BLOG: Touchscreens, digital patients and data

App stores are flooded with thousands of applications, ranging from weight reduction, to nutrition, diabetes management and blood pressure control – enabling the generation of large data volumes. Those can be shared with our doctors, partners, friends, parents, colleagues or peers for support, feedback and collective behaviour change. What is the data source? Our lives!


Estimates suggest that mHealth app downloads exceeded the three billion threshold in 2016, reaching almost four billion the year after [1]. Despite its emerging and often subtle integration in our everyday lives, the concept of patient-generated health data remains for many unknown, while researchers seem to be challenged with providing on a uniform definition. A White Paper for the US Office of the National Coordinator for Health Information Technology defines patient-generated health data as any health information created, recorded, gathered or inferred by or from patients and their designees, outside of clinical settings [2]. Others exclude patient-reported outcomes and ecological momentary assessments from that definition, arguing those are often informed and driven by healthcare providers [3]. In fact, no matter whether we choose a broad or narrow explanation, the common denominator of patient responsibility remains unchanged as they are solely responsible for creating and sharing their health information. Once technology is being added to the equation, the use, forms, aims and power of these data seems to drastically expand.  

Diverse use of apps

Despite the varying definitions, digital patient-generated health information finds a plethora of applications, ranging from health promotion to prevention, management and treatment. Existing scientific interventions provide a snapshot of that applicability. Breastfeeding women can use electronic monitoring systems to interactively capture their feeding patterns, including time, frequency and duration. The generated data are entered into the system’s smart components, which detect breastfeeding problems, alert the lactation consultant and generate tailored solutions [4]. Smartphone holders can record their alcohol consumption and associated caloric intake, including the quantity and type of consumed alcoholic drinks. The application generates personalised feedback to facilitate increased health impact awareness, while allowing users to set goals and identify geographic locations that trigger higher consumption [5]. Chronically ill patients may use a variety of digital devices, such as Bluetooth-enabled blood pressure monitors, to continuously and remotely capture and share their vitals. Those are wirelessly transmitted to mobile phones and central servers, triggering alerts whenever deviating from normal values [6].

Benefits, risks and the future

As technologies become smarter, the internet faster and health services increasingly digitalised, the examples provided above are expected to multiply exponentially. Understanding associated, and often hidden, benefits and risks is a crucial process of that growth. On the one hand, allowing patients to generate and meaningfully use their own data arguably paves the way to empowerment and patient-centeredness, transforming them into active agents of their own healthcare. Simultaneously, providers may fill their information gaps, benefit from expanded knowledge on their patients’ health status and facilitate an improved patient-provider communication. From a financial perspective, and only focusing on the diabetes epidemic, estimations suggest that IT-enabled remote monitoring contributed to a cost reduction of more than $300m (€255m approx.) over the course of decade [7].

However, data-generating technologies might indeed be intrusive, interfere with a patient’s daily life and pose challenges in correctly interpreting one’s own health information. Related to that, healthcare providers are often facing significant time and manpower constraints, quickly turning new and rich data streams into a burden. We should not forget that incoming health information needs to be appraised, filtered, validated and then processed into meaningful messages. While patients who generate and share their health data might expect physicians to complete those steps, the other side often remains reluctant and raises concerns on the trustworthiness of that information. Touchscreens, digital patients and their data seem to be an important aspect of future healthcare, and we, as consumers or providers, need a better understanding of all their risks and challenges, and learn how to overcome them.


  1. Number of mHealth app downloads worldwide from 2013-2017.
  2. Shapiro M, Johnston D, Wald J, et al. Patient-generated health data White Paper. Research Triangle Park, NC: RTI International, 2012.
  3. Cohen DJ, Keller SR, Hayes GR, et al. Integrating patient-generated health data into clinical care settings or clinical decision-making: lessons learned from project
  4. Ahmed, A., & Ouzzani, M. (2013). Development and assessment of an interactive web-based breastfeeding monitoring system (LACTOR). Maternal and child health journal17(5), 809-815.
  5. Attwood, S., Parke, H., Larsen, J., & Morton, K. L. (2017). Using a mobile health application to reduce alcohol consumption: a mixed-methods evaluation of the drinkaware track & calculate units application. BMC public health17(1), 394.
  6. Logan, Alexander G., Warren J. McIsaac, Andras Tisler, M. Jane Irvine, Allison Saunders, Andrea Dunai, Carlos A. Rizo et al. "Mobile phone–based remote patient monitoring system for management of hypertension in diabetic patients." American journal of hypertension 20, no. 9 (2007): 942-948.
  7. Bu, D., Pan, E., Walker, J., Adler-Milstein, J., Kendrick, D., Hook, J. M., ... & Middleton, B. (2007). Benefits of information technology–enabled diabetes management. Diabetes Care30(5), 1137-1142.

Vasileios Nittas

Doctoral Researcher in Epidemiology & Health Policy, University of Zurich.

Vasileios Nittas received his BSc (First Class Honors) in European Public Health from the University of Maastricht (Netherlands) and completed his MSc in Evidence-Based Social Intervention and Policy Evaluation (with Distinction) at the University of Oxford in the UK. Nittas is primarily interested in the development, implementation and evaluation of digital health solutions for disease surveillance, prevention and control. 

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