Research Topic

Creating Evidence from Real World Patient Digital Data

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N-of-1 randomized controlled trials (RCTs) provide an opportunity to evaluate individual patient response to interventions, by randomly allocating different time periods within an individual to repeated intervention and control conditions and comparing responses. N-of-1 observational studies involve the ...

N-of-1 randomized controlled trials (RCTs) provide an opportunity to evaluate individual patient response to interventions, by randomly allocating different time periods within an individual to repeated intervention and control conditions and comparing responses. N-of-1 observational studies involve the repeated measurement of an outcome (e.g. pain) in a patient over time, but with no intervention implemented, in order to draw conclusions about naturally-occurring patterns and predictors of outcomes over time. Both N-of-1 RCTs and observational studies can have a ‘self-study’ design, where an individual conducts the study on themselves, to answer research questions they have generated themselves. N-of-1 RCTs and observational studies provide individualized findings that can be aggregated to produce results equivalent to those found in traditional group-based RCTs and population-level epidemiological studies, respectively, but requiring fewer patients for the same power.

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. Since 2015, the number of N-of-1 articles has doubled annually.
Similarly, digital health is an exploding field, with over 1,000 studies registered on clinicaltrials.gov.
Digital health, and digital therapeutics in particular, complement N-of-1 RCTs and observational studies by providing relevant individualized health data from, for example, worn sensors, implants, regular lab assays, or -omics sequencing. Such data can be compared to population-health databases to target a patient’s strongest possible treatment option (as in cancer-risk studies) and, in turn, inform the design of an N-of-1 RCT to evaluate it.
Digital health data can also be continuously monitored during the study itself and used to help tailor a treatment to the needs and preferences of patients in real time.

This Research Topic will cover digital health applications, delivery, and analysis of N-of-1 RCTs and observational studies (including self-studies) in any health discipline.
The focus is on:
- mobile health (mHealth) and applications (apps)
- wearable devices, sensors and implants,
- real-time tracking, data analytics and online registries,
- patient experience of digital health and mobile health, patients as collaborators in personalised medicine, self-tracking in citizen science, etc.

The articles can be original research, methodology pieces, opinion pieces, reviews, systematic reviews, protocols, short reports, or case studies.

Topic Editor Dr Eric Daza is Senior Statistician at Clarify Health Solutions. All other Topic Editors declare no competing interests with regards to the Research Topic subject.


Keywords: data analytics, implants, personalized health, trials, sensors, real-time tracking, mobile health, N-of-1, RCT, digital health, individualized research, citizen science


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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