Measuring physical activity with objective measures, such as accelerometers, is becoming more common with researchers, as evidenced through the increasing number of research articles using accelerometers (R Troiano, 2005). Accelerometers measure acceleration multiple times within a given frequency and summarize this as a count over a pre-specified time period or epoch. The resultant count represents acceleration over the epoch length. Accelerometers are advantageous because they eliminate language or literacy difficulties, recall bias, and social desirability bias present with self-report measures of physical activity.
With the increasingly widespread use of accelerometers, standardization of how the data are collected, cleaned, and reported across studies would be useful. In 2005, Masse et al (Masse et al., 2005) identified five methodological issues regarding accelerometer data reduction, the process of reducing the data in order to derive summary measures. These included (1) identifying wearing time of the accelerometer, (2) defining minimal wear time for a valid day, (3) identifying spurious data, (4) computing summary variables and aggregating days of data, and (5) extracting bouts of activity. This paper focuses on the first issue, namely identifying nonwearing time of the accelerometer. Understanding whether the accelerometer is being worn is used to assess compliance and to determine if the participant’s data will contribute to the resultant analyses. Defining wearing and nonwearing time also affects the derivation of summary physical activity measures based on this time, such as average counts per hour worn or minutes in sedentary, light, moderate, or vigorous activity (Corder, Brage, & Ekelund, 2007). How to define and assess nonwearing time of the accelerometer is not currently standardized across research studies.
Participants are typically instructed to wear the accelerometer during all waking hours and to remove the device for showering or swimming activities, although some devices are now waterproof. The challenge is that participants often take the accelerometer off for other personal reasons. When an accelerometer such as an ActiGraph or an Actical is not being worn, it will typically register as zero counts over the epoch. However, it is possible for a person to be still (such as sleeping or sitting without movement), while wearing the accelerometer, and also register zero counts over the epoch. This can occur for multiple minutes at a time and are likely more common among those who are very sedentary throughout the day. Some have recommended that participants keep a log sheet to help determine when the participant is not wearing the monitor (Esliger, Copeland, Barnes, & Tremblay, 2005; Trost, McIver, & Pate, 2005). The challenge with this approach is that the process of keeping a log introduces a self-reporting aspect of data collection and compliance can be hampered. For this reason the use of a log was included for the National Health and Nutrition Examination Survey (NHANES) pilot study and subsequently dropped for the deployment into the main study in 2003-04 (R Troiano, 2006; RP Troiano et al., 2008).
An alternative to a log kept by participants is to deduce the most reasonable nonwearing time based on the accelerometer data. This approach is more commonly used by researchers. Some recent studies of youth and adults utilizing the ActiGraph have, for example, used 10 minutes (Brage et al., 2004; Ekelund, Yngve, Brage, Westerterp, & Sjostrom, 2004; Riddoch et al., 2004), 15 minutes (Rousham, Clarke, & Gross, 2005), 20 minutes (Jilcott, Evenson, Laraia, & Ammerman, 2007; Savitz et al., 2006; Treuth et al., 2004), 30 minutes, or 60 minutes (Matthews et al., 2008; RP Troiano et al., 2008) to define the timespan of zeros on the accelerometer to indicate nonwearing time. Many other studies do not indicate how nonwearing time is defined. Dr. Masse and colleagues (Masse et al., 2005) explored the effect of differing definitions of nonwearing time on results, comparing 20 minutes and 60 minutes of consecutive zeros as an indicator of nonwearing time among a sample of 40 to 70 year old African American and Hispanic women. However, in the same analyses this study also varied other accelerometer data reduction decisions, making the interpretation of the influence of the varying of nonwear time only difficult.
The purpose of this study was to describe the effect of changing the definition of accelerometer nonwear time on an aggregated sample of participants, prior to using the data to analyze physical activity by individuals, and to assist others in replicating this process. To do this, we utilized data from the third Pregnancy, Infection, and Nutrition (PIN3) Postpartum Study, which collected accelerometer data on women at 3- and 12-months postpartum.