Estimation of Energy Expenditure and Physical Activity Classification With Wearables
Condition: Energy Metabolism; Algorithms
Study type: Observational

Summary: 
      Regular physical activity (PA) is proven to help prevent and treat several non-communicable
      diseases such as heart disease, stroke, and diabetes. Intensity is a key characteristic of PA
      that can be assessed by estimating energy expenditure (EE). However, the accuracy of the
      estimation of EE based on accelerometers are lacking. It has been suggested that the addition
      of physiological signals can improve the estimation. How much each signal can add to the
      explained variation and how they can improve the estimation is still unclear.

      The goal of the current study is twofold:

      to explore the contribution of heart rate (HR), breathing rate (BR) and skin temperature to
      the estimation of EE develop and validate a statistical model to estimate EE in simulated
      free-living conditions based on the relevant physiological signals.
    

Detailed description: 
      Physical activity (PA) is defined as any bodily movement produced by skeletal muscle that
      requires energy expenditure. The scientific evidence for the beneficial effects are
      irrefutable. Regular PA is proven to help prevent and treat several non-communicable diseases
      such as heart disease, stroke, diabetes and different forms of cancer.

      PA is a complex behaviour that is characterized by frequency, intensity, time and type
      (FITT). In order to understand the effect of PA on health and our general well-being, it is
      essential to monitor all four characteristics of PA. A PA classification algorithm can assess
      the amount of time spent in different body postures and activity. Making it possible to
      assess frequency, time and type. In order to completely characterize PA, intensity needs to
      be estimated. This can be done by the estimation of energy expenditure (EE).

      Wearables play a crucial role in the monitoring of PA. They are practical way to collect
      objective PA data in daily life, in an unobtrusive way, at a relatively low cost. Furthermore
      they can be applied as a motivational tool to increase PA. Accelerometry has been routinely
      used to quantify PA and to predict EE using linear and non-linear models. However, the
      relationship between EE and acceleration differs from one activity to another. For example,
      cycling can generate the same acceleration amplitude as running, but the EE may differ
      greatly. It is clear that acceleration alone has a limited accuracy to estimate EE from
      different activities.

      Improving the estimation of EE could be achieved by first classifying the activity type. For
      each type of activity, different estimations can be used. There are numerous methods to
      classify PA and estimate EE. Literature describes the use of regression based equations
      combined with cut-points, linear models, non-linear models, decision trees, artificial neural
      networks, etc. It is still unclear what would be the best method to estimate EE, not to
      mention which features would contribute to the model.

      Another possibility is to add a relevant bio-signal to the estimation model. Heart rate,
      breathing rate, temperature are all signals that have a response related to an increase in
      PA. Heart rate has been used previously to improve the EE estimation in combination with
      accelerometry. The breathing rate and temperature could contribute to the estimation of EE is
      still unclear.

      Therefore, the goal of the current study is twofold. Firstly, to explore the contribution of
      different variables (physiological signals) to the estimation of EE and the classification of
      PA. Secondly, develop and validate a model to estimate EE and classify PA in simulated
      free-living conditions based on the relevant variables.
    

Intervention name(s): No Intervention
Intervention type(s): Other
Intervention description(s): No intervention
Observational model: Other
Time perspective: Other
Sampling method: Non-Probability Sample
Study population: 
        Healthy adults that are able to be physically active
      
Biospecimen description: 
      Urine samples for deutrium dilution analysis to asses total body water
    
Keywords: Energy expenditure; Physical Activity; Wearables