LAUSR

The present study aimed to examine the multi-faceted self-supporting ability profiles of rural empty-nesters in northwestern China on the basis of the self-care ability, economic self-support ability, health self-maintenance ability, physical health self-maintenance ability, and psychological health self-maintenance ability using latent profile analysis. It identified the association of self-supporting ability profiles with demographic variables and sense of coherence. The analysis included 1066 participants (mean age = 70.2; SD = 4.3). The results of latent profile analysis identified three distinctive patterns of self-supporting ability―low physical health self-maintenance ability (C1, 20.5%), low psychological health self-maintenance ability (C2, 31.4%), and high social self-adaption ability (C3, 48.0%). The specific demographic variable age (p < 0.05), monthly income (p < 0.05), education level (p < 0.05), how often their children visit (p < 0.05), how often their children contact them (p < 0.05), whether they drink (p < 0.05), the frequency of physical exercise (p < 0.05), relationship with children (p < 0.05), relationship with neighbours (p < 0.05), medical insurance (p < 0.05), and the number of chronic diseases (p < 0.05) were significantly different among the identified three profiles. A statistically significant positive association existed between self-supporting ability profiles and sense of coherence (SOC) (p < 0.001). The results of multinomial logistic regression showed that a greater sense of coherence (SOC), age ≥ 80, monthly income (RMB) (RMB is the abbreviation for Renminbi) < 1000, a good relationship with neighbours, and one type of chronic disease were significantly associated with C1 when compared with C3 (p < 0.05). Furthermore, a greater SOC, their children visiting and contacting them many times per week or once per week were more significantly related to C2 than to C3 (p < 0.05). This study revealed three groups of self-supporting ability and its related predictors in empty-nesters. The predictors related to particular classes of self-supporting ability can provide information for targeted interventions to improve the self-supporting ability of empty-nesters living in rural areas.