HEART: heart exercise and remote technologies: a randomized controlled trial study protocol.

BACKGROUND: Cardiovascular disease (CVD) is the leading cause of death worldwide. Cardiac rehabilitation (CR) is aimed at improving health behaviors to slow or reverse the progression of CVD disease. Exercise is a central element of CR. Technologies such as mobile phones and the Internet (mHealth) offer potential to overcome many of the psychological, physical, and geographical barriers that have been associated with lack of participation in exercise-based CR. We aim to trial the effectiveness of a mobile phone delivered exercise-based CR program to increase exercise capacity and functional outcomes compared with usual CR care in adults with CVD. This paper outlines the rationale and methods of the trial.

METHODS: A single-blinded parallel two-arm randomized controlled trial is being conducted. A total of 170 people will be randomized at 1:1 ratio either to receive a mHealth CR program or usual care. Participants are identified by CR nurses from two metropolitan hospitals in Auckland, New Zealand through outpatient clinics and existing databases. Consenting participants are contacted to attend a baseline assessment. The intervention consists of a theory-based, personalized, automated package of text and video message components via participants' mobile phones and the Internet to increase exercise behavior, delivered over six months. The control group will continue with usual CR. Data collection occurs at baseline and 24 weeks (post-intervention). The primary outcome is change in maximal oxygen uptake from baseline to 24 weeks. Secondary outcomes include post-intervention measures on self-reported physical activity (IPAQ), cardiovascular risk factors (systolic blood pressure, weight, and waist to hip ratio), health related quality of life (SF-36), and cost-effectiveness.

DISCUSSION: This manuscript presents the protocol for a randomized controlled trial of a mHealth exercise-based CR program. Results of this trial will provide much needed information about physical and psychological well-being, and cost-effectiveness of an automated telecommunication intervention. If effective, this intervention has enormous potential to improve the delivery of CR and could easily be scaled up to be delivered nationally (and internationally) in a very short time, enhancing the translational aspect of this research. It also has potential to extend to comprehensive CR (nutrition advice, smoking cessation, medication adherence).

Supporting adaptive tour with high level petri nets

One of the issues for tour planning applications is to adaptively provide personalized advices for different types of tourists and tour activities. This paper proposes a high level Petri Nets based approach to providing some level of adaptation by implementing adaptive navigation in a tour node space. The new model supports dynamic reordering or removal of tour nodes along a tour path; it supports multiple travel modes and incorporates multimodality within its tour planning logic to derive adaptive tour. Examples are given to demonstrate how to realize adaptive interfaces and personalization. Future directions are also discussed at the end of this paper.

Exploring the effectiveness of true abnormal data elimination in context-aware web services recommendation

Recent years have witnessed a growing interest in context-aware recommender system (CARS), which explores the impact of context factors on personalized Web services recommendation. Basically, the general idea of CARS methods is to mine historical service invocation records through the process of context-aware similarity computation. It is observed that traditional similarity mining process would very likely generate relatively big deviations of QoS values, due to the dynamic change of contexts. As a consequence, including a considerable amount of deviated QoS values in the similarity calculation would probably result in a poor accuracy for predicting unknown QoS values. In allusion to this problem, this paper first distinguishes two definitions of Abnormal Data and True Abnormal Data, the latter of which should be eliminated. Second, we propose a novel CASR-TADE method by incorporating the effectiveness of True Abnormal Data Elimination into context-aware Web services recommendation. Finally, the experimental evaluations on a real-world Web services dataset show that the proposed CASR-TADE method significantly outperforms other existing approaches.