Predicting depressive symptoms using smartphone data

S Ware, C Yue, R Morillo, J Lu, C Shang, J Bi… - Smart Health, 2020 - Elsevier
Depression is a serious mental illness. The symptoms associated with depression are both
behavioral (in appetite, energy level, sleep) and cognitive (in interests, mood …

[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 …

Automatic depression prediction using internet traffic characteristics on smartphones

C Yue, S Ware, R Morillo, J Lu, C Shang, J Bi… - Smart Health, 2020 - Elsevier
Depression is a serious mental health problem. Recently, researchers have proposed novel
approaches that use sensing data collected passively on smartphones for automatic …

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 …

The accuracy of passive phone sensors in predicting daily mood

A Pratap, DC Atkins, BN Renn, MJ Tanana… - Depression and …, 2019 - Wiley Online Library
Background Smartphones provide a low‐cost and efficient means to collect population level
data. Several small studies have shown promise in predicting mood variability from …

Predicting symptoms of depression and anxiety using smartphone and wearable data

I Moshe, Y Terhorst, K Opoku Asare, LB Sander… - Frontiers in …, 2021 - frontiersin.org
Background: Depression and anxiety are leading causes of disability worldwide but often
remain undetected and untreated. Smartphone and wearable devices may offer a unique …

[HTML][HTML] Challenges in using mHealth data from smartphones and wearable devices to predict depression symptom severity: retrospective analysis

S Sun, AA Folarin, Y Zhang, N Cummins… - Journal of medical …, 2023 - jmir.org
Background Major depressive disorder (MDD) affects millions of people worldwide, but
timely treatment is not often received owing in part to inaccurate subjective recall and …

Moodable: On feasibility of instantaneous depression assessment using machine learning on voice samples with retrospectively harvested smartphone and social …

A Dogrucu, A Perucic, A Isaro, D Ball, E Toto… - Smart Health, 2020 - Elsevier
Depression is a leading cause of disability and is associated with suicide risk. However, a
quarter of patients with major depression remain undiagnosed. Prior work has demonstrated …

STDD: short-term depression detection with passive sensing

N Narziev, H Goh, K Toshnazarov, SA Lee, KM Chung… - Sensors, 2020 - mdpi.com
It has recently been reported that identifying the depression severity of a person requires
involvement of mental health professionals who use traditional methods like interviews and …

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 …