The postexercise alteration in pulmonary gas exchange in high-aerobically trained subjects depends on both the intensity and the duration of exercise (G. Manier, J. Moinard, and H. Stoïcheff. J. Appl. Physiol. 75: 2580-2585, 1993; G. Manier, J. Moinard, P. Techoueyres, N. Varène, and H. Guénard. Respir. Physiol. 83: 143-154, 1991). In a recent study that used lung computerized tomography (CT), evidence was found for accumulation of water within the lungs after exercise (C. Caillaud, O. Serre-Cousine, F. Anselme, X. Capdevilla, and C. Prefaut. J. Appl. Physiol. 79: 1226-1232, 1995). On representative slices of the lungs, mean lung density increased by 0.040 +/- 0.007 g/cm(3) (19%, P < 0.001) in athletes after a triathlon. To verify and quantify the mechanism, we determined the change in pulmonary density and mass after strenuous and prolonged exercise using another exercise protocol and methodology for CT scanning. Nine trained runners (age 30-46 yr) volunteered to participate in the study. Each subject ran for 2 h on a treadmill at a rate corresponding to 75% of maximum O(2) consumption. CT measurements were made before and immediately after the exercise test with the subject supine and holding his breath at a point close to functional residual capacity. The lungs were scanned from the apex to the diaphragm and reconstructed in 8-mm-thick slices. Attenuation values of X-rays in each part of the lung were expressed in Hounsfield units (HU), which are related to density (D): D = 1 + HU/1,000. No significant alteration in pulmonary density (0.37 +/- 0.04 vs. 0.35 +/- 0.03, not significant) was observed after the 2-h run test. Although lung volume slightly increased (change of 166 +/- 205 ml, P < 0.05), lung mass remained stable because of a change in density distribution. We failed to detect any changes in postexercise lung mass, suggesting that other mechanisms need to be considered to explain the observed alterations in pulmonary gas exchange after prolonged strenuous exercise.

BackgroundMorbidity before retirement has a huge cost, burdening both public health and workplace finances. Multiple factors increase morbidity such as stress at work, sedentary behavior or low physical activity, and poor nutrition practices. Nowadays, the digital world offers infinite opportunities to interact with workers. The WittyFit software was designed to understand holistic issues of workers by promoting individualized behavior changes at the workplace.ObjectiveThe shorter term feasibility objective is to demonstrate that effective use of WittyFit will increase well-being and improve health-related behaviors. The mid-term objective is to demonstrate that WittyFit improves economic data of the companies such as productivity and benefits. The ultimate objective is to increase life expectancy of workers.MethodsThis is an exploratory interventional cohort study in an ecological situation. Three groups of participants will be purposefully sampled: employees, middle managers, and executive managers. Four levels of engagement are planned for employees: commencing with baseline health profiling from validated questionnaires; individualized feedback based on evidence-based medicine; support for behavioral change; and formal evaluation of changes in knowledge, practices, and health outcomes over time. Middle managers will also receive anonymous feedback on problems encountered by employees, and executive top managers will have indicators by division, location, department, age, seniority, gender and occupational position. Managers will be able to introduce specific initiatives in the workplace. WittyFit is based on two databases: behavioral data (WittyFit) and medical data (WittyFit Research). Statistical analyses will incorporate morbidity and well-being data. When a worker leaves a workplace, the company documents one of three major explanations: retirement, relocation to another company, or premature death. Therefore, WittyFit will have the ability to include mortality as an outcome. WittyFit will evolve with the waves of connected objects further increasing its data accuracy. Ethical approval was obtained from the ethics committee of the University Hospital of Clermont-Ferrand, France.ResultsWittyFit recruitment and enrollment started in January 2016. First publications are expected to be available at the beginning of 2017.ConclusionsThe name WittyFit came from Witty and Fitness. The concept of WittyFit reflects the concept of health from the World Health Organization: being spiritually and physically healthy. WittyFit is a health-monitoring, health-promoting tool that may improve the health of workers and health of companies. WittyFit will evolve with the waves of connected objects further increasing its data accuracy with objective measures. WittyFit may constitute a powerful epidemiological database. Finally, the WittyFit concept may extend healthy living into the general population.Trial RegistrationClinicaltrials.gov: NCT02596737; https://www.clinicaltrials.gov/ct2/show/NCT02596737 (Archived by WebCite at h...

AimThe second Diabetes, Attitudes, Wishes and Needs (DAWN2™) multinational cross-sectional study was aimed at generating insights to facilitate innovative efforts by people with diabetes (PWD), family members (FMs), and health care professionals (HCPs) to improve self-management and psychosocial support in diabetes. Here, the French data from the DAWN2™ study are described.MethodsIn France, 500 PWD (80 with type 1 diabetes [T1] and 420 with type 2 diabetes [T2]), 120 FMs, and 288 HCPs were recruited. The questionnaires assessed the impact of diabetes on quality of life and mood, self-management, attitudes/beliefs, and care/support.ResultsDiabetes negatively impacted the emotional well-being of 59% of people with T1 versus 45% of people with T2 (P<0.05) and about half of FMs. A high level of distress was felt by about half of PWD and FMs. About half of HCPs reported assessing depression in their patients. Sixty-two percent of FMs considered managing diabetes to be a burden. Hypoglycemia was a source of concern for 64% of people with T1 and 73% of FMs of insulin users. About two-thirds of non-insulin-medicated people with T2 agreed to start insulin if prescribed, while half of HCPs preferred to delay insulin initiation. A discrepancy between HCPs’ perceptions of their interactions with their patients and PWD’s recollection of these interactions with regard to patients’ personal needs and distress was also observed.ConclusionWhile distress remains under-assessed by HCPs, the negative impact of diabetes on the lives of PWD and FMs clearly induces distress on both groups. These findings provide new understanding of barriers precluding optimal management of diabetes. Developing strategies to overcome these barriers is now warranted.

Background: Sedentary behaviour is a major risk of mortality. However, data are contradictory regarding the effects of active commuting on mortality.Objectives: To perform a systematic review and meta-analysis on the effects of active commuting on mortality. Method:The PubMed, Cochrane Library, Embase, and Science Direct databases were searched for studies reporting mortality data and active commuting (walking or cycling) to or from work.We computed meta-analysis stratified on type of mortality, type of commuting, and level of commuting, each with two models (based on fully adjusted estimates of risks, and on crude or less adjusted estimates).Results: 17 studies representing 829 098 workers were included. Using the fully adjusted estimates of risks, active commuting decreased all-cause mortality by 9% (95% confidence intervals 3 to 9%), and cardiovascular mortality by 15% (3 to 27%) (p<0.001). For stratification by type of commuting, walking decreased significantly all-cause mortality by 13% (1 to 25%), and cycling decreased significantly both all-cause mortality by 21% (11 to 31%) and cardiovascular mortality by 33% (10 to 55%) (p<0.001). For stratification by level of active commuting, only high level decreased all-cause mortality by 11% (3 to 19%) and both intermediate and high level decreased cardiovascular mortality. Low level did not decrease any type of mortality. Cancer mortality did not decrease with walking nor cycling, and the level of active commuting had no effect. Low level walking did not decrease any type of mortality, intermediate level of walking decreased only all-cause mortality by 15% (2 to 28%), and high level of walking decreased both all-cause and cardiovascular mortality by 19% (8 to 30%) and by 31% (9 to 52%), respectively. Both low, intermediate and high intensities of cycling decreased all-cause mortality. Meta-analysis based on crude or less fully adjusted estimates retrieved similar results, with also significant reductions of cancer mortality with cycling (23%, 5 to 42%), high level of active commuting (14%, 4 to 24%), and high level of active commuting by walking (16%, 0 to 32%). Conclusion:Active commuting decreases mainly all-cause and cardiovascular mortality, with a dose-response relationship, especially for walking. Preventive strategies should focus on the benefits of active commuting.