IS THE USE OF MOBILE HEALTH APPS HEALTHY OR TOXIC TO CONSUMERS?

Increasingly omnipresent and powerful mobile technology has the potential to address long-standing issues in the healthcare sector. mHealth (mobile health) apps can be used by consumers or patients for their wellness, prevention or treatment management. This study explored the scale of awareness of mHealth apps and the perception of using mHealth apps for monitoring health in Mauritius. The study also explored the barriers they faced. The results have shown that the people of Mauritius are aware and have downloaded and used mHealth apps. Fitness seems to be important, as most of the respondents find the fitness training app most useful. The findings have also shown that biggest barrier that prevents the respondents from using a mHealth app is cost concerns and privacy or security. Hence, it is recommended that the cost of mobile apps be investigated. Furthermore, there should be communication from app creators about the benefits of using a particular app, as well as the security measures and protection of private users and their information.


Introduction
There has been a renewed focus on health worldwide and people are more engaged on what they consume than even a decade ago. The mHealth app is emerging due to the increase in the development of apps in healthcare. According to Research2Guidance (2017), there were around 3.7 billion of downloads of mHealth apps around the globe and there are 325,000 health apps (health, fitness and medical apps) available on all major app storesthe most there has ever been. At this pace, the global market for health apps is projected to reach $102.43 billion by 2022 (Medium, 2017).
Due to the growth in the interest and use of apps, the management of these apps are important. mHealth apps can be used by consumers or patients as part of their wellness, prevention or treatment regimens. In this report, we share the results of a study we have undertaken to look more closely at the usage of healthcare apps among the By 2020, mobile apps are forecasted to generate around $189 billion in revenues via app stores and in-app advertising. As of fourth quarter of 2019, there were 2.57 million available apps at Google Play Store and 1.84 million apps available in the Apple's App Store, the two leading app stores in the world (Statista, 2019). The growth of download numbers is driven mainly by downloads from Android and iOS. As per mHealth Solutions Market (2019), the global mHealth solutions market is predicted to reach USD 90.49 Billion by 2022. The growing demand of healthcare applications and advanced connectivity have led to the growing interest in the development of mHealth apps.
A mobile application (or mobile app) is a software application designed to run on smartphones, tablet computers and other mobile devices. They are usually available through application distribution platforms, which are typically operated by the owner of the mobile operating system, such as the Apple App Store, Google Play (Android), Windows Phone Store, and BlackBerry App World. Table 1 shows some applications that have been developed and their functionalities.

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ISSN 2345-0282 (online) http://jssidoi.org/jesi/ 2020  (2016), over 165 000 healthcare consumer qualified apps were selected for review from both the Apple iOS and Google app platforms. Through review and selection criteria, to include prioritisation of the most downloaded apps, 26 864 were selected as representative of the most widely used mHealth apps by consumers. mHealth apps can be divided into two main categories: those which facilitate overall wellness such as exercise and diet, and those which specifically focus on disease management. However, the awareness and knowledge of these apps need to be increased to reach out to both the public and healthcare professionals in using the apps.
The systematic review by Mosa, Yoo, & Sheets (2012) acknowledged the importance of mHealth in medicine and healthcare. The Economist Intelligence Unit in PwC report (2014) examined the current state and potential of mHealth apps in developed and emerging markets, the ongoing barriers to its adoption and the implications for companies in the field. It was found that consumers have high expectations for mHealth apps, particularly in developing countries due to increasingly omnipresent mobile technologies and mobile subscriptions. Based on the research, the key findings were:  Widespread adoption of mHealth apps will require changes in behaviour of actors who are trying to protect their interests.  Patients want more convenient provision of healthcare, but they also want greater control.  Patients in emerging markets are much more likely to use mHealth apps or services than those in developed countries.  Widespread mHealth app adoption requires services and products that appeal to current payers because patients, highly sensitive to price, will provide little income.
Mobile health (mHealth) apps have shown to improve health indicators, but concerns remain about the inclusion of populations from low and middle-income countries in these new technologies.

Aim and methodology
The aim of this study was to explore the scale of awareness of mHealth apps and the perception of using mHealth apps for monitoring health in a middle-income country like Mauritius. The study also explored the barriers they faced.
This study involved a survey in the form of questionnaires for the general public. The questionnaires consisted of demographic-related questions and Likert-scale questions to determine whether respondents owned a smartphone, used apps, were aware of mHealth apps and barriers preventing them from using these apps. The questionnaire was piloted to distribution on a sample of 10 members of the public where it was found to be viable.
Based on convenient sampling, 385 questionnaires were distributed across the Mauritian population in August 2018. According to the Worldmeter (2018 Mauritius had a population of 1 268 315 people in 2018 and a sample of 385 at a 95% confidence level and 5% margin of error. A total of 141 useable questionnaires were received back, that is an 8.25% margin of error at a 95% confidence level at a 50% response distribution (Raosoft, 2004). The questionnaires were distributed both electronically and by hand through a researcher in Mauritius. The statistical package SPSS was used for analysing the data after it was captured and cleaned.
The demographic profile of the respondent group is presented in table 2 below. The majority of respondents (43.3%) were between 25 and 34 years of age. The gender split for the respondent group is male dominated, with 56% of the respondents being male. All (100%) of the respondents owned a smartphone and the majority, 85.8% (n =121) reported that they were aware of mHealth apps.

Results
The respondents were asked a series of questions regarding mHealth apps to determine whether they are aware of mHealth apps, the identification and use of the mHealth apps. Questions regarding the usefulness and barriers that prevent the use of mHealth apps were also asked.

Awareness
Most of the respondents (85.8%, n=121) are aware of mobile health applications for smartphones, figure 1.

Identification
Note the question regarding the identification of mHealth apps is a multiple response question and therefore the groups of respondents for the different apps are not independent. This is why the percentages of cases add up to more than 100%, see figure 2.

Downloads and/or use of mHealth apps
Note the question regarding the download and/or use of mHealth apps is a multiple response question and therefore the groups of respondents for the different apps are not independent. This is why the percentages of cases add up to more than 100%.
On average, each respondent selected 1.76 apps that they have used or downloaded, see figure 2. The most popular app is SamsungHealth with more than 50% (52.7%, n=58) of the respondents having selected it. GoogleFit (24.5%, n=27) is the second most popular app, followed by FloPeriodOvulationTracker (23.6%, n=26). Of those that selected FloPeriodOvulationTracker, 42.0% (n=21) are female, see figure 3. The respondents were requested to indicate what factors do they considered as important when downloading the mHealth app. The variables were firstly treated as categorical and secondly as numerical in the measurement level.
Treating the variables as categorical in the measurement level.
Considering the combined size of the Important and Very important proportions in the graph below, one can see that Cost of App (78.0%, n=110) is the most important factor when deciding which mHealth app to download, see figure 4.  Treating the variables as numerical in the measurement level. These variables are Ordinal (categorical) in measurement level and as such, they are sometimes treated as numerical (scale in measurement level), provided that the number of values in the scale is no less than four. However, when interpreting the mean scale values for these variables, it must always be done relative to the scale. By no means should a mean value be construed as the average importance of the influencing factor. The mean scale value should be interpreted relative to the middle value of the scale (in this case it is 3). For example, if the mean of the scale values is higher than the middle value of the scale, then you can deduce that the respondents tended more to consider this factor to be important rather than not important.

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On average, Cost (M=4.17, SD=0.925) is rated as the most important influencing factor when deciding which mHealth app to download, see figure 5. These results carry the same information as the previous section in a more parsimonious way.

Usefulness of mHealth apps
Respondents were asked to rate the usefulness of the mHealth apps on a 5-point Likert scale ranging from not useful at all to very useful. The variables were firstly treated as categorical and secondly as numerical in the measurement level.

Treating the variables as categorical in the measurement level.
Considering the combined size of the not useful at all to very useful proportions in the graph below (figure 6) one can see that Fitness Training Apps (83.7%, n=118) is the most useful mHealth app.  Treating the variables as numerical in the measurement level. These variables are Ordinal (categorical) in measurement level and as such, they are sometimes treated as numerical (scale in measurement level), provided that the number of values in the scale is no less than four. However, when interpreting the mean scale values for these variables, it must always be done relative to the scale. By no means should a mean value be construed as the average usefulness of the app. The mean scale value should be interpreted relative to the middle value of the scale (in this case it is 2.5). For example, if the mean of the scale values is higher than the middle value of the scale, then you can deduce that the respondents tended more to find this app to be useful rather than not.

Barriers that prevent the use of the mHealth app
Respondents were asked to indicate barriers that prevent people from using the (mHealth apps on a 5-point Likert scale ranging from not strongly disagree to strongly agree. The variables were firstly treated as categorical and secondly as numerical in the measurement level.

Treating the variables as categorical in the measurement level
Considering the combined size of the Agree and Strongly agree proportions in the graph below, one can see that there is a close tie with Cost concerns (78.8%, n=111) and privacy or security (78.0%, n=110) for being the biggest barrier that prevents people from using a mobile health app, see figure 8.  However, when interpreting the mean scale values for these variables, it must always be done relative to the scale. By no means should a mean value be construed as the extent to which this factor is a barrier to using a mobile health app on average. The mean scale value should be interpreted relative to the middle value of the scale (in this case it is 3). For example, if the mean of the scale values is higher than the middle value of the scale, then you can deduce that the respondents tended more to find this factor to be a barrier rather than not.  9. Barriers that prevent the use of the mHealth app: numerical measurement

Discussion
A mobile app is software programs that are designed for mobile devices such as a tablet computer or a smartphone and requires an operating system to run. Apps are designed for the end user and allow the user to perform specific tasks. Mobile apps were originally intended for productivity assistance, but the demand for apps caused a rapid expansion into other areas for instance retailing, gaming, medicine etcetera, there are literally millions of apps available. One such an app is mobile health or mHealth that provides health related services for smartphones and tablet PCs. As these apps are accessible from both home and on-the-go, health apps are of the movement towards mobile health programmes in health care (Rouse 2011).
The aim of the article is to explore the scale of awareness of mHealth apps and the perception of using mHealth apps for monitoring health in a middle-income country like Mauritius.
respondents the most useful apps are apps dealing with fitness training, health data and medical reminders. The biggest barrier that prevents the respondents from using a mobile health app is cost concerns and privacy or security.

Conclusions and recommendations
Smartphones are mobile devices that are readily available and used every day in a middle-income country like Mauritius. The people of Mauritius have knowledge of apps and are aware specialised of mobile applications (apps) such as mHealth. Fitness seems to be important as most of the respondents find the fitness training app use most useful. As can be expected from a middle-income country, the people are concerned about cost of downloading and associated costs such as data, as data is a prerequisite for using apps. Besides the cost aspect, privacy is very important to the people and is therefore seen as a barrier that prevents them from using apps. It is recommended that the cost of mobile apps be investigated. Furthermore, there should be communication and education from app creators regarding the benefits of using a particular app, as well as the security measures and protection of private users and their information.
Based on the findings of the research it seems that the type of health app used as well as the barriers to using these apps are similar in developed and developing countries. There is also not a significant difference between the type of app used and barriers to use in a Western society and Africa.