Beyond the mean: A systematic review on the correlates of daily intraindividual variability of sleep/wake patterns

Abstract

Features of an individual's sleep/wake patterns across multiple days are governed by two dimensions, the mean and the intraindividual variability (IIV). The existing literature focuses on the means, while the nature and correlates of sleep/wake IIV are not well understood. A systematic search of records in five major databases from inception to November 2014 identified 53 peer-reviewed empirical publications that examined correlates of sleep/wake IIV in adults. Overall, this literature appeared unsystematic and post hoc, with under-developed theoretical frameworks and inconsistent methodologies. Correlates most consistently associated with greater IIV in one or more aspects of sleep/wake patterns were: younger age, non-White race/ethnicity, living alone, physical health conditions, higher body mass index, weight gain, bipolar and unipolar depression symptomatology, stress, and evening chronotype; symptoms of insomnia and poor sleep were associated with higher sleep/wake IIV, which was reduced following sleep interventions. The effects of experimentally reduced sleep/wake IIV on daytime functioning were inconclusive. In extending current understanding of sleep/wake patterns beyond the mean values, IIV should be incorporated as an additional dimension when sleep is examined across multiple days. Theoretical and methodological shortcomings in the existing literature, and opportunities for future research are discussed.

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

“…This within-person approach is motivated by calls to increase our understanding of the dynamics involved in substance abuse recovery (McKay et al, 2006; Shiffman, 2009), at the micro, daily level (Zheng et al, 2015). The need for this approach is further underscored by findings of day-to-day variability in the constructs of craving, affect, and sleep (Bei et al, in press; Cleveland & Harris, 2010; Peacock et al, 2015). …”

Examining multiple sleep behaviors and diurnal salivary cortisol and alpha-amylase: Within- and between-person associations

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Examining the Variability of Sleep Patterns during Treatment for Chronic Insomnia: Application of a Location-Scale Mixed Model

“…We selected a simple model for illustrative purposes, but more sophisticated models can include covariates (e.g., age, partner status, BMI) to examine the impact of these variables on the mean and IIV of sleep efficiency or other sleep parameters. Such analyses can be used to test specific hypotheses and develop a theoretical framework for the antecedents and consequences of IIV, as recommended by Bei et al 3 Furthermore, the model could be used to explore different trajectories of change in IIV over time by treating time as a polynomial factor rather than a linear factor. This could yield important patterns regarding the patient's sleep experience during the course of treatment.…”

“…Furthermore, these analyses frequently did not control for the mean values when examining IIV. 3 Therefore, the authors recommended a two-dimensional approach when analyzing longitudinal sleep data that includes the mean as one dimension and the IIV as a second dimension. 3 Recently, a novel statistical technique called the locationscale mixed model has emerged which appears particularly well-suited for examining the mean and IIV as different dimensions of the sleep experience.…”

“…3 Therefore, the authors recommended a two-dimensional approach when analyzing longitudinal sleep data that includes the mean as one dimension and the IIV as a second dimension. 3 Recently, a novel statistical technique called the locationscale mixed model has emerged which appears particularly well-suited for examining the mean and IIV as different dimensions of the sleep experience. This model was developed for the analysis of intensive longitudinal data, and is an extension of a traditional linear mixed model (aka multilevel or hierarchical linear modeling; HLM) that accounts for the clustering of observations within subjects, but additionally allows the between-subjects (inter-individual) and within-subject…”