Lie Ming Tang

Electronic Dietary Intake Assessment (e-DIA): Comparison of a Mobile Phone Digital Entry App for Dietary Data Collection With 24-Hour Dietary Recalls

Background The electronic Dietary Intake Assessment (e-DIA), a digital entry food record mobile phone app, was developed to measure energy and nutrient intake prospectively. This can be used in monitoring population intakes or intervention studies in young adults. Objective The objective was to assess the relative validity of e-DIA as a dietary assessment tool for energy and nutrient intakes using the 24-hour dietary recall as a reference method. Methods University students aged 19 to 24 years recorded their food and drink intake on the e-DIA for five days consecutively and completed 24-hour dietary recalls on three random days during this 5-day study period. Mean differences in energy, macro-, and micronutrient intakes were evaluated between the methods using paired t tests or Wilcoxon signed-rank tests, and correlation coefficients were calculated on unadjusted, energy-adjusted, and deattenuated values. Bland-Altman plots and cross-classification into quartiles were used to assess agreement between the two methods. Results Eighty participants completed the study (38% male). No significant differences were found between the two methods for mean intakes of energy or nutrients. Deattenuated correlation coefficients ranged from 0.55 to 0.79 (mean 0.68). Bland-Altman plots showed wide limits of agreement between the methods but without obvious bias. Cross-classification into same or adjacent quartiles ranged from 75% to 93% (mean 85%). Conclusions The e-DIA shows potential as a dietary intake assessment tool at a group level with good ranking agreement for energy and all nutrients.

Towards a Long Term Model of Virtual Reality Exergame Exertion

Virtual reality (VR) exergames have the potential to be a fun way to get exercise. People have different preferences and responses when it comes to both exercising an playing games, meaning that there are potential benefits from creating a user model for exergaming. This could support various forms of personalization, such as game recommenders, and personalization within a game. We define a VR exergame user model, VRex, that represents a users exertion as well a their goals and preferences for exercise and for games We illustrate the use of VRex to represent 1 users who played 4 games, based on data about their actual and perceived exertion and their satisfaction with each game. This demonstrates the diversity of the user models, in terms of the user models components. This is the first work to explore the design of user models for virtual reality exergames and has the potential to serve as a foundation for game personalization, recommenders and open model interfaces.

Harnessing Long Term Physical Activity Data—How Long-term Trackers Use Data and How an Adherence-based Interface Supports New Insights

Increasingly, people are amassing long term physical activity data which could play an important role for reflection. However, it is not clear if and how existing trackers use their long term data and incomplete data is a potential challenge. We introduced the notion of adherence to design iStuckWithIt, a custom calendar display that integrates and embeds daily adherence (days with data and days without), hourly adherence (hours of wear each day) and goal adherence (days people achieved their activity goals). Our study of 21 long term FitBit users (average: 23 months, 17 over 1 year) began with an interview about their use and knowledge of long term physical activity data followed by a think-aloud use of iStuckWithIt and a post-interview. Our participants gained new insights about their wearing patterns and they could then use this to overcome problems of missing data, to gain insights about their physical activity and goal achievement. This work makes two main contributions: new understanding of the ways that long term trackers have used and understand their data; the design and evaluation of iStuckWithIt demonstrating that people can gain new insights through designs that embed daily, hourly adherence data with goal adherence.

Daily & Hourly Adherence: Towards Understanding Activity Tracker Accuracy

We tackle the important problem of understanding the accuracy of activity tracker data. To do this, we introduce the notions of daily and hourly adherence, key aspects of how consistently people wear trackers. We hypothesise that these measures provide a valuable means to address accuracy problems in population level activity tracking data. To test this, we conducted a semester-long study of 237 University students: 88 Information Technology, 149 Medical Science. We illustrate how our adherence measures provide new ways to interpret data and valuable insights that take account of tracker data accuracy. Finally, we discuss broader roles for daily and hourly adherence measures in activity tracker data.

SAL: a small, simple, situated, ambient logger

This demonstration introduces SAL, the small, simple, situated, ambient logger. We designed SAL to help people achieve long term goals, by tracking and monitoring each days progress on a minimalist logging interface. People can flexibly configure SAL to log a broad range of behaviours that they intend to do each day. A SAL logger should be situated in just the right place, so that the display serves as an ambient reminder of daily goals and progress on them. This demonstration presents the SAL logger interface. Our key contribution is the definition and realisation of a simple, but flexible and valuable class of ubicomp device.

Formative studies of SAL, simple situated ambient loggers

The SAL (simple situated ambient logger) is an infrastructure and interface designed to enable people to do logging. They can use it to configure what they want to log each day and they are able to place their logger in a convenient location that works for them. Their logged data is stored in their personal store and the SALs interface shows a history of their progress in the last month. This paper describes the user view of the SAL logger and reports two formative studies of SAL, the first a general auto-ethnography study by the authors and a second, small study for logging food intake. Our key contributions are the insights from these formative studies of the SAL loggers.

Can SAL Support Self Reflection for Health and Nutrition

Important long-term goals for good health and wellbeing are challenging to achieve. Emerging technology offers the promise of easy data collection to help people monitor progress toward such goals. We designed SAL (simple, situated ambient logger) to help people track food eaten. It is minimalist with a core goal of making it easy for people to log progress towards goals, in particular, diet goals. We recruited ten participants who used SAL food loggers over 2 weeks. Our analysis of SAL use and interviews indicates it is usable and supports reflection on food intake. Our key contributions are insights into the potential of, and future directions of SAL loggers.

Scaffolding for an OLM for Long-Term Physical Activity Goals

An important role of open learner models (OLMs) is to support self-reflection. We explore how to do this for an OLM based on fine-grained long term physical activity tracker data that many people are accumulating. We aim to tackle two well-documented challenges that people face, in making effective use of an OLM for reflection. 1. We created a tutorial to scaffold sense-making needed to understand the meaning of the OLM. 2. We integrated an interface scaffold to help users consider key questions for effective reflection. We report the results of a qualitative think-aloud lab study with 21 participants viewing their own long term OLM. To evaluate the tutorial scaffolding, we split participants into an experimental group, who did a tutorial before exploring the OLM and a control group which explored the interface without the tutorial. To evaluate the reflection scaffolding, all participants first explored the interface as they wished. We then provided goal prompts to scaffold reflection. Our study revealed that, under lab conditions, the tutorial scaffolding was not needed - all participants in both groups could readily understand the OLM. However, we found that several of the goal prompts were important to help participants consider key questions for effective reflection. Our key contribution is insights into the design of scaffolding for reflection in a life-long learning context of gaining insights and setting goals for physical activity.

A Short Workshop on Next Steps Towards Long Term Self Tracking

Long term self tracking of health for periods of years, decades or ultimately lifelong provides tremendous opportunities for personal health. However, people face barriers that many find insurmountable for making use of self tracking. It has become clear that considerable work is needed to turn tracking from a toy to a tool. We suggest three research themes: The users double role in long-term self-tracking as a consumer of information and as a producer of data, as they try to make sense of long term data, and the needs, challenges and opportunities arising for creating new applications. As a cross-topic issue, we address challenges for HCI research on long term tracking.

Defining Adherence: Making Sense of Physical Activity Tracker Data

Increasingly, people are collecting detailed personal activity data from commercial trackers. Such data should be able to give important insights about their activity levels. However, people do not wear or carry tracking devices all day, every day and this means that tracker data is typically incomplete. This paper aims to provide a systematic way to take account of this incompleteness, by defining adherence, a measure of data completeness, based on how much people wore their tracker. We show the impact of different adherence definitions on 12 diverse datasets, for 753 users, with over 77,000 days with data, interspersed with over 73,000 days without data. For example, in one data set, one adherence measure gives an average step count of 6,952 where another gives 9,423. Our results show the importance of adherence when analysing and reporting activity tracker data. We provide guidelines for defining adherence, analysing its impact and reporting it along with the results of the tracker data analysis. Our key contribution is the foundation for analysis of physical activity data, to take account of data incompleteness.