Journal of psychiatric research · 2026
Core insomnia symptoms associated with cognitive flexibility in insomnia disorder: A network analysis.
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Yang Shiyan, Zhang Zilu, Lei Xu
Аннотация
OBJECTIVE: Cognitive flexibility, as a key component of executive function, is often impaired in people with insomnia. This study combined network analysis and multiple linear regression methods aimed to systematically explore the insomnia symptoms associated with cognitive flexibility in individuals with insomnia.
METHOD: Based on 426 insomnia individuals who completed the Insomnia Severity Index (ISI) and the Cognitive Flexibility Inventory (CFI). The network model was construct between ISI items and CFI subdimensions. Furthermore, multiple linear regression analyses were performed to examine the predictive effects of specific insomnia symptoms on the subdimensions of cognitive flexibility.
RESULTS: Network analysis revealed that ISI6 (noticeability of impaired quality of life) exhibited the highest node strength and expected influence in the network. Linear regression findings indicated that ISI4 (dissatisfaction about current sleep pattern), ISI6 (noticeability of impaired quality of life) and ISI7 (worry about current sleep pattern) could significantly predict CFI_C; ISI2 (difficulty staying asleep) significantly predicted CFI_A.
CONCLUSION: These findings indicate that impaired quality of life is the core symptom of the "Insomnia-Cognitive Flexibility Network", and different dimensions of cognitive flexibility may be affected by different insomnia symptoms. This study enhances our understanding of the relationship between insomnia and cognitive flexibility in individuals and suggests new targets for personalized intervention in insomnia.
METHOD: Based on 426 insomnia individuals who completed the Insomnia Severity Index (ISI) and the Cognitive Flexibility Inventory (CFI). The network model was construct between ISI items and CFI subdimensions. Furthermore, multiple linear regression analyses were performed to examine the predictive effects of specific insomnia symptoms on the subdimensions of cognitive flexibility.
RESULTS: Network analysis revealed that ISI6 (noticeability of impaired quality of life) exhibited the highest node strength and expected influence in the network. Linear regression findings indicated that ISI4 (dissatisfaction about current sleep pattern), ISI6 (noticeability of impaired quality of life) and ISI7 (worry about current sleep pattern) could significantly predict CFI_C; ISI2 (difficulty staying asleep) significantly predicted CFI_A.
CONCLUSION: These findings indicate that impaired quality of life is the core symptom of the "Insomnia-Cognitive Flexibility Network", and different dimensions of cognitive flexibility may be affected by different insomnia symptoms. This study enhances our understanding of the relationship between insomnia and cognitive flexibility in individuals and suggests new targets for personalized intervention in insomnia.