Smartphone App-based Integrated Cognitive Control Training for Anxiety Disorders: Study Protocol for a Multi-site Randomized Controlled Trial

Background: Cognitive deficits in anxiety disorders (ADs) contribute to clinical and socio-occupational dysfunction, necessitating targeted interventions. Novelty: Integrated cognitive control training (ICCT) has demonstrated benefits in other disorders; however, it remains unexplored in ADs. With its process-specific training and multi-pronged exercises for stimulation, metacognitive training, and generalization, it has potential for enhancing cognitive functions and reducing anxiety symptoms in ADs. Objectives: This article describes the study protocol for a multi-site randomized controlled trial to test the efficacy of ICCT in individuals with ADs. Methods: Adults diagnosed with ADs ( n = 100) will be recruited across two sites. Following baseline assessments, they will be randomized to either ICCT (eight weekly sessions) or treatment as usual. ICCT will be delivered through once-weekly therapist-guided, smartphone app-based (CogTrain) homework (20–30 min/session, 4–6 times/week). Multimodal assessments will be carried out at baseline, post-intervention (10–12 weeks), and follow-up (20–24 weeks). The primary measure comprises the Hamilton Anxiety Rating Scale, with secondary measures of the Work and Social Adjustment Scale (socio-occupational functioning), neuropsychological tests (attention, memory, and executive functions), and functional magnetic resonance imaging of the cognitive control (CC) circuits. Intervention feasibility and acceptance metrics (response rate, intervention relevance) will also be recorded. Quality assurance and ethical procedures will be documented. Expected Outcome: The ICCT is expected to enhance cognitive functioning in adults with ADs, in addition to reducing symptoms, altering the underlying neural circuits of CC, and improving overall functioning. Digital delivery via a smartphone app may provide a cost-effective, scalable intervention useful in resource-constrained settings.