Validation of actigraphy sleep metrics in children aged 8 to 16 years: considerations for device type, placement and algorithms.

Kim A Meredith-JonesJ J HaszardA Graham-DeMelloA CampbellT StewartB C GallandA CoxG KennedyS DuncanR W Taylor

Published in: The international journal of behavioral nutrition and physical activity (2024)

Although the performance of existing count-based sleep algorithms varies markedly, wrist-worn devices provide more accurate measures of most sleep measures compared to other sites. Overall, the HDZCA algorithm showed the greatest accuracy, although the most appropriate algorithm depends on the sleep measure of focus.

Keyphrases

machine learning
sleep quality
deep learning
physical activity
high resolution
depressive symptoms
mass spectrometry