NIH grant: Assessing the Efficacy of Mindfulness Apps (1R01AT013324-01)

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NIH grant 1R01AT013324-01: Assessing the Efficacy of Mindfulness Apps
Institute: {'code': 'AT', 'abbreviation': 'NCCIH', 'name': 'National Center for Complementary and Integrative Health', 'admin_org_id': '12500007', 'admin_funding_url': ''}. PI: John  Torous. Award: $837845.
Start: 2026-06-19T00:00:00.
PROJECT SUMMARY:
Rates of depression continue to rise and the mental health impact of COVID-19 has only accelerated trends.
While mental health apps, specifically mindfulness apps, are not a panacea, they are popular tools that millions
are turning to today for easy access, affordable, and low-stigma help. But increased reliance on mindfulness
apps has not been supported by rigorous scientific evidence exemplified by few studies employing appropriate
control conditions. Thus, this research is designed to focus on using 100% remote but robust methodology to
assess the efficacy of mindfulness apps by applying a novel precision medicine framework.
Our study first assesses the impact of the Digital Working Alliance by matching people with depression with a
mindfulness app that may better support their personalized needs. We will compare those randomized to the to
this matching condition to a digital placebo to better evaluate the efficacy of these mindfulness apps. For the first
six weeks, participants will be asked to use the mindfulness app or digital placebo daily, and if not engaged, will
receive reminders, allowing for the analysis of clinical outcomes during ideal usage patterns. For an additional
six weeks, participants will be asked to use the app or digital placebo naturally, allowing for the elucidation of
naturalistic usage patterns and evaluation if these usage patterns impact clinical outcomes.
Across the entire study, we will capture smartphone-based digital phenotypes of behaviors (eg sleep, step,
screen time), environments (eg home time, greenspace exposure), and symptoms (longitudinal ecological
momentary assessment) to create personalized and predictive models of response that can be utilized to better
understand factors impacting the efficacy of mindfulness apps, and in the future, better tailor apps to each
person.

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