: Wearable consumer activity trackers have become a popular tool for longitudinal monitoring of sleep quality. However, sleep data were routinely visualized in isolation from other contextual information. In this paper, we proposed a sleep analytics method to identify the associations between sleep quality and the contextual data that are readily measurable with a single Fitbit device. Different from prior studies that only focused on the daily aggregation of the contextual factors (e.g., total step counts), our method considers the intraday temporal patterns of these factors. Time-domain, frequency-domain, and nonlinear features were derived using the minute-by-minute intraday step and heart rate time series. The results showed that some of the identified contextual features such as the zero-crossing of steps and the absolute energy of heart rate could lead to actionable insights. While the nonlinear features—such as the average and longest diagonal line length derived through the recurrent quantitative analysis of the step time series—may not lead to insights that can be immediately acted on, they generated new hypotheses for further scientific studies. The results also showed that when dealing with data of consumer wearables, the individual-level analysis could generate more personally relevant insight than the cohort-level analysis.
{"title":"Context-aware Sleep Analysis with Intraday Steps and Heart Rate Time Series Data from Consumer Activity Trackers","authors":"Zilu Liang, Huyen Hoang Nhung, Lauriane Bertrand, Nathan Cleyet-Marrel","doi":"10.5220/0010892900003123","DOIUrl":"https://doi.org/10.5220/0010892900003123","url":null,"abstract":": Wearable consumer activity trackers have become a popular tool for longitudinal monitoring of sleep quality. However, sleep data were routinely visualized in isolation from other contextual information. In this paper, we proposed a sleep analytics method to identify the associations between sleep quality and the contextual data that are readily measurable with a single Fitbit device. Different from prior studies that only focused on the daily aggregation of the contextual factors (e.g., total step counts), our method considers the intraday temporal patterns of these factors. Time-domain, frequency-domain, and nonlinear features were derived using the minute-by-minute intraday step and heart rate time series. The results showed that some of the identified contextual features such as the zero-crossing of steps and the absolute energy of heart rate could lead to actionable insights. While the nonlinear features—such as the average and longest diagonal line length derived through the recurrent quantitative analysis of the step time series—may not lead to insights that can be immediately acted on, they generated new hypotheses for further scientific studies. The results also showed that when dealing with data of consumer wearables, the individual-level analysis could generate more personally relevant insight than the cohort-level analysis.","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80777673","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 : 2022-01-01DOI: 10.5220/0010843200003123
Muhammad Sulaiman, Anne Håkansson, Randi Karlsen
Health promotion is to enable people to take control over their health. Digital health with mHealth empowers users to establish proactive health, ubiquitously. The users shall have increased control over their health to improve their life by being proactive. To develop proactive health with the principles of prediction, prevention, and ubiquitous health, artificial intelligence with mHealth can play a pivotal role. There are various challenges for establishing proactive mHealth. For example, the system must be adaptive and provide timely interventions by considering the uniqueness of the user. The context of the user is also highly relevant for proactive mHealth. The context provides parameters as input along with information to formulate the current state of the user. Automated decision-making is significant with user-level decision-making as it enables decisions to promote well-being by technological means without human involvement. This paper presents a design framework of AI-enabled proactive mHealth that includes automated decision-making with predictive analytics, Just-in-time adaptive interventions and a P5 approach to mHealth. The significance of user-level decision-making for automated decision-making is presented. Furthermore, the paper provides a holistic view of the user's context with profile and characteristics. The paper also discusses the need for multiple parameters as inputs, and the identification of sources e.g., wearables, sensors, and other resources, with the challenges in the implementation of the framework. Finally, a proof-of-concept based on the framework provides design and implementation steps, architecture, goals, and feedback process. The framework shall provide the basis for the further development of AI-enabled proactive mHealth.
{"title":"A Framework for AI-enabled Proactive mHealth with Automated Decision-making for a User's Context","authors":"Muhammad Sulaiman, Anne Håkansson, Randi Karlsen","doi":"10.5220/0010843200003123","DOIUrl":"https://doi.org/10.5220/0010843200003123","url":null,"abstract":"Health promotion is to enable people to take control over their health. Digital health with mHealth empowers users to establish proactive health, ubiquitously. The users shall have increased control over their health to improve their life by being proactive. To develop proactive health with the principles of prediction, prevention, and ubiquitous health, artificial intelligence with mHealth can play a pivotal role. There are various challenges for establishing proactive mHealth. For example, the system must be adaptive and provide timely interventions by considering the uniqueness of the user. The context of the user is also highly relevant for proactive mHealth. The context provides parameters as input along with information to formulate the current state of the user. Automated decision-making is significant with user-level decision-making as it enables decisions to promote well-being by technological means without human involvement. This paper presents a design framework of AI-enabled proactive mHealth that includes automated decision-making with predictive analytics, Just-in-time adaptive interventions and a P5 approach to mHealth. The significance of user-level decision-making for automated decision-making is presented. Furthermore, the paper provides a holistic view of the user's context with profile and characteristics. The paper also discusses the need for multiple parameters as inputs, and the identification of sources e.g., wearables, sensors, and other resources, with the challenges in the implementation of the framework. Finally, a proof-of-concept based on the framework provides design and implementation steps, architecture, goals, and feedback process. The framework shall provide the basis for the further development of AI-enabled proactive mHealth.","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80358277","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 : 2022-01-01DOI: 10.5220/0010850000003123
Hasnae Zerouaoui, A. Idri
Breast cancer (BC) is a leading cause of death among women worldwide. It remains a critical challenge, causing over 10 million deaths globally in 2020. Medical images analysis is the most promising research area since it provides facilities for diagnosing several diseases such as breast cancer. The present paper carries out an empirical evaluation of recent deep Convolutional Neural Network (CNN) architectures for a binary classification of breast cytological images based fined tuned versions of seven deep learning techniques: VGG16, VGG19, DenseNet201, InceptionResNetV2, InceptionV3, ResNet50 and MobileNetV2. The empirical evaluations used: (1) four classification performance criteria (accuracy, recall, precision and F1score), (2) Scott Knott (SK) statistical test to select the best cluster of the outperforming architectures, and (3) borda count voting system to rank the best performing architectures. All the evaluations were over the FNAC dataset which contain 212 images. Results showed the potential of deep learning techniques to classify breast cancer in malignant and benign, therefor the findings of this study recommend the use of MobileNetV2 for the classification of the breast cancer cytological images since it gave the best results with an accuracy of
乳腺癌(BC)是全世界妇女死亡的主要原因。它仍然是一个严峻的挑战，2020年在全球造成1000多万人死亡。医学影像分析可以诊断乳腺癌等多种疾病，是最有前途的研究领域。本文对基于VGG16、VGG19、DenseNet201、InceptionResNetV2、InceptionV3、ResNet50和MobileNetV2这七种深度学习技术的精细化版本的乳腺细胞学图像二元分类的最新深度卷积神经网络(CNN)架构进行了实证评估。实证评价采用:(1)4个分类性能标准(准确率、召回率、精度和F1score)， (2) Scott Knott (SK)统计检验选择表现优异的架构的最佳聚类，(3)borda计数投票系统对表现最佳的架构进行排名。所有的评估都是在包含212张图像的FNAC数据集上进行的。结果显示深度学习技术在乳腺癌的恶性和良性分类方面的潜力，因此本研究的发现推荐使用MobileNetV2进行乳腺癌细胞学图像的分类，因为它给出了最好的结果，准确率为
{"title":"Classifying Breast Cytological Images using Deep Learning Architectures","authors":"Hasnae Zerouaoui, A. Idri","doi":"10.5220/0010850000003123","DOIUrl":"https://doi.org/10.5220/0010850000003123","url":null,"abstract":"Breast cancer (BC) is a leading cause of death among women worldwide. It remains a critical challenge, causing over 10 million deaths globally in 2020. Medical images analysis is the most promising research area since it provides facilities for diagnosing several diseases such as breast cancer. The present paper carries out an empirical evaluation of recent deep Convolutional Neural Network (CNN) architectures for a binary classification of breast cytological images based fined tuned versions of seven deep learning techniques: VGG16, VGG19, DenseNet201, InceptionResNetV2, InceptionV3, ResNet50 and MobileNetV2. The empirical evaluations used: (1) four classification performance criteria (accuracy, recall, precision and F1score), (2) Scott Knott (SK) statistical test to select the best cluster of the outperforming architectures, and (3) borda count voting system to rank the best performing architectures. All the evaluations were over the FNAC dataset which contain 212 images. Results showed the potential of deep learning techniques to classify breast cancer in malignant and benign, therefor the findings of this study recommend the use of MobileNetV2 for the classification of the breast cancer cytological images since it gave the best results with an accuracy of","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90714799","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 : 2022-01-01DOI: 10.5220/0010748600003123
Bushra Kundi, Dhayananth Dharmalingam, Rediet Tadesse, Alexandra Creighton, Rachel Gorman, P. Maret, Fabrice Muhlenbach, Alexis Buettgen, E. Dua, T. Mgwigwi, S. Dinca-Panaitescu, Christo El Morr
The lack of readily available disability data is a major barrier for disability advocacy globally. The collection and access to disability data is crucial to address social inequities, discrimination, and human rights violations within the disability community. The Disability Wiki project intends to use AI techniques such as Machine Learning and Semantic Web to extract and store existing disability-related data into one platform (Wikibase) and to provide a multilingual natural language enabled search engine and a screen-reader-accessible for its
{"title":"Disability Advocacy using a Smart Virtual Community","authors":"Bushra Kundi, Dhayananth Dharmalingam, Rediet Tadesse, Alexandra Creighton, Rachel Gorman, P. Maret, Fabrice Muhlenbach, Alexis Buettgen, E. Dua, T. Mgwigwi, S. Dinca-Panaitescu, Christo El Morr","doi":"10.5220/0010748600003123","DOIUrl":"https://doi.org/10.5220/0010748600003123","url":null,"abstract":"The lack of readily available disability data is a major barrier for disability advocacy globally. The collection and access to disability data is crucial to address social inequities, discrimination, and human rights violations within the disability community. The Disability Wiki project intends to use AI techniques such as Machine Learning and Semantic Web to extract and store existing disability-related data into one platform (Wikibase) and to provide a multilingual natural language enabled search engine and a screen-reader-accessible for its","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89219621","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 : 2022-01-01DOI: 10.5220/0010854900003123
Pedro Morais, Ana Pinheiro, M. Fonseca, Carla Quintão
{"title":"The Mindfulness Meditation Effect on States of Anxiety, Depression, Stress and Quality of Life","authors":"Pedro Morais, Ana Pinheiro, M. Fonseca, Carla Quintão","doi":"10.5220/0010854900003123","DOIUrl":"https://doi.org/10.5220/0010854900003123","url":null,"abstract":"","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89953454","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 : 2022-01-01DOI: 10.5220/0010742300003123
Luna El Bizri, N. Badr
Non-communicable diseases (NCDs) are still the number one killer in the world. Their economic burden is heavy, notably in low-and-middle-income countries. Lebanon is a middle-income country in the Eastern Mediterranean region. The arising COVID-19 pandemic, political and economic instability, inadequate funding, and deteriorated infrastructure have rendered the country a fragile setting, significantly affecting persons with non-communicable diseases. Improving the patient journey during the COVID-19 pandemic and a comprehensive approach to NCD management is important during emergencies.This paper used a quantitate literature review to provide a theoretical framework touching NCDs patients in their journey during emergencies and crisis. It further adopted the Sendai Framework to draw the road for these patients in Lebanon. The ultimate goal is better preparedness and response in case of emergencies and disasters. It calls for a clear and coordinated action plan addressing the challenges posed by NCDs to a resilient country's response. This paper provides an overview of the situation of NCD patients in Lebanon during the COVID19 pandemic. It suggests strategies to address noncommunicable diseases guided by the Sendai Framework's four priorities, based on previous experiences.
{"title":"Protecting Non-communicable Diseases Patients during Pandemics: Fundamental Rules for Engagement and the Case of Lebanon","authors":"Luna El Bizri, N. Badr","doi":"10.5220/0010742300003123","DOIUrl":"https://doi.org/10.5220/0010742300003123","url":null,"abstract":"Non-communicable diseases (NCDs) are still the number one killer in the world. Their economic burden is heavy, notably in low-and-middle-income countries. Lebanon is a middle-income country in the Eastern Mediterranean region. The arising COVID-19 pandemic, political and economic instability, inadequate funding, and deteriorated infrastructure have rendered the country a fragile setting, significantly affecting persons with non-communicable diseases. Improving the patient journey during the COVID-19 pandemic and a comprehensive approach to NCD management is important during emergencies.This paper used a quantitate literature review to provide a theoretical framework touching NCDs patients in their journey during emergencies and crisis. It further adopted the Sendai Framework to draw the road for these patients in Lebanon. The ultimate goal is better preparedness and response in case of emergencies and disasters. It calls for a clear and coordinated action plan addressing the challenges posed by NCDs to a resilient country's response. This paper provides an overview of the situation of NCD patients in Lebanon during the COVID19 pandemic. It suggests strategies to address noncommunicable diseases guided by the Sendai Framework's four priorities, based on previous experiences.","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86529103","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 : 2022-01-01DOI: 10.5220/0010972300003123
A. Hausberger, B. Tappeiner, René Baranyi, T. Grechenig
: A significant part of patients who have recovered from COVID-19 has been experiencing COVID-like symptoms for weeks and months after the initial disease. These patients have been called “Long COVID patients”. Currently, guidelines are being published that inform about these symptoms and may deliver a basis for medical diagnoses. In this paper, the possibility of a Long COVID patient support app is being discussed. To gain insight into the needs and requirements of patients with Long COVID, a questionnaire was conducted with 193 participants from a self help-group in Austria. The results show that Long COVID has a profound nega-tive impact on the daily lives of people who are suffering from the disease. Also, the results show a demand for more support and indicate the role that a Long COVID symptom tracking app could play in this context. Concerning digital support, ten crucial features of a potential Long COVID support app were identified by analyzing the answers to the questionnaire. Apart from health and symptom tracking features, sharing of data with medical professionals, appointment management, and news features were considered important features to support patients suffering from Long COVID throughout their journey to get better.
{"title":"Long COVID Diary: A User Centered Approach for the Design of a Mobile Application Supporting Long COVID Patients","authors":"A. Hausberger, B. Tappeiner, René Baranyi, T. Grechenig","doi":"10.5220/0010972300003123","DOIUrl":"https://doi.org/10.5220/0010972300003123","url":null,"abstract":": A significant part of patients who have recovered from COVID-19 has been experiencing COVID-like symptoms for weeks and months after the initial disease. These patients have been called “Long COVID patients”. Currently, guidelines are being published that inform about these symptoms and may deliver a basis for medical diagnoses. In this paper, the possibility of a Long COVID patient support app is being discussed. To gain insight into the needs and requirements of patients with Long COVID, a questionnaire was conducted with 193 participants from a self help-group in Austria. The results show that Long COVID has a profound nega-tive impact on the daily lives of people who are suffering from the disease. Also, the results show a demand for more support and indicate the role that a Long COVID symptom tracking app could play in this context. Concerning digital support, ten crucial features of a potential Long COVID support app were identified by analyzing the answers to the questionnaire. Apart from health and symptom tracking features, sharing of data with medical professionals, appointment management, and news features were considered important features to support patients suffering from Long COVID throughout their journey to get better.","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91350117","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 : 2022-01-01DOI: 10.5220/0010915400003123
E. Teeple, P. Joshi, R. Pande, Y. Huang, Akshat Karambe, M. Latta-Mahieu, S. Sardi, A. Cedazo-Mínguez, Katherine W. Klinger, A. Flores-Morales, S. Madden, D. Rajpal, Dinesh Kumar
Transfer of cell type labels as part of the comprehensive integration of multiple single nucleus RNA sequencing (snRNAseq) datasets offers a powerful tool for comparing cell populations and their activation states in normal versus disease conditions. Another potential use for these methods is annotation alignments between samples from different anatomic areas. This study describes and evaluates an integration analysis applied for profiling of oligodendrocyte lineage nuclei sequenced from human brain putamen region tissue samples for healthy Control (n = 3), Parkinson’s Disease (PD; n = 3) and Multiple System Atrophy (MSA; n = 3) subjects with label transfer to substantia nigra region tissue samples for healthy Control (n = 5) subjects. PD and MSA are both synucleinopathies, progressive neurodegenerative disorders characterized by nervous system aggregates of α-synuclein, a protein encoded by the SNCA gene. Histologic findings and genetic evidence suggest links between oligodendrocyte biology and synucleinopathy pathogenesis. In this work, we first identify disease-associated changes among transcriptionally distinct oligodendrocyte subpopulations in putamen. We then apply label transfer methods to generalize our findings from putamen to substantia nigra, a brain region characteristically impacted in PD and variably affected in MSA. Interestingly, our analysis predicts oligodendrocytes in substantia nigra include a significantly greater proportion of an oligodendrocyte subpopulation identified in putamen as most highly overexpressing SNCA in PD. Our results provide new insights into oligodendrocyte biology in PD and MSA and our workflow provides an example of label transfer methods applied for cross-dataset exploratory purpose.
{"title":"Integrated Label Transfer for Oligodendrocyte Subpopulation Profiling in Parkinson's Disease and Multiple System Atrophy","authors":"E. Teeple, P. Joshi, R. Pande, Y. Huang, Akshat Karambe, M. Latta-Mahieu, S. Sardi, A. Cedazo-Mínguez, Katherine W. Klinger, A. Flores-Morales, S. Madden, D. Rajpal, Dinesh Kumar","doi":"10.5220/0010915400003123","DOIUrl":"https://doi.org/10.5220/0010915400003123","url":null,"abstract":"Transfer of cell type labels as part of the comprehensive integration of multiple single nucleus RNA sequencing (snRNAseq) datasets offers a powerful tool for comparing cell populations and their activation states in normal versus disease conditions. Another potential use for these methods is annotation alignments between samples from different anatomic areas. This study describes and evaluates an integration analysis applied for profiling of oligodendrocyte lineage nuclei sequenced from human brain putamen region tissue samples for healthy Control (n = 3), Parkinson’s Disease (PD; n = 3) and Multiple System Atrophy (MSA; n = 3) subjects with label transfer to substantia nigra region tissue samples for healthy Control (n = 5) subjects. PD and MSA are both synucleinopathies, progressive neurodegenerative disorders characterized by nervous system aggregates of α-synuclein, a protein encoded by the SNCA gene. Histologic findings and genetic evidence suggest links between oligodendrocyte biology and synucleinopathy pathogenesis. In this work, we first identify disease-associated changes among transcriptionally distinct oligodendrocyte subpopulations in putamen. We then apply label transfer methods to generalize our findings from putamen to substantia nigra, a brain region characteristically impacted in PD and variably affected in MSA. Interestingly, our analysis predicts oligodendrocytes in substantia nigra include a significantly greater proportion of an oligodendrocyte subpopulation identified in putamen as most highly overexpressing SNCA in PD. Our results provide new insights into oligodendrocyte biology in PD and MSA and our workflow provides an example of label transfer methods applied for cross-dataset exploratory purpose.","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90784081","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 : 2022-01-01DOI: 10.5220/0010826700003123
Sherine Franckenstein, S. Appelbaum, T. Ostermann
: Decision making is one of the most complex tasks in human behavior. In the past, researchers have tried to understand how humans make decisions by designing neuropsychological tests to assess reward related decision making by evaluating the preference for smaller but immediate rewards over larger but delayed rewards or by evaluating the tolerance of risk in favor of a desired reward. The latter are also known as gambling tasks. Today, information technology offers a variety of possibilities to investigate behaviour under risk. After a short introduction on gambling tasks and in particular the game of dice task, this article describes the development and implementation of a JavaScript-based gambling tool for online surveys based on a game of dice task. In a pilot feasibility study with 170 medical students, participants were randomly assigned to a “REAL condition”, based on the probabilities of the chosen bet and a “FAKE condition” where participants lose all the time independently of the chosen bet. We were able to show that the software was well accepted with only 14.7% of drop outs. Moreover, we also found a difference between the FAKE and the REAL group: Participants in the FAKE condition in the mean steadily increased their stake while then control group quite early tended to run a safer strategy. This is also obvious when the overall stake mean is compared: While in the REAL condition the mean stake is 310.89 ± 222.98 €, the FAKE condition has an overall mean of 390.38 ± 296.50 €. In conclusion, this article clearly indicates how a JavaScript based gambling tool can be used for psychological online research.
{"title":"Implementation and Feasibility Analysis of a Javascript-based Gambling Tool Device for Online Decision Making Task under Risk in Psychological and Health Services Research","authors":"Sherine Franckenstein, S. Appelbaum, T. Ostermann","doi":"10.5220/0010826700003123","DOIUrl":"https://doi.org/10.5220/0010826700003123","url":null,"abstract":": Decision making is one of the most complex tasks in human behavior. In the past, researchers have tried to understand how humans make decisions by designing neuropsychological tests to assess reward related decision making by evaluating the preference for smaller but immediate rewards over larger but delayed rewards or by evaluating the tolerance of risk in favor of a desired reward. The latter are also known as gambling tasks. Today, information technology offers a variety of possibilities to investigate behaviour under risk. After a short introduction on gambling tasks and in particular the game of dice task, this article describes the development and implementation of a JavaScript-based gambling tool for online surveys based on a game of dice task. In a pilot feasibility study with 170 medical students, participants were randomly assigned to a “REAL condition”, based on the probabilities of the chosen bet and a “FAKE condition” where participants lose all the time independently of the chosen bet. We were able to show that the software was well accepted with only 14.7% of drop outs. Moreover, we also found a difference between the FAKE and the REAL group: Participants in the FAKE condition in the mean steadily increased their stake while then control group quite early tended to run a safer strategy. This is also obvious when the overall stake mean is compared: While in the REAL condition the mean stake is 310.89 ± 222.98 €, the FAKE condition has an overall mean of 390.38 ± 296.50 €. In conclusion, this article clearly indicates how a JavaScript based gambling tool can be used for psychological online research.","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90849218","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 : 2022-01-01DOI: 10.5220/0010818000003123
E. Chan, M. Chan, Shyrene Ching, Stanley Lawrence Sie, Angelyn R. Lao, J. M. A. Bernadas, C. Cheng
Twitter is a popular platform for disseminating health information. Unfortunately, there is no clear way to monitor how information reaches the intended audiences. This research examined how health information spreads on Twitter and identified factors that affect the spreading within the Philippines. We created a process whose goal is to generate results that experts can deeply analyze to reveal insights into information spread. The process consists of crawling Twitter data, transforming the data and applying sentiment identification and topic modeling, and performing Social Network Analysis (SNA). The SNA graphs allow for the study of the interactions between Twitter users and tweets while giving insights on influential users and topics discussed across clusters. The study explored and utilized tuberculosis-related tweets. Though the algorithms were meant to process tweets written in Filipino, the process is mostly language-agnostic and can be applied to Twitter data. The results also help in identifying strategies that can improve health information spread on Twitter in the Philippines.
{"title":"How Health Information Spreads in Twitter: The Whos and Whats of Philippine TB-data","authors":"E. Chan, M. Chan, Shyrene Ching, Stanley Lawrence Sie, Angelyn R. Lao, J. M. A. Bernadas, C. Cheng","doi":"10.5220/0010818000003123","DOIUrl":"https://doi.org/10.5220/0010818000003123","url":null,"abstract":"Twitter is a popular platform for disseminating health information. Unfortunately, there is no clear way to monitor how information reaches the intended audiences. This research examined how health information spreads on Twitter and identified factors that affect the spreading within the Philippines. We created a process whose goal is to generate results that experts can deeply analyze to reveal insights into information spread. The process consists of crawling Twitter data, transforming the data and applying sentiment identification and topic modeling, and performing Social Network Analysis (SNA). The SNA graphs allow for the study of the interactions between Twitter users and tweets while giving insights on influential users and topics discussed across clusters. The study explored and utilized tuberculosis-related tweets. Though the algorithms were meant to process tweets written in Filipino, the process is mostly language-agnostic and can be applied to Twitter data. The results also help in identifying strategies that can improve health information spread on Twitter in the Philippines.","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87561720","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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