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BackgroundAssessment 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. ObjectiveOur 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. MethodsThis 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. ResultsOur 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. ConclusionsThis 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

BackgroundThere is a strong demand for an accurate and objective means of assessing acute pain among hospitalized patients to help clinicians provide pain medications at a proper dosage and in a timely manner. Heart rate variability (HRV) comprises changes in the time intervals between consecutive heartbeats, which can be measured through acquisition and interpretation of electrocardiography (ECG) captured from bedside monitors or wearable devices. As increased sympathetic activity affects the HRV, an index of autonomic regulation of heart rate, ultra–short-term HRV analysis can provide a reliable source of information for acute pain monitoring. In this study, widely used HRV time and frequency domain measurements are used in acute pain assessments among postoperative patients. The existing approaches have only focused on stimulated pain in healthy subjects, whereas, to the best of our knowledge, there is no work in the literature building models using real pain data and on postoperative patients. ObjectiveThe objective of our study was to develop and evaluate an automatic and adaptable pain assessment algorithm based on ECG features for assessing acute pain in postoperative patients likely experiencing mild to moderate pain. MethodsThe study used a prospective observational design. The sample consisted of 25 patient participants aged 18 to 65 years. In part 1 of the study, a transcutaneous electrical nerve stimulation unit was employed to obtain baseline discomfort thresholds for the patients. In part 2, a multichannel biosignal acquisition device was used as patients were engaging in non-noxious activities. At all times, pain intensity was measured using patient self-reports based on the Numerical Rating Scale. A weak supervision framework was inherited for rapid training data creation. The collected labels were then transformed from 11 intensity levels to 5 intensity levels. Prediction models were developed using 5 different machine learning methods. Mean prediction accuracy was calculated using leave-one-out cross-validation. We compared the performance of these models with the results from a previously published research study. ResultsFive different machine learning algorithms were applied to perform a binary classification of baseline (BL) versus 4 distinct pain levels (PL1 through PL4). The highest validation accuracy using 3 time domain HRV features from a BioVid research paper for baseline versus any other pain level was achieved by support vector machine (SVM) with 62.72% (BL vs PL4) to 84.14% (BL vs PL2). Similar results were achieved for the top 8 features based on the Gini index using the SVM method, with an accuracy ranging from 63.86% (BL vs PL4) to 84.79% (BL vs PL2). ConclusionsWe propose a novel pain assessment method for postoperative patients using ECG signal. Weak supervision applied for labeling and feature extraction improves the robustness of the approach. Our results show the viability of using a machine learning algorithm to accurately and objectively assess acute pain among hospitalized patients. International Registered Report Identifier (IRRID)RR2-10.2196/17783

Finnish nursing students (n=85) specializing in child nursing had mainly positive attitudes about caring for children in pain, but lacked knowledge of medications and pain assessment. There were no knowledge differences between older and younger students or those with more or less work experience. (SK)

The purpose of this study was to describe the decision-making processes of 369 public health nurses in Canada, Finland, Norway, and the United States, and to discuss any differences observed [...]

BackgroundAccurate, objective pain assessment is required in the health care 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, particularly for 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 features of emotional states and anxiety induced by varying pain levels. This study used different statistical features extracted from GSR data collected from postoperative patients to detect their pain intensity. To the best of our knowledge, this is the first work building pain models using postoperative adult patients instead of healthy subjects. ObjectiveThe goal of this study was to present an automatic pain assessment tool using GSR signals to predict different pain intensities in noncommunicative, postoperative patients. MethodsThe study was designed to collect biomedical data from postoperative patients reporting moderate to high pain levels. We recruited 25 participants aged 23-89 years. First, a transcutaneous electrical nerve stimulation (TENS) unit was employed to obtain patients' baseline data. In the second part, the Empatica E4 wristband was worn by patients while they were performing low-intensity activities. Patient self-report based on the numeric rating scale (NRS) was used to record pain intensities that were correlated with objectively measured data. The labels were down-sampled from 11 pain levels to 5 different pain intensities, including the baseline. We used 2 different machine learning algorithms 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. ResultsFour 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 higher accuracy for the first 3 pain models than the 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 an accuracy of 72.1 using a k-nearest-neighbor classifier. ConclusionsWe are the first to propose and validate a 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 Identifier (IRRID)RR2-10.2196/17783

The current Finnish version of AORN's Perioperative Nursing Data Set consists of nursing diagnoses and interventions but not outcomes. During the early Finnish validation phase, researchers determined that almost half of the outcomes listed in the AORN version were impossible to measure in Finland. We conducted this study to determine the applicability of the nursing outcomes identified in the Perioperative Nursing Data Set, 2nd ed Rev, and to test the use of the outcomes by exploring the relevance of the outcome indicators for eight selected outcomes. We collected the data by using two surveys with structured questionnaires that were provided to an expert panel of nurses and physicians. Panel members found that most of the outcomes were acceptable for use in Finnish perioperative care; however, further validation of the outcome indicators is needed to develop tools to support nurses in their attempts to reach desirable nursing-sensitive outcomes for patients. [ABSTRACT FROM AUTHOR]

(2011) Journal of Nursing Management 209-217 To describe important information in the care processes of patients with cardiac symptoms. Process-based work-flow models are increasingly being used in healthcare. At the same time, developments in information systems offer the possibility of supporting improvements in process and information management in healthcare. To better utilize these possibilities we need to understand more about important information content and flow during treatment processes. A qualitative approach involving the critical incident technique was used. Critical incidents were collected using a semi-structured questionnaire (50 respondents) and interviews ( n = 10). Three incident categories of important information were identified: (1) process-related incidents, (2) managerial incidents and (3) clinical incidents. Process-related incidents focused on agreed-care practices and the importance of the care environment. Managerial incidents focused on human and material resources. Clinical incidents focused on medical and nursing care and the importance of patient education. Information content, information flow and the timing of such information should be modelled further in order to improve the management of care processes. Increasing knowledge about essential points of information as part of nursing management is important. [ABSTRACT FROM AUTHOR]

Background: There is a strong demand for an accurate and objective means of assessing acute pain among hospitalized patients to help clinicians provide pain medications at a proper dosage and in a timely manner. Heart rate variability (HRV) comprises changes in the time intervals between consecutive heartbeats, which can be measured through acquisition and interpretation of electrocardiography (ECG) captured from bedside monitors or wearable devices. As increased sympathetic activity affects the HRV, an index of autonomic regulation of heart rate, ultra-short-term HRV analysis can provide a reliable source of information for acute pain monitoring. In this study, widely used HRV time and frequency domain measurements are used in acute pain assessments among postoperative patients. The existing approaches have only focused on stimulated pain in healthy subjects, whereas, to the best of our knowledge, there is no work in the literature building models using real pain data and on postoperative patients.Objective: The objective of our study was to develop and evaluate an automatic and adaptable pain assessment algorithm based on ECG features for assessing acute pain in postoperative patients likely experiencing mild to moderate pain.Methods: The study used a prospective observational design. The sample consisted of 25 patient participants aged 18 to 65 years. In part 1 of the study, a transcutaneous electrical nerve stimulation unit was employed to obtain baseline discomfort thresholds for the patients. In part 2, a multichannel biosignal acquisition device was used as patients were engaging in non-noxious activities. At all times, pain intensity was measured using patient self-reports based on the Numerical Rating Scale. A weak supervision framework was inherited for rapid training data creation. The collected labels were then transformed from 11 intensity levels to 5 intensity levels. Prediction models were developed using 5 different machine learning methods. Mean prediction accuracy was calculated using leave-one-out cross-validation. We compared the performance of these models with the results from a previously published research study.Results: Five different machine learning algorithms were applied to perform a binary classification of baseline (BL) versus 4 distinct pain levels (PL1 through PL4). The highest validation accuracy using 3 time domain HRV features from a BioVid research paper for baseline versus any other pain level was achieved by support vector machine (SVM) with 62.72% (BL vs PL4) to 84.14% (BL vs PL2). Similar results were achieved for the top 8 features based on the Gini index using the SVM method, with an accuracy ranging from 63.86% (BL vs PL4) to 84.79% (BL vs PL2).Conclusions: We propose a novel pain assessment method for postoperative patients using ECG signal. Weak supervision applied for labeling and feature extraction improves the robustness of the approach. Our results show the viability of using a machine learning algorithm to accurately and objectively assess acute pain among hospitalized patients.International Registered Report Identifier (irrid): RR2-10.2196/17783. [ABSTRACT FROM AUTHOR]

Objectives: The Internet and social media provide various possibilities for online peer support. The aim of this review was to explore Internet-based peer-support interventions and their outcomes for parents. Design: A systematic integrative review. Data sources: The systematic search was carried out in March 2014 in PubMed, Cinahl, PsycINFO and Cochrane databases. Review methods: Two reviewers independently screened the titles (n = 1793), abstracts and full texts to decide which articles should be chosen. The inclusion criteria were: (1) an Internet-based community as an intervention, or at least as a component of an intervention; (2) the participants in the Internet-based community had to be mothers and/or fathers or pregnant women; (3) the parents had to interact and communicate with each other through the Internet-based community. The data was analysed using content analysis. When analysing peer-support interventions only interventions developed by researchers were included and when analysing the outcomes for the parents, studies that focused on mothers, fathers or both parents were separated. Results: In total, 38 publications met the inclusion criteria. Most of the studies focused on Internet-based peer support between mothers (n = 16) or both parents (n = 15) and seven focused on fathers. In 16 studies, the Internet-based interventions had been developed by researchers and 22 studies used already existing Internet peer-support groups, in which any person using the Internet could participate. For mothers, Internet-based peer support provided emotional support, information and membership in a social community. For fathers, it provided support for the transition to fatherhood, information and humorous communication. Mothers were more active users of Internet-based peer-support groups than fathers. In general, parents were satisfied with Internet-based peer support. The evidence of the effectiveness of Internet-based peer support was inconclusive but no harmful effects were reported in these reviewed studies. Conclusions: Internet-based peer support provided informational support for parents and was accessible despite geographical distance or time constraints. Internet-based peer support is a unique form of parental support, not replacing but supplementing support offered by professionals. Experimental studies in this area are needed. [ABSTRACT FROM AUTHOR]

Patient self-reporting of pain is not always possible, in those cases automated objective pain assessment could lead to reliable pain assessment. In this context, physiological measurements have been studied and one of the promising signals is skin conductance (SC). In this study, 1Hz SC signal acquisition is performed while gradually increasing heat and electrical pain stimuli are induced. Three labeled study periods are defined based on pain stimuli presence, self-reported pain threshold and pain tolerance. Different classification and regression models are compared, together with selected SC features. The model performances are evaluated using c-index. Results show good predictability, especially for the slow tonic component decomposed from the SC signal.

This study is about what adolescents with cancer think about their life situation, the support they get, and the information they receive about their illness. The data for this qualitative and descriptive study were collected in 3 focus group interviews with 20 adolescents aged 13 to 18 years attending a cancer adjustment camp. Interpretation was based on the method of inductive content analysis. The adolescents' experiences of their current situation were analyzed into 5 categories: views on life here and now, negative experiences of self because of the illness, resources recognized in self, difficulties caused by the illness in relation to life around them, and resources identified in the world around. They made very little, if any, conscious effort to plan ahead for the future. The information received by the adolescents concerned their illness and its treatment here and now, various practical matters, as well as the future impacts of the illness and its treatments. Most of this information focused on the here and now, whereas the adolescents' information needs were mainly oriented to the future. As for the adolescents' chances to take part in making decisions about their care and life, the analysis yielded 6 categories: joint decision making, inadequate chances for decision making, independent decision making, illusion of decision making, reluctant to make decisions, and excluded from decision making. Finally, the adolescents' hopes for improvement were focused on staff activities, physical care facilities, chances to discuss and work through their experiences of the illness, and the support received from society.

The purpose of this study was to describe patients' learning needs after hip arthroplasty and compare them prior to and 2 weeks after hospital discharge. Data were collected in two phases from 22 surgical wards in 17 hospitals in Finland using a Canadian Patient Learning Needs Scale (Galloway et al., 1996). The first questionnaire (n=212, 81%) was completed before hospital discharge and the second (n=144, 55%) was completed at home after hospital discharge. Results indicated that patients' learning needs diminished significantly after hospital discharge. In both questionnaires patients felt that the most important information was about complications and symptoms. Information about medication was ranked the second most important. Demographic variables such as age, gender, education and working life were clearly related to learning needs. Women over 60 years old, and less educated and retired respondents had many learning needs.