Within the past several years, accelerometry has emerged as an important means of assessing the duration and intensity of physical activity and has served to define primary outcome measures in several observational (1,4,20) and experimental studies (6,7,11,15,17). It is currently being used in the large group-randomized Trial of Activity in Adolescent Girls (TAAG) to examine the effect of a school- and community-based intervention on physical activity in middle-school girls. The uniaxial accelerometer considered in this trial, the ActiGraph, formerly known as the Computer Science and Applications (CSA) and Manufacturing Technologies Inc. (MTI) (ActiGraph, LLC, Fort Walton Beach, FL) is a small (5 × 4 × 1.5 cm) and lightweight (45 g) device that captures vertical acceleration. Acceleration is sampled 10× s−1, and the data are summed over a user-specified time interval (e.g., 30 s, 1 min) and the summed value or activity “count” is stored in memory. Although the measurement protocols vary, most studies involve monitoring physical activity over several days to ensure reliable estimates of usual physical activity behavior and to account for potentially important differences in activity patterns on weekdays versus weekend days (19). Participants are typically instructed to wear the accelerometer during waking hours, except when bathing and showering. Data from accelerometers are summarized in numerous ways, including the mean total count per day (11) and the mean minutes per day spent in moderate or vigorous physical activity (using established count cut points to distinguish specific intensity levels). The analysis is complicated in that 1) activity levels vary among days of the week and times of day and 2) over multiple days of monitoring, missing data arising from removal of the monitor are a common occurrence. Although participant noncompliance accounts for a large fraction of the missing data, legitimate reasons for removing the monitor, such as complying with mandated sports league safety regulations or participation in water-related activity (for monitors that are not waterproof), also contribute to data loss. Thus, the timing and amount of data contributed by each individual vary. If summary statistics are computed using the observed data only, these statistics have the potential to be biased. For example, the total count for a given day clearly underestimates the true level of activity on days in which the monitor is worn only part of a day. Some researchers have tried to minimize this bias by computing summary statistics after excluding accelerometer data on days in which the monitor is worn only part of the day. These are called incomplete days of observation. This strategy, however, is not without issues. First, even after excluding days with, say, less than 8 h of wearing time, the number of hours the monitor is worn is still likely to vary. Moreover, if included days have intervals when the subject was awake but not wearing the monitor, then total activity will be underestimated. Second, this approach ignores possible differences in activity levels on complete days and incomplete days, making the estimated summary statistics from complete days subject to selection bias. This paper proposes an analytical approach whereby the observed data are used to help predict activity levels for segments of the day in which the monitor is not worn. The resultant data set is pseudo-complete in the sense that each individual will have either observed or imputed data for all segments of each day in which the monitor was intended to be worn. Summary statistics are then estimated from this pseudo-complete data set. This imputation strategy is analogous to imputing missing item responses on multi-item questionnaires. The literature contains numerous examples in which this treatment of item nonresponse has been found to reduce bias effectively (5,21). In the following section, accelerometer data collected during the feasibility phase of the TAAG trial are used to demonstrate the potential for bias in estimating physical activity when all observed data are used as well as when a subset of data that excludes incomplete days of monitoring is used. We then describe procedures for filling in missing data using single imputation through expectation maximization (EM) and multiple imputation (MI) (9,14). The remaining sections of the paper describe the design of a simulation study to assess the effectiveness of the imputation approaches, present its results, and discuss the effectiveness of imputation as a strategy for dealing with missing data in the context of accelerometry.