Publication

Publication: An Explainable Transformer Model for Pain Intensity Assessment Using Multi-Modal Facial Sequential Images

Authors: Xian Du, Meysam Safarzadeh, Maoqin Zhu, Shishir Prasad, Sudeshna Das, Joohyun Chung Abstract Pain monitoring and assessment traditionally rely on subjective methods such as self-reports and caregiver evaluations, which can be costly and often inaccurate due to their inherent subjectivity and reliance on the individual's communication skills. Many objective methods have been introduced to address these issues, primarily utilizing single or multiple wearable sensor modalities. However, these approaches face challenges in home care settings, particularly concerning continuous wearability and discomfort, especially among elderly users. An alternative solution is using patient monitoring tools such as various imaging modalities to detect pain-related facial expressions. In this paper, we developed a new transformer model to extract pain-related features from facial expressions captured through three imaging modalities—RGB, thermal, and depth across sequential images. This method can leverage the…

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Preprint: Detecting Preclinical Alzheimer’s Disease Risk in Cognitively Normal Adults Using Speech Acoustics: Validation with Plasma p-Tau217 and APOE-ε4 Status

Authors: Mehrdad Dadgostar, Lindsay C. Hanford, Maryam Tavakoli, Steven E. Arnold, David H. Salat, Tatiana Sitnikova, Pia Kivisakk Webb, Jordan R. Green, Hengru Liu, Brian D. Richburg, Mariam Tkeshelashvili, Marziye Eshghi Abstract INTRODUCTION We tested whether spontaneous speech acoustics provide a scalable digital marker of biologically defined Alzheimer’s disease (AD) risk. METHODS Forty-nine cognitively unimpaired older adults were stratified within APOE genotype into Low-, Moderate-, and High-Risk groups based on log₁₀-transformed plasma p-tau217. Acoustic features were extracted from spontaneous speech and entered into multiclass SVM classifiers with leave-one-out cross-validation, with and without genetic-algorithm feature selection and age. Parallel models using neuropsychological measures were evaluated for comparison. Feature contributions were interpreted using SHAP. RESULTS Speech-based models substantially outperformed cognition-only models and exceeded chance performance for three-group classification (33.3%), achieving up to 77% accuracy compared with 47%…

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Publication: Validation of commercial sleep-tracking wearables and nearables in healthy young and older adults

Authors: M.E. Searles, A. Licata, M. Cucinotta, K. Kainec, and R.M.C. Spencer Abstract Study objectives: Changes in sleep with aging are associated with risk for Alzheimer’s and other neurological diseases, risk of accidents, and can be a predictor of health decline. For this reason, continuous sleep monitoring is of great interest for researchers, clinicians, and family members. The objective of this study was to assess the validity of consumer sleep-tracking devices in older relative to young adults. Methods: Analyses were based on one night of sleep assessed in young (19-24 years; n=13) and older adults (56-80 years; n=19). Participants wore sleep-tracking wearables (Fitbit Sense 2, Oura Ring) and nearables (Withings Sleep Mat, Sleep Score Max) were positioned nearby. Sleep measures were compared to polysomnography. Results: Results suggest that devices may be less accurate in older…

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Publication: Replicable Bandits for Digital Health Interventions

Authors: Kelly W Zhang, Nowell Closser, Anna L Trella, Susan A Murphy Abstract Adaptive treatment assignment algorithms, such as bandit algorithms, are increasingly used in digital health intervention clinical trials. Frequently the data collected from these trials is used to conduct causal inference and related data analyses to decide how to refine the intervention, and whether to roll-out the intervention more broadly. This work studies inference for estimands that depend on the adaptive algorithm itself; a simple example is the mean reward under the adaptive algorithm. Specifically, we investigate the replicability of statistical analyses concerning such estimands when using data from trials deploying adaptive treatment assignment algorithms. We demonstrate that many standard statistical estimators can be inconsistent and fail to be replicable across repetitions of the clinical trial, even as the sample size grows large.…

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Publication: Kinematic correlates of early speech motor changes in cognitively intact APOE-ε4 carriers: a preliminary study using a color-word interference task

Authors: Mehrdad Dadgostar, Lindsay C Hanford, Jordan R Green, Brian D Richburg, Averi Taylor Cannon, Nelson V Barnett, David H Salat, Steven E Arnold, Marziye Eshghi Abstract Introduction: Alzheimer's disease (AD) is the most prevalent form of dementia and a major public health challenge. In the absence of a cure, accurate and innovative early diagnostic methods are essential for proactive life and healthcare planning. Speech metrics have shown promising potential for identifying individuals with mild cognitive impairment (MCI) and AD, prompting investigation into whether speech motor features can detect elevated risk even prior to cognitive decline. This preliminary study examined whether speech kinematic features measured during a color-word interference task could distinguish cognitively normal APOE-ε4 carriers (ε4+) from non-carriers (ε4-). Methods: Sixteen cognitively normal older adults (n = 9 ε4+, n = 7 ε4-) completed…

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Publication: AI-Driven Sleep Staging Using Instantaneous Heart Rate and Accelerometry: Insights from an Apple Watch Study

Authors: Tzu-An Song, Yubo Zhang, Ziyuan Zhou, Luke Hou, Masoud Malekzadeh, Aida Behzad, Joyita Dutta Abstract Polysomnography, the gold standard for sleep evaluations, involves complex setup and data acquisition protocols and requires manual scoring of sleep data. Smartwatches and other multi-sensor consumer wearable devices with automated sleep staging capabilities offer a promising and scalable alternative for routine and long-term sleep evaluations in individuals. We conducted a multi-night study using a smartwatch for sleep assessment and created an AI-driven automated sleep staging framework based on instantaneous heart rate (IHR) and accelerometry data using sleep stage labels based on electroencephalography (EEG) as the reference. 47 healthy adults were recruited to record their sleep for up to seven consecutive nights using an Apple Watch Series 6 and a Dreem 2 Headband. Our sleep staging framework relies on a…

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Publication: Just-in-Time Adaptive Interventions: Where Are We Now and What Is Next?

Authors: Inbal Nahum-Shani, Susan A Murphy Abstract The past decade has seen a surge in developing just-in-time adaptive interventions (JITAIs)-an intervention approach that leverages advancements in digital technologies to address the rapidly changing needs of individuals in daily life. This article provides an overview of the state of science on JITAI development and highlights important directions for future research. We explain what a JITAI is (and what it is not) and review the scientific and practical rationales underlying this approach. We also call attention to three key challenges relating to the development of JITAIs. The first challenge is that individuals may not be able to engage with (i.e., invest energy in) an intervention when they need it most in daily life. The second concerns the generally suboptimal engagement of individuals in interventions that leverage digital…

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