scholarly journals Public Perceptions around mHealth Applications during COVID-19 Pandemic: A Network and Sentiment Analysis of Tweets in Saudi Arabia

2021 ◽  
Vol 18 (24) ◽  
pp. 13388
Author(s):  
Samar Binkheder ◽  
Raniah N. Aldekhyyel ◽  
Alanoud AlMogbel ◽  
Nora Al-Twairesh ◽  
Nuha Alhumaid ◽  
...  
Keyword(s):  
Saudi Arabia ◽  
Sentiment Analysis ◽  
Health Sector ◽  
Public Perceptions ◽  
Support Vector ◽  
The Public ◽  
Mobile Health Applications ◽  
Twitter Users ◽  
Mhealth Apps ◽  
Health Applications

A series of mitigation efforts were implemented in response to the COVID-19 pandemic in Saudi Arabia, including the development of mobile health applications (mHealth apps) for the public. Assessing the acceptability of mHealth apps among the public is crucial. This study aimed to use Twitter to understand public perceptions around the use of six Saudi mHealth apps used during COVID-19: “Sehha”, “Mawid”, “Sehhaty”, “Tetamman”, “Tawakkalna”, and “Tabaud”. We used two methodological approaches: network and sentiment analysis. We retrieved Twitter data using specific mHealth apps-related keywords. After including relevant tweets, our final mHealth app networks consisted of a total of 4995 Twitter users and 8666 conversational relationships. The largest networks in size (i.e., the number of users) and volume (i.e., the conversational relationships) among all were “Tawakkalna” followed by “Tabaud”, and their conversations were led by diverse governmental accounts. In contrast, the four remaining mHealth networks were mainly led by the health sector and media. Our sentiment analysis approach included five classes and showed that most conversations were neutral, which included facts or information pieces and general inquires. For the automated sentiment classifier, we used Support Vector Machine with AraVec embeddings as it outperformed the other tested classifiers. The sentiment classifier showed an accuracy, precision, recall, and F1-score of 85%. Future studies can use social media and real-time analytics to improve mHealth apps’ services and user experience, especially during health crises.

scholarly journals COVID-19 and Black Fungus: Analysis of the Public Perception through Machine Learning

2021 ◽  
Author(s):  
Muhammad Nazrul Islam ◽  
Nafiz Imtiaz Khan ◽  
Tahasin Mahmud
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Public Perception ◽  
Public Perceptions ◽  
Support Vector ◽  
Machine Model ◽  
The Public ◽  
Class Labels ◽  
Media Data

While COVID-19 is ravaging the lives of millions of people across the globe, a second pandemic 'black fungus' has surfaced robbing people of their lives especially people who are recovering from coronavirus. Again, the public perceptions regarding such pandemics can be investigated through sentiment analysis of social media data. Thus the objective of this study is to analyze public perceptions through sentiment analysis regarding black fungus during the time of the COVID-19 pandemic. To attain the objective, first, a Support Vector Machine model, with an average AUC of 82.75\%, was developed to classify user sentiments in terms of anger, fear, joy, and sad. Next, this Support Vector Machine is used to supervise the class labels of the public tweets (n = 6477) related to COVID-19 and black fungus. As outcome, this study found that public perceptions belong to sad (n = 2370, 36.59 \%), followed by joy ( n = 2095, 32.34\%), fear ( n = 1914, 29.55 \%) and anger ( n = 98, 1.51\%) towards black fungus during COVID-19 pandemic. This study also investigated public perceptions of some critical concerns (e.g., education, lockdown, hospital, oxygen, quarantine, and vaccine) and it was found that public perceptions of these issues varied. For example, for the most part, people exhibited fear in social media about education, hospital, vaccine while some people expressed joy about education, hospital, vaccine, and oxygen.

Conversations around mHealth applications during COVID-19: a network and sentiment analysis of Tweets in Saudi Arabia (Preprint)

2021 ◽  
Author(s):  
Samar Binkheder ◽  
Raniah N Aldekhyyel ◽  
Alanoud Almegbil ◽  
Nora Al-Twairesh ◽  
Nuha Alhumaid ◽  
...  
Keyword(s):  
Social Media ◽  
Saudi Arabia ◽  
Network Analysis ◽  
Sentiment Analysis ◽  
Data Augmentation ◽  
Support Vector ◽  
Learning Approaches ◽  
The Government ◽  
The Impact ◽  
Mhealth Apps

BACKGROUND In Saudi Arabia, the first novel coronavirus disease (COVID-19) confirmed case was reported on March 2, 2020, which followed a series of mitigation efforts imposed by the government. The development of specific mobile health applications (mHealth apps) for public use was one of the response strategies employed by the Saudi government. Assessing the impact of these mHealth apps through the opinions of the public posted on social media is crucial to improve mHealth services offered by governments. OBJECTIVE Our aim was to utilize Twitter, as a source of data, to understand conversations and perceptions of users around the use of six mHealth apps developed by the Saudi Ministry of Health, by conducting a network and sentimental analysis of Tweets. The mHealth apps included in our study were “Sehha”, “Mawid”, “Sehhaty”, “Tetamman”, “Tawakkalna”, and “Tabaud”. METHODS We collected mHealth-related Twitter data on December 12, 2020. After including relevant tweets, our final mHealth app networks consisted of a total of 4,995 Twitter users and 8,666 relationships. We used NodeXL to perform the network analysis and visualization. We performed a sentiment analysis using a human-in-the-loop and machine learning approaches. Our manual annotation initially included five classes (positive, neutral, negative, indeterminate, and sarcasm). We excluded indeterminate and sarcasm classes as they usually cause ambiguity for the sentiment classifier. We applied data augmentation techniques to ensure sentiment polarity (positive, negative) in the tweets. The sentiment classifier dataset consisted a total of 4,719 tweets with 26.6% positive, 52.2% neutral, and 21.2% negative. Data preprocessing and normalization were also performed. For building the sentiment classifier, we used the Support Vector Machine with the word2vec embeddings of AraVec. RESULTS Our network analysis showed that “Sehhaty”, “Tawakkalna”, and “Tabaud” had similar patterns and more interactions in conversations than other networks. “Tawakkalna” and “Tabaud” were the largest networks among all, and their conversations were led by various governmental accounts. In comparison, “Sehha”, “Mawid”, “Sehhaty”, and “Tetamman” networks were mainly led by a health sector and media. Our sentiment analysis showed that the majority of Twitter conversations around the six mHealth apps were neutral, which encompassed facts or information pieces, neutral suggestions, and general inquires. Positive tweets focused on appreciation, positive opinions, and expressions around government trust. In contrast, negative tweets included suggestions to overcome weaknesses, issues faced with apps, negative opinions, and negative psychological impact. Our sentiment classifier showed an accuracy, precision, recall, and an F1-score of 85%. CONCLUSIONS Social media can be used as a data source to understand public perceptions on the use of mHealth apps during pandemics. Real-time analytics of social media can help health authorities to address issues and concerns about mHealth apps during public health crises.

Algorithm Comparation of Naive Bayes and Support Vector Machine based on Particle Swarm Optimization in Sentiment Analysis of Freight Forwarding Services

2020 ◽  
Vol 4 (2) ◽  
pp. 362-369
Author(s):  
Sharazita Dyah Anggita ◽  
Ikmah
Keyword(s):  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
The Public ◽  
Svm Algorithm ◽  
Bayes Algorithm ◽  
Freight Forwarding ◽  
Improved Accuracy

The needs of the community for freight forwarding are now starting to increase with the marketplace. User opinion about freight forwarding services is currently carried out by the public through many things one of them is social media Twitter. By sentiment analysis, the tendency of an opinion will be able to be seen whether it has a positive or negative tendency. The methods that can be applied to sentiment analysis are the Naive Bayes Algorithm and Support Vector Machine (SVM). This research will implement the two algorithms that are optimized using the PSO algorithms in sentiment analysis. Testing will be done by setting parameters on the PSO in each classifier algorithm. The results of the research that have been done can produce an increase in the accreditation of 15.11% on the optimization of the PSO-based Naive Bayes algorithm. Improved accuracy on the PSO-based SVM algorithm worth 1.74% in the sigmoid kernel.

EFFECTIVNESS OF SPECIFIC MOBILE HEALTH APPLICATIONS (mHEALTH-APPS)in GESTATIONAL DIABTETES MELLITUS: A SYSTEMATIC REVIEW (Preprint)

2020 ◽  
Author(s):  
Claudia Eberle ◽  
Maxine Löhnert
Keyword(s):  
Mobile Health ◽  
Cochrane Library ◽  
Clinical Parameters ◽  
Control Groups ◽  
Manual Search ◽  
Mobile Health Applications ◽  
Short And Long Term ◽  
Mhealth Apps ◽  
Health Applications

BACKGROUND Gestational diabetes mellitus (GDM) emerges worldwide and is closely associated with short- and long-term health issues in women and their offspring, such as pregnancy and birth complications respectively comorbidities, Type 2 Diabetes (T2D), Metabolic Syndrome (MetS) as well as cardiovascular disease (CD). Against this background mobile health applications (mHealth-Apps) do open up new possibilities to improve the management of GDM clearly. OBJECTIVE Since there is – to our knowledge – no systematic literature review published, which focusses on the effectiveness of specific mHealth-Apps on clinical health-related short and long-term outcomes of mother and child, we conducted these much-needed analyses. METHODS Data sources: A systematic literature search in Medline (Pubmed), Cochrane Library, Embase, CINAHL and Web of Science was performed including full text publications since 2008 up to date. An additional manual search in references and Google Scholar was conducted subsequently. Study Eligibility Criteria: Women diagnosed with GDM using specific mHealth-Apps during pregnancy compared to control groups, which met main clinical parameters and outcomes in GDM management as well as maternity and offspring care. Study appraisal and synthesis methods: Study quality was assessed and rated “strong”, “moderate” or “weak” by using the Effective Public Health Practice Project (EPHPP) tool. Study results were strongly categorized by outcomes; an additional qualitative summary was assessed. Study selection: Overall, n= 114 studies were analyzed, n= 46 duplicates were removed, n=5 studies met the eligible criteria and n=1 study was assessed by manual search subsequently. In total, n=6 publications, analyzing n=408 GDM patients in the interventional and n=405 women diagnosed with GDM in the control groups, were included. These studies were divided into n=5 two-arm randomized controlled trials (RCT) and n=1 controlled clinical trial (CCT). RESULTS Distinct improvements in clinical parameters and outcomes, such as fasting blood glucoses (FBG), 2-hour postprandial blood glucoses (PBG), off target blood glucose measurements (OTBG), delivery modes and patient compliance were analyzed in GDM patients using specific mHealth-Apps compared to matched control groups. CONCLUSIONS mHealth-Apps clearly improve clinical outcomes in management of GDM effectively. More studies need to be done more in detail.

The Functionality, Quality and Consistency with International Guidelines of Asthma Mobile Health Applications: Protocol for a Systematic Review. (Preprint)

2021 ◽  
Author(s):  
Billy Robinson ◽  
Enying Gong ◽  
Brian Oldenburg ◽  
Katharine See
Keyword(s):  
Systematic Review ◽  
Mobile Health ◽  
Airflow Limitation ◽  
Significant Variable ◽  
Assessment Framework ◽  
Mobile Health Applications ◽  
Gina Guidelines ◽  
Mhealth Apps ◽  
Health Applications

BACKGROUND Asthma is a chronic respiratory disorder defined clinically as a combination of typical respiratory symptoms, and significant variable reversible airflow limitation. In addition to pharmacotherapy, a key aspect of asthma management is empowering patients to manage their condition and recognise and respond to asthma exacerbations. Mobile health applications (mHealth apps) represent a potential medium through which patients could improve the ability to self-manage their asthma. Few studies have conducted a systematic evaluation of both free and paid asthma mobile applications for the quality and functionality of the apps using a validated tool and to our knowledge none have systematically assessed these applications for the quality of information that they provide compared to available international best practice guidelines. This represents the first study that will undertake both of these evaluations for all available mHealth Apps in Australia targeted towards adult asthmatics. The Global Initiative for Asthma (GINA) guidelines represent a regularly updated guideline based on reviews of the available scientific literature by an international panel of experts. This review will examine the functionality and quality of available asthma mobile health applications and the consistency of these available applications with recommendations from the GINA guidelines. OBJECTIVE The objective of this study is to conduct a systematic review of adult-targeted asthma mobile health applications on the Australian market. As part of this review the potential for an mHealth app to improve asthma self-management and the overall quality of the application will be evaluated, using the Mobile App Rating Scale (MARS) framework, and the quality of the information within an app, using the current GINA guidelines as a reference, will be assessed. METHODS A methodological stepwise approach was taken in creating this review. First the most recent GINA guidelines were independently reviewed by two authors to identify key recommendations that could feasibly be incorporated into a mHealth app. These identified recommendations were then compared to a previously developed asthma application assessment framework. A modified assessment framework was created, ensuring all of these identified recommendations were included. Two popular App stores were then reviewed to identify potential mHealth Apps and then a screening process based on pre-defined inclusion and exclusion criteria occurred to establish what mHealth Apps would be evaluated. Application evaluation then occurred. Technical information was obtained from publicly available information on the application store or within the app itself. The next step was to perform an application quality assessment using the validated MARS framework to objectively determine the quality of the application. Application functionality was then assessed using the IMS Institute for Health Informatics Functionality Scoring system. Finally, the mHealth applications will be assessed using a checklist that we have developed based on what was identified from the international GINA guidelines. RESULTS To date, funding has been received for the project from the Respiratory Department at Northern Health, Victoria. Three reviewers have been recruited to systematically evaluate the applications. Results for this study are expected by the end of this year. CONCLUSIONS Nil as protocol CLINICALTRIAL PROSPERO 269894

scholarly journals Patient Self-Management of Asthma Using Mobile Health Applications: A Systematic Review of the Functionalities and Effects

2017 ◽  
Vol 08 (04) ◽  
pp. 1068-1081 ◽  
Author(s):  
Mehrdad Farzandipour ◽  
Ehsan Nabovati ◽  
Reihane Sharif ◽  
Marzieh Arani ◽  
Shima Anvari
Keyword(s):  
Quality Of Life ◽  
Systematic Review ◽  
Mobile Health ◽  
Self Management ◽  
Cochrane Central Register ◽  
Patient Reported ◽  
Mobile Health Applications ◽  
Mhealth Apps ◽  
Health Applications

Objective The aim of this systematic review was to summarize the evidence regarding the effects of mobile health applications (mHealth apps) for self-management outcomes in patients with asthma and to assess the functionalities of effective interventions. Methods We systematically searched Medline, Scopus, and the Cochrane Central Register of Controlled Trials. We included English-language studies that evaluated the effects of smartphone or tablet computer apps on self-management outcomes in asthmatic patients. The characteristics of these studies, effects of interventions, and features of mHealth apps were extracted. Results A total of 10 studies met all the inclusion criteria. Outcomes that were assessed in the included studies were categorized into three groups (clinical, patient-reported, and economic). mHealth apps improved asthma control (five studies) and lung function (two studies) from the clinical outcomes. From the patient-reported outcomes, quality of life (three studies) was statistically significantly improved, while there was no significant impact on self-efficacy scores (two studies). Effects on economic outcomes were equivocal, so that the number of visits (in two studies) and admission and hospitalization-relevant outcomes (in one study) statistically significantly improved; and in four other studies, these outcomes did not improve significantly. mHealth apps features were categorized into seven categories (inform, instruct, record, display, guide, remind/alert, and communicate). Eight of the 10 mHealth apps included more than one functionality. Nearly all interventions had the functionality of recording user-entered data and half of them had the functionality of providing educational information and reminders to patients. Conclusion Multifunctional mHealth apps have good potential in the control of asthma and in improving the quality of life in such patients compared with traditional interventions. Further studies are needed to identify the effectiveness of these interventions on outcomes related to medication adherence and costs.

scholarly journals Public Perceptions of the COVID-19 Pandemic Management in Bangladesh: A Qualitative Exploration

2020 ◽  
Author(s):  
Taufique Joarder ◽  
Muhammad Nahian Bin Khaled ◽  
Mohammad Ainul Islam Joarder
Keyword(s):  
Public Perception ◽  
Service Providers ◽  
Health Sector ◽  
Public Perceptions ◽  
Social Needs ◽  
Decision Makers ◽  
The Public ◽  
The People ◽  
Ability To Act ◽  
Sector Decision

Abstract BackgroundSince the emergence of COVID-19 outbreak, the Government of Bangladesh (GoB) has taken various measures to restrict virus transmission and inform the people of the situation. However, success of such measures largely depends on a positive public perception of the government’s ability to act decisively and the transparency of its communication. As the public perceptions of pandemic management efforts by the Bangladeshi health sector decision-makers have never been explored, this gap was addressed in this qualitative study.MethodsAs this qualitative research was conducted during COVID-19 pandemic, data was gathered through seven online mixed-gender focus group discussions involving 50 purposively selected clinicians and non-clinicians. The discussion transcripts were subsequently subjected to conventional content analysis.ResultsThe study participants concurred that, from the outset, decision-makers failed to engage the right kind of experts, which resulted in poor pandemic management that included imposing lockdown in periphery areas without arranging patient transport to the center, declaring certain hospitals as COVID-19 dedicated without preparing the facilities or the staff, and engaging private hospitals in care without allowing them to test the patients for COVID-19 infection. Several participants also commented on ineffective actions on behalf of the GoB, such as imposing home quarantine instead of an institutional one, weak point-of-entry screening, corruption, miscommunication, and inadequate private sector regulation.Perception of the people regarding service providers is that they lacked responsiveness (i.e., addressing the social needs of the patients) in providing COVID-19 treatment, with some doctors misleading the public by sharing misinformation on social and mainstream media. They also cited involvement of some doctors in running unauthorized testing centers, and promoting unproven medicines.Service providers, on the other hand, observed that decision-makers failed to provide them with proper training, PPE and workplace security, which has resulted in a high number of deaths among medical staff.ConclusionsThe Bangladeshi health sector decision-makers should learn from their mistakes to prevent further unnecessary loss of life and long-term economic downturn. They should adopt a science-based response to COVID-19 pandemic in the short term, while striving to develop a more resilient health system in the long run.

“Doctor Smartphone”

2016 ◽  
Vol 8 (1) ◽  
pp. 1-16 ◽  
Author(s):  
Margaret Machniak Sommervold
Keyword(s):  
Design Process ◽  
Mobile Health ◽  
Rapid Growth ◽  
Participatory Design ◽  
Public Discourse ◽  
Discursive Practices ◽  
The Public ◽  
Mainstream Media ◽  
Mobile Health Applications ◽  
Health Applications

The rapid growth in the field of m-health has not gone unnoticed by the mainstream media in Norway. Norwegian newspapers have a strong presence and outreach and hence play an important role in shaping of the public discourse on various subjects with m-health being no exception. This article presents a Dispositive Analysis of 23 articles from 6 national newspapers concerning mobile health applications. The analysis resulted in an interpretation of the press's technology views as theories of technology, which informed the discussion in this paper. Further, the newspaper articles were understood as discursive practices and analyzed by applying the concept of dispositives. The results of the analysis suggest inclusion of Dispositive Analysis as a step in Participatory Design process as means of enriching the design practices as well as uncovering the marginalized ‘voices' and thus addressing the call for democratization of technology.

An assessment of the effects of widespread use of mobile applications in the health sector: An exploratory study of its success and failures

2016 ◽  
pp. 145-149
Author(s):  
Nadire Cavus
Keyword(s):  
Health Care ◽  
Mobile Applications ◽  
Health Sector ◽  
Vital Signs ◽  
Early Warning Systems ◽  
Mobile Technologies ◽  
Warning Systems ◽  
Medical Practitioners ◽  
Mobile Health Applications ◽  
Health Applications

Abstract Prior to the introduction of mobile technologies, the manual system of checking patients’ vital signs after approximately seven hours increased the health risk of the patients. Some of the patients’ health was jeopardised, worsening their situation, others re-admitted and others even passing on. The introduction and extensive use of mobile technologies has transformed the delivery of health care. Mobile applications with early warning systems are now dominating the health sector in an attempt to alert medical practitioners to act promptly to the patients’ needs. This paper reviews effects of mobile applications in the health sector as well as the success and failures of Mobile health applications. The assimilation of mobile applications in health care is marking an incredible venture in the health care industry. Keywords: mHealth, mobile applications, success, failures, health sector, mobile technologies, adoption, patients, hospitals.

scholarly journals A Survey on Trend, Opportunities and Challenges of mHealth Apps

2017 ◽  
Vol 11 (6) ◽  
pp. 73 ◽  
Author(s):  
Shaidah Jusoh
Keyword(s):  
Mobile Technology ◽  
Healthcare Systems ◽  
Literature Survey ◽  
Mobile Technologies ◽  
Health Issues ◽  
Health Concerns ◽  
Current State ◽  
Mobile Health Applications ◽  
Mhealth Apps ◽  
Health Applications

Advancement of mobile technologies such as smartphones and PC tablets has given a great impact on healthcare systems. The mobile technology offers innovative approaches to addressing complex health concerns. Many mobile health applications (mHealth apps) are currently available on marketplaces. These apps are designed to facilitate various health issues and problems, and are intended to be used outside clinics.  However, very little research has been conducted to address trend, opportunities, and challenging issues of the apps. The purpose of this study is to investigate the current state of mHealth. A literature survey was conducted. Major findings of this study include, smartphones will be the major platform for mHealth apps, the number of published software is much higher than published scientific research, current mHealth apps lacking in grounded based theory and evaluation, and security and usability issues are still vulnerable. The findings suggest that involvement of all healthcare stakeholders is critical to the success of mHealth apps.

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