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Patterns of discussion on neuroticism and self-management behaviors in type 2 diabetes: a scoping review using
Wei Sun1, Dong Xie1, Menglin Zhang1
1School of Nursing, Changchun University of Chinese Medicine, Jilin, China.
Background:
Self-management behaviors, including diet control, medication adherence, blood glucose monitoring, and physical activity, are crucial for type 2 diabetes management. Neuroticism, a personality trait associated with anxiety and stress sensitivity, may significantly influence these behaviors. However, a comprehensive synthesis of evidence is lacking.
Objective:
This scoping review aims to systematically map and synthesize how neuroticism has been examined in relation to self-management behaviors among adults with type 2 diabetes, and to identify recurring thematic patterns and knowledge gaps through machine learning-assisted text mining.
Methods:
A scoping review was conducted in PubMed, Scopus, Web of Science, Embase, CINAHL, PsycINFO, and the Cochrane Library, covering the period from database inception to September 2025. The search strategy included keywords such as "neuroticism," "personality traits," "type 2 diabetes," "self-management," and "adherence." We used machine learning-assisted literature mining to summarize thematic patterns across included studies. The study selection process and workflow were conducted in accordance with the PRISMA-ScR guidelines.
Results:
Ten studies were included. Across the literature, neuroticism was most frequently discussed alongside blood glucose monitoring, followed by diet control, medication taking, and exercise. Psychological constructs such as anxiety, stress sensitivity, and social support were commonly co-mentioned in these discussions. Machine learning-assisted analyses highlighted recurring topics, concept clusters, and co-occurrence patterns that characterize the discourse on neuroticism and T2DM self-management.
Conclusion:
This scoping review characterizes how neuroticism is positioned within the discourse on T2DM self-management behaviors and delineates prominent thematic linkages and gaps. Machine learning-assisted text mining proved useful for organizing and visualizing dispersed evidence. Findings describe patterns in the literature rather than estimating causal effects, and can inform future hypothesis-driven studies and tailored clinical inquiry.
Systematic Review Registration:
Unique Identifier: 10.17605/OSF.IO/54NJD; publicly accessible URL: https://doi.org/10.17605/OSF.IO/54NJD.
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