Abstract : BACKGROUND:
The screening of digital footprint for clinical purposes relies on the
capacity of wearable technologies to collect data and extract relevant
information's for patient management. Artificial intelligence (AI)
techniques allow processing of real-time observational information and
continuously learning from data to build understanding. We designed a
system able to get clinical sense from digital footprints based on the
smartphone's native sensors and advanced machine learning and signal
processing techniques in order to identify suicide risk.
METHOD/DESIGN:
The Smartcrisis study is a cross-national comparative study. The study
goal is to determine the relationship between suicide risk and changes
in sleep quality and disturbed appetite. Outpatients from the Hospital
Fundación Jiménez Díaz Psychiatry Department (Madrid, Spain) and the
University Hospital of Nimes (France) will be proposed to participate to
the study. Two smartphone applications and a wearable armband will be
used to capture the data. In the intervention group, a smartphone
application (MEmind) will allow for the ecological momentary assessment
(EMA) data capture related with sleep, appetite and suicide ideations.
DISCUSSION:
Some concerns regarding data security might be raised. Our system
complies with the highest level of security regarding patients' data.
Several important ethical considerations related to EMA method must also
be considered. EMA methods entails a non-negligible time commitment on
behalf of the participants. EMA rely on daily, or sometimes more
frequent, Smartphone notifications. Furthermore, recording participants'
daily experiences in a continuous manner is an integral part of EMA.
This approach may be significantly more than asking a participant to
complete a retrospective questionnaire but also more accurate in terms
of symptoms monitoring. Overall, we believe that Smartcrises could
participate to a paradigm shift from the traditional identification of
risks factors to personalized prevention strategies tailored to
characteristics for each patient.
Sciences cognitives
Sciences du Vivant [q-bio] / Médecine humaine et pathologie / Psychiatrie et santé mentale
https://hal.umontpellier.fr/hal-02561989
Soumis le : vendredi 5 juin 2020 - 14:31:00
Fichier s12888-019-2260-y.pdf
Source https://hal.umontpellier.fr/hal-02561989/document
