Background: The Danish Myelodysplastic Syndromes Database (DMDSD) comprises nearly all patients diagnosed with myelodysplastic syndromes (MDS) in Denmark since 2010. The DMDSD has not yet been used for epidemiological research and the quality of registered variables remains to be investigated.

Objective: To describe characteristics of the patients registered in the DMDSD and to calculate predictive values and the proportion of missing values of registered data records.

Methods: We performed a nationwide cross-sectional validation study of recorded disease and treatment data on MDS patients during 2010-2019. Patient characteristics and the proportion of missing values were tabulated. A random sample of 12% was drawn to calculate predictive values with 95% confidence intervals (CIs) of 48 variables using information from medical records as a reference standard.

Results: Overall, 2284 patients were identified (median age: 76 years, men 62%). Of these, 10% had therapy-related MDS, and 6% had an antecedent hematological disease. Hemoglobin level was less than 6.2 mmol/L for 59% of patients. Within the first two years of treatment, 59% received transfusions, 35% received erythropoiesis-stimulating agents, and 15% were treated with a hypomethylating agent. For the majority of variables (around 80%), there were no missing data. A total of 260 medical records were available for validation. The positive predictive value of the MDS diagnosis was 92% (95% CI: 88-95). Predictive values ranged from 64% to 100% and exceeded 90% for 36 out of 48 variables. Stratification by year of diagnosis suggested that the positive predictive value of the MDS diagnosis improved from 88% before 2015 to 95% after.

Conclusion: In this study, there was a high accuracy of recorded data and a low proportion of missing data. Thus, the DMDSD serves as a valuable data source for future epidemiological studies on MDS.

Introduction: In general, patients with type 2 diabetes have lower cardiorespiratory fitness levels and perform exercise at lower intensities compared to healthy controls. Since metformin (MET) has been shown to increase the rate of perceived exertion (RPE) during exercise with a fixed intensity, MET per se may reduce self-selected exercise intensity. The aim of this study was to assess the effect of MET on self-selected exercise intensity.

Methods: Healthy males were eligible for this crossover, counterbalanced study with two treatment periods: MET and placebo (PLA), each lasting 17 days. Treatment dose was gradually increased and reached 2 g/day on treatment day 9, and continued at that level for the rest of the treatment period. The two periods were performed in randomized order. Two experimental days (A+B) were conducted on Day 15 (A) and Day 17 (B) of each period, respectively. Day A consisted of an exercise bout with self-selected exercise intensity (equal to RPE = 14-15 on the Borg Scale). Day B consisted of an exercise bout with fixed intensity (70% of VO2peak). Oxygen consumption rate was assessed continuously during both exercise bouts.

Results: Fifteen males (age 23.7 ± 0.6 years, BMI 22.3 ± 2.0, VO2peak 3.5 ± 0.6 L/min) were included in the study. On Day B, RPE was higher in MET compared to PLA (14.8 ± 0.4 vs. 14.0 ± 0.3, P = 0.045). On Day A, no difference in self-selected exercise intensity measured by oxygen consumption rate (PLA 2.33 ± 0.09 L O2/min, MET 2.42 ± 0.10 L O2/min, P = 0.09) was seen between treatment periods.

Conclusions: Self-selected exercise intensity was not reduced by MET in healthy males, despite the fact that MET increased RPE during an exercise bout with fixed intensity.

INTRODUCTION: OMERACT uses an evidence-based framework known as the 'OMERACT Filter Instrument Selection Algorithm' (OFISA) to guide decisions in the assessment of outcome measurement instruments for inclusion in a core outcome set for interventional and observational clinical trials.

METHODS: A group of OMERACT imaging and patient-centered outcome methodologists worked with imaging outcome groups to facilitate the selection of imaging outcome measurement instruments using the OFISA approach. The lessons learned from this work influenced the evolution to Filter 2.2 and necessitated changes to OMERACT's documentation and processes.

RESULTS: OMERACT has revised documentation and processes to incorporate the evolution of instrument selection to Filter 2.2. These revisions include creation of a template for detailed definitions of the target domain which is a necessary first step for instrument selection, modifications to the Summary of Measurement Properties (SOMP) table to account for sources of variability, and development of standardized reporting tables for each measurement property.

CONCLUSIONS: OMERACT Filter 2.2 represents additional modifications of the OMERACT guide for working groups in their rigorous assessment of measurement properties of instruments of various types, including imaging outcome measurement instruments. Enhanced reporting aims to increase the transparency of the evidence base leading to judgements for the endorsement of instruments in core outcome sets.

There is substantial evidence documenting the effects of behavioural interventions on weight loss (WL). However, behavioural approaches to initial WL are followed by some degree of longer-term weight regain, and large trials focusing on evidence-based approaches to weight loss maintenance (WLM) have generally only demonstrated small beneficial effects. The current state-of-the-art in behavioural interventions for WL and WLM raises questions of (i) how we define the relationship between WL and WLM, (ii) how energy balance (EB) systems respond to WL and influence behaviours that primarily drive weight regain, (iii) how intervention content, mode of delivery and intensity should be targeted to keep weight off, (iv) which mechanisms of action in complex interventions may prevent weight regain and (v) how to design studies and interventions to maximise effective longer-term weight management. In considering these issues a writing team within the NoHoW Consortium was convened to elaborate a position statement, and behaviour change and obesity experts were invited to discuss these positions and to refine them. At present the evidence suggests that developing the skills to self-manage EB behaviours leads to more effective WLM. However, the effects of behaviour change interventions for WL and WLM are still relatively modest and our understanding of the factors that disrupt and undermine self-management of eating and physical activity is limited. These factors include physiological resistance to weight loss, gradual compensatory changes in eating and physical activity and reactive processes related to stress, emotions, rewards and desires that meet psychological needs. Better matching of evidence-based intervention content to quantitatively tracked EB behaviours and the specific needs of individuals may improve outcomes. Improving objective longitudinal tracking of energy intake and energy expenditure over time would provide a quantitative framework in which to understand the dynamics of behaviour change, mechanisms of action of behaviour change interventions and user engagement with intervention components to potentially improve weight management intervention design and evaluation.

BACKGROUND: Weight-loss programmes often achieve short-term success though subsequent weight regain is common. The ability to identify predictive factors of regain early in the weight maintenance phase is crucial.

OBJECTIVE: To investigate the associations between short-term weight variability and long-term weight outcomes in individuals engaged in a weight-loss maintenance intervention.

METHODS: The study was a secondary analysis from The NoHoW trial, an 18-month weight maintenance intervention in individuals who recently lost ≥5% body weight. Eligible participants (n = 715, 64% women, BMI = 29.2 (SD 5.0) kg/m2, age = 45.8 (SD 11.5) years) provided body-weight data by smart scale (Fitbit Aria 2) over 18 months. Variability in body weight was calculated by linear and non-linear methods over the first 6, 9 and 12 weeks. These estimates were used to predict percentage weight change at 6, 12, and 18 months using both crude and adjusted multiple linear regression models.

RESULTS: Greater non-linear weight variability over the first 6, 9 and 12 weeks was associated with increased subsequent weight in all comparisons; as was greater linear weight variability measured over 12 weeks (up to AdjR2 = 4.7%). Following adjustment, 6-week weight variability did not predict weight change in any model, though greater 9-week weight variability by non-linear methods was associated with increased body-weight change at 12 (∆AdjR2 = 1.2%) and 18 months (∆AdjR2 = 1.3%) and by linear methods at 18 months (∆AdjR2 = 1.1%). Greater non-linear weight variability measured over 12 weeks was associated with increased weight at 12 (∆AdjR2 = 1.4%) and 18 (∆AdjR2 = 2.2%) months; and 12-week linear variability was associated with increased weight at 12 (∆AdjR2 = 2.1%) and 18 (∆AdjR2 = 3.6%) months.

CONCLUSION: Body-weight variability over the first 9 and 12 weeks of a weight-loss maintenance intervention weakly predicted increased weight at 12 and 18 months. These results suggest a potentially important role in continuously measuring body weight and estimating weight variability.