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

Abstract

Study Objectives: The purpose of this study was to introduce a novel statistical technique called the location-scale mixed model that can be used to analyze the mean level and intra-individual variability (IIV) using longitudinal sleep data. Methods: We applied the location-scale mixed model to examine changes from baseline in sleep efficiency on data collected from 54 participants with chronic insomnia who were randomized to an 8-week Mindfulness-Based Stress Reduction (MBSR; n = 19), an 8-week Mindfulness-Based Therapy for Insomnia (MBTI; n = 19), or an 8-week self-monitoring control (SM; n = 16). Sleep efficiency was derived from daily sleep diaries collected at baseline (days 1-7), early treatment (days 8-21), late treatment (days 22-63), and post week (days 64-70). The behavioral components (sleep restriction, stimulus control) were delivered during late treatment in MBTI. Results: For MBSR and MBTI, the pre-to-post change in mean levels of sleep efficiency were significantly larger than the change in mean levels for the SM control, but the change in IIV was not significantly different. During early and late treatment, MBSR showed a larger increase in mean levels of sleep efficiency and a larger decrease in IIV relative to the SM control. At late treatment, MBTI had a larger increase in the mean level of sleep efficiency compared to SM, but the IIV was not significantly different. Conclusions:The location-scale mixed model provides a two-dimensional analysis on the mean and IIV using longitudinal sleep diary data with the potential to reveal insights into treatment mechanisms and outcomes. I NTRO DUCTI O NThe sleep disturbance experienced by individuals with insomnia tends to have considerable variability across nights.1,2 This experience is not captured by the mean level of sleep across nights, but instead requires examination of the intra-individual variability (IIV), or night-to-night variability of sleep. A recent review revealed inconsistent methodology among studies that examined IIV in sleep. The authors found that most studies of IIV used methods such as within-subject standard deviations, coefficient of variance, or mean square of successive differences. 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. 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 (intra-individual) variances to be modeled in ...

Family contexts and sleep during adolescence

“…To date, the mixed effects location-scale model has been used in only one other sleep study – an analysis of 54 participants with chronic insomnia (Ong, Hedeker, Wyatt, & Manber, 2016). We employed this modeling technique to estimate the associations between the family context measures and both mean and intra-individual variance in sleep, controlling for the demographic youth and caregiver characteristics and whether the reported sleep night was a school night or not.…”

Dismantling Mindfulness-Based Cognitive Therapy: Creation and validation of 8-week focused attention and open monitoring interventions within a 3-armed randomized controlled trial

“…The results of each allocation were recorded by the statistician and communicated to the PI, who was one of the co-therapists for each treatment. Following the methods of other multi-arm MBCT and MBSR trials (Ong, Hedeker, Wyatt, & Manber, 2016; Segal, et al, 2010) treatment allocation was performed at the group level. For example, each group of 10–12 participants was randomized into one of three treatments (i.e., group allocation) nine different times.…”

Can mindfulness‐based interventions improve outcomes in cognitive‐behavioural therapy for chronic insomnia disorder in the general population? Systematic review and meta‐analysis

“…Table 1 describes the main characteristics of these nine studies included. MBI protocols were MBSR (Gross et al, 2011;Ong et al, 2008Ong et al, , 2009Ong et al, , 2014Ong et al, , 2016Ong et al, , 2018 and MBCT (Lee et al, 2018;Wong et al, 2016), two adaptations of them that included CBT-I elements, respectively named mindfulness-based therapy for insomnia (MBTI) (Ong et al, 2014(Ong et al, , 2016(Ong et al, , 2018 and mindfulness-based cognitive therapy for insomnia (MBCT-I) (Heidenreich et al, 2006;Larouche et al, 2015;Wong et al, 2017), and free MBI protocols (Siritienthong et al, 2018;Vanhuffel et al, 2018). Duration of MBIs ranged between 6 and 8 weeks.…”