Use of a goal setting intervention to increase adherence to low back pain rehabilitation: A randomized controlled trial

Objective: To examine the effects of a goal setting intervention on self efficacy, treatment efficacy, adherence and treatment outcome in patients undergoing low back pain rehabilitation. Design: A mixed-model 2 (Time) x 3 (Group) randomized controlled trial. Setting: A residential rehabilitation centre for military personnel. Subjects: UK military personnel volunteers (N=48); mean age was 32.9 (SD 7.9) with a diagnosis of non-specific low back pain. Interventions: Subjects were randomly assigned to either a goal setting experimental group (Exp, n=16), therapist-led exercise therapy group (C1, n=16), or non-therapist-led exercise therapy group (C2, n=16). Treatment duration for all groups was 3 weeks. Main measures: Self-efficacy, treatment efficacy and treatment outcome were recorded before and after the treatment period. Adherence was rated during regularly scheduled treatment sessions using the Sports Injury Rehabilitation Adherence Scale (SIRAS). The Biering-Sørensen test was used as the primary measure of treatment outcome. Results: ANCOVA results showed that adherence scores were significantly higher in the experimental group (13.70 ± 1.58) compared with C2 (11.74 ± 1.35), (P<0.025). There was no significant difference for adherence between the experimental group and C1 (P=0.13). Self-efficacy was significantly higher in the experimental group compared to both C1 and C2 (P<0.05), whereas no significant difference was found for treatment efficacy. Treatment outcome did not differ significantly between the experimental and two control groups. Conclusions: The findings provide partial support for the use of goal setting to enhance adherence in clinical rehabilitation

Review of precision cancer medicine: Evolution of the treatment paradigm.

In recent years, biotechnological breakthroughs have led to identification of complex and unique biologic features associated with carcinogenesis. Tumor and cell-free DNA profiling, immune markers, and proteomic and RNA analyses are used to identify these characteristics for optimization of anticancer therapy in individual patients. Consequently, clinical trials have evolved, shifting from tumor type-centered to gene-directed, histology-agnostic, with innovative adaptive design tailored to biomarker profiling with the goal to improve treatment outcomes. A plethora of precision medicine trials have been conducted. The majority of these trials demonstrated that matched therapy is associated with superior outcomes compared to non-matched therapy across tumor types and in specific cancers. To improve the implementation of precision medicine, this approach should be used early in the course of the disease, and patients should have complete tumor profiling and access to effective matched therapy. To overcome the complexity of tumor biology, clinical trials with combinations of gene-targeted therapy with immune-targeted approaches (e.g., checkpoint blockade, personalized vaccines and/or chimeric antigen receptor T-cells), hormonal therapy, chemotherapy and/or novel agents should be considered. These studies should target dynamic changes in tumor biologic abnormalities, eliminating minimal residual disease, and eradicating significant subclones that confer resistance to treatment. Mining and expansion of real-world data, facilitated by the use of advanced computer data processing capabilities, may contribute to validation of information to predict new applications for medicines. In this review, we summarize the clinical trials and discuss challenges and opportunities to accelerate the implementation of precision oncology

Enhancing Undergraduate AI Courses through Machine Learning Projects

It is generally recognized that an undergraduate introductory Artificial Intelligence course is challenging to teach. This is, in part, due to the diverse and seemingly disconnected core topics that are typically covered. The paper presents work funded by the National Science Foundation to address this problem and to enhance the student learning experience in the course. Our work involves the development of an adaptable framework for the presentation of core AI topics through a unifying theme of machine learning. A suite of hands-on semester-long projects are developed, each involving the design and implementation of a learning system that enhances a commonly-deployed application. The projects use machine learning as a unifying theme to tie together the core AI topics. In this paper, we will first provide an overview of our model and the projects being developed and will then present in some detail our experiences with one of the projects – Web User Profiling which we have used in our AI class

Current Approaches to Improving the Value of Care: A Physician's Perspective

Evaluates the utility of judgment-based approaches to quality improvement -- pay-for-performance, public reporting, consumer-directed health plans, and tiering -- as ways to control costs. Recommends incentive- and accountability-based programs

Tour recommendation for groups

Consider a group of people who are visiting a major touristic city, such as NY, Paris, or Rome. It is reasonable to assume that each member of the group has his or her own interests or preferences about places to visit, which in general may differ from those of other members. Still, people almost always want to hang out together and so the following question naturally arises: What is the best tour that the group could perform together in the city? This problem underpins several challenges, ranging from understanding people’s expected attitudes towards potential points of interest, to modeling and providing good and viable solutions. Formulating this problem is challenging because of multiple competing objectives. For example, making the entire group as happy as possible in general conflicts with the objective that no member becomes disappointed. In this paper, we address the algorithmic implications of the above problem, by providing various formulations that take into account the overall group as well as the individual satisfaction and the length of the tour. We then study the computational complexity of these formulations, we provide effective and efficient practical algorithms, and, finally, we evaluate them on datasets constructed from real city data