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.
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-topost time recordings.
The aim of the study herein reported was to review mobile health (mHealth) technologies and explore their use to monitor and mitigate the effects of the COVID-19 pandemic. Methods: A Task Force was assembled by recruiting individuals with expertise in electronic Patient-Reported Outcomes (ePRO), wearable sensors, and digital contact tracing technologies. Its members collected and discussed available information and summarized it in a series of reports. Results: The Task Force identified technologies that could be deployed in response to the COVID-19 pandemic and would likely be suitable for future pandemics. Criteria for their evaluation were agreed upon and applied to these systems. Conclusions: mHealth technologies are viable options to monitor COVID-19 patients and be used to predict symptom escalation for earlier intervention. These technologies could also be utilized to monitor individuals who are presumed noninfected and enable prediction of exposure to SARS-CoV-2, thus facilitating the prioritization of diagnostic testing.
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