Digital phenotyping in bipolar disorder: Using longitudinal Fitbit data and personalized machine learning to predict mood symptomatology
JM Lipschitz, S Lin, S Saghafian… - Acta Psychiatrica …, 2024 - Wiley Online Library
Background Effective treatment of bipolar disorder (BD) requires prompt response to mood
episodes. Preliminary studies suggest that predictions based on passive sensor data from …
episodes. Preliminary studies suggest that predictions based on passive sensor data from …
Electrodermal activity in bipolar disorder: Differences between mood episodes and clinical remission using a wearable device in a real-world clinical setting
G Anmella, A Mas, M Sanabra… - Journal of Affective …, 2024 - Elsevier
Background Bipolar disorder (BD) lacks objective measures for illness activity and treatment
response. Electrodermal activity (EDA) is a quantitative measure of autonomic function …
response. Electrodermal activity (EDA) is a quantitative measure of autonomic function …
[HTML][HTML] Towards a consensus roadmap for a new diagnostic framework for mental disorders
Current nosology claims to separate mental disorders into distinct categories that do not
overlap with each other. This nosological separation is not based on underlying …
overlap with each other. This nosological separation is not based on underlying …
[HTML][HTML] Automated speech analysis in bipolar disorder: the CALIBER study protocol and preliminary results
G Anmella, M De Prisco, JB Joyce… - Journal of Clinical …, 2024 - mdpi.com
Background: Bipolar disorder (BD) involves significant mood and energy shifts reflected in
speech patterns. Detecting these patterns is crucial for diagnosis and monitoring, currently …
speech patterns. Detecting these patterns is crucial for diagnosis and monitoring, currently …
Measuring algorithmic bias to analyze the reliability of AI tools that predict depression risk using smartphone sensed-behavioral data
AI tools intend to transform mental healthcare by providing remote estimates of depression
risk using behavioral data collected by sensors embedded in smartphones. While these …
risk using behavioral data collected by sensors embedded in smartphones. While these …
[HTML][HTML] Wearable Data From Subjects Playing Super Mario, Taking University Exams, or Performing Physical Exercise Help Detect Acute Mood Disorder Episodes via …
Background Personal sensing, leveraging data passively and near-continuously collected
with wearables from patients in their ecological environment, is a promising paradigm to …
with wearables from patients in their ecological environment, is a promising paradigm to …
Wearable Signal Fusion through Deep Learning: Discriminating Affective States Levels in the Wild among Healthy and Depressed
CG Vázquez, C Eicher, R Huber, G Kronenberg… - 2024 - researchsquare.com
The increasing prevalence of depression underscores the critical need for improved
monitoring and personalized treatment options. While traditional assessment methods …
monitoring and personalized treatment options. While traditional assessment methods …
[PDF][PDF] Wearable data from students, teachers or subjects with alcohol use disorder help detect acute mood episodes via self-supervised learning
AV Hidalgo-Mazzei - s3.ca-central-1.amazonaws.com
Background: Personal sensing, leveraging data passively and near-continuously collected
with wearables from patients in their ecological environment, is a promising paradigm to …
with wearables from patients in their ecological environment, is a promising paradigm to …
[引用][C] Special Issue on Digital Psychiatry
LB Glenthøj, M Faurholt‐Jepsen - Acta Psychiatrica …, 2024 - Wiley Online Library
Despite a growing recognition of mental health challenges worldwide, there remains a
significant gap between the demand for and the availability of mental health services. The …
significant gap between the demand for and the availability of mental health services. The …