Abstract

National survey data from 884 families were used to examine the overall health of children and adults with fragile X syndrome. Results indicate the rate of obesity in adults with fragile X syndrome is similar to the general population (∼30%). Male children with fragile X syndrome, however, had higher rates of obesity (31%) when compared with typically developing same-aged peers (18%). Both males and females displayed food selectivity, especially with regard to texture. Physical activity levels for children were higher than for adults, but neither group met recommended levels. Several cognitive and behavioral characteristics, food selectivity, and physical activity were related to overall health and body mass index. Continued monitoring of the health status of individuals with fragile X syndrome is recommended.

Obesity is a major health issue in the United States and around the world, impacting both children and adults (Hedley et al., 2004; Odgen, Carrol, & Flegal, 2008). Recent government reports have highlighted specific concerns regarding the weight and overall health of individuals with intellectual disabilities. Healthy People 2010 outlines 28 focus areas, ranging from preventing heart disease and stroke to improving health and physical fitness; over half of the focus areas specify objectives for people with disabilities (U.S. Department of Health and Human Services, 2000). A report of the Surgeon General's conference on health disparities and intellectual disability proposes six goals to address the health needs of individuals with disabilities and their families, including integrating health promotion into community environments and increasing knowledge and understanding of health and disability (U.S. Public Health Services, 2002).

Obesity, a primary indicator of individual and population health, is defined as a body mass index (BMI) of 30 kg/m2 or higher (Centers for Disease Control, 2009a), and reported rates vary depending on the population sampled and year in which data were collected. Research suggests that the percentage of adults with disabilities who are obese is substantially higher than that of the general population (Bhaumik, Watson, Thorp, Tyrer, & McGrother, 2008; McGuire, Daly, & Smyth, 2007). For example, Yamaki (2005), using population survey data from 1997–2000, found that 35% of adults with intellectual disabilities were obese compared with 21% of those without intellectual disabilities. In a smaller study, Rimmer and Wang (2005) documented obesity rates of adults with Down syndrome (71%) and intellectual disability (61%), more than double the rate in the general population (31%, 1999–2000 data). Moreover, certain subgroups of adults with disabilities are more likely to be obese: women, older individuals, those with more moderate disabilities or with specific genetic conditions, and those living at home or with a family member (Rimmer, Braddock, & Fujiura, 1993; Rimmer, Braddock, & Marks, 1995; Rubin, Rimmer, Chicoine, Braddock, & McGuire, 1998).

The problem of obesity extends to children as well. Reports indicate that between 25% and 35% of children in the United States are overweight or obese (Jenssen et al., 2005; Odgen et al., 2006; Sherry, Mei, Scanlon, Mokdad, & Grummer-Strawn, 2004). Children who are obese are more likely to be obese in adulthood (Senese, Almeida, Fath, Smith, & Loucks, 2009; Thorp, Marx, May, Helgerson, & Frieden, 2004; Whitaker, Wright, Pepe, Seidel, & Dietz, 1997). There are several contributing factors to the development of obesity in childhood and adulthood, including poor nutrition, lack of physical activity, home and peer influences, as well as socioeconomic factors. Children with disabilities are at increased risk for being overweight, due, in part, to a more sedentary lifestyle (Bandini, Curtin, Hamad, Tybor, & Must, 2005; Fragala-Pinkham, Haley, Rabin, & Kharasch, 2005), and for some children, a possible biological predisposition to overeating or being overweight (Ebbeling, Pawlak, & Ludwig, 2002).

The combined direct and indirect United States lifetime costs of having a disability are estimated at over $51 billion (Honeycutt et al., 2004). Obesity substantially impacts these costs due to increased likelihood of secondary conditions, such as hypertension, Type 2 diabetes, cardiovascular risk, and depression (Daniels et al., 2000). Physical activity can reduce rates of obesity, but many individuals do not regularly participate in the recommended amount (Pratt, Stevens, & Daniels, 2008; U.S. Department of Health and Human Services, 2000). Adults with disabilities have been shown to have even lower rates of physical activity than those without disabilities (Beange, McElduff, & Baker, 1995; de Winter, Magilsen, van Alfen, Penning, & Evenhuis, 2009; Draheim, Williams, & McCubbin, 2002; Graham, & Reid, 2000; Stanish, Temple, & Frey, 2006). Recent survey data indicate that fewer adults with disabilities meet the recommended amount of physical activity when compared with adults without disabilities (38% vs. 49%, respectively) and a greater percentage (26% vs. 13%, respectively) were physically inactive (Rimmer, Wolf, Armour, & Sinclair, 2007). In their review Temple, Frey, and Stanish (2006) found that only one third of adults with intellectual disabilities engaged in a sufficient amount of physical activity to achieve health benefits.

Similarly, children with disabilities are not achieving target levels of physical activity. According to the American Academy of Pediatrics, children with disabilities have lower levels of cardiorespiratory fitness and muscular endurance than do typically developing children (Murphy, Carbone, & Council on Children With Disabilities, 2008). Children with disabilities are less active in various activity-oriented settings (e.g. physical education class, recess) when compared with age-matched peers (Frey, Stanish, & Temple, 2008). However, King et al. (2003) found that families of children with disabilities reported a number of barriers to participation in physical activity, including the child's functional limitations (18%), cost (15%), and the lack of nearby facilities or programs (10%).

Complementing the findings on high levels of obesity and low amounts of physical activity is research indicating that adults with disabilities have poor eating habits, inadequate diets, and nutritional deficits (Bertoli et al., 2006; Humphries, Traci, Seekins, & Brisin, 2004). Adults living in community settings tend to have diets that are high in calories and fat and low in fruits and vegetables (Brainschweig et al., 2004; Draheim, Stanish, Williams, & McCubbin, 2007). Although most investigators highlighted the types of food consumed, a few examined factors influencing the dietary patterns of adults with disabilities. For example, Robertson et al. (2000) reported that dietary intake is correlated with the amount of care and supervision adults receive.

Munk and Repp (1994) described three categories of feeding problems in children with disabilities: food refusal, food selectivity by type, and food selectivity by texture. Recent studies show that children with disabilities have high levels of food refusal, ranging from 34% to 56%, and approximately 25% are selective by type and texture (Ahearn, Castine, Nault, & Green, 2001; Field, Garland, & Williams, 2003). Children were more likely to choose (a) carbohydrates over protein and fruits and vegetables and (b) pureed foods over solids.

Although research on obesity, physical activity, and food selectivity in children and adults with disabilities is emerging, some findings may be syndrome specific. Fragile X syndrome, a single gene disorder carried on the X chromosome, is the most commonly inherited form of intellectual disability. Approximately 1 in 4,000 males and 1 in 8,000 females are affected (Crawford, Acuna, & Sherman, 2001; Hagerman, 2008). The FMR1 gene instructs cells to make fragile X mental retardation protein (FMRP), which is essential for normal brain functioning. The genetic code for the FMR1 gene contains a limited number of repetitions of two nucleotides—cytosine and guanine—that occur in CGG triplets. In most individuals, this trinucleotide repeat occurs between 5 and 50 times. However, individuals with fragile X syndrome have an expanded number of repeats that results in either premutation carrier status (50 to 200 repeats) or full mutation status (greater than 200 repeats). Fragile X syndrome is associated with cognitive delays, attention problems, hyperactivity, autism, and behavior problems (Bailey, Raspa, Olmsted, & Holiday, 2008), and those with the full mutation of fragile X syndrome are more severely affected than premutation carriers (Loesch et al., 2007). Most males have moderate to severe intellectual impairment, whereas females are more mildly affected, with one third to one half having average intellectual function (Loesch et al., 2002; Loesch, Huggins, & Hagerman, 2004).

Most studies of individuals with fragile X syndrome have been focused on challenges associated with their development, behavior, or adaptive skills. However, some case studies and clinical reports suggest that individuals with fragile X syndrome might be at increased risk for obesity, although findings have been inconsistent. Early researchers found no weight differences between males with and those without fragile X syndrome (Butler et al., 1991; Butler, Pratesi, Breg, & Singh, 1993; Partington, 1984). However, in a later study Lisik, Szymanska-Parkieta, and Galecka (2000) compared the weight of 16 males with fragile X syndrome with that of 26 without fragile X syndrome and found significantly higher weight for males with fragile X syndrome. Some case study reports have suggested that there may be a subgroup of children with fragile X syndrome who exhibit symptoms of Prader-Willi syndrome, such as obesity and hyperphagia (de Vries et al., 1993; Nowicki et al., 2007). Bear (2005) suggested that obesity in fragile X syndrome could be due to exaggerated mGluR5 signaling, a process known to be disrupted in fragile X syndrome and shown to be related to appetite and weight in mGluR5 knockout mice.

The research on weight and obesity in fragile X syndrome has been limited to small sample sizes and has not factored height or age into the comparisons, as is the case when calculating a BMI, nor have researchers used standardized population reference points. To address these limitations and provide more definitive insights into questions regarding obesity in fragile X syndrome and its relationship to food selectivity and physical activity, in this article we drew on a survey with a large sample of families of children with fragile X syndrome to (a) determine the BMI of adults and children with fragile X syndrome, (b) describe the common types of food selectivity and amounts of physical activity experienced by individuals with fragile X syndrome, and (c) evaluate the extent to which overall health and BMI are associated with the nature and severity of behavioral and intellectual disabilities most common to individuals with fragile X syndrome.

Method

Participants

The study was part of a larger survey assessing the needs of families who had at least one child with fragile X syndrome. In this article we focused on a subset of 848 families who had at least one child with the full mutation and provided data about their child's overall health. Ninety percent were Caucasian, 5% Hispanic or Latino, 2% African American or Black, 1% Asian, and 2% from other races or ethnicities (e.g., Pacific Islander or American Indian). Respondents typically were mothers (90%); an additional 8% were fathers and 2% were other family members. The majority had a 4-year college degree or graduate degree (59%) and an annual income of at least $75,000 (56%). The families had 1,075 children with the full mutation (839 males and 236 females). Children ranged in age from 2 to 56 years. The percentages of children by age group were birth to 5 years (17%), 6 to 10 years (25%), 11 to 15 years (20%), 16 to 20 years (14%), and over 20 years (25%).

Procedure

Before recruitment began, the research protocol was approved by an Institutional Review Board. Study participants were recruited in two phases. Between August and October 2007, families were sent a letter and brochure from our research partners inviting them to enroll in the study. Our partners were three foundations (National Fragile X Foundation, FRAXA Research Foundation, Conquer Fragile X Foundation), researchers, and clinicians. In March 2008, we contacted families who enrolled and asked them to participate in a comprehensive survey of family needs. Families were given the option to enroll and complete the survey online (79%) or over the phone. A total of 1,250 families enrolled and 1,075 (86%) completed the survey.

Instruments

During enrollment, families provided demographic information (e.g., income, education) as well as information about each child in the family (e.g., date of birth, gender, genetic status). Families were asked to rate each child's overall thinking, reasoning, and learning ability as well as their ability to adapt to new situations. In addition, families indicated whether or not each child had been diagnosed or treated for developmental delay or any of eight co-occurring conditions (attention problems, hyperactivity, aggressiveness, self-injury, autism, seizures, anxiety, depression).

The survey covered a broad array of topics on the needs of families and children with fragile X syndrome. In this article we focus on the items concerning their child's health. Families were asked to record their child's current height and weight as well as their overall health (i.e., “How would you describe _____ [child's] overall health?”). Families also rated their child's functional eating skills, specifically whether the child ate at a regular pace and ate the right amount of food at each meal, and indicated whether the child displayed any food selectivity, including issues related to texture, color, smell, and type of food. Physical activity items asked about amount and frequency, types of activities, and barriers to participation. For adult children, families provided information on where the child currently lived (e.g., with family, in a group home, independently).

Data Analysis

For all analyses, we used the Statistical Analysis System (SAS, Version 9). We calculated percentages for the descriptive data, presented separately for males and females by age group, and report Cochran-Mantel-Haenszel (CMH) statistics and p values to indicate differences across the age groups. Body mass index (BMI) was calculated by dividing weight in kilograms by the square of height in meters, as recommended by the Centers for Disease Control and Prevention (2009b). BMI data were available for a subset of the sample (n  =  963 or 90%). Both adults and children were placed into one of four weight categories: (a) underweight, (b) healthy weight, (c) overweight, and (d) obese. As per the Centers for Disease Control (CDC) guidelines, weight categories for children are based on percentiles (less than 5th percentile, 5th to 85th percentile, 85th to 95th percentile, over 95th percentile), and adult weight categories are based on BMI (less than 18.5, 18.5–24.9, 25–29.9, 30 or over). Adults were further classified as extremely obese if their BMI was 40 kg/m2 or higher.

We calculated logistic regressions for two health outcomes: overall health and BMI. Predictors included seven child variables (thinking, reasoning, and learning ability; ability to adapt; co-occurring conditions; physical activity; pace of eating, amount of food eaten, food selectivity) and two covariates (child age, respondent education). Each regression model contained one of the seven child predictor variables and both covariates. Models were run separately for males and females.

Predictor variables were coded such that a higher number indicated more of the characteristic and outcome variables were coded such that the higher number indicated a better outcome. These variables were reduced from a 4-point scale to dichotomous variables because many had very low frequencies for some of the responses. The two child characteristic variables (thinking, reasoning, and learning ability; ability to adapt) were coded as 1 (poor or fair ability) or 2 (good or very good ability). The total co-occurring conditions item was coded as 1 (fewer than five co-occurring conditions) or 2 (5 or more co-occurring conditions). Eating at a regular pace and amount of food were coded as 1 (perform this task or performs this task but not well) or 2 (performs this task fairly well or very well). Food selectivity was coded as 1 (not at all or a little different than peers) or 2 (somewhat or a lot different than peers). The family covariate variable (education) was also scored dichotomously (1  =  some college but no degree, or less; 2  =  2 year college degree, or higher). The outcome variables were coded as follows: (a) overall health: 0  =  good, fair, or poor and 1  =  excellent or very good and (b) BMI: 0  =  overweight or obese; 1  =  underweight or normal weight.

Results

Results are organized into four sections: (a) BMI, (b) descriptions of food selectivity, (c) descriptions of physical activity, and (d) models predicting health status. Results for males and females are reported separately. BMI data were divided into two groups (children 20 years and under, n  =  718; adults over 20 years, n  =  245) to correspond with the CDC recommended calculation of weight categories. The final three sections on food selectivity, physical activity, and predictors of health status report results by 5 age groups: (a) birth to 5 years, (b) 6 to 10 years, (c) 11 to 15 years, (d) 16 to 20 years, and (e) over 20 years old.

Body Mass Index

Only 5% of male children and 7% of female children were underweight (BMI < 5th percentile). An additional 46% of male and 60% of female children were normal weight (BMI 5th to 85th percentile). Approximately 18% of male and female children were overweight (BMI 85th to 95th percentile), and 31% of male and 15% of female children were obese (BMI > 95th percentile).

Approximately 3% of adult males were underweight (BMI < 18.5 kg/m2) and 37% were normal weight (BMI 18.5 to 24.9 kg/m2). The majority of adult males were either overweight (37%, BMI 25 to 29.9 kg/m2), obese (19%, BMI > 30 kg/m2), or extremely obese (4%, BMI > 40 kg/m2). Adult females had similar percentages, with 3% underweight, 40% normal weight, 29% overweight, 24% obese, and 4% extremely obese. Table 1 provides a comparison of the percentages of males and females with fragile X syndrome, both children and adults, who are overweight or obese relative to the general population.

Table 1

Comparison of Body Mass Index (BMI) Categories by Group (in %)

Comparison of Body Mass Index (BMI) Categories by Group (in %)
Comparison of Body Mass Index (BMI) Categories by Group (in %)

To determine whether the BMI for adults with fragile X syndrome differed as a function of living arrangement, we grouped adult males and females into three categories: (a) living independently, (b) living with parents or guardians, or (c) living in a community setting (e.g., group home, with a roommate). For adult males, there was a statistically significant difference between BMI and living arrangement, with the highest level of obesity found among adult males who lived independently (39% compared with 24% living with family and 12% living in community). For adult females, there were no statistically significant differences across living environments.

Food Selectivity

Table 2 presents data on four types of food selectivity: texture, type, color, and smell. The most common type of food avoidance for males across all age cohorts was texture, with percentages ranging from 39% to 63%. A linear association was found across the five age groups, CMH  =  14.98, p < .001, with younger males displaying more texture avoidance than older males. Families listed both hard and soft foods as well as slippery foods as textures that males avoided. Fewer males were selective based on color, and there were no differences across the five age groups. Males had high percentages of food aversion based on smell, with lower percentages reported for males from birth to 5 years, χ2(4, 811)  =  34.37, p < .001. High percentages were also reported for food selectivity based on type of food, although no differences were found across the five age groups. The most commonly cited type of food avoidance was vegetables, followed by fruits and meats. Families reported that 38% of males had food preferences that were “not at all different” when compared with others of a similar age. However, 15% reported there were “a lot” of differences, with percentages ranging from 10% (over 20 years) to 22% (16 to 20 years).

Table 2

Food Selectivity for Individuals With Full Mutation Fragile X Syndrome (in %)

Food Selectivity for Individuals With Full Mutation Fragile X Syndrome (in %)
Food Selectivity for Individuals With Full Mutation Fragile X Syndrome (in %)

Females tended to have fewer food selectivity issues, although there was more variation across the age cohorts. Texture was the most frequently reported form of food selectivity, including avoidance of both soft and hard foods, with percentages ranging from 19% to 44%. A linear association was found across the age groups, with younger females displaying greater food avoidance based on texture, CMH  =  6.18, p < .05. Similar to males, females were not as selective based on food color. However, unlike males, aversion based on smell was highest in females from birth to 5 years (38%) and declined in subsequent age cohorts, CMH  =  6.23, p < .05. Younger females displayed higher percentages of avoidance based on type of food when compared with adults over 20, CMH  =  8.87, p < .01. The most commonly cited type of food avoidance for females was vegetables, followed by meats. In general, the majority of families (59%) indicated that the food preferences of females were “not at all different” when compared with others of a similar age.

Physical Activity

Table 3 displays activity levels across the five age groups. There were significant differences in physical activity for both males and females as age increased, CMHs  =  68.28 and 20.23, ps < .001, respectively. Up until about child age 10, most parents reported that their children had about the same level of physical activity as did other children their age. As age increased, the discrepancy from activity level of same age peers increased. The difference was more pronounced for males than females.

Table 3

Activity Levels of Individuals With Full Mutation Fragile X Syndrome Compared With Same Age Peers (in %)

Activity Levels of Individuals With Full Mutation Fragile X Syndrome Compared With Same Age Peers (in %)
Activity Levels of Individuals With Full Mutation Fragile X Syndrome Compared With Same Age Peers (in %)

The number of days per week of physical activity had a significant linear association with age groups for both males and females, CMHs  =  119.23 and 61.93, ps < .001, respectively. Table 4 shows that over 60% of males and females up to age 5 were physically active for 6 or 7 days a week. However, these percentages decreased across the age groups; only 18% of males and 15% of females ages 20 or older were active for more than 5 days a week. Similarly, there was a significant linear association between the amount of time spent in physical activity and age group for both males and females (CMHs  =  7.52 and 22.75, respectively, ps < .001. As shown in Table 5, younger children were more likely than older children or adults to participate in physical activity for longer amounts of time. A higher percentage of males and females up to age 10 were engaged in physical activity for at least 60 min when compared with those in the other three age groups.

Table 4

Number of Days a Week of Physical Activity for Individuals With Full Mutation Fragile X Syndrome (in %)

Number of Days a Week of Physical Activity for Individuals With Full Mutation Fragile X Syndrome (in %)
Number of Days a Week of Physical Activity for Individuals With Full Mutation Fragile X Syndrome (in %)
Table 5

Amount of Time Spent in Physical Activity for Individuals With Full Mutation Fragile X Syndrome (in % of Minutes)

Amount of Time Spent in Physical Activity for Individuals With Full Mutation Fragile X Syndrome (in % of Minutes)
Amount of Time Spent in Physical Activity for Individuals With Full Mutation Fragile X Syndrome (in % of Minutes)

Males and females participated in similar types of activities. Walking and swimming were two of the most frequently reported activities across all age groups. Younger children also participated in unstructured free play whereas older children were more likely to bike or participate in team sports. Younger children who were involved in organized sports participated through a community organization. Frequently reported activities for older children included belonging to a school sports team or participating in Special Olympics.

A significant linear association was found between the age groups of both males and females and how often they needed encouragement to participate in physical activity, CMHs  =  34.83 and 12.36, respectively, ps < .001 (see Table 6), For example, 43% of males and 53% of females ages birth to 5 years seldom needed to be prompted. Conversely, 25% of males and 26% of females in the 20 and over age group almost always needed encouragement.

Table 6

How Often Individuals With Full Mutation Fragile X Syndrome Need Encouragement to Participate in Physical Activity (in %)

How Often Individuals With Full Mutation Fragile X Syndrome Need Encouragement to Participate in Physical Activity (in %)
How Often Individuals With Full Mutation Fragile X Syndrome Need Encouragement to Participate in Physical Activity (in %)

Barriers to participating in physical activity were similar for males and females, although differences were seen across the age groups. Families of younger children up to age 15 were more likely to report that difficulty interacting with others was the primary reason for not participating in physical activity. The next most frequent barrier was that the activity was too difficult. Families of adolescents and adults were more likely to cite lack of interest and difficulty interacting with others as the main reasons for lack of participation. Most families reported that transportation was not a barrier to being involved in physical activity.

Predictors of Health Status

Using logistic regressions, we examined whether the seven child variables were related to two health outcomes, adjusting for age and respondent education (see Table 7). Descriptive statistics for the covariate, predictor, and outcome variables are displayed in Table 8. For males (mean age  =  14.7 years [SD  =  10.1]), thinking, reasoning, and learning ability and ability to adapt to new situations were related to overall health; males with very good or good overall ability had better health. Males with fewer co-occurring conditions and less problems with food selectivity had better overall health. Males who did not eat at a regular pace or did not eat the right amount of food had worse overall health and worse BMI. Finally, higher levels of physical activity were predictive of better BMI (i.e., normal weight or underweight). Fewer significant relationships were found for females (mean age  =  15.3 [SD  =  10.4]). Ability to adapt and thinking, reasoning, and learning ability were both related to overall health, with higher levels of each characteristic predictive of better health. Similar to males, females with fewer co-occurring conditions had better overall health. For both males and females, age was a statistically significant covariate for the BMI models. For males, education was a statistically significant covariate for overall health. For females, there was an interaction between age and eating at a regular pace as well as age and eating the right amount of food for three of the four models. Therefore, these models were not interpreted or reported.

Table 7

Predictors of Health Status for Individuala With Full Mutation Fragile X Syndrome

Predictors of Health Status for Individuala With Full Mutation Fragile X Syndrome
Predictors of Health Status for Individuala With Full Mutation Fragile X Syndrome
Table 8

Descriptive Statistics for the Predictor and Outcome Variables for Individuals With Full Mutation Fragile X Syndrome (in %)

Descriptive Statistics for the Predictor and Outcome Variables for Individuals With Full Mutation Fragile X Syndrome (in %)
Descriptive Statistics for the Predictor and Outcome Variables for Individuals With Full Mutation Fragile X Syndrome (in %)

Discussion

Summary of Findings

In this study we found that the rate of obesity in adults with fragile X syndrome is less than that of the general population: 23% of males and 28% of females over 20 years of age were obese or extremely obese. However, the general population rates varied depending on the study sample and year of the data. On average, obesity rates for adults in the general population are approximately 35% (Moran et al., 2005; Ogden et al., 2007; Yamaki, 2005). In addition, when compared with rates of other subgroups of adults with disabilities reported in earlier research, the rate of obesity in adults with fragile X syndrome is substantially lower. Previous studies have found rates in adults with disabilities and for adults with Down syndrome to be as high as 71% (Rimmer & Wang, 2005). However, when both the overweight and obese categories were examined, more than half of adult males (60%) and females (57%) had BMIs higher than recommended. Approximately 4% of both males and females met the criteria for extreme obesity (BMI > 40 kg/m2). Obesity rates for the general population have increased over the past decade (Odgen et al., 2006), and research suggests that obesity and extreme obesity lead to health complications as well as lower quality of life (Heo, Allison, Faith, Zhu, & Fontaine, 2003; McGuire et al., 2007). Given these findings, it is important to monitor the rates of BMIs of adults with fragile X syndrome.

Twice as many male children were categorized as obese compared with females (31% and 15%, respectively). Moreover, almost twice as many male children with fragile X syndrome were categorized as obese (BMI > 95th percentile) when compared with children in the general population (17%) (Ogden et al., 2006). This finding is consistent with some published research (Lisik et al., 2000). In addition, the models predicting overall health and BMI indicated that males who did not eat at a regular pace or who did not eat the proper amount of food at each meal had worse outcomes. These findings demonstrate eating-related problems with a subset of males with fragile X syndrome and lend some support to the belief that there may be a Prader-Willi phenotype in this sample of children (de Vries et al., 1993; Nowicki et al., 2007).

Food selectivity was salient for both males and females. Younger children, more so than adolescents or adults, tended to display aversions to food based on texture and type. These percentages are substantially higher when compared with earlier reports for children with disabilities (Ahearn et al., 2001; Field et al., 2003). In a recent study on children with autism, Williams, Dalrymple, and Neal (2000) reported that 67% of parents described their child as a picky eater. Selectivity based on smell was high for children ages 6 through 20 years of age. For most males and females, color was not a significant issue. In addition, fewer food selectivity issues were predictive of better overall health for males.

Complementing the findings related to obesity, we found that physical activity levels for adults were low. About 60% of males and 50% of females were less active than their same aged peers in adolescence and adulthood. Very few adults met the current recommendation of 5 or more days a week of physical activity for 30 min. Only about 15% of adults over 20 were active for more than 5 days and 35% were active between 30 and 45 min. In previous research, Rimmer et al. (2007) found that about 38% of adults get the recommended amount of physical activity. Although our percentages are lower, it may be due to how we asked the question; our response categories (e.g., more than 5 days) were different than the CDC categories (5 or more days).

Children with fragile X syndrome were more active than adolescents and adults. Families indicated that approximately 25% of males and 20% of females up to age 10 were more active than their same-aged peers. Moreover, higher percentages of children were active for more than 5 days a week, with averages around 55% for males and 60% for females. Children were also more likely to spend longer amounts of time in physical activity than were adults. These percentages were higher for female children. Although there are no national estimates of physical activity rates in children, the CDC recommends that children should do 60 min or more of physical activity each day (Centers for Disease Control, 2009c). Our data indicate that between 20% and 37% of children with fragile X syndrome met these recommendations. Not surprisingly, the modeling results indicate that higher levels of physical activity were related to lower BMI (for males only).

Several child characteristics were predictive of overall health and BMI. Individuals with fragile X syndrome who had a higher ability to adapt to new situations and higher thinking, reasoning, and learning ability had better overall health. Lower numbers of co-occurring conditions were predictive of better overall health as well.

Limitations

There are three main limitations of our analyses. First, we did not include medication usage as a predictor of overall health and BMI. Many individuals with fragile X syndrome who have co-occurring conditions are likely to take stimulants or antipsychotics that may impact appetite and weight. Second, the survey was designed around a broad range of topics, resulting in limited information on nutrition and physical activity. Additional information on eating patterns (e.g., caloric intake) and environmental or behavioral data (e.g., amount of TV watching or other inactivity) would add value to future research on the health of individuals with fragile X syndrome. Finally, although the study sample is not representative of the general population and relies exclusively on parent report, the sample size and use of standardized BMI measures and reference points provide important new information about obesity and fragile X syndrome.

Future Research

The Centers for Disease Control and Prevention have taken an active role in providing recommendations to address the growing epidemic of obesity and lack of physical activity. For example, Healthy People 2010 includes two broad goals and 467 specific objectives in 28 focus areas designed to promote the health and well-being of all people (U.S. Department of Health and Human Services, 2000). One focus area is devoted to disability and secondary conditions, with 13 specific objectives outlined, some of which are related to the obesity and physical activity (e.g., accessibility of health and wellness programs, environmental barriers affecting participation in activities, surveillance and health promotion programs). Additional recommendations have been designed with specific strategies for the community or schools (Centers for Disease Control, 2009a, 2009b, 2009c; Khan et al., 2009). However, further research and recommendations need to be developed specifically for adults and children with disabilities. Although the Surgeon General's office convened a conference on health disparities and intellectual disability, this was almost 10 years ago (U.S. Public Health Services, 2002).

There are a number of steps families and professionals can take to address obesity and physical activity in children and adults. Although many of these are universal, the specific needs of individuals with fragile X syndrome need to be taken into account. For example, parents and physicians need to monitor weight and help individuals with fragile X syndrome participate in regular physical activity. Ideally, activities should be geared towards individual interests and the individual's abilities to interact and socialize with others should be taken into account. Moreover, it is important for individuals with fragile X syndrome to maintain healthy diets, including limiting portion size, minimizing food selectivity, and eating food at a regular pace. Families, clinicians, and caregivers at group homes and other residential organizations are encouraged to recognize barriers to physical activity, incorporate motivating exercise programs into the daily schedule, utilize nutritional specialists to help with meal design when necessary, and focus on ways to improve the overall health of individuals with fragile X syndrome.

Acknowledgments

Preparation of this article was supported in part by the Centers for Disease Control and Prevention (CDC) and the Association for Prevention Teaching and Research (APTR) Cooperative Agreement No. U50/CCU300860, Project TS-1380. The findings and conclusions in this publication are those of the authors and do not necessarily represent the views of CDC or APTR. The authors express their appreciation to the research collaborators and organizations who supported the recruitment of study participants, the families who completed the survey, and RTI staff who assisted in survey programming (Venkat Yetukuri), data management (Anne Kenyon and Kirstin Miller), and call center services (Ellen Fay). Special thanks to Liz Berry-Kravis and Randi Hagerman, who provided comments on earlier drafts. E-mail: mraspa@rti.org

References

References
Ahearn
,
W. H.
,
T.
Castine
,
K.
Nault
, and
G.
Green
.
2001
.
An assessment of food acceptance in children with autism or pervasive developmental disorder–not otherwise specified.
Journal of Autism and Developmental Disorders
31
:
505
511
.
Bailey
Jr,
D. B.
,
M.
Raspa
,
M.
Olmsted
, and
D. B.
Holiday
.
2008
.
Co-occurring conditions associated with FMR1 gene variations: Findings from a national parent survey.
American Journal of Medical Genetics
146A
:
2060
2069
.
Bandini
,
L. G.
,
C.
Curtin
,
C.
Hamad
,
D. J.
Tybor
, and
A.
Must
.
2005
.
Prevalence of overweight in children with developmental disorders in the continuous National Health and Nutrition Examination Survey (NHANES)—1999–2002.
Journal of Pediatrics
146
:
738
743
.
Beange
,
H.
,
A.
McElduff
, and
W.
Baker
.
1995
.
Medical disorders of adults with mental retardation: A population study.
American Journal on Mental Retardation
99
:
595
604
.
Bear
,
M.
2005
.
Therapeutic implications of the mGluR theory of fragile X mental retardation.
Genes, Brain, & Behavior
4
:
393
398
.
Bertoli
,
S.
,
A.
Battezzati
,
G.
Merati
,
V.
Margonato
,
M.
Maggioni
,
G.
Testolin
, and
A.
Veicsteinas
.
2006
.
Nutritional status and dietary patterns in disabled people.
Nutrition, Metabolism, and Cardiovascular Diseases
16
:
100
112
.
Bhaumik
,
S.
,
J. M.
Watson
,
C. S.
Thorp
,
F.
Tyrer
, and
C. W.
McGrother
.
2008
.
Body mass index in adults with intellectual disability; Distribution, associations, and service implications: A population-based prevalence study.
Journal of Intellectual Disability Research
52
:
287
298
.
Brainschweig
,
C.
,
S.
Gomez
,
P.
Sheean
,
K.
Tomey
,
J.
Rimmer
, and
T.
Heller
.
2004
.
Nutritional status and risk factors for chronic diseases in urban-dwelling adults with Down syndrome.
American Journal on Mental Retardation
109
:
186
193
.
Butler
,
M. G.
,
G. A.
Alen
,
J. L.
Haynes
,
D. N.
Singh
,
M. J.
Watson
, and
W. R.
Breg
.
1991
.
Anthropometric comparisons of mentally retarded males with and without the fragile X syndrome.
American Journal of Medical Genetics
38
:
260
268
.
Butler
,
M. G.
,
R.
Pratesi
,
W. R.
Breg
, and
D. N.
Singh
.
1993
.
Anthropometric and craniofacial patterns in mentally retarded males with emphasis on the fragile X syndrome.
Clinical Genetics
44
:
129
138
.
Centers for Disease Control and Prevention
2009a
.
Defining overweight and obesity.
.
Centers for Disease Control and Prevention
2009b
.
A SAS program for the CDC growth charts.
.
Centers for Disease Control and Prevention
2009c
.
How much physical activity do children need?
.
Centers for Disease Control and Prevention
2010
.
School-based obesity prevention strategies for state policymakers.
.
Crawford
,
D. C.
,
J. M.
Acuna
, and
S. L.
Sherman
.
2001
.
FMR1 and the fragile X syndrome: Human genome epidemiology review.
Genetics in Medicine
3
:
359
371
.
Daniels
,
S. R.
,
D. K.
Arnett
,
R. H.
Eckel
,
S. S.
Gidding
,
L. L.
Hayman
,
S.
Kumanyika
, et al
.
2005
.
Overweight in children and adolescents: Pathophysiology, consequences, prevention, and treatment.
Circulation
111
:
1999
2012
.
de Vries
,
B. B.
,
J. P.
Fryns
,
M.
Butler
,
F.
Canziani
,
E.
Wesby-van Swaay
,
J. O.
van Hemel
, et al
.
1993
.
Clinical and molecular studies of fragile X patients with a Prader-Willi phenotype.
Journal of Medical Genetics
30
:
761
766
.
De Winter
,
C. F.
,
K. W.
Magilsen
,
J. C.
van Alfen
,
C.
Penning
, and
H. M.
Evenhuis
.
2009
.
Prevalence of cardiovascular risk factors in older people with intellectual disability.
American Journal on Intellectual and Developmental Disabilities
114
:
427
436
.
Draheim
,
C. C.
,
H. I.
Stanish
,
D. P.
Williams
, and
J. A.
McCubbin
.
2007
.
Dietary intake of adults with mental retardation who reside in community settings.
American Journal on Mental Retardation
112
:
392
400
.
Draheim
,
C. C.
,
D. P.
Williams
, and
J. A.
McCubbin
.
2002
.
Prevalence of physical inactivity and recommended physical activity in adults with mental retardation residing in community settings.
Mental Retardation
40
:
436
444
.
Ebbeling
,
C. B.
,
D. B.
Pawlak
, and
D. S.
Ludwig
.
2002
.
Childhood obesity: Public-health crisis, common sense cure.
The Lancet
360
:
473
482
.
Field
,
D.
,
M.
Garland
, and
K.
Williams
.
2003
.
Correlates of specific childhood feeding problems.
Journal of Paediatrics and Child Health
39
:
299
304
.
Fragala-Pinkham
,
M. A.
,
S. M.
Haley
,
J.
Rabin
, and
V. S.
Kharasch
.
2005
.
A fitness program for children with disabilities.
Physical Therapy
85
:
1182
1200
.
Frey
,
G. C.
,
H. I.
Stanish
, and
V. A.
Temple
.
2008
.
Physical activity of youth with intellectual disabilities: Review and research agenda.
Adapted Physical Activity Quarterly
25
:
95
117
.
Graham
,
A.
and
G.
Reid
.
2000
.
Physical fitness of adults with an intellectual disability.
Research Quarterly for Exercise and Sport
71
:
152
161
.
Hagerman
,
P. J.
2008
.
The fragile X prevalence paradox.
Journal of Medical Genetics
45
:
498
499
.
Heo
,
M.
,
D.
Allison
,
M.
Faith
,
S.
Zhu
, and
K.
Fontaine
.
2003
.
Obesity and quality of life: Mediating effects of pain and comorbidities.
Obesity Research
11
:
209
216
.
Honeycutt
,
A.
,
L.
Dunlap
,
M. A.
Chen
,
G.
al Homsi
,
C. S.
Grosse
, and
D.
Schendel
.
2004
.
Economic costs associated with mental retardation, cerebral palsy, hearing loss, and vision impairment—United States, 2003.
MMWR Morbidity and Mortality Weekly Report
53
:
57
59
.
Humphries
,
K.
,
M.
Traci
,
T.
Seekins
, and
J.
Brusin
.
2004
.
A preliminary assessment of the nutrition and food-system environment of adults with intellectual disabilities living in supported arrangements in the community.
Ecology of Food and Nutrition
43
:
517
532
.
Jenssen
,
I.
,
P. T.
Katzmarzyk
,
W. F.
Boyce
,
C.
Vereecken
,
C.
Mulvill
,
C.
Roberts
, et al
.
2005
.
Comparisons of overweight and obesity prevalence in school-aged youth from 34 countries and their relationships with physical activity and dietary patterns.
Obesity Reviews
6
:
123
132
.
Khan
,
L. K.
,
K.
Sobush
,
D.
Keener
,
K.
Goodman
,
A.
Lowry
,
J.
Kakietek
, and
S.
Zaro
.
2009
.
Recommended community strategies and measurement to prevent obesity in the United States.
MMWR Recommendations and Reports
7
:
1
26
.
King
,
G.
,
M.
Law
,
S.
King
,
P.
Rosenbaum
,
M. K.
Kertoy
, and
N. L.
Young
.
2003
.
A conceptual model of the factors affecting the recreation and leisure participation of children with disabilities.
Physical & Occupational Therapy in Pediatrics
23
:
63
90
.
Lisik
,
M.
,
K.
Szymanska-Parkieta
, and
U.
Galecka
.
2000
.
The comparison of anthropometric variables in mentally retarded boys with and without fragile X syndrome.
Clinical Genetics
57
:
456
458
.
Loesch
,
D. Z.
,
Q.
Bui
,
C.
Dissanayake
,
S.
Clifford
,
E.
Gould
,
D.
Bulhak-Paterson
, et al
.
2007
.
Molecular and cognitive predictors of the continuum of autistic behaviors in fragile X.
Neuroscience Biobehavioral Reviews
31
:
315
326
.
Loesch
,
D. Z.
,
R. M.
Huggins
,
Q. M.
Bui
,
J. L.
Epstein
,
A. K.
Taylor
, and
R. J.
Hagerman
.
2002
.
Effect of the deficits of fragile X mental retardation protein on cognitive status of fragile X males and females assessed by robust pedigree analysis.
Journal of Developmental & Behavioral Pediatrics
23
:
416
423
.
Loesch
,
D. Z.
,
R. M.
Huggins
, and
R. J.
Hagerman
.
2004
.
Phenotypic variation and FMRP levels in fragile X.
Mental Retardation and Developmental Disabilities Research Reviews
10
:
31
41
.
McGuire
,
B. E.
,
P.
Daly
, and
F.
Smyth
.
2007
.
Lifestyle and health behaviours of adults with an intellectual disability.
Journal of Intellectual Disability Research
51
:
497
510
.
Moran
,
R.
,
W.
Drane
,
S.
McDermott
,
S.
Dasari
,
J. B.
Scurry
, and
T.
Platt
.
2005
.
Obesity among people with and without mental retardation across adulthood.
Obesity Research
13
:
342
349
.
Munk
,
D. D.
and
A. C.
Repp
.
1994
.
Behavioral assessment of feeding problems of individuals with severe disabilities.
Journal of Applied Behavior Analysis
27
:
241
250
.
Murphy
,
N. A.
and
P. S.
Carbone
.
Council on Children With Disabilities
2008
.
Promoting the participation of children with disabilities in sports, recreation, and physical activities.
Pediatrics
121
:
1057
1061
.
Nowicki
,
S. T.
,
F.
Tassone
,
M. Y.
Ono
,
J.
Ferranti
,
M. F.
Croquette
,
B.
Goodlin-Jones
, and
R. J.
Hagerman
.
2007
.
The Prader-Willi phenotype of fragile X syndrome.
Journal of Developmental Behavioral Pediatrics
28
:
133
138
.
Odgen
,
C. L.
,
M. D.
Carrol
,
L. R.
Curtin
,
M. A.
Dowell
,
C. J.
Tabak
, and
K. M.
Flegal
.
2006
.
Prevalence of overweight and obesity in the U.S., 1999–2004.
Journal of the American Medical Association
295
:
1549
1555
.
Ogden
,
C. L.
,
M. D.
Carroll
, and
K. M.
Flegal
.
2008
.
High body mass index for age among U.S. children and adolescents, 2003–2006.
Journal of the American Medical Association
299
:
2401
2405
.
Ogden
,
C. L.
,
M. D.
Carroll
,
M. A.
McDowell
, and
K. M.
Flegal
.
2007
.
Obesity among adults in the United States—No change since 2003–2004. NCHS data brief no. 1
.
Hyattsville, MD
National Center for Health Statistics
.
Partington
,
M. W.
1984
.
The fragile X syndrome II: Preliminary data on growth and development in males.
American Journal of Medical Genetics
17
:
175
194
.
Pratt
,
C. A.
,
J.
Stevens
, and
S.
Daniels
.
2008
.
Childhood obesity prevention and treatment: Recommendations for future research.
American Journal of Preventive Medicine
35
:
249
252
.
Rimmer
,
J. H.
,
D.
Braddock
, and
G.
Fujiura
.
1993
.
Prevalence of obesity in adults with mental retardation: Implications for health promotion and disease prevention.
Mental Retardation
31
:
105
110
.
Rimmer
,
J. H.
,
D.
Braddock
, and
B.
Marks
.
1995
.
Health characteristics and behaviors of adults with mental retardation residing in three living arrangements.
Research in Developmental Disabilities
16
:
489
499
.
Rimmer
,
J. H.
and
E.
Wang
.
2005
.
Obesity prevalence among a group of Chicago residents with disabilities.
Archives of Physical Medicine and Rehabilitation
86
:
1461
1464
.
Rimmer
,
J. H.
,
L. A.
Wolf
,
B. S.
Armour
, and
L. B.
Sinclair
.
2007
.
Physical activity among adults with a disability—United States, 2005.
MMWR Morbidity and Mortality Weekly Report
56
:
1021
1024
.
Robertson
,
J.
,
E.
Emerson
,
N.
Gregory
,
C.
Hatton
,
S.
Turner
,
S.
Kessissoglou
, and
A.
Hallam
.
2000
.
Lifestyle related risk factors for poor health in residential settings for people with intellectual disabilities.
Research in Developmental Disabilities
21
:
469
486
.
Rubin
,
S. S.
,
J. H.
Rimmer
,
B.
Chicoine
,
D.
Braddock
, and
D. E.
McGuire
.
1998
.
Overweight prevalence in persons with Down syndrome.
Mental Retardation
36
:
175
181
.
Senese
,
L. C.
,
N. D.
Almeida
,
A. K.
Fath
,
B. T.
Smith
, and
E. B.
Loucks
.
2009
.
Associations between childhood socioeconomic position and adulthood obesity.
Epidemiology Reviews
31
:
21
57
.
Sherry
,
B.
,
Z.
Mei
,
K. S.
Scanlon
,
A. H.
Mokdad
, and
L. M.
Grummer-Strawn
.
2004
.
Trends in state-specific prevalence of overweight and underweight 2- through 4-year-old children from low-income families from 1989 through 2000.
Archives of Pediatrics and Adolescent Medicine
158
:
1116
1124
.
Stanish
,
H. I.
,
V. A.
Temple
, and
G. C.
Frey
.
2006
.
Health-promoting physical activity of adults with mental retardation.
Mental Retardation and Developmental Disabilities Research Reviews
12
:
13
21
.
Temple
,
V. A.
,
G. C.
Frey
, and
H. I.
Stanish
.
2006
.
Physical activity of adults with mental retardation: Review and research needs.
American Journal of Health Promotion
21
:
2
12
.
Thorp
,
L.
,
D.
List
,
T.
Marx
,
L.
May
,
S.
Helgerson
, and
T.
Frieden
.
2004
.
Childhood obesity in New York City elementary school students.
American Journal of Public Health
94
:
1496
1500
.
U.S. Department of Health and Human Services
2000
.
11
Healthy People 2010: Understanding and improving health (2nd ed.)
.
Washington, DC
U.S. Government Printing Office
.
U.S. Public Health Service
2002
.
Closing the gap: A national blueprint for improving the health of individuals with mental retardation. Report of the Surgeon General's Conference on Health Disparities and Mental Retardation
.
Rockville, MD
Office of the Surgeon General
.
Whitaker
,
R. C.
,
J. A.
Wright
,
M. S.
Pepe
,
K. D.
Seidel
, and
W. H.
Dietz
.
1997
.
Predicting obesity in young adulthood from childhood and parental obesity.
New England Journal of Medicine
337
:
869
873
.
Williams
,
P. G.
,
N.
Dalrymple
, and
J.
Neal
.
2000
.
Eating habits of children with autism.
Pediatric Nursing
26
:
259
264
.
Yamaki
,
K.
2005
.
Body weight status among adults with intellectual disability in the community.
Mental Retardation
43
:
1
10
.

Author notes

Editor-in-charge: Leonard Abbeduto