Liang Z, Melcer EF, Khotchasing K, Chen S, Hwang D, Hoang NH. (2024) The Role of Relevance in Shaping Perceptions of Sleep Hygiene Games Among University Students: Mixed Methods Study. JMIR Serious Games. Doi: 22/09/2024:64063. [SCI/Scopus/PubMed]
Liang Z, Melcer E, Khotchasing K, Hoang NH. (2024) Co-design Personal Sleep Health Technology for and with University Students. Front. Digit. Health - Human Factors and Digital Health 6:1371808. Doi: 10.3389/fdgth.2024.1371808. [SCI/Scopus/PubMed]
Liang Z. (2024) Developing Probabilistic Ensemble Machine Learning Models for Home-Based Sleep Apnea Screening using Overnight SpO2 Data at Varying Data Granularity. Sleep and Breathing. Doi: 10.1007/s11325-024-03141-x. [SCI/Scopus/PubMed]
Liang Z. (2024) More Haste, Less Speed?: Relationship between Response Time and Response Accuracy in Gamified Online Quizzes in an Undergraduate Engineering Course. Front. Educ. - Higher Education, 9. Doi: 10.3389/feduc.2024.1412954. [SCI/Scopus]
Liang Z. (2023) Novel method combining multiscale attention entropy of overnight blood oxygen level and machine learning for easy sleep apnea screening. Digital Health 9: 1-19. [PubMed/SCI/Scopus]
Liang Z. (2022). Context-aware sleep health recommender systems (CASHRS): a narrative review. Electronics 2022, 11(20), 3384. Doi: 10.3390/electronics1120338. [SCI/Scopus]
Liang Z. (2022). Mining associations between glycemic variability in awake-time and in-sleep among non-diabetic adults. Frontiers in Medical Technology (Section: Medtech Data Analytics). [PubMed/SCI/Scopus]
Liang Z. (2021) What does sleeping brain tell about stress? A pilot fNIRS study into stress-related cortical hemodynamic features during sleep. Frontiers in Computer Science (Section: Mobile and Ubiquitous Computing) 3:774949. Doi: 10.3389/fcomp.2021.774949. [SCI /Scopus]
Liang Z, Chapa-Martell MA. (2021) A multi-level classification approach for sleep stage prediction with processed data derived from consumer wearable activity trackers. Frontiers in Digital Health (Section: Health Informatics) 3:665946. Doi: 10.3389/fdgth.2021.665946. [PubMed /Scopus]
Liang Z, Ploderer B. (2020) “How does Fitbit measure brainwaves”: a qualitative study into the credibility of sleep-tracking technologies. PACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT) 4(1):Article 17. [SCI/Scopus]
Liang Z, Chapa-Martell MA. (2019) Accuracy of Fitbit wristbands in measuring sleep stage transitions and the effect of user-specific factors. JMIR mHealth and uHealth 7(6):e13384, DOI:10.2196/13384. [PubMed/SCI/Scopus]
Liang Z, Chapa-Martell MA. (2018) Validity of consumer activity wristbands and wearable EEG for measuring overall sleep parameters and sleep structure in free-living conditions. Journal of Healthcare Informatics Research 2 (1-2): 152-178. [PubMed/SCI/Scopus]
Liang Z, Ploderer B, Liu W, Nagata Y, Bailey J, Kulik L, Li Y. (2016). SleepExplorer: A visualization tool to make sense of correlations between personal sleep data and contextual factors. Personal and Ubiquitous Computing 20(6): 985-1000. [SCI/Scopus]
