Single Subject (N-of-1) Research Design, Data Processing, and Personal Science

Co-designing a wiki-based community knowledge management system for personal science

Personal science is the practice of addressing personally relevant health questions through self-research. Implementing personal science can be challenging, owing to the need to develop and adopt research protocols, tools and methods. While online communities can provide valuable peer support, tools for systematically accessing community knowledge are lacking. The objective of this study is to apply a participatory design process involving a community of personal science practitioners to develop a peer-produced knowledge base that supports the needs of practitioners as consumers and contributors of knowledge. The process led to the development of the Personal Science Wiki, an open repository for documenting and accessing individual self-tracking projects while facilitating the establishment of consensus knowledge. After initial design iterations and a field testing phase, we performed a user study with 21 participants to test and improve the platform, and to explore suitable information architectures. The study deepened our understanding of barriers to scaling the personal science community, established an infrastructure for knowledge management actively used by the community and provided lessons on challenges, information needs, representations and architectures to support individuals with their personal health inquiries.

Systematic Review of Descriptions and Justifications Provided for Single-Case Quantification Techniques

There are currently a multitude of quantification techniques that have been developed for use with single-case designs. As a result, choosing an appropriate quantification technique can be overwhelming and it can be difficult for researchers to properly describe and justify their use of quantification techniques. However, providing clear descriptions and justifications is important for enhancing the credibility of single-case research, and allowing others to evaluate the appropriateness of the quantification technique used. The aim of this systematic literature review is to provide an overview of the quantification techniques that are used to analyze single-case designs, with a focus on the descriptions and justifications that are provided. A total of 290 quantifications occurred across 218 articles, and the descriptions and justifications that were provided for the quantification techniques that were used are systematically examined. Results show that certain quantification techniques, such as the non-overlap indices, are more commonly used. Descriptions and justifications provided for using the quantification techniques are sometimes vague or subjective. Single-case researchers are encouraged to complement visual analysis with the use of quantification techniques for which they can provide objective and appropriate descriptions and justifications, and are encouraged to use tools to guide their choice of quantification techniques.

“One button in my pocket instead of the smartphone”: A methodological assemblage connecting self-research and autoethnography in a digital disengagement study

In this article we present a “methodological assemblage” and technological prototype connecting autoethnography to the practices of self-research in personal science. As an experimental process of personal data gathering, one of the authors used a low-tech device for the active registration of events and their perception, in a case study on disengaging from his smartphone. For the visualization of this data the other author developed a novel treatment of fieldnotes in analytic autoethnography through an open source, interactive notebook. As a proof of concept, we provide a detailed description of the corresponding protocol and prototype, also making available the notebook source code and the quantitative-qualitative open dataset behind its visualization. This highly personalized methodological assemblage represents a technological appropriation that combines self-research and autoethnography—two disciplinary perspectives that share a type of inquiry based on situated knowledge, departing from personal data as empirical basis. Despite recent autoethnographic literature on the phenomenon of self-tracking and the Qualified Self, our contribution addresses a lack of studies in the opposite direction: how the practice of self-research mediated by technology can lead to bridges with digital autoethnography, validating their hybrid combination. After addressing diverse conceptual, ontological and methodological similarities and differences between personal science and autoethnography, we contextualize the case study of digital disengagement and provide a detailed description of the developed self-protocol and the tools used for data gathering.

Wearable-Measured Sleep and Resting Heart Rate Variability as an Outcome of and Predictor for Subjective Stress Measures: A Multiple N-of-1 Observational Study

The effects of stress may be alleviated when its impact or a decreased stress-resilience are detected early. This study explores whether wearable-measured sleep and resting HRV in police officers can be predicted by stress-related Ecological Momentary Assessment (EMA) measures in preceding days and predict stress-related EMA outcomes in subsequent days. Eight police officers used an Oura ring to collect daily Total Sleep Time (TST) and resting Heart Rate Variability (HRV) and an EMA app for measuring demands, stress, mental exhaustion, and vigor during 15-55 weeks. Vector Autoregression (VAR) models were created and complemented by Granger causation tests and Impulse Response Function visualizations. Demands negatively predicted TST and HRV in one participant. TST negatively predicted demands, stress, and mental exhaustion in two, three, and five participants, respectively, and positively predicted vigor in five participants. HRV negatively predicted demands in two participants, and stress and mental exhaustion in one participant. Changes in HRV lasted longer than those in TST. Bidirectional associations of TST and resting HRV with stress-related outcomes were observed at a weak-to-moderate strength, but not consistently across participants. TST and resting HRV are more consistent predictors of stress-resilience in upcoming days than indicators of stress-related measures in prior days.

Using an Individual-Centered Approach to Gain Insights From Wearable Data in the Quantified Flu Platform: Netnography Study

BACKGROUND

Wearables have been used widely for monitoring health in general, and recent research results show that they can be used to predict infections based on physiological symptoms. To date, evidence has been generated in large, population-based settings. In contrast, the Quantified Self and Personal Science communities are composed of people who are interested in learning about themselves individually by using their own data, which are often gathered via wearable devices.

OBJECTIVE

This study aims to explore how a cocreation process involving a heterogeneous community of personal science practitioners can develop a collective self-tracking system for monitoring symptoms of infection alongside wearable sensor data.

METHODS

We engaged in a cocreation and design process with an existing community of personal science practitioners to jointly develop a working prototype of a web-based tool for symptom tracking. In addition to the iterative creation of the prototype (started on March 16, 2020), we performed a netnographic analysis to investigate the process of how this prototype was created in a decentralized and iterative fashion.

RESULTS

The Quantified Flu prototype allowed users to perform daily symptom reporting and was capable of presenting symptom reports on a timeline together with resting heart rates, body temperature data, and respiratory rates measured by wearable devices. We observed a high level of engagement; over half of the users (52/92, 56%) who engaged in symptom tracking became regular users and reported over 3 months of data each. Furthermore, our netnographic analysis highlighted how the current Quantified Flu prototype was a result of an iterative and continuous cocreation process in which new prototype releases sparked further discussions of features and vice versa.

CONCLUSIONS

As shown by the high level of user engagement and iterative development process, an open cocreation process can be successfully used to develop a tool that is tailored to individual needs, thereby decreasing dropout rates.

The digital biomarker discovery pipeline: An open-source software platform for the development of digital biomarkers using mHealth and wearables data

INTRODUCTION

Digital health is rapidly expanding due to surging healthcare costs, deteriorating health outcomes, and the growing prevalence and accessibility of mobile health (mHealth) and wearable technology. Data from Biometric Monitoring Technologies (BioMeTs), including mHealth and wearables, can be transformed into digital biomarkers that act as indicators of health outcomes and can be used to diagnose and monitor a number of chronic diseases and conditions. There are many challenges faced by digital biomarker development, including a lack of regulatory oversight, limited funding opportunities, general mistrust of sharing personal data, and a shortage of open-source data and code. Further, the process of transforming data into digital biomarkers is computationally expensive, and standards and validation methods in digital biomarker research are lacking.

METHODS

In order to provide a collaborative, standardized space for digital biomarker research and validation, we present the first comprehensive, open-source software platform for end-to-end digital biomarker development: The Digital Biomarker Discovery Pipeline (DBDP).

RESULTS

Here, we detail the general DBDP framework as well as three robust modules within the DBDP that have been developed for specific digital biomarker discovery use cases.

CONCLUSIONS

The clear need for such a platform will accelerate the DBDP's adoption as the industry standard for digital biomarker development and will support its role as the epicenter of digital biomarker collaboration and exploration.

From self-tracking to self-expertise: The production of self-related knowledge by doing personal science

This article explores the production and type of knowledge acquired in the course of specific digital self-tracking activities that resemble research and are common among followers of the Quantified Self movement. On the basis of interviews with self-trackers, it is shown that this knowledge can be characterised as a verified and practical self-knowledge, and that science in the form of scientific sources, methods and quality criteria plays a key role in its production. It is argued that this self-related knowledge can be conceptualised as self-expertise, and its production as personal science. The article then discusses the implications for the science-society relationship. In contrast to self-tracking data, so far self-knowledge has hardly caused any resonance in science, although science currently appears open to the insights from single subject (N-of-1) research. As a new mode of public engagement with science, personal science instead mainly leads to an individual self-expertisation.