BACKGROUND: More than half of patients with rheumatoid arthritis complain of insomnia, which is predominantly treated with hypnotic drugs. However, cognitive behavioural therapy for insomnia is recommended as the first-line treatment in international guidelines on sleep. Patients with rheumatoid arthritis suffer from debilitating symptoms, such as fatigue and pain, which can also be linked to sleep disturbance. It remains to be determined whether cognitive behavioural therapy for insomnia can be effective in patients with rheumatoid arthritis. The aim of the Sleep-RA trial is to investigate the efficacy of cognitive behavioural therapy for insomnia on sleep and disease-related symptoms in patients with rheumatoid arthritis. The primary objective is to compare the effect of cognitive behavioural therapy for insomnia relative to usual care on changes in sleep efficiency from baseline to week 7 in patients with rheumatoid arthritis. The key secondary objectives are to compare the effect of cognitive behavioural therapy for insomnia relative to usual care on changes in sleep onset latency, wake after sleep onset, total sleep time, insomnia, sleep quality, fatigue, impact of rheumatoid arthritis and depressive symptoms from baseline to week 26 in patients with rheumatoid arthritis.

METHODS: The Sleep-RA trial is a randomised controlled trial with a two-group parallel design. Sixty patients with rheumatoid arthritis, insomnia and low-to-moderate disease activity will be allocated 1:1 to treatment with cognitive behavioural therapy for insomnia or usual care. Patients in the intervention group will receive nurse-led, group-based cognitive behavioural therapy for insomnia once a week for 6 weeks. Outcome assessments will be carried out at baseline, after treatment (week 7) and at follow-up (week 26).

DISCUSSION: Data on treatment of insomnia in patients with rheumatoid arthritis are sparse. The Sleep-RA trial is the first randomised controlled trial to investigate the efficacy of cognitive behavioural therapy for insomnia in patients with rheumatoid arthritis. Because symptoms of rheumatoid arthritis and insomnia have many similarities, we also find it relevant to investigate the secondary effects of cognitive behavioural therapy for insomnia on fatigue, impact of rheumatoid arthritis, depressive symptoms, pain, functional status, health-related quality of life and disease activity. If we find cognitive behavioural therapy for insomnia to be effective in patients with rheumatoid arthritis this will add weight to the argument that evidence-based non-pharmacological treatment for insomnia in rheumatological outpatient clinics is eligible in accordance with the existing international guidelines on sleep.

TRIAL REGISTRATION: ClinicalTrials.gov: NCT03766100. Registered on 30 November 2018.

BACKGROUND: Individuals who share the same comorbidity profile are usually similar with regard to their disease severity, use of health care, and clinical outcomes. The identification of comorbidity clusters therefore bears prognostic information. The objective of this study was to identify and characterize comorbidity clusters in individuals with COPD in Denmark.

METHODS: Data from the Danish national registers were used. The study population included all individuals ≥16 y old who lived in the Danish Capital Region on January 1, 2012, and were diagnosed with COPD (N = 70,274). Comorbid chronic conditions were identified using diagnostic algorithms. A 2-step cluster analysis was performed.

RESULTS: 81% of subjects with COPD had chronic comorbidities; the most common was hypertension (47.6%), and the least common was anxiety (0.1%). Three comorbidity clusters were identified. Cluster 1 contained 16% of the studied individuals with COPD, with all having heart disease in addition to the remaining comorbidities. Cluster 2 contained 30% of the studied individuals with COPD, of whom approximately 1 in 3 suffered from allergies, while the rest had no comorbidities. Cluster 3 contained 54% of the studied individuals with COPD, where all comorbidities but heart disease were represented. Cluster 1 contained the highest proportion of individuals over the age of 65 y, as well as the individuals with the lowest education. After adjusting for sociodemographic characteristics, individuals in Cluster 1 had the highest rates of hospitalizations and bed days.

CONCLUSIONS: The presence of heart disease in individuals with COPD is a strong prognostic factor for socioeconomic and health vulnerability.

BACKGROUND: Several studies have suggested that reduced sleep duration and quality are associated with an increased risk of obesity and related metabolic disorders, but the role of sleep in long-term weight loss maintenance (WLM) has not been thoroughly explored using prospective data.

METHODS AND FINDINGS: The present study is an ancillary study based on data collected on participants from the Navigating to a Healthy Weight (NoHoW) trial, for which the aim was to test the efficacy of an evidence-based digital toolkit, targeting self-regulation, motivation, and emotion regulation, on WLM among 1,627 British, Danish, and Portuguese adults. Before enrolment, participants had achieved a weight loss of ≥5% and had a BMI of ≥25 kg/m2 prior to losing weight. Participants were enrolled between March 2017 and March 2018 and followed during the subsequent 12-month period for change in weight (primary trial outcome), body composition, metabolic markers, diet, physical activity, sleep, and psychological mediators/moderators of WLM (secondary trial outcomes). For the present study, a total of 967 NoHoW participants were included, of which 69.6% were women, the mean age was 45.8 years (SD 11.5), the mean baseline BMI was 29.5 kg/m2 (SD 5.1), and the mean weight loss prior to baseline assessments was 11.4 kg (SD 6.4). Objectively measured sleep was collected using the Fitbit Charge 2 (FC2), from which sleep duration, sleep duration variability, sleep onset, and sleep onset variability were assessed across 14 days close to baseline examinations. The primary outcomes were 12-month changes in body weight (BW) and body fat percentage (BF%). The secondary outcomes were 12-month changes in obesity-related metabolic markers (blood pressure, low- and high-density lipoproteins [LDL and HDL], triglycerides [TGs], and glycated haemoglobin [HbA1c]). Analysis of covariance and multivariate linear regressions were conducted with sleep-related variables as explanatory and subsequent changes in BW, BF%, and metabolic markers as response variables. We found no evidence that sleep duration, sleep duration variability, or sleep onset were associated with 12-month weight regain or change in BF%. A higher between-day variability in sleep onset, assessed using the standard deviation across all nights recorded, was associated with weight regain (0.55 kg per hour [95% CI 0.10 to 0.99]; P = 0.016) and an increase in BF% (0.41% per hour [95% CI 0.04 to 0.78]; P = 0.031). Analyses of the secondary outcomes showed that a higher between-day variability in sleep duration was associated with an increase in HbA1c (0.02% per hour [95% CI 0.00 to 0.05]; P = 0.045). Participants with a sleep onset between 19:00 and 22:00 had the greatest reduction in diastolic blood pressure (DBP) (P = 0.02) but also the most pronounced increase in TGs (P = 0.03). The main limitation of this study is the observational design. Hence, the observed associations do not necessarily reflect causal effects.

CONCLUSION: Our results suggest that maintaining a consistent sleep onset is associated with improved WLM and body composition. Sleep onset and variability in sleep duration may be associated with subsequent change in different obesity-related metabolic markers, but due to multiple-testing, the secondary exploratory outcomes should be interpreted cautiously.

TRIAL REGISTRATION: The trial was registered with the ISRCTN registry (ISRCTN88405328).

OBJECTIVE: To compare estimated treatment effects of physical therapy (PT) between patient-reported outcome measures (PROMs) and outcomes measured in other ways.

STUDY DESIGN AND SETTING: We selected randomized trials of PT with both a PROM and a non-PROM included in Cochrane systematic reviews (CSRs). Two reviewers independently extracted data and risk-of-bias assessments. Our primary outcome was the ratio of odds ratios (RORs), used to quantify how effect varies between PROMs and non-PROMs; an ROR > 1 indicates larger effect when assessed by using PROMs. We used REML-methods to estimate associations of trial characteristics with effects and between-trial heterogeneity.

RESULTS: From 90 relevant CSRs, 205 PT trials were included. The summary ROR across all the comparisons was not statistically significant (ROR, 0.88 [95% CI: 0.70-1.12]; P = 0.30); however, the heterogeneity was substantial (I2 = 88.1%). When stratifying non-PROMs further into clearly objective non-PROMs (e.g., biomarkers) and other non-PROMs (e.g., aerobic capacity), the PROMs appeared more favorable than did clearly objective non-PROMs (ROR, 1.92 [95% CI: 0.99-3.72]; P = 0.05).

CONCLUSION: Estimated treatment effects based on PROMs are generally comparable with treatment effects measured in other ways. However, in our study, PROMs indicate a more favorable treatment effect compared with treatment effects based on clearly objective outcomes.