Personalized Physician-Assisted Sleep Advice for Shift Workers: Algorithm Development and Validation Study
Although our current system requires physician input, an accurate machine learning algorithm shows promise for automatic advice without compromising the trustworthiness of the selected recommendations. Despite its decent performance, the algorithm is currently limited to the 7 most popular messages. Further studies are needed to enable predictions for less frequent advice labels.
Authors
- Akane Sano (ORCID: https://orcid.org/0000-0003-4484-8946)
- Asami Ito-Masui (ORCID: https://orcid.org/0000-0002-2086-4231)
- Ryota Sakamoto (ORCID: https://orcid.org/0000-0003-0411-856X)
- Han Yu (ORCID: https://orcid.org/0000-0003-1944-4173)
- Motomu Shimaoka (ORCID: https://orcid.org/0000-0001-5764-2598)
- Alicia Choto Olivier
- Yufei Shen (ORCID: https://orcid.org/0009-0009-4123-7710)
Institutions
- Mie University (JP)
- Tsu City College (JP)
- Rice University (US)
- The University of Texas at Austin (US)
Publication Details
- Journal
- JMIR Formative Research
- Published
- 2025-02-12
- DOI
- https://doi.org/10.2196/65000
- Citations
- 1
- Primary Topic
- Sleep and Work-Related Fatigue
- Type
- article
- Field-Weighted Citation Impact
- 2.12