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Continuous cardiovascular health evaluation by wearables

Grant number: 18/22818-9
Support type:Scholarships in Brazil - Doctorate
Effective date (Start): September 01, 2019
Effective date (End): April 30, 2022
Field of knowledge:Health Sciences - Physiotherapy and Occupational Therapy
Principal Investigator:Aparecida Maria Catai
Grantee:Maria Cecília Moraes Frade
Home Institution: Centro de Ciências Biológicas e da Saúde (CCBS). Universidade Federal de São Carlos (UFSCAR). São Carlos , SP, Brazil
Associated research grant:16/22215-7 - Impact of inspiratory muscle training and aging on metabolic maping and autonomic modulation at rest and on cardiovascular, respiratory and metabolic responses to exercise in healthy men, AP.TEM


Cardiorespiratory Fitness (CRF) impairments are related with the onset of Non-Communicable Chronic Diseases (NCD) and the increase of mortality. However, despite the extensive clinical relevance of CRF evaluation, indexes related to CRF are commonly evaluated by medical equipment that are not available to general population and realistic settings. Therefore, there is a need to further explore new technologies for the continuous CRF evaluation that are more accessible to general population. The main objective of this research project is to use wearable technologies, associated with Artificial Intelligence (AI) algorithms, for the continuous evaluation of CRF to predict the onset of NCD in the future. AI algorithms will be developed based on ambulatory biological signals obtained by wearable sensors embedded into a smart shirt. This is an observational and cross-sectional research project that will evaluate healthy, with risk factors for NCD and with NCD subjects. The first stage of this project will be composed by the gold-standard laboratory evaluation of the CRF in controlled environment. The second stage will be composed by longitudinal data collection (for 7 days) by a smart shirt during non-supervised environments. Finally, the last stage will be focusing on the development of AI algorithms that will be trained to predict CRF based on biological signals streamed from the smart shirt during unsupervised activities. The AI algorithms will be validated by standard statist tests. This research project will impact the development of technologies for NCD prevention. In addition, the findings of this project will be applied in NCD rehabilitations programs, allowing individual and real-time adjustments, improving the effectiveness of such programs. (AU)