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Measurement(s) brain activity • Blood Oxygen Level-Dependent Functional MRI • regional part of brain • cerebral white matter structure • cerebral blood flow Technology Type(s) Magnetoencephalography • Functional Magnetic Resonance Imaging • Magnetic Resonance Imaging • Diffusion Tensor Imaging • Arterial Spin Labeling Magnetic Resonance Imaging Sample Characteristic - Organism Homo sapiens Sample Characteristic - Location United States of America

Abstract Objectives The DSM‐5 Level 1 Cross‐Cutting Symptom Measure (DSM‐XC) was developed by the American Psychiatric Association as a transdiagnostic mental health symptom survey. Despite its promise as a screening tool, few studies have assessed its latent dimensionality or provided guidance on interpreting responses. We examined the factor structure of the DSM‐XC in a convenience sample of participants with varying degrees of psychopathology. Methods Participants (n = 3533) were enrolled in an online study on the mental health impact of COVID‐19 (NCT04339790). We used a factor analytic framework with exploratory and confirmatory analyses to evaluate candidate factor solutions. Convergent validity analysis with concurrent study measures was also performed. Results Six‐factor and bifactor candidate solutions both had good fit and full measurement invariance across age, sex, and enrollment date. The six‐factor solution resulted in constructs labeled as: mood, worry, activation, somatic, thought, and substance use. A general psychopathology factor and two residual factors (mood and anxiety constructs) explained the variance of the bifactor solution. Conclusions Our analysis supports that the DSM‐XC is a multidimensional instrument spanning many mental health symptoms. We provide scoring solutions for two factor structures that capture broader constructs of psychopathology. Use of a convenience sample may limit generalizability of findings.

BackgroundThe COVID-19 pandemic and its associated restrictions have been a major stressor that has exacerbated mental health worldwide. Qualitative data play a unique role in documenting mental states through both language features and content. Text analysis methods can provide insights into the associations between language use and mental health and reveal relevant themes that emerge organically in open-ended responses. ObjectiveThe aim of this web-based longitudinal study on mental health during the early COVID-19 pandemic was to use text analysis methods to analyze free responses to the question, “Is there anything else you would like to tell us that might be important that we did not ask about?” Our goals were to determine whether individuals who responded to the item differed from nonresponders, to determine whether there were associations between language use and psychological status, and to characterize the content of responses and how responses changed over time. MethodsA total of 3655 individuals enrolled in the study were asked to complete self-reported measures of mental health and COVID-19 pandemic–related questions every 2 weeks for 6 months. Of these 3655 participants, 2497 (68.32%) provided at least 1 free response (9741 total responses). We used various text analysis methods to measure the links between language use and mental health and to characterize response themes over the first year of the pandemic. ResultsResponse likelihood was influenced by demographic factors and health status: those who were male, Asian, Black, or Hispanic were less likely to respond, and the odds of responding increased with age and education as well as with a history of physical health conditions. Although mental health treatment history did not influence the overall likelihood of responding, it was associated with more negative sentiment, negative word use, and higher use of first-person singular pronouns. Responses were dynamically influenced by psychological status such that distress and loneliness were positively associated with an individual’s likelihood to respond at a given time point and were associated with more negativity. Finally, the responses were negative in valence overall and exhibited fluctuations linked with external events. The responses covered a variety of topics, with the most common being mental health and emotion, social or physical distancing, and policy and government. ConclusionsOur results identify trends in language use during the first year of the pandemic and suggest that both the content of responses and overall sentiments are linked to mental health.