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

RCTs - Protocols/Proposals (funded, already peer-reviewed, eHealth) (402) mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1774) Hypertension Prevention and Treatment (306) Mobile Health (mhealth) (3549) Design and Usability of Consumer Health Tech and Home Monitoring Devices (337) Machine Learning (2154) Formative Evaluation of Digital Health Interventions (3336) Artificial Intelligence (2229) AI Applications in Biomedical Engineering (17)

Published on in Vol 13 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/55615, first published .
Personalized AI-Driven Real-Time Models to Predict Stress-Induced Blood Pressure Spikes Using Wearable Devices: Proposal for a Prospective Cohort Study

Personalized AI-Driven Real-Time Models to Predict Stress-Induced Blood Pressure Spikes Using Wearable Devices: Proposal for a Prospective Cohort Study

Personalized AI-Driven Real-Time Models to Predict Stress-Induced Blood Pressure Spikes Using Wearable Devices: Proposal for a Prospective Cohort Study

Authors of this article:

Ali Kargarandehkordi1 Author Orcid Image ;   Christopher Slade1 Author Orcid Image ;   Peter Washington1 Author Orcid Image
Article Authors Cited by (13) Tweetations (2) Metrics

Journals

  1. Serrano D, Luciano F, Anaya B, Ongoren B, Kara A, Molina G, Ramirez B, Sánchez-Guirales S, Simon J, Tomietto G, Rapti C, Ruiz H, Rawat S, Kumar D, Lalatsa A. Artificial Intelligence (AI) Applications in Drug Discovery and Drug Delivery: Revolutionizing Personalized Medicine. Pharmaceutics 2024;16(10):1328 View
  2. Slade C, Benzo R, Washington P. Design Guidelines for Improving Mobile Sensing Data Collection: Prospective Mixed Methods Study. Journal of Medical Internet Research 2024;26:e55694 View
  3. Li S, Fan C, Kargarandehkordi A, Sun Y, Slade C, Jaiswal A, Benzo R, Phillips K, Washington P. Monitoring Substance Use with Fitbit Biosignals: A Case Study on Training Deep Learning Models Using Ecological Momentary Assessments and Passive Sensing. AI 2024;5(4):2725 View
  4. Benouis M, Andre E, Can Y. Balancing Between Privacy and Utility for Affect Recognition Using Multitask Learning in Differential Privacy–Added Federated Learning Settings: Quantitative Study. JMIR Mental Health 2024;11:e60003 View
  5. Ubelmann C, Heudel P. Impact et potentiel du numérique dans les approches psycho-corporelles en cancérologie. Bulletin du Cancer 2025;112(4):370 View
  6. Kargarandehkordi A, Li S, Lin K, Phillips K, Benzo R, Washington P. Fusing Wearable Biosensors with Artificial Intelligence for Mental Health Monitoring: A Systematic Review. Biosensors 2025;15(4):202 View
  7. Slade C, Sun Y, Chao W, Chen C, Benzo R, Washington P. Current challenges and opportunities in active and passive data collection for mobile health sensing: a scoping review. JAMIA Open 2025;8(4) View
  8. Washington P. Personalization of AI Using Personal Foundation Models Can Lead to More Precise Digital Therapeutics. JMIR AI 2025;4:e55530 View
  9. Sun Y, Jaiswal A, Slade C, Phillips K, Benzo R, Washington P. Associations Between Social Determinants of Health and Adherence in Mobile-Based Ecological Momentary Assessment: Scoping Review. Journal of Medical Internet Research 2025;27:e69831 View
  10. Baumann S, Stone R, Abdelall E. Smart Healthcare at Home: A Review of AI-Enabled Wearables and Diagnostics Through the Lens of the Pi-CON Methodology. Sensors 2025;25(19):6067 View
  11. Li X, Liu L, Liu D, Wang M, Liu F. Advance Quantum Wavelet Transformation-based intelligent system for Personalized Nursing Care Using IoT Devices. SPIN 2025;15(05) View

Conference Proceedings

  1. Gahane S, Khode P, Verma P. 2024 5th International Conference on Data Intelligence and Cognitive Informatics (ICDICI). Adaptive AI Algorithms for Real-Time Monitoring and Intervention in Pediatric Healthcare Systems View
  2. Musnmusawi M, Deepa R, Shankar S, G N, Khanam P. 2025 International Conference on Intelligent Systems and Computational Networks (ICISCN). Real-Time Blood Pressure Monitoring and Hypertension Prediction based on Multitask Temporal Convolutional Network View