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Insomnia Disorders: Nosology and Classification Past, Present, and Future
Shi-Hui Poon1, Shin-Yi Quek1, Tih-Shih Lee1
1Department of Neuroscience and Behavioral Disorders, Duke-NUS Medical School, Singapore (Poon, Lee); and Ng Teng Fong General Hospital, Singapore (Quek).
Summary
The definition of insomnia disorder has evolved across major systems, shifting focus from symptoms to treatment. Current classifications are works in progress, emphasizing standardization for research and diagnosis.
Area of Science:
- Sleep Medicine
- Psychiatry
- Medical Classification Systems
Background:
- Insomnia is a prevalent sleep disorder with evolving diagnostic criteria.
- Historical definitions of insomnia disorder have varied across major classification systems.
- Understanding the evolution of insomnia nosology is crucial for accurate diagnosis and treatment.
Purpose of the Study:
- To trace the evolution of insomnia disorder definitions across key classification systems.
- To examine the empirical basis for current insomnia disorder definitions.
- To survey future research directions for insomnia nosology.
Main Methods:
- Review of major insomnia classification systems: International Classification of Sleep Disorders, International Classification of Diseases, and DSM.
- Analysis of historical shifts in classification emphasis (symptomatology, etiology, treatment).
- Survey of ongoing research, including biomarker discovery and data-driven subtyping.
Main Results:
- Contemporary insomnia definitions show convergence across major classification systems.
- Classification emphasis has shifted from symptomatology to etiology and treatment implications.
- Insomnia subtypes have consolidated, with nonrestorative sleep criterion removal being controversial.
Conclusions:
- Current insomnia diagnostic classification systems are evolving and considered works in progress.
- Refinement and convergence of classification approaches are vital for standardizing insomnia research, diagnosis, and treatment.
- Future research directions include data-driven subtyping and transdiagnostic perspectives.
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