The association between mindfulness, psychological flexibility, and rumination in predicting mental health and well-being among university students using machine learning and structural equation modeling
Objectives: This study explores the intricate relationships between mindfulness, psychological flexibility, rumination, and their combined impact on mental health and well-being. Methods: Random forest regression on survey data from 524 undergraduate students was used to identify significant predict...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Machine Learning with Applications |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666827024000902 |
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| author | Ruohan Feng Vaibhav Mishra Xin Hao Paul Verhaeghen |
| author_facet | Ruohan Feng Vaibhav Mishra Xin Hao Paul Verhaeghen |
| author_sort | Ruohan Feng |
| collection | DOAJ |
| description | Objectives: This study explores the intricate relationships between mindfulness, psychological flexibility, rumination, and their combined impact on mental health and well-being. Methods: Random forest regression on survey data from 524 undergraduate students was used to identify significant predictors from a comprehensive set of psychological variables. Neural networks were then trained on various combinations of these predictors to evaluate their performance in predicting mental health and well-being outcomes. Finally, structural equation modeling (SEM) was employed to validate a model based on the identified key predictors, focusing on pathways from mindfulness through psychological flexibility to rumination and well-being. Results: The random forest analysis revealed that the mindfulness variables exerted their influence partially indirectly through psychological flexibility and rumination. The deep neural network analysis supported these findings and additionally showed that the mindfulness manifold model (consisting of self-awareness, self-regulation, and self-transcendence) was superior to the Five Facet Mindfulness Questionnaire variables in predicting mental health outcomes. The SEM analysis confirmed that psychological flexibility, particularly its avoidance and acceptance components, mediated the relationship between mindfulness and mental health. The hypothesized serial mediation pathway—mindfulness affecting psychological flexibility, which then influences rumination and subsequently mental health and well-being—was supported by the data. Self-transcendence was a particularly powerful predictor of mental health outcomes. Conclusions: The findings underscore the critical role of psychological flexibility and rumination in mediating the effects of mindfulness on mental health and well-being, suggesting that enhancing mindfulness and psychological flexibility might significantly reduce rumination, thereby improving overall mental health and well-being. |
| format | Article |
| id | doaj-art-90bcc18b886f41a98e26ed6940cff05e |
| institution | DOAJ |
| issn | 2666-8270 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Machine Learning with Applications |
| spelling | doaj-art-90bcc18b886f41a98e26ed6940cff05e2025-08-20T02:45:50ZengElsevierMachine Learning with Applications2666-82702025-03-011910061410.1016/j.mlwa.2024.100614The association between mindfulness, psychological flexibility, and rumination in predicting mental health and well-being among university students using machine learning and structural equation modelingRuohan Feng0Vaibhav Mishra1Xin Hao2Paul Verhaeghen3School of Psychology, Georgia Institute of Technology, GA 30332, USA; Correspondong author at: School of Psychology, Georgia Institute of Technology, North Ave, Atlanta, GA 30332, USA.School of Computer Science, Georgia Institute of Technology, GA 30332, USADepartment of Psychology and Human Development, Vanderbilt University, TN 37235, USASchool of Psychology, Georgia Institute of Technology, GA 30332, USAObjectives: This study explores the intricate relationships between mindfulness, psychological flexibility, rumination, and their combined impact on mental health and well-being. Methods: Random forest regression on survey data from 524 undergraduate students was used to identify significant predictors from a comprehensive set of psychological variables. Neural networks were then trained on various combinations of these predictors to evaluate their performance in predicting mental health and well-being outcomes. Finally, structural equation modeling (SEM) was employed to validate a model based on the identified key predictors, focusing on pathways from mindfulness through psychological flexibility to rumination and well-being. Results: The random forest analysis revealed that the mindfulness variables exerted their influence partially indirectly through psychological flexibility and rumination. The deep neural network analysis supported these findings and additionally showed that the mindfulness manifold model (consisting of self-awareness, self-regulation, and self-transcendence) was superior to the Five Facet Mindfulness Questionnaire variables in predicting mental health outcomes. The SEM analysis confirmed that psychological flexibility, particularly its avoidance and acceptance components, mediated the relationship between mindfulness and mental health. The hypothesized serial mediation pathway—mindfulness affecting psychological flexibility, which then influences rumination and subsequently mental health and well-being—was supported by the data. Self-transcendence was a particularly powerful predictor of mental health outcomes. Conclusions: The findings underscore the critical role of psychological flexibility and rumination in mediating the effects of mindfulness on mental health and well-being, suggesting that enhancing mindfulness and psychological flexibility might significantly reduce rumination, thereby improving overall mental health and well-being.http://www.sciencedirect.com/science/article/pii/S2666827024000902MindfulnessPsychological flexibilityRumination, Well-beingMachine learning, Random Forest (RF) |
| spellingShingle | Ruohan Feng Vaibhav Mishra Xin Hao Paul Verhaeghen The association between mindfulness, psychological flexibility, and rumination in predicting mental health and well-being among university students using machine learning and structural equation modeling Machine Learning with Applications Mindfulness Psychological flexibility Rumination, Well-being Machine learning, Random Forest (RF) |
| title | The association between mindfulness, psychological flexibility, and rumination in predicting mental health and well-being among university students using machine learning and structural equation modeling |
| title_full | The association between mindfulness, psychological flexibility, and rumination in predicting mental health and well-being among university students using machine learning and structural equation modeling |
| title_fullStr | The association between mindfulness, psychological flexibility, and rumination in predicting mental health and well-being among university students using machine learning and structural equation modeling |
| title_full_unstemmed | The association between mindfulness, psychological flexibility, and rumination in predicting mental health and well-being among university students using machine learning and structural equation modeling |
| title_short | The association between mindfulness, psychological flexibility, and rumination in predicting mental health and well-being among university students using machine learning and structural equation modeling |
| title_sort | association between mindfulness psychological flexibility and rumination in predicting mental health and well being among university students using machine learning and structural equation modeling |
| topic | Mindfulness Psychological flexibility Rumination, Well-being Machine learning, Random Forest (RF) |
| url | http://www.sciencedirect.com/science/article/pii/S2666827024000902 |
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