Comparing the activPAL software's Primary Time in Bed Algorithm against Self-Report and van der Berg's Algorithm

Area of Science:

• Physical activity monitoring
• Sleep science
• Biomedical engineering

Background:

• Accurate measurement of sleep and wake patterns is crucial for understanding health behaviors.
• Wearable activity trackers like activPAL offer objective data but require validation against established methods.
• Previous algorithms for estimating sleep parameters have varying degrees of accuracy.

Purpose of the Study:

• To compare activPAL algorithm-estimated time in bed (TIB), wake time (WT), and bedtime (BT) with self-report and a validated algorithm.
• To assess the agreement and equivalence of activPAL's estimations for key sleep-wake variables.
• To provide recommendations for optimizing the use of activPAL in research settings.

Main Methods:

• Secondary analysis of baseline data from the Community Activity for Prevention Study (CAPS).
• Participants (adults ≥ 18 years) wore the activPAL device for seven consecutive days.
• Statistical comparison using mixed-effects models, Bland-Altman plots for agreement, and two-one-sided tests for equivalence.

Main Results:

• activPAL algorithm was not equivalent to self-report or the van der Berg algorithm for estimating TIB.
• activPAL demonstrated equivalence with self-report for estimating BT.
• activPAL showed equivalence with the van der Berg algorithm for estimating WT.

Conclusions:

• The current activPAL algorithm requires modifications for reliable TIB estimation in research.
• Manual input of self-reported BT and WT into activPAL can facilitate accurate TIB calculation.
• These findings support the use of activPAL, with adjustments, for comprehensive 24-hour movement and sleep pattern analysis.