Abstract
Low levels of physical activity in sedentary individuals constitute a major concern in public health. Physical activity interventions can be designed relying on mobile technologies such as smartphones. The
purpose of this work is to find a dynamical model of a social norm physical
activity intervention relying on Social Cognitive Theory, and using a data
set obtained from a previous experiment.
The model will serve as a framework for the design of future optimized interventions. To obtain model parameters, two strategies are
developed: first, an algorithm is proposed that randomly varies the values of each model parameter around initial guesses. The second approach utilizes traditional system identification concepts to obtain model
parameters relying on semi-physical identification routines. For both cases the obtained model is assessed through the computation of percentage fits to a validation data set, and by the development of a correlation analysis.
purpose of this work is to find a dynamical model of a social norm physical
activity intervention relying on Social Cognitive Theory, and using a data
set obtained from a previous experiment.
The model will serve as a framework for the design of future optimized interventions. To obtain model parameters, two strategies are
developed: first, an algorithm is proposed that randomly varies the values of each model parameter around initial guesses. The second approach utilizes traditional system identification concepts to obtain model
parameters relying on semi-physical identification routines. For both cases the obtained model is assessed through the computation of percentage fits to a validation data set, and by the development of a correlation analysis.
| Original language | English |
|---|---|
| Title of host publication | 2nd IEEE Ecuador Technical Chapters Meeting |
| Publication status | Published - 16 Oct 2017 |