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Introduction of new products in the market results in cannibalization of the existing products. A study incorporating this cannibalization effect in forecasting models is presented.

A case study on beverage company, which examines the impact of product cannibalization on product innovation and market share, is presented.

Patient engagement can be enhanced through continuous monitoring of patient' health knowledge and self-efficacy levels across different co-morbid conditions and key aspects in health improvement and recovery. While selfreported test batteries and computerized instruments have been designed to perform evaluation of patient literacy and self-efficacy, they are not practical to be used in scenarios where concurrent tests are needed to understand the change over time. In this paper we propose an adaptive system that can progressively compose the most pertinent test for each user based on the provisional estimates of a patient's latent trait being measured. This requires modeling not only the latent health literacy and self-efficacy levels of patients and the difficulty and discriminating factors of test items, but also the temporal dependency among concurrent tests. The goal is to account for changes over the course of patient engagement history as the basis for devising personalized patient plans. [ABSTRACT FROM AUTHOR]

Disease self-management programs and intervention/care plan monitoring are often unable to systematically leverage patient-generated information, especially those requiring interpretation of the temporal contexts of the measurement. While existing techniques help in capturing and storing the relevant data, their ability to determine appropriate metrics most sensitive to that individual is limited or non-existent. This is attributable to the lack of unifying models for enabling such interpretations and the non-trivial process required to generate meaningful feature abstractions to support individualized prognosis. To address these issues, a data-driven approach designed to identify the right abstractions for key features relevant to personalization and monitoring of care is discussed. [ABSTRACT FROM AUTHOR]

As one of the causes of increasing population with chronic diseases, low compliance with healthy lifestyle has been a major challenge in the healthcare industry. Not seeing immediate positive outcomes, many people became frustrated and stopped adhering to health plans that offer long-term benefits. Studies have suggested that peer supports are effective to improve compliance, and social cognitive theories have indicated that personal realization and confidence enhanced through entertaining gaming elements could encourage behavior change. In this study, we propose a web-based behavior motivation tool to help improve patients' health compliance based on previous work and theories. Key components in our service design are illustrated through a case study, including a social gaming and learning portal, an engineering approach to support different application scenarios, and information interventions based on predefined rules to achieve effective compliance. © 2012 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Creation of a personalized adherence feedback loop is crucial for initiating and sustaining health behavior change. However, self reports are not sufficient to measure actual adherence. Recording and recognizing personal activities in a ubiquitous environment has thus emerged as a promising solution. In this work, we present a model-driven sensor data assessment mechanism capable of identifying high level adherence-related activity patterns from low level signals. The proposed intelligent sensing algorithm can learn from a population-based training data set and adapt quickly to an individual's exercise patterns using the acquired personal data. Upon the recognition of each activity, the system can further provide personalized feedback such as exercise coaching, fitness planning, and abnormal event detection. The resulted system demonstrates the feasibility of a portable real-time personalized adherence feedback system that could be used for advanced healthcare services. [ABSTRACT FROM AUTHOR]

Disease self-management programs and intervention/care plan monitoring are often unable to systematically leverage patient-generated information, especially those requiring interpretation of the temporal contexts of the measurement. While existing techniques help in capturing and storing the relevant data, their ability to determine appropriate metrics most sensitive to that individual is limited or non-existent. This is attributable to the lack of unifying models for enabling such interpretations and the non-trivial process required to generate meaningful feature abstractions to support individualized prognosis. To address these issues, a data-driven approach designed to identify the right abstractions for key features relevant to personalization and monitoring of care is discussed.

Patient engagement can be enhanced through continuous monitoring of patient' health knowledge and self-efficacy levels across different co-morbid conditions and key aspects in health improvement and recovery. While self-reported test batteries and computerized instruments have been designed to perform evaluation of patient literacy and self-efficacy, they are not practical to be used in scenarios where concurrent tests are needed to understand the change over time. In this paper we propose an adaptive system that can progressively compose the most pertinent test for each user based on the provisional estimates of a patient's latent trait being measured. This requires modeling not only the latent health literacy and self-efficacy levels of patients and the difficulty and discriminating factors of test items, but also the temporal dependency among concurrent tests. The goal is to account for changes over the course of patient engagement history as the basis for devising personalized patient plans.