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The COVID-19 pandemic spurred major, and possibly enduring, changes in paid work. In this chapter, we explore the continuity and change in several work day dimensions, including where it is performed, the amount of time spent working, the length of the work day, and who people are with when they work, as well as variation across population subgroups. We use nationally representative data from the American Time Use Survey (ATUS) to analyze change across the 2019 to 2021 period. While the shift to working primarily at home in 2020 is dramatic and continuing into 2021, working primarily at the workplace remains the modal experience for Americans. We find differences by gender, education, parental status, and age in which workers perform their jobs at home, and we find much more continuity in how much people work and when they work.

The study of family mobilities necessitates an examination of how practices are orchestrated in time as well as space. Conventional approaches to the study of family time use either quantitative analysis of time-use data or qualitative studies of time pressure and work/life balance. The limitation with these approaches is that they assume a rather static family structure that is dominated by parents with young children. Moreover, these studies do not capture the dualistic quality of time; that time constitutes and is a constituent of family life. In this chapter, I use one-day diaries on organising and experiencing time, collated as part of the UK Mass Observation Project in Autumn 2017, to interrogate the relationality of family time. The analysis examines how family practices maybe sequential, synchronous, planned or serendipitous and how these different temporalities permeate the busyness of time pressure. These one-day accounts confirm how time is experienced through and by family and intimate relationships.

Purpose – Departure time choice not only depends on the desire to carry out activities at certain times and places; it is a complex decision making process influenced by travel conditions, congestion levels, activity schedules, and external trip factors. To estimate departure time choice models capturing the factors influencing it in appropriate form, a complex data collection procedure allowing to obtain detailed input data from different sources and at different time periods is required.

The main aim of this chapter is to describe and discuss the survey methodology we used in a time-of-day choice project, involving the collection of revealed preference (RP) and stated preference (SP) data to estimate hybrid discrete departure time choice models incorporating latent variables. Preliminary model results are also presented as an example.

Methodology/approach – Data was obtained from 405 workers at different private and public institutions located in the centre of Santiago, Chile. The survey process had three different stages and used various collection methods (e-mail, web-page, and personal interviews at the workplace) in order to satisfy efficiency, reliability and cost criteria.

The RP component survey design was based on the last origin-destination survey implemented in Santiago (i.e. a travel diary filled under an activity recall framework). Relevant level-of-service measures at different time periods were obtained from GPS data measured from instrumented vehicles in the public and private transport networks. A SP-off-RP optimal design considering dependence among attribute levels was also developed. Finally, several 1–7 Likert scale questions were included to incorporate the latent variables.

Findings – The survey methodology described in this chapter represents a successful experience in terms of collecting high quality data, from different sources, with the aim of estimating appropriate time-of-day choice models. The data collection process was carried out in different stages, by means of web pages, email, and personal interviews. The data was further enriched with level-of-service attributes measured at different times of the day with unusual precision. Preliminary results reported in this chapter show that data obtained through this methodology are appropriate to model time-of-day choices.

Originality/value of chapter – The novelty of the survey methodology described in this chapter is the collection of data of a different nature for time-of-day choice modelling through the integration of different collection techniques.

Acquisition of very precise information about preferred departure/arrival times, level of service at different times of the day, detailed information about flexibility in schedules, employment information and attitudes towards departure times, should allow practitioners to estimate hybrid time-of-day choice models incorporating latent variables.

In this chapter, we review empirical research evidence on the relationship between stressors and catecholamines (i.e., adrenaline and noradrenaline) and cortisol. With respect to acute stressors, both laboratory and field research have shown that the exposure to stressors leads to an increase in catecholamine and cortisol levels. With respect to more chronic stressors, research evidence is less consistent. Chronic mental workload was found to be related to elevated adrenaline levels. With respect to cortisol responses the interaction between workload and other variables seems to play a role. Empirical studies suggest that chronic stressors affect the responsivity to acute stressors. Research showed that after the exposure to stressors catecholamine and cortisol recovery is delayed.