Children with Down syndrome often have high body mass index scores, brought on by hypothyroidism, poor mastication, decreased metabolic rates, and inconsistent physical fitness routines. Along with various genotypic characteristics, several behavioral tendencies accompany the diagnosis of Down syndrome. People with this condition often engage in noncompliant behaviors in an attempt to escape work-related tasks such as exercising. A lack of a consistent fitness regimen may result in additional health complications for this particular group of people, as well as ensuing concerns from the parents or guardians who care for them. Because of the propensities for poor physical health in people with Down syndrome, it is imperative that this group of people include exercise-related activities in their health-care routines to help promote a positive well-being from childhood to adulthood.The purpose of this study is to report on the results of an intervention which utilized high-probability tasks and principles of generalization to address noncompliant behaviors in a 9-year-old boy who had Down syndrome and a history of engaging in refusal towards exercise-related activities. Gross motor skills adapted from the Test of Gross Motor Development assessment were used throughout the study to evaluate both compliance and accuracy of the pre-selected movements. This study used a changing conditions design to assess John’s growth throughout 5 distinct phases. Results from both the high-probability tasks and generalization interventions showed an overall increase in the participant’s compliance and accuracy of skill development throughout all stages of the experiment. Implications from this study provide positive support for using antecedent-based interventions to help individuals with Down syndrome engage in exercise-related activities.



College and Department

David O. McKay School of Education; Counseling Psychology and Special Education



Date Submitted


Document Type





Down syndrome, noncompliance, exercise, antecedent interventions, high-probability requests, generalization