I’m David, a physics phd student and a pretty lazy quantified selfer. To help fix that, I’ve been working on developing a platform for data analysis. I’m currently in the NSF I-Corps program looking to help verify the problem I’m trying to solve exists in the first place. I’m looking for potential end-users to interview about their general habits and issues in tracking. If you’re interested in learning more about the platform and/or participating in a short 5-10 minute interview please reach out to me.
Thanks for that list of resources. We’re trying to build something to tackle the issue surrounding the inconvenience of tracking and data analysis which we don’t see reflected in any of those existing resources. If you’re interested I’d love to ask you a few questions about what you currently use and what you think about the current solutions in this space.
We are building an iOS app to aggregate data, track psychological well-being and habits, and automatically present insights into that data that quantify the effects of habits.
We are conducting interviews to get a better understanding of the current problems with habit-tracking and data-analysis. This data is continuously refining our specific implementation of our features.
We are building tools for data visualization and analysis, but these aren’t currently open-source or meant for individual tweaking.
Thanks for your feedback. We’re aware of these problems and do take them very seriously in determining if we can actually build a useful and viable product. We are focusing on a small subset of well defined data types and plan to introduce a system for users to easily create their own custom data types for tracking.
We are targeting a more general audience that already uses journal and habit tracking apps available on mobile devices. With this in mind we don’t seek to include all possible data types that might be relevant to more “hardcore” tracking enthusiasts right away. We are also working with a psychologist to help determine the few most commonly relevant psychological metrics as well.
Another consideration is that what users hope to get out of tracking does vary widely. To start we are focusing on a few predefined health and mood goals to help curate insights to users and look to also slowly expand this into a more robust system; however, from our initial interviews we have identified weight loss, mood improvement, lowered anxiety, and gym performance as the most important health goals we will be trying to help users achieve.
I will most certainly keep this forum updated on our progress moving forward. We’re currently building out the MVP based off our customer discovery interviews. If things don’t work out I will also definitely post a “lessons learned”.
I thought I’d write a quick update about my progress. I’ve continued working on this project and it evolved into something a little different than initially conceptualized due to all the interviewee feedback we received.
We have an iOS app in public beta now and I’d be very appreciative of any feedback. It’s perhaps somewhat rudimentary for hardcore quantified self folks. The current implementation is a daily questionnaire that is compiled from a configurable list of interests. For example, we have a mindfulness question bank that asks questions like “How many recent moments of serenity can you remember?”. We then provide a weekly insight, for example: how sleep hours are impacting those moments of serenity.
Right now we’re working on developing those insights to be more insightful than just a simple linear regression and make them more readily understandable. We’re also bringing on a new team member to help write wellness articles that integrate your personal data into the story. For example, many articles mention caffeine’s general impact on sleep, but we want you to see a graph of your caffeine intake vs sleep quality inline.
I’m interested in hearing anyone’s comments on this methodology and our app. If you’re interested, we’d be thrilled if signup for our beta at https://lifemetrics.io