Accelerometers are predominantly used to objectively measure the entire range of activity intensities – sedentary behaviour (SED), light physical activity (LPA) and moderate to vigorous physical activity (MVPA). However, studies consistently report results without accounting for systematic accelerometer wear-time variation (within and between participants), jeopardizing the validity of these results. This study describes the development of a standardization methodology to understand and minimize measurement bias due to wear-time variation. Accelerometry is generally conducted over seven consecutive days, with participants' data being commonly considered 'valid' only if wear-time is at least 10 hours/day. However, even within ‘valid’ data, there could be systematic wear-time variation. To explore this variation, accelerometer data of Smart Cities, Healthy Kids study (www.smartcitieshealthykids.com) were analyzed descriptively and with repeated measures multivariate analysis of variance (MANOVA). Subsequently, a standardization method was developed, where case-specific observed wear-time is controlled to an analyst specified time period. Next, case-specific accelerometer data are interpolated to this controlled wear-time to produce standardized variables. To understand discrepancies owing to wear-time variation, all analyses were conducted pre- and post-standardization. Descriptive analyses revealed systematic wear-time variation, both between and within participants. Pre- and post-standardized descriptive analyses of SED, LPA and MVPA revealed a persistent and often significant trend of wear-time’s influence on activity. SED was consistently higher on weekdays before standardization; however, this trend was reversed post-standardization. Even though MVPA was significantly higher on weekdays both pre- and post-standardization, the magnitude of this difference decreased post-standardization. Multivariable analyses with standardized SED, LPA and MVPA as outcome variables yielded more stable results with narrower confidence intervals and smaller standard errors. Standardization of accelerometer data is effective in not only minimizing measurement bias due to systematic wear-time variation, but also to provide a uniform platform to compare results within and between populations and studies.