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A Systems Approach to Stress and Resilience in Humans: Mindfulness Meditation, Aging, and Cognitive Function

Psychological stress is common and contributes to many physical and mental health problems. Its effects are mediated by a complex neurobiological system centering in the brain with effectors including autonomic nervous system, hypothalamic-pituitary-adrenal axis, inflammatory system, and gene expression. A stressor pushes the human physiological system away from its baseline state towards a lower utility state. The physiological system may return towards the original state but may be shifted to a lower utility state. While some physiological changes induced by stressors may benefit health, chronic stressors usually have negative effects on health. In contrast to this stressor effect is the system's resilience which influences its ability to return to the high utility attractor basin following a perturbation by increasing the likelihood and/or speed of returning to the baseline state following a stressor.
Age-related cognitive decline is a major public health issue with few preventative options. Stress contributes to this cognitive decline, and mindfulness meditation (MM) is a behavioral intervention that reduces stress and stress reactivity in many health conditions. A randomized clinical trial was performed to determine if MM in older adults would improve measures of cognitive function, as well as psychology and physiology, and to determine what factors might predict who would improve. 134 at least mildly stressed 50-85 year olds were randomized to a MM intervention or a wait-list control. Outcome measures included a broad cognitive function battery with emphasis on attention and executive function, self-rated psychological measures of affect and stress, and physiological measures of stress. Self-rated measures related to negative affect and stress were all significantly improved as a result of the MM intervention compared to wait-list control. There were no changes in cognition, salivary cortisol, and heart rate variability. Potential explanations for the discrepancy between the beneficial mental health outcomes and lack of impact on cognitive and physiological outcomes are discussed.
To determine which factors predict MM responsiveness, a responder was defined by determining if there was a minimum clinically important improvement in mental health. Predictors included demographic information and selected self-rated baseline measures related to stress and affect. Classification was performed using decision tree analysis. There were 61 responders and 60 non-responders. Univariate statistical analysis of the baseline measures demonstrated significant differences between the responder and non-responders in several self-rated mental health measures. However, decision tree was unable to achieve a reliable classification rate better than 65%.
A number of future research directions were suggested by this study, including to optimize the MM intervention itself, to better select participants who would benefit from MM, and to improve the outcome measures perhaps by focusing on decreased reactivity to stressful events. Finally, a less well-defined but always present future research direction is the development of better models and better quantitative analysis approaches to the multivariate but dynamically limited human empirical data that can be practically collected.

Identiferoai:union.ndltd.org:pdx.edu/oai:pdxscholar.library.pdx.edu:open_access_etds-3705
Date07 March 2016
CreatorsOken, Barry S.
PublisherPDXScholar
Source SetsPortland State University
Detected LanguageEnglish
Typetext
Formatapplication/pdf
SourceDissertations and Theses

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