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

Formative Evaluation of Digital Health Interventions (2823) mHealth for Data Collection and Research (1082) Methods and New Tools in Mental Health Research (499) Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (1808) Wearable Devices and Sensors (996)

Published on in Vol 7 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/39425, first published .
Objective Prediction of Next-Day’s Affect Using Multimodal Physiological and Behavioral Data: Algorithm Development and Validation Study

Objective Prediction of Next-Day’s Affect Using Multimodal Physiological and Behavioral Data: Algorithm Development and Validation Study

Objective Prediction of Next-Day’s Affect Using Multimodal Physiological and Behavioral Data: Algorithm Development and Validation Study

Authors of this article:

Salar Jafarlou1 Author Orcid Image ;   Jocelyn Lai2 Author Orcid Image ;   Iman Azimi1, 3 Author Orcid Image ;   Zahra Mousavi2 Author Orcid Image ;   Sina Labbaf1 Author Orcid Image ;   Ramesh C Jain1, 3 Author Orcid Image ;   Nikil Dutt1 Author Orcid Image ;   Jessica L Borelli4 Author Orcid Image ;   Amir Rahmani1, 3, 5 Author Orcid Image
Article Authors Cited by (11) Tweetations (2) Metrics

Journals

  1. Cai H, Lin Q, Liu H, Li X, Xiao H. Recognition of human mood, alertness and comfort under the influence of indoor lighting using physiological features. Biomedical Signal Processing and Control 2024;89:105661 View
  2. Abd-alrazaq A, Alajlani M, Ahmad R, AlSaad R, Aziz S, Ahmed A, Alsahli M, Damseh R, Sheikh J. The Performance of Wearable AI in Detecting Stress Among Students: Systematic Review and Meta-Analysis. Journal of Medical Internet Research 2024;26:e52622 View
  3. Jafarlou S, Azimi I, Lai J, Wang Y, Labbaf S, Nguyen B, Qureshi H, Marcotullio C, Borelli J, Dutt N, Rahmani A, Chang L. Objective monitoring of loneliness levels using smart devices: A multi-device approach for mental health applications. PLOS ONE 2024;19(6):e0298949 View
  4. Zhang P, Jung G, Alikhanov J, Ahmed U, Lee U. A Reproducible Stress Prediction Pipeline with Mobile Sensor Data. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2024;8(3):1 View
  5. Patel J, Hung C, Katapally T. Evaluating predictive artificial intelligence approaches used in mobile health platforms to forecast mental health symptoms among youth: a systematic review. Psychiatry Research 2025;343:116277 View
  6. Sprint G, Schmitter-Edgecombe M, Weaver R, Wiese L, Cook D. CogProg: Utilizing Large Language Models to Forecast In-the-Moment Health Assessment. ACM Transactions on Computing for Healthcare 2025;6(2):1 View

Books/Policy Documents

  1. Mehnaz F, Basthikodi M, Rahiman H, Kodikal R. Wearable Devices and Smart Technology for Educational Teaching Assistance. View

Conference Proceedings

  1. Khatibi E, Abbasian M, Azimi I, Labbaf S, Feli M, Borelli J, Dutt N, Rahmani A. 2023 IEEE 19th International Conference on Body Sensor Networks (BSN). Impact of COVID-19 Pandemic on Sleep Including HRV and Physical Activity as Mediators: A Causal ML Approach View
  2. Feli M, Kazemi K, Azimi I, Wang Y, Rahmani A, Liljeberg P. 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). End-to-End PPG Processing Pipeline for Wearables: From Quality Assessment and Motion Artifacts Removal to HR/HRV Feature Extraction View
  3. Subramanian A, Yang Z, Azimi I, Rahmani A. 2024 IEEE 20th International Conference on Body Sensor Networks (BSN). Graph-Augmented LLMs for Personalized Health Insights: A Case Study in Sleep Analysis View
  4. Wang Y, Yang Z, Azimi I, Rahmani A, Liljeberg P. 2024 IEEE 20th International Conference on Body Sensor Networks (BSN). Attention-Based Explainable AI for Wearable Multivariate Data: A Case Study on Affect Status Prediction View