Real-time Alerts Research Articles

Dual OS Smart Mirror Using Raspberry Pi (Allure Mirror)

This paper describes the designing and implementation of a dual operating system wall mirror, called “Allure Mirror”. It is a device that can act as a mirror as well as a dual -OS immersive display which can display time, date, weather, news, can play video (via. YouTube, Prime Video, Netflix, etc.), can do video calls, use social media, check mails, can help in fitness and many more. Voice instructions can be used to communicate with it. The Allure Mirror includes features such as real-time data and information alerts, voice orders, content viewing via LCD display, microphone, and webcam, and more. Voice instructions can be used to communicate with the Allure mirror.

Evaluation of a Mobile Application for Heart Failure Telemonitoring.

Adequate adherence to treatment is indispensable in preventing adverse consequences in heart failure patients. Such adherence can be managed through heart failure clinics and various methods of follow-up. In recent years, the use of telemonitoring has shown promising benefits in supporting clinicians' follow-up, as well as contributing to patients' self-care. This article presents the development and evaluation of a telemonitoring application for heart failure, through a Web-based interface for clinicians and a mobile application for patients. The application was evaluated through a 6-month pilot observational descriptive study in 20 outpatients with reduced ejection fraction and two nurses, in the context of a heart failure clinic. A technological acceptance questionnaire was applied to all patients and nurses at the end of the study period. In use, the application generated 64 real-time alerts for early decision-making to prevent complications, and 91% of patients did not present hospital readmissions. Such results, along with high user acceptance, show potential utility of the application as an effective complementary strategy for follow-up of patients with heart failure.

A Trend-Based Early Warning Score Can Be Implemented in a Hospital Electronic Medical Record to Effectively Predict Inpatient Deterioration.

To determine whether a statistically derived, trend-based, deterioration index is superior to other early warning scores at predicting adverse events and whether it can be integrated into an electronic medical record to enable real-time alerts. Forty-three variables and their trends from cases and controls were used to develop a logistic model and deterioration index to predict patient deterioration greater than or equal to 1 hour prior to an adverse event. Two large Australian teaching hospitals. Cases were considered as patients who suffered adverse events (unexpected death, unplanned ICU transfer, urgent surgery, and rapid-response alert) between August 1, 2016, and April 1, 2019. The logistic model and deterioration index were tested on historical data and then integrated into an electronic medical record for a 6-month prospective "silent" validation. Data were acquired from 258,732 admissions. There were 8,002 adverse events. The addition of vital sign and laboratory trend values to the logistic model increased the area under the curve from 0.84 to 0.89 and the sensitivity to predict an adverse event 1-48 hours prior from 0.35 to 0.41. A 48-hour simulation showed that the logistic model had a higher area under the curve than the Modified Early Warning Score and National Early Warning Score (0.87 vs 0.74 vs 0.71). During the silently run prospective trial, the sensitivity of the deterioration index to detect adverse event any time prior to the adverse event was 0.474, 0.369 1 hour prior, and 0.327 4 hours prior, with a specificity of 0.972. A deterioration prediction model was developed using patient demographics, ward-based observations, laboratory values, and their trends. The model's outputs were converted to a deterioration index that was successfully integrated into a live hospital electronic medical record. The sensitivity and specificity of the tool to detect inpatient deterioration were superior to traditional early warning scores.

DiaM- Integrated Mobile-Based Diabetes Management

Mobile technology is showing a wide acceptance in the health-care system. As technology is changing there are many health-care applications in the market. The need of these applications is increasing day by day because of unhealthy lifestyle. Among the various ailments, the most common lifestyle disease is diabetes. As we know diabetes can’t be cured, it can only be controlled by managing one’s lifestyle. In this paper, we propose a new application DiaM for managing and mentoring a diabetic patient. DiaM is Artificial Intelligence (AI) based mobile application which not only connect to manage one’s lifestyle but to build the strong will power. Our application has been designed in such a way that all the aspects are covered to manageone’s lifestyle. We have implemented an AI based system which smartly categorizes the patients in different states, which utilizes the different parameter like eating habit, stress, exercise, sleep time, smoking, genetic, BMI, and various other symptoms. On the basis of these states suggestions are been provided to the patient for improving the lifestyle. DiaM also enables patient to connect with the Doctor for monitoring and mentoring. Our application provides real-time alerts to doctor so that they can monitor patient anytime. It is extremely necessary to manage health conditions therefore our application provides a better,accurate and sustainable healthcare system.

Real-Time Personal Fever Alert Monitoring by Wearable Detector Based on Thermoresponsive Hydrogel

Quick fever screening at a mass scale is proven effective during a pandemic to single out the ones with suspected symptoms of infectious diseases. However, achieving affordable and real-time fever alert at an individual level, although more preferable, remains elusive. Herein, we report an inexpensive and highly sensitive fever detector, which possesses a sharp color transition temperature window tailor-tuned for fever screening. The sensing component of the detector is rationally designed thermoresponsive agarose@poly(N-isopropylacrylamide)-co-acrylamide hydrogel. The hydrogel turns from transparent to opaque white when its temperature is higher than its cloud point. As a proof of concept of its practical applicability, a wearable fever monitoring device was fabricated in the form of a wristband. When the wrist temperature is higher than the threshold of a human fever, the device shows a remarkable color change, alerting an elevated body temperature. The wearable detector provides a promising strategy for real-time fever alert monitoring and is capable of making contributions to inhibit the spread of infectious diseases during a pandemic.

The effects of clinical decision support system for prescribing medication on patient outcomes and physician practice performance: a systematic review and meta-analysis

BackgroundClinical Decision Support Systems (CDSSs) for Prescribing are one of the innovations designed to improve physician practice performance and patient outcomes by reducing prescription errors. This study was therefore conducted to examine the effects of various CDSSs on physician practice performance and patient outcomes.MethodsThis systematic review was carried out by searching PubMed, Embase, Web of Science, Scopus, and Cochrane Library from 2005 to 2019. The studies were independently reviewed by two researchers. Any discrepancies in the eligibility of the studies between the two researchers were then resolved by consulting the third researcher. In the next step, we performed a meta-analysis based on medication subgroups, CDSS-type subgroups, and outcome categories. Also, we provided the narrative style of the findings. In the meantime, we used a random-effects model to estimate the effects of CDSS on patient outcomes and physician practice performance with a 95% confidence interval. Q statistics and I2 were then used to calculate heterogeneity.ResultsOn the basis of the inclusion criteria, 45 studies were qualified for analysis in this study. CDSS for prescription drugs/COPE has been used for various diseases such as cardiovascular diseases, hypertension, diabetes, gastrointestinal and respiratory diseases, AIDS, appendicitis, kidney disease, malaria, high blood potassium, and mental diseases. In the meantime, other cases such as concurrent prescribing of multiple medications for patients and their effects on the above-mentioned results have been analyzed. The study shows that in some cases the use of CDSS has beneficial effects on patient outcomes and physician practice performance (std diff in means = 0.084, 95% CI 0.067 to 0.102). It was also statistically significant for outcome categories such as those demonstrating better results for physician practice performance and patient outcomes or both. However, there was no significant difference between some other cases and traditional approaches. We assume that this may be due to the disease type, the quantity, and the type of CDSS criteria that affected the comparison. Overall, the results of this study show positive effects on performance for all forms of CDSSs.ConclusionsOur results indicate that the positive effects of the CDSS can be due to factors such as user-friendliness, compliance with clinical guidelines, patient and physician cooperation, integration of electronic health records, CDSS, and pharmaceutical systems, consideration of the views of physicians in assessing the importance of CDSS alerts, and the real-time alerts in the prescription.

COVID-19 and Your Smartphone: BLE-Based Smart Contact Tracing.

While contact tracing is of paramount importance in preventing the spreading of infectious diseases, manual contact tracing is inefficient and time consuming as those in close contact with infected individuals are informed hours, if not days, later. This article proposes a smart contact tracing (SCT) system utilizing the smartphone’s Bluetooth low energy signals and machine learning classifiers to automatically detect those possible contacts to infectious individuals. SCT’s contribution is two-fold: a) classification of the user’s contact as high/low-risk using precise proximity sensing, and b) user anonymity using a privacy-preserving communication protocol. To protect the user’s privacy, both broadcasted and observed signatures are stored in the user’s smartphone locally and only disseminate the stored signatures through a secure database when a user is confirmed by public health authorities to be infected. Using received signal strength each smartphone estimates its distance from other user’s phones and issues real-time alerts when social distancing rules are violated. Extensive experimentation utilizing real-life smartphone positions and a comparative evaluation of five machine learning classifiers indicate that a decision tree classifier outperforms other state-of-the-art classification methods with an accuracy of about 90% when two users carry their smartphone in a similar manner. Finally, to facilitate research in this area while contributing to the timely development, the dataset of six experiments with about 123 000 data points is made publicly available.

Sensors on the Move: Onboard Camera-Based Real-Time Traffic Alerts Paving the Way for Cooperative Roads.

European road safety has improved greatly in recent decades. However, the current numbers are still far away to reach the European Commission’s road safety targets. In this context, Cooperative Intelligent Transport Systems (C-ITS) are expected to significantly improve road safety, traffic efficiency and comfort of driving, by helping the driver to make better decisions and adapt to the traffic situation. This paper puts forward two vision-based applications for traffic sign recognition (TSR) and real-time weather alerts, such as for fog-banks. These modules will support operators in road infrastructure maintenance tasks as well as drivers, giving them valuable information via C-ITS messages. Different state-of-the-art methods are analysed using both publicly available datasets (GTSB) as well as our own image databases (Ceit-TSR and Ceit-Foggy). The selected models for TSR implementation are based on Aggregated Chanel Features (ACF) and Convolutional Neural Networks (CNN) that reach more than 90% accuracy in real time. Regarding fog detection, an image feature extraction method on different colour spaces is proposed to differentiate sunny, cloudy and foggy scenes, as well as its visibility level. Both applications are already running in an onboard probe vehicle system.

Design of Detection System with Motion Sensor and Notification on Android Based on Arduino Microcontroller

The rapid development of technology nowadays makes people want to always be creative and triggers to create something that is needed to create a security system, because the difficulty of today's economy makes many people act criminally by robbery or theft. An alternative security system that is often used today is to use CCTV (Closed Circuit Television), but the limitations in conducting CCTV surveillance include that it still has to be monitored directly at the monitoring location. From the problems that occur, the authors developed a thesis that is capable of monitoring and monitoring by sending realtime alerts in the form of notifications to the user on an Android Smartphone device by building an IoT (Internet of Thing) based detection system using a PIR (Passive Infrared) motion sensor which entitled "Design of Detection System with Motion Sensor and Notification on Android Based on Arduino Microcontroller".

Collaborative learning in mathematics classrooms: Can teachers understand progress of concurrent collaborating groups?

Dashboards have been hypothesized to facilitate adaptive guidance in classrooms. Thus far, however, empirical studies in this domain are badly missing. We present a dashboard, which enables teachers to observe concurrent groups as they engage in mathematical tasks using Dynamic Geometry tools and to guide groups when their intervention is needed. We rely on a case study with this dashboard in which a single teacher could successfully orchestrate the progression of several groups working on difficult problems in geometry, when being sent realtime alerts about critical moments, moments whose occurrence may impinge on further co-construction of knowledge. In the present study, we chose six 20-min long demo videos of small groups in a variety of sessions of unguided group work, where each video describes the work of one of six small groups (dyads or triads) dealing simultaneously with identical set tasks. Two versions of this demo were prepared, one with alerts of critical moments generated according to predefined rules, and the other without alerts. The teachers observing concurrent groups and being updated about critical moments (N1 = 21) better understand progression in groups than teachers that are not informed about these critical moments (N2 = 18). In a second study we found that the dispatching of alerts impinges on the ways the teachers navigate across rooms. In particular, they pay attention to certain alerts and at the same time, focus on learning processes in mathematics. We conclude that such tools may help bringing collaborative settings more often in classrooms.

Toward an Efficient Uncertainty Quantification of Streamflow Predictions Using Sparse Polynomial Chaos Expansion

Reliable hydrologic models are essential for planning, designing, and management of water resources. However, predictions by hydrological models are prone to errors due to a variety of sources of uncertainty. More accurate quantification of these uncertainties using a large number of ensembles and model runs is hampered by the high computational burden. In this study, we developed a highly efficient surrogate model constructed by sparse polynomial chaos expansion (SPCE) coupled with the least angle regression method, which enables efficient uncertainty quantifications. Polynomial chaos expansion was employed to surrogate a storage function-based hydrological model (SFM) for nine streamflow events in the Hongcheon watershed of South Korea. The efficiency of SPCE is investigated by comparing it with another surrogate model, full polynomial chaos expansion (FPCE) built by a well-known, ordinary least square regression (OLS) method. This study confirms that (1) the performance of SPCE is superior to that of FPCE because SPCE can build a more accurate surrogate model (i.e., smaller leave-one-out cross-validation error) with one-quarter the size (i.e., 500 versus 2000). (2) SPCE can sufficiently capture the uncertainty of the streamflow, which is comparable to that of SFM. (3) Sensitivity analysis attained through visual inspection and mathematical computation of the Sobol’ index has been of great success for SPCE to capture the parameter sensitivity of SFM, identifying four parameters, α, Kbas, Pbas, and Pchn, that are most sensitive to the likelihood function, Nash-Sutcliffe efficiency. (4) The computational power of SPCE is about 200 times faster than that of SFM and about four times faster than that of FPCE. The SPCE approach builds a surrogate model quickly and robustly with a more compact experimental design compared to FPCE. Ultimately, it will benefit ensemble streamflow forecasting studies, which must provide information and alerts in real time.

Integrated Air Quality Monitoring and Alert System Based on Two Image Analysis Techniques for Reportable Fire Events

In this paper, a new monitoring alert system for air pollution emergencies is proposed. The proposed system can perform air quality monitoring to provide real-time alerts of an individual event. The system uses two image analysis techniques, namely pixel recognition and haze extraction, for video fire smoke detection. The image analysis process is divided into daytime and nighttime image analyses, which involve the analysis of red-green-blue (RGB) and gray scale images. The images analyzed in this study were captured by the video camera of an air quality monitoring station. Seven fire accidents around a selected industrial park and downtown area were analyzed in detail. Among these accidents, three occurred at daytime, one occurred over 7 days, and three occurred at nighttime. Alert models based on pixel recognition and haze extraction were established. These models incorporated the formulas of haze equivalent (HT(t)) and separated pixels (XT(t)), as well as the threshold equations of haze equivalent (∇H) and separated pixels (∇X). An alert signal is sent to the administrator when HT(t) > ∇H or XT(t) > ∇X. The obtained results indicate that a real-time observation and alert system based on two image analysis techniques can be designed for air quality monitoring without expensive hardware devices. This alert system can be used by administrators to understand the course of a reportable event, especially as evidence for the appraisal of fire accidents. It is recommended that this system be connected to the fire brigades in order to obtain early fire information.

An intelligent healthcare system for predicting and preventing dengue virus infection

Dengue is a mosquito-borne pandemic viral infection, which transmits to humans from Female Aedes albopictis or Aedes agypti mosquitoes. It progressively deteriorates the health of infected individuals and poses a high threat of human morbidity and mortality. This paper proposes an intelligent healthcare system which identifies, monitors, and alerts dengue virus (DeV) infected individuals and other stakeholders in real-time and control the DeV infection outbreak using cloud computing, internet of things and fog computing paradigms. The proposed system uses Naive Bayesian Network (NBN) for diagnosing the possibly DeV infected individuals and generating real-time alerts for suggesting and alerting the concerned stakeholders for taking on-time necessary actions at the fog subsystem. The proposed system also uses Social Network Analysis at the cloud subsystem, to provide Global Positioning Systems (GPS)-based global risk assessment of the DeV infection on Google Maps (Google-based web map service) and control DeV infection outbreak. The analysis of the experimental results acknowledges the efficiency of the NBN-based DeV infection diagnosis, alert generation, and GPS-based risk assessment functionality, of the proposed system, via various statistical measures and experimental approaches.

Natural Language Processing-Based Virtual Cofacilitator for Online Cancer Support Groups: Protocol for an Algorithm Development and Validation Study.

BackgroundCancer and its treatment can significantly impact the short- and long-term psychological well-being of patients and families. Emotional distress and depressive symptomatology are often associated with poor treatment adherence, reduced quality of life, and higher mortality. Cancer support groups, especially those led by health care professionals, provide a safe place for participants to discuss fear, normalize stress reactions, share solidarity, and learn about effective strategies to build resilience and enhance coping. However, in-person support groups may not always be accessible to individuals; geographic distance is one of the barriers for access, and compromised physical condition (eg, fatigue, pain) is another. Emerging evidence supports the effectiveness of online support groups in reducing access barriers. Text-based and professional-led online support groups have been offered by Cancer Chat Canada. Participants join the group discussion using text in real time. However, therapist leaders report some challenges leading text-based online support groups in the absence of visual cues, particularly in tracking participant distress. With multiple participants typing at the same time, the nuances of the text messages or red flags for distress can sometimes be missed. Recent advances in artificial intelligence such as deep learning–based natural language processing offer potential solutions. This technology can be used to analyze online support group text data to track participants’ expressed emotional distress, including fear, sadness, and hopelessness. Artificial intelligence allows session activities to be monitored in real time and alerts the therapist to participant disengagement.ObjectiveWe aim to develop and evaluate an artificial intelligence–based cofacilitator prototype to track and monitor online support group participants’ distress through real-time analysis of text-based messages posted during synchronous sessions.MethodsAn artificial intelligence–based cofacilitator will be developed to identify participants who are at-risk for increased emotional distress and track participant engagement and in-session group cohesion levels, providing real-time alerts for therapist to follow-up; generate postsession participant profiles that contain discussion content keywords and emotion profiles for each session; and automatically suggest tailored resources to participants according to their needs. The study is designed to be conducted in 4 phases consisting of (1) development based on a subset of data and an existing natural language processing framework, (2) performance evaluation using human scoring, (3) beta testing, and (4) user experience evaluation.ResultsThis study received ethics approval in August 2019. Phase 1, development of an artificial intelligence–based cofacilitator, was completed in January 2020. As of December 2020, phase 2 is underway. The study is expected to be completed by September 2021.ConclusionsAn artificial intelligence–based cofacilitator offers a promising new mode of delivery of person-centered online support groups tailored to individual needs.International Registered Report Identifier (IRRID)DERR1-10.2196/21453

IoHT-MBA: An Internet of Healthcare Things (IoHT) Platform based on Microservice and Brokerless Architecture

Internet of Thing (IoT), currently, is one of the technology trends that are most interested. IoT can be divided into five main areas including: Health-care, Environmental, Smart city, Commercial and Industrial. The IoHT-MBA Platform is considered the backbone of every IoT architecture, so the optimal design of the IoHT-MBA Platform is essential issue, which should be carefully considered in the different aspects. Although, IoT is applied in multiple domains, however, there are still three main features that are challenge to improve: i) data collection, ii) users, devices management, and iii) remote device control. Today’s medical IoT systems, often too focused on the big data or access control aspects of participants, but not focused on collecting data accurately, quickly, and efficiently; power redundancy and system expansion. This is very important for the medical sector - which always prioritizes the availability of data for therapeutic purposes over other aspects. In this paper, we introduce the IoHT Platform for Healthcare environment which is designed by microservice and brokerless architecture, focusing strongly on the three aforementioned characteristics. In addition, our IoHT Platform considers the five other issues including (1) the limited processing capacity of the devices, (2) energy saving for the device, (3) speed and accurate of the data collection, (4) security mechanisms and (5) scalability of the system. Also, in order for the IoHT Platform to be suitable for the field of health monitoring, we also add realtime alerts for the medical team. In the evaluation section, moreover, we describe the evaluation to prove the effectiveness of the proposed IoHT Platform (i.e. the proof-of-concept) in the performance, non-error, and non affected by geographical distance. Finally, a complete code solution is publicized on the authors’ GitHub repository to engage further reproducibility and improvement.

Media Systems and the Coverage of COVID-19 Pandemic: Assessment of Emerging Issues

The COVID-19 pandemic received enormous media coverage from the very beginning when it was first noticed in Wahan, China in December 2019. The media system which was usually defined by existing state political system radically changed during the COVID-19 period due to the rise of digital media. A new media system emerged with robust communicative power. The paper used secondary data (document analysis) to descriptively analyzed issues that arose from media systems coverage of the COVID-19 pandemic. These issues included emergence of a new media/medicine relations, the change in pattern of health news consumption and persistent public fear and panic. The paper recommended for a regulatory framework for telehealth service delivery, governments engagement of the public using social media platform and development of real-time alerts system for public health officials to respond quickly to misinformation during pandemics.

313. Using a Clinical Decision Prediction Tool to Improve Empirical Antimicrobial Therapy in Ceftriaxone-Resistant Enterobacterales Bloodstream Infections

BackgroundThere are several clinical tools for prediction of antimicrobial resistance. However, their utility in management of bloodstream infections (BSI) due to resistant bacteria remains unclear. This quasi-experimental cohort examined the impact of utilization of the extended-spectrum beta-lactamase (ESBL) prediction score on time to initiation of appropriate antimicrobial therapy (AAT) in BSI due to ceftriaxone-resistant (CRO-R) Enterobacterales.MethodsAdults with first episodes of monomicrobial BSI due to CRO-R Enterobacterales at Prisma Health-Midlands Hospitals in South Carolina from January 2010 to December 2017 were included. Antimicrobial stewardship intervention was implemented in January 2014 consisting of real-time alerts for positive blood cultures and rapid diagnostics for identification of bloodstream isolates. The ESBL prediction score was used to stratify risk of resistance prior to conventional antimicrobial susceptibility results. Student’s t-test was used to compare mean time to AAT before and after intervention.ResultsAmong 92 patients with BSI due to CRO-R Enterobacterales, median age was 66 years, 52 (57%) were men, 68 (74%) had community-onset BSI, and 52 (57%) had urinary source of infection. Escherichia coli 52 (57%) and Klebsiella species 27 (29%) were the most common bloodstream isolates and the majority 63 (68%) produced ESBLs. There were no significant differences in demographics or clinical characteristics of patients before (n=45) and after (n=47) antimicrobial stewardship intervention. Mean time to AAT was 3.2 days prior and 1.7 days after utilization of ESBL prediction score (p=0.021). Results were consistent among ESBL-producing Enterobacterales (time to AAT 2.8 before and 1.8 days after intervention, p=0.025).ConclusionUtilization of the ESBL prediction score as part of a real-time antimicrobial stewardship intervention significantly reduced time to AAT in BSI due to CRO-R and ESBL-producing Enterobacterales. The ESBL prediction score represents a viable tool to improve antimicrobial management in clinical settings that lack access to multiplex PCRs for detection of antimicrobial resistance genes and novel methods for rapid phenotypic antimicrobial susceptibility testing.DisclosuresJulie Ann Justo, PharmD, MS, BCPS-AQ ID, bioMerieux (Speaker’s Bureau)TRC Healthcare (Speaker’s Bureau)

Primates in SISS-Geo: potential contributions of mobile technology, health surveillance and citizen science to support species conservation in brazil

The Information System on Wildlife Health (SISS-Geo) is based on citizen science and the use of mobile technologies, producing data and georeferenced alerts in real time for society, public health professionals, and conservation managers. From 2014 to 2020, the system recorded 4,909 non-human primates (NHP) in 23 states of Brazil. Joint action by the General Coordination for Surveillance of Arboviruses in the Ministry of Health (CGARB/DEIDT/SVS/MS), the SISS-Geo Platform, and state health departments in southern Brazil led to an increase of 1,141 % in the number of animals recorded since July 2016 with the spread of sylvatic yellow fever (YF). These sources reported the occurrence of 30 species, 14 of which are threatened with extinction, and 2,358 dead NHP, 75 % of which were Alouatta guariba clamitans. Despite the observed mortality, the limitations in records in areas distant from human presence and without collection of biological samples to confirm the cause of death prevent measurement of the real impact of YF on primates, which are vulnerable to a wide range of socio-environmental and climatic factors. The use of SISS-Geo by health professionals allowed more agile surveillance of epizootics and favored the application of various forecasting models for priority areas for NHP surveillance and human vaccination. Additionally, it provides information for various actions listed in the national plans for primate conservation, even though effective interventions against emerging infectious diseases are still not available for protecting NHP.

Recommendations for the use of long-term experience sampling in bipolar disorder care: a qualitative study of patient and clinician experiences

BackgroundSelf-monitoring has been shown to improve the self-management and treatment of patients with bipolar disorder. However, current self-monitoring methods are limited to once-daily retrospectively assessed mood, which may not suit the rapid mood fluctuations in bipolar disorder. The experience sampling method (ESM), which assesses mood in real-time several times a day, may overcome these limitations. This study set out to assess the experiences of patients and clinicians with the addition of ESM monitoring, real-time alerts, and personalized feedback to clinical care. Participants were twenty patients with bipolar disorder type I/II and their clinicians. For four months, patients completed five ESM assessments per day on mood, symptoms, and activities. Weekly symptom questionnaires alerted patients and clinicians to potential episodes. After the monitoring, a personalized feedback report based on the patient’s data was discussed between patient and clinician. Three months later, patient and clinician were both interviewed.ResultsThematic analysis of the transcripts resulted in four themes: perceived effects of the monitoring, alerts, and feedback, and recommendations for implementation of ESM. ESM was perceived as helping patients to cope better with their disorder by increasing awareness, offering new insights, and encouraging life style adjustments. ESM was further believed to facilitate communication between patient and clinician and to lead to new treatment directions. However, high assessment burden and pre-occupation with negative mood and having a disorder were also described. Patients and clinicians advocated for increased personalization and embedding of ESM in care.ConclusionsThis study demonstrates that long-term ESM monitoring, alerts, and personalized feedback are perceived as beneficial to the treatment and self-management of patients with bipolar disorder. Future research should further test the clinical utility of ESM. Clinically relevant feedback and technology need to be developed to enable personalized integration of ESM in clinical care.

Percentile charts for speeding based on telematics information

Reference charts are widely used as a graphical tool for assessing and monitoring children’s growth given gender and age. Here, we propose a similar approach to the assessment of driving risk. Based on telematics data, and using quantile regression models, our methodology estimates the percentiles of the distance driven at speeds above the legal limit depending on drivers’ characteristics and the journeys made. We refer to the resulting graphs as percentile charts for speeding and illustrate their use for a sample of drivers with Pay-How-You-Drive insurance policies. We find that percentiles of distance driven at excessive speeds depend mainly on total distance driven, the percentage of driving in urban areas and the driver’s gender. However, the impact on the estimated percentile for these covariates is not constant. We conclude that the heterogeneity in the risk of driving long distances above the speed limit can be easily represented using reference charts and that, conversely, individual drivers can be scored by calculating an estimated percentile for their specific case. The dynamics of this risk score can be assessed by recording drivers as they accumulate driving experience and cover more kilometres. Our methodology should be useful for accident prevention and, in the context of Manage-How-You-Drive insurance, reference charts can provide real-time alerts and enhance recommendations for ensuring safety.