QuantifyMe: An Open-Source Automated Single-Case Experimental Design Platform

Smartphones and wearable sensors have enabled unprecedented data collection, with many products now providing feedback to users about recommended step counts or sleep durations. However, these recommendations do not provide personalized insights that have been shown to be best suited for a specific individual. A scientific way to find individualized recommendations and causal links is to conduct experi ments using single-case experimental design; however, properly designed single-case experiments are not easy to conduct on oneself. We designed, developed, and evaluated a novel platform, QuantifyMe, for novice self-experimenters to conduct proper-methodology single-case self-experiments in an automated and scientific manner using their smartphones. We provide software for the platform that we used (available for free on GitHub), which provides the methodological elements to run many kinds of customized studies. In this work, we evaluate its use with four different kinds of personalized investigations, examining how variables such as sleep duration and regularity, activity, and leisure time affect personal happiness, stress, productivity, and sleep efficiency. We conducted a six-week pilot study (N = 13) to evaluate QuantifyMe. We describe the lessons learned developing the platform and recommendations for its improvement, as well as its potential for enabling personalized insights to be scientifically evaluated in many individuals, reducing the high administrative cost for advancing human health and wellbeing. Keywords: single-case experimental design; mobile health; wearable sensors; self-experiment; self-trackin

Working Together in a PhamilySpace: Facilitating Collaboration on Healthy Behaviors Over Distance

Studies have shown that interpersonal relationships such as families and friends are an important source of support and encouragement to those who seek to engage in healthier habits. However, challenges related to geographic distance may hinder those relationships from fully collaborating and engaging in healthy living together. To explore this domain, we developed and deployed a lightweight photo-based application called PhamilySpace with a week-long intervention. Our goal is to examine family members\u27 and friends\u27 engagement and awareness on healthy behaviors while living apart. Our analysis of the semi-structured interviews, pre/post-intervention instruments, and application logs suggests three main benefits of interventions for health promotion in this context: (1) increased awareness on acts of health; (2) reciprocal sharing of health information supports social accountability over distance; and (3) positive dialogue around health enhances support on healthy living. By providing insights into distributed family/friends interactions and experiences with the application, we identify benefits, challenges, and opportunities for future design interventions that promote healthy behaviors

Appropriation of digital tracking tools in an online weight loss community: Individual and shared experiences

Online health communities provide a space where people seek out and provide support for weight loss activities, including tracking. Our study examined the experiences of members of an online community (r/loseit on Reddit.com) who posted about using digital tracking tools for weight loss. A targeted search garnered 379 public posts, which were analyzed using Thematic Analysis. Four themes reflected members’ individual and shared experiences: Tracking as gaining insight, Tracking as a vehicle of control, Confronting challenges in sustaining tracking and Teaching and learning the skills of tracking. We highlight complex socio-technical processes that members developed around tracking tools and discuss how knowledge of these appropriations can be applied to designing future user-centered tracking tools to support weight loss. We discuss how the social context of an online health community can shape both the usage of tracking tools and self-regulatory processes for health behaviour change

Personal Data Stores (PDS): A Review

Internet services have collected our personal data since their inception. In the beginning, the personal data collection was uncoordinated and was limited to a few selected data types such as names, ages, birthdays, etc. Due to the widespread use of social media, more and more personal data has been collected by different online services. We increasingly see that Internet of Things (IoT) devices are also being adopted by consumers, making it possible for companies to capture personal data (including very sensitive data) with much less effort and autonomously at a very low cost. Current systems architectures aim to collect, store, and process our personal data in the cloud with very limited control when it comes to giving back to citizens. However, Personal Data Stores (PDS) have been proposed as an alternative architecture where personal data will be stored within households, giving us complete control (self-sovereignty) over our data. This paper surveys the current literature on Personal Data Stores (PDS) that enable individuals to collect, control, store, and manage their data. In particular, we provide a comprehensive review of related concepts and the expected benefits of PDS platforms. Further, we compare and analyse existing PDS platforms in terms of their capabilities and core components. Subsequently, we summarise the major challenges and issues facing PDS platforms’ development and widespread adoption

A Data Science approach to behavioural change: large scale interventions on physical activity and weight loss

This PhD thesis is a quantitative investigation combining Behaviour Change Science with a Data Science approach in search of more effective large scale, multi-component behavioural interventions for health and well-being. There is limited evidence about how technology-based interventions (including those using wearable physical activity monitors and apps) are efficacious for increasing physical activity and nutrition. The relevance of this research is the systematic approach to overcome previous studies’ limitations in method and measurement: restricted research about multi-component interventions, limited analysis about the impact of social networking, the inclusion of components without sufficient evidence about the components’ effectiveness, the absence of a control group(s), small sample sizes, subjective physical activity reporting, among other limitations. The research was done in conjunction with Tictrac Ltd as the industrial partner, and the UCL Centre for Behaviour Change. Tictrac Ltd builds platforms for the collection and aggregation of personal data generated by the users’ devices and mobile apps. The collaboration with the UCL Centre for Behaviour Change has been instrumental to design, implement, evaluate and analyse behaviour change interventions that impact wellbeing and health. The thesis comprises three areas of research: 1. Computational platforms for large scale behavioural interventions. To support this research, computational platforms were designed, built, deployed and used for randomised behavioural interventions with control groups. The interventions were implemented as experiments related to the behavioural impact on physical activity, weight loss and change in diet. / 2. Behaviour change experiments. The two experiments use the Behaviour Change Wheel framework for behaviour change, intervention design and evaluation. A Data Science approach was used to test hypotheses, determine and quantify the effect of the fundamental intervention components and their interactions. The effective use of tracking devices and apps was determined by comparing the results of ‘structured intervention’ –vs- those of the control group. / Experiment 1: Large scale intervention in a corporate wellness setting. Multi-component behavioural intervention with: control group, self-defined goals, choice architecture and personal dashboards for physical activity and weight loss. The analysis covers network effects of social interactions, the role of being explicit about a type of goal, the impact of making part of team, among other relevant outcomes. / Experiment 2: Identification of critical factors of a technology-based intervention. Multi-component behavioural intervention with simultaneous target behaviours related to weight loss and physical activity, inspired by factorial design for the determination of critical factors and effective components. The analysis comprises: components’ interactions (coach, challenge, team, action plans, forum), non-linear relationships (BMI, change in diet habit), five personality traits, among other relevant results. / 3. Frameworks for future large scale interventions in behaviour change. The implementation of both experiments required an applied use of theoretical and practical principles for the design of the experimental computational platforms. As a result, two frameworks were suggested for future interventions: an implementation framework and a data strategy framework

Making Sense of Long-Term Physical Activity Tracker Data: The challenge of Incompleteness

Millions of people have already collected weeks, months and even years of data about their own health and physical activity levels. The potential is enormous for use in personal applications as well as for public health analysis of large populations at low cost. However, the reality is many people fail to wear their tracker and record data all day every day especially over the long-term. The resulting incompleteness in data poses an important challenge for interpreting long-term tracker data, in terms of both making sense of it and in dealing with the uncertainty of inferences based on it. Surprisingly, there has been little work into defining the problem, its extent and how it should be measured and addressed. This thesis tackles this key challenge and we demonstrate the need for a term to describe and quantify this challenge. We introduce the term, adherence, which quantifies the completeness in such data. We also offer interface designs that accounted for adherence to support self-monitoring and reflection. Bringing these together, we provide broader definitions and guidelines for incorporating adherence when making sense of long-term physical activity tracker data, both in personal applications and in public health research results. This thesis is based on three studies. First is a semester-long study of tracker use by 237 University students. Second is a study of 21 existing long-term physical activity trackers and provided the first richly qualitative exploration of physical activity and adherence of such users. It also evaluated the iStuckWithIt, a long-term physical activity data user interface, and reported on insights gained within and as aided by a tutorial and reflection scaffolding. In the final study, we drew on 12 diverse datasets, for 753 users, with over 77,000 days with data and 73,000 days missing to explore the impact of different definitions of adherence and methods for dealing with its implications

Democratising data science : effective use of data by communities for civic participation, advocacy and action

PhD ThesisWe live in an age of data, where it is being collected and archived in tremendous volumes and at great velocity. Smart cities are a good example of how we generate and use data with the aim of improving the lives of citizens. Cities adopting more technologies and embedding them in the physical fabric of the city will drastically change the way decisions are made in the city, in addition to the way citizens interact with the city. Research to date has predominantly focused on engineering agendas or has narrowly focused on citizens’ participation as passive producers of data in the smart city. This thesis takes a more holistic approach by focusing on both the engineering problem-solving agenda and community problem-solving activities. Taking a participatory research approach, the thesis explores such a context through three case studies that involve the design, development and analysis of two Community Informatics (CI) systems. In addition to producing two open-source CI technologies (SenseMyStreet and Data:In Place) for active citizen participation, this study posits a Citizen Advocacy Framework and Community-Data Interaction (CDI) model as novel theoretical framings that enable researchers to discuss and design for the effective use of data by communities. Furthermore, this thesis provides a practical example of the use of CDI for supporting communities to take local action. This improved understanding of the relationship between data and communities demonstrates a better direction for future research and the design of CI technologies as they work towards democratising data science and enabling the effective use of data by communities for active civic participation, advocacy and action