Internet of Things Applied to Mental Health: Concepts, Applications, and Perspectives

Teles, Ariel Soares; Barros, Flávio de; Rodrigues, Ivan; Barbosa, Allan; Silva, Francisco; Coutinho, Luciano Galvão; Teixeira, Silmar

doi:10.1007/978-3-030-42934-8_4

Cited by 2 publications

(1 citation statement)

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“…DPMH solutions require collecting and analyzing large amounts of different types of social and behavioral data that can represent experiences of the users and their interactions with people, places, and devices. These context data can be passively gathered, for instance, from ubiquitous sensors, social media, and health care systems [ 16 ]. After collection, pieces of raw data are usually preprocessed and transformed into useful data or data sets to be mined [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

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

Marques¹,

Moura²,

Ven³

et al. 2022

J Med Internet Res

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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 the sensing applications and public data sets for DPMH from a technical perspective. Methods We performed a systematic review of scientific literature and data sets. We searched 8 digital libraries and 20 data set repositories to find results that met the selection criteria. We conducted a data extraction process from the selected articles and data sets. For this purpose, a form was designed to extract relevant information, thus enabling us to answer the research questions and identify open issues and research trends. Results A total of 31 sensing apps and 8 data sets were identified and reviewed. Sensing apps explore different context data sources (eg, positioning, inertial, ambient) to support DPMH studies. These apps are designed to analyze and process collected data to classify (n=11) and predict (n=6) mental states/disorders, and also to investigate existing correlations between context data and mental states/disorders (n=6). Moreover, general-purpose sensing apps are developed to focus only on contextual data collection (n=9). The reviewed data sets contain context data that model different aspects of human behavior, such as sociability, mood, physical activity, sleep, with some also being multimodal. Conclusions This systematic review provides in-depth analysis regarding solutions for DPMH. Results show growth in proposals for DPMH sensing apps in recent years, as opposed to a scarcity of public data sets. The review shows that there are features that can be measured on smart devices that can act as proxies for mental status and well-being; however, it should be noted that the combined evidence for high-quality features for mental states remains limited. DPMH presents a great perspective for future research, mainly to reach the needed maturity for applications in clinical settings.

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Section: Introductionmentioning

confidence: 99%

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

Marques¹,

Moura²,

Ven³

et al. 2022

J Med Internet Res

Self Cite

View full text Add to dashboard Cite

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

Mendes¹,

Moura²,

Ven³

et al. 2021

Preprint

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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.

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Internet of Things Applied to Mental Health: Concepts, Applications, and Perspectives

Cited by 2 publications

References 65 publications

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

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

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

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