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Study Objectives: This study examined empirically derived symptom cluster profiles among patients who present with insomnia using clinical data and polysomnography. Methods: Latent profile analysis was used to identify symptom cluster profiles of 175 individuals (63% female) with insomnia disorder based on total scores on validated self-report instruments of daytime and nighttime symptoms (Insomnia Severity Index, Glasgow Sleep Effort Scale, Fatigue Severity Scale, Beliefs and Attitudes about Sleep, Epworth Sleepiness Scale, Pre-Sleep Arousal Scale), mean values from a 7-day sleep diary (sleep onset latency, wake after sleep onset, and sleep efficiency), and total sleep time derived from an in-laboratory PSG. Results: The best-fitting model had three symptom cluster profiles: "High Subjective Wakefulness" (HSW), "Mild Insomnia" (MI) and "Insomnia-Related Distress" (IRD). The HSW symptom cluster profile (26.3% of the sample) reported high wake after sleep onset, high sleep onset latency, and low sleep efficiency. Despite relatively comparable PSG-derived total sleep time, they reported greater levels of daytime sleepiness. The MI symptom cluster profile (45.1%) reported the least disturbance in the sleep diary and questionnaires and had the highest sleep efficiency. The IRD symptom cluster profile (28.6%) reported the highest mean scores on the insomnia-related distress measures (eg, sleep effort and arousal) and waking correlates (fatigue). Covariates associated with symptom cluster membership were older age for the HSW profile, greater obstructive sleep apnea severity for the MI profile, and, when adjusting for obstructive sleep apnea severity, being overweight/obese for the IRD profile. Conclusions: The heterogeneous nature of insomnia disorder is captured by this data-driven approach to identify symptom cluster profiles. The adaptation of a symptom cluster-based approach could guide tailored patient-centered management of patients presenting with insomnia, and enhance patient care.

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.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.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.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.

Cognitive behavioral therapy for insomnia (CBT-I) has been shown to improve both sleep and depressive symptoms, but predictors of depression outcome following CBT-I have not been well examined. This study investigated how chronotype (i.e., morningness-eveningness trait) and changes in sleep efficiency (SE) were related to changes in depressive symptoms among recipients of CBT-I.Included were 419 adult insomnia outpatients from a sleep disorders clinic (43.20% males, age mean ± standard deviation = 48.14 ± 14.02). All participants completed the Composite Scale of Morningness and attended at least 4 sessions of a 6-session group CBT-I. SE was extracted from sleep diary; depressive symptoms were assessed using the Beck Depression Inventory (BDI) prior to (Baseline), and at the end (End) of intervention.Multilevel structural equation modeling revealed that from Baseline to End, SE increased and BDI decreased significantly. Controlling for age, sex, BDI, and SE at Baseline, stronger evening chronotype and less improvement in SE significantly and uniquely predicted less reduction in BDI from Baseline to End. Chronotype did not predict improvement in SE.In an insomnia outpatient sample, SE and depressive symptoms improved significantly after a CBT-I group intervention. All chronotypes benefited from sleep improvement, but those with greater eveningness and/or less sleep improvement experienced less reduction in depressive symptom severity. This suggests that evening preference and insomnia symptoms may have distinct relationships with mood, raising the possibility that the effect of CBT-I on depressive symptoms could be enhanced by assessing and addressing circadian factors.

Patients undergoing treatment for cancer experience a myriad of disease- and treatment-related symptoms. Fatigue, excessive daytime sleepiness, pain, depressed mood, and disturbed sleep are among the most common symptoms self-reported by patients undergoing treatment for cancer.1–4 The rate of insomnia in cancer patients exceeds that of the general population and varies according to the definition used to make this diagnosis. The percentage of patients meeting formal criteria for an insomnia disorder has been reported at 19%,5 and patients self-reporting disturbed sleep is as high as 63%.6 Nocturnal sleep duration, as measured objectively by ambulatory polysomnography, has been shown to be shorter in patients with cancer.7 In addition to decreased total sleep time at night, patients demonstrate increased sleep fragmentation and increased sleep time during the day. Understanding the causal direction among these symptoms will lead to improved symptom management strategies. This is especially important in the context of cancer, in which symptom management is needed to assure delivery of optimal anti-neoplastic treatment. Fatigue can be a dose-limiting toxicity, leading to dose reduction or treatment discontinuation, thereby reducing treatment efficacy. Several recent studies have shown a systematic relation between sleep, depression, pain, and fatigue. For example, insomnia has been linked with increased rates of depression, decreased quality of life, and increased fatigue in other patient populations.8–10 Recent work has also shown decreased pain thresholds in research subjects following sleep deprivation.11 Clinical management of cancer is complicated by these symptoms, and supportive care interventions may be needed to facilitate the successful delivery of planned treatment regimens. It is difficult to understand the significance of a single symptom in isolation since there are complex patterns of co-variation expected among these symptoms. In the past it was thought that insomnia occurred as a consequence of depression, such that the term secondary insomnia was used to describe insomnia in the presence of depression. Studies have now shown that individuals with chronic insomnia are at increased risk to develop depression,12 suggesting a bi-directional relation between these conditions. Fatigue and pain have both been shown to commonly co-occur with insomnia. The same pattern has been noted in the relation between pain and disturbed sleep, such that pain is noted to cause insomnia, and short sleep causes increased pain.11,13 Although these relationships have been shown in non-cancer patient populations, they would be expected to extend to cancer patients. Awareness of the common co-occurrence of cancer-related symptoms has led to emerging research on a set of symptoms, or symptom cluster, rather than individual symptoms in isolation. A symptom cluster has been defined as a group of symptoms that are interrelated but do not necessarily share a common etiology.14 Pain, fatigue, sleep disturbance, and depressed mood have received notable attention as a symptom cluster.3,4,14,15 One study found that a subgroup of outpatients who reported high levels on all symptoms in the cluster reported the lowest quality of life scores.3 Another study found a 3-way interaction between pain, fatigue, and insomnia, with no significant change when covariates of age, comorbidities, and stage of cancer were added.4 Understanding how these common symptoms affect quality of life and treatment outcome in the context of cancer is important but difficult. While previous research has focused on the identification of a symptom cluster or subgroup of patients with a specific symptom profile, the relationship among these symptoms in the context of cancer remains unclear. Small sample sizes typically limit the statistical analyses that can be conducted to examine the potential effects of one variable on another. However, when adequate sample sizes are available, structural equation modeling (SEM) provides a framework for simultaneously examining the relationship among many variables. This includes modeling of causal relations among variables, examination of direct and indirect effects, modeling of variables as latent or observed, and testing of competing models.16,17 Employing SEM to examine data from a large sample of cancer patients could yield important insights into the causal relationships among variables in a symptom cluster. To our knowledge, SEM has not been previously used to investigate the relationship among insomnia, fatigue, pain, and depressed mood in cancer, particularly with a cross-section of patients such as reported in this study. Therefore, the aim of this paper was to examine the relationship among these variables in a sample of patients with cancer, heterogeneous with respect to tumor type and stage of treatment, using SEM. This analysis was intended to be exploratory, and specifically to evaluate the interrelationships among these specific symptoms to develop a causal model to inform further research in this area.