Sleep-inducing algorithms: can artificial intelligence help shiftworkers and those working nonstandard hours sleep...

Area of Science:

• Sleep Science
• Human-Computer Interaction
• Artificial Intelligence

Background:

• Nonstandard work hours disrupt sleep, leading to negative health, safety, and productivity outcomes.
• Current sleep interventions are often one-size-fits-all, failing to address individual variability in needs and work contexts.
• Wearable sleep technology and AI offer new possibilities for personalized interventions.

Purpose of the Study:

• To propose the integration of AI and wearable consumer sleep technology with just-in-time-adaptive-interventions (JITAIs).
• To explore the potential of JITAIs for delivering personalized, timely behavioral support to nonstandard-hour workers.
• To identify challenges in implementing AI-driven JITAIs for sleep improvement.

Main Methods:

• Conceptual paper proposing a framework for integrating AI, wearable sleep technology, and JITAIs.
• Discussion of the potential benefits and challenges of such an integrated approach.
• Review of relevant advancements in AI (reinforcement learning, large language models) and consumer sleep technology.

Main Results:

• AI and wearable sleep technology can enable highly tailored just-in-time-adaptive-interventions (JITAIs).
• These JITAIs can provide personalized, timely behavioral support adapting to individual contexts and schedules.
• Successful implementation requires addressing challenges like trust, accuracy, user engagement, and cost.

Conclusions:

• Integrating AI and wearable sleep technology with JITAIs offers a promising new standard for personalized health, safety, and productivity interventions for nonstandard-hour workers.
• Tailored, context-aware digital interventions can effectively address the unique sleep challenges faced by this population.
• Careful consideration of implementation challenges is crucial for realizing the full potential of these technologies.