Digital twins for health: a scoping review

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Abstract

The use of digital twins (DTs) has proliferated across various fields and industries, with a recent surge in the healthcare sector. The concept of digital twin for health (DT4H) holds great promise to revolutionize the entire healthcare system, including management and delivery, disease treatment and prevention, and health well-being maintenance, ultimately improving human life. The rapid growth of big data and continuous advancement in data science (DS) and artificial intelligence (AI) have the potential to significantly expedite DT research and development by providing scientific expertise, essential data, and robust cybertechnology infrastructure. Although various DT initiatives have been underway in the industry, government, and military, DT4H is still in its early stages. This paper presents an overview of the current applications of DTs in healthcare, examines consortium research centers and their limitations, and surveys the current landscape of emerging research and development opportunities in healthcare. We envision the emergence of a collaborative global effort among stakeholders to enhance healthcare and improve the quality of life for millions of individuals worldwide through pioneering research and development in the realm of DT technology.

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Just-in-Time Adaptive Interventions (JITAIs) in Mobile Health: Key Components and Design Principles for Ongoing Health Behavior Support

Inbal Nahum-Shani, Shawna Smith, Bonnie J Spring … (2017)

Background The just-in-time adaptive intervention (JITAI) is an intervention design aiming to provide the right type/amount of support, at the right time, by adapting to an individual’s changing internal and contextual state. The availability of increasingly powerful mobile and sensing technologies underpins the use of JITAIs to support health behavior, as in such a setting an individual’s state can change rapidly, unexpectedly, and in his/her natural environment. Purpose Despite the increasing use and appeal of JITAIs, a major gap exists between the growing technological capabilities for delivering JITAIs and research on the development and evaluation of these interventions. Many JITAIs have been developed with minimal use of empirical evidence, theory, or accepted treatment guidelines. Here, we take an essential first step towards bridging this gap. Methods Building on health behavior theories and the extant literature on JITAIs, we clarify the scientific motivation for JITAIs, define their fundamental components, and highlight design principles related to these components. Examples of JITAIs from various domains of health behavior research are used for illustration. Conclusions As we enter a new era of technological capacity for delivering JITAIs, it is critical that researchers develop sophisticated and nuanced health behavior theories capable of guiding the construction of such interventions. Particular attention has to be given to better understanding the implications of providing timely and ecologically sound support for intervention adherence and retention We clarify the scientific motivation for the Just-In-Time Adaptive Interventions, define its fundamental components, and discuss key design principles for each component.

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Clinical development success rates for investigational drugs.

Michael B Hay, David Thomas, John Craighead … (2014)

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Digital Phenotyping

Thomas Insel (2017)

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Author and article information

Contributors

Jun Deng: Jun.Deng@yale.edu

Journal

Journal ID (nlm-ta): NPJ Digit Med

Journal ID (iso-abbrev): NPJ Digit Med

Title: NPJ Digital Medicine

Publisher: Nature Publishing Group UK (London )

ISSN (Electronic): 2398-6352

Publication date (Electronic): 22 March 2024

Publication date PMC-release: 22 March 2024

Publication date Collection: 2024

Volume: 7

Electronic Location Identifier: 77

Affiliations

[1 ]VA Informatics and Computing Infrastructure, Salt Lake City, UT 84148 USA

[2 ]Department of Radiation Oncology, University of South Florida, ( https://ror.org/032db5x82) Tampa, FL 33606 USA

[3 ]Department of Mathematics, University of South Carolina, ( https://ror.org/02b6qw903) Columbia, SC 29208 USA

[4 ]GRID grid.264727.2, ISNI 0000 0001 2248 3398, Department of Health Services Administration and Policy, , Temple University, ; Philadelphia, PA 19122 USA

[5 ]Department of Mathematics and Statistics, University of Massachusetts Amherst, ( https://ror.org/0072zz521) Amherst, MA 01003 USA

[6 ]Department of Mechanical Engineering, Massachusetts Institute of Technology, ( https://ror.org/042nb2s44) Cambridge, MA 02139 USA

[7 ]Department of Psychiatry, Massachusetts General Hospital, ( https://ror.org/002pd6e78) Boston, MA 02139 USA

[8 ]Department of Computer and Information Sciences, Florida A&M University, ( https://ror.org/00c4wc133) Tallahassee, FL 32307 USA

[9 ]Department of Chemical Engineering, Virginia Polytechnic Institute and State University, ( https://ror.org/02smfhw86) Blacksburg, VA 24060 USA

[10 ]McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, ( https://ror.org/03gds6c39) Houston, TX 77030 USA

[11 ]Precision Neurotherapeutics Innovation Program & Department of Neurosurgery, Mayo Clinic, ( https://ror.org/02qp3tb03) Phoenix, AZ 85003 USA

[12 ]Department of Radiation Oncology, University of Pennsylvania, ( https://ror.org/00b30xv10) Philadelphia, PA 19104 USA

[13 ]GRID grid.481551.c, IBM Almaden Research Center, ; San Jose, CA 95120 USA

[14 ]Department of Therapeutic Radiology, Yale University, ( https://ror.org/03v76x132) New Haven, CT 06510 USA

Author information

Leili Shahriyari http://orcid.org/0000-0001-6234-8449

Jinwei Liu http://orcid.org/0000-0003-2298-5974

Article

Publisher ID: 1073

DOI: 10.1038/s41746-024-01073-0

PMC ID: 10960047

PubMed ID: 38519626

SO-VID: 95831b2f-681f-427f-974a-ccef0c069c07

License:

Open Access This 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/.

History

Date received : 22 August 2023

Date accepted : 7 March 2024

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Keywords: drug development,clinical trial design

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Keywords: drug development, clinical trial design

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Most cited references 50

Just-in-Time Adaptive Interventions (JITAIs) in Mobile Health: Key Components and Design Principles for Ongoing Health Behavior Support

Clinical development success rates for investigational drugs.

Digital Phenotyping

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