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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Detecting Depression and Predicting its Onset Using Longitudinal Symptoms Captured by Passive Sensing
Prerna Chikersal, Afsaneh Doryab, Michael Tumminia, et al.
ACM Transactions on Computer-Human Interaction (2021) Vol. 28, Iss. 1, pp. 1-41
Open Access | Times Cited: 100

Showing 26-50 of 100 citing articles:

Smartphone Digital Phenotyping in Mental Health Disorders: A Review of Raw Sensors Utilized, Machine Learning Processing Pipelines, and Derived Behavioral Features
Jake Linardon, Kelly Chen, Shruti Gajjar, et al.
Psychiatry Research (2025), pp. 116483-116483
Closed Access
Predictive Modeling of Mental Illness Onset Using Wearable Devices and Medical Examination Data: Machine Learning Approach
Tomoki Saito, Hikaru Suzuki, Akifumi Kishi
Frontiers in Digital Health (2022) Vol. 4
Open Access | Times Cited: 20
Investigation of physical activity, sleep, and mental health recovery in treatment resistant depression (TRD) patients receiving repetitive transcranial magnetic stimulation (rTMS) treatment
Chris Griffiths, Ksenija Maravic da Silva, Chloe Leathlean, et al.
Journal of Affective Disorders Reports (2022) Vol. 8, pp. 100337-100337
Open Access | Times Cited: 16
A Fast and Minimal System to Identify Depression Using Smartphones: Explainable Machine Learning–Based Approach
Md. Sabbir Ahmed, Nova Ahmed
JMIR Formative Research (2023) Vol. 7, pp. e28848-e28848
Open Access | Times Cited: 10
Sequence Modeling of Passive Sensing Data for Treatment Response Prediction in Major Depressive Disorder
Bochao Zou, Xiaolong Zhang, Le Xiao, et al.
IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Vol. 31, pp. 1786-1795
Open Access | Times Cited: 9
Digital Phenotyping for Stress, Anxiety, and Mild Depression: Systematic Literature Review
Ann Choi, Aysel Ooi, Danielle Lottridge
JMIR mhealth and uhealth (2023) Vol. 12, pp. e40689-e40689
Open Access | Times Cited: 9
Use of smartphone sensor data in detecting and predicting depression and anxiety in young people (12–25 years): A scoping review
Joanne R Beames, Jin Han, Artur Shvetcov, et al.
Heliyon (2024) Vol. 10, Iss. 15, pp. e35472-e35472
Open Access | Times Cited: 3
GLOBEM: Cross-Dataset Generalization of Longitudinal Human Behavior Modeling
Xuhai Xu, Xin Liu, Han Zhang, et al.
GetMobile Mobile Computing and Communications (2024) Vol. 28, Iss. 2, pp. 23-30
Closed Access | Times Cited: 3
Enhancing the convenience of frailty index assessment for elderly Chinese people with machine learning methods
Li Huang, Huajian Chen, Zhenzhen Liang
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 3
Investigating the Feasibility of Assessing Depression Severity and Valence-Arousal with Wearable Sensors Using Discrete Wavelet Transforms and Machine Learning
Abdullah Ahmed, Jayroop Ramesh, Sandipan Ganguly, et al.
Information (2022) Vol. 13, Iss. 9, pp. 406-406
Open Access | Times Cited: 14
Machine learning for anxiety and depression profiling and risk assessment in the aftermath of an emergency
Guillermo Villanueva Benito, Ximena Goldberg, Nicolai Brachowicz, et al.
Artificial Intelligence in Medicine (2024) Vol. 157, pp. 102991-102991
Open Access | Times Cited: 2
Predicting Multiple Sclerosis Outcomes During the COVID-19 Stay-at-home Period: Observational Study Using Passively Sensed Behaviors and Digital Phenotyping
Prerna Chikersal, Shruthi Venkatesh, Karman Masown, et al.
JMIR Mental Health (2022) Vol. 9, Iss. 8, pp. e38495-e38495
Open Access | Times Cited: 12
Predicting future depressive episodes from resting-state fMRI with generative embedding
Herman Galioulline, Stefan Frässle, Samuel J. Harrison, et al.
NeuroImage (2023) Vol. 273, pp. 119986-119986
Open Access | Times Cited: 6
DepreST-CAT
ML Tlachac, Ricardo Flores, Miranda Reisch, et al.
Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies (2022) Vol. 6, Iss. 2, pp. 1-32
Open Access | Times Cited: 10
Remote Assessment of Depression Using Digital Biomarkers From Cognitive Tasks
Regan L. Mandryk, Max V. Birk, Sarah Vedress, et al.
Frontiers in Psychology (2021) Vol. 12
Open Access | Times Cited: 13
Text Generation to Aid Depression Detection: A Comparative Study of Conditional Sequence Generative Adversarial Networks
ML Tlachac, Walter Gerych, Kratika Agrawal, et al.
2021 IEEE International Conference on Big Data (Big Data) (2022), pp. 2804-2813
Closed Access | Times Cited: 9
Detecting Social Contexts from Mobile Sensing Indicators in Virtual Interactions with Socially Anxious Individuals
Zhiyuan Wang, Maria A. Larrazabal, Mark Rucker, et al.
Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies (2023) Vol. 7, Iss. 3, pp. 1-26
Open Access | Times Cited: 5
Evaluation of Sleep Parameters and Sleep Staging (Slow Wave Sleep) in Athletes by Fitbit Alta HR, a Consumer Sleep Tracking Device
Yu Kawasaki, Takatoshi Kasai, Yuko Sakurama, et al.
Nature and Science of Sleep (2022) Vol. Volume 14, pp. 819-827
Open Access | Times Cited: 8
Symptom Detection with Text Message Log Distributions for Holistic Depression and Anxiety Screening
ML Tlachac, Michael V. Heinz, Miranda Reisch, et al.
Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies (2024) Vol. 8, Iss. 1, pp. 1-28
Closed Access | Times Cited: 1
Framework for Ranking Machine Learning Predictions of Limited, Multimodal, and Longitudinal Behavioral Passive Sensing Data: Combining User-Agnostic and Personalized Modeling
Tahsin Mullick, Sam Shaaban, Ana Radović, et al.
JMIR AI (2024) Vol. 3, pp. e47805-e47805
Open Access | Times Cited: 1
Predicting and Monitoring Symptoms in Diagnosed Depression Using Mobile Phone Data: An Observational Study
Arsi Ikäheimonen, Nguyen Luong, Ilya Baryshnikov, et al.
medRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 1
Predicting and Monitoring Symptoms in Diagnosed Depression Using Smartphone Data: An Observational Study (Preprint)
Arsi Ikäheimonen, Nguyen Luong, Ilya Baryshnikov, et al.
Journal of Medical Internet Research (2024) Vol. 26, pp. e56874-e56874
Open Access | Times Cited: 1
Role of AI/ML in the study of mental health problems of the students: a bibliometric study
S. S. Rajkishan, A. Jiran Meitei, Abha Singh
International Journal of Systems Assurance Engineering and Management (2023) Vol. 15, Iss. 5, pp. 1615-1637
Closed Access | Times Cited: 4
Mobile Monitoring of Mood (MoMo-Mood): a Multimodal Digital Phenotyping Study with Major Depressive Patients and Healthy Controls (Preprint)
Talayeh Aledavood, Nguyen Luong, Ilya Baryshnikov, et al.
(2024)
Closed Access | Times Cited: 1
A novel machine learning approach to shorten depression risk assessment for convenient uses
Yuan Hong Sun, Qijian Liu, Nathan Yee Lee, et al.
Journal of Affective Disorders (2022) Vol. 312, pp. 275-291
Closed Access | Times Cited: 6

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