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

A pilot sequential multiple assignment randomized trial (SMART) protocol for developing an adaptive coaching intervention around a mobile application for athletes to improve carbohydrate periodization behavior

Background: It has recently been identified that manipulating carbohydrate availability around exercise activity can enhance training-induced metabolic adaptations. Despite this approach being accepted in the athletic populations, athletes do not systematically follow the guidelines. Digital environments appear to allow nutritionists to deliver this intervention at scale, reducing expensive human coaching time. Yet, digitally delivered dietary behavior change interventions for athletes and the coaching strategy to support them are still novel concepts within sports nutrition. Methods/design: We aim to recruit 900 athletes across the UK. 500 athletes will be recruited to test the feasibility of a novel menu planner mobile application with coaching for 6 weeks. 250 athletes with pre-existing nutritionist support will also be recruited as control. We will then conduct a 4-week pilot sequential multiple assignment randomized trial (SMART) with an additional 150 athletes. In the SMART, athletes will be given the application and additional coaching according to their engagement responses. The primary outcomes are the mobile application and coach uptake, retention, engagement, and success in attaining carbohydrate periodization behavior. Secondary outcomes are changes in goal, weight, carbohydrate periodization self-efficacy, and beliefs about consequences. Due to the high attrition nature of digital interventions, all quantitative analyses will be carried out based on both the intention-to-treat and per-protocol principles. Discussion: This study will be the first to investigate improving carbohydrate periodization using a digital approach and tailored coaching strategies under this context. Foundational evidence from this study will provide insights into the feasibility of the digital approach

Transcriptomic Analysis of <em>Prunus domestica</em> Undergoing Hypersensitive Response to <em>Plum Pox Virus</em> Infection

Plum pox virus (PPV) infects Prunus trees around the globe, posing serious fruit production problems and causing severe economic losses. One variety of Prunus domestica, named 'Jojo', develops a hypersensitive response to viral infection. Here we compared infected and non-infected samples using next-generation RNA sequencing to characterize the genetic complexity of the viral population in infected samples and to identify genes involved in development of the resistance response. Analysis of viral reads from the infected samples allowed reconstruction of a PPV-D consensus sequence. De novo reconstruction showed a second viral isolate of the PPV-Rec strain. RNA-seq analysis of PPV-infected 'Jojo' trees identified 2,234 and 786 unigenes that were significantly up- or downregulated, respectively (false discovery rate; FDR≤0.01). Expression of genes associated with defense was generally enhanced, while expression of those related to photosynthesis was repressed. Of the total of 3,020 differentially expressed unigenes, 154 were characterized as potential resistance genes, 10 of which were included in the NBS-LRR type. Given their possible role in plant defense, we selected 75 additional unigenes as candidates for further study. The combination of next-generation sequencing and a Prunus variety that develops a hypersensitive response to PPV infection provided an opportunity to study the factors involved in this plant defense mechanism. Transcriptomic analysis presented an overview of the changes that occur during PPV infection as a whole, and identified candidates suitable for further functional characterization

The LHCb upgrade I

International audienceThe LHCb upgrade represents a major change of the experiment. The detectors have been almost completely renewed to allow running at an instantaneous luminosity five times larger than that of the previous running periods. Readout of all detectors into an all-software trigger is central to the new design, facilitating the reconstruction of events at the maximum LHC interaction rate, and their selection in real time. The experiment's tracking system has been completely upgraded with a new pixel vertex detector, a silicon tracker upstream of the dipole magnet and three scintillating fibre tracking stations downstream of the magnet. The whole photon detection system of the RICH detectors has been renewed and the readout electronics of the calorimeter and muon systems have been fully overhauled. The first stage of the all-software trigger is implemented on a GPU farm. The output of the trigger provides a combination of totally reconstructed physics objects, such as tracks and vertices, ready for final analysis, and of entire events which need further offline reprocessing. This scheme required a complete revision of the computing model and rewriting of the experiment's software

The LHCb upgrade I

International audienceThe LHCb upgrade represents a major change of the experiment. The detectors have been almost completely renewed to allow running at an instantaneous luminosity five times larger than that of the previous running periods. Readout of all detectors into an all-software trigger is central to the new design, facilitating the reconstruction of events at the maximum LHC interaction rate, and their selection in real time. The experiment's tracking system has been completely upgraded with a new pixel vertex detector, a silicon tracker upstream of the dipole magnet and three scintillating fibre tracking stations downstream of the magnet. The whole photon detection system of the RICH detectors has been renewed and the readout electronics of the calorimeter and muon systems have been fully overhauled. The first stage of the all-software trigger is implemented on a GPU farm. The output of the trigger provides a combination of totally reconstructed physics objects, such as tracks and vertices, ready for final analysis, and of entire events which need further offline reprocessing. This scheme required a complete revision of the computing model and rewriting of the experiment's software

The LHCb upgrade I

International audienceThe LHCb upgrade represents a major change of the experiment. The detectors have been almost completely renewed to allow running at an instantaneous luminosity five times larger than that of the previous running periods. Readout of all detectors into an all-software trigger is central to the new design, facilitating the reconstruction of events at the maximum LHC interaction rate, and their selection in real time. The experiment's tracking system has been completely upgraded with a new pixel vertex detector, a silicon tracker upstream of the dipole magnet and three scintillating fibre tracking stations downstream of the magnet. The whole photon detection system of the RICH detectors has been renewed and the readout electronics of the calorimeter and muon systems have been fully overhauled. The first stage of the all-software trigger is implemented on a GPU farm. The output of the trigger provides a combination of totally reconstructed physics objects, such as tracks and vertices, ready for final analysis, and of entire events which need further offline reprocessing. This scheme required a complete revision of the computing model and rewriting of the experiment's software