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Article ; Online: Understanding Loneliness in Younger People

Hurmat Ali Shah / Mowafa Househ

Interactive Journal of Medical Research, Vol 12, p e

Review of the Opportunities and Challenges for Loneliness Interventions

2023 Volume 45197

Abstract: Loneliness affects the quality of life of people all around the world. Loneliness is also shown to be directly associated with mental health issues and is often the cause of mental health problems. It is also shown to increase the risk of heart diseases ... ...

Abstract	Loneliness affects the quality of life of people all around the world. Loneliness is also shown to be directly associated with mental health issues and is often the cause of mental health problems. It is also shown to increase the risk of heart diseases and other physical illnesses. Loneliness is studied both from the social and medical sciences perspectives. There are also interventions on the basis of health informatics, information and communication technologies (ICTs), social media, and other technological solutions. In the literature, loneliness is studied from various angles and perspectives ranging from biological to socioeconomical and through anthropological understandings of technology. From the ICT and technological sides, there are multiple reviews studying the effectiveness of intervention strategies and solutions. However, there is a lack of a comprehensive review on loneliness that engulfs the psychological, social, and technological studies of loneliness. From the perspective of loneliness informatics (ie, the application of health informatics practices and tools), it is important to understand the psychological and biological basis of loneliness. When it comes to technological interventions to fight off loneliness, the majority of interventions focus on older people. While loneliness is highest among older people, theoretical and demographical studies of loneliness give a U-shaped distribution age-wise to loneliness; that is, younger people and older people are the demographics most affected by loneliness. But the strategies and interventions designed for older people cannot be directly applied to younger people. We present the dynamics of loneliness in younger people and also provide an overview of the technological interventions for loneliness in younger people. This paper presents an approach wherein the studies carried out from the perspectives of digital health and informatics are discussed in detail. A comprehensive overview of the understanding of loneliness and the study of the overall ...
Keywords	Computer applications to medicine. Medical informatics ; R858-859.7 ; Medical technology ; R855-855.5
Language	English
Publishing date	2023-11-01T00:00:00Z
Publisher	JMIR Publications
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Mapping loneliness through social intelligence analysis

Mowafa Househ / Hurmat Ali Shah

BMJ Health & Care Informatics, Vol 30, Iss

a step towards creating global loneliness map

2023 Volume 1

Abstract: Objectives Loneliness is a prevalent global public health concern with complex dynamics requiring further exploration. This study aims to enhance understanding of loneliness dynamics through building towards a global loneliness map using social ... ...

Abstract	Objectives Loneliness is a prevalent global public health concern with complex dynamics requiring further exploration. This study aims to enhance understanding of loneliness dynamics through building towards a global loneliness map using social intelligence analysis.Settings and design This paper presents a proof of concept for the global loneliness map, using data collected in October 2022. Twitter posts containing keywords such as ‘lonely’, ‘loneliness’, ‘alone’, ‘solitude’ and ‘isolation’ were gathered, resulting in 841 796 tweets from the USA. City-specific data were extracted from these tweets to construct a loneliness map for the country. Sentiment analysis using the valence aware dictionary for sentiment reasoning tool was employed to differentiate metaphorical expressions from meaningful correlations between loneliness and socioeconomic and emotional factors.Measures and results The sentiment analysis encompassed the USA dataset and city-wise subsets, identifying negative sentiment tweets. Psychosocial linguistic features of these negative tweets were analysed to reveal significant connections between loneliness, socioeconomic aspects and emotional themes. Word clouds depicted topic variations between positively and negatively toned tweets. A frequency list of correlated topics within broader socioeconomic and emotional categories was generated from negative sentiment tweets. Additionally, a comprehensive table displayed top correlated topics for each city.Conclusions Leveraging social media data provide insights into the multifaceted nature of loneliness. Given its subjectivity, loneliness experiences exhibit variability. This study serves as a proof of concept for an extensive global loneliness map, holding implications for global public health strategies and policy development. Understanding loneliness dynamics on a larger scale can facilitate targeted interventions and support.
Keywords	Computer applications to medicine. Medical informatics ; R858-859.7
Subject code	910
Language	English
Publishing date	2023-12-01T00:00:00Z
Publisher	BMJ Publishing Group
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Telehealth interventions during COVID-19 pandemic

Mowafa Househ / Muhammad Tukur / Ghassan Saad / Fahad M AlShagathrh / Marco Agus

BMJ Health & Care Informatics, Vol 30, Iss

a scoping review of applications, challenges, privacy and security issues

2023 Volume 1

Abstract: Background The COVID-19, caused by the SARS-CoV-2 virus, proliferated worldwide, leading to a pandemic. Many governmental and non-governmental organisations and research institutes are contributing to the COVID-19 fight to control the pandemic.Motivation ...

Abstract	Background The COVID-19, caused by the SARS-CoV-2 virus, proliferated worldwide, leading to a pandemic. Many governmental and non-governmental organisations and research institutes are contributing to the COVID-19 fight to control the pandemic.Motivation Numerous telehealth applications have been proposed and adopted during the pandemic to combat the spread of the disease. To this end, powerful tools such as artificial intelligence (AI)/robotic technologies, tracking, monitoring, consultation apps and other telehealth interventions have been extensively used. However, there are several issues and challenges that are currently facing this technology.Objective The purpose of this scoping review is to analyse the primary goal of these techniques; document their contribution to tackling COVID-19; identify and categorise their main challenges and future direction in fighting against the COVID-19 or future pandemic outbreaks.Methods Four digital libraries (ACM, IEEE, Scopus and Google Scholar) were searched to identify relevant sources. Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) was used as a guideline procedure to develop a comprehensive scoping review. General telehealth features were extracted from the studies reviewed and analysed in the context of the intervention type, technology used, contributions, challenges, issues and limitations.Results A collection of 27 studies were analysed. The reported telehealth interventions were classified into two main categories: AI-based and non-AI-based interventions; their main contributions to tackling COVID-19 are in the aspects of disease detection and diagnosis, pathogenesis and virology, vaccine and drug development, transmission and epidemic predictions, online patient consultation, tracing, and observation; 28 telehealth intervention challenges/issues have been reported and categorised into technical (14), non-technical (10), and privacy, and policy issues (4). The most critical technical challenges are: ...
Keywords	Computer applications to medicine. Medical informatics ; R858-859.7
Language	English
Publishing date	2023-12-01T00:00:00Z
Publisher	BMJ Publishing Group
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: The use of technology in tracking soccer players’ health performance

Jassim Almulla / Abdulrahman Takiddin / Mowafa Househ

BMC Medical Informatics and Decision Making, Vol 20, Iss 1, Pp 1-

a scoping review

2020 Volume 10

Abstract: Abstract Background Quantifying soccer players’ performance using different types of technologies helps coaches in making tactical decisions and maintaining players’ health. Little is known about the relation between the performance measuring ... ...

Abstract	Abstract Background Quantifying soccer players’ performance using different types of technologies helps coaches in making tactical decisions and maintaining players’ health. Little is known about the relation between the performance measuring technologies and the metrics they measure. The aim of this study is to identify and group the different types of technologies that are used to track the health-related performance metrics of soccer players. Methods We conducted a systematic search for articles using IEEE Xplore, PubMed, ACM DL, and papers from the Sports Medicine Journal. The papers were screened and extracted by two reviewers. The included papers had to fall under several criteria, including being about soccer, measuring health-related performance, and using technology to measure players’ performance. A total of 1,113 papers were reviewed and 1,069 papers were excluded through the selection process. Results We reviewed 44 papers and grouped them based on the technology used and health-related metrics tracked. In terms of technology, we categorized the used technologies into wearable technologies (N=27/44) and in-field technologies (N=14/44). We categorized the tracked health-related metrics into physiological metrics (N=16/44) and physical metrics (N=44/44). We found out that wearable technologies are mainly used to track physical metrics (N=27/27) and are also used to track physiological metrics (N=14/27). In-field technologies are only used to track physical metrics (N=24/24). Conclusion Understanding how technology is related to players’ performance and how it is used leads to an improvement in the monitoring process and performance outcomes of the players.
Keywords	Soccer ; Player ; Health ; Performance ; Technology ; Computer applications to medicine. Medical informatics ; R858-859.7
Subject code	796
Language	English
Publishing date	2020-08-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Freely Available Arabic Corpora

Arfan Ahmed / Nashva Ali / Mahmood Alzubaidi / Wajdi Zaghouani / Alaa A Abd-alrazaq / Mowafa Househ

Computer Methods and Programs in Biomedicine Update, Vol 2, Iss , Pp 100049- (2022)

A Scoping Review

2022

Abstract: Background: Corpora play a vital role when training machine learning (ML) models and building systems that use natural language processing (NLP). It can be challenging for researchers to access corpora in a language other than English, and even more so ... ...

Abstract	Background: Corpora play a vital role when training machine learning (ML) models and building systems that use natural language processing (NLP). It can be challenging for researchers to access corpora in a language other than English, and even more so if the corpora are not available for free of cost. The Arabic language is used by more than 1.5 billion Muslims and is the native language of over 250 million people as the Quran, the core text of Islam, is written in Arabic. Objective: To highlight peer-reviewed literature reporting free and accessible Arabic corpora. We aimed to benefit researchers by providing insights into freely available Arabic and accessible corpora, allowing them to achieve their research goals with ease. Methods: By conducting a scoping review using PRISMA guidelines, we searched the most common information technology (IT) databases and identified free of cost and accessible Arabic corpora. Results: We identified a total of 48 accessible corpora sources available free of cost in the Arabic language, we present our findings according to categories to further help readers understand the corpora with direct links where available. The results were classified by corpora type into five categories based on their primary purpose. Conclusion: Arabic is underrepresented considering freely available corpora as most such corpora are available in English. Although previous studies have performed searches for corpora, ours is the first of its kind as it follows the PRISMA guidelines and includes peer-reviewed articles in the literature, obtained by searching the most common IT databases and source recommendations from language experts.
Keywords	Arabic ; Open source ; Free ; Corpora ; Corpus ; NLP ; Computer applications to medicine. Medical informatics ; R858-859.7
Subject code	400
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Arabic chatbot technologies

Arfan Ahmed / Nashva Ali / Mahmood Alzubaidi / Wajdi Zaghouani / Alaa Abd-alrazaq / Mowafa Househ

Computer Methods and Programs in Biomedicine Update, Vol 2, Iss , Pp 100057- (2022)

A scoping review

2022

Abstract: Background: Chatbots have been widely used in many spheres of life from customer services to mental health companions. Despite the breakthroughs in achieving human-like conversations, Arabic language chatbots driven by AI and NLP are relatively scarce ... ...

Abstract	Background: Chatbots have been widely used in many spheres of life from customer services to mental health companions. Despite the breakthroughs in achieving human-like conversations, Arabic language chatbots driven by AI and NLP are relatively scarce due to the complex nature of the Arabic language. Objective: We aim to review published literature on Arabic chatbots to gain insight into the technologies used highlighting the gap in this emerging field. Methods: To identify relevant studies, we searched eight bibliographic databases and conducted backward and forward reference checking. Two reviewers independently performed study selection and data extraction. The extracted data was synthesized using a narrative approach. Results: We included 18 of 1755 retrieved publications. Thirteen unique chatbots were identified from the 18 studies. ArabChat was the most common chatbot in the included studies (n = 5). The type of Arabic language in most chatbots (n = 13) was Modern Standard Arabic. The input and output modalities used in 17 chatbots were only text. Most chatbots (n = 14) were able to have long conversations. The majority of the chatbots (n = 14) were developed to serve a specific purpose (Closed domain). A retrieval-based model was used for developing most chatbots (n = 17). Conclusion: Despite a large number of chatbots worldwide, there is relatively a small number of Arabic language chatbots. Furthermore, the available Arabic language chatbots are less advanced than other language chatbots. Researchers should develop more Arabic language chatbots that are based on more advanced input and output modalities, generative-based models, and natural language processing (NLP).
Keywords	Chatbot ; Conversational agents ; Artificial intelligence ; Arabic language ; Computer applications to medicine. Medical informatics ; R858-859.7
Subject code	400
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: The performance of artificial intelligence-driven technologies in diagnosing mental disorders

Alaa Abd-alrazaq / Dari Alhuwail / Jens Schneider / Carla T. Toro / Arfan Ahmed / Mahmood Alzubaidi / Mohannad Alajlani / Mowafa Househ

npj Digital Medicine, Vol 5, Iss 1, Pp 1-

an umbrella review

2022 Volume 12

Abstract: Abstract Artificial intelligence (AI) has been successfully exploited in diagnosing many mental disorders. Numerous systematic reviews summarize the evidence on the accuracy of AI models in diagnosing different mental disorders. This umbrella review aims ...

Abstract	Abstract Artificial intelligence (AI) has been successfully exploited in diagnosing many mental disorders. Numerous systematic reviews summarize the evidence on the accuracy of AI models in diagnosing different mental disorders. This umbrella review aims to synthesize results of previous systematic reviews on the performance of AI models in diagnosing mental disorders. To identify relevant systematic reviews, we searched 11 electronic databases, checked the reference list of the included reviews, and checked the reviews that cited the included reviews. Two reviewers independently selected the relevant reviews, extracted the data from them, and appraised their quality. We synthesized the extracted data using the narrative approach. We included 15 systematic reviews of 852 citations identified. The included reviews assessed the performance of AI models in diagnosing Alzheimer’s disease (n = 7), mild cognitive impairment (n = 6), schizophrenia (n = 3), bipolar disease (n = 2), autism spectrum disorder (n = 1), obsessive-compulsive disorder (n = 1), post-traumatic stress disorder (n = 1), and psychotic disorders (n = 1). The performance of the AI models in diagnosing these mental disorders ranged between 21% and 100%. AI technologies offer great promise in diagnosing mental health disorders. The reported performance metrics paint a vivid picture of a bright future for AI in this field. Healthcare professionals in the field should cautiously and consciously begin to explore the opportunities of AI-based tools for their daily routine. It would also be encouraging to see a greater number of meta-analyses and further systematic reviews on performance of AI models in diagnosing other common mental disorders such as depression and anxiety.
Keywords	Computer applications to medicine. Medical informatics ; R858-859.7
Subject code	150
Language	English
Publishing date	2022-07-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Overview of the role of big data in mental health

Arfan Ahmed / Marco Agus / Mahmood Alzubaidi / Sarah Aziz / Alaa Abd-Alrazaq / Anna Giannicchi / Mowafa Househ

Computer Methods and Programs in Biomedicine Update, Vol 2, Iss , Pp 100076- (2022)

A scoping review

2022

Abstract: Background: Big Data offers promise in the field of mental health and plays an important part when it comes to automation, analysis and prediction of mental health disorders.Objective: The purpose of this scoping review is to explore how big data was ... ...

Abstract	Background: Big Data offers promise in the field of mental health and plays an important part when it comes to automation, analysis and prediction of mental health disorders.Objective: The purpose of this scoping review is to explore how big data was exploited in mental health. This review specifically addresses the volume, velocity, veracity and variety of collected data as well as how data was attained, stored, managed, and kept private and secure.Methods: Six databases were searched to find relevant articles. PRISMA Extension for Scoping Reviews (PRISMA-ScR) was used as a guideline methodology to develop a comprehensive scoping review. General and Big Data features were extracted from the studies reviewed, and analyzed in the context of data collection, protection, storage and for what concerns data processing, targeted disorder and application purpose.Results: A collection of 23 studies were analyzed, mostly targeting depression (n=13) and anxiety (n=4). For what concerns data sources, mostly social media posts (n=5), tweets (n=7), and medical records (n=6) were used. Various Big Data technologies were used: for data protection, only 7 studies faced the problem, with anonymization schemes for medical records and only surveys (n=4), and safe authentication methods for social media (n=3). For data processing, Machine Learning (ML) models appeared in 22 studies of which Random Forest (RF) was the most widely used (n=5). Logistic Regression (LR) was used in 4 studies, and Support Vector Machine (SVM) was used in 3 studies.Conclusion: In order to utilize Big Data as a way to mitigate mental health disorders and predict their appearance a great effort is still needed. Integration and analysis of Big Data coming from different sources such as social media and health records and information exchange between multiple disciplines is also needed. Doctors and researchers alike can find patterns in otherwise difficult to identify data by making use of Artificial Intelligence (AI) and Machine Learning (ML) techniques. ...
Keywords	Big Data ; Mental health ; Analytics ; Mental disorder ; Artificial Intelligence ; Computer applications to medicine. Medical informatics ; R858-859.7
Subject code	306
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Machine learning models to detect anxiety and depression through social media

Arfan Ahmed / Sarah Aziz / Carla T. Toro / Mahmood Alzubaidi / Sara Irshaidat / Hashem Abu Serhan / Alaa A. Abd-alrazaq / Mowafa Househ

Computer Methods and Programs in Biomedicine Update, Vol 2, Iss , Pp 100066- (2022)

A scoping review

2022

Abstract: Despite improvement in detection rates, the prevalence of mental health disorders such as anxiety and depression are on the rise especially since the outbreak of the COVID-19 pandemic. Symptoms of mental health disorders have been noted and observed on ... ...

Abstract	Despite improvement in detection rates, the prevalence of mental health disorders such as anxiety and depression are on the rise especially since the outbreak of the COVID-19 pandemic. Symptoms of mental health disorders have been noted and observed on social media forums such Facebook. We explored machine learning models used to detect anxiety and depression through social media. Six bibliographic databases were searched for conducting the review following PRISMA-ScR protocol. We included 54 of 2219 retrieved studies. Users suffering from anxiety or depression were identified in the reviewed studies by screening their online presence and their sharing of diagnosis by patterns in their language and online activity. Majority of the studies (70%, 38/54) were conducted at the peak of the COVID-19 pandemic (2019–2020). The studies made use of social media data from a variety of different platforms to develop predictive models for the detection of depression or anxiety. These included Twitter, Facebook, Instagram, Reddit, Sina Weibo, and a combination of different social sites posts. We report the most common Machine Learning models identified. Identification of those suffering from anxiety and depression disorders may be achieved using prediction models to detect user's language on social media and has the potential to complimenting traditional screening. Such analysis could also provide insights into the mental health of the public especially so when access to health professionals can be restricted due to lockdowns and temporary closure of services such as we saw during the peak of the COVID-19 pandemic.
Keywords	Anxiety ; Depression ; Social media ; Social networking ; Artificial intelligence ; Machine learning ; Computer applications to medicine. Medical informatics ; R858-859.7
Subject code	150
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Understanding the stakeholders’ preferences on a mobile application to reduce door to balloon time in the management of ST-elevated myocardial infarction patients – a qualitative study

Nour Alkamel / Amr Jamal / Omar Alnobani / Mowafa Househ / Nasriah Zakaria / Mohammad Qawasmeh / Shabana Tharkar

BMC Medical Informatics and Decision Making, Vol 20, Iss 1, Pp 1-

2020 Volume 10

Abstract: Abstract Background ST-elevated myocardial infarction (STEMI) is a critical and time-sensitive emergency. The survival depends on prompt initiation of treatment requiring high precision and multi-level coordination between healthcare staff. The use of a ... ...

Abstract	Abstract Background ST-elevated myocardial infarction (STEMI) is a critical and time-sensitive emergency. The survival depends on prompt initiation of treatment requiring high precision and multi-level coordination between healthcare staff. The use of a mobile application may facilitate prompt management and shorten the door-to-balloon time by capturing information at the point of care and provide immediate feedback to all healthcare staff involved in STEMI management. The objective of the present study has two primary components: (i) to explore the suggestions and opinions of stakeholders in the development of a novel mobile app for code activation in management of STEMI patients (ii) to find out the healthcare workers’ expectations including facilitating steps and challenges in the activation process of the proposed mobile app. Methods Unstructured interviews were conducted with key informants (n = 2) to identify all stakeholders, who also helped in developing the interview protocol and prototype designs. In-depth, semi-structured, open-ended, face to face interviews were conducted on 22 stakeholders involved in managing STEMI patients. All interviews were recorded and transcribed verbatim. Data were analyzed using ATLAS.ti 8 software, allowing themes and subthemes to emerge. Results The 22 participants included in the study were cardiology physicians (n = 3), emergency consultants (n = 4), emergency room (ER) senior nurses (n = 10), and cardiac catheterization lab staff (n = 5). The main themes identified during analysis were workflow and the App. The themes identified from the interviews surrounding the App were: 1) facilitating ideas 2) management steps needed 3) features 4) preferred code activation method 5) steps of integration 6) possible benefits of the App 7) barriers and 8) possible solutions to the suggested barriers. Most of the interviewed stakeholders expressed their acceptance after viewing the proposed mobile app prototype. Conclusion The study identified the mandatory features and the management steps needed from the stakeholder’s perspectives. The steps for integrating the current paper-based workflow with the suggested mobile app were identified. The expected benefits of the App may include improved and faster management, accuracy, better communication, and improvement in data quality. Moreover, the possible barriers might comprise of doubtful acceptability, device-related issues, and time and data-related challenges.
Keywords	ST elevation myocardial infarction ; Chest pain ; Patient management ; Mobile applications ; Software ; Cell phone ; Computer applications to medicine. Medical informatics ; R858-859.7
Subject code	005
Language	English
Publishing date	2020-08-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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