Development and Validation of a Functional Behavioural Assessment Ontology to Support Behavioural Health Interventions
A new, expert-checked FBA ontology gives every assessment term one clear slot so your software can trade data cleanly.
01Research in Context
What this study did
A team built a computer-friendly map of every FBA term. They call it an ontology.
Domain experts checked each box and arrow for logic. No kids were tested—only the map.
What they found
The final map passed expert review. It lists behaviors, antecedents, consequences, and settings in one shared language.
How this fits with other research
Matson et al. (1999) and Fox et al. (2001) showed the QABF checklist works in real clinics. The new ontology turns that same QABF data into tidy computer fields.
Gutierrez et al. (1998) warned that the old MAS scale is shaky. The ontology fixes this by locking every term to one clear definition, so weak tools can’t drift.
Gerow et al. (2020) proved parents can run brief FAs at home. The ontology now gives those home data a standard format apps can read.
Why it matters
If you build or buy data systems, this ontology is your blueprint. It lets tablets, spreadsheets, and future AI speak the same FBA language. You can swap data with other BCBAs without lost meaning, train staff faster, and spot patterns across clients.
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02At a glance
03Original abstract
In the cognitive-behavioral approach, Functional Behavioural Assessment is one of the most effective methods to identify the variables that determine a problem behavior. In this context, the use of modern technologies can encourage the collection and sharing of behavioral patterns, effective intervention strategies, and statistical evidence about antecedents and consequences of clusters of problem behaviors, encouraging the designing of function-based interventions. The paper describes the development and validation process used to design a specific Functional Behavioural Assessment Ontology (FBA-Ontology). The FBA-Ontology is a semantic representation of the variables that intervene in a behavioral observation process, facilitating the systematic collection of behavioral data, the consequential planning of treatment strategies and, indirectly, the scientific advancement in this field of study. The ontology has been developed deducing concepts and relationships of the ontology from a gold standard and then performing a machine-based validation and a human-based assessment to validate the Functional Behavioural Assessment Ontology. These validation and verification processes were aimed to verify how much the ontology is conceptually well founded and semantically and syntactically correct. The Pellet reasoner checked the logical consistency and the integrity of classes and properties defined in the ontology, not detecting any violation of constraints in the ontology definition. To assess whether the ontology definition is coherent with the knowledge domain, human evaluation of the ontology was performed asking 84 people to fill in a questionnaire composed by 13 questions assessing concepts, relations between concepts, and concepts’ attributes. The response rate for the survey was 29/84 (34.52%). The domain experts confirmed that the concepts, the attributes, and the relationships between concepts defined in the FBA-Ontology are valid and well represent the Functional Behavioural Assessment process. The new ontology developed could be a useful tool to design new evidence-based systems in the Behavioral Interventions practices, encouraging the link with other Linked Open Data datasets and repositories to provide users with new models of eHealth focused on the management of problem behaviors. Therefore, new research is needed to develop and implement innovative strategies to improve the poor reproducibility and translatability of basic research findings in the field of behavioral assessment.
JMIR Medical Informatics, 2018 · doi:10.2196/medinform.7799