Predictive Factors of Response to Mindfulness-Based Cognitive Therapy (Mbct) for Patients with Depressive Symptoms: The Machine Learning’s Point of View
M. Dethoor, Francois-Benois Vialatte, M. Martinelli, P. Péri, C. Lançon, Marion Trousselard
OBM Integrative and Complementary Medicine December 28, 2022 DOI: 10.21926/obm.icm.2204058 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Observational cohort with machine learning analysis Peer reviewed |
|---|---|
| Sample size | 76 |
| Population | Psychiatric outpatients with depressive symptoms at a university hospital mental health service |
| Intervention | Mindfulness-Based Cognitive Therapy |
| Duration | 8 sessions |
| Topics | Meditation |
| Key findings | MBCT is most effective for patients with high baseline depression severity (Beck score >25) and high trait mindfulness (FFMQ >90). |
Abstract
While there is abundant literature on the benefits of Mindfulness-Based Interventions (MBI), data about factors associated with their Efficiency are scarce. Our study attempts to determine the moderators of efficacy and adherence in Mindfulness-Based Cognitive Therapy (MBCT) with a machine learning analysis. Seventy-six psychiatric outpatients at “university hospital mental health service” had a prescription for MBCT from their referring psychiatrist. They suffer from various psychiatric illnesses with depressive symptoms. They completed a battery of clinical, mindfulness, and psychological functioning self-report questionnaires before and after the MBCT intervention of 8 sessions. Changes (after minus before) in scores were used for efficacy. Scores before MBCT were used to study the adherent profile (8 sessions of MBCT) versus non-adherent patients (stopping MBCT before the eight sessions). For efficacy and adherence profiles, machine learning analysis based on the support vector machine (SMV) method was applied to complement classical statistical analyses. Results: For efficacy factors, the SVM analysis finds a two-dimensional profile of patients. The patients for whom MBCT is most effective are patients with a high Beck score (>25) and high trait mindfulness (FFMQ >90). The percentage of misclassified validation examples is 24.6 (LOO = 75.4). The model's sensitivity is 79.3%, and the specificity is 71.9%. For adherence factors, a three-dimensional model is found. The patients who perform the 8 sessions of the MBCT have a profile with high or low trait mindfulness, high or low bodily dissociation, and low self-compassion. The percentage of misclassified validation examples is 37.3 (LOO = 62.7). The model's sensitivity is 48.4%, and the specificity is 71.9%. These results provide preliminary evidence that the predictive power of machine learning may allow the designing of standard patient profiles, which can contribute to 3 more personalized care for patients with symptoms of depression and anxiety. Also, including more psychoeducation in MBCT programs can maximize clinical benefits and adherence to this therapy. However, further studies are needed to explore this topic in more detail.
Comparable studies
Other observational and cohort studies on meditation, most cited first.
| Study | Year | Design | Participants |
|---|---|---|---|
| Using Self-Report Assessment Methods to Explore Facets of Mindfulness Undergraduate students | 2006 | Observational cohort | |
| Effectiveness of a meditation-based stress reduction program in the treatment of anxiety disorders Patients with generalized anxiety disorder or panic disorder with or without agoraphobia | 1992 | Observational cohort | n = 22 |
| Meditation experience is associated with increased cortical thickness Participants with extensive Insight meditation experience and matched controls | 2005 | Observational cohort | n = 20 |
| Meditation experience is associated with differences in default mode network activity and connectivity Experienced meditators and matched meditation-naive controls | 2011 | Observational cohort | |
| Long-term meditators self-induce high-amplitude gamma synchrony during mental practice Long-term Buddhist practitioners and controls | 2004 | Observational cohort |
Citations in the library
Cited by 3
- Toward precision mindfulness: Predicting mindfulness outcomes after Mindfulness-Based Stress Reduction using interpretable machine learning Behaviour Research and Therapy August 19, 2026
- Mapping neural effects of mindfulness-based cognitive therapy in ADHD using EEG microstates and machine learning models. Frontiers in Psychiatry 2025
- FIRECARE: An Evidence-Based Prevention Program to Reduce Burnout among Prehospital Caregivers: Benefits of a Combined Mindfulness, Heart Coherence Training, and Positive Psychology Intervention. Prehospital emergency care 2024
Cites 27
- The benefits of being present: Mindfulness and its role in psychological well-being. Journal of Personality and Social Psychology 2003
- Mindfulness: Theoretical Foundations and Evidence for its Salutary Effects Psychological Inquiry October 19, 2007
- Construct Validity of the Five Facet Mindfulness Questionnaire in Meditating and Nonmeditating Samples Assessment February 29, 2008
- Prevention of relapse/recurrence in major depression by mindfulness-based cognitive therapy. Journal of Consulting and Clinical Psychology 2000
- Mindfulness-Based Cognitive Therapy for Depression: Replication and Exploration of Differential Relapse Prevention Effects. Journal of Consulting and Clinical Psychology 2004
and 22 more in the library