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This chapter provides a historical contextualisation of health tracking and public health communication from the post-World War Two development of the welfare state, through the birth of neoliberalism, until today’s individualising practices of digital health tracking and quantification of bodies. Through an examination of these three phases of public health quantification of bodies, encompassing the socio-economic, cultural and political shifts since 1948, combined with the development and wide adoption of digital health and self-quantifying technologies, this chapter traces the changing landscape and the dramatic implications this has had for shifting who is responsible for maintaining ‘good’ health. This chapter illustrates how neoliberal free market principles have reigned over UK public health discourse for many decades, seeing health as no longer binary to illness, but as a practice of individual self-quantification and self-care. In turn, the chapter explores how the quantification and health tracking of bodies has become a dominant discourse in public health promotion, as well as individual citizenship and patient practices. This discourse still exists pervasively as we move into the digital society of the 2020s, through the Covid-19 pandemic and beyond; with public health strategies internationally promoting the use of digital health tools in our everyday, further positioning citizens as entrepreneurial subjects, adopting extensive technological measures in an attempt to measure and ‘optimise’ health, normalising the everyday quantification of bodies.

This chapter focuses on the state-of-the-art modeling approaches used in Intelligent Tutoring Systems (ITSs) and the frameworks for researching and operationalizing individual and group models of performance, knowledge, and interaction. We adapt several ITS methodologies to model team performance as well as individuals’ performance of the team members. We briefly describe the point processes proposed by von Davier and Halpin (2013), and we also introduce the Competency Architecture for Learning in teaMs (CALM) framework, an extension of the Generalized Intelligent Framework for Tutoring (GIFT) (Sottilare, Brawner, Goldberg, & Holden, 2012) to be used for team settings.

The COVID-19 pandemic and its related economic meltdown and social unrest severely challenged most countries, their societies, economies, organizations, and individual citizens. Focusing on both more and less successful country-specific initiatives to fight the pandemic and its multitude of related consequences, this chapter explores implications for leadership and effective action at the individual, organizational, and societal levels. As international management scholars and consultants, the authors document actions taken and their wide-ranging consequences in a diverse set of countries, including countries that have been more or less successful in fighting the pandemic, are geographically larger and smaller, are located in each region of the world, are economically advanced and economically developing, and that chose unique strategies versus strategies more similar to those of their neighbors. Cultural influences on leadership, strategy, and outcomes are described for 19 countries. Informed by a cross-cultural lens, the authors explore such urgent questions as: What is most important for leaders, scholars, and organizations to learn from critical, life-threatening, society-encompassing crises and grand challenges? How do leaders build and maintain trust? What types of communication are most effective at various stages of a crisis? How can we accelerate learning processes globally? How does cultural resilience emerge within rapidly changing environments of fear, shifting cultural norms, and profound challenges to core identity and meaning? This chapter invites readers and authors alike to learn from each other and to begin to discover novel and more successful approaches to tackling grand challenges. It is not definitive; we are all still learning.

Self-tracking is becoming a prominent and ubiquitous feature in contemporary practices of health and wellness management. Over the last few years, we have witnessed a rapid development in digital tracking devices, apps and platforms, together with the emergence of health movements such as the Quantified Self. As the world is becoming increasingly ruled by metrics and data, we are becoming ever more reliant on technologies of tracking and measurement to manage and evaluate various spheres of our lives including work, leisure, performance, and health. This chapter begins with a brief outline of some of the key theoretical approaches that have been informing the scholarly debates on the rise of self-tracking. The chapter then moves on to discuss at length the findings of an international survey study conducted by the author with users of self-tracking technologies to discuss the ways in which they perceive and experience these practices, and the various rationales behind their adoption of self-tracking in the first place. The chapter also addresses participants’ attitudes towards issues of privacy and data sharing and protection which seem to be dominated by a lack of concern regarding the use and sharing of self-tracking data with third parties. Some of the overarching sentiments vis-à-vis these issues can be roughly categorised according to feelings of ‘trust’ towards companies and how they handle data, a sense of ‘resignation’ in the face of what is perceived as an all-encompassing and ubiquitous data use, feelings of ‘self-insignificance’ which translates into the belief that one’s data is of no value to others, and the familiar expression of ‘the innocent have nothing to hide’. Overall, this chapter highlights the benefits and risks of self-tracking practices as experienced and articulated by the participants, while providing a critical reflection on the rise of personal metrics and the culture of measurement and quantification.

Purpose — In this chapter, we will review several alternative methods of collecting data from mobile phones for human mobility analysis. We propose considering cellular network location data as a useful complementary source for human mobility research and provide case studies to illustrate the advantages and disadvantages of each method.

Methodology/approach — We briefly describe cellular phone network architecture and the location data it can provide, and discuss two types of data collection: active and passive localization. Active localization is something like a personal travel diary. It provides a tool for recording positioning data on a survey sample over a long period of time. Passive localization, on the other hand, is based on phone network data that are automatically recorded for technical or billing purposes. It offers the advantage of access to very large user populations for mobility flow analysis of a broad area.

Findings — We review several alternative methods of collecting data from mobile phone for human mobility analysis to show that cellular network data, although limited in terms of location precision and recording frequency, offer two major advantages for studying human mobility. First, very large user samples – covering broad geographical areas – can be followed over a long period of time. Second, this type of data allows researchers to choose a specific data collection methodology (active or passive), depending on the objectives of their study. The big mobile phone localization datasets have provided a new impulse for the interdisciplinary research in human mobility.

Originality/value of chapter — We propose considering cellular network location data as a useful complementary source for transportation research and provide case studies to illustrate the advantages and disadvantages of each proposed method. Mobile phones have become a kind of “personal sensor” offering an ever-increasing amount of location data on mobile phone users over long time periods. These data can thus provide a framework for a comprehensive and longitudinal study of temporal dynamics, and can be used to capture ephemeral events and fluctuations in day-to-day mobility behavior offering powerful tools to transportation research, urban planning, or even real-time city monitoring.

This chapter describes five disciplinary domains of research or lenses that contribute to the design of a team tutor. We focus on four significant challenges in developing Intelligent Team Tutoring Systems (ITTSs), and explore how the five lenses can offer guidance for these challenges. The four challenges arise in the design of team member interactions, performance metrics and skill development, feedback, and tutor authoring. The five lenses or research domains that we apply to these four challenges are Tutor Engineering, Learning Sciences, Science of Teams, Data Analyst, and Human–Computer Interaction. This matrix of applications from each perspective offers a framework to guide designers in creating ITTSs.