The within-person association between alcohol use and sleep duration and quality in situ: An experience sampling study

Abstract

Objective Despite evidence for detrimental effects of alcohol on sleep quality in laboratory studies, alcohol is commonly used as a self-prescribed sleep aid. This study examined the within-person associations of alcohol use with sleep duration and quality in everyday life to gain insight into the ecological validity of laboratory findings on the association between sleep and alcohol. Method A sample of 150 adults (age 19–89 years) were followed for 60+ days as part of an intensive experience sampling study wherein participants provided daily reports of their alcohol use, sleep duration, and sleep quality. Within-person and between-person associations of daily sleep duration and quality with alcohol use were examined using multilevel models. Results A significant, negative within-person association was observed between sleep quality and alcohol use. Sleep quality was lower on nights following alcohol use. Sleep duration did not vary as a function of within-person variation in alcohol use. Conclusions In line with laboratory assessments, alcohol use was associated with low sleep quality but was not associated with sleep duration, suggesting that laboratory findings generalize to everyday life. This examination of individuals’ daily lives suggests that alcohol does not systematically improve sleep quality or duration in real life.

Association of Dietary Behaviors and Sleep Quality: Results from the Adults Chronic Diseases and Risk Factors Survey of 2015 in Ningbo, China

“…Notably, a significantly positive association between PSQI total score and alcohol consumption was also observed in our study. Previous studies also reported significant associations between alcohol consumption and sleep quality [ 8 , 9 , 10 , 11 ]. This is due to the fact that alcohol could reduce genioglossal muscle tone, lead to upper airway collapse [ 41 ] and increase the upper airway resistance [ 42 ], and finally contributed to the risk of obstructive sleep apnoea (OSA) [ 8 ].…”

“…It is well known that poor sleep quality contributes significantly to cancer [ 3 ], depression [ 4 ], and dementia [ 5 ]. Unhealthy habits, such as smoking [ 6 , 7 ] and drinking [ 8 , 9 , 10 , 11 ] are associated with poor sleep quality. Besides, the sleep quality of adults has decreased along with lifestyle changes [ 12 , 13 ].…”

“…Epidemiological evidence indicated a positive relationship between dietary behaviors and sleep quality [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. Alcohol consumption was associated with a higher risk of sleep apnoea in adults [ 8 ].…”

Daily sleep quality affects drug craving, partially through indirect associations with positive affect, in patients in treatment for nonmedical use of prescription drugs

“…Furthermore, the current study examined the association between sleep and craving, and not treatment outcome. Participants exhibited dependence for a range of substances and some were on medications (Table 1), including substances that exert influences on affect and sleep (Lydon et al, 2016; Oberndorfer et al, 2000; Weddington et al, 1990). While this polydrug and medication use does not allow conclusions to be drawn on associations between sleep, craving, affect, and NMUPD specifically, it is representative of NMUPD in the context of other drugs observed in nationally representative samples (McCabe et al, 2008; McCabe et al, 2007).…”

The Promise and Challenges of Intensive Repeated Measures for Cognitive Neuroscience Models of Adolescent Substance Use

“…Recent innovations in intensive repeated measures data collection, whereby individuals are repeatedly assessed over timescales much longer than typical scans (e.g., 5 hours of resting state data collection), are providing opportunities to reliably characterize individuals without relying on group level data (Filevich et al, 2017;Gordon et al, 2017;Laumann et al, 2015) Notably, while the intensive repeated measures associated with fMRI data lend themselves to person-specific models, time series approaches capable of capturing heterogeneity across persons may also be applied to behavioral data. This is routinely done (e.g., Lydon et al, 2016;Russell et al, 2016) within the context of multilevel models that accommodate the nested nature (with measurement occasions nested within participants) of repeated measures data (Snijders & Bosker, 2012). In this approach, random effects may be specified for an independent variable.…”