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BackgroundRisk 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.MethodsWe 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.ResultsConsistently 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.ConclusionsAcross 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.

Introduction: Body image has been shown to be subject to explicit, body-related feedback influences, yet little attention has been paid to subtle feedback about overall social acceptability. The current study tested competing hypotheses regarding women's body image after being primed with cued social rejection or acceptance. Whereas sociometer theory predicts that women will perceive their bodies more negatively following a social rejection prime, self-enhancement theory predicts the opposite. Method: A total of 105 female undergraduate students underwent a conditioning procedure in which distinctive tones were paired with signs of social rejection or acceptance. After this, body distortion (measured by a participant-adjusted virtual image), self-reported body satisfaction and affect were measured, while one of the tones sounded repeatedly in the background. Results: Compared with the acceptance condition, rejection condition participants reported greater state negative affect, F(1, 103) = 4.41, p = .04, greater state positive affect, F(1, 103) = 5.52, p = .02, and perceived their bodies as being smaller, F(1, 103) = 6.53, p = .01, consistent with the expectations of self-enhancement theory. Discussion: Implications of these findings are discussed regarding improved understanding of body image disturbance as well as development of novel clinical treatments.

Body image is a multidimensional construct that reflects the way we perceive and feel about our physical appearance. This inside view of our body heavily influences our self-esteem, mental health, and overall well-being. Under the influence of mass media, peers and family, individuals, especially women, may feel pressured to conform to the societal standards of beauty, engage in upward social comparison, and consequently experience negative body image. While our sociocultural surroundings plays a role in the internalization process, other intrapersonal factors, such as appearance-based rejection sensitivity and lack of self-concept clarity, may heighten the risk for some individuals. Body image disturbances can be manifested in forms of avoidance behaviors, monitoring, eating restraints, and body modification. In order to promote body acceptance, we ought to gain insights into the formation of our body image and challenge the commonly held belief on who defines beauty.

OBJECTIVE Machine learning algorithms in electronic medical records can classify patients by suicide risk, but no research has explored clinicians' perceptions of suicide risk flags generated by these algorithms, which may affect algorithm implementation. The objective of this study was to evaluate clinician perceptions of suicide risk flags. METHODS Participants (n = 139; 68 with complete data) were mental health clinicians recruited to complete online surveys from October 2018 to April 2019. RESULTS Most participants preferred to know which features resulted in a patient receiving a suicide flag (94.12%) and reported that knowing those features would influence their treatment (88.24%). Clinicians were more likely to report that some algorithm features (increased thoughts of suicide) would alter their clinical decisions more than others (age, physical health conditions; χ² = 270.84, P < .001). Clinicians were more likely to report that they would create a safety/crisis response plan in response to a suicide risk flag compared to other interventions (χ² = 227.02, P < .001), and 21% reported that they would complete a no-suicide contract following a suicide risk flag. CONCLUSIONS Clinicians overwhelmingly reported that suicide risk flags in electronic medical records would alter their clinical decision making. However, clinicians' likelihood of acting in response to a suicide risk flag was tied to which features were highlighted rather than the presence of the risk flag alone. Thus, the utility of a suicide risk algorithm will be reduced if clinical features underlying the algorithm are hidden from clinicians or if clinicians do not view the clinical features as intuitively meaningful predictors of suicide risk.

While psychiatric disorder prevalence is higher among patients living with HIV (PLWH), there are no prior studies on rates of psychiatric diagnosis documentation in electronic medical records (EMRs) of PLWH, which was the goal of this study. Participants (n = 2,336) were enrolled in the Center for AIDS Research (CFAR) Longitudinal Database study. Diagnostic codes were extracted to assess the prevalence rates of depressive, alcohol/substance use, PTSD, sleep, and adjustment disorders. Prevalence rates for psychiatric disorders were extracted from national epidemiological studies for comparison. Rates of Major Depressive Disorder in the EMR were comparable to prior epidemiological reports in PLWH. In contrast, rates of PTSD, substance use, alcohol use, adjustment, and insomnia disorders were all markedly lower compared to epidemiological studies. While clinicians appropriately documented depression, other psychiatric comorbidities were largely overlooked. This indicates a potential bias in the detection or documentation of psychiatric disorders in PLWH.

Background Prior research has demonstrated the efficacy of internet-based cognitive behavioral therapy (ICBT) for social anxiety disorder (SAD). However, it is unclear how shame influences the efficacy of this treatment. Objective This study aimed to investigate the role shame played in the ICBT treatment process for participants with SAD. Methods A total of 104 Chinese participants (73 females; age: mean 24.92, SD 4.59 years) were randomly assigned to self-help ICBT, guided ICBT, or wait list control groups. For the guided ICBT group, half of the participants were assigned to the group at a time due to resource constraints. This led to a time difference among the three groups. Participants were assessed before and immediately after the intervention using the Social Interaction Anxiety Scale (SIAS), Social Phobia Scale (SPS), and Experience of Shame Scale (ESS). Results Participants’ social anxiety symptoms (self-help: differences between pre- and posttreatment SIAS=−12.71; Cohen d=1.01; 95% CI 9.08 to 16.32; P Conclusions The findings of this study suggest that participants’ engagement in the exposure module in ICBT alleviates social anxiety symptoms by reducing the levels of shame proneness. Our study provides a new perspective for understanding the role of shame in the treatment of social anxiety. The possible mechanisms of the mediation effect and clinical implications are discussed. Trial Registration Chinese Clinical Trial Registry ChiCTR1900021952; http://www.chictr.org.cn/showproj.aspx?proj=36977

The increasing number of nodes in wireless multi-hop Ad hoc network is one of the greatest challenges for its further development since the subsequent high scheduling delay and overhead will jeopardize the performance of network, such as transmission efficiency. To address the scalability problem, this paper establishes a novel Centralized Scheduling based Hierarchical Ad hoc Network (CS-HAN) using frequency division clustering mechanism, in which the scheduling overhead and delay is reduced by narrowing the range of neighbor maintenance. The interference and system capacity utilization rate model are given with joint consideration of the impacts of inter-cluster frequency reuse parameters, network parameters, scheduling parameters and frame structure design. Simulation results show that the system capacity utilization rate gradually arises first and decreases later as the frequency number increases. The reason is though the frequency interference decreases in the beginning, the bandwidth of channel at each frequency point becomes too narrow to support transmission. The influence of how many clusters use the same frequency, network scale and traffic parameters on system capacity utilization rate is also revealed.