Active Ingredients in Digital Cognitive Interventions: Integrating Dismantling Designs With Mechanistic Neuroscience
Sarah Shizuko Morimoto1, Cutter Augustus Lindbergh2, Alexander Conley3
1Division of Health Systems Innovation and Research, Department of Population Health Sciences, University of Utah, Williams Building, 295 Chipeta Way, Salt Lake City, UT, 84108, United States, 1-801-587-1288.
JMIR Mental Health
|July 16, 2026
Summary
Digital cognitive interventions (DCIs) show promise, but understanding their active components is crucial. Mechanistic trials are needed to identify specific features driving therapeutic effects for personalized treatment.
Area of Science:
- Neuroscience
- Psychiatry
- Gerontology
Background:
- Digital cognitive interventions (DCIs) offer scalable treatment for cognitive dysfunction.
- Current research lacks understanding of which DCI components drive therapeutic effects or their neurocognitive mechanisms.
- This knowledge gap hinders interpretation of null findings, intervention refinement, and personalized treatment.
Purpose of the Study:
- To advocate for a shift from broad efficacy testing to mechanistic trials for DCIs.
- To identify the "active ingredients" within DCIs responsible for engaging neurocognitive targets and yielding clinical benefits.
- To integrate Research Domain Criteria (RDoC) constructs, neuroscience, and digital behavioral data for outcome identification.
Main Methods:
- Propose adapting psychotherapy's dismantling design methodology for DCI research.
- Emphasize linking target specification and engagement with clinical outcomes, aligning with NIMH's experimental therapeutics framework.
- Utilize high-resolution digital behavioral data to analyze DCI features like adaptive difficulty, feedback, and task variability.
Main Results:
- Mechanistic dismantling trials can determine the necessity, sufficiency, or synergy of specific DCI features.
- These trials can identify components engaging neural circuitry and producing durable, meaningful cognitive transfer.
- Null or negative trial findings can be transformed into mechanistically interpretable results.
Conclusions:
- Mechanistic trials are essential for optimizing DCI design and personalization.
- This approach clarifies disease mechanisms and supports the development of effective, tailored DCIs.
- Understanding active DCI components is key to advancing cognitive dysfunction treatment across diverse populations.
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