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activPAL data are presented in Chapters 4 and 5 and ActiGraph data in Chapter 5. The data collection and reduction methods for those studies are detailed within this chapter.
3.1. ActivPAL
Participants wore an activPAL inclinometer (PAL Technologies Ltd, Glasgow, UK) on the anterior aspect of the right thigh, placed within a nitrile sleeve and attached using hypoallergenic medical dressing (Hypafix, BSN Medical), for 7 days, sampling at 20 Hz and providing triaxial accelerometer data. The device was waterproofed and a 24 h wear protocol was adopted. The activPAL is a valid measure of free living sitting and standing in children in a classroom setting and during daily free-living activities when compared to direct observation (sitting time Rho (mean difference) = 0.86 (-5.6%)) (see Chapter 1 section 1.4.2.2. for more details of these study findings) (52,60). activPAL data explored in Chapters 4 and 5 included minutes spent sitting, standing and stepping, steps, and sit-to-stand transitions, all accumulated at school, after school and during total waking hours on week days and weekend days (Chapter 4 only). Participants were requested to record when they woke up, went to bed and when either of the devices (the activPAL and ActiGraph) were removed (or fell off) in a daily monitor log.
3.1.1. Data management
ActivPAL data were downloaded (PAL files) using standard manufacturer software (activPAL Professional v.7.2.29 and v.7.2.32). The PAL files were visually inspected once downloaded within the Activity Summary feature of the software as a basic compliance check; files that included less than 2 days of data with <500 steps/day (61) were not included in any later analysis. Files with sufficient data were converted to 15- second epochs (epoch.csv files) and then processed with a customised Microsoft Excel macro. The customised macro provided the frequency of and accumulated minutes spent sitting and standing in bouts of 5-10min, 10+min and 30+min (Chapter
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4 only), comparable to bout lengths applied in a recent observational study in children (266). During school days, several periods of interests were isolated using the Excel macros (see section 3.1.1.1). In scenarios where a bout of sitting or standing spanned across two periods of interest, the bout was only included within the period of interest it began. For example, if a child engaged in a 15-minute siting bout that began during lesson 2 of the school day (e.g. 12:10pm) and continued into the lunch period (12:21 onwards), the bout would be included within lesson 2 data, and the macro would terminate the bout at the start of the next period of interest (12:21pm). While this will not capture the bout in its entirety in this instance, it is highly likely that children will change location and therefore posture between different school periods. Consequently, the example above is unlikely to be a frequent occurrence. Proportions of wear time spent sitting, standing and stepping and sit-to-stand transitions per hour of wear time were also calculated using the Excel macro. In Chapter 4, as an indicator of a sufficient level of physically activity, the recommendations of Tudor-Locke et al. (267) of ≥ 13,000 steps/day for 6–11 year-old boys; ≥ 11,000 steps/day for 6–11 year-old girls were applied to the step count data.
3.1.1.1. Wear time
In Chapter 4, wear time compliance was set at ≥10h/day on ≥3 school days and ≥1 weekend day to align with a similar cross-sectional study (87). Due to the exploratory nature of the pilot study in Chapter 5, participants were included in the analyses if they provided at least 8 hours of activPAL data per day on at least 2 weekdays, as applied elsewhere (268). The hours of 11pm-6am were set as sleep time and thus removed from the data (90). A non-wear time of 20 minutes was also applied using the accelerometer function of the device, determining additional sleep periods (between 6am and 11pm) or when the device was not being worn during waking hours. To identify periods of sleep during the designated waking hours (6am-11pm), the 3-axis acceleration data in Chapters 4 and 5 will have detected periods of no movement. If these periods exceed 20 mins then this period will have been excluded as non-wear. The effect of the non-wear criteria on sleep removal and waking hour data is discussed in Chapter 4 section 4.4.2. The use of non-wear methods (e.g. Troiano (65)) to identify sleep periods is a strategy currently recommended within activPAL research (61). The
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Excel macros did not include a tolerance or interruption allowance for non-wear time (or wear time), in contrast to the ActiGraph criteria (see section 3.2.1.1. below). The non-wear time and epoch parameters are consistent with previous activPAL research (90), and are recommended (58). School hours were based on each school’s timetable (intervention school 08:50-15:10, control school 08:41-15:15).
Nine different periods of interest were applied to the time stamped epoch file data on school days (morning, lesson 1, lesson 2, morning break, lunch, afternoon lessons, after school (until 6pm), evening (until 11pm), full waking day) using each schools timetable (Chapters 4 and 5). It was decided not necessary to include a minimum wear time of compliance for each of these periods of interest. No periods of interest were applied to weekend data (full waking day only).
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3.2. ActiGraph
ActiGraph data are presented in Chapter 5. To determine time spent in different intensities of PA during class time, school break times, after school and during full week days, participants wore an ActiGraph GT3X triaxial accelerometer (ActiGraph LLC, Pensacola, FL, USA) on the waist above the right hip on a belt (sampling at 100 Hz) during the same seven days as the activPAL. Sampling at 100hz is common practice and generally recommended for physical activity research in children (62). This device has been found to be a valid measure of different PA intensities in children (Moderate-to-vigorous PA ROC-AUC = 0.90, excellent accuracy) (53). The ActiGraph device is an established measurement of PA in children (62). Participants were requested to remove the device during sleep, when bathing or during anytime in water.
3.2.1. Data management
ActiGraph data were downloaded using standard manufacturing software (ActiLife v.6.11.9) also at 15-second epochs as recommended in PA research in children (269,270). Only data from the vertical axis were used during data processing to replicate a key validation study (53). Trost et al. (2011) (53) compared and validated different accelerometer cut points based on vertical axis data only, generated from the GT1M ActiGraph model. The present study used a more recent ActiGraph model (the GT3X), however, Hänggi et al. (2013) (271) compared GT1M and GT3X models using vertical axis outputs and found that both devices categorised a range of activities within the same activity intensity (sedentary, Light intensity PA (LPA), MVPA). This is important because it demonstrates that the GT3X, used in the present study, is comparable to an older version of ActiGaph that has been validated in identifying different intensities of PA in children (271).
3.2.1.1. Wear time
The same non-wear time (20 mins) and minimum wear time criteria (8 hrs/day on at least 2 weekdays) applied to the activPAL data in Chapter 5 were applied to the ActiGraph data within the ActiLife software. While non-wear time was customised to 20 minutes, the default parameters within the Troiano (2007) (65) criteria were applied.
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Specifically, Activity Threshold and Use Max Counts were set at zero counts/min,
Spike Tolerance was set at 2 spikes/min, Spike Level to Stop was set at 100
counts/min and the Required Consecutive Epochs Outside of the Activity Threshold was selected.
School hours were based on each school’s timetable as follows: Intervention school 08:50-15:10, control school 08:41-15:15. The timetables were used to determine school break periods, class time and outside of school time (the remaining wear time of a waking week day following the recognised school hours).
3.2.1.2. Cut points
After wear time validation was calculated, the time spent in different activity thresholds (sedentary, LPA, MVPA) were determined using the Freedson age-adjusted cut points (see equation below) within ActiLife (272,273). These cut points were selected because they have been recently implemented within a standing desk study of the same 8 month duration, using the same sit-stand desks and evaluated using the same GT3X ActiGraph model (268). Consequently, using the same cut points improves the comparability of study findings. The Freedson age-adjusted cut points have demonstrated excellent accuracy (area under the receiver operating characteristic curve (ROC–AUC) = 0.90) in categorising MVPA in 6-18 year olds (272). While these cut points were less accurate for determining LPA than the most accurate cut points (274) within the (272) validation study, the difference was negligible (ROC–AUC = 0.69 (Freedson) vs 0.70 (Evenson). A single mean age across all three study groups (two intervention, one control) during the baseline 7-day wear period were used for all data sets (baseline, 4 months and 8 months) to determine the age-adjusted cut points. This mean age (9.8 years) was entered into the below equation:
METs = 2.757 + (0.0015 * counts per minute) – (0.08957 * age (yr)) - (0.000038 * counts per minute * age (yr))
R2 = 0.74 SEE = 1.1 METs (Freedson)
The MET values for sedentary, light, moderate and vigorous physical activity were based on those used within the Trost et al. (53) validation study. Applying the mean
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sample age to the equation provided the below cut point thresholds, which were applied to all ActiGraph data.
Table 3.1. Thresholds for MET intensities and resulting
accelerometer cut point thresholds.
Intensity Cut point threshold
(counts per minute)
Sedentary, <1.5 METs ≤100 Light, ≥1.5 and <4 METs 101-1880 Moderate, ≥4 and <6 METs 1881-3654
Vigorous, ≥6 METs >3655
Within accelerometer research in children a wide range of cut points have been applied (64) and one of the drivers of this is the varied MET values applied to intensities of PA (light, moderate and vigorous). With this in mind, when calculating the age-adjusted cut point thresholds for Chapter 5, the same MET value thresholds applied within the key Trost validation study (53) were utilised.
Once ActiGraph wear time and cut point parameters were applied, data were exported to Microsoft Excel to further organise the minutes and proportions of wear time spent in different activity intensities (LPA, MVPA) during different domains (class time, school break times, after school, full school day, full weekend day).
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