This paper is in the following e-collection/theme issue:

Wearable Devices and Sensors (903) Machine Learning (1501) Pregnancy Information, Education and Lifestyle Interventions (304) mHealth for Data Collection and Research (981) Evaluation and Research Methodology for mHealth (210) mHealth for Wellness, Behavior Change and Prevention (2922) Ecological Momentary Assessment (EMA) (321) Obstetrics, Gynecology, and Reproductive Medicine (235)

Published on in Vol 10, No 8 (2022): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/33850, first published .
Predicting the Next-Day Perceived and Physiological Stress of Pregnant Women by Using Machine Learning and Explainability: Algorithm Development and Validation

Predicting the Next-Day Perceived and Physiological Stress of Pregnant Women by Using Machine Learning and Explainability: Algorithm Development and Validation

Predicting the Next-Day Perceived and Physiological Stress of Pregnant Women by Using Machine Learning and Explainability: Algorithm Development and Validation

Authors of this article:

Ada Ng1 Author Orcid Image ;   Boyang Wei1 Author Orcid Image ;   Jayalakshmi Jain1 Author Orcid Image ;   Erin A Ward2 Author Orcid Image ;   S Darius Tandon2 Author Orcid Image ;   Judith T Moskowitz2 Author Orcid Image ;   Sheila Krogh-Jespersen2 Author Orcid Image ;   Lauren S Wakschlag2 Author Orcid Image ;   Nabil Alshurafa2 Author Orcid Image
Article Authors Cited by (9) Tweetations (5) Metrics

Journals

  1. Cummings P, Petitclerc A, Moskowitz J, Tandon D, Zhang Y, MacNeill L, Alshurafa N, Krogh-Jespersen S, Hamil J, Nili A, Berken J, Grobman W, Rangarajan A, Wakschlag L. Feasibility of Passive ECG Bio-sensing and EMA Emotion Reporting Technologies and Acceptability of Just-in-Time Content in a Well-being Intervention, Considerations for Scalability and Improved Uptake. Affective Science 2022;3(4):849 View
  2. Naegelin M, Weibel R, Kerr J, Schinazi V, La Marca R, von Wangenheim F, Hoelscher C, Ferrario A. An interpretable machine learning approach to multimodal stress detection in a simulated office environment. Journal of Biomedical Informatics 2023;139:104299 View
  3. Abdelaal Y, Al-Thani D. Accessibility first: detecting frustration in web browsing for visually impaired and sighted smartphone users. Universal Access in the Information Society 2023 View
  4. Maugeri A, Barchitta M, Agodi A. How Wearable Sensors Can Support the Research on Foetal and Pregnancy Outcomes: A Scoping Review. Journal of Personalized Medicine 2023;13(2):218 View
  5. Ratul I, Nishat M, Faisal F, Sultana S, Ahmed A, Al Mamun M. Analyzing Perceived Psychological and Social Stress of University Students: A Machine Learning Approach. Heliyon 2023;9(6):e17307 View
  6. Terehov A, Yakovlev M. Mathematical Modeling of the Risks of Stress-Related Diseases: a Review. Bulletin of Rehabilitation Medicine 2023;22(4):159 View
  7. Mirzaei A, Hiller B, Stelzer I, Thiele K, Tan Y, Becker M. Computational Approaches for Connecting Maternal Stress to Preterm Birth. Clinics in Perinatology 2024;51(2):345 View
  8. Nasarian E, Alizadehsani R, Acharya U, Tsui K. Designing interpretable ML system to enhance trust in healthcare: A systematic review to proposed responsible clinician-AI-collaboration framework. Information Fusion 2024;108:102412 View
  9. Keeler Bruce L, González D, Dasgupta S, Smarr B. Biometrics of complete human pregnancy recorded by wearable devices. npj Digital Medicine 2024;7(1) View