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. Author manuscript; available in PMC: 2022 Jan 1.
Published in final edited form as: Res Dev Disabil. 2020 Nov 18;108:103809. doi: 10.1016/j.ridd.2020.103809

Weight outcomes in children with developmental disabilities from a multidisciplinary clinic

Byron Alexander Foster 1, Kimberly Reynolds 2, Alicia Callejo-Black 3, Natasha Polensek 4, Beau C Weill 5
PMCID: PMC7775337  NIHMSID: NIHMS1646118  PMID: 33220529

Abstract

Background:

Children with developmental disabilities experience disparately high rates of obesity yet there are few reports detailing clinical outcomes for this population.

Aim:

To describe outcomes of obesity treatment for children with developmental disabilities and a comparison group of children without developmental disabilities.

Methods and Procedures:

We examined weight outcomes of children with and without developmental disabilities seen in a family-centered, multidisciplinary treatment center over a ten-year period. We stratified by age and developmental disability diagnosis. We assessed whether intake demographic or health behavior data was associated with successful reduction of adiposity over six and twelve month follow-up periods, using a ≥5% absolute reduction in percent over the 95th percentile body mass index (BMIp95) as the primary outcome.

Outcomes and Results:

Over a ten-year period, 148 of 556 children in the obesity clinic (27%) had a developmental disability. In children <12 years of age, 36% of children with developmental disabilities reduced their adiposity compared with 18% of children without developmental disabilities at six months, p = .01. This pattern continued at twelve months. Active transport to school was associated with reduced adiposity for those without a disability. Older children with disabilities rarely had a significant reduction (2 of 26 children), and they took more medications with weight-related side effects.

Conclusions and Implications:

Younger children with developmental disabilities experienced relative success in reducing their adiposity. Challenges to addressing obesity in this population include structural barriers to physical activity and medications for behavioral management with weight-related side effects.

1. Introduction

1.1. Obesity interventions for children with developmental disabilities

Children with developmental disabilities experience higher rates of overweight and obesity compared to typically developing children, both overall and also among sub-groups with strong behavioral components (Bandini et al., 2015; Foley, Lloyd, Vogl, & Temple, 2014; Hinckson, Dickinson, Water, Sands, & Penman, 2013). There are limited data regarding outcomes for individuals with intellectual and developmental disabilities (Must et al., 2014).

Multidisciplinary clinics to address childhood obesity provide the potential for addressing the multiple drivers of weight status. This type of approach may occur in tertiary care settings or be facilitated by a primary care office and include experts in physical activity (physical or occupational therapists), nutrition (registered dieticians), medical well-being (e.g., physicians, nurse practitioners), and behavioral specialists (e.g., psychologists, social workers, or other behavioral health specialists), consistent with recommendations by the American Academy of Pediatrics (Spear et al., 2007).

The outcomes from multidisciplinary clinics for childhood obesity vary, by both sociodemographic status and adiposity-related outcomes. A recent analysis of a dataset from a national consortium of multidisciplinary clinics showed that youth with obesity who actively participated in a treatment intervention experienced a small but significant reduction in adiposity (Kumar et al., 2019). They used ≥5 point absolute reduction in percent of the 95th percentile for body mass index (BMIp95), which may be an important proxy for cardiovascular outcomes. This study showed significant heterogeneity in outcomes over sub-groups of age, race/ethnicity and obesity severity, highlighting the need for further evaluation (Kumar et al., 2019).

A systematic review published in 2014 found only two case reports from the United States and several case series from other countries examining clinical outcomes for children with developmental disabilities (Maiano, Normand, Aime, & Begarie, 2014). Since then there have been a few randomized clinical trials published that examine behavioral interventions in children with developmental disabilities - one study found evidence of an effect on adiposity from an intervention for children with Down syndrome (Curtin et al., 2013). Another found no evidence of an effect on adiposity using a school-based, mobile health intervention (Lee et al., 2017). A non-randomized cohort study examined children with intellectual disabilities in a weight management clinic and showed that younger children showed greater benefit with no significant change for adolescents (Pona, Dreyer Gillette, Odar Stough, Gerling, & Sweeney, 2017). A more recent systematic review of adult data identified six studies and overall no evidence of effectiveness of weight management for adults with intellectual disabilities (Harris, Melville, Murray, & Hankey, 2018).

1.2. Current study

The aim of this project was to describe the clinical outcomes for children with intellectual and developmental disabilities treated in a multidisciplinary weight management clinic and a comparison group of children without disabilities. We focused on the clinical outcomes of absolute change in percent of the 95th percentile for body mass index (BMI) and change in blood pressure because these outcomes may be amenable to change within the study time frame (6 and 12 months follow up), and are relevant predictors of future health outcomes.

2. Material and Methods

2.1. Overview of the clinical intervention in this study

The Healthy Lifestyles Clinic evaluates and treats pediatric patients who have obesity in a family-based, multidisciplinary approach. Patients are evaluated at their first visit and then seen in the clinic on an every three-months schedule to continue family-based intervention. The multidisciplinary team uses a physician, a dietician, a psychologist, and a physical therapist. Patients see each of these four disciplines at evaluation and all follow up visits for between 15–45 minutes, individualized to the needs and concerns of the patient and family.

The physician reviews the patient’s health history including risk factors in the family history, birth history, and possible obesogenic medications, and performs a physical exam. Dependent on the evaluation, referrals are placed to pediatric specialists such as sleep medicine for concerns regarding obstructive sleep apnea. The dietician reviews a three-day food diary, meal location, portion sizes, food insecurity/access, and family dynamics. With these variables in mind as well as the nutritional needs of the child (based on lab results and historical medical evaluation), they make both feeding behavior and specific food selection recommendations to families. The physical therapist performs a cardiorespiratory fitness test via a three-minute aerobic step test, assesses perceived exertion, and measures heart rate increase and time to heart rate recovery with older children. For children younger than 6 years of age, heart rate response and recovery are assessed before and after 3 minutes of age-appropriate physical activity of moderate intensity, using a faces scale for perceived exertion. Physical therapy evaluation also includes evaluation of mobility, pain, joint stability, balance and strength as well as current level of sedentary activity, all to inform developmentally appropriate and physically tailored recommendations for regular physical activity.

The psychologist assesses development, behavior, mood and sleep habits and screens for possible eating disorders. Their primary role is to support the family’s healthy lifestyle goals by promoting strategies related to limit-setting, motivation, cooperation with and from the child, tracking, arranging appropriate contingencies, and emphasizing whole family participation. Patients and families establish two to three behavioral goals during each visit, focusing on attainable incremental change towards appropriate levels and types of physical activity or improving dietary habits. Examples include walking the dog with a friend or parent three days per week for 30 minutes each time and limiting soda intake to one can per week. The behavioral goals are reviewed at subsequent clinic visits, celebrating successes, reviewing barriers, and establishing additional or revised goals if appropriate. The intervention clinic is highly individualized and tailored to the needs of each child and family. Consistent elements across families include the clinic process, appointment frequency, assessment metrics and the development of two-three behavioral goals as noted above. Details of the specific goals set for each family were not systematically evaluated as part of the current study. There were no programmatic modifications to goals set based on patient developmental disability status or age. Families with younger children were more likely to receive recommendations that are parent-driven. For example, many families were encouraged to establish a goal of incorporating a fruit or vegetable into daily snacks. Adolescents are largely responsible for implementing this recommendation independently, while younger children rely on their parents to provide the snack option.

2.2. Data sources

We used anthropometric and vital signs collected from the Healthy Lifestyles Clinic electronic medical records as well as data from intake forms completed by patients and their families just prior to or during their first visit. Data were analyzed for patients seen between January 2008 through October 2018. The local institutional review board (IRB) approved the data collection.

For classification of the children by the presence and type of developmental disability, we used the International Classification of Disease (ICD-10) codes listed in either their problem list or past medical history within the electronic medical record, for any visit within the defined date range. For this analysis, we created groups of patients based on their ICD-10 codes. Group 1 included ICD-10 codes F70-F73 and F78-F79 representing intellectual disability codes. Group 2 included codes F80-F82 for specific developmental disorders. Group 3 included all codes under F84 for pervasive developmental disorders. Group 4 included genetic disorders, namely Q90-Q93 and Q95-Q99 covering the diagnoses associated with chromosomal rearrangements. Group 5 included other diagnoses inclusive of cognitive or intellectual impairment under R41.8.

2.3. Dependent variable

The primary outcome was ≥5 point absolute reduction in percent of the 95th percentile (BMIp95) at 6 months following their index visit with Healthy Lifestyles. This outcome tracks with changes in adiposity among a population of children with obesity better than other indicators of adiposity (Freedman & Berenson, 2017). We used a categorical outcome of reduction in BMIp95 ≥5 points as a measure for clinically significant change in weight given the recently published finding of an association between this degree of change and laboratory indicators of cardiovascular risk (hemoglobin A1c level) (Kumar et al., 2019). Secondary outcomes included change in BMIp95 at 12 months and change in blood pressure at 6 and 12 months.

Given the range in follow-up visit dates, we examined specific periods of time for each follow-up: six month visits were any outpatient visit between 4 and 8 months from the initial Healthy Lifestyles visit; twelve-month visits were any outpatient visit between 10 and 14 months from the initial visit. Only outpatient visits were used as either urgent care or emergency care may introduce confounding in weight measurements due to acute illness.

2.4. Independent variables

2.4.1. Age:

We stratified the outcome analysis by age, separating pre-adolescents (<12 years of age) from adolescents (≥12 years of age). We used the rationale that the onset of puberty and adolescence generally represents a time when youth exhibit increasing independence in regards to behaviors targeted in the Healthy Lifestyles Clinic, thus there may be a differential response by age. We excluded children <2 years of age at the baseline visit from the analysis as the CDC BMI standards were used and there were only a few children this young.

2.4.2. Number of visits:

Visits to the Healthy Lifestyles Clinic were tallied. All patients by definition had one exposure. The levels of exposure were categorized by the time period in which they occurred. For example, for the time point of six months, all visits that occurred before that time point were counted towards the total exposure.

2.4.3. Intake data:

All children presenting to the clinic completed a survey of their current behaviors and practices prior to their initial visit. Independent variables from the intake form included the number of hours per day reported doing a variety of activities, separated by weekend versus weekday. Also included are the days per week that the child reported eating out, skipping meals, eating together as a family, and using different forms of screen time. The intake includes questions on the importance of having a healthy weight and the confidence that their child can attain a healthy weight using a five-point Likert scale. Anticipated challenges to behavior modification are also queried.

2.5. Statistical analysis

Descriptive statistics were used to compare groups using the potential predictor variables including the baseline behaviors reported on the intake data form. We used repeated measures linear regression to examine the primary outcome, stratified by age and developmental disability. We planned to use a multivariable model examining how the independent variables were associated with the outcome using a forward step-wise model building approach if independent variables were significant at an α<0.1. SPSS ver 25.0 (IBM, USA) was used for all analyses and modeling.

3. Results

3.1. Sample characteristics

Of all the patients seen in the 10-year period, 148 of 556 (27%) had a diagnosis indicative of a developmental or intellectual disability. About half of the children had subsequent visits at the clinic between four and eight months (272 of 556) and were included in the subsequent analyses of efficacy. Those with a developmental disability diagnosis were younger with a mean age of 9.9 years versus 11.7 years for those without a disability, p < .001, less likely to be female (42% vs. 57%, p = .02), and had similar BMIp95 at baseline compared with the typical development comparison group (overall mean of 137%, SD 29, p = .53 between groups). Children with a developmental disability had a similar number of visits in the two years following the index visit as those without a developmental disability diagnosis, p = .57. (Table 1)

Table 1.

Healthy Lifestyles Clinic Sample Characteristics by Developmental Disability Diagnosis

All (n=272) No DD (n=182) DD (n=90) p-value
Age, years, mean (SD) 11.1 (3.5) 11.7 (3.1) 9.9 (4.0) <0.001
Age in years, categories, n (%) <0.001
≤5 21 (8) 4 (3) 17 (19)
6–11 141 (52) 94 (52) 47 (52)
≥12 110 (40) 84 (46) 26 (29)
Race/ethnicity, n (%) 0.98
White, non-Latino 134 (49) 89 (49) 45 (50)
Latino 107 (39) 72 (40) 35 (39)
All others 31 (11) 21 (12) 10 (11)
Gender, % female, n (%) 142 (52) 104 (57) 38 (42) 0.02
BMI, mean (SD) 32 (8) 33 (8) 31 (8) 0.05
BMIp95, mean (SD) 137 (29) 136 (28) 138 (32) 0.53
BMIp95 categories, n (%) 0.62
Not obese (<95th percentile) 8 (3) 5 (3) 3 (3)
Class I (100–119% of 95th) 72 (27) 52 (29) 20 (22)
Class II (120–139% of 95th) 85 (31) 53 (29) 32 (36)
Class III (140% and higher) 107 (39) 72 (40) 35 (39)
Systolic blood pressure, mean (SD) 114 (12) 116 (12) 110 (9) 0.001
Diastolic blood pressure, mean (SD) 64 (9) 65 (9) 63 (9) 0.18
Total healthy lifestyles visits in six months post-index visit 0.57
1 89 (33) 57 (31) 32 (36)
2 90 (33) 64 (35) 26 (29)
3 or more 93 (34) 61 (34) 32(36)
ICD-10 diagnoses
1, mild to severe intellectual disabilities 11 (4) 0 11 (12)
2, specific developmental disorders 71 (26) 0 71 (79)
3, pervasive developmental disorders 22 (8) 0 22 (24)
4, chromosomal rearrangements 9 (3) 0 9 (10)
5, cognitive or intellectual impairment 18 (7) 0 18 (20)
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DD = developmental disability, SD= standard deviation, BMI = body mass index, BMIp95 = percent of the 95th percentile BMI

There were no differences in gender (p = .42), age category (p = .28), race/ethnicity (p = .92), or class of obesity (p = .18) between children with valid follow-up data compared and those without. The only significant difference was in the number of follow-up visits – 75% of those without valid six-month BMI data had only the initial visit, while the other 25% had a second visit that fell outside of the four to eight-month time frame.

3.2. Clinical outcomes at six and twelve months after the initial visit

Examining the outcomes by age strata and developmental disability, we observed that a greater proportion of pre-adolescent children with a developmental disability had a clinically significant reduction in their BMIp95 (36%) compared with those without a disability (18%), p = .01. In contrast, we found potential evidence of the converse in adolescents with a developmental disability with only 2 of 26 in this group (8%) showing a significant reduction in adiposity compared with 19 of 84 (23%) in the group without a disability diagnosis, p = .09. No changes in blood pressure were observed at six months for either adolescents or the younger children. (Table 2) We observed no differences in number of clinic visits between groups for the six month time point with 183 of 272 (67%) having two or more visits overall.

Table 2.

Clinical outcomes at six and twelve months after initial visit of children with and without developmental disability, stratified by age

<12 years of age ≥12 years of age
No DD (n=98) DD (n=64) p No DD (n=84) DD (n=26) p
Age in years, mean (SD) 8.9 (2.3) 8.0 (2.6) 0.002 14.4 (1.5) 14.7 (2.0) 0.31
Change in BMIp95 at 6 months, mean (SD) −0.9 (5.6) −3.4 (8.5) 0.03 −0.3 (5.8) 1.8 (6.4) 0.11
Proportion with ≥5% reduction in BMIp95 at 6 months, n (%) 18 (18) 23 (36) 0.01 19 (23) 2 (8) 0.09
Change in systolic BP at 6 months, mean (SD) 2.5 (10.9) −0.3 (11.2) 0.19 2.6 (14.4) 7.0 (17.5) 0.29
Change in diastolic BP at 6 months, mean (SD) −0.3 (11.0) 1.8 (10.5) 0.31 1.1 (11.7) 4.1 (12.8) 0.38
Change in BMIp95 at 12 months, mean (SD) 0.12 (9.5) −5.4 (12.9) 0.02 −1.4 (13.5) 0.4 (8.1) 0.61
Proportion with ≥5% reduction in BMIp95 at 12 months, n (%) 15 (25) 22 (54) 0.003 14 (29) 4 (22) 0.76
Change in systolic BP at 12 months, mean (SD) 1.6 (13.3) 4.0 (16.3) 0.52 1.4 (10.5) 10.8 (13.3) 0.01
Change in diastolic BP at 12 months, mean (SD) 2.8 (11.6) 2.5 (11.9) 0.92 −0.2 (15.1) 11.0 (11.5) 0.02
Within group estimates of clinical outcomes, using generalized linear model for repeated measures
Change in BMIp95 over time F-statistic p-value ηp2 Pairwise t1-t2 p-value Pairwise t2-t3 p-value
<12 years of age, DD 5.916 0.007 0.15 <0.001 0.78
<12 years of age, no DD 0.65 0.50 0.02 0.22 0.96
≥12 years of age, DD 0.81 0.43 0.06 0.25 0.21
≥12 years of age, no DD 0.64 0.46 0.02 0.92 0.39
Systolic BP
<12 years of age, DD 0.36 0.70 0.02 0.83 0.50
<12 years of age, no DD 1.30 0.28 0.04 0.50 0.10
≥12 years of age, DD 2.58 0.11 0.19 0.18 0.60
≥12 years of age, no DD 0.27 0.75 0.01 0.45 0.67
Diastolic BP
<12 years of age, DD 0.40 0.68 0.02 0.87 0.47
<12 years of age, no DD 0.20 0.81 0.01 0.51 0.66
≥12 years of age, DD 4.47 0.03 0.29 0.10 0.33
≥12 years of age, no DD 0.08 0.91 0.01 0.70 0.94
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DD = developmental disability, BMIp95 = percent of the 95th percentile body mass index, BP = blood pressure, SD = standard deviation, ηp2= partial eta-squared estimate of effect size, Greenhouse-Geisser estimates used for time variable estimates in generalized linear model, t1 = baseline, t2 = 6 months, t3 = 12 months

At twelve months after the initial visit, children under 12 years of age with developmental disabilities continued to experience better outcomes at twelve months with over half (54%) having a ≥5% reduction in BMIp95, compared with 25% of their typically developing peers, p = .003. No difference in adiposity outcomes was observed in the adolescent group, and there was even a relative increase in the blood pressure measurements at 12 months in children with a developmental disability.(Table 2) We also observed no differences in number of clinic visits between groups with 108 of 272 (40%) having three or more visits before twelve months.

3.3. Predictors of success using baseline data

We examined the baseline intake data on behaviors in association with the six-month outcome data for BMIp95. Children younger than 12 years of age without a developmental disability who were successful in reducing their adiposity (≥5 point decrease in BMIp95) were more likely to have an active mode of transportation (biking or walking) to school at baseline (53% versus 19% of those who were unsuccessful). Otherwise, there were similar levels of baseline screen time, physical activity and dietary habits between groups. (Table 3) For children with a developmental disability, we identified no major differences by outcome. (Table 3) Children with a developmental disability had a higher reported number of days of high physical activity with a median of 5.0 days (IQR 4.0–6.0) versus 4.5 days (IQR 3.0–5.0) for those without a disability, p =.02, η2 = .09. They also had a lower level of reported confidence in being able to attain a healthy weight with a median of 4.0 (IQR 2.0–4.0) versus a median of 4.0 (IQR 4.0–5.0) for typically developing children p = .01, η2 = .05. We also examined medication use in children with a developmental disability, and overall 11% (8/64) of the children were taking a medication with a potential weight-related side effect, though the vast majority were stimulants (data not shown), and no differences by reduction in adiposity were observed.

Table 3.

Baseline characteristics associated with six-month change in BMIp95 in children <12 years of age

No DD (n=86) DD (n=64)
≥5 point decrease in BMIp95 <5 point decrease Effect size p ≥5 point decrease in BMIp95 <5 point decrease Effect size p
Age in years, mean (SD) 8.8 (2.7) 9.4 (1.8) 0.27 0.24 8.2 (2.2) 7.7 (2.7) 0.18 0.51
Visits to Healthy Lifestyles in six months, n (%) 0.17 0.25 0.28 0.09
1 5 (28) 22 (28) 10 (44) 11 (27)
2 5 (28) 37 (46) 3 (13) 16 (39)
3 or more 8 (44) 21 (26) 10 (44) 14 (34)
Mean change in BMIp95 at 6 months, mean (SD) −9.4 (3.7) 0.9 (4.0) 2.66 <0.001 −11.5 (8.1) 1.1 (4.4) 1.94 <0.001
Physical activity and sedentary activities
TV in room (% yes), n (%) 3 (30) 15 (40) 0.08 0.58 8 (57) 9 (53) 0.04 0.82
Transit to school, % walk or bike, n (%) 8 (53) 12 (19) 0.32 0.02 3 (21) 6 (22) 0.18 0.53
Hours/day sedentary time, median (IQR) 8.4 (5.3, 15.0) 9.3 (5.5, 13.5) 0.0003 0.96 7.3 (5.6, 12.2) 7.0 (4.4, 11.9) 0.009 0.50
Hours/day PA, median (IQR) 2.0 (0, 3.5) 1.5 (0.8, 3.0) 0.0005 0.98 2.8 (1.1, 5.0) 2.5 (1.1, 3.5) 0.02 0.48
Days/week high PA, median (IQR) 5.0 (4.0, 5.5) 4.5 (2.9, 5.0) 0.007 0.78 5.0 (4.0, 7.0) 5.0 (4.1, 5.0) 0.02 0.35
Dietary habits
Fast food per week median (IQR) 1.0 (0.8, 2.0) 1.0 (0, 2.0) 0.001 0.87 1.0 (1.0, 1.9) 1.0 (0, 2.0) 0.03 0.77
Family dinner per week, median (IQR) 7.0 (5.9, 7.0) 5.8 (3.0, 7.0) 0.08 0.15 6.3 (4.0, 7.0) 7.0 (5.0, 7.0) 0.01 1.00
Soda/day, median (IQR) 0.5 (0, 1.1) 0 (0, 1.0) 0.02 0.44 0 (0, 0) 0 (0, 1.0) 0.03 0.50
Attitudes related to weight status
Importance of healthy weight, mean (SD) 4.9 (0.3) 4.7 (0.6) 0.36 0.40 4.9 (0.3) 4.5 (0.7) 0.78 0.08
Confidence in maintaining weight, mean (SD) 4.0 (0.9) 4.2 (0.9) 0.18 0.63 3.7 (1.2) 3.1 (1.2) 0.49 0.23
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PA = physical activity; IQR = interquartile range; SD = standard deviation, BMIp95 = percent of the 95th percentile body mass index;

For effect size for categorical variables, cramer’s V is presented, for non-parametric samples eta-squared and for t-tests, Cohen’s d.

For children 12 years of age and older, there were only two children with a developmental disability diagnosis who had a ≥5 point decrease in BMIp95, and so further analyses in this group were not completed. Of note, those two children only completed the baseline visit at the Healthy Lifestyles clinic with subsequent clinical data coming from visits to other clinics. For those without a developmental disability, those with a ≥5 point decrease in BMIp95 experienced an average decrease in BMIp95 of 7.8 (SD=2.5) compared with an average 1.9 unit increase (SD=4.5) in the rest of the children, p < .001. The only difference in the baseline characteristics was a higher baseline reported frequency of fast food consumption among those successful in reducing their adiposity, 2.0 days (IQR 1.0–3.0) versus 1.0 days (IQR 0–2.0), p = .04, and otherwise there were no noted differences at baseline at p < .05. Medication use in this older group of children with developmental disabilities was much more common with only 8% not taking a medication, and 10/26 (38%) of the children were taking a medication with weight gain as a potential side effect. We did not conduct a multivariable analysis of predictors of success given the very limited number of variables predictive at the bivariate level of analysis.

4. Discussion

In this retrospective cohort using 10 years of data from children seen in a multidisciplinary clinic aimed at helping children achieve a healthier weight, a significant minority of children (27%) had a developmental or intellectual disability diagnosis. In the general population, 17% of children are estimated to have developmental disabilities in the U.S. (Zablotsky et al., 2019) – this difference is possibly secondary to referral bias (providers being more likely to refer individuals with disabilities for tertiary-level obesity care) and may also reflect the relatively higher rate of obesity amongst individuals with developmental disabilities (Must et al., 2014). Examining the weight outcomes for children with developmental disabilities, we found that younger children experienced twice the success rate as their typically developing counterparts seen in the clinic. Younger children overall have been found to have greater obesity remission than adolescents (Luan, Mezuk, & Bauer, 2018), and one of the few other reports of a multidisciplinary treatment program for children with disabilities also reported better outcomes in younger children (Pona et al., 2017). One potential explanation lies in how multidisciplinary clinics approach the treatment for patients with obesity as a chronic disease (Bray, Kim, & Wilding, 2017). A multi-disciplinary clinic in North Carolina similar to that described here also found that children with cognitive disabilities had better BMI outcomes (Brown, Irby, Houle, & Skelton, 2015). Parents of children with a developmental disability may be more prepared, based on their experience dealing with their child’s other chronic conditions, to implement a plan for their child’s obesity. An alternative explanation may be that it may be easier for parents of children with a developmental disability to implement a distinct regimen of changes for their child. For typically developing children, changes for the whole family have evidence of greater effectiveness compared with individual-targeted therapy (Epstein, Valoski, Wing, & McCurley, 1994; Goldschmidt et al., 2014; Wilfley et al., 2017). Although the same effect of whole family change improving outcomes is likely similar in children with and without developmental disabilities, individually targeted interventions may be more familiar within the family system of children with developmental disabilities, and those children may be prone to cooperating with distinct expectations. Overall, the evidence from both this study and the limited additional reports suggests that younger children with developmental disabilities benefit from a multidisciplinary clinic structured to address their individualized behaviors and needs.

The findings by age may offer insight about the mechanism behind outcomes. One possibility is that health behaviors of younger children with developmental disabilities are more likely to be dictated by caregivers, and showed a greater benefit from the multidisciplinary Healthy Lifestyle interventions compared with their typically developing peers. Typically developing children’s parents may expect more independent management of health behaviors, while both groups would actually benefit from high levels of parental support. In adolescents, our findings were reversed (typically developing teens were more likely to have BMI improvements), though support a similar pattern related to the benefits of parental support are evident: adolescents are often given more autonomy over health choices, and typically developing teens may be better equipped to implement health behavior suggestions compared to their peers with developmental disabilities, who may need more caregiver involvement. This highlights the importance of intervention strategies aligning with developmentally appropriate expectations from both clinicians and parents. In particular, for all younger children (even those who are neurotypical and in many ways independent with daily living skills) parental limit-setting and structure is expected to improve outcomes with healthy lifestyle management (Gartstein, Seamon, Thompson, & Lengua, 2018; Lloyd, Lubans, Plotnikoff, Collins, & Morgan, 2014). A second possibility is that the children with developmental disabilities exhibited more behavioral challenges, and with minor remediation such as guidance about limit setting with food choices and structured physical activity plans, greater improvements were attainable in this group compared to typically developing peers. Lastly, we know that obesity becomes more recalcitrant over time with a greater likelihood of continued obesity with increasing age (Ward et al., 2017). Equipping the parents of children with developmental disabilities with skills to address healthy lifestyle behaviors early in life should have benefits for obesity and cardiovascular health long-term.

The behavioral challenges associated with providing a diverse and healthy diet in children with developmental disabilities can be significant. Selective or restrictive diets are known to be more common among children with autism spectrum disorders and other developmental disabilities (Cermak, Curtin, & Bandini, 2010). The data are mixed on the impact of these dietary patterns on nutritional differences observed at the macronutrient and micronutrient level on the group level (Graf-Myles et al., 2013; Hyman et al., 2012), though there are certainly striking case reports and individual examples that highlight the challenge families can face. An additional aspect to consider when approaching families of children with developmental disabilities who have obesity is that there may also be intentional restriction on the part of the parent intended as therapy (e.g. restricting gluten to assist in treating symptoms) (Elder, Kreider, Schaefer, & de Laosa, 2015). A multi-disciplinary approach blending dietary and behavioral recommendations with the collaborative guidance of a registered dietician and behavioral health specialist is recommended for clinical care with children with developmental disabilities.

The sample of patients described combines a variety of developmental disability diagnoses for a broad view of potential differences in this group compared with neurotypical peers. In contrast, much published data related to developmental disabilities and physical activity (indeed, many topics) is diagnosis-specific, making inferences about the current outcomes challenging. Results from existing studies are mixed with regard to whether physical activity level is related to disability status. For example, nationally representative survey data have found an association between autism spectrum disorders and obesity but no association between autism spectrum disorders and physical activity or sedentary time as reported by parents (Corvey, Menear, Preskitt, Goldfarb, & Menachemi, 2016). In children with cerebral palsy using accelerometer-measured physical activity and sedentary time, other investigators have found a significant association between Gross Motor Function Classification group and both physical activity and sedentary time in preschool aged children (Keawutan et al., 2017). Notably, in those with independent ambulation, two-thirds of them met the recommended amount of physical activity (Keawutan et al., 2017). One explanation for improved outcomes in the younger developmental disabilities group in the current sample may be their relatively higher level of physical activity. Children with developmental disabilities overall have lower levels of physical activity with many in national studies not meeting guidelines (Case, Ross, & Yun, 2020), though they do show cardiovascular benefits from participation (Collins & Staples, 2017).

Children with developmental disabilities are prescribed more medications, on average, than a typically developing child (Houghton, Ong, & Bolognani, 2017). In our sample, prescription medication use increased with age. Many of the medications used to treat challenging behaviors in these children are associated with weight-related side effects (Hasnain & Vieweg, 2013; McQuire, Hassiotis, Harrison, & Pilling, 2015), and this could explain some of the difference in outcomes by age among those with a developmental disability. These psychotropic medications have a beneficial role for some patients, though the high variability in the prescription of these psychotropic medications (Jackel et al., 2017), the association of non-clinical factors with that variation (Mandell et al., 2008), and the limited evidence base for behavioral health improvement in children should prompt our careful consideration of their use, particularly when weight-related side effects have occurred. It should also be noted that parents of children with developmental disabilities have expressed frustration that clinicians sometimes disregard addressing healthy behaviors when parents are seeking that information, with the clinician instead associating the weight gain with a medication (Polfuss, Dobson, Sawin, & Klingbeil, 2019). In some cases, there are alternative medications with less weight-associated side effects (Nihalani, Schwartz, Siddiqui, & Megna, 2012), and the use of a medication with an association with weight gain in a patient seeking care for obesity should prompt a conversation with the parent and the prescribing physician or provider about potential alternatives. Many of the medications prescribed also have the potential side effect of affecting the blood pressure (e.g. stimulants), which warrants further investigation in these children.

In examining how to provide an obesity intervention for children with disabilities, providers should be aware of and address dietary restrictions that are more common in children with developmental disabilities, consider the effect of weight-associated side effects of medications, and encourage individualized engagement in physical activity including the use of adaptive technology as there are clear health benefits, despite limitations in the scientific research (Lai et al., 2020).

4.1. Limitations

We used ICD-10 diagnosis codes to categorize children as having a developmental disability. This approach may under-estimate the proportion of children with a developmental disability as children with only a suspected diagnosis (in the process of being evaluated) may not have received a developmental disability diagnosis. Although the sample size is adequate for the analyses conducted, the current sample reflects children and families from only one urban region in one treatment setting. The sample was too small to compare the sub-groups of children with more physically restrictive disabilities compared with those who had less restrictive disabilities. Blood pressure in children with Down syndrome may be lower overall and thus comparisons with neurotypical children difficult to interpret.

Another variable that is often neglected in clinical research related to childhood illness is parental factors. In the care of chronic illnesses such as obesity, the parental role is often critical, and clinical charting practices do not often include parent-data such as self-efficacy, behavior management knowledge and confidence, parent BMI and health behaviors. These types of variables should be strongly considered in research endeavors, and we posit that a family-based model of obesity care, in which variables related to all family members are both evaluated and addressed, may lead to better outcomes.

Prospective evaluation of different treatment strategies in populations of developmentally disabled children with obesity in clinical trials would provide one path forward in examining how age and other confounding variables played a role in these results. Future studies should also include rigorous proximal outcomes related to behavior change that may precede changes in weight or blood pressure.

What this paper adds?

This paper is one of the first systematic reports of outcomes from an obesity intervention clinic serving children with developmental disabilities. It describes differential outcomes by age, predictors of successful weight loss and identifies further issues that need additional work including co-morbid medication use.

Acknowledgments

Financial support: NIH K23DK109199

Footnotes

Disclosures: None of the authors have any financial or other disclosures

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