[HTML][HTML] Wearable artificial intelligence for anxiety and depression: sco** review

A Abd-Alrazaq, R AlSaad, S Aziz, A Ahmed… - Journal of Medical …, 2023 - jmir.org
Background Anxiety and depression are the most common mental disorders worldwide.
Owing to the lack of psychiatrists around the world, the incorporation of artificial intelligence …

Parkinson's disease management via wearable sensors: a systematic review

H Mughal, AR Javed, M Rizwan, AS Almadhor… - IEEE …, 2022 - ieeexplore.ieee.org
Wearable technology has played an essential role in the Mobile Health (mHealth) sector for
diagnosis, treatment, and rehabilitation of numerous diseases and disorders. One such …

Wearable technology in clinical practice for depressive disorder

S Fedor, R Lewis, P Pedrelli… - … England Journal of …, 2023 - Mass Medical Soc
Wearable Technology in Clinical Practice for Depressive Disorder | New England Journal of
Medicine Skip to main content The New England Journal of Medicine homepage Advanced …

[HTML][HTML] Digital phenoty** for monitoring mental disorders: systematic review

P Bufano, M Laurino, S Said, A Tognetti… - Journal of Medical …, 2023 - jmir.org
Background The COVID-19 pandemic has increased the impact and spread of mental
illness and made health services difficult to access; therefore, there is a need for remote …

Systematic review and meta-analysis of performance of wearable artificial intelligence in detecting and predicting depression

A Abd-Alrazaq, R AlSaad, F Shuweihdi, A Ahmed… - NPJ Digital …, 2023 - nature.com
Given the limitations of traditional approaches, wearable artificial intelligence (AI) is one of
the technologies that have been exploited to detect or predict depression. The current …

[HTML][HTML] Mood ratings and digital biomarkers from smartphone and wearable data differentiates and predicts depression status: A longitudinal data analysis

KO Asare, I Moshe, Y Terhorst, J Vega, S Hosio… - Pervasive and Mobile …, 2022 - Elsevier
Depression is a prevalent mental disorder. Current clinical and self-reported assessment
methods of depression are laborious and incur recall bias. Their sporadic nature often …

Remote Assessment of Disease and Relapse in Major Depressive Disorder (RADAR-MDD): recruitment, retention, and data availability in a longitudinal remote …

F Matcham, D Leightley, S Siddi, F Lamers, KM White… - BMC psychiatry, 2022 - Springer
Abstract Background Major Depressive Disorder (MDD) is prevalent, often chronic, and
requires ongoing monitoring of symptoms to track response to treatment and identify early …

[HTML][HTML] From smartphone data to clinically relevant predictions: A systematic review of digital phenoty** methods in depression

IE Leaning, N Ikani, HS Savage, A Leow… - Neuroscience & …, 2024 - Elsevier
Background Smartphone-based digital phenoty** enables potentially clinically relevant
information to be collected as individuals go about their day. This could improve monitoring …

Current advances in wearable devices and their sensors in patients with depression

S Lee, H Kim, MJ Park, HJ Jeon - Frontiers in Psychiatry, 2021 - frontiersin.org
In this study, a literature survey was conducted of research into the development and use of
wearable devices and sensors in patients with depression. We collected 18 studies that had …

A model of normality inspired deep learning framework for depression relapse prediction using audiovisual data

A Othmani, AO Zeghina, M Muzammel - Computer Methods and Programs …, 2022 - Elsevier
Abstract Background: Depression (Major Depressive Disorder) is one of the most common
mental illnesses. According to the World Health Organization, more than 300 million people …