High levels of stress during pregnancy increase the chances of having a premature or lowbirthweight baby. Perceived self-reported stress does not often capture or align with the physiological and behavioral response. But what if there was a self-report measure that could better capture the physiological response? Current perceived stress self-report assessments require users to answer multi-item scales at different time points of the day. Reducing it to one question, using microinteraction-based ecological momentary assessment (micro-EMA, collecting a single in situ self-report to assess behaviors) allows us to identify smaller or more subtle changes in physiology. It also allows for more frequent responses to capture perceived stress while at the same time reducing burden on the participant. We propose a framework for selecting the optimal micro-EMA that combines unbiased feature selection and unsupervised Agglomerative clustering. We test our framework in 18 women performing 16 activities in-lab wearing a Biostamp, a NeuLog, and a Polar chest strap. We validated our results in 17 pregnant women in real-world settings. Our framework shows that the question "How worried were you?" results in the highest accuracy when using a physiological model. Our results provide further in-depth exposure to the challenges of evaluating stress models in real-world situations. 1 INTRODUCTION While immediate short-term mental and physical stress can be beneficial to health, prolonged elevated stress levels negatively affect the body's respiratory, cardiovascular, digestive, muscular, reproductive, nervous, and immune systems [18, 50, 54]. Prolonged stress can also contribute to poor health behaviors such as overeating [45], smoking [34], alcohol [42] and substance abuse [46]. These behaviors, in turn, are associated with conditions such as hypertension, heart disease, and depression [8]. Indeed, stress exposure in utero has been linked to deleterious outcomes for the child including impaired motor development, lower mental development, heightened behavioral disinhibition, and associated

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Introduction: Short message service (SMS) is a widely accepted telecommunications approach used to support health informatics, including behavioral interventions, data collection, and patient-provider communication. However, SMS delivery platforms are not standardized and platforms are typically commercial "off-the-shelf" or developed "in-house." As a consequence of platform variability, implementing SMS-based interventions may be challenging for both providers and patients. Off-the-shelf SMS delivery platforms may require minimal development or technical resources from providers, but users are often limited in their functionality. Conversely, platforms that are developed in-house are often specified for individual projects, requiring specialized development and technical expertise. Patients are on the receiving end of programming and technical specification challenges; message delays or lagged data affect quality of SMS communications. To date, little work has been done to develop a generalizable SMS platform that can be scaled across health initiatives. Objective: We propose the Configurable Assessment Messaging Platform for Interventions (CAMPI) to mitigate challenges associated with SMS intervention implementation (e.g., programming, data collection, message delivery). Method: CAMPI aims to optimize health data captured from a multitude of sources and enhance patient-provider communication through a technology that is simple and familiar to patients. Using representative examples from Michael

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BACKGROUND As mobile health (mHealth) studies become increasingly productive due to the advancements in wearable and mobile sensor technology, our ability to monitor and model human behavior will be constrained by participant receptivity. Many health constructs are dependent on subjective responses, and without such responses, researchers are left with little to no ground truth to accompany our ever-growing biobehavioral data. OBJECTIVE We examine the factors that affect participants’ responsiveness to ecological momentary assessments (EMA) in a 10-day wearable and EMA-based affect sensing mHealth study. We study the physiological relationships indicative of receptivity and affect while also analyzing the interaction between the two constructs. METHODS We collected the data from 45 healthy participants wearing two devices measuring electrodermal activity, acceleration, electrocardiography, and skin temperature while answering 10 EMAs a day containing questions related to perceived mood. Due to the nature of our constructs, we can only obtain ground truth measures for both affect and receptivity during a response. Therefore, we utilized unsupervised and supervised learning methods to infer affect when a participant did not respond. Our unsupervised method used k-means clustering to find the relationship between physiological relationships and responsiveness then inferred the emotional state during non-responses. For the supervised learning method, we primarily used Random Forest (RF) and Neural Networks (NN) to predict affect of unlabeled data points. RESULTS Based on our findings we showed that using a receptivity model to trigger EMAs will decrease the reported negative affect by more than 3 points or 0.29 standard deviation using our psychological instrument scored between 13 and 91. The findings also showed a bimodal distribution of our predicted affect during non-responses. CONCLUSIONS Our results showed a clear relationship between affect and receptivity. This relationship can affect the efficacy of a mHealth study, particularly those studies that employ a learning algorithm to trigger EMAs. Therefore, we propose a smart trigger that promotes EMA and JITI receptivity without influencing affect during sampled time points as future work.