NIDDK has ongoing clinical studies (e.g., Chronic Renal Insufficiency Cohort or CRIC) and several consortium studies planned for 2013-2018: RFA-DK-12-010 (http://grants.nih.gov/grants/guide/rfa-files/RFA-DK-12-010.html): Novel Therapies and Approaches to Reduce Morbidity and Mortality of Hemodialysis Patients. This FOA will establish a multi-center consortium to carry out pilot and feasibility studies to optimize critical elements of the study design of a subsequent full-scale randomized controlled clinical trial of novel therapies, including procedural or technical changes, psychosocial interventions and anti-inflammatory treatment(s) for end-stage renal disease patients undergoing maintenance hemodialysis (HD). The study will include data assessing drug dosing, safety and adverse events, potential biomarkers and surrogate endpoints, such as patient reported outcomes (PROs). RFA-DK-12-014 (http://grants.nih.gov/grants/guide/rfa-files/RFA-DK-12-014.html): Advancing Clinical Research in Primary Glomerular Disease. This FOA will establish a multi-center consortium to recruit a population who are diagnosed with one of four major glomerular diseases. Because of the slow progression of these diseases, the study will be a longitudinal observation with collection of phenotypic, genetic, biochemical, and tissue data. It is anticipated that ancillary studies to this consortium will develop and qualify biomarkers and surrogate outcomes (e.g., PROs) that will be used in future therapeutic trials for these diseases. RFA-DK-12-016 (http://grants.nih.gov/grants/guide/rfa-files/RFA-DK-12-016.html): Pilot Studies of Candidate Therapies for Chronic Kidney Disease. The purpose of this FOA is to form a multi-center, collaborative study group that will carry out pilot studies that optimize critical elements of the study design of subsequent randomized trial(s) of new treatment(s) for CKD. Renal and non-renal outcomes including measures of CKD progression will be determined during this study, including alternative endpoint markers such as biochemical biomarkers or PROs. RFA-DK-11-026 (http://grants.nih.gov/grants/guide/rfa-files/RFA-DK-11-026.html): Symptoms of Lower Urinary Tract Dysfunction Research Network (LURN) (U01). This FOA supports the development of a cooperative research network (Symptoms of Lower Urinary Tract Dysfunction Research Network, or LURN) to develop and qualify symptom-based instruments (patient reported outcome (PRO) measures) to measure early, late, transient, and persistent symptoms both in males and females, and to better define the phenotypes of men and women with symptoms of lower urinary tract dysfunction (LUTD). RFA-DK-12-017 (http://grants.nih.gov/grants/guide/rfa-files/RFA-DK-12-017.html): Expansion of Symptoms of Lower Urinary Tract Dysfunction Research Network (LURN). The aim of this FOA is to expand on the LURN to validate/qualify the developed Patient Reported Outcome (PRO) measures, recruit study participants, and conduct extensive characterization (phenotyping) of them. The phenotyping efforts will use a multi-disciplinary, integrated approach in order to expand our understanding of the pathophysiological causes of symptoms of LUTD; and identify methods and collect data and/or samples to find predictors and/or biomarkers of symptom initiation, flare, and progression that may inform strategies to prevent and/or manage disease. Studies such as those listed above and those that will follow these initiatives in the future have traditionally relied on research personnel to collect data either in a face-to-face visit or via a telephone interview. These same research personnel or others then transcribe the raw data into a computerized database that is often verified in quality control checks. The cost of these personnel and infrastructure is high, and the travel/telephone time often becomes burdensome for the participants, resulting in their early dropout from studies of duration of more that 12-24 months. With the movement toward large cohorts in realistic practice settings, such as the recently awarded NIH Health Care Systems Research Collaboratory, there is a growing need to collect data without the use of costly research personnel. Symptoms (e.g., PROs), habits (e.g., smoking), dietary intake, physical activity or real-life events (e.g., syncope or myocardial infarction) as part of the participants phenotype often must rely on participant recall well after the event. Adherence to a particular therapy is another issue in therapeutic trials that is often difficult to ascertain. Health care usage and hospitalizations are often study outcomes, but capturing all of these may be problematic. Biochemical data that relies on measurement in the blood or urine typically requires the participant to travel to a center or health care facility for collection and transport of the specimen to a central laboratory. This is an additional burden on the participant and on study staff. Clinical Research Importance Optimization of data collection with new technology has the potential to improve: real-time observations (e.g., biomarkers, diet, physical activity, vital signs, psychological parameters, environmental factors), reporting of patient outcomes (PROs), recording of interventions (e.g., pill intake), and adherence (e.g., diet, medicine, physical activity). These devices or techniques also have the potential to improve participant retention and to decrease research costs per participant. Successful Applications will have addressed all of the following: 1. Specification of the trial application(s) and rationale for proposed use of technology in the trial. The technology can take the form of hardware (e.g., devices that measure and/or transmit data), software (e.g., an application for a smartphone), a sensor that measures study variables and can be linked to another device that transmits data, or any other item that will enhance the collection of the desired data. 2. Detailed description of the proposed technology will include but not be limited to: If an electronic device: size, power requirements, weight If software: computer language, capability to interface with devices and other software, size of files, user interface If a sensor or measurement: interface with human body, interface with recording device, electronic characteristics (e.g., voltage, milliamps, power requirements) 3. Details of how the new technology will optimize the clinical trial. This includes but is not limited to the following: Collection of data Transmission of data to database Quality of data Timeliness of data Participant retention Participant adherence to the trial procedures Research staff required for data collection and compilation Cost vs Benefit 4. For applications to a urological trial: details of how the new technology will measure pressure within the kidney, bladder, urethra or other points relevant to function, urine flow, rectal or urethral sphincter activity. 5. Measurements of body fluids for specific trials should be discussed, and the proposed theory and approach to the determination of the biomarker(s) should be detailed and include the following, as indicated by the study design: Urine (e.g., inflammatory, proteomic, metabolomic studies demonstrating specific disease markers) Stool (e.g., individual microbiome, metabolomic studies) Blood (e.g., GFR marker(s), inflammatory markers, proteomic, metabolomic studies demonstrating specific acute and chronic disease markers) Sweat (e.g., volume status, hydration indicators) Saliva (e.g., salt concentrations, glucose concentrations, drug concentrations, enzymes) Goals for specificity, accuracy, detection limits of each proposed test Preliminary pre-clinical data for any of the above. 6. For all clinical applications that interface with the body, notification of and necessary clearance(s) by the FDA should be discussed and/or documented.