Combining Wireless Technology and Behavioral Economics to Engage Patients (WiBEEP) with cardiometabolic disease: a pilot study

Abstract Background The long-term management of cardiometabolic diseases, such as type 2 diabetes and hypertension, is complex and can be facilitated by supporting patient-directed behavioral changes. The concurrent application of wireless technology and personalized text messages (PTMs) based on behavioral economics in managing cardiometabolic diseases, although promising, has not been studied. The aim of this pilot study was to evaluate the feasibility and acceptability of the concurrent application of wireless home blood pressure (BP) monitoring (as an example of “automated hovering”) and PTMs (as an example of “nudging”) targeting pharmacotherapy and lifestyle habits in patients with cardiometabolic disease (type 2 diabetes and/or hypertension). Methods The Wireless Technology and Behavioral Economics to Engage Patients (WiBEEP) with cardiometabolic disease study was a single-arm, open-label, 7-week-long pilot study in 12 patients (mean age 58.5 years) with access to a mobile phone. The study took place at Tufts Medical Center (Boston, MA) between March and September 2017. All patients received PTMs; nine patients received wireless home BP monitoring. At baseline, patients completed questionnaires to learn about their health goals and to assess medication adherence; at the end of week 7, all patients completed questionnaires to evaluate the feasibility and acceptability of the intervention and assess medication adherence. Hemoglobin A1c was ascertained from data collected during routine clinical care in 7 patients with available data. Results The majority of patients reported the text messages to be easy to understand (88%) and appropriate in frequency (71%) and language (88%). All patients reported BP monitoring to be useful. Mean arterial pressure was lower at the end-of-study compared to baseline (− 3.4 mmHg [95% CI, − 5 to − 1.8]. Mean change in hemoglobin A1c was − 0.31% [95% CI, − 0.56 to − 0.06]. Conclusions Among patients with cardiometabolic disease, the combination of wireless BP monitoring and lifestyle-focused text messaging was feasible and acceptable. Larger studies will determine the long-term effectiveness of such an approach

Algorithms for Detecting Significantly Mutated Pathways in Cancer

Recent genome sequencing studies have shown that the somatic mutations that drive cancer development are distributed across a large number of genes. This mutational heterogeneity complicates efforts to distinguish functional mutations from sporadic, passenger mutations. Since cancer mutations are hypothesized to target a relatively small number of cellular signaling and regulatory pathways, a common approach is to assess whether known pathways are enriched for mutated genes. However, restricting attention to known pathways will not reveal novel cancer genes or pathways. An alterative strategy is to examine mutated genes in the context of genome-scale interaction networks that include both well characterized pathways and additional gene interactions measured through various approaches. We introduce a computational framework for de novo identification of subnetworks in a large gene interaction network that are mutated in a significant number of patients. This framework includes two major features. First, we introduce a diffusion proces

Connectedness of PPI network neighborhoods identifies regulatory hub proteins

Motivation: With the growing availability of high-throughput protein–protein interaction (PPI) data, it has become possible to consider how a protein's local or global network characteristics predict its function

Assessment of network module identification across complex diseases

Many bioinformatics methods have been proposed for reducing the complexity of large gene or protein networks into relevant subnetworks or modules. Yet, how such methods compare to each other in terms of their ability to identify disease-relevant modules in different types of network remains poorly understood. We launched the ‘Disease Module Identification DREAM Challenge’, an open competition to comprehensively assess module identification methods across diverse protein–protein interaction, signaling, gene co-expression, homology and cancer-gene networks. Predicted network modules were tested for association with complex traits and diseases using a unique collection of 180 genome-wide association studies. Our robust assessment of 75 module identification methods reveals top-performing algorithms, which recover complementary trait-associated modules. We find that most of these modules correspond to core disease-relevant pathways, which often comprise therapeutic targets. This community challenge establishes biologically interpretable benchmarks, tools and guidelines for molecular network analysis to study human disease biology