Previously submitted to: Journal of Medical Internet Research (no longer under consideration since Dec 05, 2024)
Date Submitted: Nov 29, 2023
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Neuroscience and the heart: emotion detection through smart wearable devices for use in cardiology
ABSTRACT
Background:
The link between neuroscience and the heart in emotions is a field of study that seeks to understand the interactions between the nervous system, brain and heart function during an individual's emotional state. Through smart wearable devices, which can be used in cardiology and monitor physiological signals, it is possible to evaluate emotional experiences related to the heart-brain axis.
Objective:
This review focused on studies that evaluated emotions by means of single-parameter and multi-parameter smart wearable devices that can be used in cardiology in order to verify their effectiveness.
Methods:
We reviewed studies that evaluated emotions through wearable devices used in cardiology. Our search was performed on the PubMed, Web of Science and Cochrane Lybrary databases. Of the initial 1227 studies from the past ten years, 8 studies met the inclusion criteria.
Results:
All studies reviewed showed that wearable devices that can be used in cardiology for emotional recognition are effective. However, the studies that used single-parameter wearable devices were more adequate than the multi-parameter studies.
Conclusions:
Devices can be valuable tools to provide information on an individual's emotional responses and consequently on the connections between the central nervous system and the cardiovascular system. Further studies are needed as the link between neuroscience and the heart needs more insights into emotional assessment through wearable devices in order to develop personalised approaches and optimise clinical interventions
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.