OpiTrack

2021 ◽  
Vol 5 (3) ◽  
pp. 1-29
Author(s):  
Bhanu Teja Gullapalli ◽  
Stephanie Carreiro ◽  
Brittany P. Chapman ◽  
Deepak Ganesan ◽  
Jan Sjoquist ◽  
...  
Keyword(s):  
Time Window ◽  
Electrodermal Activity ◽  
Clinical Care ◽  
Opioid Use Disorder ◽  
Physiological Signals ◽  
Physiological Data ◽  
Opioid Use ◽  
Painful Condition ◽  
Targeted Interventions ◽  
Opioid Administration

Opioid use disorder is a medical condition with major social and economic consequences. While ubiquitous physiological sensing technologies have been widely adopted and extensively used to monitor day-to-day activities and deliver targeted interventions to improve human health, the use of these technologies to detect drug use in natural environments has been largely underexplored. The long-term goal of our work is to develop a mobile technology system that can identify high-risk opioid-related events (i.e., development of tolerance in the setting of prescription opioid use, return-to-use events in the setting of opioid use disorder) and deploy just-in-time interventions to mitigate the risk of overdose morbidity and mortality. In the current paper, we take an initial step by asking a crucial question: Can opioid use be detected using physiological signals obtained from a wrist-mounted sensor? Thirty-six individuals who were admitted to the hospital for an acute painful condition and received opioid analgesics as part of their clinical care were enrolled. Subjects wore a noninvasive wrist sensor during this time (1-14 days) that continuously measured physiological signals (heart rate, skin temperature, accelerometry, electrodermal activity, and interbeat interval). We collected a total of 2070 hours (≈ 86 days) of physiological data and observed a total of 339 opioid administrations. Our results are encouraging and show that using a Channel-Temporal Attention TCN (CTA-TCN) model, we can detect an opioid administration in a time-window with an F1-score of 0.80, a specificity of 0.77, sensitivity of 0.80, and an AUC of 0.77. We also predict the exact moment of administration in this time-window with a normalized mean absolute error of 8.6% and R2 coefficient of 0.85.

User Experience Evaluation in Virtual Reality based on Subjective Feelings and Physiological Signals

2019 ◽  
Vol 63 (6) ◽  
pp. 60413-1-60413-11
Author(s):  
Yunfang Niu ◽  
Danli Wang ◽  
Ziwei Wang ◽  
Fan Sun ◽  
Kang Yue ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Positive Emotions ◽  
Electrodermal Activity ◽  
Self Report ◽  
Physiological Signals ◽  
Physiological Data ◽  
Self Assessment ◽  
Emotion Experience ◽  
Eye Fatigue ◽  
Electrocardiogram Ecg

Abstract At present, the research on emotion in the virtual environment is limited to the subjective materials, and there are very few studies based on objective physiological signals. In this article, the authors conducted a user experiment to study the user emotion experience of virtual reality (VR) by comparing subjective feelings and physiological data in VR and two-dimensional display (2D) environments. First, they analyzed the data of self-report questionnaires, including Self-assessment Manikin (SAM), Positive And Negative Affect Schedule (PANAS) and Simulator Sickness Questionnaire (SSQ). The result indicated that VR causes a higher level of arousal than 2D, and easily evokes positive emotions. Both 2D and VR environments are prone to eye fatigue, but VR is more likely to cause symptoms of dizziness and vertigo. Second, they compared the differences of electrocardiogram (ECG), skin temperature (SKT) and electrodermal activity (EDA) signals in two circumstances. Through mathematical analysis, all three signals had significant differences. Participants in the VR environment had a higher degree of excitement, and the mood fluctuations are more frequent and more intense. In addition, the authors used different machine learning models for emotion detection, and compared the accuracies on VR and 2D datasets. The accuracies of all algorithms in the VR environment are higher than that of 2D, which corroborated that the volunteers in the VR environment have more obvious skin electrical signals, and had a stronger sense of immersion. This article effectively compensated for the inadequacies of existing work. The authors first used objective physiological signals for experience evaluation and used different types of subjective materials to make contrast. They hope their study can provide helpful guidance for the engineering reality of virtual reality.

scholarly journals Classification of Drivers' Workload Using Physiological Signals in Conditional Automation

2021 ◽  
Vol 12 ◽  
Author(s):  
Quentin Meteier ◽  
Marine Capallera ◽  
Simon Ruffieux ◽  
Leonardo Angelini ◽  
Omar Abou Khaled ◽  
...  
Keyword(s):  
Secondary Task ◽  
Time Window ◽  
Mental Workload ◽  
Cognitive Task ◽  
Physiological Signals ◽  
Machine Learning Techniques ◽  
Physiological Data ◽  
Automated Driving ◽  
Physiological Indicators ◽  
High Level

The use of automation in cars is increasing. In future vehicles, drivers will no longer be in charge of the main driving task and may be allowed to perform a secondary task. However, they might be requested to regain control of the car if a hazardous situation occurs (i.e., conditionally automated driving). Performing a secondary task might increase drivers' mental workload and consequently decrease the takeover performance if the workload level exceeds a certain threshold. Knowledge about the driver's mental state might hence be useful for increasing safety in conditionally automated vehicles. Measuring drivers' workload continuously is essential to support the driver and hence limit the number of accidents in takeover situations. This goal can be achieved using machine learning techniques to evaluate and classify the drivers' workload in real-time. To evaluate the usefulness of physiological data as an indicator for workload in conditionally automated driving, three physiological signals from 90 subjects were collected during 25 min of automated driving in a fixed-base simulator. Half of the participants performed a verbal cognitive task to induce mental workload while the other half only had to monitor the environment of the car. Three classifiers, sensor fusion and levels of data segmentation were compared. Results show that the best model was able to successfully classify the condition of the driver with an accuracy of 95%. In some cases, the model benefited from sensors' fusion. Increasing the segmentation level (e.g., size of the time window to compute physiological indicators) increased the performance of the model for windows smaller than 4 min, but decreased for windows larger than 4 min. In conclusion, the study showed that a high level of drivers' mental workload can be accurately detected while driving in conditional automation based on 4-min recordings of respiration and skin conductance.

scholarly journals Predictors of Premature Discontinuation of Opioid Use Disorder Treatment in the United States

2021 ◽  
Author(s):  
Celia Stafford ◽  
Wesley Marrero ◽  
Rebecca B. Naumann ◽  
Kristen Hassmiller Lich ◽  
Sarah Wakeman ◽  
...  
Keyword(s):  
Risk Factors ◽  
Substance Use ◽  
The United States ◽  
Opioid Use Disorder ◽  
Geographic Region ◽  
Influential Factors ◽  
Opioid Use ◽  
Living Situation ◽  
Data Set ◽  
Targeted Interventions

Over the last few decades, opioid use disorder (OUD) and overdose have dramatically increased. Evidence shows that treatment for OUD, particularly medication for OUD, is highly effective; however, despite decreases in barriers to treatment, retention in OUD treatment remains a challenge. Therefore, understanding key risk factors for OUD treatment discontinuation remains a critical priority. We built a machine learning model using the Treatment Episode Data Set-Discharge (TEDS-D). Included were 2,446,710 treatment episodes for individuals in the U.S. discharged between January 1, 2015 and December 31, 2018 (the most recent available data). Exposures contain 32 potential risk factors, including treatment characteristics, substance use history, socioeconomic status, and demographic characteristics. Our findings show that the most influential risk factors include characteristics of treatment service setting, geographic region, primary source of payment, referral source, and health insurance status. Importantly, several factors previously reported as influential predictors, such as age, living situation, age of first substance use, race and ethnicity, and sex had far weaker predictive impacts. The influential factors identified in this study should be more closely explored to inform targeted interventions and improve future models of care.

scholarly journals Perioperative Pain and Addiction Interdisciplinary Network (PAIN): protocol of a practice advisory for the perioperative management of buprenorphine using a modified Delphi process

BMJ Open ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. e027374 ◽  
Author(s):  
Akash Goel ◽  
Saam Azargive ◽  
Joel S Weissman ◽  
Harsha Shanthanna ◽  
Karim S Ladha ◽  
...  
Keyword(s):  
Systematic Review ◽  
Perioperative Management ◽  
Management Strategies ◽  
Treatment Strategies ◽  
Clinical Excellence ◽  
Opioid Use Disorder ◽  
Opioid Use ◽  
Perioperative Pain ◽  
Modified Delphi ◽  
Opioid Administration

IntroductionThe ongoing opioid epidemic has necessitated increasing prescriptions of buprenorphine, which is an evidence-based treatment for opioid use disorder, and also shown to reduce harms associated with unsafe opioid administration. A systematic review of perioperative management strategies for patients taking buprenorphine concluded that there was little guidance for managing buprenorphine perioperatively. The aim of this project is to develop consensus guidelines on the optimal perioperative management strategies for this group of patients. In this paper, we present the design for a modified Delphi technique that will be used to gain consensus among patients and multidisciplinary experts in addiction, pain, community and perioperative medicine.Methods and analysisA national panel of experts identified by perioperative, pain and/or addiction systematic review authorship established an international profile in perioperative, pain and/or addiction research, community clinical excellence and by peer referral. A steering group will develop the first round with a list of indications to be rated by the panel of national experts, patients and allied healthcare professionals. In round 1, the expert panel will rate the appropriateness of each individual item and provide additional suggestions for revisions, additions or deletions. The definition of consensus will be seta priori. Consensus will be gauged for both appropriateness and inappropriateness of treatment strategies. Where an agreement is not reached and items are suggested for addition/deletion/modification, round 2 will take place over teleconference in order to obtain consensus.Ethics and disseminationInstitutional research ethics board provided a waiver for this modified Delphi protocol. We plan on developing a national guideline for the management of patients taking buprenorphine in the perioperative period that will be generalisable across three sets of preoperative diagnoses including opioid use disorder and/or co-occurring pain disorders. The findings will be published in peer-reviewed publications and conference presentations.

scholarly journals Cost-effectiveness of integrating buprenorphine-naloxone treatment for opioid use disorder into clinical care for persons with HIV/hepatitis C co-infection who inject opioids

2019 ◽  
Vol 72 ◽  
pp. 160-168 ◽  
Author(s):  
Joshua A. Barocas ◽  
Jake R. Morgan ◽  
David A. Fiellin ◽  
Bruce R. Schackman ◽  
Golnaz Eftekhari Yazdi ◽  
...  
Keyword(s):  
Cost Effectiveness ◽  
Hepatitis C ◽  
Clinical Care ◽  
Opioid Use Disorder ◽  
Opioid Use ◽  
Naloxone Treatment

Pharmacogenomics in Pain Management: A Review of Relevant Gene-Drug Associations and Clinical Considerations

2021 ◽  
pp. 106002802110038
Author(s):  
Emily Brandl ◽  
Zachery Halford ◽  
Matthew D. Clark ◽  
Chris Herndon
Keyword(s):  
Clinical Practice ◽  
Pain Management ◽  
Health Care Professionals ◽  
Data Extraction ◽  
Clinical Care ◽  
Genomic Medicine ◽  
Opioid Use Disorder ◽  
Patient Specific ◽  
Therapeutic Effects ◽  
Opioid Use

Objective: To provide an overview of clinical recommendations regarding genomic medicine relating to pain management and opioid use disorder. Data Sources: A literature review was conducted using the search terms pain management, pharmacogenomics, pharmacogenetics, pharmacokinetics, pharmacodynamics, and opioids on PubMed (inception to February 1, 2021), CINAHL (2016 through February 1, 2021), and EMBASE (inception through February 1, 2021). Study Selection and Data Extraction: All relevant clinical trials, review articles, package inserts, and guidelines evaluating applicable pharmacogenotypes were considered for inclusion. Data Synthesis: More than 300 Food and Drug Administration–approved medications contain pharmacogenomic information in their labeling. Genetic variability may alter the therapeutic effects of commonly prescribed pain medications. Pharmacogenomic-guided therapy continues to gain traction in clinical practice, but a multitude of barriers to widespread pharmacogenomic implementation exist. Relevance to Patient Care and Clinical Practice: Pain is notoriously difficult to treat given the need to balance safety and efficacy when selecting pharmacotherapy. Pharmacogenomic data can help optimize outcomes for patients with pain. With improved technological advances, more affordable testing, and a better understanding of genomic variants resulting in treatment disparities, pharmacogenomics continues to gain popularity. Unfortunately, despite these and other advancements, pharmacogenomic testing and implementation remain underutilized and misunderstood in clinical care, in part because of a lack of health care professionals trained in assessing and implementing test results. Conclusions: A one-size-fits-all approach to pain management is inadequate and outdated. With increasing genomic data and pharmacogenomic understanding, patient-specific genomic testing offers a comprehensive and personalized treatment alternative worthy of additional research and consideration.

scholarly journals “If You Could Wave a Magic Wand”: Treatment Barriers in the Rural Midwest

2021 ◽  
Vol 15 ◽  
pp. 117822182110533
Author(s):  
Angela Clark ◽  
Jennifer Lanzillotta-Rangeley ◽  
Jack Stem
Keyword(s):  
Disease Process ◽  
Opioid Use Disorder ◽  
Medically Underserved ◽  
Treatment Barriers ◽  
Evidence Based ◽  
Opioid Use ◽  
Treatment Needs ◽  
Evidence Based Treatment ◽  
Targeted Interventions ◽  
Community Health Survey

Introduction: The multigenerational health considerations and negative economic impacts related to the opioid epidemic are many. Increasing numbers of opioid-related fatalities are bolstered by barriers related to access to evidence-based treatment. Ohio is ranked second in the country for number of opioid-related deaths, and for many their treatment needs remain unmet due to impaired access to effective treatment, in rural, medically underserved areas of the state. Purpose: The goal of this study was to assess opioid use disorder treatment barriers in order to increase access to evidence-based treatment, wrap around services, and harm reduction efforts to support the reintegration of persons with substance use disorder back into society and subsequently reduce opioid fatalities in a rural, medically underserved region of Ohio. Methods: As part of a larger mixed-methods study design where a community health survey was randomly distributed to residents in a rural county in Ohio, this study used qualitative methods to triangulate findings. To supplement the data received from the surveys, 20persons with a diagnosed opioid use disorder (OUD) took part in focus group sessions guided by trained researchers. The sessions were transcribed, and the data was analyzed using Braun and Clarke’s thematic analysis method. Results: Three major themes emerged from the data: epigenetics and exposure, management of disease including re-integration into society, and disease process. The participant data created insight regarding the need to recognize OUD as a chronic condition that must be addressed with integrated components of medical, behavioral, and mental health morbidities throughout the lifespan and across generations. Conclusions: Findings from this study support the need for targeted interventions for integrated care and improved wrap around services such as transportation, sober living, and employment.

scholarly journals Similar COVID-19 incidence to the general population in people with opioid use disorder receiving integrated outpatient clinical care

2022 ◽  
pp. 100027
Author(s):  
Gabriel Vallecillo ◽  
Francina Fonseca ◽  
Lina Oviedo ◽  
Xavier Durán ◽  
Ignacio Martinez ◽  
...  
Keyword(s):  
General Population ◽  
Clinical Care ◽  
Opioid Use Disorder ◽  
Opioid Use

scholarly journals A Usability Study of Physiological Measurement in School Using Wearable Sensors

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5380 ◽  
Author(s):  
Nattapong Thammasan ◽  
Ivo V. Stuldreher ◽  
Elisabeth Schreuders ◽  
Matteo Giletta ◽  
Anne-Marie Brouwer
Keyword(s):  
Emotional Disorders ◽  
Electrodermal Activity ◽  
Wearable Sensors ◽  
School Setting ◽  
Physiological Signals ◽  
Quantization Noise ◽  
Physiological Data ◽  
Artifact Detection ◽  
Physiological Measurement ◽  
Analog To Digital Conversion

Measuring psychophysiological signals of adolescents using unobtrusive wearable sensors may contribute to understanding the development of emotional disorders. This study investigated the feasibility of measuring high quality physiological data and examined the validity of signal processing in a school setting. Among 86 adolescents, a total of more than 410 h of electrodermal activity (EDA) data were recorded using a wrist-worn sensor with gelled electrodes and over 370 h of heart rate data were recorded using a chest-strap sensor. The results support the feasibility of monitoring physiological signals at school. We describe specific challenges and provide recommendations for signal analysis, including dealing with invalid signals due to loose sensors, and quantization noise that can be caused by limitations in analog-to-digital conversion in wearable devices and be mistaken as physiological responses. Importantly, our results show that using toolboxes for automatic signal preprocessing, decomposition, and artifact detection with default parameters while neglecting differences between devices and measurement contexts yield misleading results. Time courses of students’ physiological signals throughout the course of a class were found to be clearer after applying our proposed preprocessing steps.

scholarly journals Measuring What Matters: How the Laboratory Contributes Value in the Opioid Crisis

2020 ◽  
Vol 5 (6) ◽  
pp. 1378-1390
Author(s):  
Jill S Warrington ◽  
Kathleen Swanson ◽  
Monique Dodd ◽  
Sheng-Ying Lo ◽  
Aya Haghamad ◽  
...  
Keyword(s):  
Clinical Laboratory ◽  
Improve Patient Safety ◽  
Clinical Care ◽  
Active Role ◽  
Opioid Use Disorder ◽  
Opioid Use ◽  
Prescribing Practices ◽  
Opioid Crisis ◽  
Clinical Laboratories ◽  
National Quality

Abstract With over 20 years of the opioid crisis, our collective response has evolved to address the ongoing needs related to the management of opioid use and opioid use disorder. There has been an increasing recognition of the need for standardized metrics to evaluate organizational management and stewardship. The clinical laboratory, with a wealth of objective and quantitative health information, is uniquely poised to support opioid stewardship and drive valuable metrics for opioid prescribing practices and opioid use disorder (OUD) management. To identify laboratory-related insights that support these patient populations, a collection of 5 independent institutions, under the umbrella of the Clinical Laboratory 2.0 movement, developed and prioritized metrics. Using a structured expert panel review, laboratory experts from 5 institutions assessed possible metrics as to their relative importance, usability, feasibility, and scientific acceptability based on the National Quality Forum criteria. A total of 37 metrics spanning the topics of pain and substance use disorder (SUD) management were developed with consideration of how laboratory insights can impact clinical care. Monitoring these metrics, in the form of summative reports, dashboards, or embedded in laboratory reports themselves may support the clinical care teams and health systems in addressing the opioid crisis. The clinical insights and standardized metrics derived from the clinical laboratory during the opioid crisis exemplifies the value proposition of clinical laboratories shifting into a more active role in the healthcare system. This increased participation by the clinical laboratories may improve patient safety and reduce healthcare costs related to OUD and pain management.

Sign in / Sign up

Export Citation Format

Share Document