Day-to-day association of napping with subsequent nighttime sleep characteristics among older adults
Centre de Recherche en Épidémiologie et Statistiques (CRESS), UMR 1153, Inserm – Université Paris Cité
Team: Équipe EpiAgeing (Epidemiology of Ageing & Neurodegenerative diseases)
Location: Université Paris Cité, Inserm U1153, 10 avenue de Verdun, 75010 Paris
Team manager: Pre Archana Singh-Manoux, Dre Séverine Sabia
Supervisor: Dre Clémence Cavaillès
Topic: Sleep patterns change with age, with older adults experiencing more fragmented and shorter nighttime sleep, increased daytime sleepiness, and more frequent naps during the day. While daytime napping may help compensate for poor sleep during the night, it may also influence subsequent nighttime sleep.
However, findings from previous studies in older adults are inconsistent. Some research has suggested that napping is associated with improved nighttime sleep outcomes, while others reported detrimental effects. This is important as poor sleep has been linked to various adverse health outcomes, including cardiovascular diseases, dementia, and mortality. Moreover, many of these studies use self-reported nap/sleep data, involve small sample sizes, and lack comprehensive assessments of nap/sleep characteristics. Consequently, it remains unclear how napping, and specific aspects such as nap frequency, duration, and timing, may differently influence nighttime sleep patterns.
In the Whitehall II cohort, 4,000 participants aged 60 to 83 underwent an in-depth assessment of their 24-hour sleep-wake patterns. They wore a wrist accelerometer for nine full days, allowing for objective assessment of both daytime napping (frequency, duration, timing) and nighttime sleep (duration, quality, fragmentation), while accounting for their dependency. Understanding the link between objectively measured daytime naps and nighttime sleep is essential for developing sleep recommendations that support healthy aging.
This project aims to examine the day-to-day association of daytime napping with subsequent nighttime sleep characteristics among older adults.
Linear mixed models will be performed to investigate these objectives. Analyses will be controlled for numerous confounding factors thanks to the detailed data collection of socio-demographic, lifestyle, and health measures within the Whitehall II study. A good understanding of statistical models, some basis in epidemiology and very good knowledge of programming (R software) is essential to carry out the analyses successfully.
Contact : Clémence Cavaillès, email : clemence.cavailles@inserm.fr
