So what is this project about?
As someone who strongly believes that good mental and physical health are central to maintaining well-being, I became interested in finding out about the current issues with the latter and this project gave me the opportunity to work towards eradicating them.
Long-lasting confusion often arises when new people decide to join the gym without prior guidance. They usually rely on online routines or choose to watch video footage of activities that they think will benefit them. Some videos are long, hard to follow and / or cause a delay in beginning the desired routine. When no pre-workout preparation is done, it is difficult to acquire techniques that match the individual's expectations and body characteristics. Without the proper session materials, many would resort to improvising and conducting activities that are unrelated to their current level. In addition, the exercises selected may be carried out improperly, resulting in injury.
The search term frequency of both "best workout" and "mobile app" has increased globally over the last 10 years (Google, 2019) (see Figures 1 and 2), indicating an increase in people's interest in finding the best online solution using a simple google search. However, because there is a great deal of variation in people's bodies and their ambitions, generic workouts are not always appropriate and can often be misleading. In addition, the progress of daily workout sessions (e.g. weight increase, number of training sessions performed, etc.) must be recorded manually using paper and pen or notes on their mobile devices.
The application is connected to a database server and allows the user accounts to be created securely via methods of encoding.
Yes, the application is successfully connected and additionally uses Firebase’s data storage for some of the images displayed throughout the workout creation process.
The workouts that are developed are personalised and user specific. The data is linked via a unique user identification number that is created any time a new user is generated.
Prior to the generation of workouts, a variety of criteria are used that carry the value of the target of the user (for example, whether they want to gain / lose / maintain their current weight).
An algorithm was created that generates a personalised workout for the user. Workouts for beginners include more functional strength exercises, as their muscles might not have grown as much, whereas workouts for intermediate or advanced gym users would be created by adding more structural strength exercises to better form the muscle structure.
I have included the option of swiping the exercise away, which would delete it from the workout and, after the removal, the exercise is replaced by a similar one. However, I have decided against implementing the option of adding an exercise, as this would completely remove the intent of the automatically generated routine as it would allow too much influence and hence the possibility of incorrectness as the addition of an exercise might have a counter-productive effect over the user's workload and not help the user but instead end up causing them harm.
When the workout is loaded, the exercises are put into an ordered list. This allows the user to follow through. There is no separate screen in which the application runs step by step.
The user can record notes onto an exercise of their choice. This happens after the workout selection, where the exercises are displayed. By inputting the note in the text field and clicking the update button, the note is being saved into the database and loaded next time the screen refreshes.
The application does not include a progress tracking mechanism. I believe that this feature is a separate project itself as it has proven to involve too many elements. I was not able to deliver on this requirement as it was straying too far away from the purpose of my project and it resembled more of a market feature.
I have introduced a function that would take the workout, format it into a bitmap image format, and export the file to the user device's internal storage device. This allows them to access the routine without a connection to the internet. The routine could still be followed through accordingly.
A reminder section has been created for the software. Whenever the user selects a timer for a specific day, the application sends a call to the device and triggers a notification at the time chosen by the user.
The application contains a fully - functional BMI calculator. It takes into account the weight and height of the individual and calculates the BMI. This value is generated every time a user account is created and the user-friendly screen that holds the value can be accessed by a user click.
I have developed a macronutrients calculator similar to that of the BMI. Similarly, It considers the weight and height, but this time it also takes into account the gender of the person, their age and the amount of exercise they do weekly.
This option is used in the Settings tab where the user enters their weight and height. Not only does it offer the conversion option, but if there is any value entered in one of the metrics, the value will be transferred to the next layout upon clicking on the switch.
Burton, M. (2015). Android application development for dummies. Hoboken, Nj: Wiley.
Pearman, G. and Goodwill, J. (2006). Pro .NET 2.0 extreme programming. Berkeley, Ca: Apress, 1st edition
Google Trends. (2018). Google Trends. [online] Available at: Google Trends (Accessed Dec 2020).
Weiler, R., and Stamatakis, E. (2010). ‘Physical activity in the UK: a unique crossroad? British Journal of Sports Medicine’, 44(13), 912–914.
Burnham, A. (2009) NHS Health and Well-being Review. Interim Report. Available at: Publication (Accessed: 23 December 2019).
Chuah, M. and Sample, S. (n.d.). Fitness Tour: A Mobile Application for Combating Obesity. [online] Available at: Publication [Accessed 27 Dec. 2020].
Sama, P.R., Eapen, Z.J., Weinfurt, K.P., Shah, B.R. and Schulman, K.A. (2014). An Evaluation of Mobile Health Application Tools. JMIR mHealth and uHealth, 2(2), p.e19.
Horton, T.J., Drougas, H., Brachey, A., Reed, G.W., Peters, J.C. and Hill, J.O. (1995). Fat and carbohydrate overfeeding in humans: different effects on energy storage. The American Journal of Clinical Nutrition, 62(1), pp.19–29.
Rehunen, S.K.J., Kautiainen, H., Eriksson, J.G. and Korhonen, P.E. (2017). Adult height and glucose tolerance: a re-appraisal of the importance of body mass index. Diabetic Medicine, 34(8), pp.1129–1135.
Kremers, H.M., Nicola, P.J., Crowson, C.S., Ballman, K.V. and Gabriel, S.E. (2004). Prognostic importance of low body mass index in relation to cardiovascular mortality in rheumatoid arthritis. Arthritis & Rheumatism, 50(11), pp.3450–3457.
Honma, N., Saji, S., Mikami, T., Yoshimura, N., Mori, S., Saito, Y., Murayama, S. and Harada, N. (2017). Estrogen-Related Factors in the Frontal Lobe of Alzheimer’s Disease Patients and Importance of Body Mass Index. Scientific Reports, 7(1).
Wadkar, M.C. and Patil, P.P. (2018). Traditional Infrastructure vs. Firebase Infrastructure. International Journal of Trend in Scientific Research and Development, Volume-2(Issue-4), pp.2050–2053.
Martyn, E. (2020) How Long It Actually Takes To See Results From A New Workout. Available at: Publication (Accessed: 23 April 2020).
Barkley, J.E., Lepp, A., Santo, A., Glickman, E. and Dowdell, B. (2020). The relationship between fitness app use and physical activity behavior is mediated by exercise identity. Computers in Human Behavior, 108, p.106313.
Fiori, F., Bravo, G., Parpinel, M., Messina, G., Malavolta, R. and Lazzer, S. (2020). Relationship between body mass index and physical fitness in Italian prepubertal schoolchildren. PLOS ONE, 15(5), p.e0233362.