We arguably ubiquitously now have the ability to track our physical lives in data, and can use this data to positively impact our wellbeing [1]. It is possible that being able to monitor our cognitive lives in data might produce similar benefits. However, because there is not yet a means to objectively measure interpretable brain activity in an uncontrolled and longitudinal environment [2], it is unclear what it means for people to view this type of data. This means that when measuring cognitive activity in daily life is possible, it would be beneficial to have explored how users understand, interpret and reflect upon their objective cognitive activity data in order to develop the most useful and meaningful method of presenting the data back to the user; this is the focus of the PhD.

But cognitive activity is a broad term and thus the specific cognition presented back to users must, like physical activity sensors, have the potential to be a useful and accurate measure. Therefore, we have chosen mental workload as the cognition of interest; this is often described as the amount of mental effort required to complete a task [3] and has been widely studied within workplaces to ensure optimal work performance [4]. In order to investigate what it means to track mental workload data in daily life, the following research questions have been developed:

• What measures can be used to capture uncontrolled and longitudinal mental workload data in order to establish how it is considered?

• How can personal life mental workload data be effectively communicated?

• How can mental workload be contextualised?

Up until now, mental workload research using physiological measures has mainly been conducted in laboratory settings and almost always has involved the careful manipulation of mental workload levels. In order to capture objective mental workload data in an unconstrained environment, an initial exploratory study will collect a large amount of physiological (brain scanning, cardiac, etc), situational (location, online activity, etc), and subjective data (diary data, ratings) over a period of 5 uncontrolled days. We will then investigate how people think about their mental workload data and which physiological and situational measures best capture mental workload levels in the wild. Further studies will likely be conducted throughout the PhD, their nature dependent upon the study direction of the initial exploratory study.

References

1. Patel MS, Asch DA, Volpp KG. Wearable Devices as Facilitators, Not Drivers, of Health Behavior Change. JAMA. 2015;313(5):459–460.
2. Cinaz, B., Arnrich, B., La Marca, R., & Tröster, G. (2011). Monitoring of mental workload levels during an everyday life office-work scenario. Personal and Ubiquitous Computing, 17(2), 229-239.
3. Horia A Maior, Matthew Pike, Max L Wilson, and Sarah Sharples. 2014. Continuous detection of workload overload: An fnirs approach. In Contemporary Ergonomics and Human Factors 2014: Proceedings of the international conference on Ergonomics & Human Factors 2014, Southampton, UK, 7-10 April 2014. CRC Press, 450.
4. John R Wilson and Sarah Sharples. 2015. Evaluation of human work. CRC press.