Variation in Behavior Analysts’ Treatment Intensity Recommendations for Patients with Autism Spectrum Disorder

Abstract

Most outcome research on Applied Behavior Analysis (ABA) treatment suggests that high intensity yields the best outcomes for patients with autism spectrum disorder (ASD). However, little is known about what impacts the determinations behavior analysts make regarding individualized treatment intensity recommendations. We conducted a cross-sectional survey of behavior analysts with experience developing and overseeing behavior analytic treatment for individuals with ASD (N = 559). We asked participants to report how 36 patient, familial, and logistical factors impact their treatment intensity recommendations using a 7-point Likert scale (ranging from significantly decrease to significantly increase recommended treatment intensity). Results indicated variation in the factors that impact recommendations as well as the direction of impact, with the greatest agreement that patient diagnosis and skills deficits lead to increased treatment intensity recommendations. Although the data reveal patterns and areas of seemingly greater consensus, the significant variability in clinicians’ approaches to individualizing treatment intensity recommendations indicates a need for future research on training and standards for clinicians to ensure appropriate treatment intensity recommendations are provided to all patients receiving ABA treatment.

Applied behavior analysis (ABA) is the scientific study of behavior, applying the principles of behavior to evoke or elicit targeted behavioral change (Furman & Lepper, 2018). Outcome studies published in the past several decades have demonstrated the positive impact of interventions based on the principles of behavior to modify the developmental trajectory of children with autism spectrum disorder (ASD; Cohen et al., 2006; Eikeseth et al., 2002, 2007, 2009; Eldevik et al., 2006; Howard et al., 2005; Lovaas, 1987; Remington et al., 2007; Smith & Iadarola, 2015). ABA treatment approaches for individuals with ASD are supported by extensive evidence and have become widely accepted among educators and healthcare professionals (Centers for Disease Control and Prevention [CDC], 2022).

ABA treatment can vary in terms of treatment intensity, duration, and goals based on the needs of the individual receiving treatment and their response to treatment (Council of Autism Service Providers [CASP] 2024). Treatment intensity, also referred to as treatment dosage, typically comprises both the number of hours of direct treatment per week and the total duration of treatment.​ Research indicates that high-intensity treatment produces the largest improvements for young children with ASD (Eldevik et al., 2009; Klintwall et al., 2015; Virues-Ortega et al., 2013; cf. Rogers et al., 2021). A minimum of 36 weekly hours of direct ABA treatment for at least two years is associated with clinically significant, reliable changes in cognitive and adaptive skills (Eldevik et al., 2010). Low intensity ABA treatment results in smaller improvements than high-intensity ABA treatment (Eldevik et al., 2006, 2012; Green, 2011; Peters-Scheffer et al., 2010). Eclectic treatment comprising some ABA treatment plus a mixture of other therapies or methods is ineffective (at best) for most children with ASD, even when it is individualized and intensive (Eikeseth et al., 2002, 2007; Eldevik et al., 2009, 2010; Howard et al., 2005, 2014).

Although most outcome research on ABA treatment suggests that high-intensity treatment produces the best outcomes, there is considerable variability in the number of ABA treatment hours that clinicians may recommend depending on individual patient needs. Clinicians may implement focused treatment, which is provided for a limited number of behavioral targets ranging between 10 and 25 h per week, or comprehensive treatment, defined as treatment which addresses multiple affected developmental domains as well as challenging behaviors ranging from 30 to 40 h per week (CASP, 2024).

Recommendations regarding the specific intensity of treatment should be based on the medical necessity of the treatment for each individual patient (CASP, 2024). Although no single definition for medical necessity exists and criteria vary across managed health care organizations, medically necessary services are: “Health care services… that a prudent physician would provide to a patient for the purpose of preventing, diagnosing or treating an illness, injury, disease or its symptoms in a manner that is: (a) in accordance with generally accepted standards of medical practice; (b) clinically appropriate in terms of type, frequency, extent, site, and duration; and (c) not primarily for the economic benefit of the health plans and purchasers or for the convenience of the patient, treating physician, or other health care provider” (American Medical Association [AMA], 2016).1

Medical necessity determinations are part of the careful construction and individualization of behavior analytic treatment and are an essential element of ABA practice that are not well understood. Despite a general consensus that ABA intervention should be individualized depending on patient characteristics and response to treatment, little guidance exists on how to best accomplish this (Toby et al., 2023). To date, there is no research on how behavior analysts individualize dosage for children who present with varied skills, needs, ages, and family contexts (Pellecchia et al., 2019). Given that there is a high degree of variability reported in the number of treatment hours a patient receives in clinical practice, researchers can only speculate as to the reasoning that clinicians apply when making treatment intensity recommendations (Linstead et al., 2017).

One approach to understanding specific aspects of the clinical process is to survey clinicians and document common practices. Using a cross-sectional survey of 211 early and intensive behavioral intervention (EIBI) program supervisors, Love, Carr, Almason, and Petursdottir (2009) documented intervention characteristics and practices, including information on curriculum, program size, therapist expertise and training, supervision, data collection, and other aspects of the clinical process. Results indicated that there is considerable variability in treatment practices among clinicians (e.g., selection and use of assessment, reinforcement, and acquisition procedures, maintenance and generalization procedures, data collection procedures, etc.). Although this study provided useful information regarding general characteristics of intervention programs (e.g., whether therapy occurred in the home, clinic, or school; if the patients were also attending school and for how many hours on average, etc.), the authors did not report information related to treatment intensity recommendation practices.

Brandel and Frome Loeb (2011) conducted a cross-sectional survey of 1897 school-based speech and language pathologists (SLPs) to better understand how SLPs make recommendations regarding the intensity of speech services to children with ASD and other language deficits. SLPs were asked to select the three most important characteristics out of several student, practitioner, and workplace characteristics they considered when making recommendations regarding which program intensity and service delivery model to use for a specific student. Results suggested that student characteristics may not be the most important factor considered when making recommendations, as reported by the sample. Instead, caseload size and years of practice appeared to influence SLPs’ recommendations (Brandel & Frome Loeb, 2011). These data are from a related discipline, and to our knowledge, similar investigations have not been published in the field of ABA.

Considering that treatment intensity may be one of the most important dimensions of intervention outcome, it is surprising that this aspect of practice has not been investigated. Research on treatment intensity recommendation practices may be useful to supervisors as they assess the status of their own practices and to researchers as they identify areas for future investigation. Therefore, the purpose of this study was to investigate the factors that impact the determinations made by behavior analysts regarding treatment intensity recommendations. Notably, this study did not examine specific hour recommendations made by clinicians, but rather examined the patient and familial factors that clinicians consider and the direction of their influence (e.g., increase or decrease treatment intensity recommendations).

Method

Survey Development

We developed a draft survey, and in September of 2021, we asked six Board Certified Behavior Analysts® (BCBAs) at a non-profit organization that provides ABA services for patients with ASD to pilot this early version of the survey. The original list of factors included in the survey were generated by the first author based on a review of the literature on moderators of ABA treatment outcomes for individuals with ASD. Additional patient, family, and logistical factors that had the potential to influence recommendations were also included. Pilot participants were asked to provide feedback on the survey as a whole, with particular focus on the comprehensiveness of the listed factors. Revisions to the survey were made based on the pilot feedback. We asked these BCBAs not to complete the final distributed survey. After repeated testing during this development phase, we estimated that the survey would take approximately 15 min to complete.

The survey included 61 questions, questions included 8 demographic questions, (e.g., age, gender, race, ethnicity, state of residence, state of practice, county of residence, county of practice), 9 training and experience questions (e.g., highest level of credential, experience developing and overseeing ABA programming for individuals with ASD, graduate program and type, Behavior Analyst Certification Board® (BACB) certification pathway, year of certification, total years of experience), 8 workplace questions (e.g., type of workplace, setting, classification), and 8 patient and caseload questions (e.g., patient age range, funding source). We then provided a list of 10 factors related to patient diagnoses and skills (e.g., DSM-5 ASD diagnosis severity level, presence/severity of restricted interests), 10 factors related to medical history factors (e.g., presence of seizure disorder, history of hospitalizations), 11 logistical factors (e.g., patient school schedule, family availability for treatment), and 5 additional patient factors (e.g., patient age at onset of treatment, patient “readiness to learn”). About these factors we asked, “When determining treatment intensity, how does the factor impact the intensity you ultimately recommend?” Respondents selected a response from the following options: (1) significantly decrease intensity, (2) moderately decrease intensity, (3) slightly decrease intensity, (4) no impact on intensity, (5) slightly increase intensity, (6) moderately increase intensity, and (7) significantly increase intensity. A complete list of the survey questions can be obtained by contacting the first author.

Data and Sample

Participants included BCBAs, Board Certified Behavior Analysts – Doctoral® (BCBA-Ds), and Board Certified Assistant Behavior Analysts® (BCaBAs) who had experience developing and overseeing behavior analytic programming for individuals with ASD. Participants were recruited through the BACB® mass email service, which included an initial email inviting BCBAs, BCBA-Ds, and BCaBAs residing within the United States to participate, with a follow-up reminder email sent one week later. Registered Behavior Technicians® were excluded, as well as any certificant residing outside the United States. We also recruited participants via social media (LinkedIn and Facebook Groups geared toward BCBA membership, e.g., BCBA Clinical Questions, etc.), emails to colleagues with large professional networks in which we requested support distributing the survey link (snowball sampling; Remler & Van Ryzin, 2011), and word of mouth. The total number of individuals who received the link to the survey is unknown; therefore, a response rate could not be calculated.

The survey portal remained open from April 13, 2022 through September 3, 2022. Total responses were 730. Of these, 559 participants completed at least 50% of survey items. Of these 559 surveys, 412 surveys (73.7%) were fully completed and 147 (26.3%) were partially completed. Three individuals reviewed the informed consent document and declined participation; these individuals are not included in the total number of participants.

Prior to completing the survey, participants were presented with an electronic consent document. The consent document included the purpose of the research, the voluntary nature of participation, the estimated time to complete the survey, the risks and benefits of participating, and contact information for the approving institutional review board and investigator. After viewing the consent documents, those who wished to continue to the survey items selected the option, “I wish to proceed to the survey items,” where answering survey items conveyed consent as explicitly stated in the consent document. Persons who selected the option, “I wish to exit without proceeding to the survey items”, were routed to the end of the survey.

We incentivized participation by providing an opportunity for participants to opt into a lottery for a chance to win one of five $50 Amazon gift cards. We created a confidential lottery in Qualtrics, which generated a separate survey with email addresses not connected to the main survey responses. Most participants in the analytic sample (n = 470; 84.1%) opted into the lottery.

Security options were enabled to protect the survey from duplicate or unwanted responses. The survey was set to prevent multiple submissions by placing a cookie on the respondents’ browser when they submitted a response. If a respondent clicked on the survey link after completing a submission, they were redirected to the end-of-survey message. Bot detection was also enabled with reCAPTCHA to ensure only valid respondents could complete the survey.

Data Analysis

To examine the factors that impact clinician-reported intensity recommendations, we presented respondents with a list of variables across three domains. We asked, “When determining the treatment intensity to recommend individual patients, how do the following impact the intensity you ultimately recommend?” To ease interpretation of the results, we grouped the response options by their direction of influence. For example, the response options “significantly increase,” “moderately increase,” and “slightly increase” were combined, as were the options indicating a decrease in recommendations. This created three main categories of recommendation impact: increase intensity recommendations, decrease intensity recommendations, or no impact on intensity recommendations. Next, we assessed whether there was consensus among respondents on the overall influence of each factor. Consensus was conservatively defined as more than 50% of respondents agreeing on one of the primary options. Factors were considered to have “no consensus” if none of the three options received over 50% of responses, indicating greater variability in how the factor was interpreted and its influence on recommendations across the sample.

We conducted all data management and analyses using SAS® 9.4. All results are presented here as descriptive statistics using the PROC FREQ and PROC UNIVARIATE procedures.

Results

Respondent Characteristics

Participant demographics are shown in Table 1. A majority of respondents were white (82.1%), female (73.3%), and BCBAs (80.1%). The largest proportion of respondents were aged 25 through 34 years (44.7%). A majority had completed behavior-analytic coursework (61.2%) in an online degree program (44.4%). Most had been credentialed between 5 and 9 years (43.1%) and reported between 5 and 9 years of programming experience (37.7%).

Table 1.

Participant demographics (N = 559)

		N	%
Ethnicity
	Hispanic	166	38.8
	White non-Hispanic	393	61.2
Race
	American Indian and Alaska Native	19	3.4
	Black or African American	22	3.9
	Asian	12	2.1
	Native Hawaiian or Other Pacific Islander	16	2.9
	Some Other Race	18	3.2
	Multiple Race	13	2.3
	White	459	82.1
Sex
	Male	137	24.5
	Female	417	73.3
	Other/non-binary	4	0.7
	Missing	1	0.2
Age
	18–24 years	25	4.5
	25–34 years	250	44.7
	35–44 years	213	38.1
	45–54 years	56	10
	55–64 years	12	2.1
	65 +	2	0.3
	Missing	1	0.2
Credential
	BCaBA	69	12.3
	BCBA	448	80.1
	BCBA-D	42	7.51
Certification pathway
	ABAI-accredited degree	181	32.4
	Behavior-analytic coursework	342	61.2
	Faculty teaching and research	29	5.2
	Postdoctoral experience	6	1.1
	Missing	1	0.2
Program type
	On campus	219	39.2
	Online	248	44.4
	Blended/hybrid	91	16.3
	Missing	1	0.2
Years credentialed
	0–4 years	156	27.9
	5–9 years	241	43.1
	10–14 years	116	20.8
	15–19 years	33	5.9
	20 + years	12	2.1
	Missing	1	0.2
Years programming experience
	0–4 years	172	30.8
	5–9 years	211	37.7
	10–14 years	90	16.1
	15–19 years	52	9.3
	20 + years	33	5.9
	Missing	1	0.2

BCaBA Board Certified assistant Behavior Analyst, BCBA Board Certified Behavior Analyst, BCBA-D Board Certified Behavior Analyst Doctoral Level, ABAI Association for Behavior Analysis International

Participant workplace and caseload characteristics are shown in Table 2. Most respondents were employed by for-profit ABA organizations (58.3%), followed by public schools (18.2%), nonprofit ABA organizations (17.3%), and university clinics (10.4%). More respondents worked for organizations spanning multiple treatment locations (58%) as opposed to organizations with a single treatment location (41.9%). Various treatment and service delivery models were reported across respondents, including focused treatment (69.4%), comprehensive treatment (73.3%), and consultation services (44.5%), with most respondents working in tiered service delivery models, primarily in person (64.4%). Respondents were nearly evenly divided on whether their workplace had a minimum treatment intensity requirement or not (49.6% and 50.2%, respectively). For respondents working for organizations requiring minimum treatment intensity requirements of their patients, 26.1% reported a minimum requirement between 1 and 5 h, 35.3% reported a minimum requirement between 6 and10 hours, 18.4% reported a minimum requirement between 11 and 15 h, 11% reported a minimum requirement between 16 and 20 h, and 9.2% reported a minimum treatment intensity requirement between 20 and 40 h. Most reported between 5 and 9 patients on their caseload (35.1%) ranging from pre-K (57.1%) to adults (26%), and private insurance (71%) as a source of funding. The proportion of patients receiving recommended treatment intensity ranged from none (7.8%) to all (11.4%), with the largest proportion of respondents reporting that “most” patients on their caseload were receiving the recommended treatment intensity (34.7%).

Table 2.

Participant workplace and caseload characteristics (N = 559)

		N	%
Current workplace
	Public school	102	18.2
	Private school	48	8.6
	For profit ABA organization	326	58.3
	Nonprofit ABA organization	97	17.3
	University clinic	58	10.4
	Self-employed	23	4.1
	Other	21	3.8
	Missing	1	0.2
Workplace size
	Single location	234	41.9
	Multiple locations	324	58.0
	Missing	1	0.2
Treatment models offered
	Focused (25 h or less)	388	69.4
	Comprehensive (30 h or more)	410	73.3
	Consultation (caregiver support without direct therapy hours)	249	44.5
	Missing	2	0.4
Treatment delivery model and modality
	Tiered, primary in person	360	64.4
	Tiered, primarily telehealth	85	15.2
	Direct, primarily in person	13	2.3
	Direct, primarily telehealth	100	17.9
	Missing	1	0.2
Workplace minimum treatment intensity requirement
	Yes	277	49.6
	No	281	50.2
	Missing	1	0.2
Minimum treatment intensity requirement (n = 272)
	1–5 h	71	26.1
	6–10 h	96	35.3
	11–15 h	50	18.4
	16–20 h	30	11.0
	20–40 h	25	9.2
Patients on caseload
	Fewer than 5	105	18.8
	5–9	196	35.1
	10–14	119	21.3
	15–19	54	9.6
	20 +	59	10.6
	Missing	26	4.6
Age range of patients on caseload
	0–5 years (Pre-K)	319	57.1
	6–9 years (Elementary)	381	68.2
	10–14 years (Middle School)	287	51.3
	15–18 years (High School)	192	34.3
	18 + (Adults)	145	26
	Missing	3	0.5
Sources of funding
	Private insurance	397	71
	Medicaid	349	62.4
	Private pay	180	32.2
	School district	106	19
	Other	30	5.4
	Missing	3	0.5
Proportion of patients on caseload receiving recommended treatment intensity
	None	43	7.8
	Some	112	20.3
	About half	143	25.9
	Most	192	34.7
	All	63	11.4
	Missing	6	1.1

We asked respondents, “When patients on your current caseload do not receive the recommended treatment intensity, why does this occur?” The four most commonly reported reasons included “staff turnover” (47.9%), “the patient's school schedule conflicts with the recommended treatment intensity” (44.9%), “staff are unable to fulfill the recommended treatment intensity” (37.8%), and “caregivers cancel sessions frequently” (32.2%). A less commonly reported reason included, “Caregivers did not agree to the recommended treatment intensity” (2%), followed by “other” (7%).

Factors Influencing Treatment Intensity Recommendations

The impact of “Patient Diagnoses and Skills” on treatment intensity recommendations is shown in Fig. 1. At least 50% of respondents reported that almost all factors in this domain increased their ultimate treatment intensity recommendation, including a Level 3 DSM-5 ASD diagnosis severity classification (62.9%), the presence of restricted interests (61.7%), the presence of repetitive behavior (58.3%), a low level of verbal communication (73.7%), severe social skills deficits (72.8%), severe self-care skills deficits (72.6%), severe coping deficits (74.1%), severe leisure skills deficits (71.9%), and a low score on normative assessment (56.9%). The factor most commonly reported by clinicians to have “no impact” on treatment intensity recommendations was severe academic skills deficits (36.1%). Although more respondents reported that the presence of these factors resulted in an increase in recommendations for treatment intensity, some respondents (between 15 and 22%) reported that these factors resulted in a recommendation for decreased treatment intensity.

Fig. 1.

Impact of patient diagnosis and skills on treatment intensity recommendations (N = 559)

Figure 2 summarizes how intensity recommendations are impacted by “patient medical history.” More than 50% of participants reported a recommended increase in treatment intensity for three factors: presence of food selectivity or mealtime/feeding difficulties (62.1%), use of augmentative and alternative communication (54.7%), and history of behavior hospitalizations (59.4%). Fifty-three percent of respondents reported that the presence of a seizure disorder has “no impact” on their recommendations for treatment intensity. There was no consensus among participants on whether the following six factors within this domain impacted recommendations for treatment intensity: the presence of comorbid intellectual disability, the presence of comorbid genetic disorder, the presence of comorbid psychiatric disorder, the presence of sleep difficulties, the presence of physical limitations, and a history of medical hospitalizations.

Fig. 2.

Impact of patient medical history on treatment intensity recommendations (N = 559)

Data for “other patient factors” are summarized in Fig. 3. More than 50% of clinicians reported an increase in intensity recommendations for three factors: presence of severe challenging behavior in home/school (72.8%), severe challenging behavior in community (73.3%), and the patient exhibiting low “readiness to learn” (57.8%). There was no consensus on the impact of the remaining two factors in this domain on treatment intensity recommendations, older patient age at treatment onset and low response to previous treatments or therapies.

Fig. 3.

Impact of other patient factors on treatment intensity recommendations (N = 559)

Data for participant responses for “logistical factors” are shown in Fig. 4. More than 50% of participants reported a decrease in intensity recommendations for patients spending much or all of their day in school (52.1%). Despite there being no consensus, a majority of participants reported a decrease in treatment intensity recommendations for five other logistical factors: the patient receives other therapies outside of school (43.3%), the patient engages in extracurricular or community-based activities (48.1%), the family has limited availability (45.5%), the financial cost to the family is high (44.7%), and there are funding source restrictions (44.2%). More than 50% of participants reported no impact on treatment intensity recommendations for two factors in this domain: the family prefers treatment to occur in the home (58.5%) and the family prefers for treatment to occur during the evenings and/or weekends (51.3%). Despite there being no consensus, a majority of participants reported that three other factors in this domain would have no impact on treatment intensity recommendations: the driving distance from the family home to the clinic location is perceived as a burden by the family (49.2%), low (or no) parent or caregiver knowledge or experience with ABA at treatment onset (43.5%), and parent or caregiver commitment and willingness to participate in treatment is low at treatment onset (41.9%).

Fig. 4.

Impact of logistical factors on treatment intensity recommendations (N = 559)

Discussion

Individuals with ASD present with varied skills, needs, and family contexts. The results of this study show that clinicians use a variety of individual factors to make these determinations. Furthermore, there is some consistency across providers as to which factors they use to make determinations and the direction of influence these factors have on their determinations (i.e., to either increase or decrease the treatment intensity recommendation). There is also consensus on several factors that do not have any influence on treatment intensity recommendations. However, there remain several factors on which there is little agreement about how they factor into treatment intensity recommendations. To date, data on how clinicians individualize ABA treatment intensity recommendations based on these contexts has largely been absent from the behavioral literature (Pellecchia et al., 2019). This study is the first of its kind to offer insight into factors that influence clinicians’ treatment intensity recommendations.

Variability in decision making regarding treatment intensity recommendations across providers may have important implications for both patients and clinicians. It is critical that patients receive treatment at an intensity that maximizes their progress. At the same time, it is important to avoid treatment that is more intensive than necessary to contribute to meaningful improvement. This is expensive, both in terms of time commitment for the participant and family and real dollars for funders. Although clinicians are expected to justify their medical necessity determinations and corresponding treatment intensity recommendations to funders, the process for arriving at individual recommendations is often a private event that involves a clinician carefully considering a host of variables and how they might impact a patient’s response to treatment. The current study offers some initial insights into this process by providing data on factors that clinicians report influencing their recommendations, as well as the direction of these influences. It is vital that clinicians have the skills and support necessary to identify and properly consider factors required for appropriately titrating treatment intensity so that patients obtain the intended benefits of treatment while minimizing undesirable side effects. Given that these decisions are typically private events, which are not observable by trainees and others, it may be worth considering what discussions and trainings must take place for clinicians to make sound and consistent decisions about treatment intensity recommendations.

Among the 36 factors we evaluated, more than 70% of respondents reported recommending increased service intensity based on five factors related to patient diagnoses and skills (i.e., low level of verbal communication, severe social skills deficits, severe self-care skills deficits, severe coping skills deficits, severe leisure skills deficits), as well as two other patient factors (i.e., presence of severe challenging behavior in home/school, presence of severe challenging behavior in community). This is encouraging considering that outcome research indicates many of these factors may influence a patient’s response to ABA intervention. Specifically, moderators of ABA treatment outcomes include comorbid intellectual disability (Hedvall et al., 2015; Magiati et al.,2011), the presence of social avoidance and withdrawal behavior (Robain et al., 2020), communication and play behavior (Contaldo et al., 2020; Fossum et al., 2018), and challenging behavior (Hedvall et al., 2015; Robain et al., 2020). The results of this study indicate that many behavior analysts adjust their treatment intensity recommendations based on the presence of these treatment moderators.

Although there was consensus on how to adjust treatment intensity recommendations for many of the aforementioned moderators of ABA treatment outcomes, some moderators had less consensus. For example, although 48.3% of participants indicated that the presence of a comorbid intellectual disability would increase their treatment intensity recommendation, 30.1% indicated this factor would have no impact on their treatment intensity recommendation. Another striking example of varied practices is related to patient age at treatment onset. Previous research demonstrates that initiating intervention early and delivering it intensively leads to better outcomes for patients (Landa, 2018). However, when considering patients of an older age at treatment onset, participants were relatively equally split regarding how patient age would impact their treatment intensity recommendation. Approximately one third of participants indicated they would recommend a higher intensity of services for older children, one third indicated they would recommend a decrease in treatment intensity for older children, and one third indicated that older age would have no impact on their treatment intensity recommendation. This variability is perhaps understandable considering that sources of information conflict regarding the influence of age on response to treatment. Although it has been long accepted that early intervention leads to the best outcome, other research has suggested that the evidence supporting this assertion is scarce (Cerasuolo et al., 2022). It is also possible that broader systemic issues (e.g., funder age limitations, limited availability of service providers offering treatment to older individuals, clinicians’ scope of competence when working with adult populations) influence participant recommendations related to patient age. Nevertheless, ABA practice guidelines specify that ABA intervention is effective for individuals with ASD across the lifespan, and treatment should not be constrained to a specific age range (CASP, 2024). It may be that age of treatment onset interacts with several other factors in influencing practitioner recommendations for treatment intensity.

There was a total of 17 factors for which there was no consensus among participants regarding how they impacted service intensity recommendations. These factors were primarily related to medical and logistical considerations and included, severe academic skills deficits; the presence of comorbid intellectual disability, comorbid genetic disorder, psychiatric disorder, sleep difficulties, or physical limitations; a history of medical hospitalizations; older patient age at treatment onset; low response to previous treatments or therapies; the patient receives other therapies outside of school; the patient engages in extracurricular or community-based activities; the family has limited availability; the driving distance to a clinic is perceived as a burden; low/no caregiver knowledge/experience with ABA; low caregiver commitment and willingness to participate in treatment; the financial cost to family is high; and there are funding source restrictions. CASP’s (2024) practice guidelines advise that, “When there is uncertainty about the appropriate level of service intensity, the practitioner should err on the side of caution by providing a higher level of service intensity” (p. 33). Therefore, factors on which respondents reported little consensus may require greater contextualization for a nuanced determination.

There are several limitations of the study that warrant discussion. First, the sample size, though comparable to other survey research conducted with ABA clinicians, was relatively small compared to the number of BACB certificants. Thus, it is not clear whether it is appropriate to generalize these results to all certificants. Second, owing to the various methods used to distribute the survey and recruit participants, it was not possible to calculate a response rate. Therefore, the representativeness of the data and the degree to which the survey suffered from bias is unknown. Third, we asked respondents to report how individual factors in isolation influence their treatment intensity recommendations. As mentioned above, it is very likely that the process of making complex determinations, such as the intensity of behavior analytic services, requires the clinician to consider multiple variables and how they might interact when making their recommendations. For example, whether an individual patient spends much or all of their day in school may influence the ultimate treatment intensity recommendation in different ways depending on additional information about the patient’s success in that environment. If, for example, there is evidence that the patient is responding well to the treatments and therapies they are receiving through special education services (e.g., speech therapy, occupational therapy, social work, special resource room), this information may lead a clinician to recommend lower treatment intensity. Alternatively, if there has been little response to the therapies offered in the school through special education or if little is known about the patient’s success learning new skills in the school environment, the current number of hours in school may have no influence on the clinician’s recommendation for intensity of services. Similarly, in practice, a clinician may identify several factors that suggest a patient would benefit from a focused treatment approach. However, the inclusion of just one or two additional factors (e.g., presence of severe challenging behavior and history of behavior hospitalizations) may indicate the need for comprehensive services, altering their ultimate treatment intensity recommendation. Future research should evaluate how factors interact with each other to influence decisions about treatment intensity recommendations.

Finally, it is important to note that the current study was designed to provide insight into only which factors behavior analysts consider when making recommendations for treatment intensity. This study was not designed to identify the factors that should and should not be considered in determining treatment intensity. Researchers should use caution in extrapolating our results to identify specific factors that should and should not determine treatment intensity recommendations. Having said that, the CASP (2024) practice guidelines advise, “The recommended intensity of treatment should be based on what is medically necessary for the patient independent of the patient’s schedule of activities outside of treatment or previous utilization of services. Practical variables may be considered, but when there is conflict that may impact treatment outcomes, medical necessity considerations should be paramount” (p. 33). This guidance appears to caution against the use of many of the logistical factors when making treatment intensity recommendations, at least in most cases. However, the variability among respondents in how logistical factors influence their treatment intensity recommendations indicates clinicians are not following clear or standard guidelines to make such determinations. Future research should explore and identify the specific factors that should be considered by clinicians when making treatment intensity recommendations. Such research could help develop decision-making guidelines that help practitioners incorporate and appropriately weight the factors related to appropriate intensity of ABA interventions for individuals with ASD (see Toby et al., 2023 for an example). A variety of other decision-making models have been proposed for clinicians providing ABA intervention to individuals with ASD (Brodhead & Truckenmiller, 2021; Geiger et al., 2010; Kipfmiller et al., 2019; LeBlanc et al., 2015; Suarez et al., 2022). LeBlanc et al. (2015) explain that such models “consist of a series of questions that can be answered to lead a practitioner to recommendations about interventions that are optimally matched to clinical considerations” (p. 77). Such models can help to operationalize the underlying processes clinicians navigate when making these complex decisions. Decision-making models that guide medical necessity determinations and corresponding treatment intensity recommendations may be useful for clinicians given the large proportion of novice behavior analysts currently practicing (more than half of all BCBA certificants were certified within the past 5 years; BACB, n.d.).

It will be important for future research to build on the work presented in this study by recruiting a more generalizable sample and exploring how and to what degree multiple interacting factors influence treatment intensity recommendations. Despite the study’s limitations, the data presented here provide an important step forward in our understanding of how clinicians who are engaged in the development and oversight of behavior analytic programming for individuals with ASD make determinations regarding intensity of services. This represents an aspect of clinical practice that is essential to the success of ABA intervention. More importantly, it is an important question for ensuring maximal success and functioning of individuals with ASD in ways that are ethical, efficacious, and cost effective (CASP, 2024). The process of making treatment intensity recommendations is an ethical decision requiring clinicians to engage in thoughtful and principled judgment. However, the current ABA landscape may, in some cases, create financial incentives that influence these decisions, such as business models that stand to benefit from inflated treatment intensity recommendations. To safeguard ethical practice, future research must continue to examine the factors that should guide these decisions and evaluate evidence-based frameworks to support clinicians in making ethical and appropriate treatment intensity recommendations.

Data Availability

He datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.

Declarations

Ethical approval

Procedures performed in this study involved the use of human participants and all were in accordance with the ethical standards of the institutional research board at the University of Louisville, as well as the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

Conflict of interest

There are no conflicts of interest to disclose.