Reporting the Reliability of Accelerometer Data with and without Missing Values

OBJECTIVES: Participants with complete accelerometer data often represent a low proportion of the total sample and, in some cases, may be distinguishable from participants with incomplete data. Because traditional reliability methods characterize the consistency of complete data, little is known about reliability properties for an entire sample. This study employed Generalizability theory to report an index of reliability characterizing complete (7 days) and observable (1 to 7 days) accelerometer data. DESIGN: Cross-sectional. METHODS: Accelerometer data from the Study of Early Child Care and Youth Development were analyzed in this study. Missing value analyses were conducted to describe the pattern and mechanism of missing data. Generalizability coefficients were derived from variance components to report reliability parameters for complete data and also for the entire observable sample. Analyses were conducted separately by age (9, 11, 12, and 15 yrs) and daily wear time criteria (6, 8, 10, and 12 hrs). RESULTS: Participants with complete data were limited (<34%) and, most often, data were not considered to be missing completely at random. Across conditions, reliability coefficients for complete data were between 0.74 and 0.87. Relatively lower reliability properties were found across all observable data, ranging from 0.52 to 0.67. Sample variability increased with longer wear time criteria, but decreased with advanced age. CONCLUSIONS: A reliability coefficient that includes all participants, not just those with complete data, provides a global perspective of reliability that could be used to further understand group level associations between activity and health outcomes.