Literature DB >> 27096291

Is it on? An algorithm for discerning wrist-accelerometer non-wear times from sleep/wake activity.

Anastasi Kosmadopoulos1,2, David Darwent1, Gregory D Roach1.   

Abstract

The accuracy of sleep/wake estimates derived with actigraphy is often dependent on researchers being able to discern non-wear times from sleep or quiescent wakefulness when confronted by discrepancies in a sleep log. Without knowing when an accelerometer is being worn, non-wear could be inferred from periods of inactivity unlikely to occur while in bed. Data collected in our laboratory suggest that more than 50% of inactive periods during time in bed are <8 min in duration. This duration may be an appropriate minimum threshold for routine non-wear classification during self-reported wake. Higher thresholds could be chosen to derive non-wear definitions for self-reported bedtimes depending on the desired level of certainty. To determine non-wear at thresholds of 75%, 95% and 99%, for example, would require periods of inactivity lasting ≥18 min, ≥53 min and ≥85 min, respectively.

Entities:  

Keywords:  Actigraphy; activity cycles; biostatistics; nonwear; physical activity; sedentary behavior; sleep

Mesh:

Year:  2016        PMID: 27096291     DOI: 10.3109/07420528.2016.1167720

Source DB:  PubMed          Journal:  Chronobiol Int        ISSN: 0742-0528            Impact factor:   2.877


  2 in total

1.  Designing Videogames to Crowdsource Accelerometer Data Annotation for Activity Recognition Research.

Authors:  Aditya Ponnada; Seth Cooper; Binod Thapa-Chhetry; Josh Aaron Miller; Dinesh John; Stephen Intille
Journal:  Proc Annu Symp Comput Hum Interact Play       Date:  2019-10

2.  Ambulatory sleep scoring using accelerometers-distinguishing between nonwear and sleep/wake states.

Authors:  Amna Barouni; Jörg Ottenbacher; Johannes Schneider; Bernd Feige; Dieter Riemann; Anne Herlan; Driss El Hardouz; Darren McLennan
Journal:  PeerJ       Date:  2020-01-02       Impact factor: 2.984

  2 in total

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