Although risk markers for depressive disorders (DD) are dynamic, especially during adolescence, few studies have examined how change in risk levels during adolescence predict DD onset during transition to adulthood. We compared two competing hypotheses of the dynamic effects of risk. The risk escalation hypothesis posits that worsening of risk predicts DD onset beyond risk level. The chronic risk hypothesis posits that persistently elevated risk level, rather than risk change, predicts DD onset.

Our sample included 393 girls (baseline age 13.5–15.5 years) from the adolescent development of emotions and personality traits project. Participants underwent five diagnostic interviews and assessments of risk markers for DD at 9-month intervals and were re-interviewed at a 6-year follow-up. We focused on 17 well-established risk markers. For each risk marker, we examined the prospective effects of risk level and change on first DD onset at wave six, estimated by growth curve modeling using data from the first five waves.

For 13 of the 17 depression risk markers, elevated levels of risk during adolescence, but not change in risk, predicted first DD onset during transition to adulthood, supporting the chronic risk hypothesis. Minimal evidence was found for the risk escalation hypothesis.

Participants who had a first DD onset during transition to adulthood have exhibited elevated levels of risk throughout adolescence. Researchers and practitioners should administer multiple assessments and focus on persistently elevated levels of risk to identify individuals who are most likely to develop DD and to provide targeted DD prevention.

Risk factors for depressive disorders (DD) change substantially over time, but the prognostic value of these changes remains unclear. Two basic types of dynamic effects are possible. The ‘Risk Escalation hypothesis’ posits that worsening of risk levels predicts DD onset above average level of risk factors. Alternatively, the ‘Chronic Risk hypothesis’ posits that the average level rather than change predicts first-onset DD.

We utilized data from the ADEPT project, a cohort of 496 girls (baseline age 13.5–15.5 years) from the community followed for 3 years. Participants underwent five waves of assessments for risk factors and diagnostic interviews for DD. For illustration purposes, we selected 16 well-established dynamic risk factors for adolescent depression, such as depressive and anxiety symptoms, personality traits, clinical traits, and social risk factors. We conducted Cox regression analyses with time-varying covariates to predict first DD onset.

Consistently elevated risk factors (i.e. the mean of multiple waves), but not recent escalation, predicted first-onset DD, consistent with the Chronic Risk hypothesis. This hypothesis was supported across all 16 risk factors.

Across a range of risk factors, girls who had first-onset DD generally did not experience a sharp increase in risk level shortly before the onset of disorder; rather, for years before onset, they exhibited elevated levels of risk. Our findings suggest that chronicity of risk should be a particular focus in screening high-risk populations to prevent the onset of DDs. In particular, regular monitoring of risk factors in school settings is highly informative.

Recently, artificial intelligence-powered devices have been put forward as potentially powerful tools for the improvement of mental healthcare. An important question is how these devices impact the physician-patient interaction.

Aifred is an artificial intelligence-powered clinical decision support system (CDSS) for the treatment of major depression. Here, we explore the use of a simulation centre environment in evaluating the usability of Aifred, particularly its impact on the physician–patient interaction.

Twenty psychiatry and family medicine attending staff and residents were recruited to complete a 2.5-h study at a clinical interaction simulation centre with standardised patients. Each physician had the option of using the CDSS to inform their treatment choice in three 10-min clinical scenarios with standardised patients portraying mild, moderate and severe episodes of major depression. Feasibility and acceptability data were collected through self-report questionnaires, scenario observations, interviews and standardised patient feedback.

All 20 participants completed the study. Initial results indicate that the tool was acceptable to clinicians and feasible for use during clinical encounters. Clinicians indicated a willingness to use the tool in real clinical practice, a significant degree of trust in the system's predictions to assist with treatment selection, and reported that the tool helped increase patient understanding of and trust in treatment. The simulation environment allowed for the evaluation of the tool's impact on the physician–patient interaction.

The simulation centre allowed for direct observations of clinician use and impact of the tool on the clinician–patient interaction before clinical studies. It may therefore offer a useful and important environment in the early testing of new technological tools. The present results will inform further tool development and clinician training materials.

Performance monitoring entails rapid error detection to maintain task performance. Impaired performance monitoring is a candidate pathophysiological process in psychotic disorders, which may explain the broader deficit in executive function and its known associations with negative symptoms and poor functioning. The current study models cross-sectional pathways bridging neurophysiological measures of performance monitoring with executive function, symptoms, and functioning.

Data were from the 20-year assessment of the Suffolk County Mental Health Project. Individuals with psychotic disorders (N = 181) were originally recruited from inpatient psychiatric facilities. Data were also collected from a geographically and demographically matched group with no psychosis history (N = 242). Neural measures were the error-related negativity (ERN) and error positivity (Pe). Structural equation modeling tested mediation pathways.

Blunted ERN and Pe in the clinical cohort related to impaired executive function (r = 0.26–0.35), negative symptom severity (r = 0.17–0.25), and poor real-world functioning (r = 0.17–0.19). Associations with executive function were consistent across groups. Multiple potential pathways were identified in the clinical cohort: reduced ERN to inexpressivity was mediated by executive function (β = 0.10); reduced Pe to global functioning was mediated by executive function and avolition (β = 0.10).

This supports a transdiagnostic model of psychotic disorders by which poor performance monitoring contributes to impaired executive function, which contributes to negative symptoms and poor real-world functioning. If supported by future longitudinal research, these pathways could inform the development of targeted interventions to address cognitive and functional deficits that are central to psychotic disorders.

Classic conceptual frameworks explaining the relationship of personality traits to depression include the precursor and predisposition models. The former hypothesizes that depression is predicted by traits alone whereas the latter hypothesizes that stress, together with personality, predicts depression. Dynamic vulnerability models (DVM) expand on these perspectives by incorporating fluctuations in personality over time. The stress generation model provides an alternative view, positing that depression generates stress, creating a self-perpetuating cycle. However, these conceptual models are rarely directly compared.

We tested these models, focusing on neuroticism and stressful life events that the participant may have contributed to, using path analysis in a sample of 550 never-depressed, adolescent females assessed five times over 3 years.

A dynamic precursor model with stress generation was best supported. For the precursor component, neuroticism predicted subsequent depression across four assessment intervals. For the dynamic trait component, stressful life events predicted subsequent neuroticism at three of four intervals. Finally, in line with stress generation, depression consistently predicted subsequent stressful life events, and life events then predicted depression.

Finding support for the DVM is noteworthy, as this is the first comprehensive test of this model. Moreover, results supported integrating stress generation with trait vulnerability. Continued use of integrated approaches and refining the statistical implementation of these theories is necessary to advance understanding of the development of depression.