ORCID Profile
0000-0002-4662-3911
Current Organisation
University of Basel
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Publisher: Elsevier BV
Date: 06-2023
Publisher: University of Bern
Date: 14-02-2023
Abstract: 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 erse 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 in iduals 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 in iduals 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 & 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 in iduals. 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 in iduals 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., & 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. 0.1080/02640414.2019.1624313 Kodama, S., Saito, K., Tanaka, S., Maki, M., Yachi, Y., Asumi, M., Sugawara, A., Totsuka, K., Shimano, H., Ohashi, Y., Yamada, N., & 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), 2024-35.0.1001/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., & 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. 0.1038/s41598-022-09469-2 Migueles, J. H., Rowlands, A. V., Huber, F., Sabia, S., & 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. 0.1123/jmpb.2018-0063 Mok, A., Khaw, K.-T., Luben, R., Wareham, N., & Brage, S. (2019). Physical activity trajectories and mortality: Population based cohort study. BMJ, 365, l2323. 0.1136/bmj.l2323 Rowlands, A. V., Edwardson, C. L., Davies, M. J., Khunti, K., Harrington, D. M., & Yates, T. (2018). Beyond cut points: Accelerometer metrics that capture the physical activity profile. Medicine & Science in Sports & Exercise, 50(6), 1323-32. 0.1249/MSS.0000000000001561 Rowlands, A. V., Fairclough, S. J., Yates, T., Edwardson, C. L., Davies, M., Munir, F., Khunti, K., & Stiles, V. H. (2019). Activity intensity, volume, and norms: Utility and interpretation of accelerometer metrics. Medicine & Science in Sports & Exercise, 51(11), 2410-2422. 0.1249/MSS.0000000000002047 Stasinopoulos, D. M., & Rigby, R. A. (2008). Generalized additive models for location scale and shape (GAMLSS) in R. Journal of Statistical Software, 23(7), 1 - 46. 0.18637/jss.v023.i07 Troiano, R. P., McClain, J. J., Brychta, R. J., & Chen, K. Y. (2014). Evolution of accelerometer methods for physical activity research. British Journal of Sports Medicine, 48(13), 1019-1023. 0.1136/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., & 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. 0.1371/journal.pone.0061691 Wagner, J., Knaier, R., Infanger, D., Arbeev, K., Briel, M., Dieterle, T., Hanssen, H., Faude, O., Roth, R., Hinrichs, T., & Schmidt-Trucksäss, A. (2019). Functional aging in health and heart failure: The COmPLETE Study. BMC Cardiovascular Disorders, 19, Article 180. 0.1186/s12872-019-1164-6
Publisher: Oxford University Press (OUP)
Date: 08-02-2021
Abstract: Commercially available health technologies such as smartphones and smartwatches, activity trackers and eHealth applications, commonly referred to as wearables, are increasingly available and used both in the leisure and healthcare sector for pulse and fitness/activity tracking. The aim of the Position Paper is to identify specific barriers and knowledge gaps for the use of wearables, in particular for heart rate (HR) and activity tracking, in clinical cardiovascular healthcare to support their implementation into clinical care. The widespread use of HR and fitness tracking technologies provides unparalleled opportunities for capturing physiological information from large populations in the community, which has previously only been available in patient populations in the setting of healthcare provision. The availability of low-cost and high-volume physiological data from the community also provides unique challenges. While the number of patients meeting healthcare providers with data from wearables is rapidly growing, there are at present no clinical guidelines on how and when to use data from wearables in primary and secondary prevention. Technical aspects of HR tracking especially during activity need to be further validated. How to analyse, translate, and interpret large datasets of information into clinically applicable recommendations needs further consideration. While the current users of wearable technologies tend to be young, healthy and in the higher sociodemographic strata, wearables could potentially have a greater utility in the elderly and higher-risk population. Wearables may also provide a benefit through increased health awareness, democratization of health data and patient engagement. Use of continuous monitoring may provide opportunities for detection of risk factors and disease development earlier in the causal pathway, which may provide novel applications in both prevention and clinical research. However, wearables may also have potential adverse consequences due to unintended modification of behaviour, uncertain use and interpretation of large physiological data, a possible increase in social inequality due to differential access and technological literacy, challenges with regulatory bodies and privacy issues. In the present position paper, current applications as well as specific barriers and gaps in knowledge are identified and discussed in order to support the implementation of wearable technologies from gadget-ology into clinical cardiology.
Publisher: Springer Science and Business Media LLC
Date: 22-02-2022
Publisher: Cold Spring Harbor Laboratory
Date: 25-04-2023
DOI: 10.1101/2023.04.19.23288786
Abstract: To compare the association between cardiorespiratory fitness (CRF) and cut-point-free accelerometer metrics (intensity gradient [IG] and average acceleration [AvAcc]) to that with traditional metrics in 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. 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 ‘rawacceleration’ application was developed to translate findings into clinical practice. IG and AvAcc yield complementary information on PA with both IG (p=0.009) and AvAcc (p .001) independently associated with CRF in healthy in iduals. Only IG was independently associated with CRF in patients with heart failure (p=0.043). The best cut-point-free and cut-point-based model had similar predictive value for CRF in both cohorts. However, unlike traditional metrics, IG and AvAcc are comparable across populations and the most commonly used accelerometers. We produced age- and sex-specific reference values and percentile curves for IG, AvAcc, moderate-to-vigorous, and vigorous activity for healthy adults. 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. Our reference values enhance the utility of cut-point-free metrics and facilitate their interpretation. This study was registered on clinicaltrials.gov ( NCT03986892 ). What is already known on this topic – Cut-point free accelerometer metrics are valuable to assess physical activity because of their comparability across populations and association with various health parameters (e.g. body fat content or physical functioning). Yet, their interpretation is not straightforward. What this study adds – This study found a strong and independent association of cut-point-free metrics with cardiorespiratory fitness, a vital sign, in healthy in iduals aged between 20 to 89 years and patients with heart failure. We produced the first reference values based on healthy in iduals across the age span. How this study might affect research, practice or policy – Our reference values together with the new open-source application may simplify the interpretation of cut-point-free accelerometer metrics and their use in clinical practice and research.
Publisher: BMJ
Date: 07-2023
DOI: 10.1136/BMJSEM-2023-001626
Abstract: Non-communicable diseases (NCDs), including coronary heart disease, stroke, hypertension, type 2 diabetes, dementia, depression and cancers, are on the rise worldwide and are often associated with a lack of physical activity (PA). Globally, the levels of PA among in iduals are below WHO recommendations. A lack of PA can increase morbidity and mortality, worsen the quality of life and increase the economic burden on in iduals and society. In response to this trend, numerous organisations came together under one umbrella in Hamburg, Germany, in April 2021 and signed the ‘Hamburg Declaration’. This represented an international commitment to take all necessary actions to increase PA and improve the health of in iduals to entire communities. In iduals and organisations are working together as the ‘Global Alliance for the Promotion of Physical Activity’ to drive long-term in idual and population-wide behaviour change by collaborating with all stakeholders in the community: active hospitals, physical activity specialists, community services and healthcare providers, all achieving sustainable health goals for their patients/clients. The ‘Hamburg Declaration’ calls on national and international policymakers to take concrete action to promote daily PA and exercise at a population level and in healthcare settings.
No related grants have been discovered for Arno Schmidt-Trucksäss.