Abstract

Accelerometers and other wearable devices are increasingly being used in clinical trials to provide an objective measure of the impact of an intervention on physical activity. Missing data are ubiquitous in this setting, typically for one of two reasons: patients may not wear the device as per protocol, and/or the device may fail to collect data (e.g. flat battery, water damage). However, it is not always possible to distinguish whether the participant stopped wearing the device, or if the participant is wearing the device but staying still. Further, a lack of consensus in the literature on how to aggregate the data before analysis (hourly, daily, weekly) leads to a lack of consensus in how to define a "missing" outcome. Different trials have adopted different definitions (ranging from having insufficient step counts in a day, through to missing a certain number of days in a week). We propose an analysis framework that uses wear time to define missingness on the epoch and day level, and propose a multiple imputation approach, at the day level, which treats partially observed daily step counts as right censored. This flexible approach allows the inclusion of auxiliary variables, and is consistent with almost all the primary analysis models described in the literature, and readily allows sensitivity analysis (to the missing at random assumption) to be performed. Having presented our framework, we illustrate its application to the analysis of the 2019 MOVE-IT trial of motivational interviewing to increase exercise.