scholarly journals Pain Assessment Tool with Electrodermal Activity for Post-Operative Patients: A Method Validation Study (Preprint)

10.2196/25258 ◽  
2020 ◽  
Author(s):  
Seyed Amir Hossein Aqajari ◽  
Rui Cao ◽  
Emad Kasaeyan Naeini ◽  
Michael-David Calderon ◽  
Kai Zheng ◽  
...  
Keyword(s):  
Method Validation ◽  
Validation Study ◽  
Pain Assessment ◽  
Assessment Tool ◽  
Electrodermal Activity

scholarly journals Prospective Study Evaluating a Pain Assessment Tool in a Postoperative Environment: Protocol for Algorithm Testing and Enhancement (Preprint)

2020 ◽  
Author(s):  
Emad Kasaeyan Naeini ◽  
Mingzhe Jiang ◽  
Elise Syrjälä ◽  
Michael-David Calderon ◽  
Riitta Mieronkoski ◽  
...  
Keyword(s):  
Electrical Activity ◽  
Pain Assessment ◽  
Assessment Tool ◽  
Elective Surgery ◽  
Electrodermal Activity ◽  
Vital Signs ◽  
Assessment Tools ◽  
Second Phase ◽  
Moderate Pain ◽  
Management Interventions

BACKGROUND Assessment of pain is critical to its optimal treatment. There is a high demand for accurate objective pain assessment for effectively optimizing pain management interventions. However, pain is a multivalent, dynamic, and ambiguous phenomenon that is difficult to quantify, particularly when the patient’s ability to communicate is limited. The criterion standard of pain intensity assessment is self-reporting. However, this unidimensional model is disparaged for its oversimplification and limited applicability in several vulnerable patient populations. Researchers have attempted to develop objective pain assessment tools through analysis of physiological pain indicators, such as electrocardiography, electromyography, photoplethysmography, and electrodermal activity. However, pain assessment by using only these signals can be unreliable, as various other factors alter these vital signs and the adaptation of vital signs to pain stimulation varies from person to person. Objective pain assessment using behavioral signs such as facial expressions has recently gained attention. OBJECTIVE Our objective is to further the development and research of a pain assessment tool for use with patients who are likely experiencing mild to moderate pain. We will collect observational data through wearable technologies, measuring facial electromyography, electrocardiography, photoplethysmography, and electrodermal activity. METHODS This protocol focuses on the second phase of a larger study of multimodal signal acquisition through facial muscle electrical activity, cardiac electrical activity, and electrodermal activity as indicators of pain and for building predictive models. We used state-of-the-art standard sensors to measure bioelectrical electromyographic signals and changes in heart rate, respiratory rate, and oxygen saturation. Based on the results, we further developed the pain assessment tool and reconstituted it with modern wearable sensors, devices, and algorithms. In this second phase, we will test the smart pain assessment tool in communicative patients after elective surgery in the recovery room. RESULTS Our human research protections application for institutional review board review was approved for this part of the study. We expect to have the pain assessment tool developed and available for further research in early 2021. Preliminary results will be ready for publication during fall 2020. CONCLUSIONS This study will help to further the development of and research on an objective pain assessment tool for monitoring patients likely experiencing mild to moderate pain. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/17783

scholarly journals The Alberta Breakthrough Pain Assessment Tool for Cancer Patients: A Validation Study Using a Delphi Process and Patient Think-Aloud Interviews

2008 ◽  
Vol 35 (2) ◽  
pp. 136-152 ◽  
Author(s):  
Neil A. Hagen ◽  
Carla Stiles ◽  
Cheryl Nekolaichuk ◽  
Patricia Biondo ◽  
Linda E. Carlson ◽  
...  
Keyword(s):  
Cancer Patients ◽  
Validation Study ◽  
Pain Assessment ◽  
Assessment Tool ◽  
Breakthrough Pain ◽  
Delphi Process ◽  
Think Aloud

scholarly journals How to enhance and assess reflection in specialist training: a mixed method validation study of a new tool for global assessment of reflection ability

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Gunver Lillevang ◽  
Helle Ibsen ◽  
Søren Hast Prins ◽  
Niels Kristian Kjaer
Keyword(s):  
Construct Validity ◽  
Method Validation ◽  
Validation Study ◽  
Mixed Method ◽  
Global Assessment ◽  
Assessment Tool ◽  
Face Validity ◽  
Train The Trainer ◽  
Specific Complex ◽  
Mind Maps

Abstract Background In Danish GP training we had the ambition to enhance and assess global reflection ability, but since we found no appropriate validated method in the literature, we decided to develop a new assessment tool. This tool is based on individual trainee developed mind maps and structured trainer-trainee discussions related to specific complex competencies. We named the tool Global Assessment of Reflection ability (GAR) and conducted a mixed method validation study. Our goal was to investigate whether it is possible to enhance and assess reflection ability using the tool. Methods In order to investigate acceptability, feasibility, face validity, and construct validity of the tool we conducted a mixed method validation study that combined 1) qualitative data obtained from 750 GP trainers participating in train-the-trainer courses, 2) a questionnaire survey sent to 349 GP trainers and 214 GP trainees and 3) a thorough analysis of eight trainer-trainee discussions. Results Our study showed an immediate high acceptance of the GAR tool. Both trainers and trainees found the tool feasible, useful, and relevant with acceptable face validity. Rating of eight audio recordings showed that the tool can demonstrate reflection during assessment of complex competencies. Conclusions We have developed an assessment tool (GAR) to enhance and assess reflection. GAR was found to be acceptable, feasible, relevant and with good face- and construct validity. GAR seems to be able to enhance the trainees’ ability to reflect and provide a good basis for assessment in relation to complex competencies.

Development of a Cancer Pain Assessment Tool in Korea: A Validation Study of a Korean Version of the Brief Pain Inventory

Oncology ◽  
2004 ◽  
Vol 66 (6) ◽  
pp. 439-444 ◽  
Author(s):  
Young Ho Yun ◽  
Tito R. Mendoza ◽  
Dae Seog Heo ◽  
Taiwoo Yoo ◽  
Bong Yul Heo ◽  
...  
Keyword(s):  
Cancer Pain ◽  
Validation Study ◽  
Pain Assessment ◽  
Assessment Tool ◽  
Brief Pain Inventory ◽  
Korean Version

scholarly journals Pain assessment tool with Electrodermal Activity for post-operative patients: A method validation study (Preprint)

2020 ◽  
Author(s):  
Seyed Amir Hossein Aqajari ◽  
Rui Cao ◽  
Emad Kasaeyan Naeini ◽  
Michael-David Calderon ◽  
Kai Zheng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pain Assessment ◽  
Assessment Tool ◽  
Learning Algorithm ◽  
Electrodermal Activity ◽  
Machine Learning Algorithms ◽  
Adult Patients ◽  
Self Report ◽  
Physiological Data ◽  
Pain Models

BACKGROUND Accurate objective pain assessment is required in the healthcare domain and clinical settings for appropriate pain management. Automated objective pain detection from physiological data in patients provides valuable information to hospital staff and caregivers to better manage pain, in particular for those patients who are unable to self-report. Galvanic Skin Response (GSR) is one of the physiologic signals that refers to the changes in sweat gland activity, which can identify the features of emotional states and anxiety induced by varying pain levels. In this study, we used different statistical features extracted from GSR data collected from postoperative patients to detect their pain intensity. To the best of our knowledge, we are the first work building pain models using postoperative adult patients instead of healthy subjects. OBJECTIVE The goal of this paper is to present an automatic pain assessment tool using GSR signals to predict different pain intensities in non-communicative postoperative patients. METHODS The study was designed to collect biomedical data from post-operative patients reporting moderate to high pain levels. 25 subjects were recruited with the age range of 23 to 89. First, a Transcutaneous Electrical Nerve Stimulation (TENS) unit was employed to obtain patients' baselines. In the second part, the Empatica E4 wristband was attached to patients while they were performing low intensity activities. Patient self-report based on the NRS was used to record pain intensities used to correlate with the objective measured data. The labels were downsampled from 11 pain levels to 5 different pain intensities including the baseline. Two different machine learning algorithms were used to construct the models. The mean decrease impurity method was used to find the top important features for pain prediction and improve the accuracy. We compared our results with a previously published research study to estimate the true performance of our models. RESULTS Four different binary classification models were constructed using each machine learning algorithm to classify the baseline and other pain intensities (Baseline (BL) vs. Pain Level (PL) 1, BL vs. PL2, BL vs. PL3, and BL vs. PL4). Our models achieved the higher accuracy for the first three pain models in comparison with BioVid paper approach despite the challenges in analyzing real patient data. For BL vs. PL1, BL vs. PL2, and BL vs. PL4, the highest prediction accuracies were achieved when using a Random Forest classifier (86.0, 70.0, and 61.5, respectively). For BL vs. PL3, we achieved the accuracy of 72.1 using a K-nearest neighbors classifier. CONCLUSIONS We are the first to propose and validate the pain assessment tool to predict different pain levels in real postoperative adult patients using GSR signals. We also exploited feature selection algorithms to find the top important features related to different pain intensities. INTERNATIONAL REGISTERED REPORT RR2-10.2196/17783

scholarly journals Prospective Study Evaluating a Pain Assessment Tool in a Postoperative Environment: Protocol for Algorithm Testing and Enhancement

10.2196/17783 ◽  
2020 ◽  
Vol 9 (7) ◽  
pp. e17783
Author(s):  
Emad Kasaeyan Naeini ◽  
Mingzhe Jiang ◽  
Elise Syrjälä ◽  
Michael-David Calderon ◽  
Riitta Mieronkoski ◽  
...  
Keyword(s):  
Electrical Activity ◽  
Pain Assessment ◽  
Assessment Tool ◽  
Elective Surgery ◽  
Electrodermal Activity ◽  
Vital Signs ◽  
Assessment Tools ◽  
Second Phase ◽  
Moderate Pain ◽  
Management Interventions

Background Assessment of pain is critical to its optimal treatment. There is a high demand for accurate objective pain assessment for effectively optimizing pain management interventions. However, pain is a multivalent, dynamic, and ambiguous phenomenon that is difficult to quantify, particularly when the patient’s ability to communicate is limited. The criterion standard of pain intensity assessment is self-reporting. However, this unidimensional model is disparaged for its oversimplification and limited applicability in several vulnerable patient populations. Researchers have attempted to develop objective pain assessment tools through analysis of physiological pain indicators, such as electrocardiography, electromyography, photoplethysmography, and electrodermal activity. However, pain assessment by using only these signals can be unreliable, as various other factors alter these vital signs and the adaptation of vital signs to pain stimulation varies from person to person. Objective pain assessment using behavioral signs such as facial expressions has recently gained attention. Objective Our objective is to further the development and research of a pain assessment tool for use with patients who are likely experiencing mild to moderate pain. We will collect observational data through wearable technologies, measuring facial electromyography, electrocardiography, photoplethysmography, and electrodermal activity. Methods This protocol focuses on the second phase of a larger study of multimodal signal acquisition through facial muscle electrical activity, cardiac electrical activity, and electrodermal activity as indicators of pain and for building predictive models. We used state-of-the-art standard sensors to measure bioelectrical electromyographic signals and changes in heart rate, respiratory rate, and oxygen saturation. Based on the results, we further developed the pain assessment tool and reconstituted it with modern wearable sensors, devices, and algorithms. In this second phase, we will test the smart pain assessment tool in communicative patients after elective surgery in the recovery room. Results Our human research protections application for institutional review board review was approved for this part of the study. We expect to have the pain assessment tool developed and available for further research in early 2021. Preliminary results will be ready for publication during fall 2020. Conclusions This study will help to further the development of and research on an objective pain assessment tool for monitoring patients likely experiencing mild to moderate pain. International Registered Report Identifier (IRRID) DERR1-10.2196/17783

scholarly journals PO-0900 Translation Comfortneo – A Pain Assessment Tool

2014 ◽  
Vol 99 (Suppl 2) ◽  
pp. A545.2-A545
Author(s):  
R Stenkjaer ◽  
M Andersen ◽  
M Scheutz ◽  
Y Hundrup
Keyword(s):  
Pain Assessment ◽  
Assessment Tool
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