Since Apple unveiled the Health and HealthKit features as part of iOS 8 yesterday, I’ve a few people ask my thoughts. For those working with personal informatics for health and wellness, I think there’s a lot of reason to be excited but some parts of the announcement that make me skeptical about the impact of this application on its own.
One aspect of the announcement that discourages me a bit is the expectation that a dashboard of calories burned, sleep, physical activity, cholesterol, etc is a “really accurate answer” to “how’s your health?”, and that it adds up to a “clear and current overview of your health.” It’s not — at least not for the typical self-tracker. Though scales aren’t exactly new technology, people misinterpret or read too much into small changes in measurements. Even for many enthusiastic quantified selfers, this data is difficult to interpret and turn into something actionable.
Yesterday’s announcement gave scant mention to this challenge of data interpretation. To the extent that it was discussed, the focus was on sharing data with health providers, with a strong emphasis on Apple’s partnerships with the Mayo Clinic and electronic health record juggernaut EPIC.
For some conditions, such as diabetes, health providers have already developed a strong practice of using the sort of data that Apple proposes people store in HealthKit. Remote monitoring also has the potential to reduce re-admittance rates for patients following heart failure. In these cases and others where medical teams already have experience using patient collected data to improve care, HealthKit and similar tools have the potential to immediately improve the patient experience and reduce user burdens, while also potentially reducing the costs and barriers to integrating data from new devices into care.
Yet for some of the other most prevalent chronic conditions, such as weight management, there is not yet good practice around integrating the data that self-trackers collect into medical care. Weight, sleep, diet, and physical activity are some of the most commonly tracked health outcomes and behaviors. In my research, colleagues and I have talked with both patients and providers who want to, and some who try to, use this data to provide better care, but face many non-technical barriers to doing so. Providers describe feeling pressed for time, doubting the reliability or completeness of the data, feeling overwhelmed by the quantity, or lacking the expertise to suggest specific lifestyle changes based on what they see. Patients describe bringing data to providers but only being more frustrated that the health providers are unable to use it.
For these potential uses, HealthKit or other data sharing platforms seem unlikely to improve care in the short term. What they will do, however, is reduce some of the technical barriers to building systems that help researchers, designers, and health providers learn how patient-collected data can best be used in practice, and to experiment with variations on them. As a researcher working on these questions, this is an exciting development.
Individual trackers, their support networks, and the applications they use also will continue to have an important role in making sense of health data. Self-tracking tools collect more types of data, with greater precision and frequency, than ever before. Your phone can now tell you how many steps you took each minute, but unless it helps you figure out how to get what you want out of that data, this added data is just added burden. Many people I’ve talked with have given up on wearing complicated fitness trackers because they get no value from being able to know their heart rate, step count, and assorted other readings minute-by-minute.
What we need, then, is applications that can help people make sense of their data, through self-experimentation or exploration from which they can draw actionable inferences. Frank Bentley and colleagues have done some great on this with Health Mashups and colleagues and I have been working to design more actionable summaries of data available in lifelog applications such as Moves. Jawbone’s UP Coffee app is a great commercial example of giving people more actionable recommendations.
For these, HealthKit is again exciting. It means that application designers can draw on more data streams without requiring users to manually enter it, or without iOS developers having to implement support for importing from the myriad of health trackers out there. For the end user, it means that one can switch which interpretation application you use or tracking technology you use without having to start over from a blank slate of data (changing from HeathKit to another data integration platform, however, may be another story). So, here too, HealthKit has the potential to enable a lot of innovation, even if the app itself isn’t going to help anyone be healthier.
So, HealthKit is exciting — but most users are still a long way from getting a lot value from it. The best aspect of HealthKit may be that it puts reduces barriers to aggregating data about health factors and outcomes, and that it does so in a way that appears to enable people to retain reasonable control and ownership of their data.