30 In most cases, distances will be calculated from each desk to its closest office building destination type.
The model will be designed to explain the findings and predict changes in outcomes by altering various exposure variables such as the number and location of each type of office building destination alongside other attributes such as an individual's likelihood to move.
A sample of 250 participants (10 buildings with approximately 25 participants in each) will give 90% power to detect a difference of 40 min/day in occupational sitting time at the 5% significance level using a two-sample t test between participants in high-office and low-office building destination groups.
Axial and segment maps will be used to calculate four types of distances from each desk to each office building destination (as well as to stairs/elevators): (1) shortest walking distance in metres, (2) fewest steps in an axial map, (3) fewest steps in a segment map and (4) least angle change.
Office layout, and in particular the number and location of office building destinations (eg, print and meeting rooms), may influence both walking time and characteristics of sitting time.
3 14 However, before an intervention is carried out, more research is needed to identify and understand associations between office layout (eg, number and distribution of office building destinations), step counts and the characteristics of sitting time.
30 Lines in an axial map can be considered a way of representing potential routes of movement to and from individuals' workstations, as well as to and from office building destinations.
Building audits, conducted by trained researchers, will collect general information about the building (eg, location, access to public transport, availability of parking spaces, overall number of floors, age of construction and floor area), and identify the location of different types of office building destinations (eg, meeting room, kitchen, toilet and office area).
Aside from metrics specific to each participant, overall building metrics will also be calculated from the floor plans, including: floor areas (gross, net internal and net usable) and respective densities (ie, floor area per desk type), as well as floor areas of office building destinations.
To investigate associations between exposures (eg, density of office building destinations; high/low) and outcomes (eg, occupational step count and sitting time) hierarchical regression models will be carried out, adjusted for clustering of outcomes among adults within the same office building, as well as other potential confounding variables (eg, SOC code and ethnicity, etc).
Building, final destination.
