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Pervasive Sensing for Mental Health
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Pervasive Sensing for Mental Health

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 3195

Special Issue Editor

Dr. Pietro Cipresso

E-Mail Website
Guest Editor
Department of Psychology, University of Turin, Via Giuseppe Verdi, 8, 10124 Torin, TO, Italy
Interests: research methodology; psychometrics; psychophysiology; mathematical psychology; mHealth

Special Issue Information

Dear Colleagues,

The field of mental health research is rapidly advancing, and there is a growing interest in the use of pervasive sensing technologies to better understand and support individuals with mental health conditions. Pervasive sensing refers to the use of a wide range of sensors, such as accelerometers, cameras, microphones, psychophysiology sensors, medical sensors, and behavioral sensors, to passively collect data about an individual's physical, social, and emotional context. These sensors can be used to monitor various aspects of an individual's physiological and behavioral states, including heart rate, skin conductance, electroencephalography (EEG), and movements. Additionally, psychophysiology sensors, such as electrocardiograms (ECG) and respiration sensors, can be used to measure the physiological responses associated with mental and emotional states. Medical sensors, such as temperature sensors and blood glucose sensors, can be used to monitor the overall physical health of an individual. Behavioral sensors, such as cameras and microphones, can also be used to track an individual's social interactions and emotional expression. This wide range of sensors allows for a more comprehensive understanding of an individual's mental and physical health.

We are inviting submissions of original research papers that explore the use of pervasive sensing technologies for mental health. Topics of interest include, but are not limited to, the following:

  • The use of wearable and ambient sensors to monitor and track mental health symptoms;
  • The use of mobile and social media data to predict and prevent mental health crises;
  • The use of virtual and augmented reality technologies to deliver mental health interventions;
  • The ethical, legal, and social implications of pervasive sensing for mental health.

Submissions should present novel research contributions and be of a high quality, rigor and relevance. Submissions will be reviewed by an international panel of experts in the field of pervasive sensing and mental health.

Dr. Pietro Cipresso
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (2 papers)

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Research

23 pages, 797 KiB  
Article
Improving Depression Severity Prediction from Passive Sensing: Symptom-Profiling Approach
by Sabinakhon Akbarova, Myeongji Im, Suhyun Kim, Kobiljon Toshnazarov, Kyong-Mee Chung, Junghyun Chun, Youngtae Noh and Young-Ah Kim
Sensors 2023, 23(21), 8866; https://doi.org/10.3390/s23218866 - 31 Oct 2023
Cited by 1 | Viewed by 1341
Abstract
Depression is a significant mental health issue that profoundly impacts people’s lives. Diagnosing depression often involves interviews with mental health professionals and surveys, which can become cumbersome when administered continuously. Digital phenotyping offers an innovative approach for detecting and monitoring depression without requiring [...] Read more.
Depression is a significant mental health issue that profoundly impacts people’s lives. Diagnosing depression often involves interviews with mental health professionals and surveys, which can become cumbersome when administered continuously. Digital phenotyping offers an innovative approach for detecting and monitoring depression without requiring active user involvement. This study contributes to the detection of depression severity and depressive symptoms using mobile devices. Our proposed approach aims to distinguish between different patterns of depression and improve prediction accuracy. We conducted an experiment involving 381 participants over a period of at least three months, during which we collected comprehensive passive sensor data and Patient Health Questionnaire (PHQ-9) self-reports. To enhance the accuracy of predicting depression severity levels (classified as none/mild, moderate, or severe), we introduce a novel approach called symptom profiling. The symptom profile vector represents nine depressive symptoms and indicates both the probability of each symptom being present and its significance for an individual. We evaluated the effectiveness of the symptom-profiling method by comparing the F1 score achieved using sensor data features as inputs to machine learning models with the F1 score obtained using the symptom profile vectors as inputs. Our findings demonstrate that symptom profiling improves the F1 score by up to 0.09, with an average improvement of 0.05, resulting in a depression severity prediction with an F1 score as high as 0.86. Full article
(This article belongs to the Special Issue Pervasive Sensing for Mental Health)
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20 pages, 3163 KiB  
Article
Exploring Biomarkers of Mental Flexibility in Healthy Aging: A Computational Psychometric Study
by Francesca Borghesi, Alice Chirico, Elisa Pedroli, Giuseppina Elena Cipriani, Nicola Canessa, Martina Amanzio and Pietro Cipresso
Sensors 2023, 23(15), 6983; https://doi.org/10.3390/s23156983 - 6 Aug 2023
Cited by 5 | Viewed by 1388
Abstract
Mental flexibility (MF) has long been defined as cognitive flexibility. Specifically, it has been mainly studied within the executive functions domain. However, there has recently been increased attention towards its affective and physiological aspects. As a result, MF has been described as an [...] Read more.
Mental flexibility (MF) has long been defined as cognitive flexibility. Specifically, it has been mainly studied within the executive functions domain. However, there has recently been increased attention towards its affective and physiological aspects. As a result, MF has been described as an ecological and cross-subject skill consisting of responding variably and flexibly to environmental cognitive-affective demands. Cross-sectional studies have mainly focused on samples composed of healthy individual and of patients with chronic conditions such as Mild Cognitive Impairment and Parkinson’s, emphasizing their behavioral rigidity. Our study is the first to consider a sample of healthy older subjects and to outline physiological and psychological markers typical of mental flexibility, to identify functional biomarkers associated with successful aging. Our results reveal that biomarkers (respiratory and heart rate variability assessments) distinguished between individuals high vs. low in mental flexibility more reliably than traditional neuropsychological tests. This unveiled the multifaceted nature of mental flexibility composed of both cognitive and affective aspects, which emerged only if non-linear multi-variate analytic approaches, such as Supervised Machine Learning, were used. Full article
(This article belongs to the Special Issue Pervasive Sensing for Mental Health)
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