Recommender system for responsive engagement of senior adults in daily activities
| Autor: | Yuan Lu, C.A.L. Valk, Pearl Pu, Igor Kulev |
|---|---|
| Přispěvatelé: | Systemic Change, EAISI Health |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
behavior change
Activities of daily living Sociology and Political Science Geography Planning and Development Applied psychology Recommender system Task (project management) 03 medical and health sciences representation learning 0302 clinical medicine Intervention (counseling) older-adults 030212 general & internal medicine physical-activity Demography recommender system behavior Behavior change Novelty health prediction Digital health ddc nutrition quality-of-life Psychology Feature learning predictive modeling 030217 neurology & neurosurgery |
| Zdroj: | Journal of Population Ageing, 13(2), 167-185. Springer Journal of Population Ageing |
| ISSN: | 1874-7884 |
| Popis: | Understanding and predicting how people change their behavior after an intervention from time series data is an important task for health recommender systems. This task is especially challenging when the time series data is frequently sampled. In this paper, we develop and propose a novel recommender system that aims to promote physical activeness in elderly people. The main novelty of our recommender system is that it learns how senior adults with different lifestyle change their activeness after a digital health intervention from minute-by-minute fitness data in an automated way. We trained the system and validated the recommendations using data from senior adults. We demonstrated that the low-level information contained in time series data is an important predictor of behavior change. The insights generated by our recommender system could help senior adults to engage more in daily activities. |
| Databáze: | OpenAIRE |
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