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Accepted for/Published in: Journal of Medical Internet Research

Date Submitted: Mar 12, 2021
Open Peer Review Period: Mar 12, 2021 - May 7, 2021
Date Accepted: Dec 23, 2021
(closed for review but you can still tweet)

The final, peer-reviewed published version of this preprint can be found here:

Sensing Apps and Public Data Sets for Digital Phenotyping of Mental Health: Systematic Review

Marques JPM, Moura IR, Van de Ven P, Santos DV, Silva FJS, Coutinho LR, Teixeira SS, Rodrigues JJPC, Teles AS

Sensing Apps and Public Data Sets for Digital Phenotyping of Mental Health: Systematic Review

J Med Internet Res 2022;24(2):e28735

DOI: 10.2196/28735

PMID: 35175202

PMCID: 8895287

Warning: This is an author submission that is not peer-reviewed or edited.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.

Sensing Applications and Public Datasets for Digital Phenotyping of Mental Health

  • Jean P. M. Marques; 
  • Ivan R. Moura; 
  • Pepijn Van de Ven; 
  • Davi V. Santos; 
  • Francisco J. S. Silva; 
  • Luciano R. Coutinho; 
  • Silmar S. Teixeira; 
  • Joel J. P. C. Rodrigues; 
  • Ariel Soares Teles

ABSTRACT

Background:

Mental disorders are normally diagnosed exclusively on the basis of symptoms, which are identified from patients' interviews and self-reported experiences. To make mental health diagnoses and monitoring more objective, different solutions have been proposed such as Digital Phenotyping of Mental Health (DPMH), which can expand the ability to identify and monitor health conditions based on the interactions of people with digital technologies.

Objective:

This article aims to identify and characterize technically the sensing applications and public datasets for DPMH.

Methods:

We performed a systematic review of scientific literature and datasets. We searched digital libraries and dataset repositories to find results that met the selection criteria.

Results:

After applying inclusion and exclusion criteria, 31 articles and 8 datasets were selected for data extraction, in which we summarized their characteristics and identified trends and research opportunities.

Conclusions:

Results evidenced growth in proposals for DPMH sensing applications in recent years as opposed to a scarcity of public datasets. This systematic review provides in-depth analysis regarding solutions for DPMH.


 Citation

Please cite as:

Marques JPM, Moura IR, Van de Ven P, Santos DV, Silva FJS, Coutinho LR, Teixeira SS, Rodrigues JJPC, Teles AS

Sensing Apps and Public Data Sets for Digital Phenotyping of Mental Health: Systematic Review

J Med Internet Res 2022;24(2):e28735

DOI: 10.2196/28735

PMID: 35175202

PMCID: 8895287

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