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Published on: May 11, 2020
Language-based measures of mindfulness: initial validity and clinical utility
Susan E Collins1, Neharika Chawla, Sharon H Hsu
1Addictive Behaviors Research Center, University of Washington, Seattle, WA 98195, USA. susan.collins@gmx.net
Summary
Mindfulness language (ML) use in adults with alcohol and other drug use (AOD) disorders predicted fewer AOD use days post-treatment. This suggests ML is a valid measure for assessing mindfulness in addiction recovery.
Area of Science:
- Psychology
- Clinical Psychology
- Addiction Research
Background:
- Mindfulness-Based Relapse Prevention (MBRP) is an intervention for alcohol and other drug use (AOD) disorders.
- Assessing the impact of mindfulness interventions requires valid outcome measures.
- Language use may reflect an individual's engagement with mindfulness practices.
Purpose of the Study:
- To examine the relationship between mindfulness language (ML) use and treatment outcomes in adults with AOD disorders.
- To establish the validity and clinical utility of ML as a measure of mindfulness.
- To explore the predictive validity of ML for substance use relapse.
Main Methods:
- An expert panel defined two categories of ML: mindfulness state and mindfulness journey.
- Participants (n=48) with AOD disorders completed an 8-week MBRP program.
- A word count program analyzed participants' language for ML frequency in post-intervention responses; AOD data collected during follow-up.
Main Results:
- ML categories demonstrated concurrent validity, appearing more frequently in the MBRP manual than the 12-step Big Book.
- ML categories correlated with other linguistic variables associated with the mindfulness construct.
- Increased ML use significantly predicted fewer AOD use days during the 4-month follow-up period.
Conclusions:
- Mindfulness language (ML) shows promise as a valid and clinically useful measure of mindfulness in AOD treatment.
- ML may offer a complementary assessment to self-report measures, providing a richer understanding of the mindfulness experience.
- Further research is needed to replicate these findings and explore the broader application of ML in clinical settings.
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