Background Although effective mental health treatments exist, the ability to match individuals to optimal treatments is poor, and timely assessment of response is difficult. One reason for these challenges is the lack of objective measurement of psychiatric symptoms. Sensors and active tasks recorded by smartphones provide a low-burden, low-cost, and scalable way to capture real-world data from patients that could augment clinical decision-making and move the field of mental health closer to measurement-based care. Objective This study tests the feasibility of a fully remote study on individuals with self-reported depression using an Android-based smartphone app to collect subjective and objective measures associated with depression severity. The goals of this pilot study are to develop an engaging user interface for high task adherence through user-centered design; test the quality of collected data from passive sensors; start building clinically relevant behavioral measures (features) from passive sensors and active inputs; and preliminarily explore connections between these features and depression severity. Methods A total of 600 participants were asked to download the study app to join this fully remote, observational 12-week study. The app passively collected 20 sensor data streams (eg, ambient audio level, location, and inertial measurement units), and participants were asked to complete daily survey tasks, weekly voice diaries, and the clinically validated Patient Health Questionnaire (PHQ-9) self-survey. Pairwise correlations between derived behavioral features (eg, weekly minutes spent at home) and PHQ-9 were computed. Using these behavioral features, we also constructed an elastic net penalized multivariate logistic regression model predicting depressed versus nondepressed PHQ-9 scores (ie, dichotomized PHQ-9). Results A total of 415 individuals logged into the app. Over the course of the 12-week study, these participants completed 83.35% (4151/4980) of the PHQ-9s. Applying data sufficiency rules for minimally necessary daily and weekly data resulted in 3779 participant-weeks of data across 384 participants. Using a subset of 34 behavioral features, we found that 11 features showed a significant (P<.001 Benjamini-Hochberg adjusted) Spearman correlation with weekly PHQ-9, including voice diary–derived word sentiment and ambient audio levels. Restricting the data to those cases in which all 34 behavioral features were present, we had available 1013 participant-weeks from 186 participants. The logistic regression model predicting depression status resulted in a 10-fold cross-validated mean area under the curve of 0.656 (SD 0.079). Conclusions This study finds a strong proof of concept for the use of a smartphone-based assessment of depression outcomes. Behavioral features derived from passive sensors and active tasks show promising correlations with a validated clinical measure of depression (PHQ-9). Future work is needed to increase scale that may permit the construction of more complex (eg, nonlinear) predictive models and better handle data missingness.

Background The Onduo virtual diabetes clinic (VDC) for people with type 2 diabetes (T2D) combines a mobile app, remote personalized lifestyle coaching, connected devices, and live video consultations with board-certified endocrinologists for medication management and prescription of real-time continuous glucose monitoring (RT-CGM) devices for intermittent use. Objective This prospective single-arm study evaluated glycemic outcomes associated with participation in the Onduo VDC for 4 months. Methods Adults aged ≥18 years with T2D and a baseline glycated hemoglobin (HbA1c) of ≥8% to ≤12% were enrolled from 2 primary care centers from February 2019 to October 2019. Participants were asked to engage at ≥1 time per week with their care team and to participate in a telemedicine consultation with a clinic endocrinologist for diabetes medication review. Participants were asked to use a RT-CGM device and wear six 10-day sensors (total 60 days of sensor wear) intermittently over the course of 4 months. The primary outcome was change in HbA1c at 4 months from baseline. Other endpoints included change in weight and in RT-CGM glycemic metrics, including percent time <70, 70-180, 181-250, and >250 mg/dL. Changes in blood pressure and serum lipids at 4 months were also evaluated. Results Participants (n=55) were 57.3 (SD 11.6) years of age, body mass index 33.7 (SD 7.2), and 40% (22/55) female. HbA1c decreased significantly by 1.6% (SD 1%; P<.001). When stratified by baseline HbA1c of 8.0% to 9.0% (n=36) and >9.0% (n=19), HbA1c decreased by 1.2% (SD 0.6%; P<.001) and 2.4% (SD 1.3%; P<.001), respectively. Continuous glucose monitoring–measured (n=43) percent time in range (TIR) 70-180 mg/dL increased by 10.2% (SD 20.5%; P=.002), from 65.4% (SD 23.2%) to 75.5% (SD 22.7%), which was equivalent to a mean increase of 2.4 hours TIR per day. Percent time 181-250 mg/dL and >250 mg/dL decreased by 7.2% (SD 15.4; P=.005) and 3.0% (SD 9.4; P=.01), respectively. There was no change in percent time <70 mg/dL. Mean weight decreased by 9.0 lb (SD 10.4; P<.001). Significant improvements were also observed in systolic blood pressure, total cholesterol, low-density lipoprotein cholesterol, and triglycerides (P=.04 to P=<.001). Conclusions Participants in the Onduo VDC experienced significant improvement in HbA1c, increased TIR, decreased time in hyperglycemia, and no increase in hypoglycemia at 4 months. Improvements in other metabolic health parameters including weight and blood pressure were also observed. In conclusion, the Onduo VDC has potential to support people with T2D and their clinicians between office visits by increasing access to specialty care and advanced diabetes technology including RT-CGM. Trial Registration ClinicalTrials.gov NCT03865381; https://clinicaltrials.gov/ct2/show/NCT03865381

Background-After angioplasty, major complications and ischemic events occur more frequently in diabetic than nondiabetic patients. To determine whether treatment with abciximab is effective in reducing these events in diabetics, we analyzed characteristics and outcomes of diabetic patients enrolled in a large multicenter study (EPILOG). Methods and Results-Of 2792 patients enrolled, 638 (23%) were diabetic. Diabetic patients were older, shorter, and heavier; more likely to be female and have three-vessel disease, prior coronary artery bypass graft surgery, a history of hypertension, or a recent myocardial infarction; and less likely to be current smokers than their nondiabetic counterparts. During hospitalization, death, myocardial infarction, or urgent revascularization occurred in 7.1% of diabetics and 7.5% of nondiabetics. By 6 months, the composite of death and myocardial infarction had occurred in 8.8% of diabetic patients and 7.4% of nondiabetics, whereas death, myocardial infarction, or revascularization had occurred in 27.2% and 22.6%, respectively. Abciximab treatment reduced death or myocardial infarction among diabetic and nondiabetic patients (hazard ratios, 0. When standard-and low-dose heparin adjuncts were compared, diabetics receiving abciximab with standard-dose heparin had marginally greater reductions in the composite of death and myocardial infarction and in target vessel revascularization than diabetics assigned to abciximab with low-dose heparin. Conclusions-Abciximab treatment in diabetic patients led to a reduction in the composite of death and myocardial infarction, which was at least as great as that seen in nondiabetic patients. However, target vessel revascularization was reduced in nondiabetic but not diabetic patients. This effect may be associated in part with lower doses of heparin. These differences may be related to differences in the platelet and coagulation systems between diabetics and nondiabetics, the greater extent of coronary artery disease in diabetics, or patient selection and management factors. (Circulation. 1998;97:1912-1920.)

Abstract-Much of robotics research is carried out using either PICs and processors that are a decade or more out of date The alternative is custom built electronics that is expensive and/or must be reinvented every time a new project is begun. The XBC is a new design for a robot controller merging a modern ARM processor with an FPGA that allows high performance -especially in vision processing and motor control -for a cost similar to controllers with a fraction of its capabilities. Additionally, the XBC uses a new, and still free, software development system, already in wide use. The XBC is being mass produced (at least in research hardware terms) so it is readily available and does not require computer hardware or electronics skills in order to be obtained. This paper describes the system, its capabilities and some potential applications.Index Terms-robot controller, back-emf, color tracking, robot programming environment I. THE XBC/IC SYSTEM For the past half-century, Moore's law has described how general purpose computing has risen in capability while the cost has declined. Along with those changes, advances in operating systems, graphics and user interfaces have lowered the technical barrier for entry to the point where more households in the US have computers than do not [2].However, this has not been the trend for robotics in the hobbyist and research market. As general purpose machines have advanced, their ability to interface to the physical world in a straightforward manner has often declined. While the number of embedded processors has skyrocketed in recent years [12], the equipment, software and required knowledge for entry into using those processors has also skyrocketed. The robot controllers powering most homebrew robots ten years ago (the Basic STAMP [3] and the Handy Board [7]) are still the controllers for many robots today, and have been displaced in numbers only by the RCX [9] -which while easy to use, has fewer practical capabilities than the systems it displaces.The XBC/IC system (see Figure 1) is an easy to use lowcost general purpose robot controller. The system provides powerful hardware (an FPGA linked to a commodity ARM processor) combined with the easy to use and very popular Interactive C programming environment. The resulting system has vision, control and interface capabilities that far exceed previous robot controllers for this market. The XBC uses Interactive C, the easy to use C programming environment already used by tens of thousands of robotics * This work was supported in part by the KISS Institute for Practical Robotics researchers, students and hobbyists. Together, the XBC and IC allow easy entry into advanced robotics applications. II. THE XBC HARDWAREThe XBC's unique hardware uses a Gameboy Advance (GBA) as the main processor. We chose the GBA because of its powerful industry-standard ARM processor, integrated TFT color display, low cost and widespread availability.The GBA also adds a certain "fun" element to an educational robotics platform. The XBC employs custom robot...

BACKGROUND Although effective mental health treatments exist, the ability to match individuals to optimal treatment options is poor and timely assessment of response is difficult. One reason for these challenges is the lack of objective measurement of psychiatric symptoms and behaviors of daily function. Sensors and active tasks enabled by smartphones provide a low-burden, low-cost, and scalable way to capture real-world data from patients that is potentially clinically relevant and could thus augment clinical decision making to improve mental health outcomes and move the field of mental health closer to measurement-based care. OBJECTIVE Our aim was to explore the feasibility of conducting a fully remote study on individuals with clinical depression using an Android-based smartphone app to collect subjective and objective measures that may be associated with severity of mood and mood-related symptoms. Goals of the pilot study were: (a) through user-centric design, develop an engaging user interface that would lead to high task adherence, (b) test the quality of collected data from passive sensors and adherence to active tasks (e.g., weekly PHQ-9), (c) start building clinically relevant behavioral measures (“features”) from passive sensors and active inputs, and (d) preliminarily explore connections between these features and depressive mood symptoms. METHODS A total of 600 participants were asked to download the study app to join this fully remote, observational, 12-week study. The app passively collected 20 sensor data streams (e.g., ambient audio level, location, inertial measurement units), and participants were asked to complete daily tasks consisting of daily mood and behavioral surveys, and weekly voice diaries and PHQ-9 self-surveys as a validated measure of depression symptoms. Statistical analyses included: (a) univariate pairwise correlations between derived behavioral features (e.g., weekly minutes spent at home, pauses in voice diaries, average ambient audio volume level) and PHQ-9, and (b) employing these behavioral features to construct an L1-penalized multivariate logistic regression model predicting depressed vs. non-depressed PHQ-9 scores (i.e., dichotomized PHQ-9 using 10 as a cutoff). RESULTS A total of 415 individuals downloaded and logged into the app, with no reports of significant adverse events or unanticipated problems. Over the course of the 12-week study, these participants completed over 80% of the key clinical self-report outcome measure, the PHQ-9, and audio diaries. Applying data sufficiency rules for minimally necessary daily and weekly data resulted in 3,779 participant-weeks of data across 384 participants. On those data, using a subset of 34 behavioral features, we found that 12 features showed a significant (P ≤ 0.001 adjusted by Benjamini-Hochberg procedure) Spearman correlation with weekly PHQ-9, including voice diary-derived word sentiment and ambient audio levels. Restricting the data to complete cases for the 34 behavioral features, we had available 1,013 participant-weeks from 186 participants. The logistic regression model predicting depression status resulted in a 10-fold cross-validated mean area under the curve (AUC) of 0.649. CONCLUSIONS This study finds strong proof-of-concept for the use of a smartphone-based assessment of depression outcomes. Behavioral features derived from passive sensors and active tasks show promising correlations with a validated clinical measure of depression (PHQ-9). Future work is needed to increase scale that may permit derivation of more complex (e.g., non-linear) predictive models and also better handle data missingness.