Biases in Physical Activity Tracking
Primary supervisor
Additional information
- Understanding My Data, Myself: Supporting Self-Reflection with Ubicomp Technologies
- The Role of Uncertainty as a Facilitator to Reflection in Self-Tracking
- List of cognitive biases
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Other projects with the same supervisor
Funding
- Competition Funded Project (Students Worldwide)
This research project is one of a number of projects at this institution. It is in competition for funding with one or more of these projects. Usually the project which receives the best applicant will be awarded the funding. Applications for this project are welcome from suitably qualified candidates worldwide. Funding may only be available to a limited set of nationalities and you should read the full department and project details for further information.
Project description
Personal informatics and the quantified self in general and physical activity tracking is particular are all about collecting, tracking and reflecting on personal data. Assessing and reflecting on personal data is a key stage, which is recognised in personal informatics frameworks [1], for continuous engagement with the tracking system and to facilitate behaviour change. However, this stage involves uncertainty that comes from many sources (incomplete data, information overload), hindering the reflection stage [2]. The fact that data is personal and the tracking process involves uncertainty indicates that sense-making and decision-making upon self-tracking data are prone to systematic errors and may involve cognitive biases. For instance:
- Anchoring bias is exhibited when there individuals rely on the first piece of information encountered (the anchor), which affects the judgements that follow. In self-tracking is manifested for example when a self-tracking tool recommends a daily goal of 10,000 steps or 30 minutes of exercise. These anchors become reference points to assess progress and success, preventing users from establishing their own baselines.
- Confirmation bias is the natural propensity toward maintaining existing beliefs. Self-trackers may select or interpret evidence that supports their beliefs.
But there may be more including illusory correlations, availability heuristic, status-quo bias etc. The goal of this PhD project is to:
1. Explore and systematically identify the cognitive biases that may exist in physical activity tracking. This will involve a literature review on physical activity tracking, cognitive biases and phycology literature on experimental designs to identify biases.
2. Run experiments to identify the biases and how they manifest in physical activity tracking.
3. Design interventions for debiasing purposes and evaluate them through field studies.
Consequently, the project would be appropriate for a candidate who is skilled in at least two of the following: software development, experimental design and data science. If one skill was missing, the candidate must show commitment to acquire it during the course of the project. Also, it is expected that the candidate will publish academic papers in SIGCHI venues (CHI, DIS, EICS), UMAP and Ubicomp/IMWUT.
Person specification
For information
- Candidates must hold a minimum of an upper Second Class UK Honours degree or international equivalent in a relevant science or engineering discipline.
- Candidates must meet the School's minimum English Language requirement.
- Candidates will be expected to comply with the University's policies and practices of equality, diversity and inclusion.
Essential
Applicants will be required to evidence the following skills and qualifications.
- You must be capable of performing at a very high level.
- You must have a self-driven interest in uncovering and solving unknown problems and be able to work hard and creatively without constant supervision.
Desirable
Applicants will be required to evidence the following skills and qualifications.
- You will have good time management.
- You will possess determination (which is often more important than qualifications) although you'll need a good amount of both.
General
Applicants will be required to address the following.
- Comment on your transcript/predicted degree marks, outlining both strong and weak points.
- Discuss your final year Undergraduate project work - and if appropriate your MSc project work.
- How well does your previous study prepare you for undertaking Postgraduate Research?
- Why do you believe you are suitable for doing Postgraduate Research?