Fitbit launches Sleep Labs Beta program with AI-powered analysis
Fitbit has begun testing Sleep Labs, an experimental program that represents an expansion of its sleep tracking capabilities. The new initiative combines daily user input with machine learning to deliver more nuanced sleep analysis.
Users can access the program through their Fitbit app’s “You” tab by selecting “See all” under Fitbit Labs, though availability varies as the feature rolls out gradually. To participate, users need to specifically opt in to “Sleep Insights and Tips” in the Labs section.
The program asks participants to complete a brief daily survey about their sleep habits. Taking roughly a minute or two to complete, the survey includes both multiple-choice questions and an open text field where users can describe factors affecting their rest.
As previously reported, Sleep Labs slaps on a sleep journal. This data feeds into the daily summaries that connect user input with sleep tracking data, helping identify patterns that might affect sleep quality. After logging at least four daily journals, users get access to weekly recaps that outline broader sleep trends and provide specific improvement suggestions.
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The program also introduces personalized “missions” based on journal entries, offering targeted recommendations to help users develop better sleep habits. This feature appears to be Fitbit’s answer to competing services like Whoop, though with a distinct focus on sleep rather than overall recovery.
Here is how Google describes the new feature:
Fitbit Sleep Labs is a feature that analyzes your sleep patterns to help you understand your sleep habits. It provides a daily sleep journal to reflect on, identify, and track these habits. Sleep Lab also highlights how your habits and behaviors impact your sleep with daily summaries, offers personalized suggestions, and allows you to track your progress with a weekly recap.
What sets Sleep Labs apart from similar offerings is its use of large language models to analyze user responses. This AI-driven approach allows for more sophisticated pattern recognition and personalized insights compared to traditional data analysis methods.
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