In this project, we will produce an IOS app called Achieva. The purpose of Achieva is to give the users insight into how other users live and how satisfied they are with their life. The key idea is that there will be trends where certain variables (e.g., water consumption, physical activity) will correlate with how other users feel on average every day. As an example, consider the following scenario. You are a college student, and you find yourself constantly feeling groggy, tired, bored, out of breath, burned out, and just generally unhappy. You discover an app called Achieva. When you open the app, it will greet you and guide you through a survey. The survey will ask you to estimate the quantity of water you drink every day, the amount of exercise you get a week, and other key factors for good health. Finally, it will ask you how you are feeling these last few months. Once finished, it will show you what your habits look like compared to other users on the app. It will also report to you how these other users feel on average. From there, you might realize that your habits might be the reason you feel so bad lately. As you keep track of your habits with the app, it will update your position relative to other people according to your new average mood and habits. With a way to keep track of your progress, and to see others’ progress for a competitive component, you start improving how you feel every day.

One of the main goals of this app is to feature an easy-to-understand user interface. Achieva will conduct a statistical analysis of the user’s data compared to other users with similar data outside of the user’s view. Achieva will then display an easy-to-understand summary to the user so that they make a choice for themselves if they wish to emulate what “top performers” are doing. To incentivize the user to enter their data, we will implement a point system. Another main goal of the app is to make it fun to use. Not as crucial habits, like specific hobbies, will be in separate data categories that we could potentially monetize. It is critical that the app is fun to use and does not feel like a chore.

Achieva encourages the user to enter data such as the amount of water they drink, the amount of fast food they eat a day, and other variables that could factor in their quality of life. In return, they will receive points that they may use to unlock a category of data so that Achieva conducts the statistical analysis for user presentation. It is also important that the user rates how they feel on a scale from 1 to 10 at the end of their day so that Achieva can store the user’s habits and mood so that it can conduct analysis for the future. As the user collects their points throughout their day, they need to report how they are feeling on the mentioned scale so that Achieva confirms their points.

The grand idea of this project might be too ambitious for development that spans the semester. For that reason, our project will focus on building a prototype that contains the core features we want our app to provide. At the time we wrote this proposal, we did not know how difficult it would be to implement the entire project; however, we expect to implement databases, GUIs, machine learning, and statistical methods for the prototype. Due to the nature of the app’s reliance on other users, we intend to make compromises.

Upon the release of Achieva, an obvious challenge would be that there are no users to compare your data to. That begs the question: what is the point of getting the app if there are no users to compare your data with? The solution is to use publicly available data from research to jumpstart the app. An alternative to using other users’ data for comparison is that we could completely scratch the idea of using other users’ data and just stay with published studies for the entirety of the app’s development cycle. During the app development process, we will consider these alternatives more clearly. For the class, the prototype is going to use public data unless we find solutions during the development process to use user data instead for statistical analysis.

This project is intended for busy everyday people. This category ranges from college students, white-collar workers, blue-collar workers, and any other category of people that would like to improve their habits or is curious about what other people are doing. However, the primary target of this app is college students.

College students are the primary target of this app because they are generally busy and may neglect their essential habits. Since the app will keep track of their habits either by case-by-case scenario or by estimates at the end of the week, the student does not need to do any math to know where they are statistically compared to other college students or the general population. We hope that hard data compared to the student’s own data will give the student a deeper understanding of where they are in an emotional way. It’s one thing to look up good habits and compare them to your own. It’s another thing an app telling you explicitly that compared to your more successful peers, your habits are below average.

Another goal of this app is to allow the user to select a category of people to compare themselves with. Our concern is that if we have a very specific user base, less common users (perhaps blue-collar workers) will find it more difficult to compare themselves to similar users since there would not be a lot of data to support their query.

Of course, college students are not the only ones who would benefit from this app. For example, we also expect white-collar workers to be a big part of our user base since the power of data science is clearer to those with higher education. We do not intend to gatekeep this app to anyone outside of our intended user base. However, the early stages of our app development will focus on college students—and with future updates, other groups of people as well.

As mentioned before, we suspect a lot of college students of having subpar habits when it comes to their quality of life. Correcting these habits can be a daunting task for your everyday student, especially if there are multiple unattractive habits plaguing their life. The app’s purpose is to inform the student about how their habits can predict their outcome statistically. And in turn, giving them an explicit analysis to incentive them to improve their quality of life according to what they want to achieve. These “end-metrics”, which will be discussed below, are essentially the main goal for them. They may want to have a higher GPA, more money, more energy, and a general feeling of well-being. Achieva’s aim is to help our users reach their goals wherever possible.

More specifically, we imagine a college student’s battle for a stable adult life. There are a lot of things they will have to do on their own, and a lot of discipline will be demanded from them that might have not existed before enrolling in college. It’s easy to forget how critical habits such as physical exercise, amount of water you drink, the type of food you eat, the amount of time you have for leisure, and other habits are for the quality of your life. It’s especially difficult since forgoing or participating in such actions does not result in an immediate punishment/reward response. These habits produce effects over a long period of time. Therefore, these habits will usually not produce a feeling of urgency, and they may be neglected.

To fill in the void that the lack of uncertainty leaves behind, Achieva will provide them an analysis that provides a prediction of what their goals (or end-metrics) look like based on how they currently live their life. The hope is that the user will see the hard data, along with its prediction, and acknowledge the true urgency of their bad habits—and if they were not aware of these habits being regarded as “bad”, they at the very least are aware of them.

We consider the unfruitful lifestyles of some college students as problems and difficulties—and this project will attempt to address these issues. From some of our experiences as college students, we have seen that many of our peers exhibit complete or partial negligence towards their quality of life. That being said, we think this app will help as a great motivator and educator for the many that need such a support.

As mentioned in the introduction section of the proposal, we will begin by developing on the IOS platform. Limiting this platform was a decision we made due to the time constraints of the project. Once we have a sufficient version, we are happy with, we will begin the work of releasing on other platforms with Android being next in line. This could be achieved before the project deadline, and in such a case, we will work on integration on the Android platform as well as IOS. But for the sake of goal setting, we will set our sights on launching a version we are satisfied with on IOS. We have addressed the competition aspect of other platforms in another section of this proposal.

Obviously, in the grand sense of this project, we are looking to help people improve their lives by offering statistical analysis to our users with the aid of higher mathematics as well as machine learning models. Undoubtedly, this will require data. In fact, the more data the better. By gathering more data from more users, we will be able to train our models better and thus have a better statistical analysis output. To begin any type of analysis, we are going to need to begin with some data sets. We will begin by researching studies that have been conducted and have produced the same end-metrics we are looking for in our application. Along with looking at the end-metrics, we will also begin planning surveys to collect these specific end-metric datasets, and model some of the statistical analysis to these completed studies if some end-metric we are striving for was not calculated from the specific study. This will take a large amount of research but will give us a greater advantage as opposed to waiting for our database to fill from users to have enough data to produce any meaningful results.

The two main end-metrics we will be focusing on while we launch this project will be Feeling Metrics. How does the user feel physically? And how does the user feel mentally? These metrics will be on a sliding scale of one through ten. On this note, as we launch this project, all data points will be very succinct. Answers will be on a numbered scale, yes or no, or number specific. This will allow us to get our statistical and machine learning models working as intended. In the future, we would like to create less restrictive surveys for the user, but that will require the development/integration of text recognition and analytics, and possibly speech if it gets that far. For these time constraints, we will not be incorporating any recognition capabilities into the software for launch.

As previously mentioned, our application will be focusing on two Feeling Metrics for launch. To keep the user engaged in our application we will develop a points system, that will reward the user by providing data (Completing cycles, surveys, etc.). These points will be redeemable for producing more complex analysis on their data, and the ability to track other activities with different end metrics. For example, a student could track study habits with grades, GPA, attendance, etc. Someone could track their finances with spending, saving, categories of spending, etc. We will be continuing to brainstorm other end-metrics we can add for users in the future. Again because of the time constraints for this project, the two end-metrics we will focus on for launch will be for Feelings.

The goal of Achieva is simply to provide our users a more tangible insight to their everyday life trends. Ultimately, we hope to improve the user’s quality of life. Each of us on this team, as computer scientists, know the power of data and the analysis of it. Our everyday user aims to be the average person, and as such, they most likely do not have experience in high end statistical analysis. Our network aims to provide the users with the ability to see their stats when inserted into larger data sets. Combined with machine learning we hope to provide insight that would have never been discovered by the users, without using our application.

Currently, rightfully so, data gathering, and storage is a major concern to users. With our application, the end-user’s data will be protected. As a quid pro quo, they will get to see as much aggregate data proportional to the amount of data they provide. E.g., If a user completes a daily wellness survey for one of the feeling metrics, they will receive analysis with data provided by users completing the same amount of data. With that being said, individual user data will not be accessible to other users. They can only see aggregate data, which does not provide other user’s personal data linked to their account. This can also be overlooked if the user chooses to publish their information by opting in.

Another feature we would like to have in our application revolves around the social media aspect of what this application could be. Again, because of time constraints, these specific features will be very minimal at launch of the project, but we are also planning for future features that would help to entice the users to use our application as well as keeping them engaged in the application. Users could possibly opt into the social media aspect side of this application and if opted in, they could be matched to similar trend communities or groups. This enables the ability for the user to feel and obtain a sense connection to other users of the application. Having the ability to DM other users or join group message boards. Again, this is post-launch and any social media features implemented at launch will be a minimum. Two that we are considering implementing for launch include the feature of user’s can show off their tracking streaks, wellness scores, etc., onto a ‘High score’ type of public chart that all users would be able to see—if they opted into using this of course. This table will only provide username and will still not be connecting user data to a specific person.

Possible ways we will integrate this application with the user's lifestyle is firstly, to incentivize the user to input/share as much data as possible. They will share this data by completing survey questions about their trend topics. In turn, we will provide the user with statistical analysis of their data, as well as more involved analysis with more complex algorithms/machine learning for users that earn more points to spend on more complex analysis. Along with granting more technical analysis, the users will eventually be able to unlock other categories that they would be able to track and analyze as well.

Our proposed app, Achieva, falls into the health and wellness category, so we wanted to take a look at a few popular apps that people tend to use in their everyday lives. By doing a bit of research we found three apps that are very well rated among consumers and wanted to see what they did well versus what they did not do well. The apps we chose to look at were the Apple Health app, MyFitnessPal, and TalksSpace. These three apps really focus on three different aspects of health, general fitness, diet, and mental health. All provide great information/resources for the consumer, but we felt these apps all fell short in some categories or were missing some key features.

Let’s start with the Apple health app, this app does a lot of general automated tracking of your health. If you are wearing some type of wearable technology like an apple watch this app automatically tracks things like the number of steps you have taken, your heart rate, calories burned, your sleep, etc. While all this information is nice to know once the data is collected there really is not much else the app does for you. It is up to the user to decipher the information and use it how they see necessary. A lot of users have an apple watch and receive all this data but do not know what it means, or how much of something they should have, so a lot of this data is collected but just sitting there with no real guidance for the user.

The MyFitnessPal app focuses on tracking the diet of the user. The user enters their body metrics like height, weight, age, gender, and the app will recommend how many calories they should eat based on the goals that the user selects, whether that be losing weight or gaining weight. The user then has to enter what food they eat and use it essentially as a food diary. Unlike the Apple health app, this app does not automatically track information, like what you eat. But like the forementioned Apple health app once you enter the information it is up to the user to determine what their next steps are. Besides the initial recommendations the app gives you for calorie intake there are not any more suggestions after that. The user can even change the percentage of carbohydrates, proteins, and fats in their diet without any suggestion or tips from the app. Once the user starts using the app, they need to examine their progress themselves and do their own research to determine if they need to change their macronutrient breakdown or calorie intake.

TalksSpace is an app that allows the user to talk to a therapist in a variety of mediums. This app focuses on the mental health of the user. Now while this app definitely strays from what the functionality of Achieva would be it does focus on a topic that we want our Achieva to cover, mental health. To summarize this app, the user answers some questions about how they are feeling, and the app can pair them with a licensed therapist in their state. Unlike the other two apps mentioned this app is proactive and once the user enters information the app acts and tells the user what the next steps are. This app is very unlike the others as it is not a diary and data entered is not an important aspect of the app, except for the beginning quiz.

So now that we have summarized three popular health apps, we need to ask the question, how would Achieva improve on these existing apps? One common theme with most health apps is that they are just data collectors. The user enters data for them to keep track of, like a diary, then it is up to the user to use that information how they see fit. We want our app to take the information in and analyze it by comparing to other users and some pre-defined metrics to help them through their lives. Once their information is entered, we want to provide them with tips on how they could improve how they feel physically and mentally. Another point that we noticed is apps tend to either automatically track the user’s information or the user has to enter all the data themselves. We want to take a health app a step further and gather the automatically tracked information along with the information that the user wishes to enter. This would give us more data to go on and allows us to really make proactive suggestions for our users.

Jumping off from what we have previously discussed, we want Achieva to gather information in a variety of ways to give us the most data possible to analyze. When analyzing the data collected, we want to compare it with professional health recommendations to give them useful and helpful suggestions. We also want Achieva to be able to compare date between users to find patterns in how people’s actions tend to lead to how they feel. This will all be accomplished by using data analysis along with some types of machine learning algorithms.

We plan on using some type of machine learning algorithms to analyze the data that users enter. We took a look at the article “Big Data and Machine Learning in Health Care”, this article summarizes how machine learning is being used in health care to help predict medical conditions in clients. This article gives an example of using machine learning to help predict the Framingham Cardiovascular risk of a patient. By using machine learning and data from existing patients the doctors could determine if a patient was at a high risk of the disease. This is the same type of idea that we wish to use in Achieva. We want to take the data generated by each user and determine what they could change to help them improve their lives. If a user enters that they are not feeling mentally well, then our algorithms would look at other users who report feeling the same way and give recommendations to that user. Achieva could also be proactive, by noticing patterns in a specific user’s routine and letting the user know that those actions tend to lead to certain outcomes based on what other users have entered.

To accomplish the above machine learning analysis, we need to have a lot of data to allow the algorithms to do their work. To collect this data, we would have the user enter information that they chose to enter along with some automated data that will be collected if the user chooses to allow our app to access it. Modern mobile phones can automatically track a lot of useful data in regard to health and wellness. Modern phones can automatically track how many steps a user has taken, how long they have been active for, and even heart rate given that they are using some other type of wearable technology. For Achieva all this data would be very beneficial for the user to get the most out of what we are trying to accomplish. We want to use this already available technology to allow Achieva to give the best results to the user as possible.

In summary, we have big ambitions for Achieva. We believe current apps in this space are all missing important characteristics that Achieva will capitalize on. We aim to improve on these shortcomings by collecting data from users and then analyzing said data using machine learning so that Achieva can be proactive in making suggestions and recommendations to all users to help them achieve the most in their lives. We want Achieva to be the hub of health and wellness apps, combining what makes all other apps successful into one easy-to-use app.

Beam, A. L., & Kohane, I. S. (2018). Big Data and Machine Learning in Health Care. JAMA, 319(13), 1317–1318. https://doi-org.lib-e2.lib.ttu.edu/10.1001/jama.2017.18391

Achieva will have a noticeable positive impact on the user’s general wellbeing and will greatly aid the user in maintaining a healthy routine. The app will do this by suggesting various courses of action that a learning algorithm has found correlated with a state of wellbeing.

This algorithm will be fed the aforementioned health-related data that users will be incentivized to enter into the app in order to learn what courses of action lead to a better state of general wellness. This method affords the potential for the app to begin to “understand” how a person can attain or maintain a state of wellness in various categories better than a human can. It should also be said that by using a learning algorithm, Achieva will be able to offer more personalized health recommendations the longer someone uses the app. Indeed, it will be a sort of “life coach” that can grow with the user. This sort of personalized assistance is a great boon in itself, but Achieva will go a step further than that.

In order to keep the user engaged and motivated, Achieva will include a points system, and some social aspects as well. Focusing on the social aspects first, Achieva will feature a follower system that users could utilize in order to compare their habits with a friend or follow the wellness routine of someone that they respect. Achieva will also have a “groups” system that allows users with enough points to create groups centered around different methods of maintaining general wellness. It is well known that there is not one clear way to attain good health, and this feature will give users a degree of choice and will provide a community that they could feel that they belong to. The learning algorithm will also adapt to these choices by suggesting activities from the user’s chosen group(s) more often as long as they lead to a healthier state of being. These features will undoubtedly keep users engaged as they have more reasons to keep checking in, and also a way to measure their progress towards goals.

The points system included with Achieva will be simple, yet robust. Users will be awarded points for submitting requested information daily, by keeping certain habits, and by earning badges by completing goals. This system is sure to keep users invested in the app, and is also a great way for them to feel a sense of progress and satisfaction as they put their hard-earned points towards rewards. Users will be able to employ the point system to explore different categories of wellbeing, apart from the physical. To name a couple of examples, we are currently planning on including mental and social wellbeing. The learning algorithm will, of course, choose from a separate pool of data samples related to these other categories when choosing a user recommendation. Having these extra categories of wellness will allow the user to receive assistance in more aspects of their life, if they so choose it. By using a points system to unlock these new categories, users will not be overwhelmed with information and will be much more comfortable learning the more basic features in the meantime. Achieva will also allow the user to spend points on the creation of Wellness Groups, the previously mentioned social feature that will allow users to share their path to general wellness. This way the users in charge of these Wellness Groups will have ample experience with Achieva to help their group members master the app as well. This means that users will not have to worry about the experience level of these group founders and can focus on improving their lifestyle.

In order for the user to be able to use our Achieva app, there are some minimum requirements that must be met. The user will be required to have a phone that is running current a version of IOS, allows access to location information, and is capable of accessing the internet. The user will also be required to be willing to follow health trends based on data, which will come in the form of replacing old habits with new data-driven habits. Lastly, we will require that the user agree to the terms of service in order to begin using the app. Included in this is consent to share health information, waiving ability to seek legal action against Achieva for following app recommendations, confirming that the user is at least 18 years of age, and confirming that the user is located in the United States.

Since Achieva will be developed as a mobile app, it is a requirement for the user to have a phone in order to have access to it. This phone will also need to meet some requirements in order to be able to functionally use our app. The phone will need to be running on a current version of IOS since the app will only be available on the Apple app store on release. The phone will also need to be able to access the internet to communicate with the remote database as well as report user’s answers to the daily questions. The phone will also need to allow the app access to location information in order to verify that the user in located in the US for legal purposes.

We will ask that the user actually follow the recommendations provided by the Achieva app. In order for the user to get meaningful use from the app, they will need to follow recommended changes to their current habits. The goal is to potentially improve the life of the user by providing them with recommendations that are derived from trends in data collected from a mass of users.

In order to gain access to the functionality of the Achieva app, the user will be required to agree to our terms of service. Some major points of the terms of service are that the user is giving consent for Achieva to collect personal health information from the user, the user is waiving their right to seek legal action against Achieva for following the in-app recommendations, the user is confirming they are at least 18 years of age, and the user confirms they are using the app from a Location in the United States. These conditions are to protect Achieva in the case that the user violates our terms, or in the case the user has a negative experience by following the recommendations. We do not guarantee that the app will result in a better life, but believe the user may find it useful to be provided data-driven recommendations that tend to lead users to a sense of feeling better.

A user of our app, Achieva, will not occur any financial costs by using the app. However, there are other costs of using the app that the user must consider. Using Achieva will cost the user time by answering daily questions about the user's health, and by following the recommendations from our app for changes the user should make. Using Achieva will also cost the user some storage space on their smartphone in the forms of storing a user interface, data collected from the user, and networking protocols to communicate with the database on a remote server. The use of Achieva will also cost the user old habits. By following the recommendations of the Achieva app, the user will give up habits they have followed previously in favor of new actions informed by data collected from the users’ answers to daily questions.

Since Achieva is targeting a wellness audience, we have decided to not to charge the user for using the app on initial release. There will also not be a subscription fee for using the service. This will hopefully make the app more appealing to download and help Achieva increase the data that informs recommendations to users.

The user will still have costs for using the Achieva app. A main cost to the user will be time. This comes as an opportunity cost as the user could be doing something else, but is instead engaging with the features of our app. Since we will place a large emphasis on data collection, we will have the user answer a set of questions on a daily basis. Which while it won’t take much time, does add up the longer the user is on our app. The user will also forgo some activities they normally engage in by following the new recommendations from the Achieva app which also costs some time of the user.

The user will also need to consider that the Achieva app will cost some storage space on the phone. While the total amount is not currently known, it shouldn’t be a significant amount of available storage space. Some things that will need to be stored locally on the phone are the user interface, recent data collected from the user while waiting to be transmitted, and networking protocols that will be used to transfer data to the remote database as well as receive the recommendations from the app server. Modern phones usually have a large amount of storage meaning this should not be a large cost to the user.

Through the use of our Achieva app, the user will lose some old habits. This cost isn’t a guarantee, but the user should consider that the cost of their current health related habits is part of what the purpose of the app is. By following the recommendations, the user will give up old habits and replace them with new habits, given enough time using the Achieva app.