Mental health and physical activity: A COVID-19 viewpoint

COVID-19, which has been declared a pandemic by the World Health Organisation, has become a public health emergency across the globe. It is a highly contagious disease, which elicits high levels of fear amongst the world population and is considered a threat to the world economy. As a response to this pandemic, international governments have devised unconventional measures to guard the health of their citizenry. Among these are the “new normal” country lockdown that mandates working from home, home-schooling of children, and physical/social distancing from friends and family. For the majority, this has resulted in momentary job loss and loneliness, and other psychological illnesses. Hence millions are frightened, depressed and panic easily as a result of the tension due to the uncertainty, which interferes with their job performance, livelihoods, international trade and the world economy. If not mitigated, this is likely to cause physical health deterioration, with severe mental illness being the outcome. To reduce mental health illnesses during and after the COVID-19 pandemic, evidence suggests prioritising regular participation in physical activity and exercise across lifespan. It is also important for medical experts who specialise in the care and management of mental health to recognise physical activity and exercise as a medicine that can ameliorate some mental illnesses and their associated risk factors

Priming cardiac function with voluntary respiratory maneuvers and effect on early exercise oxygen uptake.

Oxygen uptake (V̇o <sub>2</sub> ) at exercise onset is determined in part by acceleration of pulmonary blood flow ([Formula: see text]). Impairments in the [Formula: see text] response can decrease exercise tolerance. Prior research has shown that voluntary respiratory maneuvers can augment venous return, but the corollary impacts on cardiac function, [Formula: see text] and early-exercise V̇o <sub>2</sub> remain uncertain. We examined 1) the cardiovascular effects of three distinct respiratory maneuvers (abdominal, AB; rib cage, RC; and deep breathing, DB) under resting conditions in healthy subjects (Protocol 1, n = 13), and 2) the impact of pre-exercise DB on pulmonary O <sub>2</sub> transfer during initiation of moderate-intensity exercise (Protocol 2, n = 8). In Protocol 1, echocardiographic analysis showed increased right ventricular (RV) cardiac output and left ventricular (LV) cardiac output (RVCO and LVCO, respectively), following AB (by +23 ± 13 and +18 ± 15%, respectively, P < 0.05), RC (+23 ± 16; +14 ± 15%, P < 0.05), and DB (+27 ± 21; +23 ± 14%, P < 0.05). In Protocol 2, DB performed for 12 breaths produced a pre-exercise increase in V̇o <sub>2</sub> (+801 ± 254 mL·min <sup>-1</sup> over ∼6 s), presumably from increased [Formula: see text], followed by a reduction in pulmonary O <sub>2</sub> transfer during early phase exercise (first 20 s) compared with the control condition (149 ± 51 vs. 233 ± 65 mL, P < 0.05). We conclude that 1) respiratory maneuvers enhance RVCO and LVCO in healthy subjects under resting conditions, 2) AB, RC, and DB have similar effects on RVCO and LVCO, and 3) DB can increase [Formula: see text] before exercise onset. These findings suggest that pre-exercise respiratory maneuvers may represent a promising strategy to prime V̇o <sub>2</sub> kinetics and thereby to potentially improve exercise tolerance in patients with impaired cardiac function.NEW & NOTEWORTHY We demonstrate that different breathing maneuvers can augment both right and left-sided cardiac output in healthy subjects. These maneuvers, when performed immediately before exercise, result in a pre-exercise "cardiodynamic" increase in oxygen uptake (V̇o <sub>2</sub> ) associated with a subsequent reduction in the "cardiodynamic" V̇o <sub>2</sub> normally seen during early exercise. We conclude that pre-exercise breathing maneuvers are a plausible tool worthy of additional study to prime V̇o <sub>2</sub> kinetics and improve exercise tolerance in patients with cardiovascular disease

Consumer Wearable Health and Fitness Technology in Cardiovascular Medicine: JACC State-of-the-Art Review.

The use of consumer wearable devices (CWDs) to track health and fitness has rapidly expanded over recent years because of advances in technology. The general population now has the capability to continuously track vital signs, exercise output, and advanced health metrics. Although understanding of basic health metrics may be intuitive (eg, peak heart rate), more complex metrics are derived from proprietary algorithms, differ among device manufacturers, and may not historically be common in clinical practice (eg, peak V˙O <sub>2</sub> , exercise recovery scores). With the massive expansion of data collected at an individual patient level, careful interpretation is imperative. In this review, we critically analyze common health metrics provided by CWDs, describe common pitfalls in CWD interpretation, provide recommendations for the interpretation of abnormal results, present the utility of CWDs in exercise prescription, examine health disparities and inequities in CWD use and development, and present future directions for research and development

Deep learned representations of the resting 12-lead electrocardiogram to predict at peak exercise.

To leverage deep learning on the resting 12-lead electrocardiogram (ECG) to estimate peak oxygen consumption (V˙O2peak) without cardiopulmonary exercise testing (CPET). V ˙ O 2 peak estimation models were developed in 1891 individuals undergoing CPET at Massachusetts General Hospital (age 45 ± 19 years, 38% female) and validated in a separate test set (MGH Test, n = 448) and external sample (BWH Test, n = 1076). Three penalized linear models were compared: (i) age, sex, and body mass index ('Basic'), (ii) Basic plus standard ECG measurements ('Basic + ECG Parameters'), and (iii) basic plus 320 deep learning-derived ECG variables instead of ECG measurements ('Deep ECG-V˙O2'). Associations between estimated V˙O2peak and incident disease were assessed using proportional hazards models within 84 718 primary care patients without CPET. Inference ECGs preceded CPET by 7 days (median, interquartile range 27-0 days). Among models, Deep ECG-V˙O2 was most accurate in MGH Test [r = 0.845, 95% confidence interval (CI) 0.817-0.870; mean absolute error (MAE) 5.84, 95% CI 5.39-6.29] and BWH Test (r = 0.552, 95% CI 0.509-0.592, MAE 6.49, 95% CI 6.21-6.67). Deep ECG-V˙O2 also outperformed the Wasserman, Jones, and FRIEND reference equations (P < 0.01 for comparisons of correlation). Performance was higher in BWH Test when individuals with heart failure (HF) were excluded (r = 0.628, 95% CI 0.567-0.682; MAE 5.97, 95% CI 5.57-6.37). Deep ECG-V˙O2 estimated V˙O2peak <14 mL/kg/min was associated with increased risks of incident atrial fibrillation [hazard ratio 1.36 (95% CI 1.21-1.54)], myocardial infarction [1.21 (1.02-1.45)], HF [1.67 (1.49-1.88)], and death [1.84 (1.68-2.03)]. Deep learning-enabled analysis of the resting 12-lead ECG can estimate exercise capacity (V˙O2peak) at scale to enable efficient cardiovascular risk stratification

Healthspan and chronic disease burden among young adult and middle-aged male former American-style professional football players.

To examine the relationships between age, healthspan and chronic illness among former professional American-style football (ASF) players. We compared age-specific race-standardised and body mass index-standardised prevalence ratios of arthritis, dementia/Alzheimer's disease, hypertension and diabetes among early adult and middle-aged (range 25-59 years) male former professional ASF players (n=2864) with a comparator cohort from the National Health and Nutrition Examination Survey and National Health Interview Survey, two representative samples of the US general population. Age was stratified into 25-29, 30-39, 40-49 and 50-59 years. Arthritis and dementia/Alzheimer's disease were more prevalent among ASF players across all study age ranges (all p<0.001). In contrast, hypertension and diabetes were more prevalent among ASF players in the youngest age stratum only (p<0.001 and p<0.01, respectively). ASF players were less likely to demonstrate intact healthspan (ie, absence of chronic disease) than the general population across all age ranges. These data suggest the emergence of a maladaptive early ageing phenotype among former professional ASF players characterised by premature burden of chronic disease and reduced healthspan. Additional study is needed to investigate these findings and their impact on morbidity and mortality in former ASF players and other athlete groups