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OBJECTIVE:This study's objective was to evaluate the effect of two common components of meditation (mindfulness and slow breathing) on potential mechanistic pathways.
METHODS:
A total of 102 combat veterans with posttraumatic stress disorder (PTSD) were randomized to (a) the body scan mindfulness meditation (MM), (b) slow breathing (SB) with a biofeedback device, (c) mindful awareness of the breath with an intention to slow the breath (MM+SB), or (d) sitting quietly (SQ). Participants had 6 weekly one-on-one sessions with 20 minutes of daily home practice. The mechanistic pathways and measures were as follows: (a) autonomic nervous system (hyperarousal symptoms, heart rate [HR], and heart rate variability [HRV]); (b) frontal cortex activity (attentional network task [ANT] conflict effect and event-related negativity and intrusive thoughts); and (c) hypothalamic-pituitary-adrenal axis (awakening cortisol). PTSD measures were also evaluated.
RESULTS:
Meditation participants had significant but modest within-group improvement in PTSD and related symptoms, although there were no effects between groups. Perceived impression of PTSD symptom improvement was greater in the meditation arms compared with controls. Resting respiration decreased in the meditation arms compared with SQ. For the mechanistic pathways, (a) subjective hyperarousal symptoms improved within-group (but not between groups) for MM, MM+SB, and SQ, while HR and HRV did not; (b) intrusive thoughts decreased in MM compared with MM+SB and SB, while the ANT measures did not change; and (c) MM had lower awakening cortisol within-group (but not between groups).
CONCLUSION:
Treatment effects were mostly specific to self-report rather than physiological measures. Continued research is needed to further evaluate mindfulness meditation's mechanism in people with PTSD.
To determine if mindfulness meditation (MM) in older adults improves cognition and, secondarily, if MM improves mental health and physiology, 134 at least mildly stressed 50-85 year olds were randomized to a six-week MM intervention or a waitlist control. Outcome measures were assessed at baseline and two months later at Visit 2. The primary outcome measure was an executive function/attentional measure (flanker task). Other outcome measures included additional cognitive assessments, salivary cortisol, respiratory rate, heart rate variability, Positive and Negative Affect Schedule (PANAS), Center for Epidemiologic Studies Depression (CESD), Perceived Stress Scale (PSS), Neuroticism-Extraversion-Openness (NEO) personality traits, and SF-36 health-related quality of life. 128 participants completed the study though Visit 2 assessments. There was no significant change in the primary or other cognitive outcome measures. Even after statistical adjustment for multiple outcomes, self-rated measures related to negative affect and stress were all significantly improved in the MM intervention compared to wait-list group (PANAS-negative, CESD, PSS, and SF-36 health-related quality of life Vitality and Mental Health Component). The SF-36 Mental Health Component score improved more than the minimum clinically important difference. There were also significant changes in personality traits such as Neuroticism. Changes in positive affect were not observed. There were no group differences in salivary cortisol, or heart rate variability. These moderate sized improvements in self-rated measures were not paralleled by improvements in cognitive function or physiological measures. Potential explanations for this discrepancy in stress-related outcomes are discussed to help improve future studies.
Context • The benefits of a mindfulness meditation (MM) intervention are most often evidenced by improvements in self-rated stress and mental health. Given the physiological complexity of the psychological stress system, it is likely that some people benefit significantly, whereas others do not. Clinicians and researchers could benefit from further exploration to determine which baseline factors can predict clinically significant improvements from MM. Objectives • The study intended to determine (1) whether the baseline measures for participants who significantly benefitted from MM training were different from the baseline measures of participants who did not, and (2) whether a classification analysis using a decision-tree, machine-learning approach could be useful in predicting which individuals would be most likely to improve. Design • The research team performed a secondary analysis of a previously completed randomized, controlled clinical trial. Setting • The study occurred at the Oregon Health & Science University (Portland, OR, USA) and in participants' homes. Participants • Participants were 134 stressed, generally healthy adults from the metropolitan area of Portland, Oregon, who were 50 to 85 y old. Intervention • Participants were randomly assigned either to a 6-wk MM intervention group or to a waitlist control group, who received the same MM intervention after the waitlist period. Outcome Measures • Outcome measures were assessed at baseline and at 2-mo follow-up intervals. A responder was defined as someone who demonstrated a moderate, clinically significant improvement on the mental health component (MHC) of the short-form health-related quality of life (SF-36) (ie, a change ≥4). The MHC had demonstrated the greatest effect size in the primary analysis of the previously mentioned randomized, controlled clinical trial. Potential predictors were demographic information and baseline measures related to stress and affect. Univariate statistical analyses were performed to compare the values of predictors in the responder and nonresponder groups. In addition, predictors were chosen for a classification analysis using a decision tree approach. Results • Of the 134 original participants, 121 completed the MM intervention. As defined previously, 61 were responders and 60 were nonresponders. Analyses of the baseline measures demonstrated significant differences between the 2 groups in several measures: (1) the positive and negative affect schedule negative subscale (PANAS-neg), (2) the SF-36-MHC, and (3) the SF-36 energy/fatigue, with clinically worse scores being associated with greater likelihood of being a responder. Disappointingly, the decision-tree analyses were unable to achieve a classification rate of better than 65%. Conclusions • The differences in predictor variables between responders and nonresponders to an MM intervention suggested that those with worse mental health at baseline were more likely to improve. Decision-tree analysis was unable to usefully predict who would respond to the intervention.