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Capturing Real-World Habitual Sleep Patterns With a Novel User-Centric Algorithm to Preprocess Fitbit Data in the All of Us Research Program: Retrospective Observational Longitudinal Study

Capturing Real-World Habitual Sleep Patterns With a Novel User-Centric Algorithm to Preprocess Fitbit Data in the All of Us Research Program: Retrospective Observational Longitudinal Study

In the conceptual example illustrated in Figure 1, there are 4 sleep logs spanning 2 calendar days: C (day 0; 10:00 AM-noon), D (day 0; 2:00-3:30 PM), B (day 0; 10:00-11:30 PM), and A (day 1; 1:30-6:30 AM). To accurately compute sleep metrics, it is imperative to identify and include all primary sleep logs that fall within the primary sleep period, while distinguishing them from nonprimary sleep logs.

Hiral Master, Jeffrey Annis, Jack H Ching, Karla Gleichauf, Lide Han, Peyton Coleman, Kelsie M Full, Neil Zheng, Douglas Ruderfer, John Hernandez, Logan D Schneider, Evan L Brittain

J Med Internet Res 2025;27:e71718