2017
DOI: 10.24966/acim-7562/100037
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Predictors of Meditation Success: A Literature Review

Abstract: Despite a wealth of research examining the effectiveness and neurobiological mechanisms of meditation approaches, there is limited information about the factors that may contribute to the successful implementation of long-term meditation practice. This literature review examines studies that explore the experiences of meditators with particular attention to those studies that investigate adherence to practice. The core purpose of this inquiry is to discover the key differences between successful long-term medi… Show more

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Cited by 2 publications
(2 citation statements)
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“…• Several ENIGMA WGs use data-driven methods, such as non-linear, multivariate, machine and deep learning approaches to predict / determine brain-aging related abnormalities in MDD (99) and schizophrenia (129), predict alcohol dependence (100), or classify anxiety disorder cases vs. controls (101) and OCD cases vs. controls (102) based on brain structure. Similarly, ENIGMA-Meditation can develop powerful computational models that use neuroscientific data to distinguish levels of meditation experience or predict individual-level/patient-level responses to specific meditation practices and MIs (130), complementing efforts that predict meditation outcomes using self-report data (131)(132)(133). Such efforts may also help demystify the neuroscientific mechanisms and predispositions underlying various documented contraindications to meditation practice (30,134).…”
Section: Future Directionsmentioning
confidence: 99%
“…• Several ENIGMA WGs use data-driven methods, such as non-linear, multivariate, machine and deep learning approaches to predict / determine brain-aging related abnormalities in MDD (99) and schizophrenia (129), predict alcohol dependence (100), or classify anxiety disorder cases vs. controls (101) and OCD cases vs. controls (102) based on brain structure. Similarly, ENIGMA-Meditation can develop powerful computational models that use neuroscientific data to distinguish levels of meditation experience or predict individual-level/patient-level responses to specific meditation practices and MIs (130), complementing efforts that predict meditation outcomes using self-report data (131)(132)(133). Such efforts may also help demystify the neuroscientific mechanisms and predispositions underlying various documented contraindications to meditation practice (30,134).…”
Section: Future Directionsmentioning
confidence: 99%
“…That other "antidote" is more or less similar to the concept of self-efficacy, which again is inspired by the social cognitive theory of Bandura (1986) and very well linked to the practice of meditation in general by Sharma (2013). Studies are also beginning to emerge, assessing the role of self-efficacy with regard to adherence in practice to yoga (Cheung et al, 2016) and mindfulness meditation (Birdee et al, 2018;Kambolis, 2017) therapy programs.…”
mentioning
confidence: 99%