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Activity Number: 12 - Statistical Methods in Mobile Health: New Directions and Innovation
Type: Invited
Date/Time: Sunday, July 28, 2019 : 2:00 PM to 3:50 PM
Sponsor: Mental Health Statistics Section
Abstract #300614
Title: Robust Tests in Online Decision-Making: Testing the Utility of Data Collected by Wearables
Author(s): Jane Kim *
Companies: Stanford University School of Medicine
Keywords: mobile health; sequential decision making; bandit; wearables

We introduce the study design of a Stanford led project using Apple watches, in which the goal is to promote physical activity by pushing sequences of notifications on the Apple Watch. We also identify issues of the analysis of adaptive sequential decision making that arise from this study that can be generalized to other mHealth studies in the area. In the Apple Watch study, a focal issue is whether the watch provides useful covariates and thus being able to test whether the variables obtained by wearable devices are truly informative plays an important role in decision-making. The validity of hypothesis testing for the model coefficients however, is predicated on the correct specification of either the policy or value models. We propose a robust test statistic that is valid when either the model of the algorithm or the auxiliary model for the variable collected from the wearable is correctly specified.

Authors who are presenting talks have a * after their name.

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