Do obese but metabolically normal women differ in intra-abdominal fat and physical activity levels from those with the expected metabolic abnormalities? A cross-sectional study

<p>Abstract</p> <p>Background</p> <p>Obesity remains a major public health problem, associated with a cluster of metabolic abnormalities. However, individuals exist who are very obese but have normal metabolic parameters. The aim of this study was to determine to what extent differences in metabolic health in very obese women are explained by differences in body fat distribution, insulin resistance and level of physical activity.</p> <p>Methods</p> <p>This was a cross-sectional pilot study of 39 obese women (age: 28-64 yrs, BMI: 31-67 kg/m²) recruited from community settings. Women were defined as 'metabolically normal' on the basis of blood glucose, lipids and blood pressure. Magnetic Resonance Imaging was used to determine body fat distribution. Detailed lifestyle and metabolic profiles of participants were obtained.</p> <p>Results</p> <p>Women with a healthy metabolic profile had lower intra-abdominal fat volume (geometric mean 4.78 l [95% CIs 3.99-5.73] vs 6.96 l [5.82-8.32]) and less insulin resistance (HOMA 3.41 [2.62-4.44] vs 6.67 [5.02-8.86]) than those with an abnormality. The groups did not differ in abdominal subcutaneous fat volume (19.6 l [16.9-22.7] vs 20.6 [17.6-23.9]). A higher proportion of those with a healthy compared to a less healthy metabolic profile met current physical activity guidelines (70% [95% CIs 55.8-84.2] vs 25% [11.6-38.4]). Intra-abdominal fat, insulin resistance and physical activity make independent contributions to metabolic status in very obese women, but explain only around a third of the variance.</p> <p>Conclusion</p> <p>A sub-group of women exists who are metabolically normal despite being very obese. Differences in fat distribution, insulin resistance, and physical activity level are associated with metabolic differences in these women, but account only partially for these differences. Future work should focus on strategies to identify those obese individuals most at risk of the negative metabolic consequences of obesity and on identifying other factors that contribute to metabolic status in obese individuals.</p

Using social and behavioural science to support COVID-19 pandemic response

The COVID-19 pandemic represents a massive global health crisis. Because the crisis requires large-scale behaviour change and places significant psychological burdens on individuals, insights from the social and behavioural sciences can be used to help align human behavior with the recommendations of epidemiologists and public health experts. Here we discuss evidence from a selection of research topics relevant to pandemics, including work on navigating threats, social and cultural influences on behaviour, science communication, moral decision-making, leadership, and stress and coping. In each section, we note the nature and quality of prior research, including uncertainty and unsettled issues. We identify several insights for effective response to the COVID-19 pandemic, and also highlight important gaps researchers should move quickly to fill in the coming weeks and months

Prognostic factors for intervention effect on neck/shoulder symptom intensity and disability among female computer workers

Introduction It has been suggested that treatments may be more effective when they are matched to patient characteristics. This study aimed at investigating potential prognostic factors for clinically relevant improvement in symptom intensity and symptom-related disability among employees with symptoms in the neck/shoulder area, receiving either ergonomics counseling or such counseling in combination with myofeedback training. Methods A randomized controlled study was performed among female computer users aged 45 or older (n = 36). A clinical examination and a questionnaire survey were performed before inclusion in the study. Symptom intensity and disability was assessed using questionnaires before the start of the interventions (baseline) and at follow-ups directly after the end of the interventions (T0) and after 3 (T3) and 6 (T6) months. Logistic regression analyses were performed in order to assess prognostic factors for clinically relevant improvement in symptom intensity and disability. Results Improvement in symptom intensity was consistently predicted by symptom intensity at baseline. Diagnosis and stress-induced lack of muscular rest were prognostic factors for improvement in symptom intensity at short term follow-up. Baseline disability and passive coping consistently served as prognostic factors for outcome in disability. Few substantial differences were found between the interventions in terms of prognostic factors. Conclusions Myofeedback training in combination with ergonomics counseling seem to be an especially beneficial tool for secondary prevention among employees with moderate levels of symptom intensity and symptom-related disability, who respond to work-related stress by increased/sustained muscle activation, and who tend to employ passive coping to deal with their neck/shoulder symptoms