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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. 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. 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. 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. 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.

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

•We formed a CNN to capture topic meaning from the combinations of words in sentences.•We contrasted this to a model treating words as discrete units irrespective of topic.•Participants read sentences under fMRI and we used RSA to compare the models.•mPFC, ATL, precuneus and AG preferentially represented higher order semantic units. When reading a sentence, individual words can be combined to create more complex meaning. In this study, we sought to uncover brain regions that reflect the representation of the meaning of sentences at the topic level, as opposed to the meaning of their individual constituent words when considered irrespective of their context. Using fMRI, we recorded the neural activity of participants while reading sentences. We constructed a topic-level sentence representations using the final layer of a convolutional neural network (CNN) trained to classify Wikipedia sentences into broad semantic categories. This model was contrasted with word-level sentence representations constructed using the average of the word embeddings constituting the sentence. Using representational similarity analysis, we found that the medial prefrontal cortex, lateral anterior temporal lobe, precuneus, and angular gyrus more strongly represent sentence topic-level, compared to word-level, meaning, uncovering the important role of these semantic system regions in the representation of topic-level meaning. Results were comparable when sentence meaning was modelled with a multilayer perceptron that was not sensitive to word order within a sentence, suggesting that the learning objective, in the terms of the topic being modelled, is the critical factor in capturing these neural representational spaces.

Inconsistencies in survey-based (eg, questionnaire) data collection across biomedical, clinical, behavioral, and social sciences pose challenges to research reproducibility. ReproSchema is an ecosystem that standardizes survey design and facilitates reproducible data collection through a schema-centric framework, a library of reusable assessments, and computational tools for validation and conversion. Unlike conventional survey platforms that primarily offer graphical user interface-based survey creation, ReproSchema provides a structured, modular approach for defining and managing survey components, enabling interoperability and adaptability across diverse research settings. This study examines ReproSchema's role in enhancing research reproducibility and reliability. We introduce its conceptual and practical foundations, compare it against 12 platforms to assess its effectiveness in addressing inconsistencies in data collection, and demonstrate its application through 3 use cases: standardizing required mental health survey common data elements, tracking changes in longitudinal data collection, and creating interactive checklists for neuroimaging research. We describe ReproSchema's core components, including its schema-based design; reusable assessment library with >90 assessments; and tools to validate data, convert survey formats (eg, REDCap [Research Electronic Data Capture] and Fast Healthcare Interoperability Resources), and build protocols. We compared 12 platforms-Center for Expanded Data Annotation and Retrieval, formr, KoboToolbox, Longitudinal Online Research and Imaging System, MindLogger, OpenClinica, Pavlovia, PsyToolkit, Qualtrics, REDCap, SurveyCTO, and SurveyMonkey-against 14 findability, accessibility, interoperability, and reusability (FAIR) principles and assessed their support of 8 survey functionalities (eg, multilingual support and automated scoring). Finally, we applied ReproSchema to 3 use cases-NIMH-Minimal, the Adolescent Brain Cognitive Development and HEALthy Brain and Child Development Studies, and the Committee on Best Practices in Data Analysis and Sharing Checklist-to illustrate ReproSchema's versatility. ReproSchema provides a structured framework for standardizing survey-based data collection while ensuring compatibility with existing survey tools. Our comparison results showed that ReproSchema met 14 of 14 FAIR criteria and supported 6 of 8 key survey functionalities: provision of standardized assessments, multilingual support, multimedia integration, data validation, advanced branching logic, and automated scoring. Three use cases illustrating ReproSchema's flexibility include standardizing essential mental health assessments (NIMH-Minimal), systematically tracking changes in longitudinal studies (Adolescent Brain Cognitive Development and HEALthy Brain and Child Development), and converting a 71-page neuroimaging best practices guide into an interactive checklist (Committee on Best Practices in Data Analysis and Sharing). ReproSchema enhances reproducibility by structuring survey-based data collection through a structured, schema-driven approach. It integrates version control, manages metadata, and ensures interoperability, maintaining consistency across studies and compatibility with common survey tools. Planned developments, including ontology mappings and semantic search, will broaden its use, supporting transparent, scalable, and reproducible research across disciplines.

In the face of the global pandemic caused by the disease COVID-19, researchers have increasingly turned to simple measures to detect and monitor the presence of the disease in individuals at home. We sought to determine if measures of neuromotor coordination, derived from acoustic time series, as well as phoneme-based and standard acoustic features extracted from recordings of simple speech tasks could aid in detecting the presence of COVID-19. We further hypothesized that these features would aid in characterizing the effect of COVID-19 on speech production systems. A protocol, consisting of a variety of speech tasks, was administered to 12 individuals with COVID-19 and 15 individuals with other viral infections at University Hospital Galway. From these recordings, we extracted a set of acoustic time series representative of speech production subsystems, as well as their univariate statistics. The time series were further utilized to derive correlation-based features, a proxy for speech production motor coordination. We additionally extracted phoneme-based features. These features were used to create machine learning models to distinguish between the COVID-19 positive and other viral infection groups, with respiratory- and laryngeal-based features resulting in the highest performance. Coordination-based features derived from harmonic-to-noise ratio time series from read speech discriminated between the two groups with an area under the ROC curve (AUC) of 0.94. A longitudinal case study of two subjects, one from each group, revealed differences in laryngeal based acoustic features, consistent with observed physiological differences between the two groups. The results from this analysis highlight the promise of using nonintrusive sensing through simple speech recordings for early warning and tracking of COVID-19.

Detecting voice disorders from voice recordings could allow for frequent, remote, and low-cost screening before costly clinical visits and a more invasive laryngoscopy examination. Our goals were to detect unilateral vocal fold paralysis (UVFP) from voice recordings using machine learning, to identify which acoustic variables were important for prediction to increase trust, and to determine model performance relative to clinician performance. Patients with confirmed UVFP through endoscopic examination (N = 77) and controls with normal voices matched for age and sex (N = 77) were included. Voice samples were elicited by reading the Rainbow Passage and sustaining phonation of the vowel \"a\". Four machine learning models of differing complexity were used. SHapley Additive exPlanations (SHAP) was used to identify important features. The highest median bootstrapped ROC AUC score was 0.87 and beat clinician’s performance (range: 0.74–0.81) based on the recordings. Recording durations were different between UVFP recordings and controls due to how that data was originally processed when storing, which we can show can classify both groups. And counterintuitively, many UVFP recordings had higher intensity than controls, when UVFP patients tend to have weaker voices, revealing a dataset-specific bias which we mitigate in an additional analysis. We demonstrate that recording biases in audio duration and intensity created dataset-specific differences between patients and controls, which models used to improve classification. Furthermore, clinician’s ratings provide further evidence that patients were over-projecting their voices and being recorded at a higher amplitude signal than controls. Interestingly, after matching audio duration and removing variables associated with intensity in order to mitigate the biases, the models were able to achieve a similar high performance. We provide a set of recommendations to avoid bias when building and evaluating machine learning models for screening in laryngology.

Military Servicemembers and Veterans are at elevated risk for suicide, but rarely self-identify to their leaders or clinicians regarding their experience of suicidal thoughts. We developed an algorithm to identify posts containing suicide-related content on a military-specific social media platform. Publicly-shared social media posts ( = 8449) from a military-specific social media platform were reviewed and labeled by our team for the presence/absence of suicidal thoughts and behaviors and used to train several machine learning models to identify such posts. The best performing model was a deep learning (RoBERTa) model that incorporated post text and metadata and detected the presence of suicidal posts with relatively high sensitivity (0.85), specificity (0.96), precision (0.64), F1 score (0.73), and an area under the precision-recall curve of 0.84. Compared to non-suicidal posts, suicidal posts were more likely to contain explicit mentions of suicide, descriptions of risk factors (e.g. depression, PTSD) and help-seeking, and first-person singular pronouns. Our results demonstrate the feasibility and potential promise of using social media posts to identify at-risk Servicemembers and Veterans. Future work will use this approach to deliver targeted interventions to social media users at risk for suicide.

Breaking negative mental health cycles, including rumination and recurring regrets, requires reflection that translates awareness into behavioral change. Grounded in the Transtheoretical Model (TTM) and Gross's Emotion Regulation (ER) Process Model, we examine how Technologies Supporting Self-Reflection (TSR) bridge reflection and action. In a 15-day in-the-wild study (N = 20), participants used a voice-based journaling system to capture regrets and wishes and engaged in WhatIf-Planning, a novel structured reflection module integrating counterfactual thinking with if-then planning. Participants were randomized to either a free-form condition or a Gross-guided condition, which maps the five processes of Gross's ER model into explicit journaling prompts. We contribute: (1) a unified reflection-to-action TSR system that operationalizes the Preparation stage of TTM to bridge Contemplation and Action, and (2) triangulated empirical evidence from an in-the-wild journaling study that first operationalizes Gross's Process Model, revealing effects on coping flexibility and emotion regulation in daily life. Results show significant pre-post improvements in coping flexibility, indicating adaptive self-regulation across conditions, with the Gross-guided group generating more counterfactual alternatives, articulating concrete if-then action plans, and implementing more plans for self-driven change.

BACKGROUND Continuous interscalene catheters are utilized for inpatient and outpatient postoperative pain control after shoulder surgery. Although prior studies have primarily been limited to adult populations, benefits of peripheral nerve catheters in ambulatory surgical centers have been shown to reduce post operative pain scores, opioid requirements, and post operative nausea. However, their use in a solely pediatric ambulatory setting has not been investigated. The purpose of this study was to retrospectively review the adverse events, analgesic effectiveness, and opioid use after continuous interscalene catheters for ambulatory shoulder surgery in pediatric and adolescent patients. METHODS A retrospective review was performed of 15 pediatric and adolescent patients (4 female, 11 male), mean age 16 years (range, 12-18 years), and mean weight 78.3 kg (range 53-130 kg), who underwent ultrasound-guided interscalene catheter placement for outpatient shoulder surgery from April 2017 through May 2018. All but one surgery were performed at a dedicated pediatric ambulatory surgery center. Catheters were initially dosed with 0.5% ropivacaine (n = 13) or 0.2% ropivacaine (n = 2) at volumes of 0.1-0.2ml/kg (total, 6-20 ml). By the conclusion of surgery, all catheters were continuously infusing 0.2% ropivacaine at 3-6ml/hr with the majority (n = 10) at 4ml/hr via elastomeric pumps. Time for catheter placement, intraoperative and postoperative intravenous opioids, post-anesthesia recovery unit (PACU) length of stay (LOS), PACU maximum pain scores and rescue medications for post-operative nausea and vomiting (PONV) were recorded. After discharge, all patients received multimodal analgesia (ibuprofen, acetaminophen, and rescue oxycodone). Patients were assessed on postoperative days (POD) 1-3 for numerical rating pain scores (0-10) at rest and with movement, supplemental oxycodone use in the past 24 hours, satisfaction, and adverse effects of treatment. RESULTS Catheter placement was 100% successful; no procedure had to be aborted during placement because of inability to place catheter. The average time for catheter placement was 11.8 minutes (range, 3-19 minutes). There were 3 adverse events, including one accidental catheter removal in PACU, one dressing failure requiring removal of catheter on POD 2, and one case of Horner’s Syndrome. There were no signs of infection, local anesthetic toxicity, or shortness of breath described in any patients. Short acting opioids (Alfentanil and Fentanyl) were used intraoperatively in all but 2 patients, with average dose of 8.5mcg/kg Alfentanil and 0.7mcg/kg Fentanyl. Two patients received long-acting opioids intraoperatively. The mean PACU maximum pain score was 1.4 (range, 0-9). Only 3 patients received rescue intravenous morphine in the PACU with a mean dose of 0.035mg/kg. Mean PACU LOS was 112 minutes (range, 85 – 210 minutes). No patients required any rescue PONV medications in the PACU. Mean pain scores for POD 1- 3 at rest were 2.1, 2.3, and 2.6, respectively; scores with movement were 3.6, 3, and 4.4, respectively. Mean doses (i.e., times of administration) of oxycodone on POD 1-3 respectively were 1.5, 1.3 and 0.5 (range 0-5 doses). All but one patient was very satisfied and would have the catheter placed again. CONCLUSIONS Outpatient use of continuous interscalene catheters in pediatric and adolescent patients provides excellent intraoperative and early postoperative analgesia during and following shoulder surgery. Their routine use should be considered as they minimize perioperative opioid use with minimal adverse events, adding only small delay to surgical time.

Background: Continuous femoral nerve blocks (cFNB) have become a popular method for post-operative analgesia for patients undergoing anterior cruciate ligament reconstruction (ACLR). However, early weight-bearing and the return of quadriceps function favor a motor sparing block, such as a continuous adductor canal nerve block (cACB). Hypothesis/Purpose: We retrospectively compared cACB to cFNB in adolescent patients undergoing ACLR, assessing early post-operative pain scores, narcotic usage, and patient satisfaction; and return of quadriceps function and sports readiness at six months post-surgery. We hypothesized that cACB compared to cFNB would result in in a greater likelihood of sports readiness at six months without having compromised analgesia in the early post-operative period. Methods: We retrospectively reviewed a consecutive series of adolescent patients who underwent ACLR between January 2016 and September 2018 and received either a cACB or cFNB for post-operative pain management. Patient demographic and surgical data, post-operative pain scores, opioid consumption, satisfaction and complications, dates and results of the Return to Sports (RTS) evaluations were collected from the medical record. Comparisons of categorical and continuous variables between groups were made using the χ 2 test, Spearman correlation test, and one-way ANOVA with Bonferroni adjustment. Results: Ninety-one patients (53 with cFNB, 38 with cACB) were reviewed for post-operative analgesia outcomes and quadriceps function at six months and beyond. Analysis of demographic and surgical data revealed no difference in the make-up of the two groups. There were no significant differences between groups in the total oxycodone use PODs 1-3 (p = 0.213), daily post-operative pain scores (p > 0.25), or satisfaction with the blocks (p = 0.93). There was no difference in time to RTS nor in the percentage of patients who achieved a 90% limb symmetry index for quadriceps strength when comparing the two groups at the six-month mark and beyond (p = 0.384). Conclusions: We found no difference in post-operative analgesic requirements and high satisfaction in both groups when comparing patients who underwent ACLR with hamstring autograft with a cACB to those who underwent a similar procedure with a cFNB. Readiness for return to sports and return of quadriceps function at six months and beyond does not appear to vary with regional technique, either cACB or cFNB, employed at surgery.