Parent Teleconsultation to Increase Bites Consumed: A Demonstration Across Foods for a Child With ARFID and ASD

Abstract

Children with autism spectrum disorder (ASD) experience feeding dysfunction at a substantially higher proportion than their neurotypical peers. Feeding concerns can provide considerable challenges for parents, and as such, helping parents of children with ASD provide effective mealtime interventions for interfering behavior is critical, especially if parents have individual circumstances that affect their ability to effectively implement these feeding interventions. This study contributes to the parent-implemented feeding-intervention literature by demonstrating that a parent with ASD can implement a pediatric feeding intervention in the home with their child with ASD, despite contributing mental health factors. To address family needs, we developed a socially valid and individualized intervention, which we delivered over telehealth. The intervention resulted in an increase in the consumption of previously nonpreferred foods, while the caregiver maintained adequate levels of procedural fidelity. Practical considerations and implications are discussed.

It is well established that youth with autism spectrum disorder (ASD) frequently exhibit food selectivity, with symptoms including food refusal, limited food variety, and high-frequency food intake (Matson & Fodstad, 2009). Children who do not consume adequate amounts of calories and nutrients are at increased risk for nutrient deficiencies and developmental delays (Volkert & Piazza, 2012). Food selectivity is often associated with behavioral problems during mealtime and increases stress for parents and caregivers (Greer et al., 2008). Recommendations include early treatment for even mild food selectivity.

Numerous studies indicate that behavioral interventions within a treatment package are effective to increase intake (Sharp et al., 2012; Volkert & Piazza, 2012; Williams & Seiverling, 2010), using techniques such as positive reinforcement (Williams & Seiverling, 2010), escape extinction (Sharp et al., 2012), and differential reinforcement of alternative behavior (DRA; Valdimarsdóttir et al., 2010). DRA has been shown to be effective in treating pediatric feeding disorders where, for example, a reinforcer is presented contingent on mouth clean, a measure of swallowing (Berth et al., 2019). However, the translation of behavioral interventions into the real world is often complex due to difficulties generalizing from the clinic to the home setting, the change in implementer from clinician to parent or caregiver, the need to adjust procedures as the intervention progresses, and errors made during implementation (Miller et al., 2018).

In addition to identifying a treatment package that is effective for the client, clinicians must consider parent acceptability as a part of their decision-making process (Vazquez et al., 2019). Considering that social validity is a foundation of the work of behavior analysts (Wolf, 1978), parent buy-in is critical to implementation effectiveness for interventions that are primarily parent delivered. Vazquez et al. (2019) conducted a survey of parents of children with ASD and other developmental disabilities to assess the acceptability of various behavioral intervention components for the treatment of pediatric feeding disorders. Across parent respondents, parents preferred a differential reinforcement procedure to other interventions, especially escape extinction. Although escape extinction is a highly effective intervention for the treatment of pediatric feeding disorders (e.g., Sharp et al., 2012), intervention alternatives may need to be considered for home-based or parent-implemented interventions.

For example, Penrod et al. (2012) implemented a high-probability request sequence with demand fading to increase the consumption of nonpreferred foods in the absence of escape extinction. The researchers systematically reinforced an increasing hierarchy of feeding demands (e.g., “touch the food,” “kiss the food,” “lick the food”) in which increasing levels of feeding demands approximated the chewing and swallowing of nonpreferred foods (Penrod et al., 2012). Both participants increased the rate of consumption of the nonpreferred target foods. Similarly, Koegel et al. (2012) evaluated systematic hierarchical exposure to nonpreferred foods where compliance with increasing feeding demands was reinforced. All participants increased the number of foods accepted from baseline to follow-up.

Parent Training

Parent behaviors and additional environmental conditions can contribute to the development and maintenance of pediatric feeding disorders, such as structuring mealtime poorly, presenting developmentally inappropriate foods, serving preferred foods in response to refusal behaviors, and modeling inappropriate feeding behavior (Mitchell et al., 2013). Descriptive analyses of environmental factors that contribute to inappropriate mealtime behavior (i.e., food refusal) indicate that caretakers often provide attention or escape from bite presentation (Borrero et al., 2010; Piazza et al., 2003). Consequently, parent training has become increasingly common as a component of pediatric feeding treatment to increase generalization and to contribute to maintained gains in the home setting.

Parent training for feeding interventions has demonstrated favorable outcomes with regard to implementation feasibility and adequate parent satisfaction (Johnson et al., 2019; Sharp et al., 2014). Several behaviorally based parent-training interventions to address feeding problems associated with ASD have been developed and tested in randomized controlled trials. For example, the Autism MEAL Plan, a parent training for children with ASD and feeding disorders, indicated positive outcomes including improved feeding behaviors and intervention feasibility (Sharp et al., 2014). Similarly, an initial randomized controlled trial with 21 families who participated in parent training for feeding problems of young children (ages 2–7 years 11 months) with ASD, which included behavioral strategies and nutrition education for 11 sessions over 20 weeks, indicated decreases in children’s disruptive behaviors during meals, high parent satisfaction ratings, and evidence of feasibility (Johnson et al., 2019).

In the current study, both the child participant and the parent who implemented the feeding intervention had ASD. Previous research suggests that adults with ASD have been successfully trained to implement behavior intervention programs for children with ASD, yet limited reports have demonstrated parent training with the parent reporting an ASD diagnosis. Lerman et al. (2015) demonstrated that a behavioral skills training (BST) approach can be successful in teaching adults with ASD and no intellectual disability the skills necessary to implement a discrete-trial training program across multiple targets. Although the skills taught to the adult participants were specific to the child participant’s program, the adults demonstrated proficiency in managing the child’s challenging behavior while implementing differential reinforcement, prompting hierarchies, and data collection procedures. In this study, the adult–child dyad shared no familial relation (Lerman et al., 2015).

Telehealth Applications

Despite the need for feeding services and the efficacy of parent-training approaches, many families encounter barriers to accessing services, including inability to travel to appointments and extensive distance from qualified service providers (Clark et al., 2019; McGrath et al., 2006; Silverman, 2010). Considering that feeding interventions typically require multiple appointments, telehealth may be an acceptable and effective delivery mode to meet the various needs of some families. Telehealth involves the delivery of health-related services using virtual means and may encompass various service-delivery models. For example, telehealth may use live video feeds between the provider and participant (e.g., videoconferencing) or asynchronous service delivery (e.g., email, store-and-forward technology, delayed feedback). Specific ethical and legal guidelines regarding the use of telehealth in practice are available for psychologists (e.g., Turvey et al., 2013; Joint Task Force for the Development of Telepsychology Guidelines for Psychologists, 2013).

The use of telehealth is becoming more common in a variety of fields such as medicine (e.g., Myers & Cain, 2008), education (e.g., Bloomfield et al., 2018), and behavior analysis (Machalicek et al., 2016). Recently, this approach was demonstrated in an interdisciplinary pediatric feeding disorders clinic where services are enhanced with the use of telehealth (Clark et al., 2019). The use of a telehealth-enhanced model in the assessment and treatment of pediatric feeding disorders demonstrated savings in travel time and costs for families. In another demonstration, Bloomfield et al. (2019) used videoconferencing and remote consultation to support parent implementation of a behavioral feeding intervention. The parent was trained to systematically increase the volume and variety of foods consumed by an 8-year-old child with avoidant/restrictive food intake disorder (ARFID). As a result, the child successfully increased their consumption to match the feeding demand placed by the parent. This intervention was rated as highly acceptable by the parent within the home environment. Still, little is known about the use of telehealth with parent-implemented interventions with pediatric feeding disorders, particularly with children (and parents) with ASD.

Purpose

This study contributes to the feeding literature by demonstrating the implementation of a pediatric feeding intervention with a child with ASD delivered by a parent with ASD in the home environment. To address family needs, the intervention was developed and evaluated over telehealth. The purpose of this study was to examine the effectiveness of a parent-implemented demand-fading and differential reinforcement intervention with a child with ASD delivered via telehealth within the home environment. The following research question was addressed: Is there a functional relation between a parent-provided feeding intervention and an increase in the level of compliance with the feeding demand?

Method

Participants

The participants included a child with ARFID and ASD, her parent, and the therapist. Prior to beginning the treatment portion of the study, the child and her parent attended an interdisciplinary pediatric feeding disorders assessment through the clinic in which the services were provided. This assessment included a comprehensive battery of interviews, questionnaires, and direct observation of feeding behaviors and interactions. The interdisciplinary assessment team consisted of a speech-language pathologist, occupational therapist, nutritionist, and behavioral health provider. The interdisciplinary team gathered information on the family diet, medical history, mealtime behavior, and family goals for the child’s feeding behavior. The in-clinic observation and evaluation assessed the child’s oral motor skills and the safety of the child’s swallow. Additionally, the behavioral health provider assessed mealtime behavior during a simulated mealtime session. Please refer to Clark et al. (2019) for additional information on the model for a telehealth-enhanced interdisciplinary feeding disorders clinic.

Child

Jennifer’s pediatrician (who was affiliated with the feeding clinic through a broader university integrated health clinic) referred her for feeding services due to her restricted diet and resulting interference in family mealtimes. Her pediatrician reported that feeding concerns caused significant interference in participating fully in social interactions that impact school and home mealtimes. Jennifer was a 5-year-old White female with ASD, and her parent reported she was a “picky eater.” Jennifer reportedly ate a limited variety of foods. According to a survey of foods typically consumed and a 3-day food record completed by her parent, Jennifer was consuming proteins such as chicken nuggets, fish sticks, and hot dogs, and starches such as white bread, French fries, plain noodles, macaroni and cheese, and pancakes. She would frequently consume snacks such as chips, pretzels, and crackers. Jennifer’s fruit intake was predominantly in the form of apple juice, orange juice, and applesauce with a smooth texture. She did not consume any vegetables prior to treatment. She was consuming significantly below the recommended daily level of vegetables and fiber. Jennifer reportedly pushed away nonpreferred foods, moved away from the table or room where nonpreferred food items were presented, and screamed when presented with nonpreferred food items. Jennifer often referred to nonpreferred food as “disgusting.” The interdisciplinary team determined that Jennifer could safely consume natural-texture foods orally. The parent selected the following foods to target during intervention: dried pineapple, banana chips, dried apricots, dried apples, and a turkey-and-cheese sandwich square (hereafter referred to as a “sandwich square”).

Parent

The parent was a 40-year-old White female who self-reported a diagnosis of ASD and generalized anxiety disorder, which she reported impacted her ability to make frequent in-clinic appointments. As such, the clinical team identified in-home telehealth services to provide parent consultation and feeding treatment. The parent was the feeder throughout all sessions.

Therapist

The therapist was a 2nd-year graduate student in a speech and language pathology master’s program and was supervised by a Board Certified Behavior Analyst with training in pediatric feeding disorders. The therapist previously had experience implementing behavior interventions as a behavior technician at a university-based early intervention program. The therapist received training in pediatric feeding disorders from Bloomfield and Clark using a BST approach.

The therapist participated in a structured BST format for feeding skills acquisition including the following intervention components: sequential presentation, differential reinforcement, shaping, and response prompting hierarchies. Initially, the therapist took coursework targeting behavioral interventions and consultation skills to support families. Additionally, the therapist participated in an online learning module for common intervention skills, which was required for onboarding in the feeding clinic. After both didactic trainings, the therapist discussed content with the feeding clinic supervisor. Next, the therapist watched videos of an advanced therapist providing similar behavioral feeding interventions and discussed those videos with the supervisor. Finally, the therapist cotreated sessions with previously trained graduate therapists and received performance feedback on the implementation of the intervention from the feeding clinic supervisor. Throughout implementation, the therapist received performance feedback from the second author, including behavior-specific praise and corrective feedback, both vocally and in writing, following sessions.

Setting

The study occurred in two settings: the interdisciplinary pediatric feeding disorders clinic and the family’s home. The feeding clinic was located within a university hospital system’s integrated medical home program for individuals with ASD and developmental disabilities. Within these settings were small, square therapy rooms in which the therapist conducted teleconsultation sessions. These rooms were well lit through ceiling lighting. The therapist sat at the table with a computer positioned in front of her with a blank taupe wall behind her. The family’s home was located 15.1 km away from the feeding clinic, and most sessions in the home occurred at the kitchen table.

Materials

Hardware and Software

The therapist used a laptop with a built-in camera and microphone to conduct all telehealth sessions remotely. In addition, the therapist used headphones to reduce echo during each session. The parent used a tablet, a smartphone, and a laptop with a webcam and microphone to conduct sessions, collect data, and share data with the therapist. The parent used her laptop and tablet for all telehealth sessions, and she used all three devices to share data with the therapist throughout the week. Although there were different hardware platforms used throughout the study, the therapist reported no observable differences between platforms in session. The telehealth sessions were conducted using Vidyo (https://www.vidyo.com), telehealth software that is compliant with the Health Insurance Portability and Accountability Act (HIPAA, 2007) and that has end-to-end encryption. Videos from telehealth treatment sessions were recorded using the Vidyo platform, downloaded, and stored on a HIPAA-compliant, encrypted, cloud-based storage platform, Box (www.box.com). The parent sent data collected to the therapist using email or text messages, and the therapist then stored the data from the parent within the same secure cloud-based storage platform as the video files. To ensure the protection of all identifiable participant information, the therapist instructed the parent not to include her child’s name or any identifiable information in the email or text message attachments.

Intervention Materials

The parent prepared plates of the target foods before she began the sessions. She also prepared the reinforcers (tangible or edible) beforehand and placed them in containers on the table. The therapist conducted a semistructured preference assessment interview to create an inventory of various tangible and edible items that Jennifer preferred. For each session, the parent had Jennifer select two or three items from that array to be used during the session. Both the parent and the therapist had a paper data sheet, which was set up in a trial-by-trial arrangement to record data during sessions. On the data sheet, the parent recorded the frequency Jennifer engaged in the target behavior for each trial, as well as the criteria required to access the reinforcer. If the number of the target behavior was less than the criteria required, Jennifer did not access the reinforcer. (A copy of the data sheet is available upon request.)

Experimental Design and Data Analysis

This study used a single-case research design methodology. Specifically, a series of changing-criterion designs was conducted across approximations of target food consumption behaviors (see Table 1 for a description of the levels of approximation). A changing-criterion design uses stepwise manipulations in a dimension of the target behavior (i.e., frequency of compliance with the demand; Klein et al., 2017). We maintained experimental control within this design by repeatedly replicating stable responding at each step of the criteria prior to changing to the next criterion. Although various recommendations have been proposed, a minimum of three criterion shifts is suggested to demonstrate sufficient experimental control (Kratochwill et al., 2013). This design is particularly useful for demonstrating systematic changes in the frequency of the target behavior where the desired change is a gradual shift toward the terminal goal, such as increasing consumption (e.g., Bloomfield et al., 2018) or decreasing smoking (e.g., Belles & Bradlyn, 1987). The observational data were then transferred from paper data sheets to a spreadsheet in Microsoft Excel. Using the graphing function in Microsoft Excel, the therapist produced a line graph for each food. The primary method of data analysis was visual analysis.

Table 1.

Hierarchy of Feeding Demand Levels and Feeding Demand Descriptions

Level	Prompt	Description
A	Touch	The child extends their hand and one or more fingers and makes contact with the target food item for 1 s or longer.
B	Hold	The child extends their hand and holds the target food item in their hand for 1 s or longer. The food item may be held using a pincer grasp with two or more fingers or placed in the palm of the hand.
C	Kiss	The child extends their hand to the target food, picks up the target food in their hand, and brings the item to the plane of the mouth. The food makes contact with the lips for 1 s or longer.
D	Lick	The child extends their hand to the target food, picks up the target food in their hand, and brings the item to the plane of the mouth. The food crosses the plane of the mouth and makes contact with the tongue for 1 s or longer.
E	Bite	The child extends their hand to the target food, picks up the target food in their hand, and brings the item to the plane of the mouth. The food crosses the plane of the mouth, and the teeth close around the food. The child may expel the item following completion.
F	Consume	The child extends their hand to the target food, picks up the target food in their hand, and brings the item to the plane of the mouth. The food crosses the plane of the mouth, the teeth close around the food, and the child chews the food. Once the food is fully masticated, the child will swallow the bolus. Success is defined as less than a pea-sized bolus remaining in the mouth.

Response Measurement

The primary dependent variable was the frequency of compliance with the target response within 10 s of a vocal or model prompt. The target response was the completion of a designated step of a self-feeding task analysis. The six steps of the task analysis of self-feeding included the following: (a) touch the food with a finger for 1 s; (b) pick up the food and sustain placement in the hand for 1 s; (c) bring the food to the mouth and contact the lips for 1 s; (d) bring the food to the mouth and make contact with the tongue for 1 s (the food can remain in the participant’s grasp); (e) bring the food to the mouth and deposit the item in the mouth for 1 s; and (f) bring the food to the mouth, deposit the item in the mouth, then masticate and swallow the entire bolus. See Table 1 for the hierarchy of food consumption target behaviors. The parent used the data sheet provided to her by making plus signs to indicate a completed trial and a checkmark to indicate she completed the target number of trials with each food. The parent sent the therapist a picture of her data after the session to assure data continued to be taken accurately.

Interobserver Agreement

During each telehealth session, the parent and the therapist collected data simultaneously. The research team completed trial-by-trial interobserver agreement (IOA) for 20% of the total sessions and had high levels of agreement (range 87.18%–95.45%) across foods. Specifically, dried pineapple had IOA of 94.87%, dried apple had IOA of 87.5%, dried apricot had IOA of 87.18%, the sandwich square had IOA of 95.45%, and the banana chip had IOA of 88.89%. The sessions that occurred outside of the live teleconsultation sessions do not have IOA data available due to our inability to record sessions.

Procedural Integrity

Procedural integrity was defined as delivering the demand, delivering the reinforcer (praise), and writing the data on the data sheet. Each trial over video was coded for the occurrence or nonoccurrence of each step in the procedural integrity checklist. By trial, procedural integrity for delivering the target demand (M = 88.35%), delivering the reinforcer (praise; M = 61.35%), and writing data on the data sheet (M = 73.62%) was variable. Overall, trial-by-trial procedural integrity was highly variable (M = 74.44%, range 0%–100%). The average procedural integrity per session was adequate, yet highly variable (M = 80.09%, range 38.89%–100%). Procedural integrity for delivering the target demand (M = 90.58%, range 50%–100%), delivering the reinforcer (praise; M = 67.89%, range 33.33%–100%), and writing data on the data sheet (M = 81.82%, range 0%–100%) was variable.

IOA on procedural integrity was conducted on 100% of telehealth sessions (e.g., approximately 20% of all session data) by a graduate student. IOA on delivering the demand (M = 84.05%) and delivering the reinforcer (praise; M = 73.01%) was moderately acceptable. IOA was not collected on the third element, writing data on the data sheet, due to limitations associated with the parent’s comfort with video recordings. The accuracy of the written data was captured through high rates of IOA between the parent and therapist for the dependent variable.

Parent Training

The therapist provided initial training to the parent but did not train to competency due to parent concerns with delaying treatment, which the therapist was concerned could jeopardize parent buy-in and subsequent implementation. The parent explicitly requested ongoing performance feedback rather than a train-to-mastery approach. First, the therapist conducted didactic training consisting of a description of the procedures, data collection, and implementation of the intervention with the parent. The therapist gave the parent written instructions on how to collect data and session procedures, such as how to present target foods and present demands to Jennifer. Between meetings, the therapist sent detailed emails to the parent describing how to present foods and respond to certain situations. Prior to each treatment session, the therapist rehearsed the intervention with the parent and answered any questions about implementation. During this time, and subsequently as the parent requested, the therapist would model the instruction or procedure used within the session. Then the parent brought Jennifer to the table to implement the intervention while the therapist observed. The therapist corrected implementation errors (e.g., prompting too soon, forgetting to provide the discriminative stimulus) in the moment using vocal feedback by specifying the error made and a corrective behavior for the next trial. This vocal feedback was through speakers and audible to the parent and child. The therapist offered the option of bug-in-the-ear training; however, the parent was not comfortable with that procedure. Between telehealth sessions, the parent conducted four sessions at home and collected data. These sessions were not video recorded; however, the parent provided the data sheets daily.

Procedures

Each session was an approximately 50-min period where the parent and Jennifer were seated at the kitchen table. The parent prepared the target food, a separate plate, the reinforcers, and the telehealth device (i.e., laptop or tablet) to be observed. More than 10 pieces of each target food, approximately 1.25 cm × 1.25 cm × 0.63 cm in size, were placed in a separate container on the table. There was one telehealth session and up to four additional sessions implemented independently by the parent per week. The parent would then call in to the joint telehealth session, for which the link was sent to the parent by the therapist prior to each session. Upon joining the telehealth session, the therapist briefly reviewed the instructions and answered any of the parent’s questions. The parent then began running trials within the session. A trial was defined as each feeding demand that began with a brief vocal prompt of the feeding demand (e.g., “Hold the banana chip.”). Jennifer was required to complete the steps of the task analysis (see Table 1) up to and including the stated prompt; no further steps of the task analysis were required. The trial was terminated upon completion of the demand and presentation of the reinforcer (intervention) or a break from feeding demands (escape baseline and intervention). The parent would randomly pick the order of foods at the beginning of each session and then systematically repeat that initial order for the remainder of the session.

Escape Baseline

The purpose of the escape baseline session was to assess Jennifer’s baseline level of compliance with the feeding demand without reinforcement for the five target foods. The parent began with Level A (see Table 1) and presented the following level of the feeding hierarchy contingent on compliance with the previous level, with some variability across foods. The parent did not provide any attention or tangible reinforcer contingent on compliance with the feeding demand. If Jennifer displayed any noncompliance with the demand (e.g., making negative statements, turning her head, pushing the food away), the parent was instructed to remove the food from her plate and state, “OK, you do not have to.” Jennifer was then presented with 30 s of escape from the feeding demand prior to the presentation of the next trial. During each trial, the therapist and parent recorded Jennifer’s compliance with the terminal feeding demand for each of the five target foods. After the initial escape baseline session, the parent conducted four additional escape baseline sessions independently using the same procedures. The parent uploaded complete data sheets after each session, and the therapist reviewed all data. The results of this assessment determined the starting points for intervention during the following telehealth treatment session. For example, Jennifer held the dried apples during some trials during the escape baseline; therefore the “hold” level was targeted first for dried apples. The telehealth escape baseline session was 52.67 min in duration.

Demand Fading + DRA

Each telehealth treatment session was 48.73 min on average (range 25.25–63.5 min) in duration across 11 weeks. The duration of the independent practice sessions was not recorded. The intervention consisted of demand fading across a hierarchy of demands for self-feeding and DRA. Additionally, a least-to-most prompting procedure was used to increase compliance with the target demand. To begin each treatment session, Jennifer selected which target food would be presented first.

In the hierarchy of feeding demands for self-feeding finger foods, the parent systematically taught and reinforced each step to self-feed the target foods in the naturally occurring sequence, beginning with the highest demand level with acceptable levels of independent compliance. The hierarchy of feeding demands consisted of a task analysis for self-feeding finger foods (see Table 1). For example, Jennifer had to first touch the food prior to picking up the food and bringing the food to her mouth. Following that, the food piece had to pass the plane of her mouth, prior to her releasing the food or chewing the food. The vocal prompts of “touch,” “hold,” “kiss,” “lick,” “bite,” and “consume” were used with Jennifer and her parent to facilitate consistent language and to ensure comprehension.

Within each step of the hierarchy of feeding demands, the reinforcement ratio was systematically thinned prior to targeting the next step of the task analysis. Within each step of the task analysis, the reinforcement ratio was thinned from fixed-ratio (FR) 1 to FR10 prior to changing the target response to the successive step in the task analysis. There was some variability within and across foods for the specific ratios of reinforcement targeted; however, Jennifer needed to demonstrate compliance with the target demand for a minimum of four of five trials before the demand was increased. This was extended beyond that success criterion at times due to miscommunication between the therapist and parent. This mainly occurred at the beginning of treatment when the parent was still learning how to conduct sessions independently.

Following success with the target demand, the parent was instructed to provide vocal praise (e.g., “Great job touching the dried apple!”) and present a preferred toy for 1 min or a bite of preferred food. If Jennifer exceeded the target demand, the parent provided praise for the target response; the parent did not provide a higher quality or magnitude of reinforcement following a response exceeding the target response. Upon completion of the 1 min with the preferred toy or the consumption of the preferred food, the parent would present the next trial. If Jennifer did not exhibit compliance with the target demand within 10 s of the vocal prompt, the parent would initiate the least-to-most prompting procedure. She would model the target demand once while issuing a second vocal prompt (e.g., “Touch the dried apple like me. Now you do it.”). If after an additional 10 s Jennifer did not exhibit compliance with the target demand, the parent would physically guide Jennifer to comply with the demand. Jennifer would not receive praise or access to the reinforcer. The parent would not physically guide food into Jennifer’s mouth; rather, the parent would use hand-over-hand guidance to pick up the food piece and bring it to Jennifer’s lips for 1 s. This was only implemented once during the intervention.

A trial was considered successful if Jennifer completed the target demand on either the initial vocal or model prompt. The model prompt was not faded from the procedures and was available as a tool to the parent if Jennifer was not initially compliant with the vocal prompt. The trial was not successful if the parent used physical guidance to help Jennifer complete the step. During each feeding session, the parent completed multiple trials of each target demand except for consumption, due to satiation effects. Instead, the trials for consumption were spread out between different treatment days.

The parent ended the session after approximately 50 min or when five trials for each target food were completed (whichever came first). The parent then praised Jennifer for completing the session and stated that the session was over. After each session, the parent sent the data to the therapist as an email or text message. Upon review of the data, the therapist would send the parent the specified target demands for the next treatment session. If Jennifer demonstrated success across four of five trials, then the therapist instructed the parent to thin the reinforcement ratio or to begin the next step of the task analysis. The parent reported she occasionally provided a highly preferred toy at the end of a treatment session to keep Jennifer motivated.

Results

Escape Baseline

The escape baseline was used to identify the starting point for intervention across target foods. Five foods were tested during the probe independently: dried pineapple, dried apricots, dried apples, a sandwich square, and banana chips. For the dried pineapple, there were low rates of responding (12%; Figure 1), with two instances of success at Level A and one instance of success at Level B (see Table 1 for a description of the levels). All subsequent trials at Level A were unsuccessful. Thus, with one trial of success at Level B without any reinforcement contingency, intervention began at Level B with the goal of increasing independent self-feeding for this nonpreferred food.

Fig. 1.

Escape Baseline Compliance With the Feeding Demand for Dried Pineapple and Dried Apricot. Note. Filled-in bars indicate independent compliance with the feeding demand, and open bars indicate noncompliance with the feeding demand. See Table 1 for a description of the hierarchy of feeding demands

For the second food, dried apricots, Jennifer initially demonstrated some success with Levels A and B of the demand contingency (14.81% of trials), with no success demonstrated during successive attempts (Fig. 1). This pattern of responding was replicated with the target food of dried apples. Low rates of independent responding occurred with dried apples, with 25.81% of trials successful at Level A or B; no independent responding was observed at any higher level (Fig. 2). Intervention for both dried apricots and dried apples began at Level B with the goal of increasing independent self-feeding.

Fig. 2.

Escape Baseline Compliance With the Feeding Demand for Dried Apple and Sandwich. Note. Filled-in bars indicate independent compliance with the feeding demand, and open bars indicate noncompliance with the feeding demand. See Table 1 for a description of the hierarchy of feeding demands

For the sandwich square, Jennifer demonstrated variable responding across Levels A, B, C, E, and F, with six successful consumption (Level F) trials across 33 total probe trials (Fig. 2; 18.18% of trials). Jennifer was to begin at Level F because that was the highest level of compliance; however, with repeated unsuccessful trials at Level A (80% of opportunities; see Fig. 2), and her parent’s concern regarding initiating this food with consumption, the intervention began at Level A.

For the banana chips, Jennifer demonstrated variable responding across the demand levels during the baseline probe, with multiple instances of successful trials at the terminal demand of consumption (Level F; 25.93% of trials; Fig. 3). Thus, intervention began at consumption for banana chips with the goal of increasing the frequency of consumption.

Fig. 3.

Escape Baseline Compliance With the Feeding Demand for Banana Chip. Note. Filled-in bars indicate independent compliance with the feeding demand, and open bars indicate noncompliance with the feeding demand. See Table 1 for a description of the hierarchy of feeding demands

Demand Fading + DRA

Jennifer demonstrated high rates of success across all foods, terminating the intervention with the consumption of 3–10 bites of each target food prior to accessing reinforcement. Fig. 4 (top panel) displays the frequency of compliance with the feeding demand for dried pineapple. The baseline probe indicates one trial of Level B from the escape baseline where Jennifer independently held the dried pineapple. Jennifer began with Level B and engaged in high levels of compliance within levels as reinforcement was thinned and across levels of reinforcement. Overall, Jennifer successfully completed or exceeded the demand for 99.35% of trials (152 of 153 trials). She only exceeded the requirements during two trials within the FR5 condition of Level B. On trials where she exceeded the demand (e.g., consuming four pieces of the food when the demand was one piece), the parent provided the reinforcer and recorded the frequency of the completed demand. No additional reinforcement or change in procedures occurred. Jennifer successfully increased the demand frequency and intensity from a requirement of one attempt at Level B to access the reinforcer to four successful bites consumed (e.g., Level F). Through the repeated demonstration of Jennifer meeting the demand criteria repeatedly across fading of the demand and systematically manipulating the frequency of the behavior, we demonstrated experimental control that Jennifer increased her compliance with the demand and her frequency of engaging in that target behavior as a result of the stated contingency.

Fig. 4.

Frequency of Compliance With the Feeding Demand for Dried Pineapple and Dried Apricot. Note. Black data points refer to telehealth sessions. Open data points refer to the parent’s independent implementation

Figure 4 (bottom panel) displays the frequency of compliance with the feeding demand for dried apricots. A brief baseline of Jennifer’s compliance with Level B demonstrates moderate compliance with the demand, without reinforcers presented. Jennifer began with Level B and engaged in high levels of compliance within levels as reinforcement was thinned and across levels of reinforcement. Overall, Jennifer was successful with 98.73% of trials (155 of 157 trials), terminating with four bites of dried apricots successfully consumed. The first unsuccessful trial was due to licking the dried apricot when the demand was to kiss, and thus Jennifer exceeded the level of demand. The second unsuccessful trial was the first demand to consume the dried apricot, where Jennifer did not comply with the demand. Steadily, across repeated demonstration across levels, Jennifer engaged in the target behavior at the current contingency to access the reinforcer, thus demonstrating adequate experimental control.

Figure 5 (top panel) displays the frequency of compliance with the feeding demand for dried apples. During the escape baseline, Jennifer demonstrated moderate compliance with the demand at Level B. Jennifer began the demand fading + DRA intervention with Level B and engaged in high levels of compliance within levels as reinforcement was thinned and across levels of reinforcement. Jennifer met or exceeded the demand requirement for all 161 trials. There were four trials in which Jennifer exceeded the frequency requirement; however, she never engaged in a higher level than what was targeted. Repeatedly, Jennifer’s behavior for complying with the feeding demand corresponded to the requirement stated for that trial, demonstrating replicated demonstrations of experimental control. Initially, Jennifer was required to hold the piece of dried apple in her hand once (Level B). At the end of the intervention, Jennifer terminated with consuming four bites of dried apple.

Fig. 5.

Frequency of Compliance With the Feeding Demand for Dried Apple and Sandwich. Note. Black data points refer to telehealth sessions. Open data points refer to the parent’s independent implementation

Figure 5 (bottom panel) displays the frequency of compliance with the feeding demand for the sandwich square. During escape baseline, Jennifer demonstrated low and stable compliance with Level A. Jennifer began the intervention with Level A and engaged in high levels of compliance within levels as reinforcement was thinned and across levels of reinforcement, demonstrating a high degree of experimental control again across this food. Jennifer had one unsuccessful trial across 182 trials of this target food, indicating 99.45% of trials were successful. During that unsuccessful trial, Jennifer consumed the sandwich square when the demand was to touch it. Jennifer completed the intervention by consuming three bites of a sandwich square.

Figure 6 displays the frequency of compliance with the feeding demand for banana chips. Jennifer engaged in low to moderate compliance with Level F during the escape baseline. During intervention, Jennifer engaged in high levels of compliance across criteria as reinforcement was thinned. Jennifer successfully completed or exceeded the demand level for 100% of trials. Initially, the demand was to consume 1 bite of a banana chip, with a terminal goal of 10 bites consumed. Jennifer demonstrated success with this target food quickly, terminating with six consecutive trials of 10 bites of banana chips after 37 intervention trials.

Fig. 6.

Frequency of Compliance With the Feeding Demand for Banana Chip. Note. Black data points refer to telehealth sessions. Open data points refer to the parent’s independent implementation

Discussion

This study evaluated the effectiveness of a behavioral feeding intervention implemented by the parent of a child with ARFID and ASD when the parent also had a diagnosis of ASD. All training procedures and data collection were conducted over telehealth, with the parent implementing the intervention with the child in their home. The intervention, demand fading + DRA, resulted in an increase in the consumption of previously nonpreferred foods, and the parent maintained adequate levels of procedural integrity. Jennifer demonstrated, in this case, a significant increase in consumption of nonpreferred foods, and her parent implemented 100% of intervention sessions. Following the intake assessment, Jennifer enrolled in weekly teleconsultation services initially consisting of the escape baseline, followed by a parent-implemented intervention. During teleconsultation sessions, the parent–child dyad targeted five foods for which to increase consumption: dried pineapple, banana chips, dried apricots, dried apples, and sandwich squares. All target foods were selected by the parent to increase the variety of food groups with limited initial consumption and were feasible to maintain in the home. The baseline probe sessions demonstrated that when problem behavior was reinforced with escape from the feeding demand, the child would engage in low levels of compliance with the feeding demand. Thus, across all foods, we started with the highest point on the feeding demand hierarchy with moderate to high success: Level A for the sandwich; Level B for dried pineapple, dried apricots, and dried apples; and Level F for banana chips (see Table 1 for levels of feeding demands).

The parent reported to the therapist that she preferred clear instructions and repeated practice prior to making intervention changes. In support of ensuring high levels of stability before making changes in the intervention, the parent required the child to demonstrate the target behavior across a minimum of four out of five consecutive trials at a target level prior to increasing the demand. Further, the parent required the child to demonstrate success with a thinned ratio of reinforcement prior to increasing the level of the demand. Thus, the child demonstrated success with Level B at an FR1 for four out of five trials prior to the reinforcement ratio being thinned to an FR2. This was repeated across foods and demand levels. Given the slow and systematic progression across the ratio of reinforcement and hierarchy of feeding demands, Jennifer demonstrated high rates of compliance with the feeding demand; there were only three trials in which Jennifer did not meet the feeding demand. For two of those three trials, Jennifer exceeded the current level of the feeding demand, and she did not comply with the feeding demand on one trial. Thus, her mother implemented the physical prompt for one trial during the intervention. There were nine trials where Jennifer exceeded the frequency of the demand. This demonstrates a high degree of experimental control, with Jennifer meeting the exact feeding demand on 664 of 676 intervention trials (98.22%). Thus, Jennifer increased her compliance with the frequency and level of the demand based on the criteria set, as specified in the changing-criterion design.

Anecdotally, the therapist stated that providing these instructions in a clear manner was helpful for the parent to conduct the intervention. Similar to these observations, Lerman et al. (2015) demonstrated that adults with ASD can successfully implement behavior-analytic interventions with high levels of procedural fidelity following BST. In their training procedure, the authors adapted training procedures to reduce the stress of their adult participants with ASD. Similar to the current study, Lerman et al. adjusted the speed and intensity of both training and intervention procedures to adapt to the parent’s needs. Further, the current study is an example of the feasibility of having parents with ASD implement behavior-analytic interventions with their children, while consulting with the therapist. The parent was able to implement an intervention that demonstrated increased consumption across five previously nonpreferred foods. An additional consideration is the use of telehealth, which allowed the parent to develop the intervention skills and implement the feeding intervention in a familiar space for both the parent and child—their home. The therapist was also available to provide additional feedback as needed.

When conducting feeding assessment and treatment through telehealth, it is critical to conduct a thorough interdisciplinary assessment prior to starting intervention services to adequately rule out medically based chew-and-swallow concerns, as well as other medical issues that could preclude the participant from accessing effective (and safe) treatment. Clark et al. (2019) described a best practice guide on how to implement a telehealth-enhanced interdisciplinary pediatric feeding disorders clinic, and it is a resource for individuals interested in developing feeding services through telehealth. Although all intervention procedures in this study were conducted remotely, the interdisciplinary assessment and medical evaluation were conducted in person prior to beginning telehealth services. It is important to assess the appropriateness of telehealth services on a case-by-case basis. Due to the potential risk of choking during feeding, and the physical distance between the parents and the therapist, parents who provide the intervention should have training in procedures to ensure the child’s safety in the event they choke during feeding procedures. Because telehealth services are a newer modality to many therapists, they must learn the videoconferencing platform and intentionally practice using that platform prior to initiating telehealth services. The therapist in this case had previous experience and training on both feeding interventions and telehealth service delivery. This becomes even more important when the parent needs support with the videoconferencing platform. It is important to first train the parent on the use of the technology, if necessary. In this case, no training was required, as the parent had previous experience with various computer platforms and telehealth for clinical services.

Further, because traditional telehealth services rely on a static computing device, it can be difficult to monitor and interact with the participant (or parent) if they move out of the view of the camera. That was a challenge in this case, as the required angle in which to arrange the device to observe Jennifer’s behavior precluded the observation of relevant environmental stimuli, including the parent completing the data sheet. One effective remedy for this potential problem is to use telepresence robots, which a remote therapist can move around and use to look at different areas of the participant’s environment. Some examples of recent behavior-analytic studies using telepresence robots, albeit in schools, used them to consult with teachers around math instruction, compliance training, and functional analyses of problem behavior (Fischer et al., 2019). In this case, and in other feeding interventions, the camera angle can be remotely manipulated to verify data collection or maneuver to view the kitchen space in the event of the child leaving the table.

The foods selected in this study were picked at the direction of the parent. She did not feel comfortable initially targeting a vegetable due to her concern that her daughter would display higher levels of challenging behavior. Additionally, the child did not consume many fruits; her primary fruit consumption came in the form of fruit juices. To ensure the intervention was socially valid for the parent while targeting foods that supported Jennifer’s nutritional needs (e.g., fruits and fiber), we focused on food products that were easy for the parent to maintain in the home. Additionally, the parent implemented a total of five sessions per week with Jennifer—one over telehealth and four independently. Although there were only 12 sessions with a therapist over 3 months, this level of independent implementation may not be feasible for all participants. In this study, the parent preferred frequent, independent rehearsal of the intervention procedures.

One criticism of the hierarchy of feeding demands used in this study is the inherent reinforcement provided for expelling food in the approximation immediately before consumption. Food expelling can be a challenging behavior to treat; however, as shown in the current study, if reinforcement contingencies and approximations of consumption are systematically managed, there is potential to avoid this concern in some cases, and this can be used as an overt step along the process of systematic desensitization (i.e., habituating to physiology, engaging in approximation, and receiving reinforcement; e.g., Koegel et al., 2012; Penrod et al., 2012). An important contextual factor in this case is the degree of severity of food refusal; Jennifer displayed some food selectivity and mild food refusal behavior that was safe to manage in the home by her parent. More elevated food refusal behaviors, or complex medical and behavioral histories, warrant heightened monitoring and support, which may require a clinical environment. In this case, we demonstrated a high degree of successful independent consumption as the terminal behavior. Future research should continue to explore this procedure as a means for parents and educators to provide accessible, ethical, and socially valid feeding interventions for children and adolescents, particularly for children and adolescents with similar presentations.

Although there were overall increases in the consumption of nonpreferred foods, and the parent descriptively told the therapist that she was happy with her daughter’s progress, there were no formal measures of social validity. Future studies should ensure that validated measures of social validity are collected to help demonstrate the acceptability of the intervention strategies. Unfortunately, generalization and maintenance data were not collected in this study. This study was conducted at the kitchen table in the child’s home with her parent as the interventionist; thus, we conducted the entire study within her natural environment, in contrast to feeding interventions conducted in a clinical setting. Future investigations should ensure that both generalization and maintenance are addressed, so that demonstration of the procedures in relation to novel foods and settings, as well as maintenance over time, is achieved.

A limitation of our escape baseline procedure is that the parent stopped presenting more advanced levels of the hierarchy of feeding demands contingent on noncompliance with a current demand. This prevented an opportunity for Jennifer to demonstrate compliance with further steps of the hierarchy of feeding demands. Future studies should also expand telehealth parent-training strategies for pediatric feeding disorders. In this study, integrity data on the feedback provided to the parent were not collected. Although parent training for behavioral feeding interventions over telehealth is a developing literature base, expanded evidence on telehealth training strategies and modifications necessary is still warranted.

Declarations

Ethical approval

This article does not contain any studies with animals performed by any of the authors; all procedures with human participants were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration and comparable ethical standards.

Informed consent

Informed consent was obtained in accordance with the institutional research committee standards.

Conflict of interest

All authors declare they have no conflict of interest.