A growth in the number of new APIs being created to act as quantified self data tools could lead to a new wave of personal health applications. Data streamed via quantified self APIs could also potentially create new empirical datasets to support emerging sciences. The Moves API, Pokelog API and a forthcoming Human API all point to greater interest in pulling personal data into new applications.
The term “Quantified Self” refers to the idea of collecting, storing and analyzing personal data over a period of time. Surprisingly, the emergence of location-specific data tools and social media networks that encourage regular updating on how you are feeling and what you are thinking at any time, has not yet led to a zeitgeist-level interest in creating personal datasets from this information. Instead, the focus has been on aggregating individual data into big data sets that give advertisers (and media platforms) greater insight into the path to purchase and buying patterns of particular demographic groupings.
But with more health-related APIs currently being released, the growth in the wearable technology market, and the inclusion of a variety of sensor technologies in smartphones, drawing together a personal stream of data and interest in the concept of a quantified self is gaining momentum.
Last week, ProgrammableWeb added the Moves API to our API directory. The Moves application for iPhone uses in-built sensors to track your location and physical exercise, without the need for a jogging bracelet or other wearable tech. The Moves API provides access to the data collected by the smartphone app and allows for it to be analyzed and opens up its potential for a greater range of end uses. Android smartphone users are eagerly awaiting an expected summer version release of the app.
Earlier this week, ProgrammableWeb listed developer Božidar Benko’s quantified self API, the Pokelog API. While in alpha version stage, the API documentation allows developers to track a disparate range of personal data sources and create visualizations based on the data.
And next weekend, a San Francisco and international online hackathon is being hosted by Human API. This project, led by AngelPad graduate Andrei Pop, allows streaming of personal data via an API in order to enable greater insights and to encourage personal behavior change. “The idea is to create an infrastructure for all human health data, not just quantified self,” Pop told ProgrammableWeb. Interest in the event – which will also see the launch of the Human API – has been so great that organizers have moved the date by a week to accommodate the so-far 600-plus registrants wanting to participate. The Human API hackathon and API launch is now expected to be held on July 6-7, 2013.
With recent APIs aimed at better connecting patients and their doctors – for example with Ringadoc API and the UK’s National Health Service product the How Are You API – these new quantified self APIs could create a more seamless workflow and data sharing between an individual’s personal health data and their health professional. It may also create new tools for care-givers: New research from the Pew Internet and American Life Project shows that in the US, for example, 4 in 10 adults are caring for someone with a significant health issue.
But perhaps having this level of data available at an individual level will also provide greater insights into emerging new sciences, or those that have been too esoteric to design empirical studies around. For example, the field of chronobiology has only emerged since the 1960s but is now recognized as a true science. This field of study is finding that humans have an inbuilt clock and that the physical body responds differently at different times of the day. For example, cancer treatment response rates are better when chemotherapy is done at one time of the day rather than another, while the impacts of shiftwork – or even late-night coding projects! – can have a detrimental effect on an individual’s health. These new quantified self and personal health data APIs could help gain quicker insights into the green fields of the health sciences.
Above: Image by Yassine Mrabet with information from “The Body Clock Guide to Better Health” by Michael Smolensky and Lynne Lamberg; Henry Holt and Company, Publishers (2000). Image published under Creative Commons licence
With new tools like the Pokelog API, Moves API and the forthcoming Human API being launched on an almost-weekly basis at present, perhaps we can expect a whole new set of insights and analytical tools to follow.