Energy MetabolismEEPACNCT05523830
Estimation of Energy Expenditure and Physical Activity Classification With Wearables
An observational study of Energy Metabolism and Algorithms, run by Maastricht University Medical Center at 1 site in Netherlands.
Trial 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). Read more about this study
- Who is it for?
- Ages 18 to 64 yearsHealthy volunteers can take part
- Conditions the study is for
- Energy Metabolism, Algorithms
- How many people
- 56 participants
- Where
- 1 siteNetherlands
- Study dates
- 18 May 2022to 29 Jun 2023
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 64 years
- All sexes
- Healthy volunteers accepted
- Adult
Study population: Healthy adults that are able to be physically active
Who may be able to join
- Aged between 18 and 64 years
- Provided written informed consent
- Able to be physically active assed with PAR-Q+
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country.
About this study
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.
Read the 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.
- Conditions
- Energy Metabolism, Algorithms
- Being tested
- No Intervention
- Keywords
- Energy expenditure, Physical Activity, Wearables
- Medical subject headings
- Motor Activity
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
No InterventionOther
No intervention
| Study group | Role | What they receive |
|---|---|---|
| Healthy Subjects | 56 healhty subjects will be recruited for the current study Receives: Other: No Intervention |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 1.5 years
Secondary outcomes (3)
Heart rate (variability) algorithm
Design and validate a heart rate (variability) algorithm
- Investigate the feasibility of modelling the instantaneous energy expenditure
Measured: 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
Measured: 1.5 years
Instantaneous energy expenditure
Investigate the feasibility of modelling the instantaneous energy expenditure
Measured: 1.5 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted31 Aug 2022
- Study start18 May 2022
- Primary completion29 Jun 2023
- Study completion29 Jun 2023
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Energy MetabolismStudies of the same condition, open and under way.
- More trials run by Maastricht University Medical CenterThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Other
- Enrolment
- 56 participants (actual)
Sponsor and investigators
- Lead sponsor
- Maastricht University Medical Center
- Sponsor type
- University, hospital or other
- Collaborators
- Ministry of Economic Affairs
- Responsible party
- Sponsor
- Investigators
- Guy Plasqui, Principal investigator, Maastricht University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05523830
- Sponsor's protocol ID
- NL80580.068.22
- Acronym
- EEPAC
- Status last verified
- Jun 2023
- First submitted
- 25 Aug 2022
- Last updated
- 3 Jul 2023
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided: The plan to share IPD is undecided
Study at a glance
From the registry record, last updated by the sponsor on 3 Jul 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Estimation of Energy Expenditure and Physical Activity Classification With Wearables
- ClinicalTrials.gov ID
- NCT05523830
- Acronym / sponsor ID
- EEPAC / NL80580.068.22
- Conditions
- Energy Metabolism, Algorithms
- Keywords
- Energy expenditure, Physical Activity, Wearables
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 56 participants (actual)
- Lead sponsor
- Maastricht University Medical CenterUniversity, hospital or other
- Collaborators
- Ministry of Economic Affairs
- Study start
- 18 May 2022 (actual)
- Primary completion
- 29 Jun 2023 (actual)
- Study completion
- 29 Jun 2023 (actual)
- First posted
- 31 Aug 2022 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Other
- Time perspective
- Other
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Healthy Subjects | – | Other: No Intervention56 healhty subjects will be recruited for the current study |
Who may qualify
- Ages
- 18 to 64 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult
- Study population
- Healthy adults that are able to be physically active
Key inclusion criteria (3 listed)
- Aged between 18 and 64 years
- Provided written informed consent
- Able to be physically active assed with PAR-Q+
Key exclusion criteria (4 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| No InterventionNo intervention | Other | Healthy Subjects |
Primary outcome
Energy Expenditure Estimation Model
Time frame: 1.5 years
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.
3 secondary outcomes
Heart rate (variability) algorithm
Time frame: 1.5 years
Contribution of different bio signals to the estimation of energy expenditure
Time frame: 1.5 years
Instantaneous energy expenditure
Time frame: 1.5 years
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 3 Jul 2023.
- Individual participant dataSharing planned: Undecided. The plan to share IPD is undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Guy Plasqui principal investigator
Maastricht University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Energy Metabolism and Algorithms, run by Maastricht University Medical Center at 1 site in Netherlands.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 3 Jul 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Netherlands. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Energy Expenditure Estimation Model (time frame: 1.5 years). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 3 Jul 2023.