Feasibility and Acceptability of a Mobile App-Based TEAM-CBT (Testing Empathy Assessment Methods-Cognitive Behavioral Therapy) Intervention (Feeling Good) for Depression: Secondary Data Analysis.

BACKGROUND: The Feeling Good App is an automated stand-alone digital mobile mental health tool currently undergoing beta testing with the goal of providing evidence-informed self-help lessons and exercises to help individuals reduce depressive symptoms without guidance from a mental health provider. Users work through intensive basic training (IBT) and ongoing training models that provide education regarding cognitive behavioral therapy principles from a smartphone. OBJECTIVE: The key objective of this study was to perform a nonsponsored third-party academic assessment of an industry-generated data set; this data set focused on the safety, feasibility, and accessibility of a commercial automated digital mobile mental health app that was developed to reduce feelings associated with depression. METHODS: The Feeling Good App development team created a waitlist cohort crossover design and measured symptoms of depression and anxiety using the Patient Health Questionnaire-9, Generalized Anxiety Disorder-7, and an app-specific measure of negative feelings called the 7 Dimension Emotion Slider (7-DES). The waitlist cohort crossover design divided the participants into 2 groups, where 48.6% (141/290) of the participants were given immediate access to the apps, while 51.4% (149/290) were placed on a 2-week waitlist before being given access to the app. Data collected by the Feeling Good App development team were deidentified and provided to the authors of this paper for analysis through a nonsponsored university data use agreement. All quantitative data were analyzed using SPSS Statistics (version 28.0; IBM Corp). Descriptive statistics were calculated for demographic variables. Feasibility and acceptability were descriptively assessed. All participants included in the quantitative data were given access to the Feeling Good App; this study did not include a control group. RESULTS: In terms of safety, there was no statistically significant change in suicidality from preintervention to postintervention time points (t288=0.0; P>.99), and there was a statistically significant decrease in hopelessness from preintervention to postintervention time points (F289=30.16; P<.01). In terms of acceptability, 72.2% (166/230) of the users who started the initial 2-day IBT went on to complete it, while 34.8% (80/230) of the users who started IBT completed the entirety of the apps' 4-week protocol (150/230, 65.22% dropout rate over 4 weeks). CONCLUSIONS: This study is the first reported proof-of-concept evaluation of the Feeling Good App in terms of safety, feasibility, and statistical trends within the data set. It demonstrates a feasible and novel approach to industry and academic collaboration in the process of developing a digital mental health technology translated from an existing evidence-informed treatment. The results support the prototype app as safe for a select nonclinical population. The app had acceptable levels of engagement and dropouts throughout the intervention. Those who stay engaged showed reductions in symptom severity of depression warranting further investigation of the app's efficacy.

Perinatal Depression Associated With Increased Pediatric Emergency Department Use And Charges In The First Year Of Life.

In New Jersey, universal screening for perinatal depression at the time of delivery has resulted in a 95 percent screening rate. The widespread availability of screening data allowed me to investigate the association between perinatal depression severity and infant emergency department (ED) use and charges in the first year of life. I used birth records linked to hospital discharge records for the period 2016-19. Compared with infants who had mothers with no symptoms, infants with mothers with mild or moderate/severe depressive symptoms had significantly higher overall and nonemergent ED use, but not significantly higher emergent ED use. The positive associations between depressive symptoms and ED charges were particularly striking for infants with Medicaid, which pays for a disproportionate share of pediatric ED care in the United States. This study contributes to the evidence base linking perinatal depression screening and pediatric ED use. Opportunities may exist within Medicaid to optimize screening and referrals for perinatal depression, with potential cost-saving benefits for reducing nonemergent pediatric ED visits.

Psychological Pain as a Risk Factor for Suicidal Ideation: An Ecological Momentary Assessment Study on Inpatients With Depression With and Without Comorbid Borderline Personality Disorder.

Objective: Psychological pain (PP) is a potentially important risk factor for suicide. However, its temporal stability and association with suicidal ideation (SI) remain obscure. Whether PP represents a risk factor for SI independently of depression, anxiety, and hopelessness or is more prominent and temporally unstable in patients with depression and borderline personality disorder (BPD) is also unclear.Methods: From November 2020 to December 2022, psychiatric inpatients with depression without (N = 37) and with (N = 30) BPD were recruited to an ecological momentary assessment (EMA) study, wherein their PP, severity of depression, SI, and hopelessness were assessed 3 times daily using visual analog scales. Multilevel regression models were estimated.Results: Altogether, 4,320 EMA observations were collected. PP correlated with hopelessness (r = 0.417), depression (r = 0.339), and anxiety (r = 0.496), but the between-patient variance of PP remained at 1.26 (95% CI, 1.025-1.533) after controlling for these variables. The within-patient variance of PP was associated with SI (ß = 0.17 [95% CI, 0.12-0.22]) with a magnitude comparable to hopelessness (ß = 0.1 [95% CI, 0.05-0.15]) and depression (ß = 0.12 [95% CI, 0.08-0.17]). Patients with depression and BPD reported higher daily PP and SI (P < .001) and a more prominent within-patient variation in PP.Conclusions: In psychiatric inpatients with depression, besides depression and hopelessness, PP represents an independent risk factor for SI, varying within a timescale of days. Depressive patients with BPD may experience more prominent and temporally unstable PP, likely underlying their higher vulnerability to SI.

The assessment of transdiagnostic dimensions of emotional disorders: Validation of the Multidimensional Emotional Disorders Inventory (MEDI) in adolescents with subthreshold anxiety and depression.

BACKGROUND: The transdiagnostic approach to psychopathology has emerged as an alternative to traditional taxonomic approaches. The Multidimensional Emotional Disorders Inventory (MEDI) is a specifically designed self-report to measure the transdiagnostic dimensions proposed by Brown and Barlow (2009). This study aims to analyse the psychometric properties of the MEDI scores in adolescents with subthreshold anxiety and depression. METHOD: The sample consisted of a total of 476 students. The mean age was 13.77 years (SD = 1.43) (range 10 to 18 years), 73.9 % were females. Several questionnaires assessing positive affect, negative affect, mental health difficulties, and quality of life were used. RESULTS: The original 9-factor structure of the MEDI was confirmed with good fit indices. Satisfactory levels of internal consistency were observed in most of the MEDI scores using McDonald's Omega, ranging from 0.58 to 0.87. The MEDI dimensions were associated with psychopathology, positive affect, negative affect, and quality of life. LIMITATIONS: Reliance on self-reported data, a cross-sectional design limiting temporal assessment, and a 73.9 % female gender imbalance. CONCLUSION: The MEDI scores showed adequate psychometric properties among adolescents with subclinical emotional symptoms. The results found might have potential clinical implications for conceptualization, assessment, intervention, and prevention of emotional disorders at both clinical and research levels.

Burden of undiagnosed depression among older adults in India: a population based study.

BACKGROUND: The study explored the levels and associated factors of undiagnosed depression among community-dwelling older Indian adults. It also identified the socio-demographic predictors of undiagnosed depression among the study population at national and state levels. METHODS: The study employed data from the Longitudinal Ageing Study in India wave-I, 2017-18. Based on the data on depression from interviewee's self-reporting and measurement on Composite International Diagnostic Interview- Short Form (CIDI-SF) and Centre for Epidemiological Studies- Depression scale (CES-D) scales, we estimated undiagnosed depression among older adults (age 60+). We estimated multivariable binary logistic regressions to examine the socio-demographic and health-related predictors of undiagnosed depression among older adults. FINDINGS: 8% (95% CI: 7.8-8.4) of the total older adults had undiagnosed depression on CIDI-SF scale and 5% (95% CI: 4.8-5.3) on the combined CIDI-SF and CES-D. Undiagnosed depression was higher among those who were widowed, worked in the past and currently not working, scheduled castes, higher educated and the richest. Lack of health insurance coverage, presence of any other physical or mental impairment, family history of Alzheimer's/Parkinson's disease/ psychotic disorder, lower self-rated health and poor life satisfaction were significant predictors of undiagnosed depression on both CIDI-SF and combined scales. CONCLUSION: To improve the health of older adults in India, targeted policy efforts integrating mental health screening, awareness campaigns and decentralization of mental healthcare to primary level is needed. Further research could explore the causal factors behind different levels of undiagnosed depression.

Can routine assessment of older people's mental health lead to improved outcomes: A regression discontinuity analysis.

OBJECTIVE: To assess whether case finding for depression among people aged 65 and above improves mental health. DESIGN: Opportunistic evaluation using a regression discontinuity analysis with data from a randomised controlled trial. SETTING: The REFORM trial, a falls prevention study that recruited patients from NHS podiatry clinics. PARTICIPANTS: 1010 community-dwelling adults over the age of 65 with at least one risk factor for falling (recent previous fall or fear of falling). INTERVENTION: Letter sent to patient's General Practitioner if they scored 10 points or above on the 15-item Geriatric Depression Scale (GDS-15) informing them of the patient's risk of depression. MAIN OUTCOME MEASURE: GDS-15 score six months after initial completion of GDS-15. RESULTS: 895 (88.6%) of the 1010 participants randomised into REFORM had a valid baseline and six-month GDS-15 score and were included in this study. The mean GDS-15 baseline score was 3.5 (SD 3.0, median 3.0, range 0-15); 639 (71.4%) scored 0-4, 204 (22.8%) scored 5-9 indicating mild depression, and 52 (5.8%) scored 10 or higher indicating severe depression. At six months follow-up, those scoring 10 points or higher at baseline had, on average, a reduction of 1.08 points on the GDS-15 scale (95% confidence interval -1.83 to -0.33, p = 0.005) compared to those scoring less than 10, using the simplest linear regression model. CONCLUSION: Case finding of depression in podiatry patients based on a GDS-15 score of 10 or more followed by a letter to their General Practitioner significantly reduced depression severity. Whether this applies to all older patients in primary care is unknown. Further research is required to confirm these findings. Regression discontinuity analyses could be prespecified and embedded within other existing research studies.

Association of perceived childhood socio-economic status and health with depressive symptoms among middle-aged and older adults in India: using data from LASI Wave I, 2017-2018.

BACKGROUND: Childhood adverse experience has been linked with poor health outcomes across the life course. Nevertheless, whether such an association or direction could be projected to older people's life remains still unclear and needs to generate more evidence, particularly in India. Therefore, this study was conducted to examine the association of childhood socio-economic status and health with depressive symptoms amongst middle- aged and older adults in India. METHODS: The data for the study was drawn from national representative survey "Longitudinal Ageing Study in India (LASI)" Wave I, 2017-2018 in order to conduct cross-sectional study. Multivariable regression analysis was used to examine the association of childhood socioeconomic status and health with depressive symptoms in the older population. RESULTS: Poor childhood health was significantly and positively associated with depressive symptoms (AoR: 1.56, CI: 1.19, 2.04). Likewise, respondents who were bedridden for a month during their childhood had high odds of developing depressive symptoms (AoR: 1.16 CI: 1.01, 1.34). In addition to this, the odds of having depressive symptoms increased significantly among the average (AoR: 1.28 CI: 1.08, 1.51) and poor childhood socioeconomic status group (AoR: 1.31 CI: 1.11, 1.55) as compared to the higher socioeconomic category. CONCLUSIONS: Childhood socioeconomic status and health have a significant role in determining mental health in later life. Results suggest that considering childhood socioeconomic status and health is important while diagnosing depression in older population in order to identify the significant associated factors in early childhood and thus help in preventing depressive symptoms in later life.

The "bubbles"-study: Validation of ultra-short scales for the assessment of depression, anxiety and stress symptoms.

Depression, anxiety and stress symptoms cause substantial psychological and economic burdens around the globe. To mitigate the negative consequences, the negative symptoms should be identified at an early stage. Therefore, the implementation of very brief valid screening tools in mental health prevention programs and in therapeutic settings is advantageous. In two studies on representative German population samples, we developed and validated three ultra-short scales-the "bubbles"-that consist of only one item based on the Depression Anxiety Stress 21 subscales (DASS-21) for the assessment of depression, anxiety and stress symptoms. The results of Study 1 (N = 1,001) and Study 2 (N = 894) revealed that the bubbles are valid instruments that fit the DASS-21 subscales on the factor level. Moreover, the bubbles replicated the association pattern of the DASS-21 subscales with demographic variables, and with variables that belong to the negative and the positive dimension of mental health. Thus, due to their time- and cost-efficiency, the bubbles can be used as brief screening tools in research (e.g., large-scale studies, longitudinal studies, experience sampling paradigms) and in praxis. Their shortness can prevent fatigue, motivation decrease, and participants' drop-out.

Multimodal digital assessment of depression with actigraphy and app in Hong Kong Chinese.

There is an emerging potential for digital assessment of depression. In this study, Chinese patients with major depressive disorder (MDD) and controls underwent a week of multimodal measurement including actigraphy and app-based measures (D-MOMO) to record rest-activity, facial expression, voice, and mood states. Seven machine-learning models (Random Forest [RF], Logistic regression [LR], Support vector machine [SVM], K-Nearest Neighbors [KNN], Decision tree [DT], Naive Bayes [NB], and Artificial Neural Networks [ANN]) with leave-one-out cross-validation were applied to detect lifetime diagnosis of MDD and non-remission status. Eighty MDD subjects and 76 age- and sex-matched controls completed the actigraphy, while 61 MDD subjects and 47 controls completed the app-based assessment. MDD subjects had lower mobile time (P = 0.006), later sleep midpoint (P = 0.047) and Acrophase (P = 0.024) than controls. For app measurement, MDD subjects had more frequent brow lowering (P = 0.023), less lip corner pulling (P = 0.007), higher pause variability (P = 0.046), more frequent self-reference (P = 0.024) and negative emotion words (P = 0.002), lower articulation rate (P < 0.001) and happiness level (P < 0.001) than controls. With the fusion of all digital modalities, the predictive performance (F1-score) of ANN for a lifetime diagnosis of MDD was 0.81 and 0.70 for non-remission status when combined with the HADS-D item score, respectively. Multimodal digital measurement is a feasible diagnostic tool for depression in Chinese. A combination of multimodal measurement and machine-learning approach has enhanced the performance of digital markers in phenotyping and diagnosis of MDD.

Pathophysiology, Assessment, and Management of Post-Stroke Cognitive Impairment, Depression, and Fatigue.

Post-stroke cognitive impairment, depression, and fatigue are common, persistent, and disabling. This review summarizes current knowledge on the pathophysiology, assessment, and management of these debilitating neuropsychiatric sequelae of stroke. We briefly review evolving knowledge on the neural mechanisms and risk factors for each condition. We describe patient-reported outcome measures and clinician rating techniques that can be used to assist in screening and comprehensive assessment. We then discuss behavioral and pharmacologic management strategies. Heterogeneity of stroke remains a challenge in management and new research is still needed to optimize and personalize treatments for stroke survivors.

Patterns of gender inequality perception and depressive symptoms among Chinese adults: The mediating role of marital life satisfaction.

OBJECTIVES: As one of the most severe public health issues, depressive symptoms have attracted wide attention around the world. This study aims to investigate the mediating role of marital life satisfaction in the relationship between gender inequality perception and depressive symptoms. METHODS: Data were obtained from the China Family Panel Studies (CFPS) conducted in 2020. This study incorporated a total of 13,409 married residents aged 20 and above. RESULTS: The residents with middle (B = 0.19, 95 % CI = 0.01, 0.40) or high perception of gender inequality (B = 0.55, 95 % CI = 0.34, 0.75) were more likely to develop depressive symptoms. Marital life satisfaction plays a mediating effect in the relationship between patterns of gender inequality perception and depressive symptoms, and the mediating effect accounted for 4.89 % or 1.37 % of the total impact in the residents with middle or high patterns of gender inequality perception. LIMITATIONS: Further studies need to understand the mechanisms of perceived patterns of gender inequality and depressive symptoms, because the cross-sectional design in this study cannot draw causal inferences. CONCLUSION: Patterns of gender inequality perception are significantly associated with depressive symptoms. Marital life satisfaction plays a mediating role in the relationship between patterns of gender inequality perception and depressive symptoms. It is plausible that nurturing healthy marital relationships could mitigate depressive symptoms in individuals who perceive high degrees of gender disparity.

A Randomized Trial of Two Remote Health Care Delivery Models on the Uptake of Genetic Testing and Impact on Patient-Reported Psychological Outcomes in Families With Pancreatic Cancer: The Genetic Education, Risk Assessment, and Testing (GENERATE) Study.

BACKGROUND & AIMS: Genetic testing uptake for cancer susceptibility in family members of patients with cancer is suboptimal. Among relatives of patients with pancreatic ductal adenocarcinoma (PDAC), The GENetic Education, Risk Assessment, and TEsting (GENERATE) study evaluated 2 online genetic education/testing delivery models and their impact on patient-reported psychological outcomes. METHODS: Eligible participants had ≥1 first-degree relative with PDAC, or ≥1 first-/second-degree relative with PDAC with a known pathogenic germline variant in 1 of 13 PDAC predisposition genes. Participants were randomized by family, between May 8, 2019, and June 1, 2021. Arm 1 participants underwent a remote interactive telemedicine session and online genetic education. Arm 2 participants were offered online genetic education only. All participants were offered germline testing. The primary outcome was genetic testing uptake, compared by permutation tests and mixed-effects logistic regression models. We hypothesized that Arm 1 participants would have a higher genetic testing uptake than Arm 2. Validated surveys were administered to assess patient-reported anxiety, depression, and cancer worry at baseline and 3 months postintervention. RESULTS: A total of 424 families were randomized, including 601 participants (n = 296 Arm 1; n = 305 Arm 2), 90% of whom completed genetic testing (Arm 1 [87%]; Arm 2 [93%], P = .014). Arm 1 participants were significantly less likely to complete genetic testing compared with Arm 2 participants (adjusted ratio [Arm1/Arm2] 0.90, 95% confidence interval 0.78-0.98). Among participants who completed patient-reported psychological outcomes questionnaires (Arm 1 [n = 194]; Arm 2 [n = 206]), the intervention did not affect mean anxiety, depression, or cancer worry scores. CONCLUSIONS: Remote genetic education and testing can be a successful and complementary option for delivering genetics care. (Clinicaltrials.gov, number NCT03762590).

The 'diagnostic gap' and 'economic burden' of depression: Global mental health in neoliberal Poland (2010s-2020s).

While critical sociologists and anthropologists of health have questioned Global Mental Health policies as postcolonial, developmental agendas, little is known on how this critique applies within the central and eastern European countries. As this article shows with the case of Poland, since the advent of capitalism and liberal democracy, the psychiatric conceptualization of depression has steadily aligned with global mental health (GMH) frameworks, amplifying pre-existing trends towards biomedical dominance in Polish psychiatry and the economic framing of mental health in policy-making. These trends are evidenced by the study of Polish Psychiatry, an official journal issued by Polish Psychiatric Association and health policy documents published since the 2010s, including statements by the Ministry of Health. Two findings are presented: first, the logic of 'closing the gap' between Poland and the West has shaped how depression prevalence data is produced and interpreted by state medical institutions and in expert psychiatric discourse; second, the reconceptualization of mental illness through its supposed economic cost has become a dominant approach to depression in Polish psychiatry and public health. Thus, in showing how the Global Mental Health agenda has permeated the specific context of Poland, promoting more individual and biomedical conceptions of mental illness, this case study enables advancing the postcolonial critique of mental health.

Socioeconomic status and incident depressive symptoms among older adults: A 3-year longitudinal study from the Japan Gerontological Evaluation Study.

OBJECTIVES: The association between socioeconomic status (SES) and the onset of depressive symptoms has attracted considerable attention. However, few studies have simultaneously examined the association of multiple SES indicators, including "assets," with the onset of depressive symptoms. Therefore, this study examined the association of four SES indicators in old age ('years of education' 'equivalent income,' 'equivalent assets,' and 'the longest-held job') with new-onset depressive symptoms in a large Japanese dataset. METHODS: This longitudinal study used panel data of cognitively and physically independent older adults from the Japan Gerontological Evaluation Study (JAGES) conducted in 2013 and 2016. Multivariate logistic regression analysis was conducted to examine the association of each SES indicator with new-onset depressive symptoms, and odds ratios and 95% confidence intervals (CIs) were calculated. RESULTS: We analyzed the data of 40,257 older adults, with a mean age (± standard deviation) of 72.9 (±5.5) years. In the follow-up survey, 4292 older adults had new-onset depression symptoms (10.7%). 39.3% had 10-12 years of education. 36.9% had an equivalent income of up to JPY 1.99 million. 24.4% had equivalent assets of JPY 4-17.99 million. Most had a clerical job for the long time. Furthermore, fewer years of education (males: OR = 1.42, 95% CI = 1.22-1.64, p-value <0.001/females: 1.26, [1.09-1.47], p = 0.002), lower income (males: 1.64, [1.34-2.01], p < 0.001/females: 1.82, [1.49-2.22], p < 0.001), and fewer assets (males: 1.40, [1.16-1.68], p < 0.001/females: 1.21, [1.02-1.42], p = 0.025) resulted in higher odds of having new-onset depressive symptoms, even when other SES indicators were entered simultaneously. CONCLUSIONS: All four SES indicators have an independent association with the development of new-onset depressive symptoms among older adults, reflecting different aspects of SES. The association between the "longest-held job" and new-onset depressive symptoms can be largely explained by other SES indicators. A multifaceted and lifetime approach is required to prevent the onset of depressive symptoms in old age.

Association between high or low-quality carbohydrate with depressive symptoms and socioeconomic-dietary factors model based on XGboost algorithm: From NHANES 2007-2018.

BACKGROUND: Depressive symptoms are a serious public mental health problem, and dietary intake is often considered to be associated with depressive symptoms. However, the relationship between the quality of dietary carbohydrates and depressive symptoms remains unclear. Therefore, this study aimed to investigate the relationship between high and low-quality carbohydrates and depressive symptoms and to attempt to construct an integrated model using machine learning to predict depressive symptoms. METHODS: A total of 4982 samples from the National Health and Nutrition Examination Survey (NHANES) were included in this study. Carbohydrate intake was assessed by a 24-h dietary review, and depressive symptoms were assessed using the Patient Health Questionnaire-9 (PHQ9). Variance inflation factor (VIF) and Relief-F algorithms were used for variable feature selection. RESULTS: The results of multivariate linear regression showed a negative association between high-quality carbohydrates and depressive symptoms (ß: -0.147, 95 % CI: -0.239, -0.056, p = 0.002) and a positive association between low-quality carbohydrates and depressive symptoms (ß: 0.018, 95 % CI: 0.007, 0.280, p = 0.001). Subsequently, we used the XGboost model to produce a comprehensive depressive symptom evaluation model and developed a corresponding online tool (http://8.130.128.194:5000/) to evaluate depressive symptoms clinically. LIMITATIONS: The cross-sectional study could not yield any conclusions regarding causality, and the model has not been validated with external data. CONCLUSIONS: Carbohydrate quality is associated with depressive symptoms, and machine learning models that combine diet with socioeconomic factors can be a tool for predicting depression severity.

Comparison of the EPDS and PHQ-9 in the assessment of depression among pregnant women: Similarities and differences.

BACKGROUND: Perinatal depression has attracted increasing attention. However, a detailed investigation of the network structure of depression is still lacking. We aim to examine the similarities and differences between the Edinburgh Postnatal Depression Scale (EPDS) and Patient Health Questionnaire (PHQ-9) from a network perspective. METHODS: A cross-sectional study was conducted from August 2020 to March 2022. We followed the STROBE checklist to report our research. Pregnant women (n = 2484) were recruited. All participants completed the EPDS and PHQ-9. We mainly used network analyses for statistical analysis and constructed two network models: the EPDS and PHQ-9 models. RESULTS: The detection rates of prenatal depression measured by the EPDS and PHQ-9 were 30.2 % and 28.2 %, respectively. In the EPDS network, the EPDS8 'sad or miserable' node (strength = 1.2161) was the most central node, and the EPDS10 'self-harming' node (strength = 0.4360) was the least central node. In the PHQ-9 network, the PHQ4 'fatigue' node (strength = 0.9815) was the most central node, and PHQ9 'suicide' was the least central symptom (strength = 0.5667). For both models, 'sad' acted as an important central symptom. CONCLUSIONS: Psychological symptoms may be more important in assessing depression using the EPDS, while physical symptoms may be more influential in assessing depression using the PHQ-9. For both the EPDS and PHQ-9, "sad" was an important central symptom, suggesting that it may be the most important target for further maternal depression interventions in the future.

Conversational assessment using artificial intelligence is as clinically useful as depression scales and preferred by users.

BACKGROUND: Depression is prevalent, chronic, and burdensome. Due to limited screening access, depression often remains undiagnosed. Artificial intelligence (AI) models based on spoken responses to interview questions may offer an effective, efficient alternative to other screening methods. OBJECTIVE: The primary aim was to use a demographically diverse sample to validate an AI model, previously trained on human-administered interviews, on novel bot-administered interviews, and to check for algorithmic biases related to age, sex, race, and ethnicity. METHODS: Using the Aiberry app, adults recruited via social media (N = 393) completed a brief bot-administered interview and a depression self-report form. An AI model was used to predict form scores based on interview responses alone. For all meaningful discrepancies between model inference and form score, clinicians performed a masked review to determine which one they preferred. RESULTS: There was strong concurrent validity between the model predictions and raw self-report scores (r = 0.73, MAE = 3.3). 90 % of AI predictions either agreed with self-report or with clinical expert opinion when AI contradicted self-report. There was no differential model performance across age, sex, race, or ethnicity. LIMITATIONS: Limitations include access restrictions (English-speaking ability and access to smartphone or computer with broadband internet) and potential self-selection of participants more favorably predisposed toward AI technology. CONCLUSION: The Aiberry model made accurate predictions of depression severity based on remotely collected spoken responses to a bot-administered interview. This study shows promising results for the use of AI as a mental health screening tool on par with self-report measures.

Using ecological momentary assessment and machine learning techniques to predict depressive symptoms in emerging adults.

The objective of this study was to predict the level of depressive symptoms in emerging adults by analyzing sociodemographic variables, affect, and emotion regulation strategies. Participants were 33 emerging adults (M = 24.43; SD = 2.80; 56.3 % women). They were asked to assess their current emotional state (positive or negative affect), recent events that may relate to that state, and emotion regulation strategies through ecological momentary assessment. Participants were prompted randomly by an app 6 times per day between 10 am and 10 pm for a seven-day period. They answered 1233 of the 2058 surveys (beeps), collectively. The analysis of observations, using Machine Learning (ML) techniques, showed that the Random Forest algorithm yields significantly better predictions than other models. The algorithm used 13 out of the 36 variables adopted in the study. Furthermore, the study revealed that age, emotion of worried and a specific emotion regulation strategy related to social exchange were the most accurate predictors of severe depressive symptoms. By carefully selecting predictors and utilizing appropriate sorting techniques, these findings may provide valuable supplementary information to traditional diagnostic methods and psychological assessments.

Assessment of vision-related quality of life and depression in patients with age-related macular degeneration.

PURPOSE: To evaluate the effect of age-related macular degeneration (AMD) on vision-related quality of life (VRQOL) and depression levels. METHODS: This cross-sectional study included 143 patients who are being followed up with a diagnosis of AMD. The Turkish versions of the National Eye Institute Visual Functioning Questionnaire-25 (NEI VFQ-25) and Geriatric Depression Scale-15 (GDS-15) were directed to the patients. The questionnaire results were analyzed based on the severity, treatment procedures for AMD, and sociodemographic characteristics of patients. RESULTS: The subscale scores obtained from the NEI VFQ-25 ranged from 47.54 for "near activities" to 84.02 for "color vision." Of the patients, 59.4% (85/143) were compatible with depression according to the GDS-15 questionnaire. There was no significant difference in the NEI VFQ-25 subscale scores between the gender groups (P > 0.05), whereas females were statistically significantly more depressive than males (P < 0.05). There were no significant differences between the injection (anti-vascular endothelial growth factors [anti-VEGF]) group and the non-injection group in terms of subscales of the NEI VFQ-25 questionnaire (P > 0.05). The depression ratio in the non-injected group was statistically significantly higher (P < 0.05). CONCLUSION: According to the present study, the association between depression and AMD is a fact that should be highlighted. Patients with depression had lower scores on the quality of life (QOL) test. Previous intravitreal injection did not affect NEI VFQ-25 scores. Female patients with AMD had higher rates of depression and lower visual acuity levels.

Bidirectional and cross-lag relationship between social media use and psychological wellbeing: evidence from an Indian adolescent cohort study.

INTRODUCTION: In an online era like today, the relationship between social media and depression among adolescents and young adults is erratic and still continues to be a debatable subject. The study aims to examine the association and bi-directional relationship between social media usage and depressive symptoms among the adolescent boys and girls in India. METHODS: The study uses data from two waves of Understanding the Lives of Adolescents and Young Adults (UDAYA) project survey conducted in two Indian states of Uttar Pradesh and Bihar. Depression was measured by a Patient Health Questionnaire. Logistic regression has been used for analyzing the data comprising the same time period, whereas the bidirectional relationship between two time periods has been evaluated by Cross-Lagged Path Model. RESULT: Findings suggest that the percentage of moderate depressive symptoms increased from 1.7% to 3.0% from Wave 1 to Wave 2. Depression among adolescent girls increased slightly from wave 1 to wave 2 whereas a slight decrement was noticed in the moderate form of depressive symptom among adolescent girls using social media for the two waves. Socioeconomic factors like education, age, gender played an important role in affecting depression among adolescents in both the Wave of the survey. The path relationship reveals that social media users in Wave 1 [ß=0.22, p<0.001] were positively associated with social media users in Wave 2. Similar patterns were observed for depressive symptoms at both the waves of the survey. However, cross lagged relations between social media use and depression could not be established between the survey periods. CONCLUSION: A significant degree of association was found between social media use and depression among adolescent boys and girls in the study. The present study concludes that factors like age, gender and education showed significant relationships with social media use and depression.