Performance assessment of new-generation Fitbit technology in deriving sleep parameters and stages

@article{Haghayegh2019PerformanceAO,
  title={Performance assessment of new-generation Fitbit technology in deriving sleep parameters and stages},
  author={Shahab Haghayegh and Sepideh Khoshnevis and Michael H. Smolensky and Kenneth R. Diller and Richard J. Castriotta},
  journal={Chronobiology International},
  year={2019},
  volume={37},
  pages={47 - 59}
}
ABSTRACT We compared performance in deriving sleep variables by both Fitbit Charge 2™, which couples body movement (accelerometry) and heart rate variability (HRV) in combination with its proprietary interpretative algorithm (IA), and standard actigraphy (Motionlogger® Micro Watch Actigraph: MMWA), which relies solely on accelerometry in combination with its best performing ‘Sadeh’ IA, to electroencephalography (EEG: Zmachine® Insight+ and its proprietary IA) used as reference. We conducted… 
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Study Objectives This study investigated, through wrist actigraphy, the activity-rest pattern, estimate nocturnal sleep parameters, and quantify the exposure of light (daylight and blue light) during
NSS_A_287048 39..53
1College of Education, Psychology and Social Work, Flinders University, Adelaide, SA, 5001, Australia; 2Adelaide Institute for Sleep Health: AFlinders Centre of Research Excellence, College of
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