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Detecting the Demands of Using Mobile Health Applications During a Covid-19 Pandemic Using Python ML

Research Sample Topic

Detecting the Demands of Using Mobile Health Applications During a Covid-19 Pandemic

1. Abstract

The requirement for newer health technologies has continued to increase for quite some time due to the increase in worldwide worries about health, increasing requirements for Mobile health applications.

Mobile health analysis applications are essential and crucial for people as they have provided an easy method to analyze the barriers patients face in their daily lives. Mhealth applications provide services online to the citizens in the covid19 pandemic situation.

We, therefore, analyze the impact of covid19 on the demand of mHealth applications in our report.

2. Background

The Covid19 pandemic began in China and quickly spread throughout the globe. The World Health Organization (WHO) classified Covid19 as a pandemic on March 11, 2020.

The pandemic has affected global trade, employment, and tourism, and many governments must take drastic measures to contain the virus's spread and reduce the weight of disease and death on the healthcare system.

Citizens have been advised to stay at home and practice the longest social gap as an effective strategy to prevent the spread of Covid19 in several nations worldwide.

Even though mobile applications have proven effective in managing chronic diseases, the current Covid19 outbreak has increased demand for improved mobile application solutions to decrease the danger of cross-contamination induced by close contact.

To restrict the spread of Covid19, mobile technology has been used in various ways. Mobile applications are accessible, acceptable, and simple to use, and they may help with a variety of causes.

As a result, they've been developed and widely used in recent months to try to flatten the curve of the rising number of Covid19 instances, as well as to provide knowledge and information to those who are trying to relieve the strain on health-care systems.

Despite the increase in mobile health solutions (Medealth) as part of the Covid19 intervention plans, there are substantial information gaps about their utility and importance in the current pandemic for medical professionals and the general public.

This system's overview, as outlined in this guide, intends to elucidate studies in the scientific literature on the use and evaluation of mobile applications to prevent, manage, treat, or track Covid19.

Other recent assessments have concentrated only on finding COVID19 mobile apps in general app stores or on apps deployed in specific countries, such as the United States, the United Kingdom, and India.

While general investigations into COVID19 information and communication technology have been conducted, they have focused on specific topics such as contact tracing; specialized medical fields such as pediatric health care, mental health care, and palliative care; and countries such as India, China, and the United Kingdom.

To our knowledge, relatively few studies have looked at a systematic analysis of case studies that show how COVID-19 mobile apps are used and evaluated in the real world.

3. Data Setup

Raw data were obtained from the Statista Research Team (look in References in the end). To answer the specific research questions needed for our research, the data was filtered only to include relevant records.

We imported required libraries like Numpy, Pandas, Matplotlib, and Seaborn. We further set the styles like the title size of the plot, and the label size of the x and y axes. Finally, we also added dark grid to make the plots appear a little better.

A total of 5 small but significant datasets were included in conveying our information. The first dataset sets our story in 2,020 where the covid 19 was engulfing people. It shows 10 leading causes of death in the united states in 2020.

After this, we show the number of medical health apps downloaded during various quarters before and contemporary with the covid19 pandemic.

Then we also see examples of % growth in downloads of mHealth apps for top countries.

All the datasets are read into the python code with the help of the read_excel function available in pandas.

Then we define a function named plot_bar_plot which takes in a data frame and its title and charts a bar graph in the output console.

This function helps reduce code repetition as we use this same function for 5 of our datasets.

4. Results

The leading causes of death in the United States are heart disease and cancer. However, in 2020, COVID-19 was the third leading cause of death in the United States, accounting for 10.4 percent of all deaths that year. In 2020, there were around 85 deaths from COVID-19 per 100,000 population, as it is clear from the above figure.

So basically just a year back there was nothing named as covid19 but suddenly this thing becomes among the top-3 diseases which kill people of the Unites States of America.

This dataset shows the growth in the number of mHealth apps downloaded in January 2020 as compared to the 'peak' month for the COVID-19 crisis in each respective country.

We see that South Korea had the highest growth, with a 135 percent increase in such downloads comparing its peak month of the pandemic with January.

It was followed by India with nearly 90% growth and then by Spain with 65% growth which is further followed by United Kingdom with a growth of 60%.

The Worldwide average growth is near to 65%. This information makes it clear that how the covid19 pandemic entered the human lives and made them download mHealth apps for their own safety which for the whole world the growth went upto 65%.

During the quarter one of the year 2022, there were nearly 51,000 healthcare and medical apps available on the Apple App Store, up by four percent compared to the previous quarter.

In 2020, the number of mHealth apps available to iOS users kept growing, and in the first quarter of 2021 reached its peak of almost 54 thousand apps.

Now the covid19 pandemic started in December 2019, so if we compare quarter four of 2019 with quarter 1 of 2020, there is a growth and it continues upto quarter two of 2020.

But with quarter three it saw a big jump which was the time of beta variant, and the quarter four further saw a big jump which was a time of delta variant.

After that we see there was a significant drop due to situation normalization but it continued grow in subsequent quarter especially during omicron version of covid19 which happened in November month of 2021.

Now this graph is a bit more important as it represents number of mHealth app available in Google Play Store which has a very high customer base in comparision to Apple App Store.

During the quarter one of 2022, there were around 52,000 healthcare and medical apps available on the Google Play Store, down by almost 20 percent compared to the previous quarter.

Now the covid19 pandemic started in December 2019, and WHO marked it as a pandemic in March 2020.

Between the beginning of quarter one of 2020 and the end of 2021, the number of mHealth apps available to Android users via the Google Play Store kept growing, reaching over 65 thousand during the ultimate quarter of the year 2021.

So if we compare quarter one of 2020 with quarter 4 of 2021, there is a continuous growth.

Also with quarter three the increment continued which was the time of beta variant, and the quarter four further saw increment in available health apps which was a time of delta variant.

Finally it continued grow in subsequent quarter and also during omicron variant of covid19 which happened in quarter four of 2021.

As of 2020, a little over seven percent of mHealth apps incorporated some level of artificial intelligence (AI), such as, for example, machine learning and deep learning, and computer vision with the help of Deep Neural Networks. This statistic depicts the proportion of mHealth apps incorporating advanced, standard, or no AI globally.

We see majority of the apps did not had any type of AI installed and used to further improve app utility.

5. Discussion

These analysis show that there are observations in the attributes of the citizens in adding these mHealth apps in their regular lifestyle. The availability of health applications presents efficient advancements that can revolutionize the mHealth industry today.

The existence of such applications can remove the barriers of space and time, which also refers that many lives can be saved. Health apps also have a large impact on refining healthy and good lifestyles that grow in community.

In addition, early and smooth accessibility to health related information also helps in increasing awareness of the public towards a healthy lifestyle.

Although, people also need to be cautious.

Though this helps society a lot, sharing data with health apps should be done only with developers of reputed background, else this could be misused and politicized, especially by regimes where there is a lack of democracy.

6. Conclusion

So, seeing the dire need of such mHealth digital solutions its good for our world to develop mobile health motivated apps.

Digital health services should be to formulated so that the tools that more and more population can become educated and the citizens awake and know what to do and what not to.

One of the major help provide by these mHealth apps is that a doctor a do a digital checkup instantly saving time of patient which can be life saving.


(1) Machine Learning Pocket Reference: Working with Structured Data in Python Book by Matt Harrison.

(2) Hands-On Machine Learning With Scikit Learn, Keras & Tensorflow by Aurelien Geron.

(3) Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures by Claus O. Wilke (Author).

(4) CDC. (December 21, 2021). Rates of the 10 leading causes of death in the United States in 2020 (per 100,000 population)* [Graph]. In Statista. Retrieved July 10, 2022

(5) Business of Apps. (October 15, 2020). Growth in the number of medical apps downloaded during the COVID-19 pandemic by country in 2020* [Graph]. In Statista. Retrieved July 10, 2022

(6) Appfigures. (April 15, 2022). Number of mHealth apps available in the Apple App Store from 1st quarter 2015 to 1st quarter 2022 [Graph]. In Statista. Retrieved July 10, 2022

(7) Appfigures. (April 15, 2022). Number of mHealth apps available in the Google Play Store from 1st quarter 2015 to 1st quarter 2022 [Graph]. In Statista. Retrieved July 10, 2022

(8) Innovation Eye. (September 18, 2020). Proportion of mHealth apps incorporating advanced and standard AI worldwide as of 2020 [Graph]. In Statista. Retrieved July 10, 2022

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