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Activity Energy Expenditure Predicts Clinical Average Levels of Physical Activity in Older Population: Results from Salus in Apulia Study.

Ilaria BortoneFabio CastellanaLuisa LampignanoRoberta ZupoBiagio MorettiGianluigi GiannelliFrancesco PanzaRodolfo Sardone
Published in: Sensors (Basel, Switzerland) (2020)
Self-report questionnaires are a valuable method of physical activity measurement in public health research; however, accuracy is often lacking. Resolving the differences between self-reported and objectively measured physical activity is an important surveillance challenge currently facing population health experts. The present work aims at providing the relationship between activity energy expenditure estimated from wrist-worn accelerometers and intensity of self-reported physical activity (InCHIANTI structured interview questionnaire) in a sub-cohort of a population-based study on aging in Southern Italy. Linear regression was used to test the association between measured and reported physical activity. We found that activity energy expenditure predicted clinical average levels of PA assessed through InCHIANTI classification.
Keyphrases
  • physical activity
  • body mass index
  • sleep quality
  • healthcare
  • machine learning
  • public health
  • mental health
  • emergency department
  • deep learning
  • middle aged