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Published in final edited form as: J Autism Dev Disord. 2022 Jan 4;53(1):89–95. doi: 10.1007/s10803-021-05415-9

The Family Nutrition and Physical Activity Survey: Comparisons with Obesity and Physical Activity in Adolescents with Autism Spectrum Disorder

Brian C Helsel 1, Rachel N S Foster 1, Joseph Sherman 1, Robert Steele 2, Lauren T Ptomey 1, Robert Montgomery 1,3, Richard A Washburn 1, Joseph E Donnelly 1
PMCID: PMC9940245  NIHMSID: NIHMS1872829  PMID: 34982325

Abstract

Adolescents with autism spectrum disorder (ASD) are at a heightened risk for obesity. Family-level measures of nutrition and physical activity may help explain factors contributing to disproportionate rates of weight gain. Twenty adolescents with ASD participated in baseline testing for a study to assess the feasibility of remotely-delivered yoga. Parents completed the Family Nutrition and Physical Activity (FNPA) survey and anthropometrics and physical activity were assessed in the adolescents. A median split was applied to the FNPA score to create high and low obesogenic environments and nonparametric O’Brien’s multiple endpoint tests were used to evaluate the differences. Between-group differences were found in anthropometrics (p = 0.01) but not physical activity (p = 0.72). Implications for a multifaceted family-based approach to obesity prevention are discussed.

Keywords: Autism spectrum disorder, Body Mass Index, Exercise, Diet, Obesity

Introduction

The risk of being overweight or obese is higher for adolescents with autism spectrum disorder (ASD) when compared to youth who are typically developing (TD). Healy et al. (2019) reported 49% higher odds of obesity (odds ratio = 1.49; p = 0.02) in youth with ASD and an over threefold risk of obesity in those with severe ASD (odds ratio = 3.35; p < 0.01). Lower levels of moderate-to-vigorous physical activity (MVPA) may contribute to the increased risk of obesity in adolescents with ASD (Liang et al., 2020) and this relationship may be intensified by parents’ perceptions of the advantages, disadvantages, facilitators, and barriers of physical activity for their children. For example, Obrusnikova and Miccinello (2012) reported that some parents of children with ASD considered physical activity to be beneficial (e.g., increased fitness, enhanced motor control), while others contemplated the detrimental effects of physical activity participation including increased fatigue, physical discomfort, and the potential of poor performance in motor skills. Parents also differed in their perceptions of the psychosocial advantages (e.g., increased socialization opportunities and improved social skills) and disadvantages (e.g., teasing and bullying, increased social isolation) of physical activity. The Family Nutrition and Physical Activity (FNPA) survey measures the family-level factors that heighten a child’s risk for developing obesity, including the parent’s perception of their child’s healthy eating practices, physical activity, screen time, and sleep routine (Peyer et al., 2020). The FNPA survey remains infrequently used in studies of children who are not TD. Therefore, the purpose of this study is to evaluate the utility of the FNPA survey in adolescents with ASD by comparing anthropometrics, objectively-measured physical activity, and determinants of physical activity (i.e., self-efficacy, social support) in low versus high obesogenic environments.

Methods

Overview

This secondary analysis uses baseline data from 20 adolescents with ASD (age: 13.2 ± 2.2 years; 60% male) and their parents who volunteered for a yoga program during a 12-week pilot study. Participants were recruited using social media and newsletters from organizations serving adolescents with ASD near Kansas City, KS between September 2020 and April 2021. Participants were included if they were between the ages of 11 and 17 years, had a physician verified diagnosis of ASD and medical consent to participate in yoga, were able to understand directions and communicate through spoken language, and participated in less than 90 min/week of parent-reported physical activity. Parent/legal guardian consent and adolescent assent were obtained from all participants prior to baseline testing. This study was approved by the Institutional Review Board at the University of Kansas Medical Center.

Outcomes

All outcomes were obtained at our exercise laboratory by a research assistant trained in the collection of anthropometrics and physical activity with previous experience working with adolescents with ASD. Participants reported to a 60 min baseline testing appointment and demographic information (i.e., age, gender, race/ethnicity) and parent-reported physical activity and screen time for the adolescent with ASD were obtained.

Family Nutrition and Physical Activity (FNPA) Survey

Parents completed the version #4 of the FNPA survey described by Peyer et al. (2020), which uses more objective response items for some of the questions compared to the original survey (e.g., “3 to 5 days” instead of “often”). The FNPA survey is a behaviorally based assessment of the obesogenic environments and practices that may augment the risk of a child becoming overweight or obese and has displayed acceptable internal consistency (α = 0.76). The FNPA survey has demonstrated strong associations with body mass index (BMI) change (Ihmels et al., 2009) and parent BMI (Williams et al., 2017). It has also been linked to parenting style with permissive parenting being associated with child obesogenic behaviors (Johnson et al., 2012) and higher levels of impulsivity, aggressiveness, and a lack of self-control and independence in children with ASD (Mohammadi & Zarafshan, 2014). Specifically, the FNPA survey uses 20 questions (range 20–80 points) to evaluate the parents’ perception of their child’s frequency of engaging in several health-related behaviors, including family meals, family eating practices, food choices, beverage choices, restriction/reward, screen time, healthy environment, family activity, child activity, and family schedule/sleep routine. These are categorized into nutrition, physical activity, screen time, and healthy sleep habit subcategory scores using the summation of both objective (i.e., 0 days, 1 or 2 days, 3 to 5 days, and 6 to 7 days) and subjective (i.e., never or almost never, sometimes, often, and always or almost always) response items. Higher scores for each subcategory and the overall score represent a family environment that is healthier with fewer obesogenic risk factors (Peyer et al., 2020).

Anthropometrics (height, weight, and waist circumference)

Standing height was assessed to the nearest centimeter with a portable stadiometer (Model #IP0955, Invicta Plastics Limited, Leicester, UK). Weight was assessed to the nearest 0.1 kg with a calibrated scale (Model #PS6600, Belfour, Saukville, WI). BMI, the deviation from a BMI at the 50th percentile (BMI50), and BMI percentile (BMI %ile) were calculated using the Centers for Disease Control and Prevention’s BMI Percentile Date Files with LMS Values (Growth Charts—Percentile Data Files with LMS Values, 2019). BMI50 was expressed as a percentage and equal to the difference between the adolescent’s BMI and the matched age- and gender-specific BMI at the 50th percentile divided by the BMI at the 50th percentile. Waist circumference was assessed using the procedures described by Lohman et al. (1988).

Physical activity and sedentary time

Physical activity and sedentary time were assessed using the ActiGraph wGT3X tri-axial accelerometer (ActiGraph LLC, Pensacola, FL) worn on the non-dominant hip at the anterior axillary line during waking hours for 7 consecutive days. Data were collected at 60 Hz and vertical axis activity counts were aggregated over 60 s epochs. A minimum of 4 valid days (at least 8 h/day) with at least 1 weekend day (i.e., Friday, Saturday, or Sunday) were required for inclusion in the analysis. Daily minutes of sedentary, light, and MVPA were estimated using the age-specific Freedson 4 MET cut-points (Freedson et al., 1998, 2005). Bouts of MVPA (≥ 10 min with an allowance of ≤ 2 min below the MVPA threshold) were also determined (da Silva et al., 2014).

Social support for physical activity

Social support for physical activity was measured in adolescents using a modified version of the 7-item Social Support for Activity for Persons with Intellectual Disabilities – Family Scale (Peterson et al., 2009). This tool used a three-response format (i.e., no, maybe, or yes) to respond to questions such as “Does anyone in your family remind you to do physical activities?” or “Does anyone in your family do physical activities with you?”. Each item is scored by the summation of the response-item values of 0 (no), 1 (maybe), and 2 (yes) to yield an overall social support score with higher scores indicating more social support.

Self-efficacy for physical activity

Self-efficacy for physical activity was assessed using the 6-item Self-Efficacy for Activity for Persons with Intellectual Disabilities Scale (Peterson et al., 2009). This tool used a three-response format (i.e., no, maybe, or yes) to respond to questions such as “Do you think you can make time for physical activities almost every day?” or “Do you think you can do physical activities when you are very busy?”. Each item is scored by the summation of the response-item values of 0 (no), 1 (maybe), 2 (yes) to yield an overall self-efficacy score with higher scores representing better self-efficacy related to physical activity.

Statistical Analysis

We compared anthropometrics (weight, waist circumference, BMI, BMI %ile, and BMI50), physical activity (sedentary, light, MVPA, and number and duration of bouts of MVPA), and social support and self-efficacy for physical activity between adolescents living in low (FNPA score > median) versus high obesogenic environments (FNPA score ≤ median) using the nonparametric O’Brien’s multiple endpoint test and Wilcoxon rank sum tests. The O’Brien’s multiple endpoint test was used to lower the risk of type I error for similar measures and was calculated by applying a t-test to the sum of the ranks for the endpoints being considered. Wilcoxon rank sum tests were used as a post-hoc analysis on the multiple endpoints and on data that were not considered to be multiple endpoints (i.e., sedentary behavior, self-efficacy, and social support). Statistical significance was set at p < 0.025 for the O’Brien’s multiple endpoint test and p < 0.05 for the Wilcoxon rank sum test. R v 4.0.5 was used for the analysis (R Core Team 2021). Data are presented as mean ± standard deviation for continuous measures or frequency and percentage for categorical variables.

Results

Twenty participants (age: 13.2 ± 2.2 years; 60% male; 90% non-Hispanic white) completed baseline testing and were evaluated. Sample characteristics are presented in Table 1. Mean BMI %ile was 69.7 ± 31.9 in the adolescents with ASD with 0% achieving the current U.S. guidelines for 60 min/day of MVPA (17.6 ± 15.0 min/day). Additionally, parents reported ≥ 2 h/day of screen time in 85% of the adolescents with ASD with low engagement in physical education classes (1.7 classes/week) and sports teams (25%). FNPA scores ranged from 43 to 67 with a median of 54.5. Mean FNPA scores were 59.4 and 49.8 for the low and high obesogenic groups, respectively.

Table 1.

Sample characteristics of the adolescents with autism spectrum disorder

N
Age (years) 20 13.2 ± 2.2
Gender: male 20 12 (60%)
Non-Hispanic White 20 18 (90%)
FNPA Survey a 20 54.6 ± 6.2
Nutrition 20 30.9 ± 3.6
Physical activity 20 11.7 ± 2.2
Screen time 20 6.4 ± 1.9
Sleep 20 5.7 ± 1.1
BMI percentile 20 69.7 ± 31.9
Sedentary (min/day)b 18 509.4 ± 82.3
MVPA (min/day)b 18 17.6 ± 15.0
Screen time ≥ 2 h/dayc 20 17 (85%)
PE classes/weekc 20 1.7 ± 1.9
Play on a sports teamc 20 5 (25%)
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Reported as mean ± standard deviation or frequency (percentage)

a

Family Nutrition and Physical Activity (FNPA) total score and subcategory scores (i.e., nutrition, physical activity, screen time, and sleep) are presented

b

Measured by ActiGraph wGT3X-BT accelerometers

c

Parent-reported measure

Results of the O’Brien’s multiple endpoint test in Table 2 showed that anthropometric measures (p = 0.01) were different between adolescents living in low versus high obesogenic environments, but no differences were found for physical activity levels measured by accelerometry (p = 0.72). Anthropometric measures, including waist circumference (p = 0.03), BMI (p = 0.03), BMI %ile, (p = 0.03), and BMI50 (p = 0.02), were significantly higher in adolescents living in high compared with low obesogenic environments. Specifically, adolescents in high obesogenic environments were on average 11.1 kg heavier and had a BMI %ile that was 54.9% higher than those in low obesogenic environments. Accelerometer wear time was similar between groups (761.3 ± 74.9 min/day; 5.8 ± 1.2 days), but no significant differences in min/day of sedentary time, light, and MVPA or the frequency or duration of MVPA bouts between adolescents living in high versus low obesogenic environments were observed. A visual representation of sedentary, light, MVPA, and all activity by gender and low and high obesogenic environmental risk can be seen in Fig. 1. On average, females participated in more min/day of physical activity compared to males in environments where the obesogenic risk was low, but males were more active in environments with higher obesogenic risk. Self-efficacy for physical activity was significantly higher in adolescents in low compared with high obesogenic environments (p = 0.04). However, no significant differences in social support for physical activity were found between adolescents living in high versus low obesogenic environments.

Table 2.

Group differences between adolescents with autism spectrum disorder who live in environments with lower or higher obesogenic risk as determined by the FNPA survey

High obesogenic environmenta N = 10 Low obesogenic enviromenta N = 10 P values
Wilcoxon rank sum O’Brien’s multiple endpoint
Anthropometrics 0.01
Weight (kg) 63.5 ± 13.8 52.4 ± 16.1 0.06
Waist (cm) 83.5 ± 12.6 70.2 ± 11.2 0.03
BMI (kg/m2) 26.6 ± 5.5 20.6 ± 4.6 0.03
BMI %ile 84.7 ± 26.0 54.7 ± 31.2 0.03
BMI50 46.4 ± 35.7 8.7 ± 26.3 0.02
Physical activityb,c 0.72
Sedentary (min/day) 508.8 ± 75.2 510.0 ± 92.4 0.76
Light (min/day) 239.5 ± 96.5 230.1 ± 67.2 0.70
MVPA (min/day) 18.3 ± 16.8 17.0 ± 14.4 > 0.99
MVPA (bouts/day) 0.3 ± 0.4 0.5 ± 0.4 0.53
MVPA (min/bout) 5.4 ± 6.0 8.1 ± 6.9 0.42
Social support 7.2 ± 3.0 8.1 ± 3.6 0.62
Self-efficacy 5.3 ± 2.1 7.8 ± 2.7 0.04
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a

Low and high obesogenic environment determined using the median of the Family Nutrition and Physical Activity (FNPA) survey score and reported as mean ± standard deviation

b

Measured using ActiGraph wGT3X-BT accelerometers; Sample size is N = 8 for high obesogenic environment and N = 10 for low obesogenic environment as 2 participants did not meet the wear time criteria

c

The results from the nonparametric O’Brien’s multiple endpoint test for physical activity does not include sedentary time, social support, and self-efficacy

Fig. 1.

Fig. 1

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Physical activity by gender and the family nutrition and physical activity low versus high obesogenic environment risk in a sample of adolescents with autism spectrum disorder

Discussion

Results from this analysis suggest the potential of the FNPA for the assessment of obesogenic risk of the family environment in adolescents with ASD. We observed higher body weight, waist circumference, BMI, BMI %ile, and BMI50 in adolescents with ASD living in high compared with low obesogenic environments as categorized by the FNPA. Parents of children who are TD may contribute to the development of obesity (Williams et al., 2017). They often underestimate their child’s weight and health status (Howe et al., 2017), leading to increased optimism regarding diet quality and physical activity (Hong et al., 2019). This creates difficulties related to the early detection and screening of obesity and the initiation of healthy lifestyle changes (Povey et al., 2020). The FNPA survey was designed to be a behaviorally based assessment of the obesogenic environment and practices that may contribute to a child’s risk of developing obesity. For example, Ihmels et al. (2009) prospectively recorded stronger negative correlations between the FNPA survey and 1-year change in BMI50 for those who were in the highest quartile for BMI percentile (5.82% BMI50 increase; r = − 0.17, p < 0.05). However, the FNPA survey is infrequently used in studies of children who are not TD and have additional development or behavior considerations that contribute to obesity risk.

Constructs measuring nutrition on the FNPA survey, including family eating practices, food choices, and beverage choices, were slightly lower in adolescents with ASD when compared to tenth grade students who are TD in a study by Peyer and Welk (2017). Some research suggests that children with ASD may experience food selectivity (e.g., preference for energy dense foods, aversion to specific textures, and food refusal) and consume fewer fruits and vegetables compared to youth who are TD (Canals-Sans et al., 2021; Schreck et al., 2004), contributing to nutrient deficiencies and inadequacies (Bandini et al., 2010; Mari-Bauset et al., 2014). It is unclear if increased food selectivity and resulting nutritional inadequacies are the specific driving factors that increase obesity risk for youth with ASD over and above those factors for TD youth (Curtin et al., 2014). In a study by Sharp et al. (2018), adolescents with ASD preferred to eat processed foods, snacks, and sweets and had a low intake of fruits and vegetables overall. However, authors indicate that heightened food selectivity does not necessarily cause overweight and obesity in this population but can be a major contributing factor. Conversely, a recent study by Upadhyay-Dhungel and Ghimire (2019) noted a significant association between food selectivity and obesity in adolescents with ASD.

Notable differences were also seen in the constructs measuring physical activity on the FNPA survey (i.e., lower scores for healthy environment, child activity, and family activity) between adolescents with ASD and tenth graders who are TD in the study by Peyer and Welk (2017). Adolescents with ASD could have behavioral and physical traits (e.g., engaging in self injurious behavior (Flowers et al., 2020), motor stereotypy (Akers et al., 2020), and gross and fine motor movement delays (Hedgecock et al., 2018)) that limit their engagement in physical activity and increase sedentary activities (Jones et al., 2017). Self-efficacy of physical activity was significantly higher in adolescents with ASD living in a low obesogenic environment, possibly counteracting the behavioral or physical limitations and increasing engagement in physical activity (Liou & Kulik, 2020) and improving fitness (Tulloch et al., 2020). Nevertheless, youth with ASD may also rely more on their parents than those who are TD and therefore, family-focused interventions to increase MVPA are warranted.

This study has several limitations to consider. First, the results may not be generalizable to a larger sample of adolescents with ASD. This study used a small sample of adolescents with ASD who enrolled in a yoga intervention and who were recruited through social media and newsletters of local organizations, which resulted in a relatively homogenous sample. The severity of ASD was not assessed, thus the application of the FNPA survey and its association with obesity in adolescents with ASD with varying physical and behavioral abilities is still unknown. The FNPA may not adequately evaluate the obesogenic environment for those with ASD who engage in severe problem behaviors, are more selective in their food choices, or have physical constraints that could interfere with their ability to perform physical activity independently. Additionally, the accelerometer protocol limits the inferences that can be made between the FNPA survey and physical activity. Decisions were made related to the collection, processing, and analyzing of the accelerometer data based on previous studies, but there is a lack of consensus on the best protocols (Cain et al., 2013; Kim et al., 2012; Rich et al., 2013). There are also no protocols for assessing sedentary behavior and physical activity in adolescents with ASD, so we applied accelerometer cut-points that were validated in children who are TD. Finally, this study uses a cross-sectional approach and does not monitor changes in the FNPA survey or BMI longitudinally. Similar to the study by Ihmels et al. (2009), it will be important to understand how the FNPA survey may predict longitudinal changes in BMI to prevent continued secular trends in the prevalence of obesity.

Future research should examine the association between the FNPA survey and longitudinal changes in obesity for a larger sample of youth with ASD. Researchers may also consider expanding this to include adolescents with other intellectual and developmental disabilities to extend the generalizability of the findings to populations with similar parent influence. Addressing the complex etiology of childhood obesity may require a multifaceted family-based obesity prevention approach (Boles et al., 2013; Robson et al., 2019), so additional interpersonal-level factors (e.g., social support, family barriers to healthy eating and physical activity) need to be considered.

Conclusion

Adolescents with ASD are at increased risk for overweight and obesity when compared to their TD peers. Multifaceted family-based approaches addressing issues specific to adolescents with ASD, including diminished motor skills, motor stereotypy, and food selectivity, may be useful in preventing excessive weight gain. Our results suggest the potential for the use of the FNPA survey to assess the obesogenic risk of the family environment in adolescents with ASD. Additional studies to estimate the association between obesogenic risk of the family environment determined by the FNPA survey and longitudinal changes in anthropometrics, physical activity, and dietary intake in larger samples of adolescents with ASD are warranted.

Funding

This study was funded by Health Resources and Services Administration (Grant No. UA3MC25735), and National Center for Advancing Translational Sciences (Grant No. TL1TR002368)

Footnotes

Conflict of interest The authors have no conflicts.

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