Can mHealth Technology Help Mitigate the Effects of the COVID-19 Pandemic?

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Design of tool for analysis of speech development disorders using landmarks and other acoustic cues

Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (page 71).Non-word repetition tasks have been used to diagnose children with various developmental difficulties with phonology, but these productions have not been phonetically analyzed to reveal the nature of the modifications produced by children diagnosed with SLI, autism spectrum disorder or dyslexia compared to those produced by typically-developing children. In this thesis, we compared the modification of predicted acoustic cues to distinctive features of manner, place and voicing for just under 30 children (ages 5-12), for the CN-Rep word inventory, in an extension of the earlier analysis in Levy et al. 2014. Feature cues, including abrupt acoustic landmarks (Stevens 2002) and other acoustic feature cues, were hand-labeled and analysis of factors that may influence feature cue modifications included position in the word, position in the syllable, word length measured in syllables, lexical stress, and manner type. Results suggest specific patterns of modification in specific contexts for specific clinical populations. These findings set the foundation for understanding how phonetic variation in speech arises in both typical and clinical populations, and for using this knowledge to develop tools to aid in more accurate and insightful diagnosis as well as improved intervention methods.by Tanya Talkar.M. Eng

A Framework for Biomarkers of COVID-19 Based on Coordination of Speech-Production Subsystems

Goal: We propose a speech modeling and signal-processing framework to detect and track COVID-19 through asymptomatic and symptomatic stages. Methods: The approach is based on complexity of neuromotor coordination across speech subsystems involved in respiration, phonation and articulation, motivated by the distinct nature of COVID-19 involving lower (i.e., bronchial tubes, diaphragm, lower trachea) versus upper (i.e., laryngeal, pharyngeal, oral and nasal) respiratory tract inflammation [1], as well as by the growing evidence of the virus' neurological manifestations [2]—[5]. Preliminary results: An exploratory study with audio interviews of five subjects provides Cohen's d effect sizes between pre-COVID-19 (pre-exposure) from post-COVID-19 (after positive diagnosis but asymptomatic) using: coordination of respiration (as measured through acoustic waveform amplitude) and laryngeal motion (fundamental frequency and cepstral peak prominence), and coordination of laryngeal and articulatory (formant center frequencies) motion. Conclusions: While there is a strong subject-dependence, the group-level morphology of effect sizes indicates a reduced complexity of subsystem coordination. Validation is needed with larger more controlled datasets and to address confounding influences such as different recording conditions, unbalanced data quantities, and changes in underlying vocal status from pre-to-post time recordings

Natural Language Processing Reveals Vulnerable Mental Health Support Groups and Heightened Health Anxiety on Reddit During COVID-19: Observational Study

Background: The COVID-19 pandemic is impacting mental health, but it is not clear how people with different types of mental health problems were differentially impacted as the initial wave of cases hit. Objective: The aim of this study is to leverage natural language processing (NLP) with the goal of characterizing changes in 15 of the world’s largest mental health support groups (eg, r/schizophrenia, r/SuicideWatch, r/Depression) found on the website Reddit, along with 11 non–mental health groups (eg, r/PersonalFinance, r/conspiracy) during the initial stage of the pandemic. Methods: We created and released the Reddit Mental Health Dataset including posts from 826,961 unique users from 2018 to 2020. Using regression, we analyzed trends from 90 text-derived features such as sentiment analysis, personal pronouns, and semantic categories. Using supervised machine learning, we classified posts into their respective support groups and interpreted important features to understand how different problems manifest in language. We applied unsupervised methods such as topic modeling and unsupervised clustering to uncover concerns throughout Reddit before and during the pandemic. Results: We found that the r/HealthAnxiety forum showed spikes in posts about COVID-19 early on in January, approximately 2 months before other support groups started posting about the pandemic. There were many features that significantly increased during COVID-19 for specific groups including the categories “economic stress,” “isolation,” and “home,” while others such as “motion” significantly decreased. We found that support groups related to attention-deficit/hyperactivity disorder, eating disorders, and anxiety showed the most negative semantic change during the pandemic out of all mental health groups. Health anxiety emerged as a general theme across Reddit through independent supervised and unsupervised machine learning analyses. For instance, we provide evidence that the concerns of a diverse set of individuals are converging in this unique moment of history; we discovered that the more users posted about COVID-19, the more linguistically similar (less distant) the mental health support groups became to r/HealthAnxiety (ρ=–0.96, P<.001). Using unsupervised clustering, we found the suicidality and loneliness clusters more than doubled in the number of posts during the pandemic. Specifically, the support groups for borderline personality disorder and posttraumatic stress disorder became significantly associated with the suicidality cluster. Furthermore, clusters surrounding self-harm and entertainment emerged. Conclusions: By using a broad set of NLP techniques and analyzing a baseline of prepandemic posts, we uncovered patterns of how specific mental health problems manifest in language, identified at-risk users, and revealed the distribution of concerns across Reddit, which could help provide better resources to its millions of users. We then demonstrated that textual analysis is sensitive to uncover mental health complaints as they appear in real time, identifying vulnerable groups and alarming themes during COVID-19, and thus may have utility during the ongoing pandemic and other world-changing events such as elections and protests.NIH (Grants 5T32DC000038-28, 5T32DC000038, 5T32HG2295-17

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Delivery to human immune cells.

<p><b>A)</b> Human T cells and MDDCs were tested for delivery of cascade blue labeled 3kDa dextran, fluorescein labeled 70kDa dextran, and APC labeled IgG1. The representative histograms for a 30–4 (T cells) and 10–7 (MDDCs) device (left) and replicates across device designs (right) are displayed. <b>B)</b> SiRNA mediated knockdown of CD4 and DC-SIGN protein levels in CD4⁺ T cells and MDDCs respectively. <b>C)</b> Knockdown of CD4 expression in human regulatory T cells in response to treatment by a 30–4 device. Dead cells were excluded for delivery or knockdown analysis. <b>D)</b> Comparison of device performance in T cells to nucleofection by Amaxa. Protein expression 72hrs after siRNA delivery and cell viability after treatment are shown. <b>E)</b> Intracellular staining for the p24 antigen was used as an indicator of HIV infection level in treated human CD4⁺ T cells 24hrs after infection. In these studies, vif and/or gag, siRNA was delivered 24hrs prior to infection while CD4 siRNA was delivered 48hrs prior to infection. <b>F)</b> Median fluorescence intensity of the p24 antigen stain across repeats (min. N = 4) of the experimental conditions. Data are represented as mean + 1 standard error.</p

Delivery methodology and performance in mouse cells.

<p><b>A)</b> Illustration of device design and delivery mechanism. <b>B)</b> Illustration of the system setup and delivery procedure. <b>C)</b> Representative histograms of T cells, B cells and myeloid cells (CD11b⁺) treated by the CellSqueeze device to deliver APC-labeled IgG1. <b>D)</b> Delivery efficiency of Cascade blue-labeled 3 kDa dextran, fluorescein-labeled 70 kDa dextran, and APC-labeled IgG1. All results were measured by flow cytometry within an hour of treatment. Dead cells were excluded by propidium iodide staining. Viability is shown in <b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0118803#pone.0118803.s002" target="_blank">S2 Fig</a></b>. Data in <b>D)</b> (mean ± SD) are from 3 independent experiments. Untreated cells were not put through the device or exposed to the biomolecules. The ‘no device’ samples were incubated with the biomolecules, but were not treated by the device. This control is meant to account for surface binding, endocytosis and other background effects.</p