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Related Experiment Video

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Video Movement Analysis Using Smartphones ViMAS: A Pilot Study
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Quantifying Human Movement Using the Movn Smartphone App: Validation and Field Study.

Ralph Maddison1,2, Luke Gemming2, Javier Monedero3

  • 1Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Burwood, Australia.

JMIR Mhealth and Uhealth
|August 19, 2017
PubMed
Summary
This summary is machine-generated.

The Movn smartphone app offers valid passive measurement of energy expenditure, integrating movement data with location for enhanced insights. This technology aids in understanding human movement patterns for potential intervention development.

Keywords:
geographic information systemshumanslocomotionphysical activitysmartphonetelemedicinevalidation studies

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Area of Science:

  • Biomedical Engineering
  • Human Movement Science
  • Digital Health

Background:

  • Smartphone sensors enable novel measurement of physical activity (PA) and human movement.
  • Big data from digital traces provide detailed insights into PA and geocoded movement patterns.
  • Understanding environmental interactions through movement data is a growing area of research.

Purpose of the Study:

  • To validate the Movn smartphone app for collecting physical activity and human movement data.
  • To assess the Movn app's accuracy in estimating energy expenditure (EE).

Main Methods:

  • Criterion and convergent validity of the Movn app for EE estimation were tested against indirect calorimetry and ActiGraph accelerometers.
  • Laboratory and free-living settings were used for validation.
  • Global Positioning System (GPS) and accelerometer data were integrated with GIS for geospatial analysis.

Main Results:

  • Movn activity counts showed strong correlation with both criterion (r=.91) and convergent (r=.92) measures for EE.
  • Movn demonstrated comparable EE to calorimetry in lab settings (bias=0.36 kcal/min) but overestimated compared to ActiGraph (bias=0.93 kcal/min).
  • Movn overestimated EE in free-living conditions (bias=1.00 kcal/min), with larger biases during high-intensity activities.

Conclusions:

  • The Movn app provides valid passive measurement of energy expenditure.
  • Movn data can be enriched with temporospatial information for contextual understanding of human movement.
  • While offering insights for intervention development, the data presents processing and analytical challenges.