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Mental Health Care Technologies: Context-Aware Stress Assessment and Stress Coping

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Mental Health Care Technologies: Context-Aware Stress Assessment and Stress Coping

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Reference/Citation: Allan Berrocal, Mental Health Care Technologies: Context-Aware Stress Assessment and Stress Coping, CUSO PhD school 2017.

Additional Reference/Citation for a latest scientific paper: Katarzyna Wac, Maddalena Fiordelli, Mattia Gustarini, Homero Rivas, Quality of Life Technologies: Experiences from the Field and Key Research Challenges, IEEE Internet Computing, Special Issue: Personalized Digital Health, July/August 2015.

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Mental Health Care Technologies: Context-Aware Stress Assessment and Stress Coping

  1. 1. Mental Health Care Technologies: Context Aware Stress Assessment and Stress Coping Allan.Berrocal@unige.ch www.qol.unige.ch Artificial Natural A Priori User surveys Experts surveys User study •Adoption likelihood •Perceived value A Posteriori Prototype testing User Interviews Functional prototype •Effectiveness •Adoption •Added value Motivation qMost People Need oUnderstand their stress levels oLearn how to cope with stress oAchieve a healthy living style qMost People Lack oTime for self-awareness oTime and money for therapies oStress management skills q Most People Have o Smartphones o Wearables o Close friends and relatives But . . . However . . . Research Plan Research Question Can commercially available technologies such as wearables and smartphones be leveraged to assess stress buildup and assist individuals in the process of coping with it? Domain My research combines elements of traditional information systems, machine learning, behavioral assessment and human computer interaction applied to the domain of quality of life technologies. Specifically à assessment and treatment of human stress Main Challenges Social Content Complexity of research: Higher in social-context Medium to high in content-context High High Low Expected Contributions qObservers Data oPeer-assessments aiming to enhance accuracy of stress, assessment and modeling qAlgorithms oTo model human stress and coping techniques in ways that help individuals qSoftware design principles oGuides, data visualization, user-aware notifications, ways to deliver persuasive recommendations qPrototype oTools, mobile app to demonstrate the operationalization of model, algorithms and constructs Evaluations Planned Related Research oPsychological theories of stress assessment and behavior change oStress assessment & prediction from monitoring of individual’s patterns oHealth interventions delivered via smartphones and wearables oHCI principles to guide m/e-health systems design oBehavioral informatics oSocio-determinants of health University of Geneva Institute of Services Science Quality of Life Technologies Lab Methods qData Collection oInterviews, surveys (online, face-to face) oAutomatic logging of individual patters and body signals (GSR, HRV, etc.) oSelf assessments (ESM, DRM, peer-ESM) qMachine Learning oStress assessment and prediction oStress coping preferences qBehavior Change oTechnology-assisted oBehavior change models qInterventions oJust in time oPassive or active

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