A Theory-Based Approach to Predict Stress Relaxation Behavior Among South Asian Americans: A Cross-Sectional Study.

South Asian Americans experience multifaceted sociocultural and acculturative stressors that influence mental well-being, yet few studies have applied contemporary behavioral theories to understand relaxation behaviors in this population. This cross-sectional study examined predictors of initiating and sustaining relaxation behaviors using the Multi-Theory Model (MTM) of health behavior change. A web-based survey of 271 South Asian adults incorporated the Perceived Stress Scale (PSS-10), MTM constructs, and sociodemographic characteristics. Reliability was high across MTM subscales (Cronbach's α = 0.81-0.93). Structural equation modeling demonstrated acceptable fit (CFI > 0.90, TLI > 0.90, RMSEA < 0.08, SRMR < 0.08). Hierarchical regressions revealed that among participants practicing relaxation (n = 202), behavioral confidence significantly predicted initiation (β = 0.481, p < 0.001), followed by participatory dialogue (β = 0.194, p < 0.05) and changes in the physical environment (β = 0.242, p < 0.01). Emotional transformation strongly predicted sustenance (β = 0.395, p < 0.001), along with practice for change (β = 0.307, p < 0.05) and changes in the social environment (β = 0.210, p < 0.05). MTM constructs explained 69.8% of initiation variance and 70.4% of sustenance variance. Among non-practitioners, participatory dialogue predicted initiation (β ≈ 0.18-0.34, p < 0.05), and emotional transformation predicted sustenance (β = 0.570, p < 0.001). These findings underscore MTM's strong predictive utility and support culturally tailored interventions enhancing confidence, emotional regulation, and social/environmental supports to promote relaxation behaviors in South Asian communities in the United States.
Mental Health
Policy

Authors

Sharma Sharma, Awan Awan, Patel Patel, Hanif Hanif, Poudel Poudel, Laeeq Laeeq, Wahi-Gururaj Wahi-Gururaj
View on Pubmed
Share
Facebook
X (Twitter)
Bluesky
Linkedin
Copy to clipboard