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  1. Background

    As mobile health (mHealth) studies become increasingly productive owing 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. This issue can significantly impact the quality of a study, particularly for populations known to exhibit lower compliance rates. To address this challenge, researchers have proposed innovative approaches that use machine learning (ML) and sensor data to modify the timing and delivery of surveys. However, an overarching concern is the potential introduction of biases or unintended influences on participants’ responses when implementing new survey delivery methods.


    This study aims to demonstrate the potential impact of an ML-based ecological momentary assessment (EMA) delivery system (using receptivity as the predictor variable) on the participants’ reported emotional state. We examine the factors that affect participants’ receptivity to EMAs in a 10-day wearable and EMA–based emotional state–sensing mHealth study. We study the physiological relationships indicative of receptivity and affect while also analyzing the interaction between the 2 constructs.


    We collected data from 45 healthy participants wearing 2 devices measuring electrodermal activity, accelerometer, electrocardiography, and skin temperature while answering 10 EMAs daily, containing questions about perceived mood. Owing to the nature of our constructs, we can only obtain ground truth measures for both affect and receptivity during responses. Therefore, we used unsupervised and supervised ML methods to infer affect when a participant did not respond. Our unsupervised method used k-means clustering to determine the relationship between physiology and receptivity and then inferred the emotional state during nonresponses. For the supervised learning method, we primarily used random forest and neural networks to predict the affect of unlabeled data points as well as receptivity.


    Our findings showed that using a receptivity model to trigger EMAs decreased the reported negative affect by >3 points or 0.29 SDs in our self-reported affect measure, scored between 13 and 91. The findings also showed a bimodal distribution of our predicted affect during nonresponses. This indicates that this system initiates EMAs more commonly during states of higher positive emotions.


    Our results showed a clear relationship between affect and receptivity. This relationship can affect the efficacy of an mHealth study, particularly those that use an ML algorithm to trigger EMAs. Therefore, we propose that future work should focus on a smart trigger that promotes EMA receptivity without influencing affect during sampled time points.

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    Free, publicly-accessible full text available January 1, 2025
  2. Shift work disrupts sleep and causes chronic stress, resulting in burnout syndrome characterized by emotional exhaustion, depersonalization, and decreased personal accomplishment. Continuous biometric data collected through wearable devices contributes to mental health research. However, direct prediction of burnout risk is still limited, and interpreting machine learning (ML) models in healthcare poses challenges. In this paper, we develop machine learning models that utilize wearable and survey data, including rhythm features, to predict burnout risk among shift workers. Additionally, we employ the DiCE (Diverse Counterfactual Explanations) framework to generate interpretable explanations for the ML model, aiding in the management of burnout risks. Our experiments on the AMED dataset show that incorporating rhythm features significantly enhances the predictive performance of our models. Specifically, sleep and heart rate features have emerged as significant indicators for accurately predicting burnout risk 
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    Free, publicly-accessible full text available November 15, 2024
  3. Physiological and behavioral data collected from wearable or mobile sensors have been used to estimate self-reported stress levels. Since stress annotation usually relies on self-reports during the study, a limited amount of labeled data can be an obstacle to developing accurate and generalized stress-predicting models. On the other hand, the sensors can continuously capture signals without annotations. This work investigates leveraging unlabeled wearable sensor data for stress detection in the wild. We propose a two-stage semi-supervised learning framework that leverages wearable sensor data to help with stress detection. The proposed structure consists of an auto-encoder pre-training method for learning information from unlabeled data and the consistency regularization approach to enhance the robustness of the model. Besides, we propose a novel active sampling method for selecting unlabeled samples to avoid introducing redundant information to the model. We validate these methods using two datasets with physiological signals and stress labels collected in the wild, as well as four human activity recognition (HAR) datasets to evaluate the generality of the proposed method. Our approach demonstrated competitive results for stress detection, improving stress classification performance by approximately 7% to 10% on the stress detection datasets compared to the baseline supervised learning models. Furthermore, the ablation study we conducted for the HAR tasks supported the effectiveness of our methods. Our approach showed comparable performance to state-of-the-art semi-supervised learning methods for both stress detection and HAR tasks. 
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  4. Free, publicly-accessible full text available September 10, 2024
  5. Abstract

    The noniterative conditional expectation (NICE) parametric g-formula can be used to estimate the causal effect of sustained treatment strategies. In addition to identifiability conditions, the validity of the NICE parametric g-formula generally requires the correct specification of models for time-varying outcomes, treatments, and confounders at each follow-up time point. An informal approach for evaluating model specification is to compare the observed distributions of the outcome, treatments, and confounders with their parametric g-formula estimates under the “natural course.” In the presence of loss to follow-up, however, the observed and natural-course risks can differ even if the identifiability conditions of the parametric g-formula hold and there is no model misspecification. Here, we describe 2 approaches for evaluating model specification when using the parametric g-formula in the presence of censoring: 1) comparing factual risks estimated by the g-formula with nonparametric Kaplan-Meier estimates and 2) comparing natural-course risks estimated by inverse probability weighting with those estimated by the g-formula. We also describe how to correctly compute natural-course estimates of time-varying covariate means when using a computationally efficient g-formula algorithm. We evaluate the proposed methods via simulation and implement them to estimate the effects of dietary interventions in 2 cohort studies.

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  6. Abstract

    Microbially-synthesized protein-based materials are attractive replacements for petroleum-derived synthetic polymers. However, the high molecular weight, high repetitiveness, and highly-biased amino acid composition of high-performance protein-based materials have restricted their production and widespread use. Here we present a general strategy for enhancing both strength and toughness of low-molecular-weight protein-based materials by fusing intrinsically-disordered mussel foot protein fragments to their termini, thereby promoting end-to-end protein-protein interactions. We demonstrate that fibers of a ~60 kDa bi-terminally fused amyloid-silk protein exhibit ultimate tensile strength up to 481 ± 31 MPa and toughness of 179 ± 39 MJ*m−3, while achieving a high titer of 8.0 ± 0.70 g/L by bioreactor production. We show that bi-terminal fusion of Mfp5 fragments significantly enhances the alignment of β-nanocrystals, and intermolecular interactions are promoted by cation-π and π-π interactions between terminal fragments. Our approach highlights the advantage of self-interacting intrinsically-disordered proteins in enhancing material mechanical properties and can be applied to a wide range of protein-based materials.

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