Brain health refers to the preservation of brain integrity and function optimized for an individual’s biological age. Several studies have demonstrated that our lifestyles habits impact our brain health and our cognitive and mental wellbeing. Monitoring such lifestyles is thus critical and mobile technologies are essential to enable such a goal. Three databases were selected to carry out the search. Then, a PRISMA and PICOTS based criteria for a more detailed review on the basis of monitoring lifestyle aspects were used to filter the publications. We identified 133 publications after removing duplicates. Fifteen were finally selected from our criteria. Many studies still use questionnaires as the only tool for monitoring and do not apply advanced analytic or AI approaches to fine-tune results. We anticipate a transformative boom in the near future developing and implementing solutions that are able to integrate, in a flexible and adaptable way, data from technologies and devices that users might already use. This will enable continuous monitoring of objective data to guide the personalized definition of lifestyle goals and data-driven coaching to offer the necessary support to ensure adherence and satisfaction.
Healthy daily activities have a positive influence on many aspects of our lives. Habits have a deep impact on our health, they help to prevent the appearance of chronic and neurodegenerative diseases and will provide a healthy and active aging. This research work is aiming to analyze the need of new approaches on monitoring daily life activities, investigating new technologies and user modelling methods for healthy habits monitoring. mHealth platforms allow to perform a multivariable monitoring for allowing effective and personalized interventions. Data analytics, data mining and gamification methodologies are being applied to investigate user experience models. This user adaptation is commonly focused on personality and mood monitoring. Furthermore, new user models are built based on monitoring data, personality of user and the daily activity patterns extracted from intelligent and personalized monitoring. The final goal is contributing to improve user´s adherence to interventions, quality of life and quality of care in mHealth applications. Keywords: Healthy habits Á Habit management Á Monitoring Á Machine learning Á Daily activity patterns Á Emotional status Á Data analytics Á User model
Brain Health is defined as the development and preservation of optimal brain integrity and neural network functioning for a given age. Recent studies have related healthy habits with better maintenance of brain health across the lifespan. As a part of the Barcelona Brain Health Initiative (BBHI), a mHealth platform has been developed with the purpose of helping people to improve and monitor their healthy habits, facilitating the delivery of health coaching strategies. A decision support system (DSS), named Intelligent Coaching Assistant (ICA), has been developed to ease the work of professional brain health coaches, helping them design and monitor adherence to multidomain interventions in a more efficient manner. Personalized recommendations are based on users’ current healthy habits, individual preferences, and motivational aspects. Taking these inputs, an initial user profile is defined, and the ICA applies an algorithm for determining the most suitable personalized intervention plan. An initial validation has been done focusing on assessing the feasibility and usability of the solution, involving 20 participants for three weeks. We conclude that this kind of technology-based intervention is feasible and implementable in real-world settings. Importantly, the personalized intervention proposal generated by the DSS is feasible and its acceptability and usability are high.
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