A Context-Aware Mobile Recommender

THE WORLD POPULATION’S average age has been increasing gradually over the past 50 years mainly because of medical and healthcare advances. However, millions of people suffer from chronic respiratory diseases, arthritis, and back pain.1 In addition, exposure to air pollution causes millions of illnesses and premature deaths annually worldwide2 and harms the health of children, the elderly, and people with chronic diseases.3 An active lifestyle can help reduce or even prevent physical issues such as cardiorespiratory problems, musculoskeletal diseases, or morbidity.4 Moreover, it has positive neurocognitive effects.5 For instance, increased cardiorespiratory fitness is associated with increased cortical thickness in both healthy adults and the elderly diagnosed with dementia.6 So, the aging of society and the need to foster healthy habits in society imply a great challenge for public healthcare systems. To address these issues, the healthcare sector has strived to integrate new technologies that promote healthy habits. Although the deployment of such services implies initial investments, in the long run, their adoption decreases healthcare system costs and improves people’s quality of life. Healthcare models are steadily shifting toward patient-centric approaches in which patients are not only passive elements but also proactive contributors to their own and their peers’ health. From a much wider perspective, the sharing of information has led to recommender systems,7 which have evolved and integrated transparently into our daily lives. Such systems exploit collaboration among users and help them make better decisions. To advance the state of the art, we’ve developed a system that augments mobile health in the context of smart health.8 It suggests exercise routes in real time, using a contextaware recommender system and a mobile app.