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Binkheder, S. Public Perceptions around mHealth Applications. Encyclopedia. Available online: https://encyclopedia.pub/entry/17761 (accessed on 09 July 2024).
Binkheder S. Public Perceptions around mHealth Applications. Encyclopedia. Available at: https://encyclopedia.pub/entry/17761. Accessed July 09, 2024.
Binkheder, Samar. "Public Perceptions around mHealth Applications" Encyclopedia, https://encyclopedia.pub/entry/17761 (accessed July 09, 2024).
Binkheder, S. (2022, January 05). Public Perceptions around mHealth Applications. In Encyclopedia. https://encyclopedia.pub/entry/17761
Binkheder, Samar. "Public Perceptions around mHealth Applications." Encyclopedia. Web. 05 January, 2022.
Public Perceptions around mHealth Applications
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This entry is adapted from the peer-reviewed paper 10.3390/ijerph182413388

This study aimed to use Twitter to understand public perceptions around the use of six Saudi mHealth apps used during the COVID-19 pandemic: “Sehha”, “Mawid”, “Sehhaty”, “Tetamman”, “Tawakkalna”, and “Tabaud”. The specific objectives of this study are: (1) to examine the difference in communication network structure across the networks generated among the six mHealth apps included in our study; (2) to analyze the sentiment surrounding the six mHealth apps conversations; and (3) to evaluate the performance of a sentiment classifier using machine learning approaches.

COVID-19 coronavirus social media Twitter mHealth applications public health sentiment analysis network analysis health informatics

1. Introduction

The novel coronavirus disease (COVID-19), caused by severe acute respiratory coronavirus 2 (SARS-CoV-2 virus), has spread around the world causing a pandemic. In Saudi Arabia, the first COVID-19 confirmed case was reported on 2 March 2020, which was followed by a series of mitigation efforts imposed by the government. These efforts included the enforcement of social distancing, closure, and suspension of schools and universities, shopping malls, restaurants, coffee shops, public parks, sports leagues and competitions, and congregational and weekly Friday prayers [1]. On 23 March 2020, the Saudi Arabian government took extra measures by announcing a national wide curfew, which lasted about two months [2][3]. During the implementation of these precautionary measures, communication technology tools and social media platforms were among the main methods that authorities used to communicate with the public. The development of specific mobile health applications (mHealth apps) for public use was also a major pandemic response by the Saudi government [1][4].
The importance of using mHealth apps for improving public health and transforming health service delivery has been recognized by the world health organization (WHO) since 1998 [5][6][7]. The Saudi Arabian national e-health initiative has also recognized the importance of e-health by mentioning e-health as an enabler of quality and safe healthcare systems [8]. During the COVID-19 pandemic, and as a response to the public health crisis, many governments leveraged technologies that played a role in combating the COVID-19 pandemic. Such technologies were focused on developing e-health applications, which included the use of mobile integrated health care programs from home, mHealth apps, artificial intelligence (AI) and machine learning decision-making apps, robotic technologies, social networking apps, contact tracing apps, AI, and blockchain-enabled decentralized apps, and health and fitness apps [4][9][10][11][12][13].
The Saudi government, in collaboration with specialized organizations, has launched six mHealth apps, which were heavily used during the pandemic [12][14]. These mHealth apps were the official apps providing free services to the public. There were three mandated apps used for COVID-19 testing, isolation, and issuing electronic permits for movement, gathering, and work [14]. Three of the mHealth apps were specifically designed in response to the pandemic during the year 2020: “Tetamman” [15] (translated to English as “rest assured”), was launched in April 2020, “Tawakkalna” [16] (translated to English as “we trust”), launched in May 2020, and “Tabaud” [17] (translated to English as “social distancing”) was launched in June 2020. The remaining three mHealth apps were developed before the pandemic, which were designed to support telemedicine services and primary health clinic appointment scheduling: “Sehha” [18] (translated to English as “health”), launched in March 2017, “Mawid” [19] (translated to English as “appointments”), launched January 2018, and “Sehhaty” (translated to English “my health”), was launched in August 2019 [4][14][20]. However, some research studies reported usability barriers among mHealth apps users during the COVID-19 pandemic, including lack of knowledge, awareness, trust, and lower users’ satisfaction [12][21][22][23]. Therefore, a critical component for enhancing the meaningfulness of the implemented mHealth apps during the COVID-19 pandemic is to understand perceptions, experiences, and acceptance among their users [24][25].
Social media platforms are a great resource to collect information regarding user experiences and perceptions due to their popularity. Many people use the platform as a method to share their opinions, experiences, and ideas, especially during public health crises [26][27][28][29][30][31]. One of the most popular social media platforms is Twitter. Twitter can be seen as an important resource, which may be used by consumers to seek health-related information, engage in behavior change interventions, and share perceptions, and by researchers and health officials to track disease outbreaks and drug use [32][33][34][35]. For example, an infoveillance approach was used to support public health decision-makers during the 2009 H1N1 pandemic by providing near real-time content and sentiment analysis [36]. Furthermore, researchers have identified health-related keywords and hashtags, which can be used in analyzing tweets during public health pandemics or outbreaks. Signorini et al. collected tweets matching a set of 15 pre-specified search keywords including “flu”, “vaccine”, “tamiflu”, and “H1N1” and built a predictive model based on 1 million influenza-related tweets [33]. Using social media and understanding public conversations can help in gaining insights into the impact of various implemented measures during a crisis, including the use of eHealth and mHealth apps.
Social network analysis and sentiment analysis from social media data have also played a significant role in supporting stakeholders, such as governments, health authorities, and policymakers, in data-driven decision making during pandemics and outbreaks for timely responses during public health emergencies [26][27][28][29][30][31]. Social network analysis is an interdisciplinary research area that examines information flow, attitudes, and patterns gained from exchanged conversations and users characteristics [37]. For instance, Park et al. investigated information-sharing patterns during the COVID-19 pandemic by applying a network and content analysis of four networks, which suggested that the spread of information was faster in the Coronavirus network than in others [38]. Similarly, sentiment analysis can help decision-makers in understanding the sentiments of people about topics, such as medical information and public health, and to improve healthcare services [39].
Several studies applied either social network analysis or sentiment analysis to explore public perceptions toward some health-related topics, such as COVID-19 pandemic [40][41], 5G COVID-19 conspiracy theory and misinformation [31], vaccination [42][43], child physical activity [44], quality of care [45], and end-of-life care [46]. Studies that combined methodologies of social network analysis and sentiment analysis were generally lower than studies that used either social network analysis or sentiment analysis. For instance, Shams et al. experimented with the combination of sentiment analysis and social network analysis in building classification rules to represent customers’ preferences and needs and found that this combination helped in classifying products based on customers’ interests [47]. Hung et al. analyzed Twitter discussions and the related sentiments toward COVID-19 and concluded that Twitter discussions and sentiments can help officials with needed information during pandemics [35]. Furthermore, Yao et al. also applied both social network analysis and sentiment analysis to the construction safety research among the public [48].
At the time of this study, there was no research in Saudi Arabia that examined public perceptions about the use of mHealth apps during COVID-19 by probing Twitter data. Even though some published research studies have evaluated the perceptions of users on the use of mHealth apps during the pandemic, these studies relied only on surveys [23][49][50]. Unlike social media-based data collection, traditional survey-based data collection might suffer from a tendency to systematic bias due to underrepresenting the sample or fall into a systematic bias due to the survey design. Furthermore, surveys require individuals to recall their experiences and sentiments regarding a specific context, while social media collects data from real-time and real-world individual interactions on a larger scale [51]. Lastly, using the conjunction of social network analysis and sentiment analysis has not experimented with the context of mHealth apps. Therefore, understanding how these methods can help in gaining insights about users’ experiences from Twitter data is beneficial to improve the usability of mHealth apps.

2. Major Findings

This study presented a novel research context by using social media conversations posted on Twitter to assess public perceptions on using mHealth apps during the COVID-19 pandemic. Two methodological approaches were used, which are the social network analysis and the sentiment analysis. Twitter data were used to identify the networks and sentiments of the public toward six mHealth apps, which were “Sehha”, “Mawid”, “Sehhaty”, “Tetamman”, “Tawakkalna”, and “Tabaud”. The social network analysis identified similar patterns in conversations among “Sehhaty”, “Tawakkalna”, and “Tabaud”. On the other hand, similar patterns were found among the following networks: “Sehha”, “Mawid”, and “Tetamman”. The apps “Tawakkalna” and “Tabaud” were the largest networks in size (the number of users) and volume (the number of conversational relationships) among all, and their conversations were led by a variety of governmental accounts. In comparison, the apps “Sehha”, “Mawid”, “Sehhaty”, and “Tetamman” networks were mainly led by a health sector or/and media. The sentiment analysis showed that conversations around the six mHealth apps were majorly neutral. Among all the six mHealth apps included in this study, we found that conversations about “Tetamman” were the highest frequency in positive sentiments. For the automated sentiment classifier, we used the SVM with AraVec embeddings as it outperformed other tested classifiers. The sentiment classifier showed an accuracy, precision, recall, and F1-score of 85%.
Overall, the social network analysis identified similar patterns in conversations among “Sehhaty”, “Tawakkalna”, and “Tabaud”. These mHealth apps had the highest number of conversations, indicating their significant role during the pandemic, and were heavily used by the public for COVID-19 health status, tracing of cases, and exposure notifications. A previous study has also reported that “Tawakkalna” and “Tabaud” mHealth apps were among the highest in the number of users during the pandemic [12]. The fact that “Tawakkalna” and “Tabaud” had distinguished Twitter user accounts while other apps did not might have also contributed to the highest number of conversations surrounding them. When examining the main conversational role-players within the networks, conversations around “Tawakkalna” and “Tabaud” were led by various governmental accounts, including education, hajj and umrah, the health sector, and media channels. This variability is most likely a result of the apps’ medical-related features and the regulations enforced by the Saudi government to combat the spread of COVID-19 through the mandatory use of “Tawakkalna” when entering universities, hospitals, workplaces, shopping malls, government buildings, and other public places.
When compared with other networks featured that had more app functionalities, the conversations of “Sehha”, “Mawid”, and “Tetamman” networks lacked interactions. A possible reason for low interactions and conversations in Twitter around “Sehha” and “Mawid” apps may be the misperceptions among the public regarding the access to MOH’s apps for only MOH patients [23]. Another reason for low interactions, some studies that surveyed physicians about their perspective on telehealth during the pandemic showed that they were concerned about the following: technological barriers, diagnostic reliability, cultural and social factors, lack of face-to-face interactions, and lack of a clear telemedicine legal framework. In addition, physicians tend to use WhatsApp® and Zoom more than the “Sehha” app [50][52][53]. Therefore, more campaigns targeting the eligibility of these mHealth apps are suggested to increase awareness about their use [23]. Lastly, even though “Tetamman” was one of the mHealth apps that were launched during the pandemic, the conversations were not as extensive as those by “Tawakkalna” and “Tabaud”. A plausible reason for this low rate of conversations is that many of the services provided by “Tetamman” were already offered by “Sehhaty” and “Tawakkalna”. This has also been found in our sentiment analysis findings where users suggested a need to integrate mHealth apps into one fully featured app.
Several findings of this study were derived from the sentiment analysis of conversations around the use of the six mHealth apps. First, the majority of conversations around mHealth apps were neutral. The dominance of neutral tweets was also reported in other similar studies depending on the research topic domains, where some topics can be more controversial than others [54][55][56]. The neutral conversations provided information or facts, neutral suggestions, and general inquires, which may indicate that Twitter can be used as an effective real-time communication platform to answer users’ questions and tackle their concerns. This is in line with many other studies that showed the use of Twitter by government officials during pandemics to communicate with the public during health crisis times [30][57][58][59]. Second, the findings also indicated several positive conversations that were relevant to the appreciation of the mHealth apps’ services and features, in addition to the positive user experiences surrounding the use of these apps. Other positive conversations were more of statements that indicated gratitude and appreciation toward healthcare providers. Many of the communication campaigns on social media platforms, and other communication outlets, which were led by the MOH during the pandemic, were focused on lifting the spirits of the public in the fight against the pandemic.
Third, when examining the type of issues raised by the public indicated by the negative sentiments, several were related to the recent digital transformation of many Saudi government services and the adaptation of mHealth and eHealth apps to facilitate health-related services, as stated by Han et al. [60]. Concerns and lack of familiarity and digital literacy by the public are expected at these early stages of adaptation [61][62]. Furthermore, the replication and overlapping of features between the mHealth apps have been a concern that was raised frequently by the public. Such duplication in services should be avoided as it could lead to confusion and avoidance of using these mHealth apps altogether, which may have contributed to the negative experience. Integrating similar features between these mHealth apps into one app may overcome these issues. Other negative conversations were related to the technical and accessibility issues experienced by users. All the mHealth apps described in our study require the use of Wi-Fi or a cellular connection, an issue with mHealth apps in general [63]. It is vital to consider this limitation when mandating the public to use a specific mHealth app, given the variability and differences in the availability of smartphones and Internet connections among the public. Another critical element raised in these conversations was the psychological impact that may be related to the use of such apps. The use of mHealth apps to track COVID-19 cases and their negative implications on the public has been a topic addressed by many researchers. Examples of these implications include increased levels of anxiety when users receive a COVID-19 exposure notification [63][64][65]. Privacy concerns have also been raised about tracking and tracing features, specifically about “Tetamman”, “Tawakkalna”, and “Tabaud”, similar to what has been reported by other mHealth apps [66][67][68]. The benefits and drawbacks of mHealth systems that raise issues with consumer privacy, must be examined critically by all stakeholders to ensure public by in and trust is not jeopardized.
To build the sentiment classifier for our data set, we experimented with the performance with different approaches. Overall, the results showed that AraVec embeddings performed better than AraBERT. This might be because AraVec embeddings were pre-trained on tweets compared to AraBERT that was pre-trained on Arabic Wikidumps and other Arabic corpora. Unlike different text sources on the Web, the nature of text in tweets is known to be informal with different characteristics. Consequently, the SVM with word embeddings sentiment classifier performed well, and it can be used in automating the detection of the sentiment of conversations around mHealth apps.

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Subjects: Computer Science, Artificial Intelligence; Medical Informatics
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Samar Binkheder
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Update Date: 10 Jan 2022
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