Linear mixed models with endogenous covariates: modeling sequential treatment effects with application to a mobile health study

Mobile health is a rapidly developing field in which behavioral treatments are delivered to individuals via wearables or smartphones to facilitate health-related behavior change. Micro-randomized trials (MRT) are an experimental design for developing mobile health interventions. In an MRT the treatments are randomized numerous times for each individual over course of the trial. Along with assessing treatment effects, behavioral scientists aim to understand between-person heterogeneity in the treatment effect. A natural approach is the familiar linear mixed model. However, directly applying linear mixed models is problematic because potential moderators of the treatment effect are frequently endogenous---that is, may depend on prior treatment. We discuss model interpretation and biases that arise in the absence of additional assumptions when endogenous covariates are included in a linear mixed model. In particular, when there are endogenous covariates, the coefficients no longer have the customary marginal interpretation. However, these coefficients still have a conditional-on-the-random-effect interpretation. We provide an additional assumption that, if true, allows scientists to use standard software to fit linear mixed model with endogenous covariates, and person-specific predictions of effects can be provided. As an illustration, we assess the effect of activity suggestion in the HeartSteps MRT and analyze the between-person treatment effect heterogeneity

Randomised trials for the Fitbit generation

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/116333/1/sign863.pd

The Micro-Randomized Trial for Developing Digital Interventions: Data Analysis Methods

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

ULEARN: Personalised Learner’s Profile Based On Dynamic Learning Style Questionnaire

The file attached to this record is the author's final peer reviewed version.E-Learning recommender system effectiveness re- lies upon their ability to recommend appropriate learning con- tents according to the learner learning style and preferences. An effective approach to handle the learner preferences is to build an efficient learner profile in order to gain adaptation and individualisation of the learning environment. It is usually necessary to know learning style and preferences of the learner on a domain before adapting the learning process and course content. This study focuses on identifying the learning styles of students in order to adapt the learning process and course content. ULEARN is an adaptive recommender learning system designed to provide learners with personalised learning environment such as course learning objects that match their adaptive profile. This paper presents the algorithm used in ULEARN to reduce dynamically the number of questions in Felder-Silverman learning style ques- tionnaire used to initialise the adaptive learner profile. Firstly, the questionnaire is restructured into four groups, one for each learning style dimension; and a study is carried out to determine the order in which questions will be asked in each dimension. Then an algorithm is built upon this ranking of questions to calculate dynamically the initial learning style of the user as they go through the questionnaire

Human identification via unsupervised feature learning from UWB radar data

This paper presents an automated approach to automatically distinguish the identity of multiple residents in smart homes. Without using any intrusive video surveillance devices or wearable tags, we achieve the goal of human identification through properly processing and analyzing the received signals from the ultra-wideband (UWB) radar installed in indoor environments. Because the UWB signals are very noisy and unstable, we employ unsupervised feature learning techniques to automatically learn local, discriminative features that can incorporate intra-class variations of the same identity, and yet reflect differences in distinguishing different human identities. The learned features are then used to train an SVM classifier and recognize the identity of residents. We validate our proposed solution via extensive experiments using real data collected in real-life situations. Our findings show that feature learning based on K-means clustering, coupled with whitening and pooling, achieves the highest accuracy, when only limited training data is available. This shows that the proposed feature learning and classification framework combined with the UWB radar technology provides an effective solution to human identification in multi-residential smart homes