Abstract
To perform a systematic review and meta-analysis of studies evaluating the effectiveness of mindfulness-based interventions (MBIs) in reducing burnout and stress among physicians. The authors searched records in MEDLINE, Embase, PsycINFO, PSYNDEX, Web of Science, CINAHL, and CENTRAL from database inception to August 8, 2019, using combinations of terms for mindfulness, interventions, and physicians. Eligible studies were randomized controlled trials (RCTs) and nonrandomized trials (NRTs), including controlled and noncontrolled before-after studies, all assessing burnout and stress among physicians preintervention and postintervention via validated instruments. Two reviewers independently screened records, extracted data, assessed risk of bias, and rated overall quality of evidence. The authors used random-effects modeling to calculate pooled effect sizes and conducted prespecified subgroup and sensitivity analyses to explore potential moderators. Of 6,831 identified records, 25 studies (with 925 physicians) were ultimately included. MBIs were associated with significant small reductions in burnout in between-group analyses (5 comparisons: standardized mean difference [SMD] = -0.26; 95% confidence interval [CI] = -0.50, -0.03) and pre-post analyses (21 comparisons: SMD = -0.26; 95% CI = -0.37, -0.15), and with a significant medium reduction in stress in between-group analyses (4 comparisons: SMD = -0.55; 95% CI = -0.95, -0.14) and a significant small reduction in stress in pre-post analyses (17 comparisons: SMD = -0.41; 95% CI = -0.61, -0.20). Versions of established MBIs showed higher effectiveness in reducing stress than other forms of MBIs or a mindfulness app. Reductions were maintained over an average follow-up of 5.3 months. The risk of bias was moderate with RCTs and high with NRTs. The overall quality of evidence was low to very low. MBIs can be effective in reducing physicians' burnout and stress. Shortcomings in the quality of evidence highlight the need for high-quality controlled trials providing long-term follow-up data.
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