Characterizing behavior change techniques in Mindset: A therapist-guided smartphone intervention for depression (Preprint)

<sec> <title>BACKGROUND</title> Worldwide, depression is a leading cause of disability, and its prevalence continues to rise. Although cognitive behavioral therapy (CBT) is an effective treatment, access to traditional face-to-face CBT is limited due to clinician availability and access barriers. Evidence-based digital interventions offer an alternative, but few document how closely they adhere to established therapeutic protocols. Understanding the content included in these apps helps researchers and clinicians understand mechanisms of action, whether they follow best practices, and how they might be improved. </sec> <sec> <title>OBJECTIVE</title> This study aims to identify and summarize evidence-based techniques in a validated, commercially available smartphone-based CBT intervention for depression, Mindset. </sec> <sec> <title>METHODS</title> Using standardized CBT strategy definitions and the Behavior Change Taxonomy (BCT) version 1 (BCTTv1), we coded all 393 app pages to identify the presence and distribution of therapeutic techniques. Coders used a collaborative consensus process, and interrater agreement was calculated </sec> <sec> <title>RESULTS</title> All 16 CBT techniques were identified in Mindset, with 528 instances coded across modules. Psychoeducation, skill building, and cognitive restructuring were most frequently applied. BCT coding found 37 of 93 possible techniques across 13 of 16 BCTTv1 categories, with shaping knowledge, repetition and substitution, and feedback and monitoring most prevalent. </sec> <sec> <title>CONCLUSIONS</title> Identifying CBT techniques and BCTs in a digital intervention provides transparency about embedded evidence-based strategies and their implementation. Findings show BCTs align with core CBT principles and that complete representation of all BCTs may not be necessary for therapeutic benefit. Using a standardized framework can help describe interventions systematically to support hypothesis generation, evaluation, and refinement. </sec>