Towards a consensus around standards for smartphone apps and digital mental health

Mental disorders impact one in four people worldwide, yet access to care is challenging for those who suffer from them1. Mental health apps offer the potential to overcome access barriers for the nearly three billion people projected to own a smartphone by 2020. Although there are over 10,000 mental health apps commercially available, there are few resources available to help end users (patients, clinicians and health care organizations) to evaluate the quality and suitability of these products. Thus, there is an urgent need for an agreement about appropriate standards, principles and practices in research and evaluation of these tools.We represent leaders in mHealth research, industry and health care systems from around the globe, and we seek here to promote consensus on implementing these standards and principles for the evaluation of mental health apps. At a minimum, standards should include consideration of: a) data safety and privacy, b) effectiveness, c) user experience/adherence, d) data integration. Our consensus on the challenges and recommendations in each of these areas is presented below

Groundwater-surface water exchange::A New Graphical User Interface for temperature time-series analysis

Riverine systems have a dynamic exchange of water with the hyporheic zone and groundwater. Exchange fluxes can be challenging to estimate because they vary spatially and temporally and depend on many geological and hydrological properties. Temperature as a tracer has become a low-cost and robust method to monitor such fluxes both at local and reach (several channel widths) scales. Here, we present the capabilities and functionality of a new graphical user interface (GUI) developed in Python which is operating system independent. The GUI integrates standard and state-of-the-art signal processing methods with data visualization and analysis techniques. The signal analysis library allows the user to select the important frequencies to improve result confidence while the advanced LPMLEn and window function in FFT to reduce leakage in the extraction process of the amplitude and phase of the signals. The GUI streamlines the entire analysis process, from evaluating the raw temperature data to obtaining end-user specified parameters such as flux and streambed thermal properties. It allows for the analysis of single-probe and multi-probe data from short to long-term data sets

iFlow:a new graphical user interface to quantify thermal properties and advection from temperature time-series analysis

The last years have seen an increase interest in quantifying hyporheic and water exchange between stream and aquifer due to the need for better water resource management. Pursuant in this effort, several approaches have been developed based on using groundwater temperature as a passive tracer. These approaches may differ in time signal analysis, solutions for the heat transport equation under different boundary conditions or solving the heat transport equation numerically to extract the advective flux. Few of these approaches have been coded in available software. However, available codes do not separate the processes of problem conceptualization and signal analysis. Many approaches only analyze a single frequency or extract only a singular information, (e.g., phase or amplitude), and rarely quantify uncertainty. However valuable information can be extracted by frequencies other than the daily frequency. Here, we provide a novel graphical user interface, GUI, that incorporates a streamlined data analysis procedure. It guides the user through data visualization and selection of signal analysis. The software allows both single- and multi-frequencies approaches, and different reconstruction techniques of the fluxes and system thermal properties based on different boundary conditions. The GUI is developed in the multi-platform Python language and with modular structure, which allows integration and additions of new signal processing approaches and solutions to partial differential equation and boundary conditions that better describe the process

In vivo quantification of human lumbar disc degeneration using T1ρ-weighted magnetic resonance imaging

Diagnostic methods and biomarkers of early disc degeneration are needed as emerging treatment technologies develop (e.g., nucleus replacement, total disc arthroplasty, cell therapy, growth factor therapy) to serve as an alternative to lumbar spine fusion in treatment of low back pain. We have recently demonstrated in cadaveric human discs an MR imaging and analysis technique, spin-lock T1ρ-weighted MRI, which may provide a quantitative, objective, and non-invasive assessment of disc degeneration. The goal of the present study was to assess the feasibility of using T1ρ MRI in vivo to detect intervertebral disc degeneration. We evaluated ten asymptomatic 40–60-year-old subjects. Each subject was imaged on a 1.5 T whole-body clinical MR scanner. Mean T1ρ values from a circular region of interest in the center of the nucleus pulposus were calculated from maps generated from a series of T1ρ-weighted images. The degenerative grade of each lumbar disc was assessed from conventional T2-weighted images according to the Pfirmann classification system. The T1ρ relaxation correlated significantly with disc degeneration (r=−0.51, P<0.01) and the values were consistent with our previous cadaveric study, in which we demonstrated correlation between T1ρ and proteoglycan content. The technique allows for spatial measurements on a continuous rather than an integer-based scale, minimizes the potential for observer bias, has a greater dynamic range than T2-weighted imaging, and can be implemented on a 1.5 T clinical scanner without significant hardware modifications. Thus, there is a strong potential to use T1ρ in vivo as a non-invasive biomarker of proteoglycan loss and early disc degeneration