Ignite Creation Date:
2024-05-06 @ 6:02 PM
Last Modification Date:
2024-10-26 @ 2:40 PM
Study NCT ID:
NCT05523830
Status:
COMPLETED
Last Update Posted:
2023-07-03
First Post:
2022-08-25
Brief Title:
Estimation of Energy Expenditure and Physical Activity Classification With Wearables
Sponsor:
Maastricht University Medical Center
Organization:
Maastricht University Medical Center
Study Overview
Official Title:
Estimation of Energy Expenditure and Physical Activity Classification With Wearables
Status:
COMPLETED
Status Verified Date:
2023-06
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
EEPAC
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
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
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 Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None