Sleep Well Despite Persistent Pain Symptoms

Background: Insomnia is 8-10 times more prevalent among patients with chronic pain than in the general population. The disrupted sleep restoration caused by insomnia can be exhausting for the patient as insomnia also impairs pain inhibition. Sleep medications often yield short-lived clinical responses and further introduce risks of "hangover", tolerance, and even addiction. Psychological sleep treatment is thus clearly preferable given sufficient effect.

Cognitive Behavioral Therapy is the recommended first-line treatment of insomnia (CBT-i). Psychological interventions are also recognized in the treatment and follow-up of chronic pain. However, the evidence of psychological treatment of comorbid insomnia in patients with chronic pain is very limited, although some few results are promising .

Insomnia is the most common sleep disorder and involves nocturnal symptoms (e.g. long sleep onset latency, nightly awakenings, and early morning awakening) and daytime problems (e.g. fatigue, mood swings, concentration difficulties, impaired function at work, school, or in social life). Acute insomnia symptoms triggered by adverse life events or acute illnesses are normal, whereas the transition to chronic insomnia is not. Chronic pain is a chronic stressor and may act both as a precipitating and perpetuating factor for insomnia, generating high levels of stress-activating hormones.

Rumination on the causes of pain is frequently observed in chronic pain, often combined with a cognitive inclination towards catastrophizing (i.e., repetitive, intrusive, and difficult-to-ignore thoughts). Meta-cognitions, such as rumination and catastrophizing may perpetuate insomnia by maintaining a high level of sleep-disruptive psychophysiological arousal and loss of deep sleep. Affective and anxiety disorders are other frequent comorbidities of chronic pain, that may further aggravate the effects of rumination and catastrophizing on both sleep and pain.

Meta-Cognitive Therapy (MCT) has well-documented effects on depression and anxiety disorders. Studies of MCT in insomnia are limited, which is unfortunate as it seems to modulate symptoms substantially across multiple health conditions. The advantage of MCT is the focus on how one relates to, evaluates, monitors, or reacts emotionally to internal thoughts rather than their specific content or experienced truth as in CBT. Change in dysfunctional cognitions about sleep is a central element of CBT-i. The strong association between insomnia and sleep-related metacognitions (thought patterns), rather than negative sleep-related cognitions (thought content), suggests that psychological treatment of insomnia should include MCT.

The sleep intervention for the present intervention study (hereafter "Sleep-Well") applies a modified version of a Norwegian CBT-i manual by the Diakonhjemmet hospital in Oslo, integrating elements of MCT. Sleep-Well is group-based and covers four modules; i.e. 1 (introduction), information about sleep physiology, causes of sleep problems, sleep hygiene and the use of sleep diaries; 2 (behavioral strategies), concerns stimulus control and sleep restriction; 3 (cognitive strategies) represent a CBT/MCT part focusing on sleep-disturbing cognitions and metacognition common to pain and sleep; and 4, maintenance and prevention of relapse.

Hypotheses: This trial examines if the Sleep-Well intervention performs better than the "treatment as usual" (TAU) group, i.e., rudimentary sleep evaluation and education plus sleep medication on indication, in terms of statistically significant improvements in:

1. (primary hypotheses) self-reported insomnia symptoms, diagnostic criteria for insomnia, health-related quality of life, and actigraphy recorded indicators of insomnia (i.e., SOL-sleep onset latency, WASO-wakeup after sleep onset, EMA-early morning awakening and SE-sleep efficiency).
2. (secondary hypotheses) self-reported symptoms of fatigue, symptoms of anxiety and depression, pain-symptoms related to acceptance, intensity and interference, and need for sleep medication. In addition, improvements in objective sleep recordings (the Home Sleep Test) related to the proportion and duration of slow-wave sleep periods, and sleep disturbance as indicated by the EEG-based arousal-index.

In addition, this trial explores

• whether mediator variables as described in the Measurement Outcomes section (under other variables) may identify treatment mechanisms related to the Sleep-Well program.
• through qualitative interviews participants' perceived feasibility and acceptability of Sleep-Well, and if qualitative contrasts are present between those benefitting well versus poorly with regard to primary insomnia symptoms.

Study design and procedures: This study uses a randomized semi-crossover design. Participants are randomized to the Sleep-Well or the active treatment-as-usual (TAU) comparator condition receiving standard sleep treatment at the hospital or their general practitioner. Participants are block randomized (block size = 4) to maintain equal treatment and TAU group sizes. After three months of waiting time, the TAU participants cross over to the Sleep-Well group.

The outcome measurements are registered at baseline (before the randomization), and after finishing the Sleep-Well intervention (posttest ~3 months). In addition, we conduct two follow-ups at three and 12 months to examine relapse. The TAU group carries out two pre-tests (before randomization, and before initiation of Sleep-Well) to control for sleep function changes during the waiting period.

Statistical power and analyses Power calculation: A meta-analysis of comparable somatic diagnoses (5 studies) indicated a standardized mean difference (Cohen´s d) between intervention and control ranging between 0.60-0.80 (Tang et al., 2015). With randomization done on an individual level, a correlation = 0.5 between pre- and post-test (R-sq=25%), alpha = 0.05, power = 0.80, and minimum Cohen´s d = 0.60, at least N = 68 patients are required to reject the null hypothesis. With an estimate of ICC (intra-class correlation) = .05 (adjustment for similarities that occur between patients due to sharing of a treatment group, cluster size=6), which yields a design effect of 1.25 (1+(6-1)*ICC), at least N = 85 participants are needed. By adding a safety margin of 20% dropout, the final sample estimate is N = 106 patients.

Statistical analyses: Generalized linear mixed models (GenLinMixed) are used as a general rule due to great flexibility in handling both continuous, binary, and count-based outcome variables, and estimation of random effects for adjustment purposes due to dependency in the outcome measures at different levels (e.g. repeated data, treatment group clusters). The analyses are carried out conservatively as intention-to-treat. Alpha < .05 and .01 are assumed for the hypotheses about primary sleep treatment outcomes (insomnia, sleep quality) and secondary/exploratory treatment effects (affective health, fatigue, quality of life) and moderators/mediators, respectively.

Qualitative analyses: The transcribed semi-structured interview data will be analyzed using the reflexive thematic analysis methodology, which across five steps seeks to identify the number of overall conceptual dimensions that meaningfully account for all the interview texts. The Consolidated Criteria for Reporting Qualitative Research will be applied.

Interaction with user representatives: The project has included two user representatives with first-hand experiences with chronic pain. They actively participate in multiple parts of the research process and are particularly consulted in questions related to relevance (e.g., regarding questionnaires and the content of Sleep-Well), the process of implementing the Sleep-Well intervention, and on issues related to avoiding over-burdening the participants.

Ethics: The Sleep-Well study has been approved by the Regional Committee for Medical and Health Research Ethics (CaseID: 500457). The project is assessed by the Center for Information Security and Privacy in Research (SIKT), and the Privacy Commissioner at UiT The Arctic University of Norway regarding Data Protection Impact Assessment (DPIA). Initiation of the study will be pending upon approval by all parties.