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Related Experiment Video

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Discrimination of Frailty Phenotype by KinectTM-Based Stepping Parameters.

Y Osuka1,2, N Takeshima3, N Kojima2

  • 1Department of Frailty Research, Center for Gerontology and Social Science, Research Institute, National Center for Geriatrics and Gerontology, Aichi, Japan.

JAR Life
|January 8, 2024
PubMed
Summary

Frailty detection is crucial for preventing falls and other adverse outcomes. Kinect-based stepping parameters show promise as a practical tool for early identification of frailty in older adults.

Keywords:
KinectTMagingdigital biomarkerfrailtymobility

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Area of Science:

  • Gerontology and Public Health
  • Biomedical Engineering
  • Clinical Diagnostics

Background:

  • Frailty is a significant risk factor for adverse health outcomes, including falls, hospitalization, and mortality in older adults.
  • There is a pressing need for accessible and practical tools for the early detection of frailty.
  • Identifying frailty early can enable timely interventions to mitigate negative health consequences.

Purpose of the Study:

  • To evaluate the effectiveness of Kinect-based stepping parameters in distinguishing individuals with the frailty phenotype.
  • To assess the discriminative capability of specific gait metrics derived from Kinect technology for frailty assessment.

Main Methods:

  • A population-based cross-sectional study was conducted involving 563 community-dwelling older adults aged 75 years and above.
  • Participants were assessed using the Kinect infrared depth sensor to measure step number (SN) and knee total movement distance (KMD) during a 20-second stepping test.
  • Exclusion criteria included mobility limitations, neurological disease, or dementia to focus on frailty in a specific population.

Main Results:

  • Approximately 9.1% of the participants (51 individuals) were identified as frail.
  • The combined parameter of SN and KMD demonstrated an acceptable discriminative ability for identifying frailty, with an area under the receiver operating characteristic curve of 0.72 (95% CI: 0.64, 0.79).

Conclusions:

  • Stepping parameters measured by the Kinect system offer a viable method for identifying the frailty phenotype.
  • This technology presents a practical screening solution for frailty that can be implemented in primary care and home-based settings.
  • The findings support the use of Kinect-based assessments as an early detection tool for frailty, potentially improving health outcomes for older adults.