Pub Date : 2024-05-09DOI: 10.3389/fdgth.2024.1374359
Irene Suilan Zeng
{"title":"Integrating omics atlas in health informatics system design-an opinion article","authors":"Irene Suilan Zeng","doi":"10.3389/fdgth.2024.1374359","DOIUrl":"https://doi.org/10.3389/fdgth.2024.1374359","url":null,"abstract":"","PeriodicalId":504480,"journal":{"name":"Frontiers in Digital Health","volume":" 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140994445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-09DOI: 10.3389/fdgth.2024.1395501
Jin Rui Edmund Neo, Joon Sin Ser, San San Tay
The utility of large language model-based (LLM) artificial intelligence (AI) chatbots in many aspects of healthcare is becoming apparent though their ability to address patient concerns remains unknown. We sought to evaluate the performance of two well-known, freely-accessible chatbots, ChatGPT and Google Bard, in responding to common questions about stroke rehabilitation posed by patients and their caregivers.We collected questions from outpatients and their caregivers through a survey, categorised them by theme, and created representative questions to be posed to both chatbots. We then evaluated the chatbots' responses based on accuracy, safety, relevance, and readability. Interrater agreement was also tracked.Although both chatbots achieved similar overall scores, Google Bard performed slightly better in relevance and safety. Both provided readable responses with some general accuracy, but struggled with hallucinated responses, were often not specific, and lacked awareness of the possibility for emotional situations with the potential to turn dangerous. Additionally, interrater agreement was low, highlighting the variability in physician acceptance of their responses.AI chatbots show potential in patient-facing support roles, but issues remain regarding safety, accuracy, and relevance. Future chatbots should address these problems to ensure that they can reliably and independently manage the concerns and questions of stroke patients and their caregivers.
基于大型语言模型（LLM）的人工智能（AI）聊天机器人在医疗保健的许多方面的作用正变得越来越明显，但它们解决患者问题的能力仍是未知数。我们试图评估 ChatGPT 和 Google Bard 这两个知名、可自由访问的聊天机器人在回答患者及其护理人员提出的有关中风康复的常见问题时的性能。我们通过调查收集了门诊患者及其护理人员提出的问题，并按主题进行了分类，然后创建了具有代表性的问题，将其提交给这两个聊天机器人。然后，我们根据准确性、安全性、相关性和可读性对聊天机器人的回答进行了评估。虽然两个聊天机器人的总分相近，但 Google Bard 在相关性和安全性方面的表现略胜一筹。这两个聊天机器人都提供了具有一定准确性的可读回复，但在幻觉回复方面有困难，回复往往不具体，而且缺乏对情绪化情况可能导致危险的认识。人工智能聊天机器人在面向患者的支持角色方面显示出潜力，但在安全性、准确性和相关性方面仍存在问题。未来的聊天机器人应解决这些问题，以确保它们能可靠、独立地处理中风患者及其护理人员的担忧和问题。
{"title":"Use of large language model-based chatbots in managing the rehabilitation concerns and education needs of outpatient stroke survivors and caregivers","authors":"Jin Rui Edmund Neo, Joon Sin Ser, San San Tay","doi":"10.3389/fdgth.2024.1395501","DOIUrl":"https://doi.org/10.3389/fdgth.2024.1395501","url":null,"abstract":"The utility of large language model-based (LLM) artificial intelligence (AI) chatbots in many aspects of healthcare is becoming apparent though their ability to address patient concerns remains unknown. We sought to evaluate the performance of two well-known, freely-accessible chatbots, ChatGPT and Google Bard, in responding to common questions about stroke rehabilitation posed by patients and their caregivers.We collected questions from outpatients and their caregivers through a survey, categorised them by theme, and created representative questions to be posed to both chatbots. We then evaluated the chatbots' responses based on accuracy, safety, relevance, and readability. Interrater agreement was also tracked.Although both chatbots achieved similar overall scores, Google Bard performed slightly better in relevance and safety. Both provided readable responses with some general accuracy, but struggled with hallucinated responses, were often not specific, and lacked awareness of the possibility for emotional situations with the potential to turn dangerous. Additionally, interrater agreement was low, highlighting the variability in physician acceptance of their responses.AI chatbots show potential in patient-facing support roles, but issues remain regarding safety, accuracy, and relevance. Future chatbots should address these problems to ensure that they can reliably and independently manage the concerns and questions of stroke patients and their caregivers.","PeriodicalId":504480,"journal":{"name":"Frontiers in Digital Health","volume":" 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140996904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-08DOI: 10.3389/fdgth.2024.1423281
Gloria Cosoli, Carlo Massaroni, Paola Saccomandi
{"title":"Editorial: Wearable sensors for the measurement of physiological signals: what about their measurement uncertainty?","authors":"Gloria Cosoli, Carlo Massaroni, Paola Saccomandi","doi":"10.3389/fdgth.2024.1423281","DOIUrl":"https://doi.org/10.3389/fdgth.2024.1423281","url":null,"abstract":"","PeriodicalId":504480,"journal":{"name":"Frontiers in Digital Health","volume":" 31","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141001099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-25DOI: 10.3389/fdgth.2024.1402810
I. Sánchez-Machín, P. Poza-Guedes, E. Mederos-Luis, R. González-Pérez
In Spain, specialist outpatient care traditionally relied on in-person consultations at public hospitals, leading to long wait times and limited clinical analysis in appointment assignments. However, the emergence of Information and Communication Technologies (ICTs) has transformed patient care, creating a seamless healthcare ecosystem. At the Allergy Department, we aimed to share our experience in transitioning form a traditional linear model of patient flow across different healthcare levels to the implementation of a digital ecosystem. By telemedicine, we can prioritize individuals based on clinical relevance, promptly and efficiently addressing potentially life-threatening conditions such as severe uncontrolled asthma or hymenoptera venom anaphylaxis. Furthermore, our adoption of telephone consultations has markedly reduced the need for in-person hospital visits, while issues with unstable patients are swiftly addressed via WhatsApp. This innovative approach not only enhances efficiency but also facilitates the dissemination of personalized medical information through various channels, contributing to public awareness and education, particularly regarding allergies. Concerns related to confidentiality, data privacy, and the necessity for informed consent must thoroughly be addressed. Also, to ensure the success of ICT integration, it is imperative to focus on the quality of educational information, its efficient dissemination, and anticipate potential unforeseen consequences. Sharing experiences across diverse health frameworks and medical specialties becomes crucial in refining these processes, drawing insights from the collective experiences of others. This collaborative effort aims to contribute to the ongoing development of a more effective and sustainable healthcare system.
{"title":"The paradigm shift in allergy consultations through a digital ecosystem","authors":"I. Sánchez-Machín, P. Poza-Guedes, E. Mederos-Luis, R. González-Pérez","doi":"10.3389/fdgth.2024.1402810","DOIUrl":"https://doi.org/10.3389/fdgth.2024.1402810","url":null,"abstract":"In Spain, specialist outpatient care traditionally relied on in-person consultations at public hospitals, leading to long wait times and limited clinical analysis in appointment assignments. However, the emergence of Information and Communication Technologies (ICTs) has transformed patient care, creating a seamless healthcare ecosystem. At the Allergy Department, we aimed to share our experience in transitioning form a traditional linear model of patient flow across different healthcare levels to the implementation of a digital ecosystem. By telemedicine, we can prioritize individuals based on clinical relevance, promptly and efficiently addressing potentially life-threatening conditions such as severe uncontrolled asthma or hymenoptera venom anaphylaxis. Furthermore, our adoption of telephone consultations has markedly reduced the need for in-person hospital visits, while issues with unstable patients are swiftly addressed via WhatsApp. This innovative approach not only enhances efficiency but also facilitates the dissemination of personalized medical information through various channels, contributing to public awareness and education, particularly regarding allergies. Concerns related to confidentiality, data privacy, and the necessity for informed consent must thoroughly be addressed. Also, to ensure the success of ICT integration, it is imperative to focus on the quality of educational information, its efficient dissemination, and anticipate potential unforeseen consequences. Sharing experiences across diverse health frameworks and medical specialties becomes crucial in refining these processes, drawing insights from the collective experiences of others. This collaborative effort aims to contribute to the ongoing development of a more effective and sustainable healthcare system.","PeriodicalId":504480,"journal":{"name":"Frontiers in Digital Health","volume":"31 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140659005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hospitalised patients could benefit from the emergence of novel technologies for nursing care. There are numerous technical products available, but these rarely find their way into practice. Further knowledge is required about the circumstances under which technology in nursing is accepted and used. In the research project “Centre for Implementing Nursing Care Innovations”, technical innovations are implemented on a trauma surgery inpatient ward in Germany. After implementation, it was investigated: Which implemented technologies are accepted/rejected, and which factors influence the acceptance/rejection of technology for nurses?A focused ethnography was used, containing two approaches: First, participant observation was conducted to examine nurses’ and patients’ interaction with technologies. Observations were fixed in a field research diary and analysed using evaluative qualitative content analysis. Second, a questionnaire was used by nurses to provide information about the use frequency and technology suitability. The results of the study were consolidated and analysed using the UTAUT model.Seven studied technologies can be summarised in four result categories: (1) A Mobilising mattress, a Special projector and a Sound pillow are accepted and used by nurses and patients, because they offer a way to provide high quality care with little additional effort. (2) A Fall prevention system is consistently used in patient care as a work obligation, but since nurses consider the system error-prone, acceptance is low. (3) An Interactive therapy ball is accepted but nurses cannot use it due to the high workload. (4) An App for nurse-patient communication and a work-equipment tracking system are not used or accepted because nurses do not see a practical benefit in the systems.Acceptance or rejection of a product does not necessarily equate to use or non-use of the technology. Before implementation, technology acceptance among users occurs as prejudice—when users are given time to experiment with technology, intention-to-use can stabilize into sustained use. Accepted and used technologies can serve to mask problems (such as staff shortages) and encourage problematic developments, such as the reduction of contact time at the bedside. Therefore, technology acceptance should be qualified in asking to what accepted technology contributes.
{"title":"Beyond technology acceptance—a focused ethnography on the implementation, acceptance and use of new nursing technology in a German hospital","authors":"Ronny Klawunn, Urs-Vito Albrecht, Deliah Katzmarzyk, Marie-Luise Dierks","doi":"10.3389/fdgth.2024.1330988","DOIUrl":"https://doi.org/10.3389/fdgth.2024.1330988","url":null,"abstract":"Hospitalised patients could benefit from the emergence of novel technologies for nursing care. There are numerous technical products available, but these rarely find their way into practice. Further knowledge is required about the circumstances under which technology in nursing is accepted and used. In the research project “Centre for Implementing Nursing Care Innovations”, technical innovations are implemented on a trauma surgery inpatient ward in Germany. After implementation, it was investigated: Which implemented technologies are accepted/rejected, and which factors influence the acceptance/rejection of technology for nurses?A focused ethnography was used, containing two approaches: First, participant observation was conducted to examine nurses’ and patients’ interaction with technologies. Observations were fixed in a field research diary and analysed using evaluative qualitative content analysis. Second, a questionnaire was used by nurses to provide information about the use frequency and technology suitability. The results of the study were consolidated and analysed using the UTAUT model.Seven studied technologies can be summarised in four result categories: (1) A Mobilising mattress, a Special projector and a Sound pillow are accepted and used by nurses and patients, because they offer a way to provide high quality care with little additional effort. (2) A Fall prevention system is consistently used in patient care as a work obligation, but since nurses consider the system error-prone, acceptance is low. (3) An Interactive therapy ball is accepted but nurses cannot use it due to the high workload. (4) An App for nurse-patient communication and a work-equipment tracking system are not used or accepted because nurses do not see a practical benefit in the systems.Acceptance or rejection of a product does not necessarily equate to use or non-use of the technology. Before implementation, technology acceptance among users occurs as prejudice—when users are given time to experiment with technology, intention-to-use can stabilize into sustained use. Accepted and used technologies can serve to mask problems (such as staff shortages) and encourage problematic developments, such as the reduction of contact time at the bedside. Therefore, technology acceptance should be qualified in asking to what accepted technology contributes.","PeriodicalId":504480,"journal":{"name":"Frontiers in Digital Health","volume":"56 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140656712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-22DOI: 10.3389/fdgth.2024.1334058
Yu-Peng Chen, Julia Woodward, Meena N Shankar, Dinank Bista, Umelo A Ugwoaba, Andrea N Brockmann, Kathryn M. Ross, Jaime Ruiz, Lisa Anthony
A growing body of research has focused on the utility of adaptive intervention models for promoting long-term weight loss maintenance; however, evaluation of these interventions often requires customized smartphone applications. Building such an app from scratch can be resource-intensive. To support a novel clinical trial of an adaptive intervention for weight loss maintenance, we developed a companion app, MyTrack+, to pair with a main commercial app, FatSecret (FS), leveraging a user-centered design process for rapid prototyping and reducing software engineering efforts. MyTrack+ seamlessly integrates data from FS and the BodyTrace smart scale, enabling participants to log and self-monitor their health data, while also incorporating customized questionnaires and timestamps to enhance data collection for the trial. We iteratively refined the app by first developing initial mockups and incorporating feedback from a usability study with 17 university students. We further improved the app based on an in-the-wild pilot study with 33 participants in the target population, emphasizing acceptance, simplicity, customization options, and dual app usage. Our work highlights the potential of using an iterative human-centered design process to build a companion app that complements a commercial app for rapid prototyping, reducing costs, and enabling efficient research progress.
{"title":"MyTrack+: Human-centered design of an mHealth app to support long-term weight loss maintenance","authors":"Yu-Peng Chen, Julia Woodward, Meena N Shankar, Dinank Bista, Umelo A Ugwoaba, Andrea N Brockmann, Kathryn M. Ross, Jaime Ruiz, Lisa Anthony","doi":"10.3389/fdgth.2024.1334058","DOIUrl":"https://doi.org/10.3389/fdgth.2024.1334058","url":null,"abstract":"A growing body of research has focused on the utility of adaptive intervention models for promoting long-term weight loss maintenance; however, evaluation of these interventions often requires customized smartphone applications. Building such an app from scratch can be resource-intensive. To support a novel clinical trial of an adaptive intervention for weight loss maintenance, we developed a companion app, MyTrack+, to pair with a main commercial app, FatSecret (FS), leveraging a user-centered design process for rapid prototyping and reducing software engineering efforts. MyTrack+ seamlessly integrates data from FS and the BodyTrace smart scale, enabling participants to log and self-monitor their health data, while also incorporating customized questionnaires and timestamps to enhance data collection for the trial. We iteratively refined the app by first developing initial mockups and incorporating feedback from a usability study with 17 university students. We further improved the app based on an in-the-wild pilot study with 33 participants in the target population, emphasizing acceptance, simplicity, customization options, and dual app usage. Our work highlights the potential of using an iterative human-centered design process to build a companion app that complements a commercial app for rapid prototyping, reducing costs, and enabling efficient research progress.","PeriodicalId":504480,"journal":{"name":"Frontiers in Digital Health","volume":"27 20","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140673365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent years, there has been increasing attention on the cluster approach to symptom management. Two significant challenges in the symptom cluster (SC) approach are identifying and predicting these clusters. This multiphase protocol aims to identify SCs in patients with advanced cancer as the primary objective, with the secondary objective of developing machine learning algorithms to predict SCs identified in the first phase.The 2-MIXIP study consists of two main phases. The first phase involves identifying SCs, and the second phase focuses on developing predictive algorithms for the identified SCs. The identification of SCs involves a parallel mixed-method design (quantitative and qualitative). Quantitative and qualitative methods are conducted simultaneously and given equal importance. The data are collected and analyzed independently before being integrated. The quantitative part is conducted using a descriptive-analytical method. The qualitative analysis is conducted using a content analysis approach. Then, the identified SCs from both parts are integrated to determine the final clusters and use them in the second phase. In the second phase, we employ a tree-based machine learning method to create predictive algorithms for SCs using key demographic and clinical patient characteristics.The findings of the 2-MIXIP study can help manage cancer patients' symptoms more effectively and enhance clinical decision-making by using SCs prediction. Furthermore, the results of this study can provide guidance for clinical trials aimed at managing symptoms.
{"title":"A multiphase study protocol of identifying, and predicting cancer-related symptom clusters: applying a mixed-method design and machine learning algorithms","authors":"Mojtaba Miladinia, Kourosh Zarea, Mahin Gheibizadeh, Mina Jahangiri, Hossein Karimpourian, Darioush Rokhafroz","doi":"10.3389/fdgth.2024.1290689","DOIUrl":"https://doi.org/10.3389/fdgth.2024.1290689","url":null,"abstract":"In recent years, there has been increasing attention on the cluster approach to symptom management. Two significant challenges in the symptom cluster (SC) approach are identifying and predicting these clusters. This multiphase protocol aims to identify SCs in patients with advanced cancer as the primary objective, with the secondary objective of developing machine learning algorithms to predict SCs identified in the first phase.The 2-MIXIP study consists of two main phases. The first phase involves identifying SCs, and the second phase focuses on developing predictive algorithms for the identified SCs. The identification of SCs involves a parallel mixed-method design (quantitative and qualitative). Quantitative and qualitative methods are conducted simultaneously and given equal importance. The data are collected and analyzed independently before being integrated. The quantitative part is conducted using a descriptive-analytical method. The qualitative analysis is conducted using a content analysis approach. Then, the identified SCs from both parts are integrated to determine the final clusters and use them in the second phase. In the second phase, we employ a tree-based machine learning method to create predictive algorithms for SCs using key demographic and clinical patient characteristics.The findings of the 2-MIXIP study can help manage cancer patients' symptoms more effectively and enhance clinical decision-making by using SCs prediction. Furthermore, the results of this study can provide guidance for clinical trials aimed at managing symptoms.","PeriodicalId":504480,"journal":{"name":"Frontiers in Digital Health","volume":" 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140682906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-19DOI: 10.3389/fdgth.2024.1284426
Mesut Yavuz, Nicolai Savaskan
{"title":"A European roadmap to a digital epidemiology in public health system","authors":"Mesut Yavuz, Nicolai Savaskan","doi":"10.3389/fdgth.2024.1284426","DOIUrl":"https://doi.org/10.3389/fdgth.2024.1284426","url":null,"abstract":"","PeriodicalId":504480,"journal":{"name":"Frontiers in Digital Health","volume":" 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140683930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-19DOI: 10.3389/fdgth.2024.1366176
Marjan Nassajpour, Mustafa Shuqair, A. Rosenfeld, M. Tolea, James E. Galvin, Behnaz Ghoraani
Accurate balance assessment is important in healthcare for identifying and managing conditions affecting stability and coordination. It plays a key role in preventing falls, understanding movement disorders, and designing appropriate therapeutic interventions across various age groups and medical conditions. However, traditional balance assessment methods often suffer from subjectivity, lack of comprehensive balance assessments and remote assessment capabilities, and reliance on specialized equipment and expert analysis. In response to these challenges, our study introduces an innovative approach for estimating scores on the Modified Clinical Test of Sensory Interaction on Balance (m-CTSIB). Utilizing wearable sensors and advanced machine learning algorithms, we offer an objective, accessible, and efficient method for balance assessment. We collected comprehensive movement data from 34 participants under four different sensory conditions using an array of inertial measurement unit (IMU) sensors coupled with a specialized system to evaluate ground truth m-CTSIB balance scores for our analysis. This data was then preprocessed, and an extensive array of features was extracted for analysis. To estimate the m-CTSIB scores, we applied Multiple Linear Regression (MLR), Support Vector Regression (SVR), and XGBOOST algorithms. Our subject-wise Leave-One-Out and 5-Fold cross-validation analysis demonstrated high accuracy and a strong correlation with ground truth balance scores, validating the effectiveness and reliability of our approach. Key insights were gained regarding the significance of specific movements, feature selection, and sensor placement in balance estimation. Notably, the XGBOOST model, utilizing the lumbar sensor data, achieved outstanding results in both methods, with Leave-One-Out cross-validation showing a correlation of 0.96 and a Mean Absolute Error (MAE) of 0.23 and 5-fold cross-validation showing comparable results with a correlation of 0.92 and an MAE of 0.23, confirming the model’s consistent performance. This finding underlines the potential of our method to revolutionize balance assessment practices, particularly in settings where traditional methods are impractical or inaccessible.
{"title":"Objective estimation of m-CTSIB balance test scores using wearable sensors and machine learning","authors":"Marjan Nassajpour, Mustafa Shuqair, A. Rosenfeld, M. Tolea, James E. Galvin, Behnaz Ghoraani","doi":"10.3389/fdgth.2024.1366176","DOIUrl":"https://doi.org/10.3389/fdgth.2024.1366176","url":null,"abstract":"Accurate balance assessment is important in healthcare for identifying and managing conditions affecting stability and coordination. It plays a key role in preventing falls, understanding movement disorders, and designing appropriate therapeutic interventions across various age groups and medical conditions. However, traditional balance assessment methods often suffer from subjectivity, lack of comprehensive balance assessments and remote assessment capabilities, and reliance on specialized equipment and expert analysis. In response to these challenges, our study introduces an innovative approach for estimating scores on the Modified Clinical Test of Sensory Interaction on Balance (m-CTSIB). Utilizing wearable sensors and advanced machine learning algorithms, we offer an objective, accessible, and efficient method for balance assessment. We collected comprehensive movement data from 34 participants under four different sensory conditions using an array of inertial measurement unit (IMU) sensors coupled with a specialized system to evaluate ground truth m-CTSIB balance scores for our analysis. This data was then preprocessed, and an extensive array of features was extracted for analysis. To estimate the m-CTSIB scores, we applied Multiple Linear Regression (MLR), Support Vector Regression (SVR), and XGBOOST algorithms. Our subject-wise Leave-One-Out and 5-Fold cross-validation analysis demonstrated high accuracy and a strong correlation with ground truth balance scores, validating the effectiveness and reliability of our approach. Key insights were gained regarding the significance of specific movements, feature selection, and sensor placement in balance estimation. Notably, the XGBOOST model, utilizing the lumbar sensor data, achieved outstanding results in both methods, with Leave-One-Out cross-validation showing a correlation of 0.96 and a Mean Absolute Error (MAE) of 0.23 and 5-fold cross-validation showing comparable results with a correlation of 0.92 and an MAE of 0.23, confirming the model’s consistent performance. This finding underlines the potential of our method to revolutionize balance assessment practices, particularly in settings where traditional methods are impractical or inaccessible.","PeriodicalId":504480,"journal":{"name":"Frontiers in Digital Health","volume":" 490","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140682621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-18DOI: 10.3389/fdgth.2024.1307817
Michael Loizou, S. Arnab, Petros Lameras, Thomas Hartley, Fernando Loizides, Praveen Kumar, Dana Sumilo
Emotions play an important role in human-computer interaction, but there is limited research on affective and emotional virtual agent design in the area of teaching simulations for healthcare provision. The purpose of this work is twofold: firstly, to describe the process for designing affective intelligent agents that are engaged in automated communications such as person to computer conversations, and secondly to test a bespoke prototype digital intervention which implements such agents. The presented study tests two distinct virtual learning environments, one of which was enhanced with affective virtual patients, with nine 3rd year nursing students specialising in mental health, during their professional practice stage. All (100%) of the participants reported that, when using the enhanced scenario, they experienced a more realistic representation of carer/patient interaction; better recognition of the patients' feelings; recognition and assessment of emotions; a better realisation of how feelings can affect patients' emotional state and how they could better empathise with the patients.
{"title":"Designing, implementing and testing an intervention of affective intelligent agents in nursing virtual reality teaching simulations—a qualitative study","authors":"Michael Loizou, S. Arnab, Petros Lameras, Thomas Hartley, Fernando Loizides, Praveen Kumar, Dana Sumilo","doi":"10.3389/fdgth.2024.1307817","DOIUrl":"https://doi.org/10.3389/fdgth.2024.1307817","url":null,"abstract":"Emotions play an important role in human-computer interaction, but there is limited research on affective and emotional virtual agent design in the area of teaching simulations for healthcare provision. The purpose of this work is twofold: firstly, to describe the process for designing affective intelligent agents that are engaged in automated communications such as person to computer conversations, and secondly to test a bespoke prototype digital intervention which implements such agents. The presented study tests two distinct virtual learning environments, one of which was enhanced with affective virtual patients, with nine 3rd year nursing students specialising in mental health, during their professional practice stage. All (100%) of the participants reported that, when using the enhanced scenario, they experienced a more realistic representation of carer/patient interaction; better recognition of the patients' feelings; recognition and assessment of emotions; a better realisation of how feelings can affect patients' emotional state and how they could better empathise with the patients.","PeriodicalId":504480,"journal":{"name":"Frontiers in Digital Health","volume":" 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140689358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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