Detecting when advanced cancer patients get in and out of bed using self-reports, movement tracking, and computer methods: Implications for measuring sleep and body clock patterns

Automated algorithms identified IN and OUT bedtimes with 93-100% consistency compared to 48-83% for patient-reported methods.

• Automated methods demonstrated greater reliability in determining sleep timings than patient diaries.
• All timing methods showed significant correlations in their findings (p < 0.001).
• Excellent agreement was found between patient-reported timings and wrist event markers (ICC 0.829-0.877) as well as thigh accelerometry (ICC 0.892).
• Event markers provided more accurate timing data than sleep diaries, aligning better with actual sleep transitions.
• The method used to determine bedtimes impacted metrics like sleep onset latency and sleep percentage.

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