A latent profile analysis of emotional expression among patients with unintentional injuries.

BACKGROUND: Emotional expression has been suggested to affect the well-being of individuals with unintentional injuries. However, few studies have investigated it as a heterogeneous phenomenon. The purpose of this study was to characterize the patterns of emotional expression among patients with unintentional injuries using latent profile analysis, and to examine the relationship among these latent profiles and cognitive processing, posttraumatic growth, and posttraumatic stress disorder. METHODS: A cross-sectional study was carried out at two general hospitals in Wenzhou, China. In total, 352 patients with unintentional injuries completed the socio-demographic questionnaire, Berkeley Expressivity Questionnaire, Ambivalence Over Emotional Expression Questionnaire, Event-Related Rumination Inventory, the Posttraumatic Growth Inventory, and PTSD Checklist-Civilian Version.  RESULTS: Three unique profiles were identified: high emotional expressivity (n = 238, 67.6%), moderate emotional expressivity (n = 45, 12.8%), and low emotional expressivity (n = 69, 19.6%). The ANOVA and chi-square tests demonstrated significant differences among the three groups concerning deliberate rumination and posttraumatic growth. Multinomial logistic regression analysis indicated that monthly income and time since injury significantly predicted profile membership. CONCLUSIONS: Most patients showed high emotional expressivity after an unintentional injury. Emotional expression profiles were associated with deliberate rumination and posttraumatic growth. Emotional expression interventions tailored for different profiles are warranted after an unintentional injury.

A fuzzy-set qualitative comparative analysis exploration of multiple paths to users' continuous use behavior of diabetes self-management apps.

BACKGROUND: Despite the obvious potential benefits of diabetes self-management apps, users' continuous use of diabetes self-management apps is still not widespread. Influential factors coexisted in information ecologies are likely to have a synthetic effect on users' continuous use behavior. However, it is less clear how factors in information ecologies combine to influence users' continuous use behavior. OBJECTIVE: The objectives of this study are to explore combinations of factors (perceived severity, information quality, service quality, system quality, and social influence) in information ecologies that lead to users' continuous use behavior of diabetes self-management apps and which combination is the most important. METHODS: Purpose sampling was used to recruit diabetes self-management app users from July 1, 2021 to January 31, 2022. Fuzzy-set qualitative comparative analysis (fsQCA) was then employed by conducting necessity and sufficiency analysis. RESULTS: In total 280 diabetes self-management app users participated. The necessity analysis indicated that no single factor was necessary to cause users' continuous use behavior, and the sufficiency analysis identified five different combinations of factors that lead to users' continuous use behavior. Of these five, the combination of high information quality, high service quality, and high social influence was found to be the most important path. CONCLUSIONS: Users' continuous use behavior of diabetes self-management apps results from the synergistic effects of factors in information ecologies. The five paths that directly contribute to users' continuous use, as well as the four user types preliminarily identified in this study may provide a reference for healthcare providers and app developers.