Impact of Mask Wearing on Post-Exercise Hemodynamics

As the guidelines regarding COVID-19 regressed, many fitness centers established regulations requiring mask-wearing during exercise. Data suggest that the impact of a mask during exercise has minimal effects on exercise hemodynamics. The post-exercise period has been described as a window of opportunity to lower blood pressure, a phenomenon called post-exercise hypotension. The impact of wearing a mask on post-exercise hemodynamics is unknown. PURPOSE: The purpose of this study was to examine the impact of mask-wearing during exercise on post-exercise hemodynamics. METHODS: Nine total participants aged 18-30 yr were recruited for this experimental cross-over study. This within-subject design involved six randomized conditions; control no mask, no exercise (CON-NE), control-surgical mask, no exercise (CON-SUR), control-exercise, no mask (CON-E), exercise surgical mask (EXS-SUR), exercise N95 mask (EXS-N95), and exercise cloth mask (EXS-CL). The exercise protocol was a HIIT 4 x 4 on a cycle ergometer. Participants exercised at 85% of VO2max for four minutes, followed by a three-minute rest period, repeated four times. Measurements of cardiac output (Q), stroke volume (SV), heart rate (HR), systemic vascular resistance (SVR), and brachial blood pressure (BP) were measured pre-exercise for 20-min, during exercise, and postexercise for 60-min. RESULTS: Exercising at high intensity with the surgical, cloth, and N95 masks showed no statistically significant differences in HR, systolic BP, diastolic BP, SV, SVR, and RPE during exercise when compared to the CON-E condition (all p \u3e 0.05). Post-exercise data revealed no statistical differences in systolic BP or diastolic BP compared to the CON-E condition (both p \u3e 0.05). HR was significantly lower (roughly 4-5 ± 1.8 bpm p \u3c 0.01) in the CON-E group compared to all exercise mask-wearing groups following exercise. Additionally, SV (p\u3c0.001) and Q (p=0.002) were significantly lower in the EXS-N95 group compared to the other exercise groups. CONCLUSION: This study is consistent with current literature in suggesting that mask-wearing during exercise, even at high intensity, has no effect physiologically during exercise and on post-exercise hemodynamics. The impact of wearing a mask during exercise may alter the mechanisms of post-exercise hypotension

The reference site collaborative network of the european innovation partnership on active and healthy ageing

Seventy four Reference Sites of the European Innovation Partnership on Active and Healthy Ageing (EIP on AHA) have been recognised by the European Commission in 2016 for their commitment to excellence in investing and scaling up innovative solutions for active and healthy ageing. The Reference Site Collaborative Network (RSCN) brings together the EIP on AHA Reference Sites awarded by the European Commission, and Candidate Reference Sites into a single forum. The overarching goals are to promote cooperation, share and transfer good practice and solutions in the development and scaling up of health and care strategies, policies and service delivery models, while at the same time supporting the action groups in their work. The RSCN aspires to be recognized by the EU Commission as the principal forum and authority representing all EIP on AHA Reference Sites. The RSCN will contribute to achieve the goals of the EIP on AHA by improving health and care outcomes for citizens across Europe, and the development of sustainable economic growth and the creation of jobs

Matching value propositions with varied customer needs:the role of service modularity

Abstract Organizations seek to manage varied customer segments using varied value propositions. The ability of a knowledge‐intensive business service (KIBS) provider to formulate value propositions into attractive offerings to varied customers becomes a competitive advantage. In this specific business based on often highly abstract service offerings, this requires the provider to have a clear overview of its knowledge and resources and how these can be configured to obtain the desired customization of services. Hence, the purpose of this paper is to investigate how a KIBS provider can match value propositions with varied customer needs utilizing service modularity. To accomplish this purpose, a qualitative multiple case study is organized around 5 projects allowing within‐case and cross‐case comparisons. Our findings describe how through the configuration of knowledge and resources a sustainable competitive advantage is created through creating the right kind of value propositions for varied customers with the help of modularity. Understanding gained through this research helps KIBS organizations in their efforts to increase organizational effectiveness through modular services

Identifying de novo Parkinson's disease with optical coherence tomography of the retina: A machine learning classification approach

Objective To identify and classify de novo Parkinson’s disease patients compared to healthy controls (HC). Background PD patients experience visual symptoms and retinal degeneration. Studies using spectral-domain optical coherence tomography (SD-OCT) have shown retinal thinning in PD, even at the beginning of disease. This study investigated the utility of these retinal changes in de novo Ldopa-naive PD patients, to evaluate if the profile of retinal thinning could serve as a classification biomarker. Methods SD-OCT data were collected in de novo Ldopa-naive PD patients at the University Medical Center Groningen. 5x5 mm macular scans of right and left eyes were made. These were compared to age-matched HC scans. Good quality scans (≥4) were segmented by Iowa Reference Algorithms [1]; each retina was segmented into 10 individual cell layers. Results 121 PD, 110 HC were included. A random forest classification of all cell layers, across both eyes was run. Data was split into 102 training, 26 validation and 31 testing. Total test accuracy was 0.74, Out of the box accuracy 0.64. True positive rate: area under the curve receiver operating characteristic (AUROC) of 0.82, to classify PD compared to HC. Conclusions Retinal cell layer changes could play an important role in a model of classifying PD; presenting with significant differences in medication naive, newly diagnosed patients, being able to provide a high true positive classification, with automatically segmented retinal cell layer data, from straightforward SD-OCT retinal imaging

Identifying de novo Parkinson's disease with optical coherence tomography of the retina:A machine learning classification approach

Objective To identify and classify de novo Parkinson’s disease patients compared to healthy controls (HC). Background PD patients experience visual symptoms and retinal degeneration. Studies using spectral-domain optical coherence tomography (SD-OCT) have shown retinal thinning in PD, even at the beginning of disease. This study investigated the utility of these retinal changes in de novo Ldopa-naive PD patients, to evaluate if the profile of retinal thinning could serve as a classification biomarker. Methods SD-OCT data were collected in de novo Ldopa-naive PD patients at the University Medical Center Groningen. 5x5 mm macular scans of right and left eyes were made. These were compared to age-matched HC scans. Good quality scans (≥4) were segmented by Iowa Reference Algorithms [1]; each retina was segmented into 10 individual cell layers. Results 121 PD, 110 HC were included. A random forest classification of all cell layers, across both eyes was run. Data was split into 102 training, 26 validation and 31 testing. Total test accuracy was 0.74, Out of the box accuracy 0.64. True positive rate: area under the curve receiver operating characteristic (AUROC) of 0.82, to classify PD compared to HC. Conclusions Retinal cell layer changes could play an important role in a model of classifying PD; presenting with significant differences in medication naive, newly diagnosed patients, being able to provide a high true positive classification, with automatically segmented retinal cell layer data, from straightforward SD-OCT retinal imaging