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

Electronic/Mobile Data Capture, Internet-based Survey & Research Methodology (782) mHealth for Data Collection and Research (907) Machine Learning (1370) Ecological Momentary Assessment (EMA) (294) Mobile Health (mhealth) (2748) Wearable Devices and Sensors (811)

Published on in Vol 24, No 4 (2022): April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/34015, first published .
Using Smartphone Sensor Paradata and Personalized Machine Learning Models to Infer Participants’ Well-being: Ecological Momentary Assessment

Using Smartphone Sensor Paradata and Personalized Machine Learning Models to Infer Participants’ Well-being: Ecological Momentary Assessment

Using Smartphone Sensor Paradata and Personalized Machine Learning Models to Infer Participants’ Well-being: Ecological Momentary Assessment

Authors of this article:

Alexander Hart1 Author Orcid Image ;   Dorota Reis1 Author Orcid Image ;   Elisabeth Prestele2 Author Orcid Image ;   Nicholas C Jacobson3 Author Orcid Image
Article Authors Cited by (5) Tweetations (4) Metrics

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  2. Goldberg S. A common factors perspective on mindfulness-based interventions. Nature Reviews Psychology 2022;1(10):605 View
  3. Langener A, Stulp G, Jacobson N, Costanzo A, Jagesar R, Kas M, Bringmann L. It’s All About Timing: Exploring Different Temporal Resolutions for Analyzing Digital-Phenotyping Data. Advances in Methods and Practices in Psychological Science 2024;7(1) View
  4. Schmitter-Edgecombe M, Luna C, Dai S, Cook D. Predicting daily cognition and lifestyle behaviors for older adults using smart home data and ecological momentary assessment. The Clinical Neuropsychologist 2024:1 View
  5. Christensen J, Rumley J, Gil-Carvajal J, Whiston H, Lough M, Saunders G. Predicting Individual Hearing-Aid Preference From Self-Reported Listening Experiences in Daily Life. Ear & Hearing 2024 View