Low Cardiorespiratory Fitness Post-COVID-19: A Narrative Review

Patients recovering from COVID-19 often report symptoms of exhaustion, fatigue and dyspnoea and present with exercise intolerance persisting for months post-infection. Numerous studies investigated these sequelae and their possible underlying mechanisms using cardiopulmonary exercise testing. We aimed to provide an in-depth discussion as well as an overview of the contribution of selected organ systems to exercise intolerance based on the Wasserman gears. The gears represent the pulmonary system, cardiovascular system, and periphery/musculature and mitochondria. Thirty-two studies that examined adult patients post-COVID-19 via cardiopulmonary exercise testing were included. In 22 of 26 studies reporting cardiorespiratory fitness (herein defined as peak oxygen uptake-VO2peak), VO2peak was < 90% of predicted value in patients. VO2peak was notably below normal even in the long-term. Given the available evidence, the contribution of respiratory function to low VO2peak seems to be only minor except for lung diffusion capacity. The prevalence of low lung diffusion capacity was high in the included studies. The cardiovascular system might contribute to low VO2peak via subnormal cardiac output due to chronotropic incompetence and reduced stroke volume, especially in the first months post-infection. Chronotropic incompetence was similarly present in the moderate- and long-term follow-up. However, contrary findings exist. Peripheral factors such as muscle mass, strength and perfusion, mitochondrial function, or arteriovenous oxygen difference may also contribute to low VO2peak. More data are required, however. The findings of this review do not support deconditioning as the primary mechanism of low VO2peak post-COVID-19. Post-COVID-19 sequelae are multifaceted and require individual diagnosis and treatment

Physical activity and brain health in patients with atrial fibrillation

Background and purpose: Vascular brain lesions, such as ischemic infarcts, are common among patients with atrial fibrillation (AF) and are associated with impaired cognitive function. The role of physical activity (PA) in the prevalence of brain lesions and cognition in AF has not been investigated. Methods: Patients from the multicenter Swiss‐AF cohort study were included in this cross‐sectional analysis. We assessed regular exercise (RE; at least once weekly) and minutes of weekly PA using a validated questionnaire. We studied associations with ischemic infarcts, white matter hyperintensities, cerebral microbleeds, and brain volume on brain magnetic resonance imaging and with global cognition measured with a cognitive construct (CoCo) score.ResultsAmong 1490 participants (mean age = 72 ± 9 years), 730 (49%) engaged in RE. In adjusted regression analyses, RE was associated with a lower prevalence of ischemic infarcts (odds ratio [OR] = 0.78, 95% confidence interval [CI] = 0.63–0.98, p = 0.03) and of moderate to severe white matter hyperintensities (OR = 0.78, 95% CI = 0.62–0.99, p = 0.04), higher brain volume (β‐coefficient = 10.73, 95% CI = 2.37–19.09, p = 0.01), and higher CoCo score (β‐coefficient = 0.08, 95% CI = 0.03–0.12, p < 0.001). Increasing weekly PA was associated with higher brain volume (β‐coefficient = 1.40, 95% CI = 0.65–2.15, p < 0.001). Conclusions: In AF patients, RE was associated with a lower prevalence of ischemic infarcts and of moderate to severe white matter disease, with larger brain volume, and with better cognitive performance. Prospective studies are needed to investigate whether these associations are causal. Until then, our findings suggest that patients with AF should be encouraged to remain physically active

Counteracting Physical Inactivity during the COVID-19 Pandemic: Evidence-Based Recommendations for Home-Based Exercise

To reduce transmission of the coronavirus, from its initial outbreak in 2019 up to now, various safety measures have been enacted worldwide by the authorities that have likely led to reduced physical activity levels in the general population. This short communication aims to briefly outline the deteriorative consequences of physical inactivity on parameters of physical fitness and ultimately to highlight associated increases of cardiovascular disease risk and mortality. Finally, evidence-based practical recommendations for exercise that can be performed at home are introduced, to help avoid physical inactivity and therefore maintain or achieve good physical health

Has Being Lost While High-Altitude Mountaineering Become Less Frequent? A Retrospective Analysis from the Swiss Alps

Background: High-altitude mountaineering is becoming more popular. Despite technical developments such as global positioning systems, mountaineers still lose their way. This study aimed to analyze characteristics of alpinists that lost their way while high-altitude mountaineering in Switzerland. Material and Methods: Data from the central registry of the Swiss Alpine Club between 2009 and 2020 were retrospectively analyzed. Changes in the number of cases and severity of injuries over time were examined using simple linear regression models. Descriptive analyses were performed for age, time of emergency occurrence, and factors associated with being lost. The Mann&ndash;Whitney U test assessed between-sex comparisons. Results: Of the 4596 emergency cases during the observation period, 275 cases (5.9%) were due to being lost (76.4% male). A mean of 22.9 &plusmn; 9.6 cases per year was detected. The number of cases did not change significantly over time. Similarly, this was the case for the NACA-Score (National Advisory Committee for Aeronautics Score) with the majority of mountaineers remaining uninjured (77.8%). The median age was 42 (35&ndash;54) years for the full sample and 45 (35&ndash;56) years and 40 (33&ndash;48) years for males and females, respectively. Fog or weather changes, exhaustion, and inadequate tour planning (time and darkness) were frequently documented by rescuers as perceived reasons for being lost. Regarding the time of emergency occurrence, three peaks were detected, around 10 am, 5 pm, and 8 pm. Conclusions: Our findings show that the number of emergencies due to being lost was stable during the 12-year period. Furthermore, we presented factors that might be associated with losing one&rsquo;s way during mountaineering. These results may form an important basis for future studies determining risk factors for being lost and the prevention of such emergencies

Methodological aspects for accelerometer-based assessment of physical activity in heart failure and health

For valid accelerometer-assessed physical activity (PA) data, several methodological aspects should be considered. We aimed to 1) visualize the applicability of absolute accelerometer cut-offs to classify PA intensity, 2) verify recommendations to measure PA over 7 days by examining inter-day variability and reactivity, 3) examine seasonal differences in PA, and 4) recommend during which 10 h day period accelerometers should be worn to capture the most PA in patients with heart failure (HEART) and healthy individuals (HEALTH).; Fifty-six HEART (23% female; mean age 66 ± 13 years) and 299 HEALTH (51% female; mean age 54 ± 19 years) of the COmPLETE study wore accelerometers for 14 days. Aim 1 was analyzed descriptively. Key analyses were performed using linear mixed models.; The results yielded poor applicability of absolute cut-offs. The day of the week significantly affected PA in both groups. PA-reactivity was not present in either group. A seasonal influence on PA was only found in HEALTH. Large inter-individual variability in PA timing was present.; Our data indicated that absolute cut-offs foster inaccuracies in both populations. In HEART, Sunday and four other days included in the analyses seem sufficient to estimate PA and the consideration of seasonal differences and reactivity seems not necessary. For healthy individuals, both weekend days plus four other days should be integrated into the analyses and seasonal differences should be considered. Due to substantial inter-individual variability in PA timing, accelerometers should be worn throughout waking time. These findings may improve future PA assessment.; The COmPLETE study was registered at clinicaltrials.gov ( NCT03986892 )

Characteristics of Victims of Fall-Related Accidents during Mountain Hiking

The study evaluated characteristics of non-fatal mountain hiking accidents caused by falls. Questionnaires were sent to mountain hikers who suffered a fall-related accident in Tyrol (Austria) during a 3-year period. The questionnaire included details of socio-demographic data, physical activity, medication intake, defective vision, breaks, fluid intake, level of fatigue, muscle soreness, use of backpacks, use of hiking sticks, and type of shoes. Data of 405 individuals (57% females and 43% males) were included in the analyses. Victims were 56 &plusmn; 15 years of age, had a body mass index of 24.8 &plusmn; 3.5, and indicated 4.2 &plusmn; 3.9 h/week regular physical activity. A defective vision was reported by 70% of the victims, breaks were frequent (in 80%), and alcohol intake was rare (4%) among the interviewed hikers. Subjective level of fatigue was low and only 5% reported muscle soreness. A backpack was carried by 83% of the victims and the average weight was higher in males compared to females. The majority (61%) of the victims wore ankle-height hiking shoes with a profiled sole. Victims of non-fatal falls in mountain hiking are older than the general population of mountain hikers and are often afflicted with defective vision

Reference values for accelerometer metrics and associations with cardiorespiratory fitness: a prospective cohort study of healthy adults and patients with heart failure

Background Accelerometry has gained increasing popularity and yields numerous physical activity (PA) outcomes (Rowlands et al., 2019). These include traditional cut-point-based (i.e. light, moderate, and vigorous PA) and cut-point-free metrics (i.e. intensity gradient [IG] and average acceleration [AvAcc]). IG reflects the intensity distribution of PA across the day (Rowlands et al., 2018; Fairclough et al., 2019). AvAcc is a proxy for the daily volume of PA ( Rowlands et al., 2018; Fairclough et al., 2019). Cut-point-based metrics are commonly expressed in minutes per day, making their interpretation simple (Troiano et al., 2014). Yet, the measured acceleration needs to be categorised by setting population- and device-dependent cut-points to obtain these metrics (Troiano et al., 2014). Cut-point-free metrics, on the other hand, are comparable across studies, accelerometer brands (Migueles et al., 2022), and diverse populations (Rowlands et al., 2018). However, their interpretation is not easy. Besides, it is unknown how cut-point-free metrics are associated with cardiorespiratory fitness (CRF), an important health indicator in healthy individuals and patient populations with impaired CRF (Kodama et al., 2009). We thus aimed to 1) compare the association of CRF with cut-point-free metrics to that with cut-point-based metrics in a prospective cohort of healthy adults aged 20 to 89 years and patients with heart failure, and 2) provide age-, sex-, and CRF-related reference values for healthy adults. Methods The COmPLETE study was cross-sectional. Healthy individuals were recruited via unaddressed letters sent to randomly selected postal districts in the Basel area (Wagner et al., 2019). Patients with heart failure were approached as described elsewhere (Wagner et al., 2019). Subjects were asked to wear GENEActiv accelerometers on their non-dominant wrist for up to 14 days and undergo cardiopulmonary exercise testing on a cycle ergometer to determine CRF. Raw accelerometer data were processed using the R-package GGIR (Migueles et al., 2019; van Hees et al., 2013). Associations between CRF and accelerometer metrics were examined using multiple linear regression models adjusted for sex, age, and body mass index. Percentile curves were generated with Generalised Additive Models for Location, Scale, and Shape (Stasinopoulos &amp; Rigby, 2008). Results Four hundred and sixty-three healthy adults and 67 patients with heart failure were included in the analyses. IG and AvAcc provide complementary information on PA. Both metrics were independently associated with CRF in healthy individuals. The best cut-point-free regression model (AvAcc+IG) performed similar to the best cut-point-based model (vigorous activity) and explained 73.9% and 74.2% of the variance in CRF, respectively. In patients with heart failure, IG was associated with CRF, independent of AvAcc. Cut-point-free models (IG+AvAcc, IG alone) had comparable predictive value for CRF as the best cut-point-based metric (moderate-to-vigorous activity). We produced age-, sex-, and CRF-related reference values for IG, AvAcc, moderate-to-vigorous, and vigorous activity for healthy adults. Moreover, we developed a web-based application (rawacceleration) facilitating the interpretation of cut-point-free metrics. Conclusions Cut-point-free metrics are not only more robust than cut-point-based metrics, but also have similar predictive value for CRF and, in turn, indirectly for the risk of mortality and longevity (Kodama et al., 2009; Mok et al., 2019). This may be the case in both healthy individuals and patients with heart failure. Our findings together with those of previous studies (Rowlands et al., 2018; Fairclough et al., 2019), therefore, provide a rationale that cut-point-free metrics facilitate the capture of the volume and intensity distribution of the PA profile across populations, and thus may be a viable alternative to cut-point-based metrics in describing PA. Our reference values will enhance the utility of IG and AvAcc and facilitate their interpretation. Finally, our web-based application will simplify this process and also support the translation of cut-point-free metrics into meaningful outcomes. References Fairclough, S. J., Taylor, S., Rowlands, A. V., Boddy, L. M., &amp; Noonan, R. J. (2019) Average acceleration and intensity gradient of primary school children and associations with indicators of health and well-being. Journal of Sports Sciences, 37(18), 2159-2167. https://doi.org/10.1080/02640414.2019.1624313 Kodama, S., Saito, K., Tanaka, S., Maki, M., Yachi, Y., Asumi, M., Sugawara, A., Totsuka, K., Shimano, H., Ohashi, Y., Yamada, N., &amp; Sone, H. (2009). Cardiorespiratory fitness as a quantitative predictor of all-cause mortality and cardiovascular events in healthy men and women: A meta-analysis. JAMA, 301(19), 2024-35.https://doi.org/10.1001/jama.2009.681 Migueles, J. H., Molina-Garcia, P., Torres-Lopez, L. V., Cadenas-Sanchez, C., Rowlands, A. V., Ebner-Priemer, U. W., Koch, E. D., Reif, A., &amp; Ortega, F. B. (2022). Equivalency of four research-grade movement sensors to assess movement behaviors and its implications for population surveillance. Science Reports, 12, Article 5525. https://doi.org/10.1038/s41598-022-09469-2 Migueles, J. H., Rowlands, A. V., Huber, F., Sabia, S., &amp; van Hees, V. T. (2019). GGIR: A research community–driven open source R package for generating physical activity and sleep outcomes from multi-day raw accelerometer data. Journal for the Measurement of Physical Behaviour, 2(3),188-96. https://doi.org/10.1123/jmpb.2018-0063 Mok, A., Khaw, K.-T., Luben, R., Wareham, N., &amp; Brage, S. (2019). Physical activity trajectories and mortality: Population based cohort study. BMJ, 365, l2323. https://doi.org/10.1136/bmj.l2323 Rowlands, A. V., Edwardson, C. L., Davies, M. J., Khunti, K., Harrington, D. M., &amp; Yates, T. (2018). Beyond cut points: Accelerometer metrics that capture the physical activity profile. Medicine &amp; Science in Sports &amp; Exercise, 50(6), 1323-32. https://doi.org/10.1249/MSS.0000000000001561 Rowlands, A. V., Fairclough, S. J., Yates, T., Edwardson, C. L., Davies, M., Munir, F., Khunti, K., &amp; Stiles, V. H. (2019). Activity intensity, volume, and norms: Utility and interpretation of accelerometer metrics. Medicine &amp; Science in Sports &amp; Exercise, 51(11), 2410-2422. https://doi.org/10.1249/MSS.0000000000002047 Stasinopoulos, D. M., &amp; Rigby, R. A. (2008). Generalized additive models for location scale and shape (GAMLSS) in R. Journal of Statistical Software, 23(7), 1 - 46. https://doi.org/10.18637/jss.v023.i07 Troiano, R. P., McClain, J. J., Brychta, R. J., &amp; Chen, K. Y. (2014). Evolution of accelerometer methods for physical activity research. British Journal of Sports Medicine, 48(13), 1019-1023. https://doi.org/10.1136/bjsports-2014-093546 van Hees, V. T., Gorzelniak, L., Dean León, E. C., Eder, M., Pias, M., Taherian, S., Ekelung, U., Renström, F., Franks, P. W., Horsch, A., &amp; Brage, S. (2013). Separating movement and gravity components in an acceleration signal and implications for the assessment of human daily physical activity. PloS one, 8(4), Article e61691. https://doi.org/10.1371/journal.pone.0061691 Wagner, J., Knaier, R., Infanger, D., Arbeev, K., Briel, M., Dieterle, T., Hanssen, H., Faude, O., Roth, R., Hinrichs, T., &amp; Schmidt-Trucksäss, A. (2019). Functional aging in health and heart failure: The COmPLETE Study. BMC Cardiovascular Disorders, 19, Article 180. https://doi.org/10.1186/s12872-019-1164-

Accelerometer Metrics: Healthy Adult Reference Values, Associations with Cardiorespiratory Fitness, and Clinical Implications

Purpose  Accelerometer-assessed physical activity (PA) can be summarised using cut-point-free or population-specific cut-point-based outcomes. We aimed to: 1) examine the interrelationship between cut-point-free (intensity gradient [IG] and average acceleration [AvAcc]) and cut-point-based accelerometer metrics, 2) compare the association between cardiorespiratory fitness (CRF) and cut-point-free metrics to that with cut-point-based metrics in healthy adults aged 20 to 89 years and patients with heart failure, and 3) provide age-, sex-, and CRF-related reference values for healthy adults. Methods  In the COmPLETE study, 463 healthy adults and 67 patients with heart failure wore GENEActiv accelerometers on their non-dominant wrist and underwent cardiopulmonary exercise testing. Cut-point-free (IG: distribution of intensity of activity across the day; AvAcc: proxy of volume of activity) and traditional (moderate-to-vigorous and vigorous activity) metrics were generated. The ‘interpretablePA’ R-package was developed to translate findings into clinical practice. Results  IG and AvAcc yield complementary information on PA with both IG (p = 0.009) and AvAcc (p Conclusions  IG and AvAcc are strongly associated with CRF and, thus, indirectly with the risk of non-communicable diseases and mortality, in healthy adults and patients with heart failure. However, unlike cut-point-based metrics, IG and AvAcc are comparable across populations. Our reference values provide a healthy age- and sex-specific comparison that may enhance the translation and utility of cut-point-free metrics in clinical practice.</p

Physical Activity and Brain Health in Patients with Atrial Fibrillation.

BACKGROUND Vascular brain lesions, such as ischemic infarcts, are common among patients with atrial fibrillation (AF) and are associated with impaired cognitive function. The role of physical activity in the prevalence of brain lesions and cognition in AF has not been investigated. METHODS Patients from the multicenter Swiss-AF cohort study were included in this cross-sectional analysis. We assessed regular exercise (at least once weekly) and minutes of weekly physical activity using a validated questionnaire. We studied associations with ischemic infarcts, white matter hyperintensities, cerebral microbleeds, and brain volume on brain MRI and with global cognition measured with a cognitive construct score (CoCo). RESULTS Among 1490 participants (mean age 72 ±9 years), 730 (49%) engaged in regular exercise. In adjusted regression analyses, regular exercise was associated with a lower prevalence of ischemic infarcts (odds ratio [OR]) 0.78, 95% CI 0.63-0.98, p=0.03) and of moderate to severe white matter hyperintensities (OR 0.78, 95% CI 0.62-0.99, p=0.04), higher brain volume (β-coefficient 10.73, 95% CI 2.37-19.09, p=0.01), and higher CoCo score (β-coefficient 0.08, 95% CI 0.03-0.12, p<0.001). Increasing weekly physical activity was associated with higher brain volume (β-coefficient 1.40, 95% CI 0.65-2.15, p<0.001). CONCLUSION In AF patients, regular exercise was associated with a lower prevalence of ischemic infarcts, of moderate to severe white matter disease, with larger brain volume and better cognitive performance. Prospective studies are needed to investigate if these associations are causal. Until then, our findings suggest that patients with AF should be encouraged to remain physically active

PLUS-IS-LESS project: Procalcitonin and Lung UltraSonography-based antibiotherapy in patients with Lower rESpiratory tract infection in Swiss Emergency Departments: study protocol for a pragmatic stepped-wedge cluster-randomized trial

Abstract Background Lower respiratory tract infections (LRTIs) are among the most frequent infections and a significant contributor to inappropriate antibiotic prescription. Currently, no single diagnostic tool can reliably identify bacterial pneumonia. We thus evaluate a multimodal approach based on a clinical score, lung ultrasound (LUS), and the inflammatory biomarker, procalcitonin (PCT) to guide prescription of antibiotics. LUS outperforms chest X-ray in the identification of pneumonia, while PCT is known to be elevated in bacterial and/or severe infections. We propose a trial to test their synergistic potential in reducing antibiotic prescription while preserving patient safety in emergency departments (ED). Methods The PLUS-IS-LESS study is a pragmatic, stepped-wedge cluster-randomized, clinical trial conducted in 10 Swiss EDs. It assesses the PLUS algorithm, which combines a clinical prediction score, LUS, PCT, and a clinical severity score to guide antibiotics among adults with LRTIs, compared with usual care. The co-primary endpoints are the proportion of patients prescribed antibiotics and the proportion of patients with clinical failure by day 28. Secondary endpoints include measurement of change in quality of life, length of hospital stay, antibiotic-related side effects, barriers and facilitators to the implementation of the algorithm, cost-effectiveness of the intervention, and identification of patterns of pneumonia in LUS using machine learning. Discussion The PLUS algorithm aims to optimize prescription of antibiotics through improved diagnostic performance and maximization of physician adherence, while ensuring safety. It is based on previously validated tests and does therefore not expose participants to unforeseeable risks. Cluster randomization prevents cross-contamination between study groups, as physicians are not exposed to the intervention during or before the control period. The stepped-wedge implementation of the intervention allows effect calculation from both between- and within-cluster comparisons, which enhances statistical power and allows smaller sample size than a parallel cluster design. Moreover, it enables the training of all centers for the intervention, simplifying implementation if the results prove successful. The PLUS algorithm has the potential to improve the identification of LRTIs that would benefit from antibiotics. When scaled, the expected reduction in the proportion of antibiotics prescribed has the potential to not only decrease side effects and costs but also mitigate antibiotic resistance. Trial registration This study was registered on July 19, 2022, on the ClinicalTrials.gov registry using reference number: NCT05463406. Trial status Recruitment started on December 5, 2022, and will be completed on November 3, 2024. Current protocol version is version 3.0, dated April 3, 2023