Mental Health Care Technologies: Context-Aware Stress Assessment and Stress Coping Thank You for referencing this work, if you find it useful! 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.

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