[HTML][HTML] Digital biomarkers for depression screening with wearable devices: cross-sectional study with machine learning modeling

Y Rykov, TQ Thach, I Bojic… - JMIR mHealth and …, 2021 - mhealth.jmir.org
Background: Depression is a prevalent mental disorder that is undiagnosed and untreated
in half of all cases. Wearable activity trackers collect fine-grained sensor data characterizing …

[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] Relationship between major depression symptom severity and sleep collected using a wristband wearable device: multicenter longitudinal observational …

Y Zhang, AA Folarin, S Sun, N Cummins… - JMIR mHealth and …, 2021 - mhealth.jmir.org
Background Sleep problems tend to vary according to the course of the disorder in
individuals with mental health problems. Research in mental health has associated sleep …

[HTML][HTML] Toward a mobile platform for real-world digital measurement of depression: user-centered design, data quality, and behavioral and clinical modeling

S Nickels, MD Edwards, SF Poole, D Winter… - JMIR mental …, 2021 - mental.jmir.org
Background Although effective mental health treatments exist, the ability to match individuals
to optimal treatments is poor, and timely assessment of response is difficult. One reason for …

[HTML][HTML] Tracking and monitoring mood stability of patients with major depressive disorder by machine learning models using passive digital data: prospective …

R Bai, L Xiao, Y Guo, X Zhu, N Li, Y Wang… - JMIR mHealth and …, 2021 - mhealth.jmir.org
Background Major depressive disorder (MDD) is a common mental illness characterized by
persistent sadness and a loss of interest in activities. Using smartphones and wearable …

[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 …

[HTML][HTML] Predicting depressive symptom severity through individuals' nearby bluetooth device count data collected by mobile phones: preliminary longitudinal study

Y Zhang, AA Folarin, S Sun, N Cummins… - JMIR mHealth and …, 2021 - mhealth.jmir.org
Background: Research in mental health has found associations between depression and
individuals' behaviors and statuses, such as social connections and interactions, working …

[HTML][HTML] Tracking and predicting depressive symptoms of adolescents using smartphone-based self-reports, parental evaluations, and passive phone sensor data …

J Cao, AL Truong, S Banu, AA Shah… - JMIR mental …, 2020 - mental.jmir.org
Background: Depression carries significant financial, medical, and emotional burden on
modern society. Various proof-of-concept studies have highlighted how apps can link …

Monitoring changes in depression severity using wearable and mobile sensors

P Pedrelli, S Fedor, A Ghandeharioun, E Howe… - Frontiers in …, 2020 - frontiersin.org
Background: While preliminary evidence suggests that sensors may be employed to detect
presence of low mood it is still unclear whether they can be leveraged for measuring …

[HTML][HTML] Mobile sensing and support for people with depression: a pilot trial in the wild

F Wahle, T Kowatsch, E Fleisch, M Rufer… - JMIR mHealth and …, 2016 - mhealth.jmir.org
Background: Depression is a burdensome, recurring mental health disorder with high
prevalence. Even in developed countries, patients have to wait for several months to receive …