Pub Date : 2024-08-09DOI: 10.3389/fdgth.2024.1403457
Alejandra Zepeda-Echavarria, Niek C. M. Ratering Arntz, A. H. Westra, Leonard J. van Schelven, Froukje E. Euwe, H. Noordmans, Melle B Vessies, R. R. van de Leur, R. Hassink, T. Wildbergh, Rien van der Zee, Pieter A. Doevendans, R. van Es, J. Jaspers
Cardiovascular diseases (CVDs) are a global burden that requires attention. For the detection and diagnosis of CVDs, the 12-lead ECG is a key tool. With technological advancements, ECG devices are becoming smaller and available for home use. Most of these devices contain a limited number of leads and are aimed to detect atrial fibrillation (AF). To investigate whether a four-electrode arrangement could provide enough information to diagnose other CVDs, further research is necessary. At the University Medical Center Utrecht in a multidisciplinary team, we developed the miniECG, a four-electrode ECG handheld system for scientific research in clinical environments (TRL6). This paper describes the process followed during the development of the miniECG. From assembling a multidisciplinary team, which includes engineers, cardiologists, and clinical physicians to the contribution of team members in the design input, design, and testing for safety and functionality of the device. Finally, we detail how the development process was composed by iterative design steps based on user input and intended use evolution. The miniECG is a device compliant for scientific research with patients within Dutch Medical Centers. We believe that hospital-based development led to a streamlined process, which could be applied for the design and development of other technologies used for scientific research in clinical environments.
{"title":"On the design and development of a handheld electrocardiogram device in a clinical setting","authors":"Alejandra Zepeda-Echavarria, Niek C. M. Ratering Arntz, A. H. Westra, Leonard J. van Schelven, Froukje E. Euwe, H. Noordmans, Melle B Vessies, R. R. van de Leur, R. Hassink, T. Wildbergh, Rien van der Zee, Pieter A. Doevendans, R. van Es, J. Jaspers","doi":"10.3389/fdgth.2024.1403457","DOIUrl":"https://doi.org/10.3389/fdgth.2024.1403457","url":null,"abstract":"Cardiovascular diseases (CVDs) are a global burden that requires attention. For the detection and diagnosis of CVDs, the 12-lead ECG is a key tool. With technological advancements, ECG devices are becoming smaller and available for home use. Most of these devices contain a limited number of leads and are aimed to detect atrial fibrillation (AF). To investigate whether a four-electrode arrangement could provide enough information to diagnose other CVDs, further research is necessary. At the University Medical Center Utrecht in a multidisciplinary team, we developed the miniECG, a four-electrode ECG handheld system for scientific research in clinical environments (TRL6). This paper describes the process followed during the development of the miniECG. From assembling a multidisciplinary team, which includes engineers, cardiologists, and clinical physicians to the contribution of team members in the design input, design, and testing for safety and functionality of the device. Finally, we detail how the development process was composed by iterative design steps based on user input and intended use evolution. The miniECG is a device compliant for scientific research with patients within Dutch Medical Centers. We believe that hospital-based development led to a streamlined process, which could be applied for the design and development of other technologies used for scientific research in clinical environments.","PeriodicalId":504480,"journal":{"name":"Frontiers in Digital Health","volume":"76 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141922308","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 : 2024-08-08DOI: 10.3389/fdgth.2024.1400369
Peter Düking, Jana Strahler, André Forster, Birgit Wallmann-Sperlich, B. Sperlich
The effect of displayed step count in smartwatches on the accuracy of daily step-count estimation and the potential underlying psychological factors have not been revealed. The study aimed for the following: (i) To investigate whether the counting and reporting of daily steps by a smartwatch increases the daily step-count estimation accuracy and (ii) to elucidating underlying psychological factors.A total of 34 healthy men and women participants wore smartwatches for 4 weeks. In week 1 (baseline), 3 (follow-up 1), and 8 (follow-up 2), the number of smartwatch displayed steps was blinded for each participant. In week 2 (Intervention), the number of steps was not blinded. During baseline and follow-ups 1 and 2, the participants were instructed to estimate their number of steps four times per day. During the 4-week wash-out period between follow-ups 1 and 2, no feedback was provided. The Body Awareness Questionnaire and the Body Responsiveness Questionnaire (BRQ) were used to elucidate the psychological facets of the assumed estimation accuracy.The mean absolute percentage error between the participants’ steps count estimations and measured steps counts were 29.49% (at baseline), 0.54% (intervention), 11.89% (follow-up 1), and 15.14% (follow-up 2), respectively. There was a significant effect between baseline and follow-up 1 [t (61.7) = 3.433, p < 0.001] but not between follow-up 1 and follow-up 2 [t (60.3) = −0.288, p = 0.774]. Only the BRQ subscale “Suppression of Bodily Sensations” appeared to be significant at the Baseline (p = 0.012; Bonferroni adjusted p = 0.048) as a factor influencing step-count estimation accuracy.The counting and reporting of daily steps with a smartwatch allows improving the subjective estimation accuracy of daily step counts, with a stabilizing effect for at least 6 weeks. Especially individuals who tend to suppress their bodily sensations are less accurate in their daily step-count estimation before the intervention.
{"title":"Smartwatch step counting: impact on daily step-count estimation accuracy","authors":"Peter Düking, Jana Strahler, André Forster, Birgit Wallmann-Sperlich, B. Sperlich","doi":"10.3389/fdgth.2024.1400369","DOIUrl":"https://doi.org/10.3389/fdgth.2024.1400369","url":null,"abstract":"The effect of displayed step count in smartwatches on the accuracy of daily step-count estimation and the potential underlying psychological factors have not been revealed. The study aimed for the following: (i) To investigate whether the counting and reporting of daily steps by a smartwatch increases the daily step-count estimation accuracy and (ii) to elucidating underlying psychological factors.A total of 34 healthy men and women participants wore smartwatches for 4 weeks. In week 1 (baseline), 3 (follow-up 1), and 8 (follow-up 2), the number of smartwatch displayed steps was blinded for each participant. In week 2 (Intervention), the number of steps was not blinded. During baseline and follow-ups 1 and 2, the participants were instructed to estimate their number of steps four times per day. During the 4-week wash-out period between follow-ups 1 and 2, no feedback was provided. The Body Awareness Questionnaire and the Body Responsiveness Questionnaire (BRQ) were used to elucidate the psychological facets of the assumed estimation accuracy.The mean absolute percentage error between the participants’ steps count estimations and measured steps counts were 29.49% (at baseline), 0.54% (intervention), 11.89% (follow-up 1), and 15.14% (follow-up 2), respectively. There was a significant effect between baseline and follow-up 1 [t (61.7) = 3.433, p < 0.001] but not between follow-up 1 and follow-up 2 [t (60.3) = −0.288, p = 0.774]. Only the BRQ subscale “Suppression of Bodily Sensations” appeared to be significant at the Baseline (p = 0.012; Bonferroni adjusted p = 0.048) as a factor influencing step-count estimation accuracy.The counting and reporting of daily steps with a smartwatch allows improving the subjective estimation accuracy of daily step counts, with a stabilizing effect for at least 6 weeks. Especially individuals who tend to suppress their bodily sensations are less accurate in their daily step-count estimation before the intervention.","PeriodicalId":504480,"journal":{"name":"Frontiers in Digital Health","volume":"47 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141929487","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}
Internet hospitals have become an important way to improve the accessibility of medical services and promote medical equity in China. However, there is still lack of research on the behavior of medical personnel during the process of using Internet medical services, and the elements of behavior that motivate doctors to actively use or resist the use of Internet hospitals are still not fully analyzed. The study applied the Theoretical Domains Framework to examine the factors affecting the engagement of medical personnel in Internet hospitals, with the aim of guiding the design of intervention to enhance Internet hospital participation.This study utilized qualitative analysis. Semi-structured questionnaires based on the Theoretical Domains Framework (TDF) and Capability-Opportunity-Motivation-Behavior (COM-B) model was developed and administered to 40 doctors and nurses at a Grade A tertiary hospital in Guangdong Province. Data was coded and analyzed using qualitative methods including Nvivo software.The research displayed 19 barriers and 7 enablers for the implementation of Internet hospitals, all 14 TDF domains impacted participation with motivation cited most frequently. Despite challenges, medical personnel exhibited a generally optimistic stance towards utilization of the Internet hospital. Major barriers include the higher requirement of diagnostic ability, objective difficulties brought by online consultation to the decision-making process, limitation of time and other resources, not ideal technological and institutional environment, lack of self-efficacy and negative expectation of results in online consultation. Key enablers include patient needs and the positive impact of online care on the medical process and patient experience.This qualitative study identified a range of barriers and enablers to Internet hospital participation according to medical personnel, providing an conceptual framework to guide further research evaluating implementation strategies. Expanded research and targeted interventions design can help optimize participation in this evolving healthcare delivery model.
{"title":"Analyzing the barriers and enablers to internet hospital implementation: a qualitative study of a tertiary hospital using TDF and COM-B framework","authors":"Xiaolong Wu, Yulin Kuang, Yonglin Guo, Ning Wei, Zichun Fan, Jingru Ling","doi":"10.3389/fdgth.2024.1362395","DOIUrl":"https://doi.org/10.3389/fdgth.2024.1362395","url":null,"abstract":"Internet hospitals have become an important way to improve the accessibility of medical services and promote medical equity in China. However, there is still lack of research on the behavior of medical personnel during the process of using Internet medical services, and the elements of behavior that motivate doctors to actively use or resist the use of Internet hospitals are still not fully analyzed. The study applied the Theoretical Domains Framework to examine the factors affecting the engagement of medical personnel in Internet hospitals, with the aim of guiding the design of intervention to enhance Internet hospital participation.This study utilized qualitative analysis. Semi-structured questionnaires based on the Theoretical Domains Framework (TDF) and Capability-Opportunity-Motivation-Behavior (COM-B) model was developed and administered to 40 doctors and nurses at a Grade A tertiary hospital in Guangdong Province. Data was coded and analyzed using qualitative methods including Nvivo software.The research displayed 19 barriers and 7 enablers for the implementation of Internet hospitals, all 14 TDF domains impacted participation with motivation cited most frequently. Despite challenges, medical personnel exhibited a generally optimistic stance towards utilization of the Internet hospital. Major barriers include the higher requirement of diagnostic ability, objective difficulties brought by online consultation to the decision-making process, limitation of time and other resources, not ideal technological and institutional environment, lack of self-efficacy and negative expectation of results in online consultation. Key enablers include patient needs and the positive impact of online care on the medical process and patient experience.This qualitative study identified a range of barriers and enablers to Internet hospital participation according to medical personnel, providing an conceptual framework to guide further research evaluating implementation strategies. Expanded research and targeted interventions design can help optimize participation in this evolving healthcare delivery model.","PeriodicalId":504480,"journal":{"name":"Frontiers in Digital Health","volume":"12 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141927433","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 : 2024-07-26DOI: 10.3389/fdgth.2024.1430245
Yuxuan Yang, H. Khorshidi, U. Aickelin
There has been growing attention to multi-class classification problems, particularly those challenges of imbalanced class distributions. To address these challenges, various strategies, including data-level re-sampling treatment and ensemble methods, have been introduced to bolster the performance of predictive models and Artificial Intelligence (AI) algorithms in scenarios where excessive level of imbalance is present. While most research and algorithm development have been focused on binary classification problems, in health informatics there is an increased interest in the field to address the problem of multi-class classification in imbalanced datasets. Multi-class imbalance problems bring forth more complex challenges, as a delicate approach is required to generate synthetic data and simultaneously maintain the relationship between the multiple classes. The aim of this review paper is to examine over-sampling methods tailored for medical and other datasets with multi-class imbalance. Out of 2,076 peer-reviewed papers identified through searches, 197 eligible papers were chosen and thoroughly reviewed for inclusion, narrowing to 37 studies being selected for in-depth analysis. These studies are categorised into four categories: metric, adaptive, structure-based, and hybrid approaches. The most significant finding is the emerging trend toward hybrid resampling methods that combine the strengths of various techniques to effectively address the problem of imbalanced data. This paper provides an extensive analysis of each selected study, discusses their findings, and outlines directions for future research.
{"title":"A review on over-sampling techniques in classification of multi-class imbalanced datasets: insights for medical problems","authors":"Yuxuan Yang, H. Khorshidi, U. Aickelin","doi":"10.3389/fdgth.2024.1430245","DOIUrl":"https://doi.org/10.3389/fdgth.2024.1430245","url":null,"abstract":"There has been growing attention to multi-class classification problems, particularly those challenges of imbalanced class distributions. To address these challenges, various strategies, including data-level re-sampling treatment and ensemble methods, have been introduced to bolster the performance of predictive models and Artificial Intelligence (AI) algorithms in scenarios where excessive level of imbalance is present. While most research and algorithm development have been focused on binary classification problems, in health informatics there is an increased interest in the field to address the problem of multi-class classification in imbalanced datasets. Multi-class imbalance problems bring forth more complex challenges, as a delicate approach is required to generate synthetic data and simultaneously maintain the relationship between the multiple classes. The aim of this review paper is to examine over-sampling methods tailored for medical and other datasets with multi-class imbalance. Out of 2,076 peer-reviewed papers identified through searches, 197 eligible papers were chosen and thoroughly reviewed for inclusion, narrowing to 37 studies being selected for in-depth analysis. These studies are categorised into four categories: metric, adaptive, structure-based, and hybrid approaches. The most significant finding is the emerging trend toward hybrid resampling methods that combine the strengths of various techniques to effectively address the problem of imbalanced data. This paper provides an extensive analysis of each selected study, discusses their findings, and outlines directions for future research.","PeriodicalId":504480,"journal":{"name":"Frontiers in Digital Health","volume":"63 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141798652","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 : 2024-07-26DOI: 10.3389/fdgth.2024.1422396
M. S. R. Jabin
{"title":"The need for a refined classification system and national incident reporting system for health information technology-related incidents","authors":"M. S. R. Jabin","doi":"10.3389/fdgth.2024.1422396","DOIUrl":"https://doi.org/10.3389/fdgth.2024.1422396","url":null,"abstract":"","PeriodicalId":504480,"journal":{"name":"Frontiers in Digital Health","volume":"43 39","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141800057","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 : 2024-07-25DOI: 10.3389/fdgth.2024.1351637
Michael Yang, Abd-Allah El-Attar, Theodora Chaspari
Machine learning (ML) algorithms have been heralded as promising solutions to the realization of assistive systems in digital healthcare, due to their ability to detect fine-grain patterns that are not easily perceived by humans. Yet, ML algorithms have also been critiqued for treating individuals differently based on their demography, thus propagating existing disparities. This paper explores gender and race bias in speech-based ML algorithms that detect behavioral and mental health outcomes.This paper examines potential sources of bias in the data used to train the ML, encompassing acoustic features extracted from speech signals and associated labels, as well as in the ML decisions. The paper further examines approaches to reduce existing bias via using the features that are the least informative of one’s demographic information as the ML input, and transforming the feature space in an adversarial manner to diminish the evidence of the demographic information while retaining information about the focal behavioral and mental health state.Results are presented in two domains, the first pertaining to gender and race bias when estimating levels of anxiety, and the second pertaining to gender bias in detecting depression. Findings indicate the presence of statistically significant differences in both acoustic features and labels among demographic groups, as well as differential ML performance among groups. The statistically significant differences present in the label space are partially preserved in the ML decisions. Although variations in ML performance across demographic groups were noted, results are mixed regarding the models’ ability to accurately estimate healthcare outcomes for the sensitive groups.These findings underscore the necessity for careful and thoughtful design in developing ML models that are capable of maintaining crucial aspects of the data and perform effectively across all populations in digital healthcare applications.
机器学习（ML）算法能够检测到人类不易察觉的细粒度模式，因此被誉为实现数字医疗辅助系统的理想解决方案。然而，ML 算法也受到了批评，因为它们会根据人口统计学对个人进行区别对待，从而扩大现有的差距。本文探讨了用于检测行为和心理健康结果的基于语音的人工智能算法中的性别和种族偏见。本文研究了用于训练人工智能的数据中潜在的偏见来源，包括从语音信号中提取的声学特征和相关标签，以及人工智能决策中的偏见。论文进一步研究了减少现有偏差的方法，即使用对个人人口信息信息量最小的特征作为 ML 输入，并以对抗的方式转换特征空间，以减少人口信息的证据，同时保留有关焦点行为和心理健康状态的信息。论文介绍了两个领域的结果，第一个领域涉及估计焦虑水平时的性别和种族偏差，第二个领域涉及检测抑郁时的性别偏差。研究结果表明，不同人口群体在声音特征和标签方面都存在显著的统计学差异，不同群体之间的 ML 性能也存在差异。标签空间中存在的统计意义上的显著差异在 ML 决策中得到了部分保留。这些发现突出表明，在开发 ML 模型时，有必要进行细致周到的设计，使其能够保持数据的关键方面，并在数字医疗应用中有效地跨越所有人群。
{"title":"Deconstructing demographic bias in speech-based machine learning models for digital health","authors":"Michael Yang, Abd-Allah El-Attar, Theodora Chaspari","doi":"10.3389/fdgth.2024.1351637","DOIUrl":"https://doi.org/10.3389/fdgth.2024.1351637","url":null,"abstract":"Machine learning (ML) algorithms have been heralded as promising solutions to the realization of assistive systems in digital healthcare, due to their ability to detect fine-grain patterns that are not easily perceived by humans. Yet, ML algorithms have also been critiqued for treating individuals differently based on their demography, thus propagating existing disparities. This paper explores gender and race bias in speech-based ML algorithms that detect behavioral and mental health outcomes.This paper examines potential sources of bias in the data used to train the ML, encompassing acoustic features extracted from speech signals and associated labels, as well as in the ML decisions. The paper further examines approaches to reduce existing bias via using the features that are the least informative of one’s demographic information as the ML input, and transforming the feature space in an adversarial manner to diminish the evidence of the demographic information while retaining information about the focal behavioral and mental health state.Results are presented in two domains, the first pertaining to gender and race bias when estimating levels of anxiety, and the second pertaining to gender bias in detecting depression. Findings indicate the presence of statistically significant differences in both acoustic features and labels among demographic groups, as well as differential ML performance among groups. The statistically significant differences present in the label space are partially preserved in the ML decisions. Although variations in ML performance across demographic groups were noted, results are mixed regarding the models’ ability to accurately estimate healthcare outcomes for the sensitive groups.These findings underscore the necessity for careful and thoughtful design in developing ML models that are capable of maintaining crucial aspects of the data and perform effectively across all populations in digital healthcare applications.","PeriodicalId":504480,"journal":{"name":"Frontiers in Digital Health","volume":"30 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141805410","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 : 2024-07-24DOI: 10.3389/fdgth.2024.1394901
A. C. D. De Moraes, Lorrane Cristine Conceição da Silva, Barbara Saldanha Lima, K. A. Marin, Ethan T. Hunt, M. Nascimento-Ferreira
We aimed to test the reliability and structural validity (also called dimensionality) of the online Pittsburgh Sleep Quality Index among college students from low-income regions.We assessed 195 Brazilian college students from a low-income region (Gini index of 0.56), of whom 117 were reassessed to evaluate the reliability. We collected all data in a self-reported online twice, 2-week apart. We evaluated reliability and structural validity.All questionnaire components showed reliability, correlation coefficient ≥0.49. In the structural validity, the confirmatory analysis showed better global model adjustment for the one-factor (RMSEA = 0.019; SRMR = 0.041; CFI = 0.992; TLI = 0.986) solution compared with two-factor (RMSEA = 0.099; SRMR = 0.070; CFI = 0.764; TLI = 0.619) and three-factor (RMSEA = 0.108; SRMR = 0.066; CFI = 0.763; TLI = 0.548) solutions, respectively.The online questionnaire presents acceptable reliability and structural validity in Brazilian low-income regions.
{"title":"Reliability and validity of the online Pittsburgh sleep quality index in college students from low-income regions","authors":"A. C. D. De Moraes, Lorrane Cristine Conceição da Silva, Barbara Saldanha Lima, K. A. Marin, Ethan T. Hunt, M. Nascimento-Ferreira","doi":"10.3389/fdgth.2024.1394901","DOIUrl":"https://doi.org/10.3389/fdgth.2024.1394901","url":null,"abstract":"We aimed to test the reliability and structural validity (also called dimensionality) of the online Pittsburgh Sleep Quality Index among college students from low-income regions.We assessed 195 Brazilian college students from a low-income region (Gini index of 0.56), of whom 117 were reassessed to evaluate the reliability. We collected all data in a self-reported online twice, 2-week apart. We evaluated reliability and structural validity.All questionnaire components showed reliability, correlation coefficient ≥0.49. In the structural validity, the confirmatory analysis showed better global model adjustment for the one-factor (RMSEA = 0.019; SRMR = 0.041; CFI = 0.992; TLI = 0.986) solution compared with two-factor (RMSEA = 0.099; SRMR = 0.070; CFI = 0.764; TLI = 0.619) and three-factor (RMSEA = 0.108; SRMR = 0.066; CFI = 0.763; TLI = 0.548) solutions, respectively.The online questionnaire presents acceptable reliability and structural validity in Brazilian low-income regions.","PeriodicalId":504480,"journal":{"name":"Frontiers in Digital Health","volume":"56 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141808957","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 : 2024-07-23DOI: 10.3389/fdgth.2024.1440986
J. Tröger, Felix Dörr, Louisa Schwed, N. Linz, Alexandra König, Tabea Thies, J. Orozco-Arroyave, J. Rusz
Dysarthria, a motor speech disorder caused by muscle weakness or paralysis, severely impacts speech intelligibility and quality of life. The condition is prevalent in motor speech disorders such as Parkinson's disease (PD), atypical parkinsonism such as progressive supranuclear palsy (PSP), Huntington's disease (HD), and amyotrophic lateral sclerosis (ALS). Improving intelligibility is not only an outcome that matters to patients but can also play a critical role as an endpoint in clinical research and drug development. This study validates a digital measure for speech intelligibility, the ki: SB-M intelligibility score, across various motor speech disorders and languages following the Digital Medicine Society (DiMe) V3 framework.The study used four datasets: healthy controls (HCs) and patients with PD, HD, PSP, and ALS from Czech, Colombian, and German populations. Participants’ speech intelligibility was assessed using the ki: SB-M intelligibility score, which is derived from automatic speech recognition (ASR) systems. Verification with inter-ASR reliability and temporal consistency, analytical validation with correlations to gold standard clinical dysarthria scores in each disease, and clinical validation with group comparisons between HCs and patients were performed.Verification showed good to excellent inter-rater reliability between ASR systems and fair to good consistency. Analytical validation revealed significant correlations between the SB-M intelligibility score and established clinical measures for speech impairments across all patient groups and languages. Clinical validation demonstrated significant differences in intelligibility scores between pathological groups and healthy controls, indicating the measure's discriminative capability.The ki: SB-M intelligibility score is a reliable, valid, and clinically relevant tool for assessing speech intelligibility in motor speech disorders. It holds promise for improving clinical trials through automated, objective, and scalable assessments. Future studies should explore its utility in monitoring disease progression and therapeutic efficacy as well as add data from further dysarthrias to the validation.
{"title":"An automatic measure for speech intelligibility in dysarthrias—validation across multiple languages and neurological disorders","authors":"J. Tröger, Felix Dörr, Louisa Schwed, N. Linz, Alexandra König, Tabea Thies, J. Orozco-Arroyave, J. Rusz","doi":"10.3389/fdgth.2024.1440986","DOIUrl":"https://doi.org/10.3389/fdgth.2024.1440986","url":null,"abstract":"Dysarthria, a motor speech disorder caused by muscle weakness or paralysis, severely impacts speech intelligibility and quality of life. The condition is prevalent in motor speech disorders such as Parkinson's disease (PD), atypical parkinsonism such as progressive supranuclear palsy (PSP), Huntington's disease (HD), and amyotrophic lateral sclerosis (ALS). Improving intelligibility is not only an outcome that matters to patients but can also play a critical role as an endpoint in clinical research and drug development. This study validates a digital measure for speech intelligibility, the ki: SB-M intelligibility score, across various motor speech disorders and languages following the Digital Medicine Society (DiMe) V3 framework.The study used four datasets: healthy controls (HCs) and patients with PD, HD, PSP, and ALS from Czech, Colombian, and German populations. Participants’ speech intelligibility was assessed using the ki: SB-M intelligibility score, which is derived from automatic speech recognition (ASR) systems. Verification with inter-ASR reliability and temporal consistency, analytical validation with correlations to gold standard clinical dysarthria scores in each disease, and clinical validation with group comparisons between HCs and patients were performed.Verification showed good to excellent inter-rater reliability between ASR systems and fair to good consistency. Analytical validation revealed significant correlations between the SB-M intelligibility score and established clinical measures for speech impairments across all patient groups and languages. Clinical validation demonstrated significant differences in intelligibility scores between pathological groups and healthy controls, indicating the measure's discriminative capability.The ki: SB-M intelligibility score is a reliable, valid, and clinically relevant tool for assessing speech intelligibility in motor speech disorders. It holds promise for improving clinical trials through automated, objective, and scalable assessments. Future studies should explore its utility in monitoring disease progression and therapeutic efficacy as well as add data from further dysarthrias to the validation.","PeriodicalId":504480,"journal":{"name":"Frontiers in Digital Health","volume":"131 45","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141811237","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 : 2024-06-14DOI: 10.3389/fdgth.2024.1337667
Berenike Lisa Blaser, Mathias Weymar, J. Wendt
Heart rate variability biofeedback (HRVB) is a well-studied intervention known for its positive effects on emotional, cognitive, and physiological well-being, including relief from depressive symptoms. However, its practical use is hampered by high costs and a lack of trained professionals. Smartphone-based HRVB, which eliminates the need for external devices, offers a promising alternative, albeit with limited research. Additionally, premenstrual symptoms are highly prevalent among menstruating individuals, and there is a need for low-cost, accessible interventions with minimal side effects. With this pilot study, we aim to test, for the first time, the influence of smartphone-based HRVB on depressive and premenstrual symptoms, as well as anxiety/stress symptoms and attentional control.Twenty-seven participants with above-average premenstrual or depressive symptoms underwent a 4-week photoplethysmography smartphone-based HRVB intervention using a waitlist-control design. Laboratory sessions were conducted before and after the intervention, spaced exactly 4 weeks apart. Assessments included resting vagally mediated heart rate variability (vmHRV), attentional control via the revised attention network test (ANT-R), depressive symptoms assessed with the BDI-II questionnaire, and stress/anxiety symptoms measured using the DASS questionnaire. Premenstrual symptomatology was recorded through the PAF questionnaire if applicable. Data analysis employed linear mixed models.We observed improvements in premenstrual, depressive, and anxiety/stress symptoms, as well as the Executive Functioning Score of the ANT-R during the intervention period but not during the waitlist phase. However, we did not find significant changes in vmHRV or the Orienting Score of the ANT-R.These findings are promising, both in terms of the effectiveness of smartphone-based HRVB and its potential to alleviate premenstrual symptoms. Nevertheless, to provide a solid recommendation regarding the use of HRVB for improving premenstrual symptoms, further research with a larger sample size is needed to replicate these effects.
{"title":"Alleviating premenstrual symptoms with smartphone-based heart rate variability biofeedback training: a pilot study","authors":"Berenike Lisa Blaser, Mathias Weymar, J. Wendt","doi":"10.3389/fdgth.2024.1337667","DOIUrl":"https://doi.org/10.3389/fdgth.2024.1337667","url":null,"abstract":"Heart rate variability biofeedback (HRVB) is a well-studied intervention known for its positive effects on emotional, cognitive, and physiological well-being, including relief from depressive symptoms. However, its practical use is hampered by high costs and a lack of trained professionals. Smartphone-based HRVB, which eliminates the need for external devices, offers a promising alternative, albeit with limited research. Additionally, premenstrual symptoms are highly prevalent among menstruating individuals, and there is a need for low-cost, accessible interventions with minimal side effects. With this pilot study, we aim to test, for the first time, the influence of smartphone-based HRVB on depressive and premenstrual symptoms, as well as anxiety/stress symptoms and attentional control.Twenty-seven participants with above-average premenstrual or depressive symptoms underwent a 4-week photoplethysmography smartphone-based HRVB intervention using a waitlist-control design. Laboratory sessions were conducted before and after the intervention, spaced exactly 4 weeks apart. Assessments included resting vagally mediated heart rate variability (vmHRV), attentional control via the revised attention network test (ANT-R), depressive symptoms assessed with the BDI-II questionnaire, and stress/anxiety symptoms measured using the DASS questionnaire. Premenstrual symptomatology was recorded through the PAF questionnaire if applicable. Data analysis employed linear mixed models.We observed improvements in premenstrual, depressive, and anxiety/stress symptoms, as well as the Executive Functioning Score of the ANT-R during the intervention period but not during the waitlist phase. However, we did not find significant changes in vmHRV or the Orienting Score of the ANT-R.These findings are promising, both in terms of the effectiveness of smartphone-based HRVB and its potential to alleviate premenstrual symptoms. Nevertheless, to provide a solid recommendation regarding the use of HRVB for improving premenstrual symptoms, further research with a larger sample size is needed to replicate these effects.","PeriodicalId":504480,"journal":{"name":"Frontiers in Digital Health","volume":"57 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141344947","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 : 2024-06-14DOI: 10.3389/fdgth.2024.1289451
Megan Fahy, Marguerite Barry
Recent studies have found that there is scope for communication technologies to increase online social capital. Although studies have linked online social capital and mental well-being, there is a need to identify the causal pathways within this relationship. This study explores the role of loneliness in the relationship between computer-mediated communication, online social capital and well-being.The study used an online questionnaire and had 217 participants. William's 2006 scale was used to measure individuals’ online social capital, and structural equational modelling (SEM) was used to explore the relationship between computer-mediated communication, use, levels of loneliness, online social capital and well-being. This study was conducted remotely during the first COVID-19 lockdown in Ireland.High levels of online communication mitigated the otherwise negative effects of loneliness on well-being when online interaction fostered online social capital.Overall, the proposed model offers qualified support for the continued analysis of technology-mediated communication as a potential source for building online social capital and improving the well-being of particular individuals with high levels of loneliness.
{"title":"Investigating the interplay of loneliness, computer-mediated communication, online social capital, and well-being: insights from a COVID-19 lockdown study","authors":"Megan Fahy, Marguerite Barry","doi":"10.3389/fdgth.2024.1289451","DOIUrl":"https://doi.org/10.3389/fdgth.2024.1289451","url":null,"abstract":"Recent studies have found that there is scope for communication technologies to increase online social capital. Although studies have linked online social capital and mental well-being, there is a need to identify the causal pathways within this relationship. This study explores the role of loneliness in the relationship between computer-mediated communication, online social capital and well-being.The study used an online questionnaire and had 217 participants. William's 2006 scale was used to measure individuals’ online social capital, and structural equational modelling (SEM) was used to explore the relationship between computer-mediated communication, use, levels of loneliness, online social capital and well-being. This study was conducted remotely during the first COVID-19 lockdown in Ireland.High levels of online communication mitigated the otherwise negative effects of loneliness on well-being when online interaction fostered online social capital.Overall, the proposed model offers qualified support for the continued analysis of technology-mediated communication as a potential source for building online social capital and improving the well-being of particular individuals with high levels of loneliness.","PeriodicalId":504480,"journal":{"name":"Frontiers in Digital Health","volume":"57 47","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141339116","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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