May 3, 2016


Chronic diseases and quality-of-life impacting conditions such as obesity, diabetes, asthma, and hypertension have placed tremendous stresses on healthcare systems worldwide. While self-management is essential, lifestyle alterations remain challenging for people to put into practice: even highly motivated individuals can struggle to maintain the required behaviors. More advanced ubiquitous systems could be of benefit to those living with these conditions, building on such accepted technologies as insulin pumps and continuous glucose monitors (CGMs). However, many questions remain regarding the ability to meet user needs without compromising self-determination.  In “Does he take sugar?: moving beyond the rhetoric of compassion” (2013) Rogers and Marsden proposed that to address such problems, HCI research should move away from third person thinking and instead embrace the design of flexible tools for self-empowerment and re-appropriation. Such promotion of user autonomy becomes especially relevant where algorithms and fully autonomous systems have the potential to dispense medication and offer advice based on standards not necessarily understood nor desired by the user.

Affordable consumer personal informatics technologies have already become pervasive with products such as Fitbit, Jawbone, LifeSum, HealthKit, smartphone and smartwatch apps assisting with the measuring, tracking, and communicating of personal biometrics and trends. And connected devices already exist for: epilepsy, ulcer prevention, hypertension, asthma, diabetes and diverse other chronic conditions. The resulting collections of highly targeted and personalized data could potentially help guide better treatment decisions. In some cases it is only with the quantities of data afforded by sensor integration across data sets and services that predictive analysis becomes effective. However, such practices raise serious challenges in terms of data ownership, privacy, and data siloing. In response to proprietary device standards, groups such as have been working with industry to open up device APIs, while citizen-initiated groups like have hacked the data protocol of continuous glucose monitors (CGMs) to allow utilization of collected data on diverse cloud-based platforms.

Such systems could be thought of as early stages of an Internet of Personal Health (IoPH) or ubiquitous systems of connected sensors and devices, which assist an individual in the management of a chronic or quality-of-life impacting health condition(s). As these systems become more capable of using methods such as big data analytics and machine learning, they will allow for personalized and contextually aware decision support. However, assessing both short and long-term effects of such technologies on the ecology of an individual’s life will be challenging and research methods to measure and analyze these effects are not well developed. In addition, there are many situations in which automated data collection is far from sufficient to ensure any benefit to the user, especially when it is dependent on user data interpretation. Thus there is need for such systems not only to gather data, but also to translate data into motivational feedback to support users in attaining their individual health goals. However, most people have diverse priorities in life such as family, career, or lifestyle that sometimes take precedence over what clinicians might consider “ideal” disease management. Therefore, it is ethically important that these systems do not erode user self-determination. Understanding of these complex user/system relationships will become increasingly important as we carry and embed ever more powerful technologies within our personal sphere.