Opinion: The digital patient - self-quantified and self-interpreted

The eHealth era is generating a shift in stakeholder roles, explains Vasileios Nittas, doctoral candidate at the Epidemiology, Biostatistics and Prevention Institute, University of Zurich.
By Vasileios Nittas
12:21 pm

The eHealth era, with all its innovations and possibilities, brings an abundance of digital tools capable of capturing our physical, mental as well as social well-being, often quantified in numbers and graphs. With the ever-increasing ownership of wearables, sensors and apps that track sleep patterns, step counts, water consumption or mood states, we are continuously creating our own personal health datasets, often large, complex and in need of translation; translation into understandable, meaningful, valid and practical health messages. 

Beyond smart technology, the eHealth era and its underlying self-quantification movement bring along an empowering shift of stakeholder roles. Enabling patients to generate, aggregate, analyse and share a large amount of personal, often multifaceted health information, previously passive “consumers” are rapidly emerging to active “agents” of care. While embracing that changing paradigm, it is essential to be reminded that empowerment rarely comes alone. The role of a digital and self-quantified patient comes with requirements and responsibilities, one of which is the skill to appraise and correctly interpret the digital information we generate. Acknowledging and promoting that skill is vital for at least two reasons. Firstly, the use of technology for health-related quantification of daily living gradually gains popularity in resource-poor and developing settings, increasingly targeting marginalised populations, often of low health and digital literacy. Secondly, the same technology is rapidly expanding across care domains, from primary prevention to treatment and rehabilitation, addressing complex conditions and multifaceted needs.  

Access: Not enough 

Statistics suggest that the digital divide of access to technology, in other words, the gap between those who can afford electronic gadgets and those who cannot is gradually closing; with previously low-access populations indicating increasing ownership trends. For example, 80 percent of Ghanaians and Senegalese are estimated to own a mobile phone, half of them possessing smartphones, many of which are capable to capture and monitor health parameters. Those trends are applicable to the largest part most Sub-Saharan region, as well as most other developing and emerging economies.

Access to technology and eHealth tools does certainly not equate to health benefits. Despite access, another type of divide, the health literacy divide remains a challenge and needs to be addressed. In the context of digital self-quantification, that divide separates those who have the skills to work with their quantified health and those who don’t. Simultaneously, it separates those who might actually experience health benefits from self-tracking tools and those who most likely won’t. A survey of self-monitoring practices among diabetic patients in Saudi Arabia indicated that only one fourth of those who generated blood glucose data actually used that information to adjust their lifestyles, while more than half indicated the need for more help and information regarding their condition. A similar trend was identified by a survey conducted in the United States, exploring effects of health literacy on blood glucose self-tracking. Low-literacy respondents kept lower test results records, which is most likely linked to the disease’s complexity, as well as relatively lower data appraisal skills. Both examples underline the essence of that divide, which lies in the fact that health information, especially if generated digitally and in large volumes, only becomes meaningful after being appraised, filtered, validated and processed appropriately. 

The skill of self-interpretation 

The health benefits of self-tracking technology are certainly more likely to be realised if access is combined with a key health literacy skill; the skill of self-interpreting one’s self-quantified self. That skill does not merely imply understanding the data one generates, but ideally, knowing how to act and react to their values. That sounds indeed easier than it actually is, especially for highly complex diseases, such as diabetes. What can be done to achieve that? Firstly, we certainly need more self-tracking interventions that are inclusive and sensitive to individual literacy needs. Secondly, we need more self-tracking technologies that are adaptable to these needs and capable of providing interpretation, as well as health action support. Finally, we require digital data that are understandable and sensitive to a person’s context and preferences. Simply equipping people with self-monitoring technologies, especially in resource-limited and low-literacy settings is most likely not going to achieve the promises of self-quantification.

Vasileios Nittas is a doctoral candidate at the Epidemiology, Biostatistics and Prevention Institute, University of Zurich.