Circadian rhythm disruption and mental health

WH Walker, JC Walton, AC DeVries… - Translational psychiatry, 2020‏ - nature.com
Circadian rhythms are internal manifestations of the solar day that permit adaptations to
predictable environmental temporal changes. These~ 24-h rhythms are controlled by …

[HTML][HTML] The foundation and architecture of precision medicine in neurology and psychiatry

H Hampel, P Gao, J Cummings, N Toschi… - Trends in …, 2023‏ - cell.com
Neurological and psychiatric diseases have high degrees of genetic and pathophysiological
heterogeneity, irrespective of clinical manifestations. Traditional medical paradigms have …

Modern views of machine learning for precision psychiatry

ZS Chen, IR Galatzer-Levy, B Bigio, C Nasca, Y Zhang - Patterns, 2022‏ - cell.com
In light of the National Institute of Mental Health (NIMH)'s Research Domain Criteria (RDoC),
the advent of functional neuroimaging, novel technologies and methods provide new …

[HTML][HTML] Predicting depression from smartphone behavioral markers using machine learning methods, hyperparameter optimization, and feature importance analysis …

K Opoku Asare, Y Terhorst, J Vega… - JMIR mHealth and …, 2021‏ - mhealth.jmir.org
Background Depression is a prevalent mental health challenge. Current depression
assessment methods using self-reported and clinician-administered questionnaires have …

[HTML][HTML] Current state of digital biomarker technologies for real-life, home-based monitoring of cognitive function for mild cognitive impairment to mild Alzheimer …

A Piau, K Wild, N Mattek, J Kaye - Journal of medical Internet research, 2019‏ - jmir.org
Background Among areas that have challenged the progress of dementia care has been the
assessment of change in symptoms over time. Digital biomarkers are defined as objective …

[HTML][HTML] Digital phenoty**: data-driven psychiatry to redefine mental health

A Oudin, R Maatoug, A Bourla, F Ferreri… - Journal of medical …, 2023‏ - jmir.org
The term “digital phenotype” refers to the digital footprint left by patient-environment
interactions. It has potential for both research and clinical applications but challenges our …

[HTML][HTML] Passive sensing of prediction of moment-to-moment depressed mood among undergraduates with clinical levels of depression sample using smartphones

NC Jacobson, YJ Chung - Sensors, 2020‏ - mdpi.com
Prior research has recently shown that passively collected sensor data collected within the
contexts of persons daily lives via smartphones and wearable sensors can distinguish those …

[HTML][HTML] Understanding people's use of and perspectives on mood-tracking apps: interview study

SM Schueller, M Neary, J Lai, DA Epstein - JMIR mental health, 2021‏ - mental.jmir.org
Background Supporting mental health and wellness is of increasing interest due to a
growing recognition of the prevalence and burden of mental health issues. Mood is a central …

[HTML][HTML] Mobile phone and wearable sensor-based mHealth approaches for psychiatric disorders and symptoms: systematic review

J Seppälä, I De Vita, T Jämsä, J Miettunen… - JMIR mental …, 2019‏ - mental.jmir.org
Background: Mobile Therapeutic Attention for Patients with Treatment-Resistant
Schizophrenia (m-RESIST) is an EU Horizon 2020-funded project aimed at designing and …

[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 …