Impact of the Mindfulness-Based Blood Pressure Reduction (MB-BP) Program on Cardiovascular Health: A Randomized Clinical Trial
Wu, F.; Brewer, L. C.; Neves, V.; Scarpaci, M.; Proulx, J. A.; Loucks, E. B.
Show abstract
BackgroundMindfulness-based interventions may improve cardiovascular health (CVH) by supporting behavioral change across multiple risk factors. This study evaluated the impact of Mindfulness-Based Blood Pressure Reduction (MB-BP), a mindfulness program targeting hypertension-related behaviors, on CVH using the American Heart Associations Lifes Essential 8 framework. MethodsThis secondary analysis of a preregistered, parallel-group, phase 2 randomized clinical trial evaluated the effects of MB-BP on CVH in 201 participants with elevated office BP ([≥]120/80 mmHg). The MB-BP group (n=101) received an 8-week program focused on mindfulness training and education targeting diet, physical activity, medication adherence, alcohol use, and stress, whereas the control group (n=100) received enhanced usual care. CVH was assessed using available Lifes Essential 8 components: systolic blood pressure, body mass index (BMI), diet (DASH adherence), physical activity, smoking, and sleep duration. Generalized estimating equations evaluated intervention effects through six months. ResultsAt 6 months follow-up, MB-BP participants significantly improved composite CVH scores compared to controls (standardized mean difference: 0.144; 95% CI: 0.023-0.266). Non-significant improvements were observed across most CVH components in MB-BP vs. control, including systolic blood pressure (-4.95 mmHg), DASH diet score (+0.27), physical activity (+47.9 MET-min/week), sleep duration (+0.34 hours/night), and BMI (-0.28 kg/m{superscript 2}). No significant changes were observed for smoking, likely due to the low baseline prevalence. ConclusionsMB-BP led to modest but clinically significant improvements in CVH, driven by multiple Lifes Essential 8 components. These findings suggest that MB-BP may be an effective behavioral intervention to support CVH and reduce risk for cardiovascular disease. ClinicalTrials.gov Preregistration IdentifiersNCT03256890, NCT03859076
Matching journals
The top 9 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Maternal obesity before pregnancy predicts offspring blood pressure at 18 years of age: A causal mediation analysis 91%
- Upregulated miR-200c may increase the risk of obese individuals to severe COVID-19 91%
- Risk factors mediating the effect of body-mass index and waist-to-hip ratio on cardiovascular outcomes: Mendelian randomization analysis 91%
Similar papers in this journal
- Community-Based Culturally Tailored Education Programs for Black Adults with Cardiovascular Disease, Diabetes, Hypertension, and Stroke: A Systematic Review Protocol 93%
- Telehealth versus Self-Directed Lifestyle Intervention to Promote Healthy Blood Pressure: A Protocol for a Randomized Controlled Trial 92%
- Effects of 12 weeks of Multi-nutrient supplementation on the Immune and Musculoskeletal systems of Older Adults in Aged-Care (The Pomerium Study): Protocol for a Randomised Controlled Trial 92%
Similar papers in this journal
- Effectiveness of a Text Message Intervention Promoting Seat Belt Use Among Targeted Young Adults: A Randomized Clinical Trial 91%
- Venous Thromboembolism and the Effects of Statin and Hormone Therapy: A Case-Control Study of 250,000 Women 50-64 years of age 90%
- Changes in cardiorespiratory fitness and body mass index due to COVID-19 mitigation measures in Austrian children aged 7 to 10 years 89%
Similar papers in this journal
- Social Networks and Cardiovascular Disease Events in the Jackson Heart Study 94%
- Associations of alcohol consumption with left atrial morphology and function in a population at high cardiovascular risk 94%
- Prevalence of Cardiovascular-Kidney-Metabolic Stages in US Adolescents and Relationship to Social Determinants of Health 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.