  Although there is much excitement surrounding the use of mobile and wearable
technology for the purposes of delivering interventions as people go through
their day-to-day lives, data analysis methods for constructing and optimizing
digital interventions lag behind. Here, we elucidate data analysis methods for
primary and secondary analyses of micro-randomized trials (MRTs), an
experimental design to optimize digital just-in-time adaptive interventions. We
provide a definition of causal "excursion" effects suitable for use in digital
intervention development. We introduce the weighted and centered least-squares
(WCLS) estimator which provides consistent causal excursion effect estimators
for digital interventions from MRT data. We describe how the WCLS estimator
along with associated test statistics can be obtained using standard
statistical software such as SAS or R. Throughout we use HeartSteps, an MRT
designed to increase physical activity among sedentary individuals, to
illustrate potential primary and secondary analyses.

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