Literature DB >> 31680327

Validity, potential clinical utility and comparison of a consumer activity tracker and a research-grade activity tracker in insomnia disorder II: Outside the laboratory.

Kellie Hamill1, Ria Jumabhoy1, Piyumi Kahawage1, Massimiliano de Zambotti2, Elizabeth M Walters1, Sean P A Drummond1.   

Abstract

Accurate assessment of sleep can be fundamental for monitoring, managing and evaluating treatment outcomes within diseases. A proliferation of consumer activity trackers gives easy access to objective sleep. We evaluated the performance of a commercial device (Fitbit Alta HR) relative to a research-grade actigraph (Actiwatch Spectrum Pro) in measuring sleep before and after a cognitive behavioural intervention in insomnia disorder. Twenty-five individuals with DSM-5 insomnia disorder (M = 50.6 ± 15.9 years) wore Fitbit and Actiwatch and completed a sleep diary during an in-laboratory polysomnogram, and for 1 week preceding and following seven weekly sessions of cognitive-behavioural intervention for insomnia. Device performance was compared for sleep outcomes (total sleep time, sleep latency, sleep efficiency and wake after sleep onset). The analyses assessed (a) agreement between devices across days and pre- to post-treatment, and (b) whether pre- to post-treatment changes in sleep assessed by devices correlated with clinical measures of change. Devices generally did not significantly differ from each other on sleep variable estimates, either night to night, in response to sleep manipulation (pre- to post-treatment) or in response to changes in environment (in the laboratory versus at home). Change in sleep measures across time from each device showed some correlation with common clinical measures of change in insomnia, but not insomnia diagnosis as a categorical variable. Overall, the Fitbit provides similar estimates of sleep outside the laboratory to a research grade actigraph. Despite the similarity between Fitbit and Actiwatch performance, the use of consumer technology is still in its infancy and caution should be taken in its interpretation.
© 2019 European Sleep Research Society.

Entities:  

Keywords:  actigraphy; activity monitor; consumer sleep tracker; night-to-night variability; wearables

Mesh:

Year:  2019        PMID: 31680327     DOI: 10.1111/jsr.12944

Source DB:  PubMed          Journal:  J Sleep Res        ISSN: 0962-1105            Impact factor:   3.981


  8 in total

1.  A standardized framework for testing the performance of sleep-tracking technology: step-by-step guidelines and open-source code.

Authors:  Luca Menghini; Nicola Cellini; Aimee Goldstone; Fiona C Baker; Massimiliano de Zambotti
Journal:  Sleep       Date:  2021-02-12       Impact factor: 5.849

2.  Regularity and Timing of Sleep Patterns and Behavioral Health Among Adolescents.

Authors:  Jessica R Lunsford-Avery; Ke Will Wang; Scott H Kollins; Richard J Chung; Casey Keller; Matthew M Engelhard
Journal:  J Dev Behav Pediatr       Date:  2021-10-25       Impact factor: 2.988

3.  Effects of Sleep-Extend on glucose metabolism in women with a history of gestational diabetes: a pilot randomized trial.

Authors:  Sirimon Reutrakul; Pamela Martyn-Nemeth; Lauretta Quinn; Brett Rydzon; Medha Priyadarshini; Kirstie K Danielson; Kelly G Baron; Jennifer Duffecy
Journal:  Pilot Feasibility Stud       Date:  2022-06-04

4.  Performance of seven consumer sleep-tracking devices compared with polysomnography.

Authors:  Evan D Chinoy; Joseph A Cuellar; Kirbie E Huwa; Jason T Jameson; Catherine H Watson; Sara C Bessman; Dale A Hirsch; Adam D Cooper; Sean P A Drummond; Rachel R Markwald
Journal:  Sleep       Date:  2021-05-14       Impact factor: 5.849

5.  Performance of Four Commercial Wearable Sleep-Tracking Devices Tested Under Unrestricted Conditions at Home in Healthy Young Adults.

Authors:  Evan D Chinoy; Joseph A Cuellar; Jason T Jameson; Rachel R Markwald
Journal:  Nat Sci Sleep       Date:  2022-03-22

6.  Predictive Modeling of Mental Illness Onset Using Wearable Devices and Medical Examination Data: Machine Learning Approach.

Authors:  Tomoki Saito; Hikaru Suzuki; Akifumi Kishi
Journal:  Front Digit Health       Date:  2022-04-14

Review 7.  Quality Evaluation of Free-living Validation Studies for the Assessment of 24-Hour Physical Behavior in Adults via Wearables: Systematic Review.

Authors:  Marco Giurgiu; Irina Timm; Marlissa Becker; Steffen Schmidt; Kathrin Wunsch; Rebecca Nissen; Denis Davidovski; Johannes B J Bussmann; Claudio R Nigg; Markus Reichert; Ulrich W Ebner-Priemer; Alexander Woll; Birte von Haaren-Mack
Journal:  JMIR Mhealth Uhealth       Date:  2022-06-09       Impact factor: 4.947

8.  A machine learning model for multi-night actigraphic detection of chronic insomnia: development and validation of a pre-screening tool.

Authors:  S Kusmakar; C Karmakar; Y Zhu; S Shelyag; S P A Drummond; J G Ellis; M Angelova
Journal:  R Soc Open Sci       Date:  2021-06-16       Impact factor: 2.963

  8 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.