Cryologger Ice Tracking Beacon: A Low-Cost, Open-Source Platform for Tracking Icebergs and Ice Islands

Icebergs and ice islands (large, tabular icebergs) present a significant hazard to marine vessels and infrastructure at a time when demand for access to Arctic waters is increasing. There is a growing demand for in situ iceberg tracking data to monitor their drift trajectories and improve models used for operational forecasting of ice hazards, yet the high cost of commercial tracking devices often prevents monitoring at optimal spatial and temporal resolutions. Here, we provide a detailed description of the Cryologger Ice Tracking Beacon (ITB), a low-cost, robust, and user-friendly data logger and telemeter for tracking icebergs and ice islands based on the Arduino open-source electronics platform. Designed for deployments of at least 2 years with an hourly sampling interval that is remotely modifiable by the end user, the Cryologger ITB provides long-term measurements of position, temperature, pressure, pitch, roll, heading, and battery voltage. Data are transmitted via the Iridium satellite network at user-specified intervals. We present the results of field campaigns in 2018 and 2019, which saw the deployment of 16 ITBs along the coasts of Greenland and Ellesmere and Baffin islands. The overall success of these ITB deployments has demonstrated that inexpensive, open-source hardware and software can provide a reliable and cost-effective method of monitoring icebergs and ice islands in the polar regions.

A Bluetooth-Enabled Device for Real-Time Detection of Sitting, Standing, and Walking: Cross-Sectional Validation Study

BACKGROUND

This study assesses the accuracy of a Bluetooth-enabled prototype activity tracker called the Sedentary behaviOR Detector (SORD) device in identifying sedentary, standing, and walking behaviors in a group of adult participants.

OBJECTIVE

The primary objective of this study was to determine the criterion and convergent validity of SORD against direct observation and activPAL.

METHODS

A total of 15 healthy adults wore SORD and activPAL devices on their thighs while engaging in activities (lying, reclining, sitting, standing, and walking). Direct observation was facilitated with cameras. Algorithms were developed using the Python programming language. The Bland-Altman method was used to assess the level of agreement.

RESULTS

Overall, 1 model generated a low level of bias and high precision for SORD. In this model, accuracy, sensitivity, and specificity were all above 0.95 for detecting sitting, reclining, standing, and walking. Bland-Altman results showed that mean biases between SORD and direct observation were 0.3% for sitting and reclining (limits of agreement [LoA]=-0.3% to 0.9%), 1.19% for standing (LoA=-1.5% to 3.42%), and -4.71% for walking (LoA=-9.26% to -0.16%). The mean biases between SORD and activPAL were -3.45% for sitting and reclining (LoA=-11.59% to 4.68%), 7.45% for standing (LoA=-5.04% to 19.95%), and -5.40% for walking (LoA=-11.44% to 0.64%).

CONCLUSIONS

Results suggest that SORD is a valid device for detecting sitting, standing, and walking, which was demonstrated by excellent accuracy compared to direct observation. SORD offers promise for future inclusion in theory-based, real-time, and adaptive interventions to encourage physical activity and reduce sedentary behavior.

Assessment of open data kit mobile technology adoption to enhance reporting of supportive supervision conducted for oral poliovirus vaccine supplementary immunization activities in Nigeria, March 2017-February 2020

Introduction

in Nigeria, supportive supervision of Supplementary Immunization Activities (SIA) is a quality improvement strategy for providing support to vaccination teams administering the poliovirus vaccines to children under 5 years of age. Supervision activities were initially reported in paper forms. This had significant limitations, which led to Open Data Kit (ODK) technology being adopted in March 2017. A review was conducted to assess the impact of ODK for supervision reporting in place of paper forms.

Methods

issues with paper-based reporting and the benefits of ODK were recounted. We determined the average utilization of ODK per polio SIA rounds and assessed the supervision coverage over time based on the proportion of local government areas with ODK geolocation data per round.

Results

a total of 17 problematic issues were identified with paper-based reporting, and ODK addressed all the issues. Open Data Kit-based supervision reports increased from 3,125 in March 2017 to 51,060 in February 2020. Average ODK submissions for national rounds increased from 84 in March 2017 to 459 in February 2020 and for sub-national rounds increased from 533 in July 2017 to 1,596 in October 2019. Supportive supervision coverage improved from 42.5% in March 2017 to 97% in February 2020.

Conclusion

the use of digital technologies in public health has comparative advantages over paper forms, and the adoption of ODK for supervision reporting during polio SIAs in Nigeria experienced the advantages. The visibility and coverage of supportive supervision improved, consequentially contributing to the improved quality of polio SIAs.

From a different angle: genetic diversity underlies differentiation of waterlogging-induced epinasty in tomato

In tomato, downward leaf bending is a morphological adaptation towards waterlogging, which has been shown to induce a range of metabolic and hormonal changes. This kind of functional trait is often the result of a complex interplay of regulatory processes starting at the gene level, gated through a plethora of signaling cascades and modulated by environmental cues. Through phenotypical screening of a population of 54 tomato accessions in a Genome Wide Association Study (GWAS), we have identified target genes potentially involved in plant growth and survival during waterlogging and subsequent recovery. Changes in both plant growth rate and epinastic descriptors revealed several associations to genes possibly supporting metabolic activity in low oxygen conditions in the root zone. In addition to this general reprogramming, some of the targets were specifically associated to leaf angle dynamics, indicating these genes might play a role in the induction, maintenance or recovery of differential petiole elongation in tomato during waterlogging.

Using Real-Time Interaction Analysis to Explore Human-Robot Interaction

Despite the increasing presence of social robots (SRs) in Human-Robot Interaction, there are few studies that quantify these interactions and explore children's attitudes by analyzing real-time data as they communicate with SRs. Therefore, we attempted to explore the interaction between pediatric patients and SRs by analyzing the interaction log collected from real-time. This study is a retrospective analysis of data collected in a prospective study conducted on 10 pediatric cancer patients at tertiary hospitals in Korea. Using the Wizard of Oz method, we collected the interaction log during the interaction between pediatric cancer patients and the robot. Out of the collected data, 955 sentences from the robot and 332 sentences from the children were available for analysis, except for the logs that were missing due to environmental errors. we analyzed the delay time from saving the interaction log and the sentence similarity of the interaction log. The interaction log delay time between robot and child was 5.01 seconds. And the child's delay time averaged 7.2 seconds, which was longer than the robot's delay time of 4.29 seconds. Additionally, as a result of analyzing the sentence similarity of the interaction log, the robot (97.2%) was higher than the children (46.2%). The results of the sentiment analysis of the patient's attitude toward the robot were 73% neutral, 13.59% positive, and 12.42% negative. The observational evaluations of pediatric psychological experts identified curiosity (n=7, 70.0%), activity (n=5, 50.0%), passivity (n=5, 50.0%), sympathy (n=7, 70.0%), concentration (n=6, 60.0%), high interest (n=5, 50.0%), positive attitude (n=9, 90.0%), and low interaction initiative (n=6, 60.0%). This study made it possible to explore the feasibility of interaction with SRs and to confirm differences in attitudes toward robots according to child characteristics. To increase the feasibility of human-robot interaction, measures such as improving the completeness of log records by enhancing the network environment are required.

Estimating County-Level Overdose Rates Using Opioid-Related Twitter Data: Interdisciplinary Infodemiology Study

BACKGROUND

There were an estimated 100,306 drug overdose deaths between April 2020 and April 2021, a three-quarter increase from the prior 12-month period. There is an approximate 6-month reporting lag for provisional counts of drug overdose deaths from the National Vital Statistics System, and the highest level of geospatial resolution is at the state level. By contrast, public social media data are available close to real-time and are often accessible with precise coordinates.

OBJECTIVE

The purpose of this study is to assess whether county-level overdose mortality burden could be estimated using opioid-related Twitter data.

METHODS

International Classification of Diseases (ICD) codes for poisoning or exposure to overdose at the county level were obtained from CDC WONDER. Demographics were collected from the American Community Survey. The Twitter Application Programming Interface was used to obtain tweets that contained any of the 36 terms with drug names. An unsupervised classification approach was used for clustering tweets. Population-normalized variables and polynomial population-normalized variables were produced. Furthermore, z scores of the Getis Ord Gi clustering statistic were produced, and both these scores and their polynomial counterparts were explored in regression modeling of county-level overdose mortality burden. A series of linear regression models were used for predictive modeling to explore the interpretability of the analytical output.

RESULTS

Modeling overdose mortality with normalized demographic variables alone explained only 7.4% of the variability in county-level overdose mortality, whereas this was approximately doubled by the use of specific demographic and Twitter data covariates based on a backward selection approach. The highest adjusted R² and lowest AIC (Akaike Info Criterion) were obtained for the model with normalized demographic variables, normalized z scores from geospatial analyses, and normalized topic counts (adjusted R²=0.133, AIC=8546.8). The z scores of the Getis Ord Gi statistic appeared to have improved utility over population-normalization alone. In this model, median age, female population, and tweets about web-based drug sales were positively associated with opioid mortality. Asian race and Hispanic ethnicity were significantly negatively associated with county-level burdens of overdose mortality.

CONCLUSIONS

Social media data, when transformed using certain statistical approaches, may add utility to the goal of producing closer to real-time county-level estimates of overdose mortality. Prediction of opioid-related outcomes can be advanced to inform prevention and treatment decisions. This interdisciplinary approach can facilitate evidence-based funding decisions for various substance use disorder prevention and treatment programs.

Exploring the Quality of Dynamic Open Government Data Using Statistical and Machine Learning Methods

Dynamic data (including environmental, traffic, and sensor data) were recently recognized as an important part of Open Government Data (OGD). Although these data are of vital importance in the development of data intelligence applications, such as business applications that exploit traffic data to predict traffic demand, they are prone to data quality errors produced by, e.g., failures of sensors and network faults. This paper explores the quality of Dynamic Open Government Data. To that end, a single case is studied using traffic data from the official Greek OGD portal. The portal uses an Application Programming Interface (API), which is essential for effective dynamic data dissemination. Our research approach includes assessing data quality using statistical and machine learning methods to detect missing values and anomalies. Traffic flow-speed correlation analysis, seasonal-trend decomposition, and unsupervised isolation Forest (iForest) are used to detect anomalies. iForest anomalies are classified as sensor faults and unusual traffic conditions. The iForest algorithm is also trained on additional features, and the model is explained using explainable artificial intelligence. There are 20.16% missing traffic observations, and 50% of the sensors have 15.5% to 33.43% missing values. The average percent of anomalies per sensor is 71.1%, with only a few sensors having less than 10% anomalies. Seasonal-trend decomposition detected 12.6% anomalies in the data of these sensors, and iForest 11.6%, with very few overlaps. To the authors' knowledge, this is the first time a study has explored the quality of dynamic OGD.

Emergency Department and Intensive Care Unit Overcrowding and Ventilator Shortages in US Hospitals During the COVID-19 Pandemic, 2020-2021

OBJECTIVE

In 2020, the COVID-19 pandemic overburdened the US health care system because of extended and unprecedented patient surges and supply shortages in hospitals. We investigated the extent to which several US hospitals experienced emergency department (ED) and intensive care unit (ICU) overcrowding and ventilator shortages during the COVID-19 pandemic.

METHODS

We analyzed Health Pulse data to assess the extent to which US hospitals reported alerts when experiencing ED overcrowding, ICU overcrowding, and ventilator shortages from March 7, 2020, through April 30, 2021.

RESULTS

Of 625 participating hospitals in 29 states, 393 (63%) reported at least 1 hospital alert during the study period: 246 (63%) reported ED overcrowding, 239 (61%) reported ICU overcrowding, and 48 (12%) reported ventilator shortages. The number of alerts for overcrowding in EDs and ICUs increased as the number of COVID-19 cases surged.

CONCLUSIONS

Timely assessment and communication about critical factors such as ED and ICU overcrowding and ventilator shortages during public health emergencies can guide public health response efforts in supporting federal, state, and local public health agencies.

NextGen Public Health Surveillance and the Internet of Things (IoT)

Recent advances in technology have led to the rise of new-age data sources (e.g., Internet of Things (IoT), wearables, social media, and mobile health). IoT is becoming ubiquitous, and data generation is accelerating globally. Other health research domains have used IoT as a data source, but its potential has not been thoroughly explored and utilized systematically in public health surveillance. This article summarizes the existing literature on the use of IoT as a data source for surveillance. It presents the shortcomings of current data sources and how NextGen data sources, including the large-scale applications of IoT, can meet the needs of surveillance. The opportunities and challenges of using these modern data sources in public health surveillance are also explored. These IoT data ecosystems are being generated with minimal effort by the device users and benefit from high granularity, objectivity, and validity. Advances in computing are now bringing IoT-based surveillance into the realm of possibility. The potential advantages of IoT data include high-frequency, high volume, zero effort data collection methods, with a potential to have syndromic surveillance. In contrast, the critical challenges to mainstream this data source within surveillance systems are the huge volume and variety of data, fusing data from multiple devices to produce a unified result, and the lack of multidisciplinary professionals to understand the domain and analyze the domain data accordingly.

Use of Ecological Momentary Assessment Through a Passive Smartphone-Based App (eB2) by Patients With Schizophrenia: Acceptability Study

Background

Ecological momentary assessment (EMA) tools appear to be useful interventions for collecting real-time data on patients’ behavior and functioning. However, concerns have been voiced regarding the acceptability of EMA among patients with schizophrenia and the factors influencing EMA acceptability.

Objective

The aim of this study was to investigate the acceptability of a passive smartphone-based EMA app, evidence-based behavior (eB2), among patients with schizophrenia spectrum disorders and the putative variables underlying their acceptance.

Methods

The participants in this study were from an ongoing randomized controlled trial (RCT) of metacognitive training, consisting of outpatients with schizophrenia spectrum disorders (F20-29 of 10th revision of the International Statistical Classification of Diseases and Related Health Problems), aged 18-64 years, none of whom received any financial compensation. Those who consented to installation of the eB2 app (users) were compared with those who did not (nonusers) in sociodemographic, clinical, premorbid adjustment, neurocognitive, psychopathological, insight, and metacognitive variables. A multivariable binary logistic regression tested the influence of the above (independent) variables on “being user versus nonuser” (acceptability), which was the main outcome measure.

Results

Out of the 77 RCT participants, 24 (31%) consented to installing eB2, which remained installed till the end of the study (median follow-up 14.50 weeks) in 14 participants (70%). Users were younger and had a higher education level, better premorbid adjustment, better executive function (according to the Trail Making Test), and higher cognitive insight levels (measured with the Beck Cognitive Insight Scale) than nonusers (univariate analyses) although only age (OR 0.93, 95% CI 0.86-0.99; P=.048) and early adolescence premorbid adjustment (OR 0.75, 95% CI 0.61-0.93; P=.01) survived the multivariable regression model, thus predicting eB2 acceptability.

Conclusions

Acceptability of a passive smartphone-based EMA app among participants with schizophrenia spectrum disorders in this RCT where no participant received financial compensation was, as expected, relatively low, and linked with being young and good premorbid adjustment. Further research should examine how to increase EMA acceptability in patients with schizophrenia spectrum disorders, in particular, older participants and those with poor premorbid adjustment.

Trial Registration

ClinicalTrials.gov NCT04104347; https://clinicaltrials.gov/ct2/show/NCT04104347

Using Machine Learning to Optimize the Quality of Survey Data: Protocol for a Use Case in India

BACKGROUND

Data quality is vital for ensuring the accuracy, reliability, and validity of survey findings. Strategies for ensuring survey data quality have traditionally used quality assurance procedures. Data analytics is an increasingly vital part of survey quality assurance, particularly in light of the increasing use of tablets and other electronic tools, which enable rapid, if not real-time, data access. Routine data analytics are most often concerned with outlier analyses that monitor a series of data quality indicators, including response rates, missing data, and reliability of coefficients for test-retest interviews. Machine learning is emerging as a possible tool for enhancing real-time data monitoring by identifying trends in the data collection, which could compromise quality.

OBJECTIVE

This study aimed to describe methods for the quality assessment of a household survey using both traditional methods as well as machine learning analytics.

METHODS

In the Kilkari impact evaluation's end-line survey amongst postpartum women (n=5095) in Madhya Pradesh, India, we plan to use both traditional and machine learning-based quality assurance procedures to improve the quality of survey data captured on maternal and child health knowledge, care-seeking, and practices. The quality assurance strategy aims to identify biases and other impediments to data quality and includes seven main components: (1) tool development, (2) enumerator recruitment and training, (3) field coordination, (4) field monitoring, (5) data analytics, (6) feedback loops for decision making, and (7) outcomes assessment. Analyses will include basic descriptive and outlier analyses using machine learning algorithms, which will involve creating features from time-stamps, "don't know" rates, and skip rates. We will also obtain labeled data from self-filled surveys, and build models using k-folds cross-validation on a training data set using both supervised and unsupervised learning algorithms. Based on these models, results will be fed back to the field through various feedback loops.

RESULTS

Data collection began in late October 2019 and will span through March 2020. We expect to submit quality assurance results by August 2020.

CONCLUSIONS

Machine learning is underutilized as a tool to improve survey data quality in low resource settings. Study findings are anticipated to improve the overall quality of Kilkari survey data and, in turn, enhance the robustness of the impact evaluation. More broadly, the proposed quality assurance approach has implications for data capture applications used for special surveys as well as in the routine collection of health information by health workers.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/17619.

Maker Buoy Variants for Water Level Monitoring and Tracking Drifting Objects in Remote Areas of Greenland

Meltwater runoff from the Greenland Ice Sheet changes water levels in glacial lakes and can lead to glacial lake outburst flooding (GLOF) events that threaten lives and property. Icebergs produced at Greenland's marine terminating glaciers drift into Baffin Bay and the North Atlantic, where they can threaten shipping and offshore installations. Thus, monitoring glacial lake water levels and the drift of icebergs can enhance safety and aid in the scientific studies of glacial hydrology and iceberg-ocean interactions. The Maker Buoy was originally designed as a low-cost and open source sensor to monitor surface ocean currents. The open source framework, low-cost components, rugged construction and affordable satellite data transmission capabilities make it easy to customize for environmental monitoring in remote areas and under harsh conditions. Here, we present two such Maker Buoy variants that were developed to monitor water level in an ice-infested glacial lake in southern Greenland and to track drifting icebergs and moorings in the Vaigat Strait (Northwest Greenland). We describe the construction of each design variant, methods to access data in the field without an internet connection, and deployments in Greenland in summer 2019. The successful deployments of each Maker Buoy variant suggest that they may also be useful in operational iceberg management strategies and in GLOF monitoring programs.

Real-Time Associations Between Glucose Levels and Fatigue in Type 2 Diabetes: Sex and Time Effects

OBJECTIVE

Fatigue is a pervasive and serious complaint among aging adults with type 2 diabetes. Anecdotally, hyperglycemia was thought to cause fatigue, but prior cross-sectional analyses failed to find any relationship between glucose levels and fatigue. However, study methodology may have caused this relationship to be missed. Our aim was to use concurrent and continuous data across 5 days to examine real-time momentary relationships between glucose and fatigue levels by week, day, and time of day. Additionally, we explored how these relationships differed by sex.

METHOD

Participants (N = 54, 51% male, 54% non-White) wore continuous glucose monitors and wrist actigraphy into which they inputted fatigue ratings 6-8 times daily during waking hours across 5 days. Generalized estimation equation models were used to explore the relationship between glucose and fatigue when averaged by week, day, and time of day. Differences by sex were also explored.

RESULTS

HbA1c and baseline and real-time fatigue were higher in women than in men. Baseline HbA1c and self-reported general fatigue were unrelated. Fatigue levels averaged by day and time of day were higher in women than in men (p < .05). Glucose and fatigue were significantly related at all levels of data (weekly, daily, and time of day) in women but not men.

CONCLUSIONS

Our findings suggest that, when measured concurrently, glucose excursions may affect fatigue levels in women.

Resource-Efficient Sensor Data Management for Autonomous Systems Using Deep Reinforcement Learning

Hyperconnectivity via modern Internet of Things (IoT) technologies has recently driven us to envision “digital twin”, in which physical attributes are all embedded, and their latest updates are synchronized on digital spaces in a timely fashion. From the point of view of cyberphysical system (CPS) architectures, the goals of digital twin include providing common programming abstraction on the same level of databases, thereby facilitating seamless integration of real-world physical objects and digital assets at several different system layers. However, the inherent limitations of sampling and observing physical attributes often pose issues related to data uncertainty in practice. In this paper, we propose a learning-based data management scheme where the implementation is layered between sensors attached to physical attributes and domain-specific applications, thereby mitigating the data uncertainty between them. To do so, we present a sensor data management framework, namely D2WIN, which adopts reinforcement learning (RL) techniques to manage the data quality for CPS applications and autonomous systems. To deal with the scale issue incurred by many physical attributes and sensor streams when adopting RL, we propose an action embedding strategy that exploits their distance-based similarity in the physical space coordination. We introduce two embedding methods, i.e., a user-defined function and a generative model, for different conditions. Through experiments, we demonstrate that the D2WIN framework with the action embedding outperforms several known heuristics in terms of achievable data quality under certain resource restrictions. We also test the framework with an autonomous driving simulator, clearly showing its benefit. For example, with only 30% of updates selectively applied by the learned policy, the driving agent maintains its performance about 96.2%, as compared to the ideal condition with full updates.

Real-Time Analysis of a Sensor's Data for Automated Decision Making in an IoT-Based Smart Home

IoT devices frequently generate large volumes of streaming data and in order to take advantage of this data, their temporal patterns must be learned and identified. Streaming data analysis has become popular after being successfully used in many applications including forecasting electricity load, stock market prices, weather conditions, etc. Artificial Neural Networks (ANNs) have been successfully utilized in understanding the embedded interesting patterns/behaviors in the data and forecasting the future values based on it. One such pattern is modelled and learned in the present study to identify the occurrence of a specific pattern in a Water Management System (WMS). This prediction aids in making an automatic decision support system, to switch OFF a hydraulic suction pump at the appropriate time. Three types of ANN, namely Multi-Input Multi-Output (MIMO), Multi-Input Single-Output (MISO), and Recurrent Neural Network (RNN) have been compared, for multi-step-ahead forecasting, on a sensor’s streaming data. Experiments have shown that RNN has the best performance among three models and based on its prediction, a system can be implemented to make the best decision with 86% accuracy.

Investigation of powered 2-wheeler accident involvement in urban arterials by considering real-time traffic and weather data

OBJECTIVE

Understanding the various factors that affect accident risk is of particular concern to decision makers and researchers. The incorporation of real-time traffic and weather data constitutes a fruitful approach when analyzing accident risk. However, the vast majority of relevant research has no specific focus on vulnerable road users such as powered 2-wheelers (PTWs). Moreover, studies using data from urban roads and arterials are scarce. This study aims to add to the current knowledge by considering real-time traffic and weather data from 2 major urban arterials in the city of Athens, Greece, in order to estimate the effect of traffic, weather, and other characteristics on PTW accident involvement.

METHODS

Because of the high number of candidate variables, a random forest model was applied to reveal the most important variables. Then, the potentially significant variables were used as input to a Bayesian logistic regression model in order to reveal the magnitude of their effect on PTW accident involvement.

RESULTS

The results of the analysis suggest that PTWs are more likely to be involved in multivehicle accidents than in single-vehicle accidents. It was also indicated that increased traffic flow and variations in speed have a significant influence on PTW accident involvement. On the other hand, weather characteristics were found to have no effect.

CONCLUSIONS

The findings of this study can contribute to the understanding of accident mechanisms of PTWs and reduce PTW accident risk in urban arterials.

Aims and approaches of Web-RADR: a consortium ensuring reliable ADR reporting via mobile devices and new insights from social media

INTRODUCTION

Advent of new technologies in mobile devices and software applications is leading to an evolving change in the extent, geographies and modes for use of internet. Today, it is used not only for information gathering but for sharing of experiences, opinions and suggestions. Web-Recognizing Adverse Drug Reactions (RADR) is a groundbreaking European Union (EU) Innovative Medicines Innovation funded 3-year initiative to recommend policies, frameworks, tools and methodologies by leveraging these new developments to get new insights in drug safety.

AREAS COVERED

Data were gathered from prior surveys, previous initiatives and a review of relevant literature was done. New technologies provide an opportunity in the way safety information is collected, helping generate new knowledge for safety profile of drugs as well as unique insights into the evolving pharmacovigilance system in general. It is critical that these capabilities are harnessed in a way that is ethical, compliant with regulations, respecting data privacy and used responsibly. At the same time, the process for managing and interpreting this new information must be efficient and effective for sustenance, thoughtful use of resources and valuable return of knowledge. These approaches should complement the ongoing progress toward personalized medicine.

EXPERT OPINION

This Web-RADR initiative should provide some directions on 'what and how' to use social media to further proactive pharmacovigilance and protection of public health. It is expected to also show how a multipronged expert consortium group comprising regulators, industry and academia can leverage new developments in technology and society to bring innovation in process, operations, organization and scientific approaches across its boundaries and beyond the normal realms of individual research units. These new approaches should bring insights faster, earlier, specific, actionable and moving toward the target of AE prevention. The possibilities of a blended targeted pharmacovigilance (PV) approach where boundaries between stakeholders blur and cultures mix point to very different future for better, healthier and longer lives.

Association Between Daily Time Spent in Sedentary Behavior and Duration of Hyperglycemia in Type 2 Diabetes

Exercise and sedentary behavior have different physiologic effects, which have yet to be fully explained. Time spent in sedentary behavior has been associated with glucose intolerance in adults at risk for type 2 diabetes, but these data have come largely from cross-sectional studies that have not explored this relationship in adults with diabetes. The specific aim of this study was to examine the relationship between time spent in sedentary behavior and glucose levels in adults diagnosed with type 2 diabetes over 3-5 days.

METHODS

Using continuous and concurrent data gathered from wrist accelerometry and a Continuous Glucose-Monitoring Sensor (CGMS), we conducted a longitudinal, descriptive study involving 86 patients with type 2 diabetes.

RESULTS

More time spent in sedentary behavior was predictive of significant increases in time spent in hyperglycemia (B = 0.12, p < .05).

CONCLUSIONS

These findings highlight the relationship between time spent sedentary and time spent in hyperglycemia, as identified through our use of objective, continuous data collection methods for both sedentary behavior and glucose levels across multiple days (Actiwatch, CGMS). For patients with type 2 diabetes, these findings emphasize the need for the development of individualized interventions aimed at decreasing the amount of time spent in hyperglycemia by reducing sedentary time.