Ching-Hua Chen

520 total citations
26 papers, 218 citations indexed

About

Ching-Hua Chen is a scholar working on Applied Psychology, Public Health, Environmental and Occupational Health and Experimental and Cognitive Psychology. According to data from OpenAlex, Ching-Hua Chen has authored 26 papers receiving a total of 218 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Applied Psychology, 5 papers in Public Health, Environmental and Occupational Health and 5 papers in Experimental and Cognitive Psychology. Recurrent topics in Ching-Hua Chen's work include Behavioral Health and Interventions (4 papers), Digital Mental Health Interventions (4 papers) and Mobile Health and mHealth Applications (4 papers). Ching-Hua Chen is often cited by papers focused on Behavioral Health and Interventions (4 papers), Digital Mental Health Interventions (4 papers) and Mobile Health and mHealth Applications (4 papers). Ching-Hua Chen collaborates with scholars based in United States, United Kingdom and Colombia. Ching-Hua Chen's co-authors include Varun Mishra, Jeffrey M. Rogers, Grace Chen, David Kotz, Sougata Sen, Hao Tian, Oshani Seneviratne, Marion J. Ball, Jane Snowdon and Kimberly N. Walter and has published in prestigious journals such as IBM Journal of Research and Development, Journal of Behavioral Medicine and Frontiers in Psychiatry.

In The Last Decade

Ching-Hua Chen

22 papers receiving 211 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Ching-Hua Chen United States 8 54 49 45 34 28 26 218
Louis Faust United States 10 63 1.2× 34 0.7× 48 1.1× 46 1.4× 26 0.9× 22 274
Yuanchao Ma Canada 7 72 1.3× 66 1.3× 55 1.2× 12 0.4× 36 1.3× 22 228
Ridwan Alam United States 10 32 0.6× 50 1.0× 59 1.3× 20 0.6× 19 0.7× 28 296
Gonzalo J. Martinez United States 11 135 2.5× 60 1.2× 26 0.6× 27 0.8× 28 1.0× 20 315
Yatharth Ranjan United Kingdom 8 46 0.9× 74 1.5× 47 1.0× 11 0.3× 9 0.3× 22 229
Sina Labbaf United States 10 99 1.8× 53 1.1× 38 0.8× 56 1.6× 18 0.6× 21 291
Joseph Nuamah United States 9 46 0.9× 23 0.5× 24 0.5× 42 1.2× 16 0.6× 27 274
Andrea Patanè United Kingdom 9 112 2.1× 82 1.7× 22 0.5× 20 0.6× 32 1.1× 28 316
Artur Rocha Portugal 10 81 1.5× 110 2.2× 43 1.0× 11 0.3× 13 0.5× 24 323
Alban Maxhuni Italy 7 117 2.2× 107 2.2× 26 0.6× 15 0.4× 26 0.9× 12 252

Countries citing papers authored by Ching-Hua Chen

Since Specialization
Citations

This map shows the geographic impact of Ching-Hua Chen's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ching-Hua Chen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ching-Hua Chen more than expected).

Fields of papers citing papers by Ching-Hua Chen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ching-Hua Chen. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ching-Hua Chen. The network helps show where Ching-Hua Chen may publish in the future.

Co-authorship network of co-authors of Ching-Hua Chen

This figure shows the co-authorship network connecting the top 25 collaborators of Ching-Hua Chen. A scholar is included among the top collaborators of Ching-Hua Chen based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ching-Hua Chen. Ching-Hua Chen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
2.
Stecher, Chad, Ching-Hua Chen, James Codella, Sara Cloonan, & James Hendler. (2024). Combining anchoring with financial incentives to increase physical activity: a randomized controlled trial among college students. Journal of Behavioral Medicine. 47(5). 751–769.
3.
Seneviratne, Oshani, Amar K. Das, Sabbir M. Rashid, et al.. (2023). Semantically enabling clinical decision support recommendations. Journal of Biomedical Semantics. 14(1). 8–8. 3 indexed citations
5.
Marsch, Lisa A., Ching-Hua Chen, Sara R. Adams, et al.. (2022). The Feasibility and Utility of Harnessing Digital Health to Understand Clinical Trajectories in Medication Treatment for Opioid Use Disorder: D-TECT Study Design and Methodological Considerations. Frontiers in Psychiatry. 13. 871916–871916. 7 indexed citations
6.
Hsueh, Pei-Yun, et al.. (2021). Applying Contemporary Machine Learning Approaches to Nutrition Care Real-World Evidence: Findings From the National Quality Improvement Data Set. Journal of the Academy of Nutrition and Dietetics. 121(12). 2549–2559.e1. 6 indexed citations
7.
Seneviratne, Oshani, et al.. (2021). Identifying Ingredient Substitutions Using a Knowledge Graph of Food. Frontiers in Artificial Intelligence. 3. 621766–621766. 22 indexed citations
8.
Chen, Ching-Hua, et al.. (2021). A Framework for Generating Summaries from Temporal Personal Health Data. 2(3). 1–43. 4 indexed citations
9.
Ding, Ying, et al.. (2021). International Workshop on Knowledge Graph: Heterogenous Graph Deep Learning and Applications. 4121–4122. 3 indexed citations
10.
Rosenthal, Sara, Subhro Das, Pei-Yun Hsueh, Ken Barker, & Ching-Hua Chen. (2020). Efficient goal attainment and engagement in a care manager system using unstructured notes. JAMIA Open. 3(1). 62–69.
11.
Craig, Kelly Jean Thomas, Laura C Morgan, Ching-Hua Chen, et al.. (2020). Systematic review of context-aware digital behavior change interventions to improve health. Translational Behavioral Medicine. 11(5). 1037–1048. 49 indexed citations
12.
Seneviratne, Oshani, et al.. (2020). Semantics-Driven Ingredient Substitution in the FoodKG.. 242–247. 1 indexed citations
13.
Seneviratne, Oshani, et al.. (2020). Applying Learning and Semantics for Personalized Food Recommendations.. 305–310. 1 indexed citations
14.
Chen, Ching-Hua, et al.. (2019). Evaluating autostereoscopic 3D baseball games using signal detection theory and receiver operating characteristic space. International Journal of Industrial Ergonomics. 72. 390–397. 4 indexed citations
15.
Chen, Yu, Oshani Seneviratne, James Codella, et al.. (2019). FoodKG Enabled Q&A Application.. 273–276. 2 indexed citations
16.
Chen, Grace, Varun Mishra, & Ching-Hua Chen. (2019). Temporal factors of listening to music on stress reduction. 907–914. 4 indexed citations
17.
Chen, Ching-Hua & Kenney Ng. (2018). Preface: User-generated health data and applications. IBM Journal of Research and Development. 62(1). 1–3. 2 indexed citations
18.
Li, Zhiguo, et al.. (2018). An Adaptive, Data-Driven Personalized Advisor for Increasing Physical Activity. IEEE Journal of Biomedical and Health Informatics. 23(3). 999–1010. 12 indexed citations
19.
Rogers, Jeffrey M., et al.. (2017). Towards Precision Stress Management: Design and Evaluation of a Practical Wearable Sensing System for Monitoring Everyday Stress. Iproceedings. 3(1). e15–e15. 8 indexed citations
20.
Yin, Zhijun, Bradley Malin, Jeremy L. Warner, Pei-Yun Hsueh, & Ching-Hua Chen. (2017). The Power of the Patient Voice: Learning Indicators of Treatment Adherence From An Online Breast Cancer Forum. Proceedings of the International AAAI Conference on Web and Social Media. 11(1). 337–346. 7 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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