Efficacy of attention bias modification training for depressed adults: a randomized clinical trial.

BACKGROUND: This study examined the efficacy of attention bias modification training (ABMT) for the treatment of depression. METHODS: In this randomized clinical trial, 145 adults (77% female, 62% white) with at least moderate depression severity [i.e. self-reported Quick Inventory of Depressive Symptomatology (QIDS-SR) ⩾13] and a negative attention bias were randomized to active ABMT, sham ABMT, or assessments only. The training consisted of two in-clinic and three (brief) at-home ABMT sessions per week for 4 weeks (2224 training trials total). The pre-registered primary outcome was change in QIDS-SR. Secondary outcomes were the 17-item Hamilton Depression Rating Scale (HRSD) and anhedonic depression and anxious arousal from the Mood and Anxiety Symptom Questionnaire (MASQ). Primary and secondary outcomes were administered at baseline and four weekly assessments during ABMT. RESULTS: Intent-to-treat analyses indicated that, relative to assessment-only, active ABMT significantly reduced QIDS-SR and HRSD scores by an additional 0.62 ± 0.23 (p = 0.008, d = -0.57) and 0.74 ± 0.31 (p = 0.021, d = -0.49) points per week. Similar results were observed for active v. sham ABMT: a greater symptom reduction of 0.44 ± 0.24 QIDS-SR (p = 0.067, d = -0.41) and 0.69 ± 0.32 HRSD (p = 0.033, d = -0.42) points per week. Sham ABMT did not significantly differ from the assessment-only condition. No significant differences were observed for the MASQ scales. CONCLUSION: Depressed individuals with at least modest negative attentional bias benefitted from active ABMT.

Neurocognitive predictors of self-reported reward responsivity and approach motivation in depression: A data-driven approach.

BACKGROUND: Individual differences in reward-related processes, such as reward responsivity and approach motivation, appear to play a role in the nature and course of depression. Prior work suggests that cognitive biases for valenced information may contribute to these reward processes. Yet there is little work examining how biased attention, processing, and memory for positively and negatively valenced information may be associated with reward-related processes in samples with depression symptoms. METHODS: We used a data-driven, machine learning (elastic net) approach to identify the best predictors of self-reported reward-related processes using multiple tasks of attention, processing, and memory for valenced information measured across behavioral, eye tracking, psychophysiological, and computational modeling approaches (n = 202). Participants were adults (ages 18-35) who ranged in depression symptom severity from mild to severe. RESULTS: Models predicted between 5.0-12.2% and 9.7-28.0% of held-out test sample variance in approach motivation and reward responsivity, respectively. Low self-referential processing of positively valenced information was the most robust, albeit modest, predictor of low approach motivation and reward responsivity. CONCLUSIONS: Self-referential processing of positive information is the strongest predictor of reward responsivity and approach motivation in a sample ranging from mild to severe depression symptom severity. Experiments are now needed to clarify the causal relationship between self-referential processing of positively valenced information and reward processes in depression.

Lived Experiences and Fear of Cancer Recurrence Among Survivors of Localized Cutaneous Melanoma.

Importance: Most of the rapid increase in cutaneous melanoma incidence in the US has been localized disease that is treated surgically and is associated with high survival rates. However, little is known about the psychological well-being of survivors in the US. Objective: To explore the lived experiences and fear of cancer recurrence among survivors of localized cutaneous melanoma. Design, Setting, and Participants: This was a qualitative and survey-based study that used semistructured interviews and the Fear of Cancer Recurrence Inventory short form (FCRI-SF) survey tool with participants recruited from an academic dermatology practice affiliated with the University of Texas, Austin. Interviews were completed via telephone or in person from August 2021 to September 2022. Each of the 9 items in the FCRI-SF was rated on a 5-point Likert scale, scored from 0 to 4, with a maximum possible score of 36 points. Data analyses were performed from February 2022 to June 2023. Main Outcomes and Measures: Semistructured interviews were analyzed for themes and subthemes associated with the lived experiences of survivors of cutaneous melanoma. The FCRI-SF scores were tabulated, with scores of 13 or greater identifying potential cases of clinically significant fear of cancer recurrence. Results: In all, 51 participants (mean [SD] age, 49.5 [11.7] years; 34 [67%] female and 17 [33%] male) with a history of localized melanoma (stage 0-IIA) completed the interview and survey. Among them, 17 (33%) had survived a diagnosis of stage 0 melanoma, and the remainder, at least 1 invasive melanoma diagnosis (stage I-IIA). Semistructured interviews revealed several themes: (1) emotions surrounding follow-up appointments, (2) intensity of melanoma surveillance, (3) lifestyle changes regarding sun exposure, and (4) thoughts about life and death. Thirty-eight of 51 participants had an FCRI-SF score above the threshold for clinical fear of cancer recurrence. Conclusions and Relevance: This qualitative and survey-based study found that despite having an excellent prognosis, some survivors of localized melanoma, even those who had stage 0, have high rates of fear of cancer recurrence and intense survivorship experiences that affect their psychological well-being.

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.

Multifactorial prediction of depression diagnosis and symptom dimensions.

While depression is a leading cause of disability, prior investigations of depression have been limited by studying correlates in isolation. A data-driven approach was applied to identify out-of-sample predictors of current depression from adults (N = 217) sampled on a continuum of no depression to clinical levels. The current study used elastic net regularized regression and predictors from sociodemographic, self-report, polygenic scores, resting electroencephalography, pupillometry, actigraphy, and cognitive tasks to classify individuals into currently depressed (MDE), psychiatric control (PC), and no current psychopathology (NP) groups, as well as predicting symptom severity and lifetime MDE. Cross-validated models explained 20.6% of the out-of-fold deviance for the classification of MDEs versus PC, 33.2% of the deviance for MDE versus NP, but -0.6% of the deviance between PC and NP. Additionally, predictors accounted for 25.7% of the out-of-fold variance in anhedonia severity, 65.7% of the variance in depression severity, and 12.9% of the deviance in lifetime depression (yes/no). Self-referent processing, anhedonia, and psychosocial functioning emerged as important differentiators of MDE and PC groups. Findings highlight the advantages of using psychiatric control groups to isolate factors specific to depression.

Association between negative cognitive bias and depression: A symptom-level approach.

Cognitive models of depression posit that negatively biased self-referent processing and attention have important roles in the disorder. However, depression is a heterogeneous collection of symptoms and all symptoms are unlikely to be associated with these negative cognitive biases. The current study involved 218 community adults whose depression ranged from no symptoms to clinical levels of depression. Random forest machine learning was used to identify the most important depression symptom predictors of each negative cognitive bias. Depression symptoms were measured with the Beck Depression Inventory-II. Model performance was evaluated using predictive R-squared (Rpred2), the expected variance explained in data not used to train the algorithm, estimated by 10 repetitions of 10-fold cross-validation. Using the self-referent encoding task (SRET), depression symptoms explained 34% to 45% of the variance in negative self-referent processing. The symptoms of sadness, self-dislike, pessimism, feelings of punishment, and indecision were most important. Notably, many depression symptoms made virtually no contribution to this prediction. In contrast, for attention bias for sad stimuli, measured with the dot-probe task using behavioral reaction time (RT) and eye gaze metrics, no reliable symptom predictors were identified. Findings indicate that a symptom-level approach may provide new insights into which symptoms, if any, are associated with negative cognitive biases in depression. (PsycINFO Database Record (c) 2019 APA, all rights reserved).