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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.

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Showing 1-25 of 53 citing articles:

End-to-end design of wearable sensors
H. Ceren Ates, Peter Q. Nguyen, Laura Gonzalez‐Macia, et al.
Nature Reviews Materials (2022) Vol. 7, Iss. 11, pp. 887-907
Open Access | Times Cited: 684
Methodological and Quality Flaws in the Use of Artificial Intelligence in Mental Health Research: Systematic Review
Roberto Tornero-Costa, Antonio Martínez-Millana, Natasha Azzopardi‐Muscat, et al.
JMIR Mental Health (2023) Vol. 10, pp. e42045-e42045
Open Access | Times Cited: 53
Systematic review and meta-analysis of performance of wearable artificial intelligence in detecting and predicting depression
Alaa Abd‐Alrazaq, Rawan AlSaad, Farag Shuweihdi, et al.
npj Digital Medicine (2023) Vol. 6, Iss. 1
Open Access | Times Cited: 46
Machine Learning for Multimodal Mental Health Detection: A Systematic Review of Passive Sensing Approaches
Lin Sze Khoo, Mei Kuan Lim, Chun Yong Chong, et al.
Sensors (2024) Vol. 24, Iss. 2, pp. 348-348
Open Access | Times Cited: 21
Development and application of emotion recognition technology — a systematic literature review
Runfang Guo, Hongfei Guo, Liwen Wang, et al.
BMC Psychology (2024) Vol. 12, Iss. 1
Open Access | Times Cited: 18
From smartphone data to clinically relevant predictions: A systematic review of digital phenotyping methods in depression
Imogen E. Leaning, Nessa Ikani, Hannah S. Savage, et al.
Neuroscience & Biobehavioral Reviews (2024) Vol. 158, pp. 105541-105541
Open Access | Times Cited: 16
Wearable Artificial Intelligence for Anxiety and Depression: Scoping Review
Alaa Abd‐Alrazaq, Rawan AlSaad, Sarah Aziz, et al.
Journal of Medical Internet Research (2022) Vol. 25, pp. e42672-e42672
Open Access | Times Cited: 54
Exploring the digital footprint of depression: a PRISMA systematic literature review of the empirical evidence
Daniel Zarate, Vasileios Stavropoulos, M. Bethany Ball, et al.
BMC Psychiatry (2022) Vol. 22, Iss. 1
Open Access | Times Cited: 51
Digital Phenotyping for Monitoring Mental Disorders: Systematic Review
Pasquale Bufano, Marco Laurino, Sara Said, et al.
Journal of Medical Internet Research (2023) Vol. 25, pp. e46778-e46778
Open Access | Times Cited: 34
Digital phenotype of mood disorders: A conceptual and critical review
Redwan Maatoug, Antoine Oudin, Vladimir Adrien, et al.
Frontiers in Psychiatry (2022) Vol. 13
Open Access | Times Cited: 29
Exploring Digital Biomarkers of Illness Activity in Mood Episodes: Hypotheses Generating and Model Development Study
Gerard Anmella, Filippo Corponi, Bryan M. Li, et al.
JMIR mhealth and uhealth (2023) Vol. 11, pp. e45405-e45405
Open Access | Times Cited: 19
Behind the Screen: A Narrative Review on the Translational Capacity of Passive Sensing for Mental Health Assessment
Anastasia C. Bryan, Michael V. Heinz, Abigail Salzhauer, et al.
Deleted Journal (2024) Vol. 2, Iss. 2, pp. 778-810
Closed Access | Times Cited: 5
The Chinese version of the immediate mood scaler (IMS): a study evaluating its validity, reliability, and responsiveness in patients with MDD in China
Xiongying Chen, Zizhao Feng, Nanxi Li, et al.
BMC Psychiatry (2025) Vol. 25, Iss. 1
Open Access
Development of a Cohesive Predictive Model for Substance Use Disorder Rehabilitation Using Passive Digital Biomarkers, Psychological Assessments, and Automated Facial Emotion Recognition (Preprint)
Andrea P. Garzón‐Partida, Kimberly Magaña-Plascencia, Diana Fernández, et al.
(2025)
Closed Access
Digitale Phänotypisierung – Integration alltagsnah erhobener Daten in die Einzelfallbeurteilung
Patricia Garatva, Harald Baumeister
Springer eBooks (2025), pp. 527-537
Closed Access
Development and Feasibility Study of HOPE Model for Prediction of Depression Among Older Adults Using Wi-Fi-based Motion Sensor Data: Machine Learning Study
Shayan Nejadshamsi, Vania Karami, Negar Ghourchian, et al.
JMIR Aging (2025) Vol. 8, pp. e67715-e67715
Open Access
Fusing Wearable Biosensors with Artificial Intelligence for Mental Health Monitoring: A Systematic Review
Ali Kargarandehkordi, Shizhe Li, Kaiying Lin, et al.
Biosensors (2025) Vol. 15, Iss. 4, pp. 202-202
Open Access
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
From smartwatch to psychopathology: longitudinally recorded digital phenotypes relate to psychopathology dimensions in patients with psychotic spectrum disorders (Preprint)
Vasiliki Garyfalli, Emmanouil Kalisperakis, Alexandros Smrynis, et al.
(2025)
Closed Access
Digital Phenotyping in Health Using Machine Learning Approaches: Scoping Review
Schenelle Dayna Dlima, Santosh Shevade, Sonia Rebecca Menezes, et al.
JMIR Bioinformatics and Biotechnology (2022) Vol. 3, Iss. 1, pp. e39618-e39618
Open Access | Times Cited: 20
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
Effectiveness of artificial intelligence in detecting and managing depressive disorders: Systematic review
Yoonseo Park, Sewon Park, Munjae Lee
Journal of Affective Disorders (2024) Vol. 361, pp. 445-456
Closed Access | Times Cited: 3
Machine learning applied to digital phenotyping: A systematic literature review and taxonomy
Marília Pit dos Santos, Wesllei Felipe Heckler, Rodrigo Simon Bavaresco, et al.
Computers in Human Behavior (2024) Vol. 161, pp. 108422-108422
Closed Access | Times Cited: 3
Accurately predicting mood episodes in mood disorder patients using wearable sleep and circadian rhythm features
Dongju Lim, Jaegwon Jeong, Yun Min Song, et al.
npj Digital Medicine (2024) Vol. 7, Iss. 1
Open Access | Times Cited: 3
Developing prediction algorithms for late-life depression using wearable devices: a cohort study protocol
Jin‐Kyung Lee, Min‐Hyuk Kim, Sangwon Hwang, et al.
BMJ Open (2024) Vol. 14, Iss. 6, pp. e073290-e073290
Open Access | Times Cited: 2
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