Assess Depression Severity Research Articles

Predictive Analytics in Mental Health: A Machine Learning Approach to Assessing Depression Severity

Contemporary healthcare reforms are being significantly shaped by the ongoing advance-ments in technology. One area where these advancements are proving crucial is in the un-derstanding and treatment of depression, which is increasingly emerging as a substantial public health concern. To address this issue, there is a growing interest in leveraging novel research methods and therapeutic approaches to identify the contributing factors to de-pression. This study adopts an innovative approach, utilizing machine learning techniques to carry out an exhaustive examination of diverse data sources. The primary aim is to gain a profound comprehension of the complex interplay between various facets of individuals' quality of life and the presence of depression. To undertake this investigation, the research-ers have harnessed the National Health and Nutrition Examination Survey (NHANES) Da-taset provided by the Centers for Disease Control and Prevention, a rich source of valuable health-related information.In this study, the focus is on exploring the behavioral and social dimensions of numerous subjects and their intricate connections to depression. To achieve this, a diverse array of machine learning classifiers has been deployed, including the Decision Tree Classifier, Ran-dom Forest Classifier, Gaussian Naive Bayes, KNN Classifier, Logistic Regression, Support Vector Machine Classifier, and Multilayer Perceptrons. By applying these classifiers, the re-searchers aim to assess their performance across various metrics, providing valuable insights into which models are best suited to discern the connection between depression and as-pects of quality of life.

Association between depression and diabetes among American adults using NHANES data from 2005 to 2020

Depression impairs self-management in diabetic patients, exacerbates insulin resistance, and elevates glycated hemoglobin (HbA1c) levels, thereby increasing diabetes risk. This study analyzed data from 30,386 participants in the National Health and Nutrition Examination Survey (NHANES), assessing depression severity using the 9-item Patient Health Questionnaire (PHQ-9) and evaluating diabetes status through clinical markers such as HbA1c, random blood glucose, and fasting blood glucose. Participants were stratified by depression severity and diabetes status to examine the relationship between depression and diabetes risk. We applied descriptive statistics, logistic regression models, subgroup analyses, and restricted cubic spline (RCS) modeling to explore this association. The results revealed that greater depression severity was significantly associated with increased diabetes incidence, elevated HbA1c, fasting glucose, and insulin levels. Multivariate regression analysis confirmed a consistent positive correlation between depression severity and diabetes risk. Subgroup analyses further identified significant relationships between depression and various demographic and behavioral factors, including gender, race, BMI, smoking status, and prediabetic conditions. Additionally, the RCS model demonstrated a clear increase in diabetes risk with rising PHQ-9 scores. In conclusion, our study demonstrates that the severity of depression is positively correlated with the risk of diabetes, and this association may be closely linked to various glycemic and lipid metabolic parameters.

The cerebral and cognitive changes after intermittent theta burst stimulation (iTBS) treatment for depression: study protocol for a randomized double-blind sham-controlled trial

BackgroundThe therapeutic use of intermittent theta burst stimulation (iTBS) delivered to the left dorsolateral prefrontal cortex (LDLPFC) is a relatively new but promising treatment option for depression. There is a need for more knowledge on the mechanisms involved in its antidepressant effects.MethodsThis is a single-centre, prospective, randomized, double-blind, placebo-controlled trial with two arms, iTBS and sham iTBS. Adult outpatients with unipolar major depressive disorder of at least moderate severity will undergo cognitive assessment with an N-back task (0-back and 2-back), functional and structural magnetic resonance imaging and assessment of depression severity before and after brain stimulation. Neuronavigated iTBS or sham stimulation will be targeted at the LDPFC once a day for 10 consecutive workdays. ITBS will be delivered with the parameters 120% of resting motor threshold, triplet 50 Hz bursts repeated at 5 Hz; 2 s on and 8 s off, 600 pulses per session with a total duration of 3 min 9 s. The severity of depression will be measured with the Montogomery Aasberg Depression Rating Scale and the Beck Depression Inventory – second edition.In the iTBS group relative to sham, we expect significant antidepressant effects and improved N-back performance, associated with increased integrity in white matter tracts functionally connected with the LDLPFC and emotion regulation areas within the rostral anterior cingulate cortices, alongside potential increases in cortical thickness in these regions. On functional imaging, we expect to observe increased brain activity in the LDPFC during the performance of the N-back condition with higher cognitive load (2-back) in the iTBS group relative to sham.DiscussioniTBS is a promising, time-efficient, and considered a safe treatment option for depression according to existing evidence. This trial aims to assess the neurocognitive impact of a 2-week, once-daily iTBS compared to sham iTBS, targeting the LDLPFC in depressed adult outpatients. The study investigates the relationships between changes in cerebral measures and cognitive performance on an N-back task in relation to the antidepressant effect following iTBS. This trial delves into the neurocognitive mechanisms of iTBS in depression, potentially offering novel scientific insights into its treatment effects and mechanisms of action.Trial registrationClinicalTrials.gov NCT06534684. Retrospectively registered on August 1st 2024.

Disruptions in Sleep Health and Independent Associations with Psychological Distress in Close Family Members of Cardiac Arrest Survivors: A Prospective Study

While recent guidelines have noted the deleterious effects of poor sleep on cardiovascular health, the upstream impact of cardiac arrest-induced psychological distress on sleep health metrics among families of cardiac arrest survivors remains unknown. Sleep health of close family members of consecutive patients with cardiac arrest admitted to an academic center (August 16, 2021-June 28, 2023) was self-reported using the Pittsburgh Sleep Quality Index (PSQI). The baseline PSQI, focused on sleep in the month before cardiac arrest, was administered during hospitalization and repeated 1 month after cardiac arrest alongside the Patient Health Questionnaire-8 (PHQ-8) to assess depression severity. Multivariable linear regressions analyzed associations between total PHQ-8 scores and changes in global PSQI scores between baseline and 1 month, with higher scores indicating deterioration. A prioritization exercise explored potential interventions categorized into the family's information and well-being needs to reduce psychological distress. In our sample of 102 close family members (mean age 52 ± 15 years, 70% female, 21% Black, 33% Hispanic), mean global PSQI scores showed a significant decline between baseline and 1 month after cardiac arrest (6.2 ± 3.8 vs. 7.4 ± 4.1; P < .01). This deterioration was notable for sleep quality, duration, and daytime dysfunction. Higher PHQ-8 scores were significantly associated with higher change in PSQI scores after adjusting for family members' age, sex, race/ethnicity, prior psychiatric history, and patient's discharge disposition (B = 0.4 [95% CI 0.24-0.48]; P < .01, β = 0.5). Most families expressed a higher priority for information-based interventions over well-being needs to help alleviate psychological distress during the first month following cardiac arrest (76% vs. 34%, P < .01). A significant sleep health decline was observed among close family members of cardiac arrest survivors during the acute period, with psychological distress associated with this disruption. Understanding these temporal associations will help guide the development of targeted interventions to support families during this uncertain time.

Explainable Multi-Layer Dynamic Ensemble Framework Optimized for Depression Detection and Severity Assessment.

Depression is a pervasive mental health condition, particularly affecting older adults, where early detection and intervention are essential to mitigate its impact. This study presents an explainable multi-layer dynamic ensemble framework designed to detect depression and assess its severity, aiming to improve diagnostic precision and provide insights into contributing health factors. Using data from the National Social Life, Health, and Aging Project (NSHAP), this framework combines classical machine learning models, static ensemble methods, and dynamic ensemble selection (DES) approaches across two stages: detection and severity prediction. The depression detection stage classifies individuals as normal or depressed, while the severity prediction stage further classifies depressed cases as mild or moderate-severe. Finally, a confirmation depression scale prediction model estimates depression severity scores to support the two stages. Explainable AI (XAI) techniques are applied to improve model interpretability, making the framework more suitable for clinical applications. The framework's FIRE-KNOP DES algorithm demonstrated high efficacy, achieving 88.33% accuracy in depression detection and 83.68% in severity prediction. XAI analysis identified mental and non-mental health indicators as significant factors in the framework's performance, emphasizing the value of these features for accurate depression assessment. This study emphasizes the potential of dynamic ensemble learning in mental health assessments, particularly in detecting and evaluating depression severity. The findings provide a strong foundation for future use of dynamic ensemble frameworks in mental health assessments, demonstrating their potential for practical clinical applications.

Correlation Between Anxiety, Depression and Hemodynamic Changes in Office-Based Laryngeal Surgery.

To investigate the correlation between anxiety, depression, and hemodynamic changes during office-based laryngeal surgery (OBLS). All patients undergoing OBLS between February 2024 until July 2024 were invited to participate in the study. Participants had their vital signs recorded throughout the procedure at a 5-min interval. They also had to fill the Generalized Anxiety Disorder scale-7 (GAD-7) to assess anxiety severity and the Patient Health Questionnaire-9 (PHQ-9) to assess depression severity. Demographic data included age, gender, history of smoking, history of reflux disease, history of cardiovascular diseases, type and duration of procedure. A total of 45 patients were recruited in the study. During OBLS, 35.5% of patients developed hypertension and 28.9% developed tachycardia. There was a significant increase in mean systolic blood pressure (SBP) by 30.16 mmHg (p < 0.001), in mean diastolic blood pressure (DBP) by 31.44 mmHg (p < 0.001), and in mean heart rate (HR) by 14.2 beats per minute (p < 0.001). There was also a significant decrease in the mean O2 saturation by 0.4% (p = 0.001). There was no correlation between anxiety and SBP, DBP, HR, and O2 (r < 0.1). There was also no correlation between depression levels and SBP, DBP, HR, O2 (r < 0.1). There was a significant increase in the mean SBP, DP, and HR and a significant decrease in the O2 saturation in patients undergoing OBLS. However, there was no correlation between anxiety, depression and the changes in these vital signs. Future investigations are needed to understand the causes of hemodynamic instability in OBLS. 2 Laryngoscope, 2024.

Changes in P300 amplitude to negative emotional stimuli correlate with treatment responsiveness to sertraline in adolescents with depression

ObjectiveAdolescents with depression is characterized by high rates of recurrence and functional impairment, with a significant association with suicide risk. Antidepressants are commonly prescribed to treat depression, yet few reproducible neurobiological markers for depression and antidepressant treatment response have been identified. Therefore, discovering a stable and reliable neurobiological marker holds significant value for both the clinical diagnosis and treatment of depression in adolescents. MethodsOne hundred and seven patients with major depressive disorder (MDD group, 30 males, 77 females, mean age: 14.80 years), and 25 healthy subjects (HC group, 13 males, 12 females, mean age: 15.72 years) were recruited to perform a two-choice oddball task related to negative emotional cues. All participants completed a self-administered questionnaire to gather demographic information. A trained psychiatrist administered the Hamilton Depression Scale (HAMD-17) to assess depression severity. Of the 107 adolescents with depression, 61 received antidepressant medication for 8 weeks, and 61 of these patients were followed up. Multichannel EEG was recorded continuously from 64 scalp electrodes using the Curry 8 system. EEG signal preprocessing and analysis was performed offline using the EEGLAB toolbox in MATLAB. The ERP component characteristics associated with emotional processing were extracted from the difference waves and statistically analyzed. ResultsAdolescents with depression exhibited significantly larger P300 amplitudes than healthy controls in response to both neutral and negative emotional cues. Following sertraline treatment, both depression scores and P300 amplitudes decreased significantly in adolescents with depression. Moreover, a strong positive correlation was observed between changes in depression scores and changes in P300 amplitude in response to negative emotional cues before and after treatment. ConclusionsChanges in neural reactivity to negative emotional stimuli among adolescents with depression can be selectively modulated by sertraline and are significantly associated with improvements in depressive symptoms. SignificanceChanges in P300 amplitude to negative emotional stimuli significantly correlate with treatment responsiveness to sertraline in adolescents with depression.

8561 Association Of Alopecia And Hirsutism With Severity Of Depression In A Sample Of Hispanic Women With Polycystic Ovary Syndrome

Abstract Disclosure: C.M. Casas Loyola: None. Y.G. Rivera-Rodriguez: None. B. Marin Rodriguez: None. J.A. Roque Torres: None. L.A. Gonzalez Rodriguez: Research Investigator; Self; Lilly USA, LLC. J. Romaguera: None. Purpose: Polycystic ovary syndrome (PCOS), a highly prevalent endocrine disorder in females has been associated with several chronic diseases. Females with PCOS have been found with a higher prevalence of mental disorders. Depression and its severity have been associated with the development and progression of cardiovascular disease. We aimed to assess depression severity and its association with PCOS related comorbidities such as alopecia and hirsutism. Methods: A cross sectional study of females aged 21-45 years was conducted in the endocrinology clinics at the RCM-Clinics. PCOS was diagnosed using the Rotterdam criteria, hirsutism was defined by modified Ferriman-Gallwey score (mFG ≥ 9), alopecia was defined using the Ludwig scale and Depression using the Patient Health Questionnaire (PHQ9). Severity of depression and presence of alopecia and hirsutism were compared among groups. Results: A total of 112 patients were included in this study (85 patients PCOS+ and 27 patients PCOS-). Mean age was 32 ± 6 years. Severity of depression in PCOS- patients was: 44% none to minimal, 30% mild and 27% moderate to severe. Severity of depression in PCOS+ patients was: 28% none to minimal, 35% mild and 36% moderate to severe. Participants with alopecia and PCOS+ had a greater percentage of moderate to severe depression (58%) compared to the control group without alopecia (30%) (p value&amp;lt;0.05). No differences were observed between hirsutism and severity of depression among groups. Conclusion: Our study showed an association between alopecia and severity of depression in patients with PCOS+. The presence of hirsutism was not associated to severity of depression. Currently, there are more available treatments for hirsutism than for alopecia. For this reason, patients with hirsutism have more opportunities to manage their condition than patients with alopecia, which often require more invasive procedures. The presence of alopecia can be considered a risk factor for developing major depressive disorder in patients with PCOS. Treatment of PCOS-related abnormalities should be emphasized soon after diagnosis. Evaluation of mental health is imperative in our PCOS patients. Presentation: 6/3/2024

Siamese Neural Network for Speech-Based Depression Classification and Severity Assessment

The evaluation of an individual’s mental health and behavioral functioning, known as psychological assessment, is generally conducted by a mental health professional. This process aids in diagnosing mental health conditions, identifying suitable treatment options, and assessing progress during treatment. Currently, national health systems are unable to cope with the constantly growing demand for such services. To address and expedite the diagnosis process, this study suggests an AI-powered tool capable of delivering understandable predictions through the automated processing of the captured speech signals. To this end, we employed a Siamese neural network (SNN) elaborating on standardized speech representations free of domain expert knowledge. Such an SNN-based framework is able to address multiple downstream tasks using the same latent representation. Interestingly, it has been applied both for classifying speech depression as well as assessing its severity. After extensive experiments on a publicly available dataset following a standardized protocol, it is shown to significantly outperform the state of the art with respect to both tasks. Last but not least, the present solution offers interpretable predictions, while being able to meaningfully interact with the medical experts.

Prevalence of Antidepressant Prescriptions for Community-Dwelling Adults Diagnosed with Depressive Disorder in the UK: A Systematic Review.

The UK National Institute for Health and Care Excellence (NICE) guidance for the treatment of depression is widely followed and has international influence. According to these guidelines, antidepressant medications are recommended for moderate to severe depression. Nonetheless, antidepressants are increasingly prescribed, including for cases of subthreshold and mild depression. This may indicate that a proportion of depressed patients uses pharmacological interventions with unclear evidence-base, though other factors such as physical and mental health comorbidities need to be accounted for. This study aims to investigate the prevalence and trends of antidepressant prescriptions among community-dwelling adults diagnosed with depression according to NICE recommendations. We conducted a systematic review of PsycInfo, PubMed/MEDLINE, and Scopus databases. Observational studies reporting on the prevalence of antidepressant treatments in UK adults diagnosed with depression were sought. Fifteen studies were included. The prevalence of antidepressants for depression treatment ranged from 30.8 to 95% and mainly concerned selective serotonin reuptake inhibitors (SSRIs) among classes of antidepressant drugs. Little information about depression severity as well as comorbid conditions was reported. High prevalence rates of antidepressant drug use highlight the widespread adoption of pharmacological interventions, while also raising concerns about compliance with NICE guidelines. Careful assessment of depressive illness severity and comorbidities is needed to ensure the delivery of adequate care to people with depression. Systematic Review Registration Number (PROSPERO) CRD42023448152.

Investigating thyroid function and iodine status in adolescents with and without paediatric major depressive disorder.

Depression has been associated with subclinical hypothyroidism and altered hypothalamic-pituitary-thyroid axis functioning. Adequate iodine nutrition is essential for healthy thyroid functioning. We therefore determined associations of iodine and thyroid status with paediatric major depressive disorder (pMDD) among Swiss adolescents and explored whether associations are sex-specific and mediated by stress. We conducted a matched case-control study in 95 adolescents with diagnosed pMDD and 95 healthy controls. We assessed depression severity using the Children's Depression Rating Scale-Revised and stress using the perceived stress scale (PSS) and measuring hair cortisol levels. We determined iodine status by measuring urinary iodine concentrations (UIC) and thyroid status by thyroid-stimulating hormone (TSH) and free thyroxine (FT4) in serum. Median (IQR) UIC did not differ between cases (121 (87, 174) µg/l) and controls (114 (66, 183) μg/l, P = 0·3). Median TSH and FT4 were lower in cases than controls (TSH: 1·36 (0·91, 2·00) mlU/l v. 1·50 (1·18, 2·06) mlU/l, P = 0·039; FT4: 14·7 (12·9, 16·9) pmol/l v. 15·7 (14·3, 17·2) pmol/l, P = 0·004). The prevalence of hypothyroxinaemia (normal TSH; low FT4) was higher among female cases than controls (21 % v. 4%, P = 0·006). PSS scores were higher while hair cortisol was lower in cases than controls (PSS: 25 (20, 28) v. 11 (7, 15), P < 0·001; cortisol: 2·50 (1·34, 3·57) pg/mg v. 3·23 (1·79, 4·43) pg/mg, P = 0·044). After adjusting for confounders, the associations of TSH and hair cortisol with pMDD were no longer significant. Furthermore, TSH and FT4 were not associated with PSS scores and hair cortisol levels. Summarising, iodine nutrition was adequate for adolescents with and without pMDD. However, FT4 concentrations were lower in those with pMDD, and 1 in 5 female adolescents with pMDD were hypothyroxinaemic.

Describing the burden of disease amongst inpatients with treatment resistant major depressive disorder in Australia.

To describe the quality of life and clinical characteristics of treatment-resistant depression (TRD) patients in an Australian patient cohort recruited cross-sectionally during admission. Inpatients admitted for TRD treatment completed a quality of life questionnaire (AQoL-8D) and a depression severity assessment (HAM-D). A chart review and patient interview occurred for demographic and patient characteristics. Comparisons between the mean AQoL-8D scores of the study population and Australian population norms occurred. 79 TRD inpatients (70.9% female), mean age of 44.8 ± 14.9 years, were recruited, with 78.5% having an anxiety disorder, 48.1% post-traumatic stress disorder, and 30.4% a personality disorder. Adjunctive to antidepressants, 92.4% were taking antipsychotics and 55.7% were taking mood stabilisers. Approximately 42% of patients received transcranial magnetic stimulation, and 35.4% received electroconvulsive therapy. Mean HAM-D score was 20.3 ± 5.2, and AQoL-8D score (120.1 ± 16.5) was significantly higher than Australian population norms (p < .001) indicating reduced quality of life. Personal and clinical characteristics of patients hospitalised for TRD were similar to TRD globally with impaired quality of life relative to the general Australian population. TRD patients on average presented with moderate/severe depression, highlighting the need for greater support for these individuals.

Mobile sensing-based depression severity assessment in participants with heterogeneous mental health conditions

Mobile sensing-based depression severity assessment could complement the subjective questionnaires-based assessment currently used in practice. However, previous studies on mobile sensing for depression severity assessment were conducted on homogeneous mental health condition participants; evaluation of possible generalization across heterogeneous groups has been limited. Similarly, previous studies have not investigated the potential of free-living audio data for depression severity assessment. Audio recordings from free-living could provide rich sociability features to characterize depressive states. We conducted a study with 11 healthy individuals, 13 individuals with major depressive disorder, and eight individuals with schizoaffective disorders. Communication logs and location data from the participants’ smartphones and continuous audio recordings of free-living from a wearable audioband were obtained over a week for each participant. The depression severity prediction model trained using communication log and location data features had a root mean squared error (rmse) of 6.80. Audio-based sociability features further reduced the rmse to 6.07 (normalized rmse of 0.22). Audio-based sociability features also improved the F1 score in the five-class depression category classification model from 0.34 to 0.46. Thus, free-living audio-based sociability features complement the commonly used mobile sensing features to improve depression severity assessment. The prediction results obtained with mobile sensing-based features are better than the rmse of 9.83 (normalized rmse of 0.36) and the F1 score of 0.25 obtained with a baseline model. Additionally, the predicted depression severity had a significant correlation with reported depression severity (correlation coefficient of 0.76, p<\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$p<$$\\end{document}0.001). Thus, our work shows that mobile sensing could model depression severity across participants with heterogeneous mental health conditions, potentially offering a screening tool for depressive symptoms monitoring in the broader population.

The relationship between wearable-derived sleep features and relapse in Major Depressive Disorder

BackgroundChanges in sleep and circadian function are leading candidate markers for the detection of relapse in Major Depressive Disorder (MDD). Consumer-grade wearable devices may enable remote and real-time examination of dynamic changes in sleep. Fitbit data from individuals with recurrent MDD were used to describe the longitudinal effects of sleep duration, quality, and regularity on subsequent depression relapse and severity. MethodsData were collected as part of a longitudinal observational mobile Health (mHealth) cohort study in people with recurrent MDD. Participants wore a Fitbit device and completed regular outcome assessments via email for a median follow-up of 541 days. We used multivariable regression models to test the effects of sleep features on depression outcomes. We considered respondents with at least one assessment of relapse (n = 218) or at least one assessment of depression severity (n = 393). ResultsIncreased intra-individual variability in total sleep time, greater sleep fragmentation, lower sleep efficiency, and more variable sleep midpoints were associated with worse depression outcomes. Adjusted Population Attributable Fractions suggested that an intervention to increase sleep consistency in adults with MDD could reduce the population risk for depression relapse by up to 22 %. LimitationsLimitations include a potentially underpowered primary outcome due to the smaller number of relapses identified than expected. ConclusionOur study demonstrates a role for consumer-grade activity trackers in estimating relapse risk and depression severity in people with recurrent MDD. Variability in sleep duration and midpoint may be useful targets for stratified interventions.

Effects of open-label transdermal nicotine antidepressant augmentation on affective symptoms and executive function in late-life depression

BackgroundLate-life depression (LLD) is characterized by a poor response to antidepressant medications and diminished cognitive performance, particularly in executive functioning. There is currently no accepted pharmacotherapy for LLD that effectively treats both mood and cognitive symptoms. This study investigated whether transdermal nicotine augmentation of standard antidepressant medications benefitted mood and cognitive symptoms in LLD. MethodsNonsmoking participants aged 60 years or older with unremitted LLD on stable SSRI or SNRI medications (N = 29) received transdermal nicotine patches up to a 21 mg daily dose over 12 weeks. Clinical measures assessed depression severity, secondary affective symptoms, and cognitive performance. Nicotine metabolite concentrations were obtained from blood samples. ResultsDepression severity significantly decreased over the trial, with a 76 % response rate and 59 % remission rate. Change in depression severity was positively associated with nicotine exposure. Participants also exhibited improvement in self-reported affective symptoms (apathy, insomnia, rumination, and generalized anxiety symptoms), negativity bias, and disability. Executive function test performance significantly improved, specifically in measures of cognitive control, as did subjective cognitive performance. Adverse events were generally mild, with 75 % of the sample tolerating the maximum dose. ConclusionThe current study extends our previous pilot open-label trial in LLD, supporting feasibility and tolerability of transdermal nicotine patches as antidepressant augmentation. Although preliminary, this open-label study supports the potential benefit of transdermal nicotine patches for both mood and cognitive symptoms of LLD. Further research, including definitive randomized, blinded trials, is warranted to confirm these findings and explore long-term risk and benefit. Trial registrationThe study was registered with clinicaltrials.gov (NCT04433767).

Heterogeneity of Treatment Effects in Internet- and Mobile-Based Interventions for Depression

While the effects of internet- and mobile-based interventions (IMIs) for depression have been extensively studied, no systematic evidence is available regarding the heterogeneity of treatment effects (HTEs), indicating to what extent patient-by-treatment interactions exist and personalized treatment models might be necessary. To investigate the HTEs in IMIs for depression as well as their efficacy and effectiveness. A systematic search in Embase, MEDLINE, Central, and PsycINFO for randomized clinical trials and supplementary reference searches was conducted on October 13, 2019, and updated March 25, 2022. The search string included various terms related to digital psychotherapy, depression, and randomized clinical trials. Titles, abstracts, and full texts were reviewed by 2 independent researchers. Studies of all populations with at least 1 intervention group receiving an IMI for depression and at least 1 control group were eligible, if they assessed depression severity as a primary outcome and followed a randomized clinical trial (RCT) design. This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting guidelines. Risk of bias was evaluated using the Cochrane Risk of Bias Tool. HTE was investigated using logarithmic variance ratios (lnVR) and effect sizes using Hedges g. Three-level bayesian meta-regressions were conducted. Heterogeneity of treatment effects was the primary outcome of this study; magnitudes of treatment effect sizes were the secondary outcome. Depression severity was measured by different self-report and clinician-rated scales in the included RCTs. The systematic review of 102 trials included 19 758 participants (mean [SD] age, 39.9 [10.58] years) with moderate depression severity (mean [SD] in Patient Health Questionnaire-9 score, 12.81 [2.93]). No evidence for HTE in IMIs was found (lnVR = -0.02; 95% credible interval [CrI], -0.07 to 0.03). However, HTE was higher in more severe depression levels (β̂ = 0.04; 95% CrI, 0.01 to 0.07). The effect size of IMI was medium (g = -0.56; 95% CrI, -0.46 to -0.66). An interaction effect between guidance and baseline severity was found (β̂ = -0.24, 95% CrI, -0.03 to -0.46). In this systematic review and meta-analysis of RCTs, no evidence for increased patient-by-treatment interaction in IMIs among patients with subthreshold to mild depression was found. Guidance did not increase effect sizes in this subgroup. However, the association of baseline severity with HTE and its interaction with guidance indicates a more sensitive, guided, digital precision approach would benefit individuals with more severe symptoms. Future research in this population is needed to explore personalization strategies and fully exploit the potential of IMI.

Global Burden Disease Estimates for Major Depressive Disorders (MDD): Areview of diagnosticinstruments used in studies ofprevalence.

Global Burden of Disease (GBD) estimates have significant policy implications nationally and internationally. Disease burden metrics, particularly for depression, have played a critical role in raising governmental awareness of mental health and in calculating the economic cost of depression. Recently, the World Health Organization ranked depression as the single largest contributor to global disability. The main aim of this paper was to assess the basis upon which GBD prevalence estimates for major depressive disorder (MDD) were made. We identify the instruments used in the 2019 GBD estimates and provide a descriptive assessment of the five most frequently used instruments. The majority of country studies, 356/566 (62.9%), used general mental health screeners or structured/semi-structured interview guides, 98/566 (17.3%) of the studies used dedicated depression screeners, and 112 (19.8%) used other tools for assessing depression. Thus, most of the studies used instruments that were not designed to make a diagnosis of depression or assess depression severity. Our results are congruent with and extend previous research that has identified critical flaws in the data underpinning the GBD estimates for MDD. Despite the widespread promotion of these prevalence estimates, caution is needed before using them to inform public policy and mental health interventions. This is particularly important in lower-income countries where resources are scarce.

Disruptions in Sleep Health and Independent Associations with Psychological Distress in Close Family Members of Cardiac Arrest Survivors: A Prospective Study.

While recent guidelines have noted the deleterious effects of poor sleep on cardiovascular health, the upstream impact of cardiac arrest-induced psychological distress on sleep health metrics among families of cardiac arrest survivors remains unknown. Sleep health of close family members of consecutive cardiac arrest patients admitted at an academic center (8/16/2021 - 6/28/2023) was self-reported on the Pittsburgh Sleep Quality Index (PSQI) scale. The baseline PSQI administered during hospitalization was cued to sleep in the month before cardiac arrest. It was then repeated one month after cardiac arrest, along with the Patient Health Questionnaire-8 (PHQ-8) to assess depression severity. Multivariable linear regressions estimated the associations of one-month total PHQ-8 scores with changes in global PSQI scores between baseline and one month with higher scores indicating deteriorations. A prioritization exercise of potential interventions categorized into family's information and well-being needs to alleviate psychological distress was conducted at one month. In our sample of 102 close family members (mean age 52±15 years, 70% female, 21% Black, 33% Hispanic), mean global PSQI scores showed a significant decline between baseline and one month after cardiac arrest (6.2±3.8 vs. 7.4±4.1; p<0.01). This deterioration was notable for sleep quality, duration, and daytime dysfunction components. Higher PHQ-8 scores were significantly associated with higher change in PSQI scores, after adjusting for family members' age, sex, race/ethnicity, and patient's discharge disposition [β=0.4 (95% C.I 0.24, 0.48); p<0.01]. Most (n=72, 76%) prioritized interventions supporting information over well-being needs to reduce psychological distress after cardiac arrest. There was a significant decline in sleep health among close family members of cardiac arrest survivors in the acute phase following the event. Psychological distress was associated with this sleep disruption. Further investigation into their temporal associations is needed to develop targeted interventions to support families during this period of uncertainty. Sleep health has been identified as a key element in maintaining cardiovascular health.Close family members of critically ill patients experience suboptimal sleep health and psychological distress may contribute to it. It is breaking new ground in understanding the sleep health dynamics of close family members of cardiac arrest survivors, a critical but often overlooked group of caregivers.The study highlights significant associations between psychological distress and poor sleep that further deteriorates within the first month after a loved one's cardiac arrest.Families of cardiac arrest survivors expressed a high priority for information-based interventions to help alleviate psychological distress during the initial month following the cardiac event emphasizing the need for targeted, accessible, resources to address their psychological and potentially sleep-related challenges.

Decoding depression: Analyzing social network insights for depression severity assessment with transformers and explainable AI

Depression is a mental state characterized by recurrent feelings of melancholy, hopelessness, and disinterest in activities, having a significant negative influence on everyday functioning and general well-being. Millions of users express their thoughts and emotions on social media platforms, which can be used as a rich source of data for early detection of depression. In this connection, this work leverages an ensemble of transformer-based architectures for quantifying the severity of depression from social media posts into four categories — non-depressed, mild, moderate, and severe. At first, a diverse range of preprocessing techniques is employed to enhance the quality and relevance of the input. Then, the preprocessed samples are passed through three variants of transformer-based models, namely vanilla BERT, BERTweet, and ALBERT, for generating predictions, which are combined using a weighted soft-voting approach. We conduct a comprehensive explainability analysis to gain deeper insights into the decision-making process, examining both local and global perspectives. Furthermore, to the best of our knowledge, we are the first ones to explore the extent to which a Large Language Model (LLM) like ‘ChatGPT’ can perform this task. Evaluation of the model on the publicly available ‘DEPTWEET’ dataset produces state-of-the-art performance with 13.5% improvement in AUC–ROC score.

ARTIFICIAL INTELLIGENT SYSTEM FOR AUTOMATIC DEPRESSION LEVEL ANALYSIS THROUGH VISUAL AND VOCAL EXPRESSIONS

Depression is a major mental health disease of human which is rapidly affecting lives worldwide. Early detection and intervention are crucial for effective treatment and management. Depression is regularly identified with thoughts of suicide. Significant depression can bring about a determination of social and physical side effects. It could remember changes in rest, craving, vitality level, concentration, day by day conduct or confidence. In recent years, deep-learned applications concentrated on neural networks have shown superior performance at hand-crafted apps in various areas. This system presents an innovative artificial intelligence (AI) system designed for automatic depression level analysis by analyzing visual and vocal expressions. Leveraging advances in computer vision and natural language processing, the proposed system extracts relevant features from facial expressions and speech patterns to assess depression severity levels accurately. Deep-learned apps that settle the above issues that may precisely assess the degree of voice and face depression. In the proposed method, Convolutionary Neural Networks (CNN) is developed for learning deep-learned features and descriptive raw waveforms for visual expressions. Second. Keywords: Artificial Intelligence, Depression, Vocal, Facial Expressions, Deep Learning