Beyond Baseline Severity: Temporal and Disease-Specific Predictors of Depression Outcomes Following Mindfulness Interventions
Muhammad Jawad Chowdhury, Sultanus Salehin, Akib Jayed Islam
arXiv (Cornell University) September 19, 2026 preprint DOI: 10.48550/arxiv.2610.08809 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Observational cohort (multi-center longitudinal clinical cohort) with machine-learning prediction modeling |
|---|---|
| Population | Patients with chronic or acute medical conditions enrolled in a multi-center longitudinal clinical cohort who participated in a mindfulness-based intervention |
| Intervention | Mindfulness-based intervention |
| Duration | Follow-up at 12 and 24 weeks |
| Measures | Beck Depression Inventory-II (BDI-II) |
| Topics | Depression Meditation |
| Key findings | Baseline BDI-II severity was the strongest overall predictor of depression scores after a mindfulness-based intervention. Ridge Regression best predicted 12-week scores (RMSE 5.186, R² 0.474) and LightGBM best predicted 24-week scores (RMSE 5.038, R² 0.525). Short-term outcomes were more associated with clinical and hospital context, while long-term outcomes depended more on behavioral adherence and demographic factors, with predictors differing across clinical subgroups. |
Abstract
Depression severity among patients with chronic or acute medical conditions is influenced by a complex interaction of baseline psychological state, demographic characteristics, clinical context, and engagement with behavioral interventions. This paper presents an interpretable machine-learning analysis of a multi-center longitudinal clinical cohort to predict Beck Depression Inventory-II (BDI-II) scores at 12 and 24 weeks following mindfulness-based intervention participation. The study uses demographic variables, clinical condition information, hospital-center identifiers, baseline BDI-II scores, and therapy engagement measures to model short-term and long-term depression outcomes. Missing follow-up outcomes were addressed using a model-based stochastic imputation procedure to preserve the modest sample size while maintaining outcome variability. Five regression models were evaluated, spanning regularized linear regression and tree-based ensemble methods. Ridge Regression achieved the best 12-week performance with an RMSE of 5.186 and R^2 of 0.474, while LightGBM achieved the best 24-week performance with an RMSE of 5.038 and R^2 of 0.525. Beyond prediction accuracy, the analysis reveals three clinically relevant patterns: baseline severity remains the strongest overall predictor, short-term outcomes are more strongly associated with clinical and hospital context, and long-term outcomes show greater dependence on behavioral adherence and demographic factors. Disease-specific and hierarchical subgroup analyses further indicate that predictors differ substantially across and within clinical categories. These findings support the use of interpretable, context-aware modeling to inform personalized mental-health support following mindfulness-based interventions.
Comparable studies
Other observational and cohort studies on depression, most cited first.
| Study | Year | Design | Participants |
|---|---|---|---|
| Metacognitive awareness and prevention of relapse in depression: Empirical evidence. Residually depressed patients and nondepressed controls; recovered depressed patients | 2002 | Observational cohort with intervention components | |
| Psilocybin for treatment-resistant depression: fMRI-measured brain mechanisms. Patients with treatment-resistant depression | 2017 | Observational cohort | n = 19 |
| Antidepressant effects of a single dose of ayahuasca in patients with recurrent depression: a preliminary report Patients with a depressive disorder | 2015 | Observational study | |
| Concomitant BDNF and sleep slow wave changes indicate ketamine-induced plasticity in major depressive disorder Patients with treatment-resistant major depressive disorder | 2012 | Observational cohort | n = 30 |
| Altered peripheral immune profiles in treatment-resistant depression: response to ketamine and prediction of treatment outcome Healthy controls and actively depressed patients with treatment-resistant depression... | 2017 | Observational cohort | n = 59 |