We were inspired by the genome data API. We wanted to utilize that genomic data to come up with an app that was more customized to the user. From there, we started thinking about the existing period tracking apps. After doing some analysis with 8 popular ones, we came up with 3 things the apps failed to address.
1). Many apps were overly feminine or childish. In fact, the color scale of each app tended towards pink. This ideology may alienate users who prefer not to have overtly feminine applications.
2).Many of the current applications overly emphasize fertility and ovulation. This may make users who are not interested in having children, can’t have children, or choose to abstain from sexual intercourse, in an uncomfortable situation.
3).Lastly, and the most important gap we wanted to address, is the fact that these apps do not have a lot of customized data. They aren’t able to make suggestions on what to eat during menstruation if for example, the user is more prone or has an iron deficiency.
What it does
First, the user would enter their genomic information or a comprehensive listing of observed SNPs (Single Nucleotide Polymorphisms). This would generate the interactive network of phenotypes that the user can use to see the impact of their genotypes based on observed phenotypes. The user can go to the calendar linked with the application and mark the begin date of their menstrual cycle. This in turn would generate the interactive network of phenotypes for each day based on the effect of a particular phase of their menstrual cycle on each phenotype. At this point the user could see certain phenotype expression increase or decrease, and distance to the center decrease and increase respectively.
How we built it
We used bootstrap templates in order to create a web application. After the foundation of the application was made, we wanted to add interactive elements. We started by looking at the genome link API to get a sense of what phenotypes that may have an effect on menstruation. Using preliminary research analysis of linkage between a phenotype and the experience of women during menstruation, we collected information from the genome link API, the comprehensive SNP-genotype listing of a single user and published studies (GWAS or Genome Wide Association Studies and otherwise) to make a comprehensive database of genotypes and phenotypes.
Challenges we ran into
Wifi and environmental issues at the physical location were also another unexpected challenge. One of our teammates’ laptop wouldn’t connect. Furthermore, half of the team couldn’t concentrate well in the loud, chaotic environment. Thus, we ended up having to pick a different location and commuting back and forth for food.
Accomplishments that we're proud of
Our project managed to solve a problem that could potentially lead to women feeling more healthy and confident during menstruation. Furthermore, we wanted to bring to light some of the potential emotional or health issues that may be exacerbated during menstruation.
Additionally, we’re all proud of the amount of work we were able to get done in the amount of time, especially given our inexperience with programming. Three out of four of the members on our team were also first-time hackers. Given all of this, we were able to get a lot done.
What we learned
Developing an application in 36 hours when no one on the team is proficient at coding, is extremely difficult. Everyone had to pitch in and do things they weren’t comfortable doing. We learned that all of us do well under pressure. Additionally, the human body can stay up for a horrifyingly long amount of time with little sleep just on caffeine and determination. However, no sleep leads to decreased productivity and we learned there is a fine balance between the two.
What's next for Orenda
Given the time constraint, we weren’t able to do the amount of research we wanted to do. We wanted to further research the effects certain phenotypes may have on menstruation.
In regards to coding and implementation, if there was more time, we would have liked to make judicious use of data mining to find quantitative correlation data between menstruation cycle (in the form of scaled experience) and each of the identified phenotypes. Another aspect of the same would have been incorporating the level of variations in each the phenotypes with each phase change of the menstrual cycle (menstrual, follicular, ovulation and lutea).
We would also have liked to test our deployment with more user data and have a functional mobile application with push notification enabled for users to easily access their information. More research into the actual molecular pathways connecting not just menstruation to certain phenotypes but phenotypes to phenotypes could provide greater insight to the user into their personal health care and lifestyle.
Lastly, we would have liked to get some user feedback on the app and iterate upon this. With any design, it’s important to make changes in order to make sure the final product is something that will be enjoyed by your users.