Can mindfulness mechanistically target worry to improve sleep disturbances? Theory and study protocol for app-based anxiety program.
Can mindfulness mechanistically target worry to improve sleep disturbances? Theory and study protocol for app-based anxiety program.
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DOI:
10.1037/hea0000874
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发表时间:
2020-09
期刊:
影响因子:
--
通讯作者:
Hoge EA
中科院分区:
文献类型:
--
作者:
Brewer JA;Roy A;Deluty A;Liu T;Hoge EA
Anxiety is associated with sleep disturbance and insomnia. Mindfulness-based interventions, such as Mindfulness-based Stress Reduction, have shown consistent anxiety reduction. Mindfulness training has been theorized to affect reinforcement learning, affecting habitual behaviors such as smoking and overeating, but a direct mechanistic link between the use of mindfulness training for anxiety reduction and improvement in sleep has not been studied. Moreover, the mechanisms by which mindfulness may affect worry and subsequent sleep disturbances have not been elucidated. This study protocol evaluates the impact an app-based mindfulness training program for anxiety has on decreasing worry, and improvement in sleep. A randomized controlled study will be conducted in approximately 80 adults with worry that interferes with their sleep. Subjects will be randomly allocated (1:1) to two groups: treatment as usual (TAU) or TAU + app-based mindfulness training (MT; Unwinding Anxiety app). The primary outcomes will be the nonreactivity subscale of the Five Facet Mindfulness Questionnaire and PROMIS sleep quality measures. Secondary outcomes will include the Penn State Worry Questionnaire, Generalized Anxiety Disorder-7, and Multidimensional Assessment of Interoceptive Awareness scale. This study is the first to test the mechanism of app-based mindfulness training on worry and sleep disturbance. Testing the mechanistic effects of mindfulness training using the Science of Behavior Change (SOBC) framework will help move the field forward both in further elucidation of potential mechanisms of mindfulness (e.g., targeting reinforcement learning), and determining whether such a platform may be a viable method for delivering high-fidelity treatment at scale and for a low cost.
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