A journey from Cure to Care- Wellness management for healthy lifestyle: Diabetes management a case study

Smart ubiquitous computing has a vital role to avoid and indicate the preventable lifestyle-based chronic diseases. It is focusing to adopt a healthy lifestyle by converging science and technology in this digital world for improving health and quality of life. From the last decade, the development of wellness applications has supported personalization and self-quantification. These applications facilitate the users through activity tracking and monitoring, based on the raw sensory data to adopt healthy behavior. The challenge of behavior change is not only to indicate the issues but also provides step-by-step coaching and guidance at real time. The realization of behavior change theories through digital technology has revolutionized the lifestyle change in a systematic and measurable manner. We have proposed a methodology to understand the behavior for generating just-in-time intervention for adopting a healthy lifestyle. Wellness platform based behavior analysis is performed using unbiased life-log and questionnaire for qualitative assessment of behavior. Behavior stage wise intervention is provided to adapt behavior for enhancing the quality of life and boost the socio-economic conditions. Personalized education is provided to understand the importance of healthy behavior and motivate the users, whereas just-in-time context-based recommendations have supported the stage-wise adaptation of unhealthy behavior. These capabilities require status evaluation of the activities and an efficient way to portray the comprehensive index of lifestyle habits. The real focus is to correlate the primarily linked habits in appropriate proportion through healthy behavior index (HBI) for personalized wellness support services. The healthy behavior index and behavior change theories through smart technologies

Assessing the structures and domains of wellness models: A systematic review

Objective: This study systematically identifies different wellness domains, explores whether we are reaching any consensus, and presents an archetype of a wellness model. Methods: Studies were selected for review if they proposed a model for assessing individuals’ wellness, the model was generic (i.e., non-context or disease-specific), designed for adults and included at least physical, psychological and social domains. Furthermore, the study needed to be peer-reviewed with a full-text available in English. Based on this, 44 models were identified and their domains were extracted and grouped using thematic analysis, and placed under themes that were created using quantitative methods. Publication year and formed groupings were used to examine the evolution of models. Median, mode, and percentages were used to form the archetype. Results: The investigated models included 379 unique domains that could be clustered into 70 groups and under 14 themes. While the numbers of published wellness models increased, no consensus on the domains was reached. The majority of the models were presented at one level with five domains. Conclusions: Incorporating wellness into everyday practice requires comparable measures to evaluate and benchmark outcomes. Hence, we need to reach a mutual understanding on the structure and domains of wellness