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Can wearable technology be used to approximate cardiopulmonary exercise testing metrics?

BACKGROUND: Consumer wrist-worn wearable activity monitors are widely available, low cost and are able to provide a direct measurement of several markers of physical activity. Despite this, there is limited data on their use in perioperative risk prediction. We explored whether these wearables could...

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Autores principales: Jones, Laura, Tan, Laura, Carey-Jones, Suzanne, Riddell, Nathan, Davies, Richard, Brownsdon, Ashleigh, Kelson, Mark, Williams-Thomas, Rhys, Busse, Monica, Davies, Michael M., Morgan, Matt P. G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7959880/
https://www.ncbi.nlm.nih.gov/pubmed/33722305
http://dx.doi.org/10.1186/s13741-021-00180-w
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author Jones, Laura
Tan, Laura
Carey-Jones, Suzanne
Riddell, Nathan
Davies, Richard
Brownsdon, Ashleigh
Kelson, Mark
Williams-Thomas, Rhys
Busse, Monica
Davies, Michael M.
Morgan, Matt P. G.
author_facet Jones, Laura
Tan, Laura
Carey-Jones, Suzanne
Riddell, Nathan
Davies, Richard
Brownsdon, Ashleigh
Kelson, Mark
Williams-Thomas, Rhys
Busse, Monica
Davies, Michael M.
Morgan, Matt P. G.
author_sort Jones, Laura
collection PubMed
description BACKGROUND: Consumer wrist-worn wearable activity monitors are widely available, low cost and are able to provide a direct measurement of several markers of physical activity. Despite this, there is limited data on their use in perioperative risk prediction. We explored whether these wearables could accurately approximate metrics (anaerobic threshold, peak oxygen uptake and peak work) derived using formalised cardiopulmonary exercise testing (CPET) in patients undergoing high-risk surgery. METHODS: Patients scheduled for major elective intra-abdominal surgery and undergoing CPET were included. Physical activity levels were estimated through direct measures (step count, floors climbed and total distance travelled) obtained through continuous wear of a wrist worn activity monitor (Garmin Vivosmart HR+) for 7 days prior to surgery and self-report through completion of the short International Physical Activity Questionnaire (IPAQ). Correlations and receiver operating characteristic (ROC) curve analysis explored the relationships between parameters provided by CPET and physical activity. DEVICE SELECTION: Our choice of consumer wearable device was made to maximise feasibility outcomes for this study. The Garmin Vivosmart HR+ had the longest battery life and best waterproof characteristics of the available low-cost devices. RESULTS: Of 55 patients invited to participate, 49 (mean age 65.3 ± 13.6 years; 32 males) were enrolled; 37 provided complete wearable data for analyses and 36 patients provided full IPAQ data. Floors climbed, total steps and total travelled as measured by the wearable device all showed moderate correlation with CPET parameters of peak oxygen uptake (peak VO(2)) (R = 0.57 (CI 0.29–0.76), R = 0.59 (CI 0.31–0.77) and R = 0.62 (CI 0.35–0.79) respectively), anaerobic threshold (R = 0.37 (CI 0.01–0.64), R = 0.39 (CI 0.04–0.66) and R = 0.42 (CI 0.07–0.68) respectively) and peak work (R = 0.56 (CI 0.27–0.75), R = 0.48 (CI 0.17–0.70) and R = 0.50 (CI 0.2–0.72) respectively). Receiver operator curve (ROC) analysis for direct and self-reported measures of 7-day physical activity could accurately approximate the ventilatory equivalent for carbon dioxide (V(E)/VCO(2)) and the anaerobic threshold. The area under these curves was 0.89 for V(E)/VCO(2) and 0.91 for the anaerobic threshold. For peak VO(2) and peak work, models fitted using just the wearable data were 0.93 for peak VO(2) and 1.00 for peak work. CONCLUSIONS: Data recorded by the wearable device was able to consistently approximate CPET results, both with and without the addition of patient reported activity measures via IPAQ scores. This highlights the potential utility of wearable devices in formal assessment of physical functioning and suggests they could play a larger role in pre-operative risk assessment. ETHICS: This study entitled “uSing wearable TEchnology to Predict perioperative high-riSk patient outcomes (STEPS)” gained favourable ethical opinion on 24 January 2017 from the Welsh Research Ethics Committee 3 reference number 17/WA/0006. It was registered on ClinicalTrials.gov with identifier NCT03328039.
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spelling pubmed-79598802021-03-16 Can wearable technology be used to approximate cardiopulmonary exercise testing metrics? Jones, Laura Tan, Laura Carey-Jones, Suzanne Riddell, Nathan Davies, Richard Brownsdon, Ashleigh Kelson, Mark Williams-Thomas, Rhys Busse, Monica Davies, Michael M. Morgan, Matt P. G. Perioper Med (Lond) Research BACKGROUND: Consumer wrist-worn wearable activity monitors are widely available, low cost and are able to provide a direct measurement of several markers of physical activity. Despite this, there is limited data on their use in perioperative risk prediction. We explored whether these wearables could accurately approximate metrics (anaerobic threshold, peak oxygen uptake and peak work) derived using formalised cardiopulmonary exercise testing (CPET) in patients undergoing high-risk surgery. METHODS: Patients scheduled for major elective intra-abdominal surgery and undergoing CPET were included. Physical activity levels were estimated through direct measures (step count, floors climbed and total distance travelled) obtained through continuous wear of a wrist worn activity monitor (Garmin Vivosmart HR+) for 7 days prior to surgery and self-report through completion of the short International Physical Activity Questionnaire (IPAQ). Correlations and receiver operating characteristic (ROC) curve analysis explored the relationships between parameters provided by CPET and physical activity. DEVICE SELECTION: Our choice of consumer wearable device was made to maximise feasibility outcomes for this study. The Garmin Vivosmart HR+ had the longest battery life and best waterproof characteristics of the available low-cost devices. RESULTS: Of 55 patients invited to participate, 49 (mean age 65.3 ± 13.6 years; 32 males) were enrolled; 37 provided complete wearable data for analyses and 36 patients provided full IPAQ data. Floors climbed, total steps and total travelled as measured by the wearable device all showed moderate correlation with CPET parameters of peak oxygen uptake (peak VO(2)) (R = 0.57 (CI 0.29–0.76), R = 0.59 (CI 0.31–0.77) and R = 0.62 (CI 0.35–0.79) respectively), anaerobic threshold (R = 0.37 (CI 0.01–0.64), R = 0.39 (CI 0.04–0.66) and R = 0.42 (CI 0.07–0.68) respectively) and peak work (R = 0.56 (CI 0.27–0.75), R = 0.48 (CI 0.17–0.70) and R = 0.50 (CI 0.2–0.72) respectively). Receiver operator curve (ROC) analysis for direct and self-reported measures of 7-day physical activity could accurately approximate the ventilatory equivalent for carbon dioxide (V(E)/VCO(2)) and the anaerobic threshold. The area under these curves was 0.89 for V(E)/VCO(2) and 0.91 for the anaerobic threshold. For peak VO(2) and peak work, models fitted using just the wearable data were 0.93 for peak VO(2) and 1.00 for peak work. CONCLUSIONS: Data recorded by the wearable device was able to consistently approximate CPET results, both with and without the addition of patient reported activity measures via IPAQ scores. This highlights the potential utility of wearable devices in formal assessment of physical functioning and suggests they could play a larger role in pre-operative risk assessment. ETHICS: This study entitled “uSing wearable TEchnology to Predict perioperative high-riSk patient outcomes (STEPS)” gained favourable ethical opinion on 24 January 2017 from the Welsh Research Ethics Committee 3 reference number 17/WA/0006. It was registered on ClinicalTrials.gov with identifier NCT03328039. BioMed Central 2021-03-16 /pmc/articles/PMC7959880/ /pubmed/33722305 http://dx.doi.org/10.1186/s13741-021-00180-w Text en © The Author(s) 2021 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Jones, Laura
Tan, Laura
Carey-Jones, Suzanne
Riddell, Nathan
Davies, Richard
Brownsdon, Ashleigh
Kelson, Mark
Williams-Thomas, Rhys
Busse, Monica
Davies, Michael M.
Morgan, Matt P. G.
Can wearable technology be used to approximate cardiopulmonary exercise testing metrics?
title Can wearable technology be used to approximate cardiopulmonary exercise testing metrics?
title_full Can wearable technology be used to approximate cardiopulmonary exercise testing metrics?
title_fullStr Can wearable technology be used to approximate cardiopulmonary exercise testing metrics?
title_full_unstemmed Can wearable technology be used to approximate cardiopulmonary exercise testing metrics?
title_short Can wearable technology be used to approximate cardiopulmonary exercise testing metrics?
title_sort can wearable technology be used to approximate cardiopulmonary exercise testing metrics?
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7959880/
https://www.ncbi.nlm.nih.gov/pubmed/33722305
http://dx.doi.org/10.1186/s13741-021-00180-w
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