National Acceptance and Determinants of Immersive Extended Reality in Health Care in China: Cross-Sectional Study.

BACKGROUND

Immersive extended reality (XR) promises to transform health care, but public acceptance and user-side determinants of acceptance remain largely unknown.

OBJECTIVE

This study aimed to estimate national immersive XR acceptance and identify sociodemographic, psychosocial, health, and digital determinants among adults in China.

METHODS

This nationwide cross-sectional survey was conducted from June to September 2024 among 35,861 Chinese adults aged 18 years or older across 33 provincial-level regions and 800 communities, using multistage sampling. Immersive XR acceptance (0-100) and 139 potential predictors across demographic, adversity, personality, literacy, lifestyle, physical, and psychosocial domains were assessed. Poststratification weights were calibrated to the national age-sex distribution. Determinants were identified using survey-weighted hierarchical linear regression with Benjamini-Hochberg false discovery rate correction. Elastic net validation assessed predictor robustness, and classification and regression tree analysis identified profiles of likely nonacceptors.

RESULTS

Mean acceptance was 63.11 (95% CI 62.75-63.46), varying by province (95% CI 47.9-72.3). Acceptance was highest in younger adults (women aged 18-24 years and men aged 30-34 years) and declined with age; sex differences were minimal. Acceptance varied across 15 chronic conditions (lowest: rare diseases, mean 54.24, 95% CI 45.40-63.07; highest: hyperlipidemia, mean 62.26, 95% CI 60.19-64.33). Acceptance was strongly associated with socioeconomic factors (higher social status: standardized β=0.17, 95% CI 0.16-0.18; higher youth socioeconomic status: standardized β=0.14, 95% CI 0.13-0.16; better youth economic environment: standardized β=0.06, 95% CI 0.04-0.07), digital capital (prior digital health use: standardized β=0.15, 95% CI 0.14-0.16; eHealth literacy: standardized β=0.09, 95% CI 0.07-0.10), and key traits (self-efficacy: standardized β=0.10, 95% CI 0.09-0.12; personal meaning: standardized β=0.06, 95% CI 0.04-0.07). Other positive predictors included having 2 types of medical insurance (standardized β=0.07, 95% CI 0.04-0.09), stable sleep duration (standardized β=0.07, 95% CI 0.03-0.10), and childhood psychological abuse (standardized β=0.07, 95% CI 0.05-0.09). Strong negative predictors included older age (standardized β=-0.08, 95% CI -0.10 to -0.06), couple-only household (standardized β=-0.08, 95% CI -0.11 to -0.04), childhood sexual abuse (standardized β=-0.08, 95% CI -0.10 to -0.06), attention-deficit/hyperactivity disorder (standardized β=-0.07, 95% CI -0.09 to -0.05), more siblings (standardized β=-0.07, 95% CI -0.08 to -0.05), childhood physical abuse (standardized β=-0.05, 95% CI -0.06 to -0.03), and collective violence exposure (standardized β=-0.05, 95% CI -0.07 to -0.03). The 6-node classification tree showed modest discrimination (test area under the curve=0.61; accuracy=0.681), high specificity (specificity=0.903), and low sensitivity (sensitivity=0.242), suggesting better identification of likely nonacceptors than likely acceptors.

CONCLUSIONS

Acceptance of immersive XR in health care in China was moderate but uneven. Adoption varied by age, region, socioeconomic resources, digital capital, psychosocial factors, household context, health status, and adversity exposure, suggesting that XR implementation is both a digital health innovation and a health equity challenge. Deployment should include targeted education, accessible demonstrations, usability support, and trusted guidance for less accepting groups, especially older adults, socioeconomically disadvantaged groups, people with limited digital health experience, and psychosocially vulnerable populations.