Clinical Trial Details
— Status: Completed
Administrative data
| NCT number |
NCT05523830 |
| Other study ID # |
NL80580.068.22 |
| Secondary ID |
|
| Status |
Completed |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
May 18, 2022 |
| Est. completion date |
June 29, 2023 |
Study information
| Verified date |
June 2023 |
| Source |
Maastricht University Medical Center |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical 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). 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.
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.
