feb 20, 2015; last revision received Apr 27, 2015; accepted Jun 4, 2015.
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Vol. 167, No. 3 Reprint requests: Elif Seda Selamet Tierney, MD, Division of Cardiology, Department of Pediatrics, Stanford School of Medicine, 750 Welch Rd, Suite 350, Palo Alto, CA 94304. E-mail: [email protected]
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September 2015 20. Cook G, Burton L, Hoogenboom B. Pre-participation screening: the use of fundamental movements as an assessment of function - part 1. N Am J Sports Phys Ther 2006;1:62-72. 21. Appel LJ, Moore TJ, Obarzanek E, Vollmer WM, Svetkey LP, Sacks FM, et al. A clinical trial of the effects of dietary patterns on blood pressure. DASH Collaborative Research Group. N Engl J Med 1997;336:1117-24. 22. Couch SC, Saelens BE, Levin L, Dart K, Falciglia G, Daniels SR. The efficacy of a clinic-based behavioral nutrition intervention emphasizing a DASH-type diet for adolescents with elevated blood pressure. J Pediatr 2008;152:494-501. 23. Sarkola T, Manlhiot C, Slorach C, Bradley TJ, Hui W, Mertens L, et al. Evolution of the arterial structure and function from infancy to adolescence is related to anthropometric and blood pressure changes. Arterioscler Thromb Vasc Biol 2012;32:2516-24. 24. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)–a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform 2009;42:377-81. 25. Skelton JA, Beech BM. Attrition in paediatric weight management: a review of the literature and new directions. Obes Rev 2011;12:e273-81. 26. Oude Luttikhuis H, Baur L, Jansen H, Shrewsbury VA, O’Malley C, Stolk RP, et al. Interventions for treating obesity in children. Cochrane Database Syst Rev 2009;1:CD001872.
ORIGINAL ARTICLES 27. Fakhouri TH, Hughes JP, Brody DJ, Kit BK, Ogden CL. Physical activity and screen-time viewing among elementary school-aged children in the United States from 2009 to 2010. JAMA Pediatr 2013;167:223-9. 28. Iannotti RJ, Wang J. Trends in physical activity, sedentary behavior, diet, and BMI among US adolescents, 2001-2009. Pediatrics 2013;132:606-14. 29. Saneei P, Hashemipour M, Kelishadi R, Rajaei S, Esmaillzadeh A. Effects of recommendations to follow the Dietary Approaches to Stop Hypertension (DASH) diet v. usual dietary advice on childhood metabolic syndrome: a randomised cross-over clinical trial. Br J Nutr 2013;110:2250-9. 30. Maiorana A, O’Driscoll G, Dembo L, Cheetham C, Goodman C, Taylor R, et al. Effect of aerobic and resistance exercise training on vascular function in heart failure. Am J Physiol Heart Circ Physiol 2000;279:H1999-2005. 31. Maiorana A, O’Driscoll G, Cheetham C, Dembo L, Stanton K, Goodman C, et al. The effect of combined aerobic and resistance exercise training on vascular function in type 2 diabetes. J Am Coll Cardiol 2001;38:860-6. 32. Maiorana A, O’Driscoll G, Dembo L, Goodman C, Taylor R, Green D. Exercise training, vascular function, and functional capacity in middle-aged subjects. Med Sci Sports Exerc 2001;33:2022-8. 33. Khadilkar VV, Pandit DS, Khadilkar AV, Chiplonkar SA, Kinare AS. Diet and exercise intervention, with special reference to micronutrients, reduces cardiometabolic risk in overweight children. Indian J Endocrinol Metab 2012;16:124-33.
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Figure 1. Study enrollment.
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