Study design exploring Qigong and Tai Chi Easy (QTC) on cardiometabolic risk factors
Author: Ramya Rameshkumar1, Linda Larkey2, Kate Alperin1, Danielle Martin1, Antonia Primus1, Dara James3
Affiliation:
1 Edson College of Nursing and Health Innovation, Arizona State University, 500 N 3rd Street, Phoenix, AZ 85004, USA.
2 Edson College of Nursing and Health Innovation, Arizona State University, 500 N 3rd Street, Phoenix, AZ 85004, USA; Center for Health Promotion and Disease Prevention, Edson College of Nursing and Health Innovation, Arizona State University, 500 N 3rd Street, Phoenix, AZ 85004, USA.
3 Edson College of Nursing and Health Innovation, Arizona State University, 500 N 3rd Street, Phoenix, AZ 85004, USA; Center for Health Promotion and Disease Prevention, Edson College of Nursing and Health Innovation, Arizona State University, 500 N 3rd Street, Phoenix, AZ 85004, USA. Electronic address: Dara.James@asu.edu.
Conference/Journal: Contemp Clin Trials
Date published: 2022 May 16
Other:
Special Notes: doi: 10.1016/j.cct.2022.106793. , Word Count: 263
Background:
Cardiovascular disease is the leading cause of death in the United States paralleled with several cardiometabolic risk factors that are on the rise such as obesity, hypertension, and diabetes. Many of these cardiometabolic risk factors are preventable by lifestyle changes in physical activity and dietary patterns. Qigong and Tai Chi Easy (QTC) exercises are considered meditative movement practices that have been shown to reduce cardiometabolic risk factors such as psychosocial stress, poor sleep quality and weight gain and is particularly suitable for older adults. Heart rate variability (HRV) is a common factor known to be related to reduction of these risks and may be enhanced using HRV biofeedback to specifically optimize effects of QTC.
Methods:
The protocol presented describes a two-group parallel randomized controlled trial testing effects of QTC vs QTC plus HRV biofeedback "priming" on HRV parameters (primary), and cardiometabolic risk factors and sequelae (secondary) (e.g., waist circumference/percent body fat, sleep quality, stress, anxiety/depression, emotional regulation, eating behaviors, and cognitive performance). We will enroll 50 adults aged 55-85 years old to participate in an 8-week intervention. Self-reported body measurements, psychosocial and behavioral questionnaires, and cognitive performance assessments will be conducted before and after the intervention.
Conclusions:
Findings from this study are expected to assess effects of QTC and elucidate the potential role of HRV in QTC relative to cardiometabolic risk factors and sequelae. Implications for how HRV may play a central role and be optimized in a meditative movement practice are discussed.
Keywords: Cardiometabolic risk factors; HRV biofeedback; Heart rate variability; Older adults; Qigong; Taiji.
PMID: 35589024 DOI: 10.1016/j.cct.2022.106793
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