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Amer NA, Shohieb SM, Eladrosy W, Liu S, Nam Y, Abdelrazek S. A gamified cognitive behavioral therapy for Arabs to reduce symptoms of depression and anxiety: A case study research. Digit Health 2024; 10:20552076241263317. [PMID: 38882250 PMCID: PMC11179503 DOI: 10.1177/20552076241263317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/29/2024] [Indexed: 06/18/2024] Open
Abstract
Background Depression and anxiety are prevalent mental health issues addressed by online cognitive behavioral therapy (CBT) via mobile applications. This study introduces Sokoon, a gamified CBT app tailored for Arabic individuals, focusing on alleviating depression and anxiety symptoms (DASDs). Objectives The objectives of this study were to: Evaluate the effectiveness of Sokoon in reducing symptoms of depression and anxiety. Assess the usability of the intervention through user engagement and adherence to CBT skills. Methods A single-group pre-post design evaluated Sokoon's impact on adults with DASDs. In consultation with psychiatrists, Sokoon integrates evidence-based skills such as relaxation, gratitude, behavioral activation, and cognitive restructuring, represented by planets. Its design incorporates Hexad theory and gamification, supported by a dynamic difficulty adjustment algorithm. The study involves 30 participants aged 18-35 (86.7% female), specifically those with mild to moderate depression and anxiety. Results Based on a sample of 30 participants, Sokoon, a smartphone-based intervention, significantly reduced symptoms of depression and anxiety (d = 2.7, d = 3.6, p < 0.001). Over a two-week trial, participants experienced a notable decrease in anxiety and depressive symptoms, indicating the effectiveness of the model. Sokoon shows potential as a valuable tool for addressing DASDs. Conclusion Sokoon, the gamified CBT application, offers an innovative approach to increasing CBT skills adherence and engagement. By leveraging Hexad theory and gamification, Sokoon provides an enjoyable and engaging user experience while maintaining the effectiveness of traditional CBT techniques. The study findings suggest that Sokoon has a positive impact on reducing symptoms of depression and anxiety.
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Affiliation(s)
- Nourhan A Amer
- Information Systems Department, Faculty of Computers and Information, Mansoura University, Mansoura, Egypt
| | - Samaa M Shohieb
- Information Systems Department, Faculty of Computers and Information, Mansoura University, Mansoura, Egypt
| | - Waleed Eladrosy
- Computer Science Department, Faculty of Computers and Information, Mansoura University, Mansoura, Egypt
| | - Shilong Liu
- Department of ICT Convergence, Soonchunhyang University, Asan, Korea
| | - Yunyoung Nam
- Department of ICT Convergence, Soonchunhyang University, Asan, Korea
| | - Samir Abdelrazek
- Information Systems Department, Faculty of Computers and Information, Mansoura University, Mansoura, Egypt
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Naegelin M, Weibel RP, Kerr JI, Schinazi VR, La Marca R, von Wangenheim F, Hoelscher C, Ferrario A. An interpretable machine learning approach to multimodal stress detection in a simulated office environment. J Biomed Inform 2023; 139:104299. [PMID: 36720332 DOI: 10.1016/j.jbi.2023.104299] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 12/16/2022] [Accepted: 01/26/2023] [Indexed: 01/31/2023]
Abstract
BACKGROUND AND OBJECTIVE Work-related stress affects a large part of today's workforce and is known to have detrimental effects on physical and mental health. Continuous and unobtrusive stress detection may help prevent and reduce stress by providing personalised feedback and allowing for the development of just-in-time adaptive health interventions for stress management. Previous studies on stress detection in work environments have often struggled to adequately reflect real-world conditions in controlled laboratory experiments. To close this gap, in this paper, we present a machine learning methodology for stress detection based on multimodal data collected from unobtrusive sources in an experiment simulating a realistic group office environment (N=90). METHODS We derive mouse, keyboard and heart rate variability features to detect three levels of perceived stress, valence and arousal with support vector machines, random forests and gradient boosting models using 10-fold cross-validation. We interpret the contributions of features to the model predictions with SHapley Additive exPlanations (SHAP) value plots. RESULTS The gradient boosting models based on mouse and keyboard features obtained the highest average F1 scores of 0.625, 0.631 and 0.775 for the multiclass prediction of perceived stress, arousal and valence, respectively. Our results indicate that the combination of mouse and keyboard features may be better suited to detect stress in office environments than heart rate variability, despite physiological signal-based stress detection being more established in theory and research. The analysis of SHAP value plots shows that specific mouse movement and typing behaviours may characterise different levels of stress. CONCLUSIONS Our study fills different methodological gaps in the research on the automated detection of stress in office environments, such as approximating real-life conditions in a laboratory and combining physiological and behavioural data sources. Implications for field studies on personalised, interpretable ML-based systems for the real-time detection of stress in real office environments are also discussed.
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Affiliation(s)
- Mara Naegelin
- Mobiliar Lab for Analytics at ETH Zurich, Department of Management, Economics, and Technology, ETH Zurich, Weinbergstrasse 56/58, Zurich, 8092, Switzerland; Chair of Technology Marketing, Department of Management, Economics, and Technology, ETH Zurich, Weinbergstrasse 56/58, Zurich, 8092, Switzerland.
| | - Raphael P Weibel
- Mobiliar Lab for Analytics at ETH Zurich, Department of Management, Economics, and Technology, ETH Zurich, Weinbergstrasse 56/58, Zurich, 8092, Switzerland; Chair of Technology Marketing, Department of Management, Economics, and Technology, ETH Zurich, Weinbergstrasse 56/58, Zurich, 8092, Switzerland
| | - Jasmine I Kerr
- Mobiliar Lab for Analytics at ETH Zurich, Department of Management, Economics, and Technology, ETH Zurich, Weinbergstrasse 56/58, Zurich, 8092, Switzerland; Chair of Technology Marketing, Department of Management, Economics, and Technology, ETH Zurich, Weinbergstrasse 56/58, Zurich, 8092, Switzerland
| | - Victor R Schinazi
- Department of Psychology, Bond University, 14 University Drive, Robina, 4226, Australia; Future Health Technologies, Singapore-ETH Centre, 1 Create Way, Singapore, 138602, Singapore
| | - Roberto La Marca
- Centre for Stress-Related Disorders, Clinica Holistica Engiadina, Plaz 40, Susch, 7542, Switzerland; Chair of Clinical Psychology and Psychotherapy, Department of Psychology, University of Zurich, Binzmuehlestrasse 14, Zurich, 8050, Switzerland
| | - Florian von Wangenheim
- Chair of Technology Marketing, Department of Management, Economics, and Technology, ETH Zurich, Weinbergstrasse 56/58, Zurich, 8092, Switzerland; Future Health Technologies, Singapore-ETH Centre, 1 Create Way, Singapore, 138602, Singapore
| | - Christoph Hoelscher
- Future Health Technologies, Singapore-ETH Centre, 1 Create Way, Singapore, 138602, Singapore; Chair of Cognitive Science, Department of Humanities, Social and Political Sciences, ETH Zurich, Clausiusstrasse 59, Zurich, 8092, Switzerland
| | - Andrea Ferrario
- Mobiliar Lab for Analytics at ETH Zurich, Department of Management, Economics, and Technology, ETH Zurich, Weinbergstrasse 56/58, Zurich, 8092, Switzerland; Chair of Technology Marketing, Department of Management, Economics, and Technology, ETH Zurich, Weinbergstrasse 56/58, Zurich, 8092, Switzerland
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Ahmed A, Aziz S, Alzubaidi M, Schneider J, Irshaidat S, Abu Serhan H, Abd-Alrazaq AA, Solaiman B, Househ M. Wearable devices for anxiety & depression: A scoping review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE UPDATE 2023; 3:100095. [PMID: 36743720 PMCID: PMC9884643 DOI: 10.1016/j.cmpbup.2023.100095] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Background The rates of mental health disorders such as anxiety and depression are at an all-time high especially since the onset of COVID-19, and the need for readily available digital health care solutions has never been greater. Wearable devices have increasingly incorporated sensors that were previously reserved for hospital settings. The availability of wearable device features that address anxiety and depression is still in its infancy, but consumers will soon have the potential to self-monitor moods and behaviors using everyday commercially-available devices. Objective This study aims to explore the features of wearable devices that can be used for monitoring anxiety and depression. Methods Six bibliographic databases, including MEDLINE, EMBASE, PsycINFO, IEEE Xplore, ACM Digital Library, and Google Scholar were used as search engines for this review. Two independent reviewers performed study selection and data extraction, while two other reviewers justified the cross-checking of extracted data. A narrative approach for synthesizing the data was utilized. Results From 2408 initial results, 58 studies were assessed and highlighted according to our inclusion criteria. Wrist-worn devices were identified in the bulk of our studies (n = 42 or 71%). For the identification of anxiety and depression, we reported 26 methods for assessing mood, with the State-Trait Anxiety Inventory being the joint most common along with the Diagnostic and Statistical Manual of Mental Disorders (n = 8 or 14%). Finally, n = 26 or 46% of studies highlighted the smartphone as a wearable device host device. Conclusion The emergence of affordable, consumer-grade biosensors offers the potential for new approaches to support mental health therapies for illnesses such as anxiety and depression. We believe that purposefully-designed wearable devices that combine the expertise of technologists and clinical experts can play a key role in self-care monitoring and diagnosis.
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Affiliation(s)
- Arfan Ahmed
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Sarah Aziz
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Mahmood Alzubaidi
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Jens Schneider
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | | | | | - Alaa A Abd-Alrazaq
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Barry Solaiman
- College of Law, Hamad Bin Khalifa University, Doha, Qatar
| | - Mowafa Househ
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
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Machine learning for suicidal ideation identification: A systematic literature review. COMPUTERS IN HUMAN BEHAVIOR 2022. [DOI: 10.1016/j.chb.2021.107095] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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Lima R, Filippetto AS, Heckler W, Barbosa JL, Leithardt VR. Towards ubiquitous requirements engineering through recommendations based on context histories. PeerJ Comput Sci 2022; 8:e794. [PMID: 35111909 PMCID: PMC8771779 DOI: 10.7717/peerj-cs.794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Accepted: 11/02/2021] [Indexed: 06/14/2023]
Abstract
The growing technological advance is causing constant business changes. The continual uncertainties in project management make requirements engineering essential to ensure the success of projects. The usual exponential increase of stakeholders throughout the project suggests the application of intelligent tools to assist requirements engineers. Therefore, this article proposes Nhatos, a computational model for ubiquitous requirements management that analyses context histories of projects to recommend reusable requirements. The scientific contribution of this study is the use of the similarity analysis of projects through their context histories to generate the requirement recommendations. The implementation of a prototype allowed to evaluate the proposal through a case study based on real scenarios from the industry. One hundred fifty-three software projects from a large bank institution generated context histories used in the recommendations. The experiment demonstrated that the model achieved more than 70% stakeholder acceptance of the recommendations.
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Affiliation(s)
- Robson Lima
- Applied Computing Graduate Program (PPGCA), University of Vale do Rio dos Sinos (UNISINOS), São Leopoldo, RS, Brazil
| | - Alexsandro S. Filippetto
- Applied Computing Graduate Program (PPGCA), University of Vale do Rio dos Sinos (UNISINOS), São Leopoldo, RS, Brazil
| | - Wesllei Heckler
- Applied Computing Graduate Program (PPGCA), University of Vale do Rio dos Sinos (UNISINOS), São Leopoldo, RS, Brazil
| | - Jorge L.V. Barbosa
- Applied Computing Graduate Program (PPGCA), University of Vale do Rio dos Sinos (UNISINOS), São Leopoldo, RS, Brazil
| | - Valderi R.Q. Leithardt
- VALORIZA–Research Centre for Endogenous Resource Valorization, Polytechnic Institute of Portalegre, Portalegre, Portugal
- COPELABS, University Lusófona–ULHT, Lisboa, Portugal
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Machine Learning and IoT Applied to Cardiovascular Diseases Identification through Heart Sounds: A Literature Review. INFORMATICS 2021. [DOI: 10.3390/informatics8040073] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
This article presents a systematic mapping study dedicated to conduct a literature review on machine learning and IoT applied in the identification of diseases through heart sounds. This research was conducted between January 2010 and July 2021, considering IEEE Xplore, PubMed Central, ACM Digital Library, JMIR—Journal of Medical Internet Research, Springer Library, and Science Direct. The initial search resulted in 4372 papers, and after applying the inclusion and exclusion criteria, 58 papers were selected for full reading to answer the research questions. The main results are: of the 58 articles selected, 46 (79.31%) mention heart rate observation methods with wearable sensors and digital stethoscopes, and 34 (58.62%) mention care with machine learning algorithms. The analysis of the studies based on the bibliometric network generated by the VOSviewer showed in 13 studies (22.41%) a trend related to the use of intelligent services in the prediction of diagnoses related to cardiovascular disorders.
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Ontology-Based Reasoning for Educational Assistance in Noncommunicable Chronic Diseases. COMPUTERS 2021. [DOI: 10.3390/computers10100128] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Noncommunicable chronic diseases (NCDs) affect a large part of the population. With the emergence of COVID-19, its most severe cases impact people with NCDs, increasing the mortality rate. For this reason, it is necessary to develop personalized solutions to support healthcare considering the specific characteristics of individuals. This paper proposes an ontology to represent the knowledge of educational assistance in NCDs. The purpose of ontology is to support educational practices and systems oriented towards preventing and monitoring these diseases. The ontology is implemented under Protégé 5.5.0 in Ontology Web Language (OWL) format, and defined competency questions, SWRL rules, and SPARQL queries. The current version of ontology includes 138 classes, 31 relations, 6 semantic rules, and 575 axioms. The ontology serves as a NCDs knowledge base and supports automatic reasoning. Evaluations performed through a demo dataset demonstrated the effectiveness of the ontology. SWRL rules were used to define accurate axioms, improving the correct classification and inference of six instantiated individuals. As a scientific contribution, this study presents the first ontology for educational assistance in NCDs.
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Ahmed A, Aziz S, Alzubaidi M, Schneider J, Irshaidat S, Abu Serhan H, Abd-alrazaq A, Solaiman B, Househ M. Features of wearable devices used for Anxiety & Depression: A scoping review (Preprint).. [DOI: 10.2196/preprints.33287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
BACKGROUND
The rates of mental health disorders such as anxiety and depression are at an all time high and the need for readily available digital health care solutions has never been greater. Wearable devices (WD) have seen a steady rise in the usage of sensors previously reserved for hospital settings. The availibity of features that make use of WDs for anxiety and depression is in its infancy, but we are seeing the potential for consumers to self monitor moods and behaviours with everyday commercially available devices and the ability to self-regulate their health needs.
OBJECTIVE
This study aims to explore features of wearable devices (WDs) used for anxiety and depression
METHODS
We have searched the following six bibliographic databases while conducting this review: MEDLINE, EMBASE, PsycINFO, IEEE Xplore, ACM Digital Library, and Google Scholar. Two reviewers independently performed study selection and data extraction; two other individual reviewers justified cross-checking of extracted data. We utilized a narrative approach for synthesizing the data.
RESULTS
From an initial 2,408 studies we assess and report the features in 58 studies that were highlighted according to our inclusion criteria. Wrist worn devices were identified in the bulk of our studies (n=42 or 71%). Depression was assessed in most of the studies (n=27 or 47%), whereas anxiety was assessed in n=15 or 25% of studies. More than a quarter (n=16 or 27%) of the included studies assessed both mental disorders. Finally n=26 or 46% of studies highlighted the wearable device host device as a smartphone.
CONCLUSIONS
The emergence of affordable, consumer-grade biosensors offers the potential for new approaches to support mental health therapies such as anxiety and depression. We see WDs having real potential in aiding with self-care and with purposefully designed WDs that combine the expertise of technologists and clinical experts WDs could play a key role in self-care monitoring and diagnosis.
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Aranda JAS, Bavaresco RS, de Carvalho JV, Yamin AC, Tavares MC, Barbosa JLV. A computational model for adaptive recording of vital signs through context histories. JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021; 14:1-15. [PMID: 33758628 PMCID: PMC7972018 DOI: 10.1007/s12652-021-03126-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 03/05/2021] [Indexed: 05/06/2023]
Abstract
Wearable devices emerged from the advancement of communication technology and the miniaturization of electronic components. These devices periodically monitor the user's vital signs and generally have a short battery life. This work introduces ODIN, a model for optimized vital signs collection based on adaptive rules. Analyzing vital sign values requires preciseness, so the adaption of these collected data allows a personalized analysis of the user's health condition. The comparison with related works indicates that ODIN is the only model that presents context-aware-adaptive vital signs collection. The implementation of a prototype allowed to perform three evaluations of ODIN. The first evaluation used simulations in different scenarios, with the adaptive approach increasing battery life by 119% through the analysis of input data compared to data collection without adaptivity. The second evaluation applied the prototype to a database of real physiologic data, which allowed reduced data collection when the user has regular vital signs. This reduction optimized battery consumption by 66% compared to collection without adaptivity. Finally, the third evaluation applied ODIN through an Arduino and a heart rate monitor (Polar H7). The average power saved across mobile devices was 21%. Consequently, the adaptive strategy presented in this work allows the optimization of computational resources during the collection and analysis of vital signs. This optimization occurs because of the reduction in energy expenditure and the reduction in the amount of data that needs to be collected and stored.
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Devi R, Kanitkar K, Narendhar R, Sehmi K, Subramaniam K. A Narrative Review of the Patient Journey Through the Lens of Non-communicable Diseases in Low- and Middle-Income Countries. Adv Ther 2020; 37:4808-4830. [PMID: 33052560 PMCID: PMC7553852 DOI: 10.1007/s12325-020-01519-3] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Accepted: 09/28/2020] [Indexed: 01/08/2023]
Abstract
Low- and middle-income countries (LMICs) are challenged with a disproportionately high burden of noncommunicable diseases (NCDs) and limited healthcare resources at their disposal to tackle the NCD epidemic. Understanding the patient journey for NCDs from the patients' perspective can help healthcare systems in these settings evolve their NCD care models to address the unmet needs of patients, enhance patient participation in their management, and progress towards better outcomes and quality of life. This paper aims to provide a theoretical framework outlining common touchpoints along the patient journey for NCDs in LMICs. It further aims to review influencing factors and recommend strategies to improve patient experience, satisfaction, and disease outcomes at each touchpoint. The co-occurrence of major NCDs makes it possible to structure the patient journey for NCDs into five broad touchpoints: awareness, screening, diagnosis, treatment, and adherence, with integration of palliative care along the care continuum pathway. The patients' perspective must be considered at each touchpoint in order to inform interventions as they experience first-hand the impact of NCDs on their quality of life and physical function and participate substantially in their disease management. Collaboratively designed health communication programs, shared decision-making, use of appropriate risk assessment tools, therapeutic alliances between the patient and provider for treatment planning, self-management tools, and improved access to palliative care are some strategies to help improve the patient journeys in LMICs. Long-term management of NCDs entails substantial self-management by patients, which can be augmented by pharmacists and nurse-led interventions. The digital healthcare revolution has heralded an increase in patient engagement, support of home monitoring of patients, optimized accurate diagnosis, personalized care plans, and facilitated timely intervention. There is an opportunity to integrate digital technology into each touchpoint of the patient journey, while ensuring minimal interruption to patients' care in the face of global health emergencies.
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