Creating Evidence from Real World Patient Digital Data

Updated: Nov 14

Authors: Jane Nikles, Eric J. Daza, Suzanne McDonald, Eric Hekler and Nicholas Schork

The number of published N-of-1 articles has doubled annually since 2015. Similarly, digital health is an exploding field, with over 1,000 relevant studies registered on clinicaltrials.gov. Digital health includes digital therapeutics and wearable devices (e.g. worn sensors, implants etc.), that can be used to monitor various aspects of an individual’s health in N-of-1 studies. Individualised data collected through digital devices can be compared to data in population health databases to target a patient’s strongest possible treatment option and, in turn, inform the design of an N-of-1 randomised controlled trial to evaluate it. Digital health data can be monitored continuously during the entire N-of-1 study, and used to help tailor a treatment to the needs and preferences of patients in real time. N-of-1 RCTs and observational studies are well-suited to complement, strengthen, and generate advances in precision medicine, patient-centred healthcare, and personalised health.

To acknowledge this emerging field of digital N-of-1 research, a team of N-of-1 experts recently completed editing a Frontiers Research Topic entitled ‘Creating Evidence from Real World Patient Digital Data’. The 13 articles written by 60 authors has generated over 30,000 views to date, reflecting the strong interest in these methods globally. The topic has had viewers from all over the world, particularly from the United States, United Kingdom, Germany, France and China (see map below). The articles covered a selection of original research, methodology pieces, opinion pieces, and study protocols, discussing important themes including the significance of technology, the emergence of the “self-scientist” and the value of using diverse N-of-1 designs.

Significance of technology

A key feature of several of the articles was the use of N-of-1 studies enabled by mobile app technology; Bobe et al. discussed the potential for clinicians and patients to collaboratively use an app-based platform for N-of-1 trials and reported the results of a survey exploring perceptions about implementing an app-based N-of-1 trial platform to support data-driven decisions around the treatment of insomnia. In addition, Kravitz et al. reported on feasibility, acceptability, and influence of mHealth-supported N-of-1 trials for enhanced cognitive and emotional well-being in US volunteers. Bauer et al. described the protocol for a feasibility study testing randomized N-of-1 trials of Tamsulosin using the PERSONAL app to track daily urinary symptoms and medication side effects. Golden et al. described the protocol for self-directed, mobile app-based N-of-1 studies to test the effects of caffeine and l-theanine on cognitive performance.

The significance of technology is not limited to mobile apps; Chrisinger outlined the opportunities to use GPS technology to create geolocated N-of-1 datasets that could be used to explore relationships between individuals, their environment and their and health, or “the quantified self-in-place”. Chrisinger argues that individual level information in real-world environmental contexts might lead to a better understanding of how treatments and interventions work, for whom, and under which conditions. A number of logistical, methodological, and ethical challenges were identified.

Emergence of the “self-scientist”

Many articles discussed the concept of the “self-scientist” or “personal science”, which has been enabled by the availability of diverse and accessible digital tools to collect personal real-world data. Wolf and de Groot outlined a 5-stage conceptual framework to guide research and education into the practice of “personal science”, which they define as using empirical methods to pursue personal health questions. Important similarities and differences between personal science, citizen science and single subject (N-of-1) research were described.

Schwartz et al. introduced the concept of the “digital twin”, where individuals have access to self-generated biobehavioural information derived from data collected from various sensors and devices. Advances in technology have led to more accurate capture of the various biometric, behavioural, emotional, cognitive and psychological aspects of daily life. Data-driven feedback from the “digital twin” may inspire users to conduct self-experiments to evaluate their own treatment responses.

Nebeker et al. describe the patient perspective of using self-study and peer-to-peer support and argue that access to digital health technologies, wearable sensors, affordable lab screenings, etc. may contribute to a paradigm shift where “sick” care may become authentic “health” care.

The value of diverse N-of-1 designs

Articles in the Research Topic covered different N-of-1 designs. McMillan and Dixon used a series of digital N-of-1 observational studies to explore self-regulatory processes, motivation to conserve resources and activity levels in people with chronic pain and were able to draw conclusions that motivational and self-regulatory processes during goal pursuit may play a key role. Similarly, Altman, Shapiro and Fisherused a series of digital N-of-1 observational studies to explore the processes and mechanisms of change over a course of psychotherapy.

N-of-1 RCTs and observational studies provide individualised findings that can be aggregated to produce results equivalent to those found in traditional group-based RCTs and population-level epidemiological studies, respectively, but may require fewer people for the same statistical power. Hendrickson et al. present the findings from statistical simulation studies they conducted to optimize aggregated N-of-1 trial designs for predictive biomarker validation. They describe a set of statistical simulation studies comparing the power of four different trial designs to detect a relationship between a predictive biomarker measured at baseline and subjects' specific response to the pharmacotherapeutic agent prazosin for Post-Traumatic Stress Disorder.

Finally, Munson et al. argued that elicitation of individualized goals, and customization of tracking to support those goals, are a critical part of conducting N-of-1 studies. This serves as an important reminder about the flexibility of these methods and their added value of tailoring to the preferences and needs of the individual through patient-centred N-of-1 designs.


Our Frontiers Research Topic covered digital health applications, delivery, and analysis of N-of-1 RCTs and observational studies (including self-studies) that can be used in any health discipline. The focus was on mobile health (mHealth) and applications (apps), wearable devices, sensors and implants, real-time tracking, data analytics, patient experience of digital health and mobile health, patients as collaborators in personalised medicine, and self-tracking in citizen science. The great variety of articles illustrates the versatility of this design and the opportunity to use digital methods to collect real world health data. We look forward to seeing the impact of this research topic on digital health and personalised medicine worldwide.

About the Authors

Jane Nikles

The University of Queensland



Eric J. Daza

Evidation Health, Inc.

San Francisco,

United States

Suzanne McDonald

The University of Queensland



Eric Hekler

University of California, San Diego

San Diego,

United States

Nicholas Schork

Translational Genomics Research Institute


United States