Monitor logs can be informative and be a source for quality checking of the monitor data. Such information can assist in isolating periods of interest within the data, such as waking times or when monitors were removed or attached, school hours, or the after- school period. However, this method has not been validated and accurate strategies for collecting such data are currently lacking (61). During school hours, the absence of a diary will be less of an issue since the school timetable can be applied to activPAL and ActiGraph data using filters. Schools typically follow these timetables closely and is therefore a reliable guide.
An important decision to make is whether to apply a minimum threshold of data compliance within a period of interest. A method used within activPAL data reduction is to apply a 50% threshold to a period of interest. This ensures that the participant has provided data for the majority of that period of interest. Without this rule, you may get incidences where very short periods of data have been provided (e.g. 20 minutes) within a period of interest (e.g. first lesson of a school day: 2 hours). Consequently, this may result in the participant registering, for example, just 15 minutes of sitting time, acting as an outlier to the data. In reality they may have sat for 1h 30 mins during this period. It would be wise to remove this outlier from the analysis.
There is a risk that bouts of activity (sitting, standing, stepping) could cross over from one filter of time (e.g. class time) to another (e.g. break times) and therefore there is the issue of dissecting a bout rather than capturing it in its entirety. In scenarios where a bout of sitting or standing spans across two periods of interest, the bout may be included within the period of interest it began. However, it is fair to assume that a change in school time period and probable location will result in a change in posture and/or activity (61). Bouts of activity crossing different periods of interest can be an issue during evenings if a blanket removal of estimated sleep time is made (e.g. from 11pm). A child may be engaging in a sedentary bout (e.g. sitting watching television) from 10-11.30pm, however, if sleep time is identified from 11pm, this bout will have been reduced to 1h instead of 1h 30 mins. This would generally apply to all waking behaviour that occurs beyond 11pm. Furthermore, this blanket approach of removing sleep time assumes that sleep occurs in a continuous single occasion for all participants. It may be that sleep
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occurs in several segments, separated by brief periods of movement (i.e. going to the toilet) (61).
The method of applying a blanket sleep period to all data to identify all sleep periods, is limited (61). For example, a sleep period of 11pm-6am would result in 1020 mins of waking data per day, however, a child could go to sleep at 9pm and wake up at 7am, meaning 3 h of data has been miss-classified as waking hours. However, to identify periods of sleep during designated waking hours (6am-11pm), 3-axis acceleration data can detect periods of no movement. If these periods exceed 20 mins then this period will be excluded as non-wear. For example, before 11pm, if a child goes to bed but is still awake with small movements that repeat within every 20 mins (e.g. legs fidgeting while reading), this will be identified as sedentary time (sitting/lying). However, if the child goes to sleep and is stationary for >20 mins (and therefore recording zero accelerometer counts) this will be identified as non-wear or sleep and therefore excluded from the waking hour analysis. 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). Like all data reduction methods, this approach has limitations. It is very possible that a participant could be asleep but still has some movement at somewhat regular intervals, resulting in sleep time registered as sitting/lying time during waking hours. For example, if a child fell asleep at 10pm but had very subtle movements up to 11pm, the acceleration channel will detect movements and 1 h of sleep time is therefore recorded as a sedentary waking hour. Consequently, some sitting time data during evening periods may be more erroneous than perhaps daytime (e.g. school-based) sitting data. A debate within the sedentary behaviour literature has recently emerged around how to classify the period of time where an individual first goes to bed in the evening and is lying in bed attempting to fall asleep (66). While the person is still awake, this is commonly interpreted as sedentary time since they are not asleep. However, it has been recently argued that this should be described as sleep-related behaviour which may be part of a natural and healthy sleep-wake cycle (66). Engaging in an activity prior to this phase, such as reading a book, could still be classified as sedentary time. Automated algorithms are likely to systematically overlook or miss classify these two different phases of behaviour prior to sleep (66). These behaviours are also likely to vary between populations (e.g. children,
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adults, males and females). This highlights the benefit of including sleep logs however these are burdensome and often insufficiently completed by younger ages (61,66). Currently there is a lack of validated methods for removing sleep from a 24 h activPAL wear protocol in children. A recently implemented strategy in adults has included identifying the first standing event after ≥2 h of sitting/lying between midnight and 9am as waking, and the final standing event before >3 h of sitting/lying after 22:30 as the beginning of sleep (67). This approach allows for varying waking periods for each participant; however, it is unlikely that all individuals once awake, immediately stand up out of bed or instantly fall asleep as soon as they lie in bed at night. Recently, an activPAL processing algorithm identifying sleep periods from event.csv files has been developed and validated in adults (68). This algorithm identifies the longest sitting/lying and sitting/lying/standing bouts >5 h as sleep within a 24 h period. Furthermore, behaviours either side of these bouts are searched and added as sleep if complying with one of several rules. Such an algorithm would be hugely beneficial in children since adequate monitor log compliance is particularly challenging in younger ages