Estimation of Energy Expenditure and Physical Activity Classification With Wearables (EEPAC)

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.

Study Overview

• Status: Completed
• Conditions: Algorithms; Energy Metabolism
• Intervention / Treatment: Other: No Intervention

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.

• Study Type: Observational
• Enrollment (Actual): 56

Contacts and Locations

• Study Contact: Name: Wouter Bijnens; Phone Number: +31 43 38 81455; Email: wouter.bijnens@maastrichtuniversity.nl

Study Locations

• Netherlands
  • Limburg
    • Maastricht, Limburg, Netherlands, 6229ER
      • Maastricht University

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 16 years to 62 years (Adult)
• Accepts Healthy Volunteers: Yes

• Sampling Method: Non-Probability Sample

Study Population

Healthy adults that are able to be physically active

Description

Inclusion Criteria:

• Aged between 18 and 64 years
• Provided written informed consent
• Able to be physically active assed with PAR-Q+

Exclusion Criteria:

• A contraindication to physical activity
• A contraindication to wearing wearables, fixed by a hypoallergenic plaster
• Chronic disease
• A pace maker or any chest-implanted device

Study Plan

How is the study designed?

Design Details

• Observational Models: Other
• Time Perspectives: Other

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Healthy Subjects; 56 healhty subjects will be recruited for the current study	Other: No Intervention; No intervention

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Energy Expenditure Estimation Model	The primary objective of this study is to develop and validate an energy expenditure estimation and physical activity classification algorithm based on wearable sensors. To do so the relevant signals contributing to the classification of physical activity and the estimation of energy expenditure will be identified.	1.5 years

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Heart rate (variability) algorithm	Design and validate a heart rate (variability) algorithm - Investigate the feasibility of modelling the instantaneous energy expenditure	1.5 years
Contribution of different bio signals to the estimation of energy expenditure	Assess the contribution of different bio signals to the estimation of energy expenditure	1.5 years
Instantaneous energy expenditure	Investigate the feasibility of modelling the instantaneous energy expenditure	1.5 years

Collaborators and Investigators

• Sponsor: Maastricht University Medical Center
• Collaborators: Ministry of Economic Affairs
• Investigators: Principal Investigator: Guy Plasqui, Maastricht University

Study record dates

Study Major Dates

• Study Start (Actual): May 18, 2022
• Primary Completion (Actual): June 29, 2023
• Study Completion (Actual): June 29, 2023

Study Registration Dates

• First Submitted: August 25, 2022
• First Submitted That Met QC Criteria: August 30, 2022
• First Posted (Actual): August 31, 2022

Study Record Updates

• Last Update Posted (Actual): July 3, 2023
• Last Update Submitted That Met QC Criteria: June 30, 2023
• Last Verified: June 1, 2023

More Information

Terms related to this study

• Keywords: Energy expenditure; Physical Activity; Wearables
• Other Study ID Numbers: NL80580.068.22

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED
• IPD Plan Description: The plan to share IPD is undecided

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No