Sleep health network analysis based on questionnaire data from 35,808 subjects concerned by their sleep

Sleep Epidemiology

卷:1

Royant-Parola Sylvie¹  Philip Pierre²  Hartley Sarah³  Lopez Régis⁴  Gauld Christophe⁵  Micoulaud-Franchi Jean-Arthur⁶ 

[1] Inserm, U1061, Université Montpellier 1, Montpellier, France;

[2] Réseau Morphée, 2 Grande rue, 92380 Garches, France;

[3] UMR CNRS 8590 IHPST, Sorbonne University, Paris 1, France;

[4] APHP Hôpital Raymond Poincaré, Sleep Center, 92380 Garches, EA 4047, Université de Versailles Saint-Quentin en Yvelines, France;

[5] Department of Psychiatry, University of Grenoble, Avenue du Maquis du Grésivaudan, 38 000 Grenoble, France;

[6] Sleep Disorders Center, Department of Neurology, Gui-de-Chauliac Hospital, CHU Montpellier, France;

关键词: Symptom;    Diagnosis;    International classification of sleep disorders;    Network approach;    Personalized medicine;   

DOI  :  

来源: DOAJ

【 摘 要 】

The concept of sleep health encompasses variables such as sleep disturbances, sleep behaviors and symptoms of sleep disorders. It is an epidemiological and public health challenge, particularly because integrating multifactorial variables makes the modelisation of sleep health difficult. However, we can advance our understanding of sleep health by network analysis, which has already been used to conceptualize the interaction of variables in other health domains including sleep diagnostic criteria and the underlying structure of sleep disorder classification. A network analysis of sleep variables related to health shows the empirical associations between heterogeneous variables, and to identify central variables involved in sleep health. The aim of this study is to investigate sleep health through a multivariable network analysis approach. Studied sleep variables (n = 39), extracted from an online questionnaire accessible via the “Réseau Morphée”, are symptoms of sleep disorders, sleep disturbances, sleep behaviors, and comorbidities, collected in a large French population of subjects concerned by their sleep, who responded to an online questionnaire. We follow the network guidelines for the computational analysis of network estimation, inferences, and robustness. Complete data was available for 35,808 participants (mean age = 42.7 (SD=15.7); mean BMI = 24.7 (SD=5.1); average ESS score = 9.48 (SD=4.9; N = 14,600 ≥ 11 – 40.7%); average ISI score = 16.3 (SD=5.2; N = 21,926 ≥ 15 – 61.2%); average HAD-A = 9.6 (SD=3.9; N = 14,099 > 11 – 39.4%); average HAD-D = 6.6 (SD=3.9; N = 5,998 > 11 – 16.7%)). Among the 39 sleep variables, the four most central variables with a strong influence in the network in terms of strength are: “Non-Restorative Sleep” (belonging to the group “Sleep disturbances”), “Excessive Daytime Sleepiness” (belonging to the group “Symptoms of sleep disorders”), “Circadian irregularity” and “Chronic Sleep Deprivation” (both belonging to the group “Sleep behaviors”). The identification of these central variables has many implications, especially as they are not always included in health models and organization of sleep health prevention.

【 授权许可】

Unknown