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.

BACKGROUND: Body weight variability (BWV) is common in the general population and may act as a risk factor for obesity or diseases. The correct identification of these patterns may have prognostic or predictive value in clinical and research settings. With advancements in technology allowing for the frequent collection of body weight data from electronic smart scales, new opportunities to analyze and identify patterns in body weight data are available.

OBJECTIVE: This study aims to compare multiple methods of data imputation and BWV calculation using linear and nonlinear approaches.

METHODS: In total, 50 participants from an ongoing weight loss maintenance study (the NoHoW study) were selected to develop the procedure. We addressed the following aspects of data analysis: cleaning, imputation, detrending, and calculation of total and local BWV. To test imputation, missing data were simulated at random and using real patterns of missingness. A total of 10 imputation strategies were tested. Next, BWV was calculated using linear and nonlinear approaches, and the effects of missing data and data imputation on these estimates were investigated.

RESULTS: Body weight imputation using structural modeling with Kalman smoothing or an exponentially weighted moving average provided the best agreement with observed values (root mean square error range 0.62%-0.64%). Imputation performance decreased with missingness and was similar between random and nonrandom simulations. Errors in BWV estimations from missing simulated data sets were low (2%-7% with 80% missing data or a mean of 67, SD 40.1 available body weights) compared with that of imputation strategies where errors were significantly greater, varying by imputation method.

CONCLUSIONS: The decision to impute body weight data depends on the purpose of the analysis. Directions for the best performing imputation methods are provided. For the purpose of estimating BWV, data imputation should not be conducted. Linear and nonlinear methods of estimating BWV provide reasonably accurate estimates under high proportions (80%) of missing data.

Osteoarthritis (OA) is defined by clinical symptoms and radiological signs. Cartilage is crucial in the development, but all tissue components in and around the joint are affected by the disease. OA aetiopathogenesis is multifactorial and may be primary (idiopathic) or secondary, with an influence of heritable factors. Contributing to OA development are age, joint trauma, other joint diseases, and overweight/obesity. The latter is of special interest being modifiable, and weight loss has proven very effective on symptoms of OA. Over the course of OA, inflammatory flares may be experienced, some associated with crystal formation in the joint, which opens for possible treatments, as argued in this review.

Objective: To investigate whether a dose-response relationship exists between volume of exercise and discontinuation of glucose-lowering medication treatment in patients with type 2 diabetes. Patients and Methods: Secondary analyses of a randomized controlled exercise-based lifestyle intervention trial (April 29, 2015 to August 17, 2016). Patients with non–insulin-dependent type 2 diabetes were randomly assigned to an intensive lifestyle intervention (U-TURN) or standard-care group. Both groups received lifestyle advice and objective target-driven medical regulation. Additionally, the U-TURN group received supervised exercise and individualized dietary counseling. Of the 98 randomly assigned participants, 92 were included in the analysis (U-TURN, n=61, standard care, n=31). Participants in the U-TURN group were stratified into tertiles based on accumulated volumes of exercise completed during the 1-year intervention. Results: Median exercise levels of 178 (interquartile range [IQR], 121-213; lower tertile), 296 (IQR, 261-310; intermediate tertile), and 380 minutes per week (IQR, 355-446; upper tertile) were associated with higher odds of discontinuing treatment with glucose-lowering medication, with corresponding odds ratios of 12.1 (95% CI, 1.2-119; number needed to treat: 4), 30.2 (95% CI, 2.9-318.5; 3), and 34.4 (95% CI, 4.1-290.1; 2), respectively, when comparing with standard care. Cardiovascular risk factors such as glycated hemoglobin A_1c levels, fitness, 2-hour glucose levels, and triglyceride levels were improved significantly in the intermediate and upper tertiles, but not the lower tertile, compared with the standard-care group. Conclusion: Exercise volume is associated with discontinuation of glucose-lowering medication treatment in a dose-dependent manner, as are important cardiovascular risk factors in well-treated participants with type 2 diabetes and disease duration less than 10 years. Further studies are needed to support these findings. Study Registration: ClinicalTrials.gov registration (NCT02417012).