Profiling commenters on mental health-related online forums : a methodological example focusing on eating disorder-related commenters

Background Understanding the characteristics of commenters on mental health-related online forums is vital for the development of effective psychological interventions in these communities. The way in which commenters interact can enhance our understanding of their characteristics. Objective Using eating disorder-related (EDR) forums as an example, this study details a methodology that aimed to determine subtypes of mental health-related forums, and profile their commenters based on the other forums to which they contributed. Methods The researchers identified all public EDR-forums (with ≥500 contributing commenters between March 2017 and February 2018) on a large online discussion platform (Reddit). A mixed-methods approach comprising network analysis with community-detection, text-mining and manual review identified subtypes of EDR-forums. For each subtype, another network analysis with community-detection was conducted using the EDR-forum commenter-overlap between 50 forums on which the commenters also commented. The topics of forums in each detected community were then manually reviewed to identify the shared interests of each subtype of EDR-forum commenters. Results Six subtypes of EDR-forums were identified, to which 14024 commenters had contributed. The results focus on two subtypes – pro-eating disorder, and thinspiration – and communities of commenters within both subtypes. Within the pro-eating disorder subtype, three communities of commenters were detected that related to the body and eating, mental health, and women, appearance and mixed topics. Regarding the thinspiration group, 78% of commenters had also commented on pornographic forums, and 17% had contributed to pro-eating disorder forums. Conclusions The article exemplifies a methodology that provides insight into subtypes of mental health-related forums, and the characteristics of their commenters. The findings have implications for future research, and online psychological interventions. With the publicly available data and code provided, researchers can easily reproduce the analyses, or utilise the methodology to investigate other mental health-related forums

Evidence for feasibility of implementing online brief cognitive‐behavioral therapy for eating disorder pathology in the workplace

Objective: CBT-T is a brief (10-week) cognitive-behavioral therapy for non-underweight eating disorders. This report describes the findings from a single center, single group, feasibility trial of online CBT-T in the workplace as an alternative to health service settings. Method: This trial was approved by the Biomedical and Scientific Research Ethics committee, University of Warwick, UK (reference 125/20-21) and was registered with ISRCTN (reference number: ISRCTN45943700). Recruitment was based on self-reported eating and weight concerns rather than diagnosis, potentially enabling access to treatment for employees who have not previously sought help and for those with sub-threshold eating disorder symptoms. Assessments took place at baseline, mid-treatment (week 4), post-treatment (week 10), and follow-up (1 and 3 months post-treatment). Participant experiences following treatment were assessed using quantitative and qualitative approaches. Results: For the primary outcomes, pre-determined benchmarks of high feasibility and acceptability were met, based on recruiting >40 participants (N = 47), low attrition (38%), and a high attendance rate (98%) over the course of the therapy. Participant experiences revealed low previous help-seeking for eating disorder concerns (21%). Qualitative findings indicated a wide range of positive impacts of the therapy and the workplace as the therapeutic setting. Analysis of secondary outcomes for participants with clinical and sub-threshold eating disorder symptoms showed strong effect sizes for eating pathology, anxiety and depression, and moderate effect sizes for work outcomes. Discussion: These pilot findings provide a strong rationale for a fully powered randomized controlled trial to determine the effectiveness of CBT-T in the workplace. Public Significance: This study demonstrates the feasibility of implementing an eating disorders intervention (online CBT-T) in the workplace as an alternative to traditional healthcare settings. Recruitment was based on self-reported eating and weight concerns rather than diagnosis, potentially enabling access to treatment for employees who had not previously sought help. The data also provide insights into recruitment, acceptability, effectiveness, and future viability of CBT-T in the workplace

A feasibility study of the delivery of online brief cognitive-behavioral therapy (CBT-T) for eating disorder pathology in the workplace

Objective CBT-T is a brief (10 sessions) version of cognitive behavioral therapy for non-underweight eating disorders. This report describes the protocol for a single center, single group, feasibility trial of online CBT-T in the workplace as an alternative to the health-service setting. By offering mental health services for eating disorders in the workplace, greater accessibility and increased help-seeking behaviors could be achieved. Method Treatment will be delivered online over 10 weeks and offered to employees based on self-referral rather than meeting diagnostic criteria, making treatment available to employees with sub-threshold eating disorder symptoms. Results Assessments will be conducted at baseline, mid-treatment (week 4), posttreatment (week 10) and at follow-up (1 month and 3 months posttreatment). For the primary outcome, measures will include recruitment, attrition and attendance data using pre-set benchmarks to determine high, medium or low feasibility and acceptability. Qualitative participant experiences data will be analyzed using thematic analysis. Impact on work engagement and effect sizes will be determined from secondary outcome measures; the latter enabling sample size calculations for future study. Discussion These pilot data will provide insights to recruitment, acceptability, effectiveness and viability of a future fully powered clinical trial of online CBT-T in the workplace. Public Significance Statement This study will present feasibility data from an eating disorders intervention (online CBT-T) using the workplace as an alternative to the healthcare setting to recruit and treat workers. Recruitment will be based on self-reported eating and weight concerns rather than diagnosis potentially enabling treatment to employees who have not previously sought help. The data will also provide insights to recruitment, acceptability, effectiveness, and future viability of CBT-T in the workplace

Predicting depression using electronic health records : a systematic review

Background Depression is one of the most significant health conditions in personal, social, and economic impact. The aim of this review is to summarize existing literature in which machine learning (ML) methods have been used in combination with Electronic Health Records (EHRs) for prediction of depression. Methods Systematic literature searches were conducted within arXiv, PubMed, PsycINFO, Science Direct, SCOPUS and Web of Science electronic databases. Searches were restricted to information published after 2010 (from 1st January 2011 onwards) and were updated prior to the final synthesis of data (27th January 2022). Results Following the PRISMA process, the initial 744 studies were reduced to 19 eligible for detailed evaluation. Data extraction identified machine learning methods used, types of predictors used, the definition of depression, classification performance achieved, sample size, and benchmarks used. Area Under the Curve (AUC) values more than 0.9 were claimed, though the average was around 0.8. Regression methods proved as effective as more developed machine learning techniques. Limitations The categorization, definition, and identification of the numbers of predictors used within models was sometimes difficult to establish, Studies were largely Western Educated Industrialised, Rich, Democratic (WEIRD) in demography. Conclusion This review supports the potential use of machine learning techniques with EHRs for the prediction of depression. All the selected studies used clinically based, though sometimes broad, definitions of depression as their classification criteria. The reported performance of the studies was comparable to or even better than that found in primary care. There are concerns over the generalizability and interpretability

Engagement with MyFitnessPal in eating disorders : qualitative insights from online forums

Objective Using calorie‐counting and fitness‐tracking technologies is concerning in relation to eating disorders. While studies in this area typically assess one aspect of use (e.g., frequency), engagement with a device or application is more complex. Consequently, important relationships between the use of these technologies and the eating disorder symptomatology might remain undetected. The current study therefore used comments from online eating disorder‐related forums to generate comprehensive qualitative insights into engagement with a popular calorie‐counting and fitness‐tracking application, MyFitnessPal. Method First, we extracted every comment mentioning MyFitnessPal made on three eating disorder‐related forums between May 2015 and January 2018 (1,695 comments from 920 commenters). Then, we conducted an inductive thematic analysis using these comments to identify important aspects of engagement with MyFitnessPal. Results The analyses resulted in three themes: Preventing misuse, describing ways in which MyFitnessPal attempts to prevent pathological use and actions taken by users to circumvent its interventions; Accuracy, outlining distrust of MyFitnessPal's accuracy and ways in which perceived inaccuracy is reduced or compensated for; and Psychosocial factors, comprising cognitive, behavioral, and social factors that influence, or are influenced by, engagement with MyFitnessPal. Discussion The qualitative insights provide a detailed overview of how people with high levels of eating disorder symptomatology likely engage with MyFitnessPal. The insights can be used as a basis to develop valid, quantitative assessment of pathological patterns of engagement with calorie‐counting and fitness‐tracking technologies. The findings can also provide clinicians with insight into how their patients likely engage with, and are affected by, these devices and applications

A direct-to-drive neural data acquisition system

Driven by the increasing channel count of neural probes, there is much effort being directed to creating increasingly scalable electrophysiology data acquisition (DAQ) systems. However, all such systems still rely on personal computers for data storage, and thus are limited by the bandwidth and cost of the computers, especially as the scale of recording increases. Here we present a novel architecture in which a digital processor receives data from an analog-to-digital converter, and writes that data directly to hard drives, without the need for a personal computer to serve as an intermediary in the DAQ process. This minimalist architecture may support exceptionally high data throughput, without incurring costs to support unnecessary hardware and overhead associated with personal computers, thus facilitating scaling of electrophysiological recording in the future.National Institutes of Health (U.S.) (Grant 1DP1NS087724)National Institutes of Health (U.S.) (Grant 1R01DA029639)National Institutes of Health (U.S.) (Grant 1R01NS067199)National Institutes of Health (U.S.) (Grant 2R44NS070453)National Institutes of Health (U.S.) (Grant R43MH101943)New York Stem Cell FoundationPaul Allen FoundationMassachusetts Institute of Technology. Media LaboratoryGoogle (Firm)United States. Defense Advanced Research Projects Agency (HR0011-14-2-0004)Hertz Foundation (Myhrvold Family Fellowship

The Nature of Infrared Emission in the Local Group Dwarf Galaxy NGC 6822 As Revealed by Spitzer

We present Spitzer imaging of the metal-deficient (Z ~30% Z_sun) Local Group dwarf galaxy NGC 6822. On spatial scales of ~130 pc, we study the nature of IR, H alpha, HI, and radio continuum emission. Nebular emission strength correlates with IR surface brightness; however, roughly half of the IR emission is associated with diffuse regions not luminous at H alpha (as found in previous studies). The global ratio of dust to HI gas in the ISM, while uncertain at the factor of ~2 level, is ~25 times lower than the global values derived for spiral galaxies using similar modeling techniques; localized ratios of dust to HI gas are about a factor of five higher than the global value in NGC 6822. There are strong variations (factors of ~10) in the relative ratios of H alpha and IR flux throughout the central disk; the low dust content of NGC 6822 is likely responsible for the different H alpha/IR ratios compared to those found in more metal-rich environments. The H alpha and IR emission is associated with high-column density (> ~1E21 cm^-2) neutral gas. Increases in IR surface brightness appear to be affected by both increased radiation field strength and increased local gas density. Individual regions and the galaxy as a whole fall within the observed scatter of recent high-resolution studies of the radio-far IR correlation in nearby spiral galaxies; this is likely the result of depleted radio and far-IR emission strengths in the ISM of this dwarf galaxy.Comment: ApJ, in press; please retrieve full-resolution version from http://www.astro.wesleyan.edu/~cannon/pubs.htm

Do mental health symptoms during the pandemic predict university non-completion in a sample of UK students? A prospective study

Mental health symptoms are highly prevalent in university students and have been further exacerbated following the COVID-19 pandemic. The aim of this study was to examine the prospective prediction of five mental health symptoms (anxiety, depression, insomnia, suicidality, substance misuse risk) on university non-completion. Baseline data were collected between July and September 2020 following the first UK lockdown and prior to the 2020/2021 academic year. Univariate binary logistic regression analyses were performed using data from 147 participants who were due to graduate at the end of the 2020/2021 academic year. Only substance misuse risk was found to predict university non-completion, with students with a higher risk of substance misuse more likely to not complete their university course. There appears to be an association between substance misuse risk and university non-completion; however, this was attenuated once study characteristic covariates (study level, changes in study hours and study engagement) were included, indicating possible associations between these variables. Future research should further consider the role of substance use in this population and the relationship with study characteristics, engagement and university completion

Optimal model complexity for terrestrial carbon cycle prediction

The terrestrial carbon cycle plays a critical role in modulating the interactions of climate with the Earth system, but different models often make vastly different predictions of its behavior. Efforts to reduce model uncertainty have commonly focused on model structure, namely by introducing additional processes and increasing structural complexity. However, the extent to which increased structural complexity can directly improve predictive skill is unclear. While adding processes may improve realism, the resulting models are often encumbered by a greater number of poorly determined or over-generalized parameters. To guide efficient model development, here we map the theoretical relationship between model complexity and predictive skill. To do so, we developed 16 structurally distinct carbon cycle models spanning an axis of complexity and incorporated them into a model–data fusion system. We calibrated each model at six globally distributed eddy covariance sites with long observation time series and under 42 data scenarios that resulted in different degrees of parameter uncertainty. For each combination of site, data scenario, and model, we then predicted net ecosystem exchange (NEE) and leaf area index (LAI) for validation against independent local site data. Though the maximum model complexity we evaluated is lower than most traditional terrestrial biosphere models, the complexity range we explored provides universal insight into the inter-relationship between structural uncertainty, parametric uncertainty, and model forecast skill. Specifically, increased complexity only improves forecast skill if parameters are adequately informed (e.g., when NEE observations are used for calibration). Otherwise, increased complexity can degrade skill and an intermediate-complexity model is optimal. This finding remains consistent regardless of whether NEE or LAI is predicted. Our COMPLexity EXperiment (COMPLEX) highlights the importance of robust observation-based parameterization for land surface modeling and suggests that data characterizing net carbon fluxes will be key to improving decadal predictions of high-dimensional terrestrial biosphere models.</p

Auditory but Not Audiovisual Cues Lead to Higher Neural Sensitivity to the Statistical Regularities of an Unfamiliar Musical Style

It is still a matter of debate whether visual aids improve learning of music. In a multisession study, we investigated the neural signatures of novel music sequence learning with or without aids (auditory-only: AO, audiovisual: AV). During three training sessions on 3 separate days, participants (nonmusicians) reproduced (note by note on a keyboard) melodic sequences generated by an artificial musical grammar. The AV group (n = 20) had each note color-coded on screen, whereas the AO group (n = 20) had no color indication. We evaluated learning of the statistical regularities of the novel music grammar before and after training by presenting melodies ending on correct or incorrect notes and by asking participants to judge the correctness and surprisal of the final note, while EEG was recorded. We found that participants successfully learned the new grammar. Although the AV group, as compared to the AO group, reproduced longer sequences during training, there was no significant difference in learning between groups. At the neural level, after training, the AO group showed a larger N100 response to lowprobability compared to high-probability notes, suggesting an increased neural sensitivity to statistical properties of the grammar; this effect was not observed in the AV group. Our findings indicate that visual aids might improve sequence reproduction while not necessarily promoting better learning, indicating a potential dissociation between sequence reproduction and learning. We suggest that the difficulty induced by auditory-only input during music training might enhance cognitive engagement, thereby improving neural sensitivity to the underlying statistical properties of the learned material