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Student focus groups and teacher interviews will last around 15-min, use semistructured questions and be audio-recorded and transcribed by the lead researcher. For student focus groups, one mixed sex group of six students per class will take part. Each teacher will be asked to select two children of lower, middle and higher overall academic ability, as done in previous physically active lesson evaluation.55 Questions will explore students' perceptions and enjoyment of Virtual Traveller sessions, examples of memorable sessions and potential improvements. Teacher interviews will be arranged according to teacher availability. Questions will investigate teacher perceptions of Virtual Traveller sessions compared to typical Ebastine Maths and English teaching. Perceived effects of Virtual Traveller on children's PA and educational outcomes will be explored, as well as challenges to implementing the programme. Analysis Outcomes The primary outcome for this study will be the change in average daily time spent in MVPA as recorded by the Actigraph accelerometer. Secondary outcomes assessed via accelerometers, observations and student questionnaires will include (1) changes in daily time spent in light, moderate and vigorous lesson-time PA, (2) changes in lesson-time spent in light, moderate and vigorous lesson-time 17-AAG cell line PA, (3) changes in on-task behaviour during lesson-time and (4) change in student engagement. Quantitative analysis Raw data will be extracted from each Actigraph accelerometer and analysed using LGK-974 cell line ActiLife software. To maximise the study sample, participants will be included in analysis if they provide at least 1?day of valid accelerometer wear time, including one Virtual Traveller day in intervention students. Sensitivity analysis will be carried out to compare results if a minimum of 3-day valid accelerometry data are included. Valid accelerometer wear time will be defined as at least 500?min wear time between 07:00 and 00:00. This replicates criteria previously used in research utilising The International Children's Accelerometry Database (ICAD),2 the world's largest resource of pooled child accelerometer data. Data will be collected in 5?s epochs31 and analysed using Pulsford cut-points56 to classify activity as sedentary: (