About this Research Topic

Abstract Submission Deadline 15 September 2022
Manuscript Submission Deadline 15 November 2022

Digital phenotyping in health aims at leveraging data harnessed from our digitally connected technologies in order to unlock numerous opportunities in health applications. For instance, this can be applied in producing objective measurements, also referred to as digital biomarkers to refine diagnostic processes, improve condition monitoring for actionable outcomes, such as early signs of relapse, and facilitate or develop new personalized interventions and tailor treatment choices.

As digital phenotyping has attracted significant research interest, presents novel interesting opportunities for researchers to unfold, and has many potential benefits to end-users, it is important to keep in mind all aspects related to this technology and bear in mind other considerations related to its application. Such considerations include, among others: (a) ethical: perpetuating bias by utilizing data that reinforces social differences among groups; (b) privacy: users' concerns about a Big Brother watching them, and sharing their data with third parties; (c) economical: sharing the economical value of users’ data between the end-users and tech corporations.

This Research Topic aims to bring together research on the benefits and drawbacks of this technology. This collection invites articles pushing the level of common considerations in this field beyond using digital biomarkers for predictive modeling in health, to unpeel the facets of digital phenotyping and its applications in health.

We welcome original contribution papers related to digital phenotyping solutions for health applications. Relevant subtopic topics include, but are not limited to:

• Novel considerations, including ethical, economical, or with regard to privacy in digital phenotyping for health.
• Case studies, using quantitative or qualitative data.
• Feasibility and exploratory work on novel digital biomarkers data collection.
• Validation and evaluation of novel digital biomarkers.
• Qualitative analysis of users’ perspective on digital phenotyping and its potential uses in health.
• Application of machine learning to digital phenotyping data to derive novel digital measures and predictive models.
• Application of digital phenotyping for monitoring or predicting clinical or subclinical outcomes.
• Application of digital phenotyping for novel interventions, care delivery, and personalized medicine.
• Novel data science techniques or mixed-method research for solving challenges in health with digital phenotyping technologies.


Topic editor Teodora S. Buda has filed over 20 patents. A list of her published patents can be found here and here. Her research is now supported by Koa Health, where she works full-time from Spain. In the past, she worked for Telefonica, IBM, and LexisNexis. Previously to that, her Ph.D was funded by IBM, Lero, and UCD.

Topic Editor Akane Sano has received travel reimbursement or honorarium payments from Gordon Research Conferences, Pola Chemical Industries, Meta, Leuven Mindgate, American Epilepsy Society, and IEEE. AS has received research support from Meta, Microsoft, Sony Corporation, NEC Corporation, and Pola Chemicals and consulting fees from Gideon Health and Suntory Global Innovation Center. AS was paid by the European Science Foundation for a grant review.

All other Topic Editors declare no competing interests with regards to the Research Topic subject.

Keywords: Digital phenotyping, health, mental health, smartphone sensing, wearable sensors, predictive modelling machine learning, deep learning, passive sensing


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Digital phenotyping in health aims at leveraging data harnessed from our digitally connected technologies in order to unlock numerous opportunities in health applications. For instance, this can be applied in producing objective measurements, also referred to as digital biomarkers to refine diagnostic processes, improve condition monitoring for actionable outcomes, such as early signs of relapse, and facilitate or develop new personalized interventions and tailor treatment choices.

As digital phenotyping has attracted significant research interest, presents novel interesting opportunities for researchers to unfold, and has many potential benefits to end-users, it is important to keep in mind all aspects related to this technology and bear in mind other considerations related to its application. Such considerations include, among others: (a) ethical: perpetuating bias by utilizing data that reinforces social differences among groups; (b) privacy: users' concerns about a Big Brother watching them, and sharing their data with third parties; (c) economical: sharing the economical value of users’ data between the end-users and tech corporations.

This Research Topic aims to bring together research on the benefits and drawbacks of this technology. This collection invites articles pushing the level of common considerations in this field beyond using digital biomarkers for predictive modeling in health, to unpeel the facets of digital phenotyping and its applications in health.

We welcome original contribution papers related to digital phenotyping solutions for health applications. Relevant subtopic topics include, but are not limited to:

• Novel considerations, including ethical, economical, or with regard to privacy in digital phenotyping for health.
• Case studies, using quantitative or qualitative data.
• Feasibility and exploratory work on novel digital biomarkers data collection.
• Validation and evaluation of novel digital biomarkers.
• Qualitative analysis of users’ perspective on digital phenotyping and its potential uses in health.
• Application of machine learning to digital phenotyping data to derive novel digital measures and predictive models.
• Application of digital phenotyping for monitoring or predicting clinical or subclinical outcomes.
• Application of digital phenotyping for novel interventions, care delivery, and personalized medicine.
• Novel data science techniques or mixed-method research for solving challenges in health with digital phenotyping technologies.


Topic editor Teodora S. Buda has filed over 20 patents. A list of her published patents can be found here and here. Her research is now supported by Koa Health, where she works full-time from Spain. In the past, she worked for Telefonica, IBM, and LexisNexis. Previously to that, her Ph.D was funded by IBM, Lero, and UCD.

Topic Editor Akane Sano has received travel reimbursement or honorarium payments from Gordon Research Conferences, Pola Chemical Industries, Meta, Leuven Mindgate, American Epilepsy Society, and IEEE. AS has received research support from Meta, Microsoft, Sony Corporation, NEC Corporation, and Pola Chemicals and consulting fees from Gideon Health and Suntory Global Innovation Center. AS was paid by the European Science Foundation for a grant review.

All other Topic Editors declare no competing interests with regards to the Research Topic subject.

Keywords: Digital phenotyping, health, mental health, smartphone sensing, wearable sensors, predictive modelling machine learning, deep learning, passive sensing


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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