ArticleJournal of sleep research2026
A Participatory Artificial Intelligence Driven Shift-Scheduling Application for Improving Sleep Among Shift-Working Caregivers: A 4-Month Non-Randomised Controlled Study With Cross-Over Design.
Article in Journal of sleep research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Human-Centered AI in Sleep Health Management: Scoping Review of Stakeholder Perspectives and Co-Design Practices.Journal of medical Internet research · 2026Article
- A Participatory Artificial Intelligence Driven Shift-Scheduling Application for Improving Sleep Among Shift-Working Caregivers: A 4-Month Non-Randomised Controlled Study With Cross-Over Design.Journal of sleep research · 2026Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Here, we examine the effectiveness of a participatory artificial intelligence (AI)-driven shift-scheduling mobile application (which reflects the local improvement needs in shift scheduling) in improving the sleep quality of shift-working geriatric caregivers. Thirty-five shift-working geriatric caregivers participated in this 4-month cross-over interventional study. Half of the participants in the first 2 months followed the intervention schedule created by the AI-driven shift-scheduling mobile application, while the remaining participants followed the manually created control schedule. The improvement needs in shift scheduling, derived from occupational-fatigue counselling, were as follows: avoiding backward rotating shifts, reducing consecutive shifts, extending shift intervals and ensuring a day-off after a night shift. Sleep phases were evaluated using a ring-type sleep tracker. The effectiveness of the intervention was examined using three-way multilevel analyses (condition × shift × time). Deep sleep (N3) and rapid eye movement sleep were significantly more pronounced in the intervention condition compared with the control condition (p = 0.016, p = 0.046, respectively). However, no significant differences were detected for other outcomes. Moreover, we examined how shift combinations affected sleep outcomes. As a result, two consecutive late shifts and backward rotating shifts significantly deteriorated sleep quality and length (all p < 0.05). Our findings suggest that the shift-scheduling app reduced the backward shift rotations, resulting in significantly better sleep outcomes than from manual schedule creation. However, the magnitude of reduction in backward rotating shifts was not so remarkable. Therefore, the positive outcomes can also be attributed to enhanced employees' working time control by reflecting the local improvement needs. Trial Registration: UMIN Clinical Trials Registry: UMIN000048495.
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