Estimation of Energy Expenditure and Physical Activity Classification With Wearables

NCT05523830 | Completed

• 1 other identifier: NL80580.068.22
• Study Type: observational
• Target: 56
• Locations: 1 country
• Sites: 1

Brief 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.

Conditions

Energy Metabolism; Algorithms

Keywords

Energy expenditure; Physical Activity; Wearables

Outcome Measures

Primary Outcomes (1)

• 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 Outcomes (3)

• Heart rate (variability) algorithm

  1.5 years

• Contribution of different bio signals to the estimation of energy expenditure

  1.5 years

• Instantaneous energy expenditure

  1.5 years

Study Arms (1)

Healthy Subjects

56 healhty subjects will be recruited for the current study

Other: No Intervention

Interventions

No Intervention OTHER

No intervention

Healthy Subjects

Eligibility Criteria

• Age: 18 Years - 64 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64)
• Sampling Method: Non-Probability Sample

Study Population

Healthy adults that are able to be physically active

You may qualify if:

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

You may not qualify if:

• 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

Sponsors & Collaborators

• Maastricht University Medical Center: lead
• Ministry of Economic Affairs: collaborator

Study Sites (1)

Maastricht University

Maastricht, Limburg, 6229ER, Netherlands

Location

Biospecimen

Retention: SAMPLES WITHOUT DNA

Urine samples for deutrium dilution analysis to asses total body water

MeSH Terms

Conditions

Motor Activity

Condition Hierarchy (Ancestors)

Behavior

Study Officials

• Guy Plasqui

  Maastricht University

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: OTHER
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: August 25, 2022
• First Posted: August 31, 2022
• Study Start: May 18, 2022
• Primary Completion: June 29, 2023
• Study Completion: June 29, 2023
• Last Updated: July 3, 2023

Record last verified: 2023-06

Locations

NL (1)