Heterogeneous trajectories of intrinsic capacity and their predictors: a Bayesian network analysis of a longitudinal cohort in Chinese nursing homes

OBJECTIVES: To identify distinct developmental trajectories of intrinsic capacity (IC) among nursing home residents in China and to elucidate the complex network relationships among their predictors using a Bayesian network model, guided by the Socio-Ecological Theory. METHODS: An 18-month longitudinal study was conducted with 592 older adults from three nursing homes in Northeast China from March 2022 to February 2024. IC was assessed at baseline (T0), 6 (T1), 12 (T2), and 18 (T3) months using the ICOPE screening tool. Latent growth mixture modeling (GMM) identified trajectory subgroups. A BN was constructed to model the interrelationships among significant predictors identified from multinomial logistic regression. RESULTS: Three distinct IC trajectories were revealed: 'High Functioning-Stable' (16.0%), 'Medium Functioning-Slow Decline' (40.5%), and 'Low Functioning-Rapid Decline' (43.4%). The Bayesian network model identified four direct predictors of trajectory membership: family function, number of chronic diseases, grip strength, and perceived stress. Health-promoting behaviors and loneliness were key indirect predictors, operating through mediators such as grip strength and perceived stress, respectively. CONCLUSION: Intrinsic capacity development among nursing home residents is heterogeneous and driven by a complex network of interlinked factors. Our findings advocate for a paradigm shift from single-factor interventions to integrated, multi-target strategies. Clinicians should simultaneously address family support, multimorbidity, physical capacity, and psychological well-being to effectively delay IC decline.