Exercise testing is a versatile tool for health purposes. When used in combination with specific devices and sensors, it can provide valuable diagnostic and prognostic information in a wide range of populations. Exercise testing outcomes are also useful for training prescriptions and defining responses to clinical trials evaluating interventions. Whole-body maximal tests (e.g., cardiopulmonary exercise testing), field tests (e.g., walking tests), and modalities isolating a muscle group (e.g., isokinetic endurance testing) all have their advantages and limits and should be viewed as complementary. Recent advances in wearable technology and artificial intelligence provide unique opportunities to broaden the application of these tests and facilitate their interpretation. In the meantime, the clinimetric properties of some widely used exercise tests are still poorly documented in several clinical populations, which hampers optimal diagnosis and management. Moreover, most exercise tests used in clinical practice are suffering from a lack of ecological validity and there is a need to develop and valid new testing modalities that best mimic daily life functioning.
This Research Topic aims to extend our knowledge regarding the validity and clinical utility of various exercise testing modalities and facilitate their interpretation. This topic supports a multimodal approach to exercise testing and welcomes reports investigating either whole-body or local muscle testing. Submission of research combining different exercise modalities and investigating their potential links are particularly encouraged. Inter-disciplinary research with studies integrating concepts, tools, and data from various disciplines like exercise physiology, biomechanics, and psychology are of particular interest for this Research Topic. This topic is not restricted in terms of age, medical conditions, or type of disease but manuscripts must have clear implications for human health.
We welcome authors to submit manuscripts covering but not limited to, the following topics:
• Development and validation of new exercise testing procedures and outcomes
• Prognostic value of exercise testing
• Ability of submaximal testing procedures to predict maximal outcomes (e.g., peak oxygen uptake)
• Establishment of normative values for exercise testing
• Ability of exercise testing outcomes to predict important health events (e.g., hospital admission)
• Sensory responses to exercise testing
• Use of artificial intelligence (machine learning, deep learning, neural networks) on exercise testing data for diagnosis and prescription purposes
• Use of wearable devices and sensors during exercise testing to monitor real-time physiological and biomechanical parameters
There are no restrictions regarding the article type beyond those defined by the journals involved in this research topic. However, we particularly encourage the submission of manuscripts describing original research.
Exercise testing is a versatile tool for health purposes. When used in combination with specific devices and sensors, it can provide valuable diagnostic and prognostic information in a wide range of populations. Exercise testing outcomes are also useful for training prescriptions and defining responses to clinical trials evaluating interventions. Whole-body maximal tests (e.g., cardiopulmonary exercise testing), field tests (e.g., walking tests), and modalities isolating a muscle group (e.g., isokinetic endurance testing) all have their advantages and limits and should be viewed as complementary. Recent advances in wearable technology and artificial intelligence provide unique opportunities to broaden the application of these tests and facilitate their interpretation. In the meantime, the clinimetric properties of some widely used exercise tests are still poorly documented in several clinical populations, which hampers optimal diagnosis and management. Moreover, most exercise tests used in clinical practice are suffering from a lack of ecological validity and there is a need to develop and valid new testing modalities that best mimic daily life functioning.
This Research Topic aims to extend our knowledge regarding the validity and clinical utility of various exercise testing modalities and facilitate their interpretation. This topic supports a multimodal approach to exercise testing and welcomes reports investigating either whole-body or local muscle testing. Submission of research combining different exercise modalities and investigating their potential links are particularly encouraged. Inter-disciplinary research with studies integrating concepts, tools, and data from various disciplines like exercise physiology, biomechanics, and psychology are of particular interest for this Research Topic. This topic is not restricted in terms of age, medical conditions, or type of disease but manuscripts must have clear implications for human health.
We welcome authors to submit manuscripts covering but not limited to, the following topics:
• Development and validation of new exercise testing procedures and outcomes
• Prognostic value of exercise testing
• Ability of submaximal testing procedures to predict maximal outcomes (e.g., peak oxygen uptake)
• Establishment of normative values for exercise testing
• Ability of exercise testing outcomes to predict important health events (e.g., hospital admission)
• Sensory responses to exercise testing
• Use of artificial intelligence (machine learning, deep learning, neural networks) on exercise testing data for diagnosis and prescription purposes
• Use of wearable devices and sensors during exercise testing to monitor real-time physiological and biomechanical parameters
There are no restrictions regarding the article type beyond those defined by the journals involved in this research topic. However, we particularly encourage the submission of manuscripts describing original research.
