TY  - JOUR
AU  - dethoor, manon
AU  - vialatte, françois
AU  - martinelli, marion
AU  - peri, pauline
AU  - lançon, christophe
AU  - TROUSSELARD, Marion
PY  - 2022
DA  - 2022/12/28
TI  - Predictive Factors of Response to Mindfulness-Based Cognitive Therapy (Mbct) for Patients with Depressive Symptoms: The Machine Learning’s Point of View
JO  - OBM Integrative and Complementary Medicine
SP  - 058
VL  - 07
IS  - 04
AB  - 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.
SN  - 2573-4393
UR  - https://doi.org/10.21926/obm.icm.2204058
DO  - 10.21926/obm.icm.2204058
ID  - dethoor2022
ER  -