The Application of a Token Economy to Reduce Disruptive Behavior in Children with Attention Deficit Hyperactivity Disorder: A Literature Review
Keywords:
Attention-Deficit/Hyperactivity Disorder; Token Economy; Disruptive Behaviour; Positive Reinforcement; Classroom InterventionAbstract
Disruptive behaviour associated with attention-deficit/hyperactivity disorder (ADHD) can interfere with children’s learning, classroom participation, and peer relationships. Token economy offers a structured way to reinforce specific, appropriate behaviours through immediate feedback and exchangeable rewards. This literature review aimed to examine the conceptual basis and empirical evidence for applying token economy to reduce disruptive behaviour in children with ADHD. A literature review approach was used to synthesize studies on token economy and related school-based behavioural interventions. The review focused on 10 selected articles published within the last decade, identified through searches of academic databases and assessed for relevance to children with ADHD, behavioural intervention, and classroom outcomes. The findings suggest that token economy may improve observable behaviours, particularly attention and on-task engagement. In single-case studies, reported on-task duration increased from approximately 1–2 minutes at baseline to 9 minutes at follow-up in one study, and from 3 to 16 minutes during intervention in a study combining shaping and token economy, before declining to 13 minutes after intervention. Broader meta-analytic evidence on classroom programmes also indicates small improvements in teacher-rated disruptive behaviour (d = −0.20) and observed on-task behaviour (d = 0.39). These broader effects cannot be attributed to token economy alone. The review concludes that token economy is a promising component of individualized school-based support, but current direct evidence on disruptive behaviour is limited. Future studies should use clear behavioural definitions, repeated direct observations, fidelity checks, and follow-up measures of maintenance and generalization.