Different types of accelerometers are increasingly being used in interdisciplinary research to objectively measure activity patterns in populations (Feinglass et al., 2011; Kristensen et al., 2010; Laguna et al., 2013; Prince et al., 2011; Straker et al., 2012;). Activity includes a wide range of physical activity and sedentary behaviours which can be measured by accelerometers. For the purpose of this study, physical activity is defined as any body movement produced by skeletal muscles that requires energy expenditure (Caspersen et al., 1985). Similarly, sedentary behaviour is defined as lack of body movement during waking time that does not increase energy expenditure substantially above the resting level (Pate et al., 2008; Sedentary Behaviour Research Network, 2012).
The popularity of accelerometers is based on their documented superiority over self-reported measures (Celis-Morales et al., 2012; Prince et al., 2008) and their ability to provide a detailed picture of frequency and duration of activity intensities--sedentary behaviour (SED), light physical activity (LPA) and moderate to vigorous physical activity (MVPA) (Baquet et al., 2007). A growing reliance on accelerometers to study patterns of activity within and between populations makes the measurement and analysis protocol of accelerometer measures a key methodological issue.
In population health studies, accelerometers are typically used to collect data during waking hours from participants over a period of 7 consecutive days--5 weekdays and 2 weekend days (Colley et al., 2011; Esliger et al., 2012; Feinglass et al., 2011; Kristensen et al., 2010; Tudor-Locke et al., 2011). Widely accepted data reduction standards (Colley et al., 2010) deem that participants are required to wear accelerometers (wear-time) for at least 10 hours on a given day to capture the entire range of activity, and such a day is termed a valid day.
Analyses are conducted using data only from the valid days (Colley et al., 2011; Esliger et al., 2012), however, even within this valid data, there is a chance for systematic variation in daily wear-time, both within (on different days of accelerometer use) and between participants. This is because, even though participants are asked to wear accelerometers from the time they wake up in the morning till the time they go to bed at night, every participant would wear or remove the accelerometer at her/his discretion, thus potentially introducing a random or non-random bias to activity measurement.
A random (but highly imprecise) bias would result if accelerometers are removed during waking hours (nonwear-time) by participants without regard to the type of activity that subsequently goes unmeasured. A nonrandom bias would result if accelerometers are removed during waking hours by participants consistently before engaging in a certain type of activity. In other words, activity measured overall is consistently different from the real activity engaged by participants. Furthermore, in large population health studies, as variation in wear-time increases, the chance of final estimates of activity being distorted increases as wear-time is directly related to the amount of activity measurement.
Specific to non-wear-time, Tudor-Locke et al (2011), using accelerometer data from the 2005-2006 National Health and Nutritional Examination Survey, concluded that non-wear-time appears to distort population estimates of all accelerometer measured activity, especially estimates of SED. However, distinct from nonwear-time, the purpose of this particular study is to explicitly address the impact of systematic variation of wear-time on estimates of activity. To our knowledge, apart from statistically controlling for wear-time in multivariable analyses by including it as an independent variable (Bond et al., 2012), most studies so far have not taken into account wear-time discrepancies and their impact, before performing final analyses.
Two studies that have explored wear-time variation arrived at inconclusive results (Catellier et al., 2005; Chen et al., 2009). For example, Catellier and colleagues (2005) utilized sophisticated imputation methods in tackling wear-time irregularities with an assumption that the data (activity) were missing at random, or completely missing at random. However, at the same time, the authors acknowledged that there is no objective way of determining whether the data are missing at random, completely missing at random, or not missing at random.
To preserve the expected objectivity of accelerometry and to avoid complicated statistical techniques that rely on many assumptions, a method of standardization of measured activity controlling for wear-time is essential. This approach would not only minimize measurement bias due to systematic wear-time variation, but would also create a uniform platform to compare estimates of activity obtained from all types of accelerometers, both within and between populations.
To advance this argument, data from Smart Cities Healthy Kids study (www.smartcitieshealthykids.com) has been used. Smart Cities, Healthy Kids which is set in Saskatoon, Saskatchewan, Canada, is an ongoing population health intervention study that investigates the influence of neighbourhood built environment on activity patterns in children aged 10-14 years. From the original sample 1610 children, objective activity data were collected using Actical accelerometers (Mini Mitter Co., Inc., Bend, OR, USA) from a subgroup of 455 children. Prior to deploying the accelerometers, a questionnaire was administered to children to capture their demographic data. Ethics approval from the University of Saskatchewan's research ethics board and both Catholic and public schools boards was obtained before accelerometers were deployed through schools from April to June in 2010. Participants were visited at their respective schools and were asked to wear the devices on their right hip using an elastic belt, every day for 7 consecutive days. They were advised to remove the accelerometers during night time sleep and during any water-based activities.
Accelerometers measured movement in 15 second epochs in order to capture the sporadic nature of children's physical activity (Bailey et al., 1995). The raw data were...