Abstract
Sedentary lifestyle and inadequate levels of physical activity represent two serious health risk factors. Nevertheless, within developed countries, 60% of people aged over 60 are deemed to be sedentary. Consequently, interest in behavior change to promote physical activity is increasing. In particular, the role of emerging mobile apps to facilitate behavior change has shown promising results. Smart technologies can help in providing rich context information including an objectiveassessment of the level of physical activity and information on the emotional and physiological state of the person. Collectively, this can be used to develop innovative persuasive solutionsfor adaptive behavior change. Such solutions offer potential in reducing levels of sedentary behavior. This work presents a study exploring new ways of employing smart technologiesto facilitate behavior change. It is achieved by means of (i) developing a knowledge base on sedentary behaviors and recommended physical activity guidelines, and (ii) a context model able to combine information on physical activity, location, and a user’s diary to develop a context-aware virtual coach with the ability to select the most appropriate behavior change strategy on a case by case basis.
| Original language | English |
|---|---|
| Title of host publication | Unknown Host Publication |
| Publisher | IEEE |
| Number of pages | 4 |
| ISBN (Print) | 978-1-5090-2809-2 |
| DOIs | |
| Publication status | Published online - 14 Sept 2017 |
| Event | The 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’17) - Jeju, Korea Duration: 14 Sept 2017 → … |
Conference
| Conference | The 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’17) |
|---|---|
| Period | 14/09/17 → … |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- Behaviour Change
- JITAI
Fingerprint
Dive into the research topics of 'Rich Context Information for Just-In-Time Adaptive Intervention promoting physical activity'. Together they form a unique fingerprint.Student theses
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Personalisation of machine learning models for human activity recognition
Cruciani, F. (Author), Cleland, I. (Supervisor), Nugent, C. (Supervisor) & Mc Cullagh, P. (Supervisor), Jul 2020Student thesis: Doctoral Thesis
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