CHILDHOOD OBESITY October 2014 j Volume 10, Number 5 ª Mary Ann Liebert, Inc. DOI: 10.1089/chi.2013.0165

School-Based Health Center-Based Treatment for Obese Adolescents: Feasibility and Body Mass Index Effects Kathy Love-Osborne, MD,1,2 Rachel Fortune, MD,3 Jeanelle Sheeder, MSPH, PhD,2,4 Steven Federico, MD,1,2 and Matthew A. Haemer, MD, MPH 2,5

Abstract Background: School-based health centers (SBHCs) may be an ideal setting to address obesity in adolescents because they provide increased access to a traditionally difficult-to-reach population. The study evaluated the feasibility of adding a health educator (HE) to SBHC teams to provide support and increase the delivery of preventive services for overweight or obese adolescents. Methods: Adolescents with BMI ‡ 85% recruited from two SBHCs were randomized to a control group (CG) or an intervention group (IG). Both groups received preventive services, including physical examinations and laboratory screening in the SBHC. The educator met with the IG during the academic year, utilizing motivational interviewing techniques to set lifestyle goals. Text messaging was used to reinforce goals between visits. Results: Eighty-two students (15.7 – 1.5 years of age; BMI, 31.9 – 6.2 kg/m2) were enrolled in the IG and 83 in the control group (16.0 – 1.5 years of age; BMI, 31.6 – 6.5 kg/m2). Retention was 94% in the IG and 87% in the CG. A total of 54.5% of the IG and 72.2% of the CG decreased or maintained BMI z-score (less than 0.05 increase; p = 0.025). Sports participation was higher in the CG (47% vs. 28% in the IG; p = 0.02). Mean BMI z-score change was - 0.05 – 0.2 for students participating in sports vs. 0.01 – 0.2 for those not ( p = 0.09). Conclusions: This SBHC intervention showed successful recruitment and retention of participants and delivery of preventive services in both groups. Meeting with an HE did not improve BMI outcomes in the IG. Confounding factors, including sports participation and SBHC utilization, likely contributed to BMI outcomes.

Introduction besity is associated with medical comorbidities, including type 2 diabetes (T2D) and cardiovascular disease.1 Obese adolescents may have worse attendance and school performance as well as more disciplinary problems, compared to normal weight peers.2 Screening obese adolescents for comorbidities of obesity has been recommended.1 Yet, in most practices, documentation of obesity as a diagnosis is poor, with rates of recommended laboratory screening less than 50%.3–5 Treatment of obesity in adolescents is difficult, with many interventions showing mixed or negative results.6 Although achieving weight loss may not always be pos-

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1

Department Department 3 Department 4 Department 5 Department 2

of of of of of

sible, evidence in adults has suggested that stopping weight gain lowers the risk for T2D.7 A study in obese children similarly demonstrated that participants that maintained weight were less likely to develop T2D.8 Several logistical challenges create barriers to treating obese adolescents. One problem is that office-based settings are likely to reach only a subset of adolescents in need of services. Multidisciplinary treatment programs typically have long wait lists and high attrition rates in the range of 25–50% dropout after the first visit.9,10 Missing school has been reported as a reason for dropping out of obesity treatment programs.9 The capacity of traditional office-based obesity treatment to have a significant impact on the current obesity epidemic is limited by reach of services, and effectiveness is

Pediatrics, Denver Health and Hospitals Authority, Denver, CO. Pediatrics, University of Colorado School of Medicine, Aurora, CO. Pediatrics, Yale School of Medicine, New Haven, CT. Obstetrics and Gynecology, University of Colorado School of Medicine, Aurora, CO. Pediatrics, Children’s Hospital Colorado, Aurora, CO. 424

CHILDHOOD OBESITY October 2014

largely untested. Inner-city schools have a higher-thanaverage percentage of the student body that are overweight or obese, and adolescents served by school-based health centers (SBHCs) are more likely to be from minority groups and be uninsured.11 Most school-based obesity interventions have been delivered to all students.12 One randomized, controlled trial (RCT) showed improved BMI outcomes with an intervention that integrated physical activity (PA) into a required health course.13 There have been few individual RCTs studying treatment of obese teens in the school setting.14 SBHCs have been utilized to improve delivery of obesity preventive services.15 They have been shown to provide opportunities for increased access to adolescents, compared with the traditional office-based setting, and therefore have the potential to deliver an individualized intervention to teens and reduce attrition rates.16 One small SBHC study did show successful BMI outcomes with the use of motivational interviewing.17 Motivational interviewing (MI) techniques have been shown to be useful in many health conditions, including obesity.18,19 MI involves identifying and enhancing intrinsic motivation to change using an empathic counseling style to elicit the patient’s own solutions and collaboratively set goals for change. The use of technology through text messaging has also been studied to improve various health outcomes.19,20 Given that adolescents are frequent users of technology, they are an ideal population to test the utilization of text messaging as a means for supporting lifestyle changes. The aim of this study was to evaluate whether a health educator (HE) providing additional contact time with students and helping them to set personal goals to improve lifestyle would lead to improved BMI outcomes in overweight or obese adolescents. The educator was incorporated into the SBHC team of a mid-level provider (nurse practitioner or physician assistant), medical assistant, and supervising physician. The hypothesis was that adding an HE to an SBHC team would lead to improved rates of delivery of recommended preventive services. More-frequent contact with intervention students by the educator using text-messaging support would increase the proportion of adolescents able to maintain or decrease BMI z-score during an academic year.

Methods During the fall of 2010, adolescents with BMI ‡ 85% were recruited from the SBHC population at two schools. Students were randomized to a control group (CG) or an intervention group (IG). Both SBHCs are located within public schools with high percentages of underserved students, largely ethnic minorities. One school is a high school (HS) and the other is a middle school/high school (MS/HS). Participants had parental consent to be treated in the SBHC. The Colorado Multiple Institutional Review Board approved the study, which included waiver of parental consent. Students completed informed consent, and

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letters were sent to parents with instructions to contact study staff if they did not want their adolescent to participate in the study.

Motivational Interviewing Before enrolling participants, study staff (including the HE, mid-level providers at both schools, project manager, and the principal investigator) attended full-day training on MI techniques conducted by a local expert. They completed a follow-up session with the trainer 2 months later. MI training has been successfully utilized in SBHCs to improve provider implementation of obesity treatment guidelines.15

Health Educator Visits IG participants were seen by the educator for visits after recruitment was complete. The educator went to participants’ classrooms and asked for students to be released from class for visits. At each visit, height and weight were measured without shoes with the same calibrated scale and stadiometer. HeartSmartKidsTM,21 a bilingual assessment tool that gathers data on dietary and exercise habits as well as family medical problems, was completed by the student at the baseline visit. Feedback from the HeartSmartKids assessment was presented to students by the educator using an MI framework to support change and start goal-setting discussions. At each visit, goals were reviewed and modified. The educator encouraged participants to choose one nutrition goal and one PA goal. Although the educator discussed the recommended target for PA duration of 1 hour daily, participants chose their own goals. The frequency of visits with the educator was designed to be participant directed; at the end of each visit, students were asked if they wished to return in 2 weeks, 1 month, or 2 months. The educator linked participants with existing resources for PA and healthy eating within the school or community, such as the free breakfast program or existing fitness centers located within the school (at the MS/HS) or in the community. In addition, the educator facilitated applications for free parks and recreation memberships for interested participants.

Self-Monitoring Self-monitoring was encouraged by asking participants to record their weight weekly and lifestyle behaviors daily on a paper log sheet. Participants were instructed to turn in log sheets weekly. Log sheets were reviewed with participants during visits as an aid in revising goals. Students returning five log sheets received a $10 gift card, and students who returned 10 additional log sheets received a second gift card. Each student could earn up to two gift cards during the school year.

Electronic Support Participants in the IG were randomized to receive two weekly text messages (TMs) or no text messages (NTMs)

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for the first semester, defined as the first educator visit of the first semester until the first educator visit of the second semester. The TM group received one individualized goalrelated TM each week to reinforce goals set at the visit and a second weekly TM reminding them to turn in log sheets. All IG participants received TMs during the second semester. Messages were sent manually through one of two systems: e-mail to text interface by Outlook webmail or directly from a study phone. Participants who did not have a cellular phone were provided with one for the duration of the study.

Medical Care Study physicians reviewed both groups’ medical records. If a physical examination and laboratory screening for comorbidities of obesity had not been done within the previous 2 years, considered standard of care within the organization, a visit was scheduled in the SBHC. Laboratory testing included hematocrit for females and total cholesterol for all adolescents. Obese adolescents and overweight adolescents with a family history of diabetes had additional testing for T2D and nonalcoholic fatty liver disease (hemoglobin A1c and alanine aminotransferase). Abnormal laboratory testing was evaluated by physician investigators and addressed within the SBHC, with referral for specialty care, if indicated.

Body Mass Index Trajectory Growth charts before study enrollment were evaluated for both groups, with BMI measurements approximately 1 year before study enrollment recorded. Results were categorized as no previous BMI measurement, BMI stable before enrollment (z-score change of - 0.1 to + 0.1), BMI increasing before enrollment (z-score ‡ 0.1), or BMI decreasing before enrollment (z-score £ - 0.1). A z-score change of 0.1 or more is considered to be a minimally clinically significant outcome.22 Because the study intervention was less than 1 year, BMI outcomes for smaller (0.05–0.1) z-score changes as well as minimally clinically significant ( ‡ 0.1) z-score changes were analyzed in order to evaluate any potential effects of the intervention on BMI z-score outcomes.

Fitness Testing The IG was scheduled to undergo fitness testing with the Fitnessgram Progressive Aerobic Cardiovascular Endurance Run (PACER) test, a validated test of cardiovascular fitness,23 at baseline and at the end of the school year (6–8 months after enrollment). For students that were participating in physical education (PE) class, PACER results were obtained from the PE teacher.

Final Visit Both groups had a final visit at the end of the school year, 6–8 months after enrollment, where they were weighed and measured. Students completed a survey about the types of

LOVE-OSBORNE ET AL.

lifestyle changes they had made during the school year, as well as participation in PE and sports during the year.

Statistical Analysis Summary statistics were used to describe the study population. For the power calculation, we estimated that 50% of the participants in the IG would either decrease or maintain their BMI z-score and that only 25% of the participants in the CG would. With 80 participants in the IG and 80 controls, we had 80% power, with an a = 0.05. Student’s t-tests (continuous variables) and chi-square tests (dichotomous variables) were used to compare the IG to the CG. For dichotomous variables where individual cell size was less than 5, Fisher’s exact tests were used. All statistics were calculated using PASW Statistics 20 (SPSS, Inc., Chicago, IL).

Results Enrollment A total of 165 adolescents were enrolled from mid-August through the end of September 2010. Participants were mostly Hispanic (Table 1). Of students approached, 90% agreed to participate in the study. One parent declined permission for the student to participate.

Attrition The end-of-year visit was completed by 72 students (87%) in the CG and 77 students (94%) in the IG (not

Table 1. Baseline Characteristics of Study Groups Intervention Control group (n582) group (n583)

p value

15.7 – 1.5

16.0 – 1.5

NS

Gender (% female)

58

46

0.10

Ethnicity (% Hispanic)

88

89

NS

2

BMI (kg/m )

31.9 – 6.2

31.6 – 6.5

NS

BMI z-score

1.92 – 0.46

1.89 – 0.52

NS

BMI 85–95%

27%

35%

BMI 95–99%

52%

46%

BMI > 99%

21%

19%

BMI stable before study (z-score change of - 0.1 to 0.1)

41%

31%

BMI increasing prior to study (z-score increase of ‡ 0.1)

31%

38%

BMI decreasing before study (z-score decrease of ‡ 0.1)

28%

Age (years)

NS, not significant.

NS

NS 31%

CHILDHOOD OBESITY October 2014

significant). Eight students (11%) in the CG and 2 (5%) in the IG withdrew from school ( p = 0.17). At the HS, 95% of IG students completed the study versus 83% of CG students ( p = 0.08). There were no differences in attrition rates at the MS/HS. Some students did not complete the final visit despite multiple attempts to schedule a visit by the HE.

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Table 2. BMI z-Score Change by Group z-score change Significant decrease ( £ 0.1) Borderline decrease ( - 0.099 to - 0.049)

Intervention Control group group (%) (n577) (%) (n572) p value* 18.2

40.3

0.02

9.1

11.1

0.45

Educator Visits

Stable ( - 0.05 to 0.05)

27.3

20.8

Reference

Intervention students had a mean of five visits with the educator (range, one to eight). Increased visit number was not associated with improved BMI outcome. Sixty percent of participant visits documented requested follow-up in 2 weeks, 33% in 1 month, and 7% in 2 or 3 months.

Borderline increase (0.051 - 0.099)

14.3

9.7

0.85

Significant increase ( ‡ 0.1)

31.2

18.1

0.57

*Chi-square test.

Electronic Support During the first semester, 38 students were randomized to the TM group. Eight of these students received a study phone free of charge. There were no demographic differences between the TM and NTM groups. Sending TMs with the Microsoft Outlook method was successful for 30 of 38 students in the TM group. Eight students were only able to receive TMs sent directly from the study cell phone. Six participants (16%) changed cell phone numbers and 2 participants (5%) lost a cell phone during the first semester. TM group participants were sent an average of 12 goalrelated texts and 12 log reminder texts during the first semester. Very few adolescents turned in log sheets in either group. Students receiving a free study phone did not have improved mean BMI z-score outcomes, compared with students who already had a cellular phone.

From enrollment to the final visit, 55% of the IG and 72% of the CG decreased or maintained a stable BMI zscore, within 0.05 of baseline ( p = 0.025; Table 2). Forty percent of the CG versus 18% of the IG decreased BMI zscore by 0.1 or more ( p = 0.02). Other categories of BMI change were not significant between groups. Post-hoc analysis revealed that reported sports participation was significantly higher in the CG (47% vs. 28% in the IG; p = 0.02). Mean BMI z-score change was - 0.05 – 0.2 for students in both groups participating in sports versus 0.01 – 0.2 for those not participating in sports ( p = 0.09). BMI outcomes were not statistically different in the TM group, compared to the NTM group.

School-Based Health Center Utilization

Age Differences

The majority of participants in both groups received preventive services within the SBHC. Almost all of the participants (98%) had a physical examination and 95% had laboratory testing done within the SBHC, either before or during the study period. At the beginning of the study, 40% of participants were current with regard to physical examination status. Of 71 students in the CG completing the final visit, 61 (86%) utilized the SBHC during the study period.

Medical Comorbidities of Obesity Several students were diagnosed with significant medical comorbidities of obesity requiring additional obesity-related visits and treatment. One CG student was diagnosed with T2D by laboratory tests sent as part of the study, and another CG student with previously diagnosed T2D who had been lost to follow-up was identified during the study. Both diabetic students were referred to pediatric endocrinology and improved glycated hemoglobin during the study period. Two students (1 IG and 1 CG) were started on statin therapy for severe dyslipidemia. All 4 students with significant medical issues experienced stabilization or decrease in BMI z-score.

BMI Outcomes

Students younger than 15 years had better BMI outcomes than older students. Of younger students in both groups, 76% maintained or decreased BMI z-score within 0.05, compared to 57% of older students ( p = 0.03).

BMI Trajectory Most (81%) participants had a previous BMI measurement, a mean of 1.1 years before enrollment. Thirty-six percent of these participants had a stable BMI z-score from previous measurement to enrollment. Twnety-nine percent had decreased BMI z-score ( £ - 0.1), and 35% had an increased BMI z-score ‡ 0.1 before enrollment. BMI outcomes did not differ by baseline BMI trajectory (data not shown).

Fitness Testing, Physical Education Class, and Sports Participation At least three attempts were made to get each student out of class to perform PACER testing. Of intervention participants, 46 (56%) completed the first PACER test. However, only 19 students (23% of the IG) completed both baseline and follow-up PACER tests. Of students completing both PACER tests, 80% improved their score from

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LOVE-OSBORNE ET AL.

Table 3. BMI z-Score Change by Baseline BMI Category and Sports Participation Intervention group (n577) BMI 85–95% Median z-score change (range) Sports participation (% yes) BMI 95–99% Median z-score change (range) Sports participation (% yes) BMI > 99% Median z-score change (range) Sports participation (% yes)

Control group (n572) p value

n = 20

n = 23

0.11 ( - 0.39 to 0.72)

- 0.03 ( - 0.67 to 0.46)

0.65*

35

54

0.20**

n = 40

n = 35

0.02 ( - 0.67 to 0.47)

- 0.07 ( - 0.48 to 0.45)

0.81

26

46

0.07

n = 17

n = 14

0.01 ( - 0.09 to 0.15)

0.01 ( - 0.22 to 0.12)

0.36

25

36

0.69

*Independent-samples median test. **Chi-square test or Fisher’s exact test for comparisons with cell sizes < 5.

fall to spring. Reasons reported by the educator for participants not completing testing included: absent (7%); not coming for testing (33%); refusal to come for the test (11%); and teacher refusal to let the student leave class (26%). Students at the MS/HS were more likely to complete the baseline PACER test (79% vs. 30% at the HS), likely reflecting higher rates of PE participation at the MS/ HS. Of IG students completing the spring visit, 20 (26%) at the MS/HS reported participating in PE class both semesters. At the HS, 10 (14%) reported participating in both semesters of PE class. Sports participation rates generally decreased with increasing severity of obesity and were higher in the CG (Table 3).

Discussion This intervention in a SBHC setting was successful with recruitment and retention. In addition, delivery of recommended preventive services (physical examination and laboratory testing for obesity comorbidities) was successfully implemented. Nearly 100% of students in both groups received recommended preventive services, compared with 40% of participants at baseline. The intervention reached a subset of students with high morbidity, including severe obesity in 20% of participants, and identified 2 cases of T2D. Three of the 4 students requiring significant medical intervention for comorbidities of obesity were CG participants, and all 4 of these students had stable or decreased BMI z-score.

The addition of an HE did not lead to improved BMI outcomes in the intervention group. Small school-based studies may suffer from contamination between treatment groups and have inadequate sample size to ensure similar distribution of possible confounders among groups.24 Confounders may include gender, age, baseline BMI, parental obesity, and home environment, including family support, mental health issues, sports participation, PE, and use of obesigenic medications, such as psychiatric medications or contraceptives. Mental health issues were present in 14% of the entire cohort, with no difference between the IG and CG in prevalence of mental health concerns at the time of the annual physical examination. Additional unidentified factors may have contributed to improved BMI outcomes in the control group. Because knowledge of the CG was limited to previous and baseline BMI, medical record review, and a follow-up questionnaire, it is unclear whether other factors differed between the groups in addition to sports participation. The unequal sports participation rate in this study population is an example of this potential limitation. A study evaluating activity of adolescents demonstrated that obese adolescents spent less energy than normal weight adolescents; 70% of this difference was attributed to differences in sports participation.25 Sports participation may also be a marker for higher engagement with the school culture. It is possible that CG students who dropped out of school were not participating in school sports, so that those engaged in sports may have been more likely to be retained in the study. Sports participation rates generally decreased as baseline BMI category increased. Adolescents in the IG did not have a high rate of using exercise resources offered by the educator, such as parks and recreation passes. This may, in part, be a result of a requirement for a parental signature by the parks and recreation centers. Alternative methods of increasing moderate-tovigorous PA in obese and especially severely obese adolescents, including increasing rates of sports participation, deserve further investigation. The use of an educator to expand contact time with this group of adolescents may have unique limitations. It is possible that a new type of clinic staff person may take up to several years to earn the trust of both the students and also the teachers within a school. Compared with providers who have established trust, it could potentially require more than 1 academic year to truly integrate a new type of provider into the SBHC team. Despite the use of z-scores to assist with evaluation of BMI change, participants that were severely obese at baseline had much less variability in z-score changes over the time period of the study. This finding of poor response to treatment in severely obese adolescents has been previously reported.26 It is likely that a more intensive intervention is required for the severely obese. Utilization of self-monitoring (log sheets) as an intended result of the counseling was not demonstrated. It is unclear whether this was a result of students not completing the log

CHILDHOOD OBESITY October 2014

sheets or not turning them in to the clinic. It appears that traditional paper logs may not be an effective tool to measure self-monitoring behavior in adolescents. Other methods of self-monitoring, including electronic methods such as two-way TM responses regarding self-monitoring behaviors, might be an alternative approach to increasing self-monitoring behavior in this population. Different strategies for text-message support, behavior-tracking incentive, or counseling training may be required to improve intervention effectiveness. School differences were apparent, despite utilizing the same educator at both schools. There may be components of school engagement that differed between the two schools. The rate of withdrawal from school was higher in the HS, suggesting lower engagement in school. In addition, the MS/HS students were younger on average, which may have been a factor in improved BMI outcome. Improved BMI outcomes have been linked to younger patients.10 The difference in school withdrawal between the groups approached significance, with more CG students withdrawing from school. This suggests that the additional support provided by the HE may have had other beneficial effects unrelated to weight status.

Limitations The most significant limitation of this study lies in the paucity of information available for potential confounders discussed previously, especially in the CG participants. Another limitation is the short duration of the intervention. Educator visits did not begin until study enrollment was complete. This led to a 6-week delay in the onset of visits. Although the majority of IG participants requested their follow-up visits in 2 weeks or 1 month, the mean number of HE visits during the school year was only five. Ideally, most participants would have had at least 10 visits based upon request. Reluctance by teachers to have students leave the classroom for visits was a significant barrier that contributed to lower numbers of visits than requested. This reluctance on the part of some teachers to release students may have also affected the educator’s comfort level in bringing participants to the clinic, given that she personally went to classrooms to ask students to come for visits. Having an unrelated person bringing students to the clinic for all appointment types might have moderated this issue. In future school-based research, it is imperative that teachers are included in the planning stage, given that their support is critical to the success of this type of intervention. The short time interval available for visits in the first semester led to randomization of TM for only 2 months. Technical difficulties and the short randomization period did not allow adequate evaluation of whether this component of the intervention might improve BMI outcomes. It is also possible that once-weekly goal-directed TM was not sufficiently intensive to have an impact on BMI outcomes. Also, the sample size may not have been sufficient to detect change for the TM versus NTM groups.

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It is likely that adolescents may have difficulty implementing the goals they set without parental support with regard to grocery shopping and foods kept within the home. This study did not include a parent component, which might have improved BMI outcomes. In addition, parental obesity has been associated with less-favorable response to intervention,27 and parental obesity data were not collected. The study intended to measure fitness, but few participants completed testing because of logistical difficulties of testing. Students who were not otherwise completing testing in PE class infrequently completed the test. These findings suggest that fitness measurement in obese adolescents may pose significant difficulties. Overweight and obese adolescents have been shown to have lower fitness utilizing the PACER test.28 Recognizing this, adolescents who do not have confidence in their physical abilities may be unwilling to complete this part of the research assessment. Those students who completed the PACER test at both time periods improved test scores; however, because so few students completed both tests, these results are likely affected by self-selection bias. In the public school system studied, very few students had two semesters of PE class. Also, though participation in sports was a significant predictor of BMI outcome, participation in PE class was not associated with improved BMI outcome. The type of sport was not consistently reported. This finding may be related to the intensity of exercise in sports being more vigorous than that of PE class. The study protocol requiring that CG students had preventive services within the SBHC may have led to contamination of the CG, because SBHC medical providers in this study had an interest in adolescent obesity and attended MI training. CG students who used the SBHC during the study year had better BMI outcomes than those who did not use the SBHC. With regard to adherence to MI techniques, this study did not contain specific measures to evaluate adherence of the educator to MI techniques. Adherence evaluation would be an important component to further studies assessing efficacy of MI as an intervention in obese adolescents. Last, there were multiple limitations regarding the TM portion of the intervention. The Microsoft Outlook method was not delivered consistently and it was difficult to determine how many participants were receiving messages with the technology that was used. The method of texting from cell phone to cell phone was cumbersome. Further, the intervention (including TM) took longer than intended to start during the first semester, which may have had an impact on outcomes. However, all participants had the same intervention period, because the intervention visits did not begin until all participants had been enrolled. One additional limitation was the bias that was introduced by providing participants with cell phones if they did not have their own, although this did not appear to have an impact on outcomes.

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Conclusions This study demonstrated feasibility of conducting an obesity intervention in a SBHC setting, although barriers to implementation and evaluation were identified. Improvement in delivery of preventive services was demonstrated in this traditionally hard-to-reach population. Further study of interventions in this setting is warranted given the potential to expand capacity to deliver weight management services. In relatively small trials such as the one presented here, it is difficult to control for the many potential confounders that may impact adolescent BMI outcomes. Future research should consider controlling for sports participation, which was identified as the most important confounder in this study. SBHC utilization was another important potential confounder in this study. Providing recommendations for care for the control group, rather than actively recruiting students for SBHC preventive care visits, might minimize SBHC utilization as a confounding factor in future research. In addition, further baseline evaluation of participants, including evaluation for mental health issues and family support, would be valuable.

8.

9.

10.

11.

12.

13.

14.

15.

Acknowledgments

16.

This study was funded by the Colorado Health Foundation. In-kind support was provided by Denver Health and Hospitals.

17.

Author Disclosure Statement

18.

No competing financial interests exist. 19.

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Address correspondence to: Kathy Love-Osborne, MD Associate Professor of Pediatrics Denver Health and Hospitals Authority 501 28th Street Denver, CO 80205 E-mail: [email protected]

School-based health center-based treatment for obese adolescents: feasibility and body mass index effects.

School-based health centers (SBHCs) may be an ideal setting to address obesity in adolescents because they provide increased access to a traditionally...
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