Annual Meeting of the NCI Cohort Consortium (Abstract Submission): Submission #4

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completed: '1726005462'
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data:
  additional_authors:
    - add_author_degree: PhD
      add_author_first_name: Asuka
      add_author_last_name: Ishihara
      add_author_middle: ''
      add_author_organization: NIDDK
    - add_author_degree: PhD
      add_author_first_name: Antoinette
      add_author_last_name: Rabel
      add_author_middle: ''
      add_author_organization: NIDDK
    - add_author_degree: PhD
      add_author_first_name: Ranganath
      add_author_last_name: Muniyappa
      add_author_middle: ''
      add_author_organization: NIDDK
    - add_author_degree: PhD
      add_author_first_name: Kong
      add_author_last_name: Chen
      add_author_middle: 'Y'
      add_author_organization: NIDDK
    - add_author_degree: PhD
      add_author_first_name: Samuel
      add_author_last_name: LaMunion
      add_author_middle: R
      add_author_organization: NIDDK
  degree_s_: MPH
  email: sdonzell@fredhutch.org
  first_name: Sidney
  last_name: Donzella
  organization: 'University of Washington; American Cancer Society '
  poster_title: 'Sleep measured by accelerometry: Comparing the performance of objective sleep algorithms'
  short_biography_: 'Background Various algorithms and software are available to score sleep data collected from accelerometer-based wearable sensors. However, little is known about how the combination of algorithms and software may influence the sleep estimates. The aim of this study was to investigate the agreement of sleep constructs scored with different sleep bounding algorithms on two commonly used software platforms (i.e., ActiLife and GGIR). Methods Thirty-five healthy volunteers were invited to wear an ActiGraph wGT3X-BT device for 7 consecutive days and complete a sleep diary. Objective sleep was scored using the count-based Cole-Kripke sleep scoring algorithm and the following sleep bounding algorithms: GGIR heuristic of the distribution in the change in Z-axis angle (HDCZA) with open-source counts, GGIR HDCZA zero crossing counts, GGIR open-source counts with sleep diary, ActiLife Tudor Locke. We included participants who had 16 hours of device wear/day and 4 days of valid device wear. We used intraclass correlation coefficient (ICC) with 95% confidence intervals to assess the agreement of objective sleep constructs with the sleep diary. Results Twenty-nine participants were included. ICC estimates for time asleep and time awake ranged from moderate to good agreement (ICC range 0.53-0.86) for all algorithms compared to the sleep diary. Compared to the sleep diary, all algorithms showed poor agreement for sleep duration (ICC range -0.64-0.47). Conclusion Sleep constructs scored using different algorithms or on different software platforms may not be generalizable to one another.  Researchers should use caution when comparing objective sleep measures across studies that used different sleep scoring procedures. '
  title: 'PhD Candidate'