Objective There are many barriers to accessing mental health assessments including cost and stigma. Even when individuals receive professional care, assessments are intermittent and may be limited partly due to the episodic nature of psychiatric symptoms. Therefore, machine‐learning technology using speech samples obtained in the clinic or remotely could one day be a biomarker to improve diagnosis and treatment. To date, reviews have only focused on using acoustic features from speech to detect depression and schizophrenia. Here, we present the first systematic review of studies using speech for automated assessments across a broader range of psychiatric disorders. Methods We followed the Preferred Reporting Items for Systematic Reviews and Meta‐Analysis (PRISMA) guidelines. We included studies from the last 10 years using speech to identify the presence or severity of disorders within the Diagnostic and Statistical Manual of Mental Disorders (DSM‐5). For each study, we describe sample size, clinical evaluation method, speech‐eliciting tasks, machine learning methodology, performance, and other relevant findings. Results 1395 studies were screened of which 127 studies met the inclusion criteria. The majority of studies were on depression, schizophrenia, and bipolar disorder, and the remaining on post‐traumatic stress disorder, anxiety disorders, and eating disorders. 63% of studies built machine learning predictive models, and the remaining 37% performed null‐hypothesis testing only. We provide an online database with our search results and synthesize how acoustic features appear in each disorder. Conclusion Speech processing technology could aid mental health assessments, but there are many obstacles to overcome, especially the need for comprehensive transdiagnostic and longitudinal studies. Given the diverse types of data sets, feature extraction, computational methodologies, and evaluation criteria, we provide guidelines for both acquiring data and building machine learning models with a focus on testing hypotheses, open science, reproducibility, and generalizability. Level of Evidence 3a

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.

The unique circumstances created by the COVID-19 pandemic pose serious challenges to mood stability and emotional regulation at all ages. Although many people tend to react resiliently to stress, others appear to display emotional anxiety and depression-related symptoms. In this study, we carried out a survey (N = 10,053) during the first week of the general lockdown (quarantine) in Argentina to measure early affective reactions in Argentine adults. Respondents showed substantial anxious and depressive symptoms, with 33% and 23% of participants reporting possible depressive and anxious syndromes, respectively, with the youngest group (18 to 25 y.o.) showing the highest prevalence of symptoms. Even if prior mental health problems predisposed or aggravated the reaction, participants without prior complaints showed signs of psychological impact. Using linear regression, the most important independent variables related to depressive symptoms were the feeling of loneliness followed by daily stress. In the case of anxious states, the strongest variables were negative repetitive thinking and feeling of loneliness. Other psychological, economic, and social factors are discussed. This study is in line with previous literature that highlight the importance of the psychological impact of pandemics, but additionally demonstrates that these reactions are present at a large scale immediately after the start of quarantine with very low infectious rates as an early anticipatory adaptive reaction leading to potential negative outcomes from adjustment disorders to major disorders. In addition, the present results provide potentially relevant information about sudden environmental impacts on affective states and specific pathways for anxiety and depression to be expressed. We end by discussing implications for public policy based on considering the most vulnerable groups.

The unique circumstances created by the COVID-19 pandemic pose serious challenges to mood stability and emotional regulation at all ages. Although many people tend to react resiliently to stress, others appear to display emotional anxiety and depression-related symptoms. In this study, we carried out a survey (N = 10,053) during the first week of the general lockdown (quarantine) in Argentina to measure early affective reactions in Argentine adults. Respondents showed substantial anxious and depressive symptoms, with 33 % and 23% of participants reporting possible depressive andanxious syndromes, respectively, with the youngest group (18 to 25 y.o.) showing the highest prevalence of symptoms. Even if prior mental health problems predisposed or aggravated the reaction, participants without prior complaints showed signs of psychological impact. Using linear regression, the most important independent variables related to depressive symptoms was the feeling of loneliness followed by daily stress. In the case of anxious states, the strongest variables were negative repetitive thinking and feeling of loneliness. Other psychological, economic, and social factors are discussed. This study is in line with previous literature that highlights the importance of the psychological impact of pandemics but additionally demonstrates that these reactions are present at a large scale immediately after the start of quarantine with very low infectious rates as an early anticipatory adaptive reaction leading to potentially negative outcomes from adjustment disorders to major disorders. In addition, the present results provide potentially relevant information about sudden environmental impacts on affective states and specific pathways for anxiety and depression to be expressed. We end by discussing implications for public policy based on considering the most vulnerable groups.