"Back Rx, a personalized mobile phone application for discogenic chronic low back pain: a prospective pilot study".

BACKGROUND: Intervertebral disc pathology is the most common identifiable cause of chronic lower back pain (CLBP). There are limited conservative alternatives to treat discogenic axial CLBP. Back Rx is a mobile application (app) developed to treat patients with this condition, following the Back Rx exercise program, assisted by a virtual coach. METHODS: Patients 18 to 65 years of age, with axial CLBP (more than 3 months), and evidence of lumbar disc pathology by magnetic resonance imaging (MRI) were enrolled to the study. Patients' symptomatology was prospectively evaluated at baseline and after 3 months of using the Back Rx app. The main outcome of the study was back pain evaluated using the visual analog scale (VAS) for pain. Secondary outcomes were the patient's functionality, the weekly pain medication intake, the patients' adherence to the app, and the patients´ satisfaction rate. RESULTS: Seventy-five patients with CLBP were enrolled in the study. All patients had a statistically significant improvement from baseline to final follow-up in the average VAS scores, and the functionality evaluations. Average VAS scores decreased from 5.17 ± 2.1 at baseline to 3.8 ± 2.6 at final follow-up (P = 0.016). Patients showed a significant decrease in the number of pain medications taken during a week (P = 0.001). Overall compliance with the app was 52%, and 65% of the patients rated the overall experience as good or excellent. CONCLUSION: The Back Rx app decreased pain and increased function in patients with discogenic axial CLBP compared to their baseline status. Further measures are needed to increase patients' compliance with the app and the Back Rx program. TRIAL REGISTRATION: Retrospectively registered in 2/2/2017 NCT03040310 (ClinicalTrials.gov).

Role of Technology in Self-Assessment and Feedback Among Hospitalist Physicians: Semistructured Interviews and Thematic Analysis.

BACKGROUND: Lifelong learning is embedded in the culture of medicine, but there are limited tools currently available for many clinicians, including hospitalists, to help improve their own practice. Although there are requirements for continuing medical education, resources for learning new clinical guidelines, and developing fields aimed at facilitating peer-to-peer feedback, there is a gap in the availability of tools that enable clinicians to learn based on their own patients and clinical decisions. OBJECTIVE: The aim of this study was to explore the technologies or modifications to existing systems that could be used to benefit hospitalist physicians in pursuing self-assessment and improvement by understanding physicians' current practices and their reactions to proposed possibilities. METHODS: Semistructured interviews were conducted in two separate stages with analysis performed after each stage. In the first stage, interviews (N=12) were conducted to understand the ways in which hospitalist physicians are currently gathering feedback and assessing their practice. A thematic analysis of these interviews informed the prototype used to elicit responses in the second stage. RESULTS: Clinicians actively look for feedback that they can apply to their practice, with the majority of the feedback obtained through self-assessment. The following three themes surrounding this aspect were identified in the first round of semistructured interviews: collaboration, self-reliance, and uncertainty, each with three related subthemes. Using a wireframe, the second round of interviews led to identifying the features that are currently challenging to use or could be made available with technology. CONCLUSIONS: Based on each theme and subtheme, we provide targeted recommendations for use by relevant stakeholders such as institutions, clinicians, and technologists. Most hospitalist self-assessments occur on a rolling basis, specifically using data in electronic medical records as their primary source. Specific objective data points or subjective patient relationships lead clinicians to review their patient cases and to assess their own performance. However, current systems are not built for these analyses or for clinicians to perform self-assessment, making this a burdensome and incomplete process. Building a platform that focuses on providing and curating the information used for self-assessment could help physicians make more accurately informed changes to their own clinical practice and decision-making.

Digital Health Apps in the Clinical Care of Inflammatory Bowel Disease: Scoping Review.

BACKGROUND: Digital health is poised to transform health care and redefine personalized health. As Internet and mobile phone usage increases, as technology develops new ways to collect data, and as clinical guidelines change, all areas of medicine face new challenges and opportunities. Inflammatory bowel disease (IBD) is one of many chronic diseases that may benefit from these advances in digital health. This review intends to lay a foundation for clinicians and technologists to understand future directions and opportunities together. OBJECTIVE: This review covers mobile health apps that have been used in IBD, how they have fit into a clinical care framework, and the challenges that clinicians and technologists face in approaching future opportunities. METHODS: We searched PubMed, Scopus, and ClinicalTrials.gov to identify mobile apps that have been studied and were published in the literature from January 1, 2010, to April 19, 2019. The search terms were ("mobile health" OR "eHealth" OR "digital health" OR "smart phone" OR "mobile app" OR "mobile applications" OR "mHealth" OR "smartphones") AND ("IBD" OR "Inflammatory bowel disease" OR "Crohn's Disease" (CD) OR "Ulcerative Colitis" (UC) OR "UC" OR "CD"), followed by further analysis of citations from the results. We searched the Apple iTunes app store to identify a limited selection of commercial apps to include for discussion. RESULTS: A total of 68 articles met the inclusion criteria. A total of 11 digital health apps were identified in the literature and 4 commercial apps were selected to be described in this review. While most apps have some educational component, the majority of apps focus on eliciting patient-reported outcomes related to disease activity, and a few are for treatment management. Significant benefits have been seen in trials relating to education, quality of life, quality of care, treatment adherence, and medication management. No studies have reported a negative impact on any of the above. There are mixed results in terms of effects on office visits and follow-up. CONCLUSIONS: While studies have shown that digital health can fit into, complement, and improve the standard clinical care of patients with IBD, there is a need for further validation and improvement, from both a clinical and patient perspective. Exploring new research methods, like microrandomized trials, may allow for more implementation of technology and rapid advancement of knowledge. New technologies that can objectively and seamlessly capture remote data, as well as complement the clinical shift from symptom-based to inflammation-based care, will help the clinical and health technology communities to understand the full potential of digital health in the care of IBD and other chronic illnesses.

Yum-Me: A Personalized Nutrient-Based Meal Recommender System.

Nutrient-based meal recommendations have the potential to help individuals prevent or manage conditions such as diabetes and obesity. However, learning people's food preferences and making recommendations that simultaneously appeal to their palate and satisfy nutritional expectations are challenging. Existing approaches either only learn high-level preferences or require a prolonged learning period. We propose Yum-me, a personalized nutrient-based meal recommender system designed to meet individuals' nutritional expectations, dietary restrictions, and fine-grained food preferences. Yum-me enables a simple and accurate food preference profiling procedure via a visual quiz-based user interface and projects the learned profile into the domain of nutritionally appropriate food options to find ones that will appeal to the user. We present the design and implementation of Yum-me and further describe and evaluate two innovative contributions. The first contriution is an open source state-of-the-art food image analysis model, named FoodDist. We demonstrate FoodDist's superior performance through careful benchmarking and discuss its applicability across a wide array of dietary applications. The second contribution is a novel online learning framework that learns food preference from itemwise and pairwise image comparisons. We evaluate the framework in a field study of 227 anonymous users and demonstrate that it outperforms other baselines by a significant margin. We further conducted an end-to-end validation of the feasibility and effectiveness of Yum-me through a 60-person user study, in which Yum-me improves the recommendation acceptance rate by 42.63%.

No need for disease: testing extinction hypotheses for the thylacine using multi-species metamodels.

Population viability analysis (PVA) is widely used to assess the extinction risk of threatened species and to evaluate different management strategies. However, conventional PVA neglects important biotic interactions and therefore can fail to identify important threatening processes. We designed a new PVA approach that includes species interactions explicitly by networking species models within a single 'metamodel'. We demonstrate the utility of PVA metamodels by employing them to reinterpret the extinction of the carnivorous, marsupial thylacine Thylacinus cynocephalus in Tasmania. In particular, we test the claim that well-documented impacts of European settlement cannot account for this extinction and that an unknown disease must have been an additional and necessary cause. We first constructed a classical, single-species PVA model for thylacines, which was then extended by incorporation within a dynamic predator-herbivore-vegetation metamodel that accounted for the influence of Europeans on the thylacine's prey base. Given obvious parameter uncertainties, we explored both modelling approaches with rigorous sensitivity analyses. Single-species PVA models were unable to recreate the thylacine's extinction unless a high human harvest, small starting population size or low maximum population growth rate was assumed, even if disease effects were included from 1906 to 1909. In contrast, we readily recreated the thylacine's demise using disease-free multi-species metamodels that simulated declines in native prey populations (particularly due to competition with introduced sheep). Dynamic, multi-species metamodels provide a simple, flexible framework for studying current species declines and historical extinctions caused by complex, interacting factors.