Controlled case series demonstrates how parents can be trained to treat paediatric feeding disorders at home

J of App Behav Analysis

Lanovaz

2022

Self Cite

Behavior analysts typically rely on visual inspection of single‐case experimental designs to make treatment decisions. However, visual inspection is subjective, which has led to the development of supplemental objective methods such as the conservative dual‐criteria method. To replicate and extend a study conducted by Wolfe et al. (2018) on the topic, we examined agreement between the visual inspection of five raters, the conservative dual‐criteria method, and a machine‐learning algorithm (i.e., the support vector classifier) on 198 AB graphs extracted from clinical data. The results indicated that average agreement between the 3 methods was generally consistent. Mean interrater agreement was 84%, whereas raters agreed with the conservative dual‐criteria method and the support vector classifier on 84% and 85% of graphs, respectively. Our results indicate that both objective methods produce results consistent with visual inspection, which may support their future use.

Section: Methodsmentioning

confidence: 99%

Agreement between visual inspection and objective analysis methods: A replication and extension

J of App Behav Analysis

Lanovaz

2022

Self Cite

“…In the current study, caregivers of children receiving behavior-analytic treatment for pediatric feeding problems participated in a comprehensive social validity analysis. The few studies in pediatric feeding that have addressed social validity typically used Likert scale questionnaires, including standardized questionnaires (e.g., Hoch, et al, 1994;Sharp et al, 2014Sharp et al, , 2016Taylor et al, 2020). For example, Bui et al (2014) used the BIRS to evaluate the social validity of nonremoval of the spoon in combination with verbal reinforcement in a child with autism.…”

Section: Social Validity In Pediatric Feeding Interventionsmentioning

confidence: 99%

Thematic and textual analysis methods for developing social validity questionnaires in applied behavior analysis

Anderson

Behavioral Interventions

Taylor³

et al. 2021

Self Cite

Social validity is often defined as the degree to which an intervention has value to the community that it effects, but it is seldom reported in the literature. Most social validity questionnaires are purposely created by the authors of the study and often lack a description of scale development process. The purpose of this study was to evaluate methods for the development of a Likert‐type social validity scale. Caregivers of children who took part in a study on behavioral treatments for pediatric feeding disorders were part of an initial interview to inform scale development. We analyzed interviews using thematic (qualitative) analysis and textual (quantitative) analysis, and used the resulting themes to generate items for two social validity questionnaires. We examined the inter‐rater reliability of the questionnaire development process and evaluated the content validity of the questionnaires resulting from each method. Textual analysis had higher inter‐rater reliability for producing themes that could be converted to questionnaire items. The textual analysis method produced a questionnaire with content validity equal to that of the thematic analysis method. The study demonstrates the successful use of a quantitative approach to the development of social validity questionnaires for behavioral interventions.

“…He had sleep problems and was on melatonin. This case history was conducted in a behaviour-analytic, short-term and intensive in-home feeding programme as described by Taylor et al (2020) with no other therapies (all other services were paused). A physician filled out a form to clear and approve participation.…”

Section: Participant Setting and Materialsmentioning

confidence: 99%

Machine Learning to Support Visual Inspection of Data: A Clinical Application

Lanovaz

2021

Behav Modif

Self Cite

Practitioners in pediatric feeding programs often rely on single-case experimental designs and visual inspection to make treatment decisions (e.g., whether to change or keep a treatment in place). However, researchers have shown that this practice remains subjective, and there is no consensus yet on the best approach to support visual inspection results. To address this issue, we present the first application of a pediatric feeding treatment evaluation using machine learning to analyze treatment effects. A 5-year-old male with autism spectrum disorder participated in a 2-week home-based, behavior-analytic treatment program. We compared interrater agreement between machine learning and expert visual analysts on the effects of a pediatric feeding treatment within a modified reversal design. Both the visual analyst and the machine learning model generally agreed about the effectiveness of the treatment while overall agreement remained high. Overall, the results suggest that machine learning may provide additional support for the analysis of single-case experimental designs implemented in pediatric feeding treatment evaluations.