Research in Autism Spectrum Disorders 7 (2013) 56–65

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Research in Autism Spectrum Disorders

Jo u rn al h om ep ag e: h t tp : / /ees .e lsev ier . co m /RASD/d efau l t .as p

Multi-method assessment of feeding problems among children withautism spectrum disorders

William G. Sharp a,b,*, David L. Jaquess a,b, Colleen T. Lukens c

a The Marcus Autism Center, United Statesb Emory University School of Medicine, United Statesc The Children’s Hospital of Philadelphia, United States

A R T I C L E I N F O

Article history:

Received 3 April 2012

Received in revised form 29 June 2012

Accepted 2 July 2012

Keywords:

Assessment

Autism

Children

Diet

Feeding

Food selectivity

Pediatric feeding disorders

A B S T R A C T

Estimates suggest that atypical eating is pervasive among children with autism spectrum

disorders (ASD); however, much remains unknown regarding the nature and prevalence of

feeding problems in this population due to methodological limitations, including lack of

adequate assessment methods and empirical evaluation of existing measures. In the current

study, a sample of 30 children ages 3–8 years completed a multi-method assessment battery

involving a standardized mealtime observation, a food preference inventory, and the Brief

Autism Mealtime Behavior Inventory (BAMBI), which represents the first attempt to assess

the correspondence between direct observation and parent-report measures of feeding

concerns and dietary intake in ASD. During the mealtime observation, fourteen participants

either rejected (n = 8) or accepted (n = 6) all bites, while the remaining 16 participants

demonstrated selective patterns of acceptance by type and/or texture. Among this subgroup,

vegetables were the most frequently rejected food type during the behavioral observation.

Vegetables were also the most frequently rejected food based on parent report for the

sample. Increased food selectivity was positively correlated with problem behaviors during

the observation, while ASD symptom severity and growth parameters were unrelated to

feeding data. We discuss findings in relation to clinical and research activities and

recommend strategies to achieve more systematic research in this area.

� 2012 Elsevier Ltd. All rights reserved.

In addition to difficulties with communication, social interaction, and behavioral flexibility, feeding problems represent afrequent concern reported by caregivers of children with autistic spectrum disorders (ASD) and more recent researchsuggests that challenging mealtime behaviors may occur at near epidemic levels in this population, with some estimatesapproaching 90% (see Cermak, Curtin, & Bandini, 2010; Ledford & Gast, 2006; Matson & Fodstad, 2009 for reviews). Foodselectivity (i.e., only eating a narrow variety of foods by type, texture, and/or presentation) is the most frequent feedingconcern documented among children with ASD, predominately in the form of strong preferences for starches, snack andprocessed foods and a bias against fruits, vegetables, and proteins (Ahearn, Castine, Nault, & Green, 2001; Field, Garland, &Williams, 2003; Lukens & Linscheid, 2008). Provisional evidence also suggest children with ASD may be at increased risk fornutritional and/or related medical issues related to atypical mealtime behaviors, including vitamin and mineral deficiencies(Bandini et al., 2010; Zimmer et al., 2011) and poor bone growth (Hediger et al., 2008). Despite the potential for serious

* Corresponding author at: Pediatric Psychology and Feeding Disorders Program, The Marcus Autism Center, 1920 Briarcliff Road, Atlanta, GA 30329,

United States. Tel.: +1 404 785 9469.

E-mail addresses: wgsharp@emory.edu, william.sharp@choa.org (W.G. Sharp).

1750-9467/$ – see front matter � 2012 Elsevier Ltd. All rights reserved.

http://dx.doi.org/10.1016/j.rasd.2012.07.001

W.G. Sharp et al. / Research in Autism Spectrum Disorders 7 (2013) 56–65 57

consequences associated with feeding problems in ASD, much remains unknown regarding the prevalence, etiology, andpossible sequelae associated with feeding problems and ASD.

Lack of adequate assessment methods has been identified as a significant barrier to progress in this area (Matson &Fodstad, 2009; Seiverling, Williams, & Sturmey, 2010). Published reports vary widely in outcome measures of mealtimebehavior problems and related nutritional concerns, including retrospective chart reviews, study-specific caregiverquestionnaires, and less commonly, direct mealtime observation or standardized assessment instruments. Replicatingprocedures outlined by Munk and Repp (1994) for classifying feeding problems of individuals with physical ordevelopmental disabilities based on direct observation, Ahearn et al. (2001) conducted the only direct observation ofmealtime behavior to assess feeding problems among a sample of children with ASD to date. The authors evaluated a groupof 30 children ages 3–14 years with autistic disorder or pervasive developmental disorder-not otherwise specified (PDD-NOS), presenting children with three food items from each of four food groups (i.e., fruit, vegetable, protein, and starch)across six sessions (120 bites) and preparing one item at pureed texture per session. Behaviors measured during theobservation included food acceptance, expulsion, and disruptive behavior. Ahearn and colleagues reported that more thanhalf of the sample (57%) exhibited food selectivity by type or texture, while more than three quarters (87%) exhibited low tomoderate food acceptance. The use of behavioral observation among children with ASD seeking intervention for feedingproblems has also been documented in single-subject research literature (see Sharp, Jaquess, Morton, & Herzinger, 2011 fora review).

Standardized questionnaires represent an alternative method for evaluating feeding behaviors and dietary intake inASD. There are, however, relatively few measures developed for pediatric feeding disorders and only one measure – theBrief Autism Mealtime Behavior Inventory (BAMBI; Lukens & Linscheid, 2008) – specifically designed to evaluatemealtime difficulties commonly seen in the ASD population; however, additional research is needed to confirmthe psychometric utility of the BAMBI, including comparing scores to direct observation of mealtime behaviors, as wellas cross validation with an independent sample. A food preference inventory (FPI) is a complimentary parent-reportmethod used to assess dietary variety in children with ASD. This method involves a list of food items across food groups,which caregivers endorse in terms of regularity (e.g., often, sometimes, never) or willingness to consume (e.g., favorite,willingly, with prodding) using a likert-type scale (Willett, 1998). Previous studies utilized the FPI to determine overallpatterns of food intake in ASD (Schreck, Williams, & Smith, 2004), as well as design and evaluate interventions targetingfood selectivity in this population (Paul, Williams, Riegel, & Gibbons, 2007; Pizzo, Williams, Paul, & Riegel, 2009). Data,however, regarding dietary variety as captured by the FPI has yet to be compared to direct observation and/orstandardized questionnaires in the ASD population.

In sum, there is a strong need to further develop assessment methods focusing on feeding problems in ASD. The currentstudy seeks to add to this line of research by assessing the correspondence between different assessment tools available tomeasure feeding concerns among children with ASD, including data derived from a structured mealtime observation, theBAMBI, and a FPI. Descriptive statistics, including mean and variance, are presented and correlations between measures areinvestigated through exploratory analysis. Through this process, we describe a multi-method assessment battery designedto capture mealtime difficulties purportedly unique to this population, including severe food selectivity, ritualistic behaviorsurrounding eating, and strong emotional responses in response to non-preferred food, with the potential for disseminationamong researchers and practitioners in the ASD community. To aide in replication and standardization of procedures, weprovide a detailed protocol for conducting a behavioral observation during meals and discuss the pros and cons regarding theuse of direct observation versus questionnaires based on pragmatic and logistical concerns.

1. Methods

1.1. Participants

Participants were recruited through local early intervention programs, parent support groups, and state and localautism organizations through flyers, list serves, and The central inclusion criterion was an ASD diagnosis (i.e., Asperger’sDisorder, PDD-NOS, and Autistic Disorder) among children between ages 3 and 8. All participants were diagnosed byprofessionals not associated with the program (based on caregiver report); however, we used the Social ResponsivenessScale (SRS; Constantino, 2005) parent form to support ASD status. The inclusion criterion required a total SRS score inthe mild, moderate, or severe range (T-score > 60). The final sample included 30 children with ASD between the ages of3 years and 8 years, 7 months. There were 23 males and 7 females, which is consistent with previous researchersindicating higher rates of autism among males (Cialdella & Mamelle, 1989). Findings from the SRS supported thecharacterization of the sample as falling on the autism spectrum, with the mean score falling in the severe range(M = 83; SD = 7.8; range: 68–91).

1.2. Procedure

Upon arriving to the clinic, children were measured to obtain anthropometric parameters (i.e., height, weight), andcaregivers completed a battery of questionnaires (outlined below). When these measures were completed, we conducted astructured mealtime observation with each parent–child dyad. Meals occurred in a 100 by 100 room equipped with one way

W.G. Sharp et al. / Research in Autism Spectrum Disorders 7 (2013) 56–6558

mirror and adjacent observation room. The room included a chair, foods, table, feeding utensils (small maroon spoons), andserving tray. We modified procedures used by Ahearn et al. (2001), with the meal involving one food from each of the fourbasic food groups: peaches (fruit), potato (starch), hot dog (protein), and green beans (vegetable). We selected these foods inconsultation with a registered dietician in order to target the presentation of commonly consumed, age-appropriate foods,while also minimizing the possibility that a child would be unable to participate in the mealtime observation due to dietaryrestrictions [e.g., following a Gluten Free Casein Free (GFCF)]. We presented each food three times at both puree and tabletexture (¼ in. � ¼ in.), with a total of 24 bites during the observation. All bites involved approximately the same volume offood (i.e., about 1 cm3 per bite) and we standardized the order of presentation across items and texture.

The presentation of each bite involved a 4-step prompting sequence (independent; verbal; model; physical). A trial beganwith the presentation of a bite on a plate in front of the child. If the bite was not independently accepted within 5 s, the feederissued the verbal instruction to ‘‘take a bite’’. If the bite was not accepted within 5 s, the feeder modeled taking a bite with aseparate spoon while simultaneously issuing the same verbal instruction. If the child did not accept the bite 5 s following themodel prompt, the feeder placed the child’s hand over the spoon and physically guided the spoon to the child’s mouth whilesimultaneously instructing the child to ‘‘take a bite’’. The child received verbal praise in response to accepting abite (regardless of the step in the prompting sequence). Escape (i.e., removal of the bite of food) was provided in response toany disruptive behavior (e.g., head turning; batting at the plate or spoon; swiping the food or spoon off the table). The feederneutrally redirected the child back to the table in response to elopement between bites, but discontinued this processfollowing significant resistance to return to the table (e.g., aggression, flopping to the floor) for more than two redirections. Insuch cases, the protocol involved presenting bites on the table in front of the child’s seat using the prompting sequence, whileissuing instructions in the direction of the child and removing the bite in response to turning away from the table/food. Abreak of approximately 20 seconds occurred between bites following either acceptance or disruption. An outline of theprotocol is illustrated in Fig. 1.

A caregiver served as the feeder throughout the mealtime observation in order to ameliorate possible separation issues orrelated behavioral concerns that could occur with the introduction of an unfamiliar feeder. Each caregiver completed a brieftraining session before the meal and they were provided with a script outlining movement through the prompting sequencefor reference during the meal. The script also included an introductory paragraph to be read to each child outlining the

Fig. 1. 4-Step prompting sequence + escape during structured mealtime observation.

W.G. Sharp et al. / Research in Autism Spectrum Disorders 7 (2013) 56–65 59

structure of the meal (a copy of this script is available upon request). A trained behavioral observer was positioned in theobservation room to assist the caregiver with the prompting sequence (when necessary), monitor the timing of bitepresentations to ensure proper cadence, and provide immediate feedback for any deviation in the protocol using a wirelesscommunication system (i.e., ‘‘bug in the ear’’). With this support in place, caregiver protocol integrity was high (>90%) for thesample of caregivers enrolled in the study.

1.3. Caregiver questionnaires

1.3.1. Demographic/personal history form

A background questionnaire included items gathering demographic information (e.g., date of birth, gender), as well asinformation regarding feeding concerns/dietary habits and previously diagnosed medical, developmental, or mental healthissues.

1.3.2. Food preference inventory (FPI)

The FPI includes 154 items across seven food categories – 30 fruits, 28 vegetables, 36 proteins, 27starches, 8 dairy, 20miscellaneous/snack (i.e., deserts, fats, and sweets such as cake, cookies, or chips), and 5 combination foods (e.g., lasagna/ravioli, taco/burrito, or soup/stews). A registered dietician reviewed the list and classified foods into each of these categoriesbased on classifications provided by the United States Department of Agriculture. The inventory employs a likert-type scaleassessing preference for consumption (e.g., Never, With Prodding, Willing, Favorite). Respondents were also given the optionof selecting ‘‘N/A’’ if an item was not part of the family’s regular diet or the child lacked exposure/experience with the food.Consistent with previous research (Bandini et al., 2010; Emond, Emmett, Steer, & Golding, 2010) we derived a food selectivityscore by dividing the number of foods a caregiver reported the child ‘‘never’’ consumed by total number of items (154)multiplied by 100. A food selectivity score for each category was derived using a similar formula, with the number of foodsfor a certain food type (e.g., fruit) identified as ‘‘never’’ consumed divided by the total number of items for that category (e.g.,30) multiplied by 100.

1.3.3. Brief autism mealtime behavior inventory (BAMBI; Lukens & Linscheid, 2008)

The BAMBI is a parent report checklist designed to measure the extent of mealtime behavior problems observed inchildren with ASD. The 18 item measure employs a Likert scale for reporting the frequency of behaviors (1 = Never/Rarely to5 = At Almost Every Meal). The scale yields a total score, as well as scores on three subscales (i.e., Limited Variety, FoodRefusal, and Features of Autism). Items on the Limited Variety subscale assess a child’s willingness to try new foods and foodpreference by preparation, texture, or type. The Food Refusal subscale focuses on problem behaviors during meals (e.g.,crying, expelling bite, disruptions during meals). Finally, the Feature of Autism subscale includes items that assessinattention, self-injury, and rigid behavior patterns during meals. The authors reported good internal consistency, high test-retest reliability, and strong construct and criterion-related validity in the initial validation study.

1.3.4. Social Responsiveness Scale (SRS) parent form (Constantino, 2005)

The SRS (parent report form) is a 65-item rating scale measuring severity of ASD symptoms as they occur in natural socialsettings. The instrument yields a total standard score (T-score), as well as T-scores on 5 subscales focusing on socialawareness, social cognition, social communication, social motivation (e.g., anxiety/avoidance), and autistic mannerisms(e.g., preoccupations). Scores from 60 to 75 reflect deficiencies that are clinically significant and lead to mild to moderateinterference in everyday social interactions consistent with mild to ‘‘high functioning’’ autism. The scale has demonstratedadequate reliability and validity (see Booker & Starling, 2011 for a review).

1.4. Mealtime observation variables

1.4.1. Acceptance

Bite acceptance was defined as the mouth opening and the child (or feeder) depositing the entire bite. Acceptance wasconverted to percentages by dividing the total number of accepted bites by the number of total bites presented multiplied by100. Similar to the analysis conducted by Ahearn et al. (2001), the percentage of bites accepted during the meal was alsosummarized to produce a profile of the overall level of acceptance for the sample (i.e., low, moderate, and high), with groupdivided into thirds based on the number of bites accepted. Specifically, a low level of acceptance was defined as accepting 8 orfewer bites; moderate acceptance was defined as accepting 9 to 16, and acceptance of 17 or more bites was considered high

acceptance. Food type and texture was also analyzed to determine the possible pattern of selectivity for the sample. Thisanalysis focused exclusively on children whose intake of bites was variable (i.e., <24 bites and >1 bite).

1.4.2. Disruptions

Disruptions included any response that interfered with a bite presentation, such as head turns (turning the head 45degrees past midline during the presentation of a bite), pushing the spoon, plate, or the feeder’s hand/arm (i.e., from theelbow through the hand) while the feeder presented the bite. Data on disruptions was converted into a percentage bydividing the total occurrence of the behavior during a session by the number of bites presented multiplied by 100.

W.G. Sharp et al. / Research in Autism Spectrum Disorders 7 (2013) 56–6560

1.4.3. Negative vocalizations

Negative vocalization was defined as crying, screaming, whining, swearing, or making negative statements/refusalstatements regarding the food or bite presentation at or above normal conversational tone. The percentage of the sessioninvolving negative vocalizations was calculated by dividing the duration of negative vocalizations by the total meal durationmultiplied by 100.

We video-recorded and a trained behavioral observer scored each tape, collecting data on the operationally definedmealtime observation variables and caregiver protocol integrity on computers using an event-recorder computer program. Asecond member of the research team independently scored 65% of the meal observations, allowing for the calculation ofinterobserver agreement (total agreements by the total agreements plus disagreements multiplied by 100%). The totalinterobserver agreement for the sample was 95% for acceptance (range, 72–100%), 95% for disruptions (range, 73–100%), and98% for negative vocalization (range, 86–100%).

2. Results

Participant demographics and medical characteristics are presented in Table 1. The age-referenced body mass index (BMI)for most participants (63.3%) fell in the normal range, while 16.7% were overweight (85–95th percentile) and 20% were obese(>95th percentile). No participant had a BMI that was considered underweight (<5th percentile), suggesting that participantswere able to maintain (at a minimum) adequate weight for height for their age. Most caregivers (80%) expressed concernsregarding their child’s eating habits in response to the general question: ‘‘Does your child have a problem with feeding?’’. Manyfamilies (40%) reported implementing a special diet to target behavioral concerns, most often in the form of a GFCF diet or use ofnutritional supplements. Consistent with an ASD diagnosis, caregivers indicated that communication and speech were the mostprominent developmental/mental health concern (reported by 73% of the sample). Less common were concerns regardingattention deficit hyperactivity disorder (ADHD) or a learning disorder (LD, 33.3%), followed by anxiety (10%) and mentalretardation (MR, 6.7%). Less than half of the sample (42%) had a history of medical concerns, most often involving a food allergy(20%) followed by gastroesophageal reflux (13.3%), constipation (13.3%), and enteral feedings (6%).

Descriptive statistics from the mealtime observation are presented in Table 2. On average, participants accepted fewerthan half of the bites (40.5%) presented during the meal; although there was high variability across the sample (SD = 37.4;range: 0–100). The percentage of disruptions (43%) was strongly negatively correlated with acceptance (r = �.716, p < .001),which likely reflects the format of the meal involving removal of food in response to refusal behaviors. The average durationof negative vocalizations for the sample was <5% of meals and only 33% of participants (n = 10) exhibited this behavior.Similar to disruptions, bite acceptance and negative vocalizations were negatively correlated at a moderate level (r = �.385;p < .05), with greater crying observed among children who accepted few or no bites.

Table 1

Participant characteristics.

Characteristic M SD Range

Age (in months): 68.7 17.3 36–104

Height (inches): 44.7 4.7 30–53.2

Weight (lbs): 49.9 14 33.1–82.5

Body mass index (BMI) 17.4 3.6 13.7–27.4

n %

BMI-for-age

Normal (5–84%) 19 63.3

Overweight (85–95%) 5 16.7

Obese (>95%) 6 20

Gender: male/female 23/7 77/23

Parent reported feeding concerns 24 80

Parent mediated dietary manipulationa 12 40bGluten free/casein free (GFCF) 9 30bVitamin supplementation 9 30bOther 5 16.7

Developmental/mental health issues 25 81bADHD/LD 10 33.3bAnxiety disorder 3 10bMental retardation 2 6.7bSpeech/language delay 22 73

Medical issues reporteda 13 42bGastroesophageal reflux 4 13.3bFood allergies 6 20bConstipation 4 13.3bPast feeding tube 2 6

a Total breakdown may exceed total number per category due to multiple dietary, medical or developmental issues per participant.b % calculated based on total sample n = 30.

Table 2

Behavioral data from mealtime observation (n = 30).

Variable M SD Range

% Acceptance 41.8 37 0–100

% Negative vocalizations 3.6 9.1 0–29.8

% Disruptions 43.0 33.8 0–100

W.G. Sharp et al. / Research in Autism Spectrum Disorders 7 (2013) 56–65 61

Table 3 presents a more detailed analysis of bite acceptance. Eight participants (26.7%) demonstrated high overallacceptance, with 6 children accepting all bites. Seven children (23.3%) displayed a moderate level of acceptance, and half ofthe sample exhibited low overall acceptance; 8 children accepted no bites. We analyzed patterns of food selectivity by typeand texture, focusing on the 16 participants who accepted between 1 and 23 bites. We examined this subgroup based onthe rationale that participants who accepted all 24 bites exhibited no issues with selectivity during the meal observation,while participants who rejected all bites appeared highly selective/displayed complete refusal, providing no data regardingspecific patterns of preference by type or texture. We conducted an ANOVA comparing mean levels of acceptance by foodtype (combining both puree and table texture bites), adopting the more conservative Greenhouse-Geisser correction. Thisanalysis revealed a significant main effect for the average number of bites accepted across food type [F (3, 60) = 5.05;p < .003; h2

p ¼ :202]. Post hoc analyses (Bonferroni corrected) indicated that hotdog was significantly more likely to beaccepted compared with green beans (p < .001) and potato (p < .05). There was no significant difference between bitesinvolving peach compared with other foods, as well as between green beans and potato.

Analysis of acceptance of bites by texture indicated that table texture bites were significantly more likely to be acceptedcompared with puree presentations (t = 3.6; p = .001; d = 1.3). Table texture hotdog was the most frequently accepted foodduring the meal observation (91.7% of bites accepted), with each of the 16 participants accepting at least 2 bites of this food.Pureed green beans was the least accepted food (10.4% of bites accepted), with only 3 participants consuming green beans atthis texture. Given the notable influence of food texture on consumption based on the observed pattern and past research inthis area (Patel, Piazza, Layer, Coleman, & Swartzwelder, 2005), we compared the mean level of acceptance by food type ateach texture using an ANOVA. For table texture foods, we detected a similar main effect regarding the average number ofbites accepted across food type [F (3, 60) = 7.4; p < .001; h2

p ¼ 2:7], with post hoc analysis indicating hotdog was significantlymore likely to be consumed compared with green beans (p < .001) and potato (p < .001). In contrast, there was no significantdifference in the acceptance of bites by food type at puree texture [F (3, 60) = 1.5; p = 2.3; h2

p ¼ :069], possibly related to thelow level of acceptance across foods at this texture.

Results from the BAMBI and FPI are presented in Table 4. In terms of dietary variety, caregivers identified nearly 40% offoods on the FPI as never consumed, with vegetables identified as the most frequently rejected food. We compared theaverage percentage of foods identified as never consumed across food groups, conducting an ANOVA utilizing the more

Table 3

Breakdown of acceptance and selectivity.

Breakdown of acceptancea n %

High 8 26.7

Moderate 7 23.3

Low 15 50.0

Total participants: 30 100

Analysis of selectivityb M SD Range

% Acceptance by type:Peaches 45.8 36.3 0–100

Hotdog 63.5 20.4 33–100

Potato 30.2 36.6 0–100

Green Bean 23.9 29.8 0–100

% Acceptance by texture:Table 56.8 26.2 25–100

Puree 25.0 23.9 0–75

% Acceptance by type and texturePeaches – Table 63.5 46.9 0–100

Peaches – Puree 29.2 36.3 0–100

Hotdog – Table 91.7 14.9 66.7–100

Hotdog – Puree 35.4 39.4 0–100

Potato – Table 35.4 42.9 0–100

Potato – Puree 25.0 35.5 0–100

Green Bean – Table 37.5 41.9 0–100

Green Bean – Puree 10.4 26.4 0–100a High: n = 6 accepted all bites; low: n = 8 accepted no bites.b Calculation based on the sample of participants who accepted >1 and <24 bites (n = 16).

Table 4

Descriptive statistics for the BAMBI and FPI.

Variable Mean (St. Dev) Range

BAMBI 49.4 (10.8) 18–75

Limited variety 26.7 (6.3) 8–34

Food refusal 11.7 (3.9) 5–21

Autism features 10.9 (3.3) 5–20

FPI

% Never consumed 39.9 (23.2) 3.9–86.4

Breakdown by food

% Fruit 42.2 (30.3) 0–86.7

% Protein 42.1 (25.2) 0–86.1

% Starch 30.6 (22.8) 0–88.8

% Vegetable 61.6 (30.0) 10.7–96.4

Table 5

Pearson correlations (sig. 2-tail) between autism severity, BMI, and feeding measures.

SRS – total BMI BAMBI – total BAMBI – LV BAMBI – FR BAMBI – FA FPI – never Accept Neg Vocs

SRS – total

BMI �.234 (.213)

BAMBI – Total �.194 (.305) .176 (.351)

BAMBI – LV �.223 (.237) .041 (.830) .846** (.000)

BAMBI – FR �.171 (.367) .209 (.267) .764** (.000) .389* (.01)

BAMBI – FA �.003 (.986) .249 (.185) .736** (.000) .381* (.038) .564* (.001)

FPI – never �.205 (.278) .074 (.699) .285 (.126) .560** (.001) �.150 (.430) .034 (.859)

Accept .058 (.757) �.017 (.929) �.101 (.596) �.367* (.046) .078 (.682) .280 (.134) �.619** (.000)

Neg Vocs .088 (.643) �.090 (.635) .237 (.208) .396* (.030) .133 (.485) �142 (.453) .408* (.025) �.385 (.035)

Disruptions �.164 (.387) .169 (.373) .056 (.770) .282 (.131) �.083 (.664) �.259 (.166) .414* (.023) �.716 (.000) .380 *(.038)

Neg Vocs: negative vocalizations.

* p < .05.

** p < .005.

W.G. Sharp et al. / Research in Autism Spectrum Disorders 7 (2013) 56–6562

conservative Greenhouse-Geisser correction during the analysis. Findings indicated a significant main effect for food group, F

(3, 116) = 6.4, p < .001, h2p ¼ :157. We used the Bonferroni correction to explore main effects during post hoc analysis, which

indicated vegetables were significantly more likely (p < .001) to be identified as never consumed compared with all otherfood groups. No other difference between food groups was detected.

The BAMBI Limited Variety subscale and the percentage of foods identified as never consumed on the FPI were stronglyand positively associated, r = .560, p < .01, suggesting that these measures tap into a similar construct (i.e., restricted dietaryintake or food selectivity). Both of these measures were also associated with maladaptive feeding behaviors during themealtime observation. The Limited Variety subscale was negatively correlated with the number of bites accepted, r = �367,p < .05, and positively correlated with negative vocalizations during the meal, r = .396, p < .05, both at moderate levels. Thepercentage of foods identified as never consumed on the FPI was strongly and negatively associated with the number of bitesaccepted, r = �.619, p < .001, as well as moderately and positively associated with negative vocalizations, r = .408, p = .05, anddisruptions, r = .414, p < .05, during the meal.

Correlations between autism severity scores on the SRS, growth status as captured by BMI, and feeding measures arepresented in Table 5. The analysis indicated no association between degree of autism severity, feeding behaviors and/ordegree of food selectivity; a finding consistent with previous research in this area (Schreck & Williams, 2006). There was alsono association between feeding measures and participants’ growth status. It is also noteworthy that the BAMBI Features ofAutism subscale was unrelated to SRS total score or any subscales of the SRS (e.g., Autism Mannerisms), suggesting that theneed to further investigate whether this factor captures characteristics and associated features of autism as first reported byLukens and Linscheid (2008). In regard to correlations among BAMBI subscales, the Limited Variety factor was positivelycorrelated with both the Food Refusal, r = .389 (p < .05), and the Features of Autism subscales, r = .381 (p < 05), while theFood Refusal subscale was positively correlated with the Features of Autism subscale, r = .564 (p < .01). This pattern isconsistent with subscale correlations reported in the initial validation of the BAMBI.

3. Discussion

The current study expands the research base regarding the assessment of feeding problems in children with ASD in anumber of key areas. Most notably, this represents the first attempt to assess the correspondence between direct observationand parent-report measures of feeding concerns and dietary intake. Findings suggest indirect measures of food selectivity, ascaptured by the BAMBI’s Limited Variety subscale and the number of foods identified as never consumed on the FPI, arenegatively associated with a child’s acceptance of bites and positively associated with disruptions during the presentation of

W.G. Sharp et al. / Research in Autism Spectrum Disorders 7 (2013) 56–65 63

foods during a structured mealtime observation. This relationship has important implications for guiding the selection ofassessment methods for clinical and research purposes based on a number of important considerations. As the traditionallyviewed ‘‘gold standard’’ for assessment, behavioral observation will continue to play an important part in any detailedassessment of feeding concerns among children with ASD. The process of conducting a behavioral observation during meals,however, is complicated by a number of factors, most notably the time requirement, questions regarding meal formatting(e.g., foods, texture, feeders, bite size) and the possibility of eliciting strong emotional responses during the assessmentprocess (described in more detail below). In addition, there is little evidence to assure that clinic-based observationscorrespond with behaviors that children exhibit in their daily home environments. As such, the use of behavioral observationas the first-line screening method may be neither feasible nor appropriate across pediatric settings.

With the current results indicating measures of food selectivity correspond to behavior during a structured meal, the useof the BAMBI or FPI may hold certain benefits in terms of ease of administration, respondent burden, and length, pendingmore comprehensive validation. The selection of a measure to include in the assessment and screening process may beguided by pragmatic and clinical considerations. For example, the relative brevity of the BAMBI in comparison to the FPI(18 items vs. 154 items, respectively) suggest that it may more readily lend itself for use as an initial screening for feedingconcerns among children with ASD. In addition, the items of the BAMBI, assessing food variety, behavioral concerns, andautism symptoms may also be more sensitive to detecting proximate changes in response to intervention not captured by abroad tally of rejected foods, such as a child’s willingness to try new foods or remaining seated during meals. Alternatively,the FPI may be utilized when a more detailed analysis of dietary patterns is warranted or longer-term shifts in dietarypatterns are an anticipated outcome of clinical or research efforts.

Findings also provide further detail regarding the BAMBI as a potential tool for measuring mealtime difficulties amongchildren with ASD, including data on two key limitations cited by Lukens and Linscheid (2008) in the initial validation study(i.e., the use of a confirmatory measure of ASD status and the comparison of scores to behavioral data obtained through directobservation). As noted above, the Limited Variety subscale may provide the most clinically salient data from the BAMBI whenassessing feeding problems in this population, with this subscale associated with bite acceptance and problem behaviorsduring the meal. We did not find a significant relationship between ASD symptoms and the BAMBI Autism Features subscaleand it is unclear why the Food Refusal subscale was unrelated to problem behaviors during the meal observation, suggestingthe need to further explore the psychometric properties of this instrument. Together, the available data suggests the BAMBImay serve best as a screening instrument for food selectivity in clinical setting, but not replace the utility of behavioral datato assess challenging behaviors during mealtime.

This study also represents one of the few descriptions of a structured mealtime observation (involving a set promptingsequence, standard bite size and pre-selected target foods) utilizing caregivers as the primary feeder available in the assessmentliterature. Outcomes from the meal observation paralleled results reported by Ahearn et al. (2001), with the majority (73%) ofparticipants demonstrating low to moderate food acceptance, with both food type and texture playing a role in bite acceptanceduring the meal. This suggests that a structured mealtime observation, as outlined in both the current study and by Ahearn et al.,represents a viable tool for clinicians and researchers interested in assessing feeding behaviors in children in ASD. There are anumber of aspects of the protocol used in the current study, which should be considered when designing future studiesinvolving a structured meal observation. We used an escape baseline condition during the meal observation, which may notprovide a complete representation of a child’s behavior during the presentation of clinic foods. With caregivers instructed toremove a bite in response to refusal behaviors, data were unavailable regarding how a child would respond if the feederpersisted with a bite presentation (which is likely to occur at some point during meals with many families). With this in mind,the finding that many children displayed high rates of problem behaviors, including pushing the food away and crying, withnominal demands for consumption further solidifies the importance of research and intervention in this area.

The type of food presented may have also influenced outcomes during the meal observation. In contrast to Ahearn et al.(2001), we selected one food (vs. three) from each food group to present during the meal. The lack of multiple food itemsfrom each food group limits conclusions that can be drawn regarding specific patterns of selectivity. For example, tabletexture hot dog was the most frequently accepted food during the meal, while pureed green beans were the least acceptedbites. This pattern is consistent with previous reports describing children with ASD as preferring dense, processed foods,while rejecting vegetables (Field et al., 2003; Lukens & Linscheid, 2008); however, it is inconsistent with reports of childrenwith ASD preferring carbohydrates and starches, while rejecting proteins. Expanding the number of food items presentedduring the meal, as well as including proteins that are not highly processed (e.g., grilled chicken or fish; beans) and starchesmore often associated with the diet of children with ASD (e.g., French fries; chips or pretzels) may yield a more consistentpicture of food preference among children with ASD. In addition, varying the structure of mealtime observations (e.g., parentdirected vs. therapist; increased food types and textures; home vs. clinic setting), as well as assessing the impact of textureand bite size modifications, would help identify the optimal means for capturing behavioral data. For example, presentingvery small bites or pureed textured foods on a spoon (particularly for older children) may have influenced rates of acceptanceor disruptions due to the likely novelty of this presentation method. In doing so, it will be necessary to continue to includealternative measures of dietary diversity (e.g., FFI; 24 h recall) in order to develop a more comprehensive, population levelprofile of the pattern of food selectivity associated with ASD, as it remains unclear if a mealtime observation is the optimalmeans to assess food variety given the possibility of idiosyncratic dietary preference in ASD. Throughout this process, it willbe important to evaluate each of these factors in terms of the balance between complexity, efficiency and incrementalvalidity of feeding measures.

W.G. Sharp et al. / Research in Autism Spectrum Disorders 7 (2013) 56–6564

It is noteworthy that ASD symptom severity was unrelated to feeding measures, highlighting remaining questionsregarding the relationship between ASD, food selectivity, and whether restricted patterns of intake are unique to thispopulations, as first raised by Williams, Gibbons, & Schreck, 2005. Past research in this area has produced conflicting results.Similar to the results of the current study, Schreck and Williams (2006) reported that scores on the Gilliam Autism RatingScale (GARS, Gilliam, 1995) overall autism quotient was unrelated to dietary diversity, although children with more selectivediets tended to live in families with less diverse diets. Lukens and Linscheid (2008), however, reported that certain mealtimebehaviors, including rigid and repetitive behavior, abnormal responses to sensory input, and short attention span, appearedunique to children with ASD compared to typically developing peers, resulting in the Features of Autism subscale beingincluded as part of the Brief Autism Mealtime Behavior Inventory (BAMBI). In addition, the Features of Autism subscale wasstrongly and positively correlated with scores on the GARS, and it was positively correlated with the Limited Varietysubscale, which assesses a child’s willingness to try new foods and food preference by preparation, texture, or type (higherscores reflecting more difficulty).

Results also highlight the importance of future research to determine the impact of aberrant feeding patterns on growth,development, and nutrition beyond anthropometric parameters in this population. Data from the current study suggests thatatypical patterns of intake may not necessarily translate to compromised gross anthropometric parameters (i.e., height,weight, and body-mass index [BMI]), which is consistent with previous research analyzing growth status in ASD (Bandiniet al., 2010; Emond et al., 2010). Provisional evidence, however, also suggests that feeding problems in ASD may translateinto other indicators of health status, such as documented deficits in micronutrient intake (Emond et al., 2010) and poor bonegrowth (Hediger et al., 2008), which may increase the risk of long-term, chronic diet related diseases among the ASDpopulation. Clearly, additional research is needed to determine the relationship between ASD symptomatology, feedingbehaviors, and health status, which will necessitate more detailed diagnostic characterization of ASD samples and increasedstandardization in the measurement of feeding concerns.

Finally, increasing the sample size and including a comparison group in future studies will be critical to determineprevalence rates and aide in the development of normative criteria for identifying feeding problems across assessmentmethods. While providing preliminary data regarding the relationship between feeding measures, as well as feasibility of amulti-method assessment battery, the small sample size and lack of comparison control likely limits the generalizability offinding, highlighting the need to replicate these procedures with larger cohorts of ASD and typically developing peers.Expanding the range and number of ASD and non-ASD participants will also help attenuate concerns regarding rangerestriction detected in the current correlation analysis of ASD symptoms and feeding problems, with most SRS scoresclustering in the high/severe range.

4. Conclusion

Findings from the current study are consistent with previous descriptions of children with ASD as exhibiting preferencesfor certain foods and displaying strong behavioral responses when presented with non-preferred food (Bowers, 2002; Collinset al., 2003; Field et al., 2003). Data from both direct and indirect assessment methods indicated high rates of food rejection,most notably vegetables. With growing research supporting the use of behavioral intervention as an effective means toexpand dietary variety among children with ASD (Laud, Girolami, Boscoe, & Gulotta, 2010; Sharp, Jaquess, Morton, & Miles,2011), future research is needed to further refine assessment methods in order to increase the identification of feedingproblems in this population. Increased standardization of measurement is essential for expanding our knowledge regardingthe feeding problems in ASD while also strengthening conclusions from intervention studies targeting mealtime concerns inthis population.

Acknowledgements

This project was funded by a 2008 Applied Research Grant sponsored by the Organization for Autism Research.

References

Ahearn, W. H., Castine, T., Nault, K., & Green, G. (2001). An assessment of food acceptance in children with autism or pervasive developmental disorder—nototherwise specified. Journal of Autism and Developmental Disorders, 31, 505–511.

Bandini, L. G., Anderson, S. E., Curtin, C., Cermak, S., Evans, E. W., Scampini, R., et al. (2010). Food selectivity in children with autism spectrum disorders andtypically developing children. The Journal of Pediatrics, 157(2), 259–264.

Booker, K. W., & Starling, L. (2011). Review of ‘Social Responsiveness Scale (SRS)’. Assessment for Effective Intervention, 36(3), 192–194.Bowers, L. (2002). An audit of referrals of children with autistic spectrum disorder to the dietetic Service. Journal of Human Nutrition and Dietetics, 15, 141–144.Cermak, S. A., Curtin, C., & Bandini, L. G. (2010). Food selectivity and sensory sensitivity in children with autism spectrum disorders. Journal of the American Dietetic

Association, 110(1), 259–264.Cialdella, P., & Mamelle, N. (1989). An epidemiological study of infantile autism in a French department (Rhone): A research note. Journal of Child Psychology and

Psychiatry, 30, 165–175.Collins, M. S., Kyle, R., Smith, S., Laverty, A., Roberts, S., & Eaton-Evans, J. (2003). Coping with the unusual family diet: Eating behavior and food choices of children

with Down’s syndrome, autistic spectrum disorders or chi du chat syndrome and comparison groups of siblings. Journal of Learning Disabilities, 7, 137–155.Constantino, J. N. (2005). The Social Responsiveness Scale (SRS). Los Angeles, CA: Western Psychological Services.Emond, A., Emmett, P., Steer, C., & Golding, J. (2010). Feeding symptoms, dietary patterns, and growth in young children with autism spectrum disorders.

Pediatrics, 126(2), 337–342.

W.G. Sharp et al. / Research in Autism Spectrum Disorders 7 (2013) 56–65 65

Field, D., Garland, M., & Williams, K. (2003). Correlates of specific childhood feeding problems. Journal of Pediatrics and Child Health, 39, 299–304.Gilliam, J. E. (1995). Gilliam Autism Rating Scale. Austin, TX: Pro-ed.Hediger, M. L., England, L. J., Molly, C. A., Yu, K. F., Manning-Courtney, P., & Mills, J. L. (2008). Reduced bone cortical thickness in boys with autism or autism

spectrum disorder. Journal of Autism and Developmental Disorders, 38, 848–856.Laud, R. B., Girolami, P. A., Boscoe, J. H., & Gulotta, C. S. (2010). Treatment outcomes for severe feeding problems in children with autism spectrum disorder.

Behavior Modification, 33, 520–536.Ledford, J. R., & Gast, D. L. (2006). Feeding problems in children with autism spectrum disorders: A review. Focus on Autism and Other Developmental Disabilities, 21,

153–166.Lukens, C. T., & Linscheid, T. R. (2008). Development and validation of an inventory to assess mealtime behavior problems in children with autism. Journal of Autism

and Developmental Disorders, 38, 342–352.Matson, J. L., & Fodstad, J. C. (2009). The treatment of food selectivity and other feeding problems in children with autism spectrum disorders. Research in Autism

Spectrum Disorders, 3(2), 455–461.Munk, D. D., & Repp, A. C. (1994). Behavioral assessment of feeding problems in individuals with severe disabilities. Journal of Applied Behavior Analysis, 27,

241–250.Patel, M. R., Piazza, C. C., Layer, S. A., Coleman, R., & Swartzwelder, D. M. (2005). A systematic evaluation of food textures to decrease packing and increase oral

intake in children with pediatric feeding disorders. Journal of Applied Behavior Analysis, 38(1), 89–100.Paul, C., Williams, K., Riegel, K., & Gibbons, B. (2007). Combining repeated taster exposure and escape prevention: An intervention for the treatment of extreme

food selectivity. Appetite, 49, 708–711.Pizzo, B., Williams, K., Paul, C., & Riegel, K. (2009). Jump start exit criterion: Exploring a new model of service delivery for the treatment of childhood feeding

problems. Behavioral Interventions, 24, 185–203.Schreck, K. A., & Williams, K. (2006). Food preferences and factors influencing food selectivity for children with autism spectrum disorders. Research in

Developmental Disabilities, 27, 353–363.Schreck, K. A., Williams, K., & Smith, A. F. (2004). A comparison of eating behavior between children with and without autism. Journal of Autism and Developmental

Disabilities, 34, 433–438.Seiverling, L., Williams, K., & Sturmey, P. (2010). Assessment of feeding problems in children with autism spectrum disorders. Journal of Developmental and Physical

Disabilities, 22, 401–413.Sharp, W. G., Jaquess, D. L., Morton, J. S., & Herzinger, C. (2011). Pediatric feeding disorders: A quantitative synthesis of treatment outcomes. Clinical Child and

Family Psychology Review, 13, 348–365.Sharp, W. G., Jaquess, D. L., Morton, J. F., & Miles, A. G. (2011). A retrospective chart review of dietary diversity and feeding behavior of children with autism

spectrum disorder before and after admission to a day treatment program. Focus on Autism and Other Developmental Disabilities, 26, 37–48.Willett, W. (1998). Food-frequency methods. In W. Willett (Ed.), Nutritional epidemiology (2nd ed., pp. 50–57). New York: Oxford University Press.Williams, K. E., Gibbons, B. G., & Schreck, K. A. (2005). Comparing selective eaters with and without developmental disabilities. Journal of Developmental and

Physical Disabilities, 17, 299–309.Zimmer, M. H., Hart, L. C., Manning-Courtney, P., Murray, D. S., Bing, N. M., & Summer, S. (2011). Food variety as predictor of nutritional status among children with

autism. Journal of Autism and Developmental Disorders. Online First.