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Area of Science:

  • Public Health
  • Kinesiology
  • Computer Science

Background:

  • Outdoor physical activity (PA) measurement on sidewalks and streets is crucial.
  • The traditional Block Walk Method (BWM) is labor-intensive and unchanged since 2006.
  • Advancements in PA measurement are needed to better understand behavior.

Purpose of the Study:

  • Develop and test a novel BWM using a wearable video device (WVD).
  • Integrate machine learning and computer vision for automated PA data extraction.
  • Improve accuracy and reduce data collection burden for street-based PA.

Main Methods:

  • Trained observers used a WVD and traditional BWM along predetermined routes.
  • Video data captured by WVD was analyzed by investigators and deep convolutional neural networks (CNNs).
  • Bland Altman methods and intraclass correlation coefficients (ICCs) assessed agreement; moderator analyses evaluated error sources.

Main Results:

  • Preliminary studies show BWM is reliable for PA mode (Cramer V=.89), location (Cohen kappa=.85), and participant count (ICC=.85).
  • PA counts correlated with sidewalk quality (r=.39) and neighborhood aesthetics (r=.49).
  • CNNs successfully detected pedestrians, vehicles, and park facility users.

Conclusions:

  • The WVD-enhanced BWM is expected to improve measurement accuracy and efficiency.
  • Future expansions will include caloric expenditure and environmental condition analysis.
  • This technology offers a significant advancement in understanding PA behavior in urban environments.