Distinguishing between excessive daytime sleepiness and fatigue: toward improved detection and treatment
Abstract
INTRODUCTION: Excessive daytime sleepiness (EDS) and fatigue occur in high percentages in the general population. They are common complaints in primary care and in specialty medicine. Although they may represent distinct or overlapping phenomena, the general medical literature does not normally distinguish between EDS and fatigue. Despite their prevalence, both EDS and fatigue are identified and treated in a relatively small proportion of those affected. The similarity of EDS and fatigue may create diagnostic ambiguity and thereby contribute to under-identification and under-treatment. Fatigue, in particular, is thought to be difficult to manage when it is identified.
METHODS: The literature was searched for reviews, meta-analysis and similar levels of papers focused on EDS or fatigue.
RESULTS: EDS and fatigue are operationalized in ways that contribute to blurring rather than to distinguishing between them. Existing measures of both EDS and fatigue may also contribute to their misidentification. Effective treatments for both symptoms have been established. Behavioral interventions are effective and underutilized.
DISCUSSION: We suggest more precise operationalization of EDS and fatigue, leading to a refinement of existing measures or development of new tools, a structured interview with fatigue and EDS sections in the clinical setting, and more consideration for behavioral interventions.
Read the paper
Related studies
- The Matrisome: In Silico Definition and In Vivo Characterization by Proteomics of Normal and Tumor Extracellular Matrices
- The Escherichia coli MG1655 in silico metabolic genotype: Its definition, characteristics, and capabilities
- In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling
- In silico prediction of splice-altering single nucleotide variants in the human genome
- In silico clinical trials: how computer simulation will transform the biomedical industry
- In silico optimization of a guava antimicrobial peptide enables combinatorial exploration for peptide design
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.