Frontiers in Psychiatry最新文献

Objective: China's abrupt exit from the zero-COVID policy in late 2022 led to a rapid surge in infections, overwhelming healthcare systems and exposing healthcare providers to intensified psychological pressures. This sudden shift exacerbated pandemic-related psychological issues, including fear, health anxiety, and emotional processing difficulties. This study aimed to unravel the relationships among pandemic fear, cyberchondria, and alexithymia following China's exit from the zero-COVID policy.

Methods: A multi-center cross-sectional survey was conducted among 4088 nurses from 43 public hospitals in China. The web-based survey comprised the Fear of COVID-19 Scale, Cyberchondria Severity Scale, and Toronto Alexithymia Scale. Network analysis was employed to explore the interconnections and identify central components within these psychological and behavioral constructs.

Results: The analysis revealed a dense network with predominantly positive connections. Specific aspects of cyberchondria and pandemic fear exhibited the highest strength centrality, indicating their critical influence. The externally oriented thinking dimension of alexithymia emerged as a crucial bridge node, linking pandemic fear and cyberchondria. The network structure demonstrated consistency across diverse educational backgrounds and career stages.

Conclusion: These findings highlight the need for targeted interventions focusing on key network components, particularly externally oriented thinking, to disrupt the detrimental cycle of pandemic fear and cyberchondria. Healthcare organizations should promote balanced objective fact-focused and problem-solving approaches while also fostering skills in emotional awareness and expression, thereby mitigating the risk of maladaptive pandemic fear responses and dysfunctional online health information-seeking behaviors.

Background: A number of studies have demonstrated that suicidal ideation in late life is associated with early-life circumstances. However, the importance of early-life circumstances in predicting suicidal ideation is not entirely clear. This study aims to use a machine learning approach to evaluate the importance of 32 early-life circumstances from six domains in predicting suicidal ideation in old age.

Methods: The data in this study come from a cross-national longitudinal survey, the Survey of Health, Aging and Retirement in Europe (SHARE). Participants recalled information on early-life circumstances in SHARE wave 7 and reported suicidal ideation in SHARE wave 8. The XGBoost model was employed to evaluate the importance of 32 circumstances in six domains (early-life socioeconomic status, early-life health and healthcare, early-life relationship, etc.) in predicting the suicidal ideation of middle-aged and older adults over 50.

Results: There were 46,498 participants in this study, of which 26,672 (57.36%) were females and 19,826 (42.64%) were males. XGBoost showed a strong predictive performance, with an area under the curve of 0.80 and accuracy of 0.77. Top predictors were mainly in the domains of childhood relationship, childhood socioeconomic status, childhood health, and healthcare. In particular, having a group of friends most critically influences suicidal ideation in old age.

Discussion: These findings suggest that early-life circumstances may modestly predict suicidal ideation in late life. Preventive measures can be taken to lower the risk of suicidal ideation in middle-aged and older individuals.

Introduction: Peripheral neurofilament light chain (NfL) reflect neuronal and axonal damage. Most studies have been focused on NfL cerebrospinal fluid measures since peripheral levels were difficult to detect. However, with recent advent of single molecule array (SIMOA) technology, NfL is now detectable peripherally at small concentrations (pg/ml). In neurodegenerative disorders, NfL peripheral levels have been found significantly elevated compared against psychiatric disorders. However, there is still controversy of whether NfL peripheral levels might be altered in psychiatric disorders like major depressive disorder (MDD) when compared against a normal population.

Methods: We have measured NfL plasma levels by using single molecule array (SIMOA) technology in a Spanish small cohort of MDD patients (n = 15) and a HC group (n = 15). We have used subjective scales to estimate depression severity (HDRS), anhedonia (SAAS), the general cognitive state (MMSE) and episodic memory (FCSRT) in MDD patients.

Results: We have not detected a significant alteration in NfL plasma levels in MDD patients when compared against the HC subjects (U = 97, p-value = 0.532). Moreover, we have not detected any significant correlation between NfL plasma levels with any subjective scales. The only parameter that significantly and positively correlated with NfL plasma levels was age in both MDD and HC.

Discussion: Significant alteration in NfL plasma levels in MDD patients might reflect neurobiological changes behind the predisposition to develop future neurodegenerative disorders such as Alzheimer's or Parkinson's diseases for which depression represents a risk factor. However, whether there is an increase in NfL due to MDD regardless of the ageing process is still a matter of debate.

Background: Remnant cholesterol (RC) has received increasing attention due to its association with a variety of diseases. However, comprehensive population-based studies elucidating the relationship between RC and major depressive disorder (MDD) are limited. The current study aimed to determine the association between RC and MDD in US adults.

Methods: Cross-sectional data of US adults with complete RC and depression information were obtained from the National Health and Nutrition Examination Survey (NHANES) 2005-2018. MDD was evaluated using the Patient Health Questionnaire (PHQ-9). Multivariate logistic regression, sensitivity analysis, and spline smoothing plot method were conducted to explore the relationship between RC and depression. The cut-off point was calculated using recursive partitioning analysis when segmenting effects emerged. The area under the receiver operating characteristic (ROC) curve (AUC), calibration curve, Hosmer-Lemeshow test, the decision curve analysis (DCA), and clinical impact curve (CIC) were employed to evaluate the performance of RC in identifying MDD. Subgroup analyses and interaction tests were performed to explore whether the association was stable in different populations.

Results: A total of 9,173 participants were enrolled and participants in the higher RC quartile tended to have a higher PHQ-9 score and prevalence of MDD. In the fully adjusted model, a positive association between RC and PHQ-9 score and MDD was both observed (β=0.54, 95% CI 0.26~0.82; OR=1.43, 95% CI 1.15~1.78). Participants in the highest RC quartile had a 0.42-unit higher PHQ-9 score (β=0.42, 95% CI 0.15~0.69) and a significantly 32% higher risk of MDD than those in the lowest RC quartile (OR=1.32, 95% CI 1.05~1.66). Spline smoothing plot analysis further confirmed the positive and non-linear association between RC and PHQ-9 and MDD. ROC analysis (AUC=0.762), the Hosmer-Lemeshow test (χ² = 6.258, P=0.618), and calibration curve all indicated a high performance and goodness-of-fit of the multivariate model. DCA and CIC analysis similarly demonstrated a positive overall net benefit and clinical impact for the model. Subgroup analyses and interaction tests suggested that the relationship between RC and depression remained stable across subgroups and was unaffected by other factors other than diabetes, hypertension, or hyperlipidemia.

Conclusion: An elevated RC is associated with a higher risk of prevalent MDD among US adults, especially in those with diabetes, hypertension, or hyperlipidemia. The present results suggested that the management of RC levels and comorbidities may contribute to alleviating the occurrence of MDD.

Introduction: Digital health interventions specifically those realized as chatbots are increasingly available for mental health. They include technologies based on artificial intelligence that assess user's sentiment and emotions for the purpose of responding in an empathetic way, or for treatment purposes, e.g. for analyzing the expressed emotions and suggesting interventions.

Methods: In this paper, we study the ethical dimensions of integrating these technologies in chatbots for depression intervention using the digital ethics canvas and the DTx Risk Assessment Canvas.

Results: As result, we identified some specific risks associated with the integration of sentiment and emotion analysis methods into these systems related to the difficulty to recognize correctly the expressed sentiment or emotion from statements of individuals with depressive symptoms and the appropriate system reaction including risk detection. Depending on the realization of the sentiment or emotion analysis, which might be dictionary-based or machine-learning based, additional risks occur from biased training data or misinterpretations.

Discussion: While technology decisions during system development can be made carefully depending on the use case, other ethical risks cannot be prevented on a technical level, but by carefully integrating such chatbots into the care process allowing for supervision by health professionals. We conclude that a careful reflection is needed when integrating sentiment and emotion analysis into chatbots for depression intervention. Balancing risk factors is key to leveraging technology in mental health in a way that enhances, rather than diminishes, user autonomy and agency.

The Artificial Intelligence (AI) technology holds immense potential in the realm of automated diagnosis for Major Depressive Disorder (MDD), yet it is not without potential shortcomings. This paper systematically reviews the research progresses of integrating AI technology with depression diagnosis and provides a comprehensive analysis of existing research findings. In this context, we observe that the knowledge-driven first-generation of depression diagnosis methods could only address deterministic issues in structured information, with the selection of depression-related features directly influencing identification outcomes. The data-driven second-generation of depression diagnosis methods achieved automatic learning of features but required substantial high-quality clinical data, and the results were often obtained solely from the black-box models which lack sufficient explainability. In an effort to overcome the limitations of the preceding approaches, the third-generation of depression diagnosis methods combined the strengths of knowledge-driven and data-driven approaches. Through the fusion of information, the diagnostic accuracy is greatly enhanced, but the interpretability remains relatively weak. In order to enhance interpretability and introduce diagnostic criteria, this paper offers a new approach using Large Language Models (LLMs) as AI agents for assisting the depression diagnosis. Finally, we also discuss the potential advantages and challenges associated with this approach. This newly proposed innovative approach has the potential to offer new perspectives and solutions in the diagnosis of depression.

Addiction is a complex neurobiological disorder characterized by compulsive drug-seeking and use despite harmful consequences. While abstinence-based approaches have long been the cornerstone of addiction treatment, recent years have seen legitimate challenges from harm reduction clinicians, and within the food addiction realm, the eating disorder treatment practitioners. This perspective emphasizes the role of abstinence in food addiction recovery using the Koob model and its concept of hyperkatifeia despite these reservations. However, further research is essential before abstinence can be recommended. We need to 1) identify what qualifies as abstinence in relation to ultra-processed food, 2) clarify suitable situations and disease progression for optimal implementation of this approach, 3) provide clear guidelines when it is harmful, and 4) conduct clinical studies to confirm the effectiveness of this strategy for long-term recovery from late-stage food addiction.

Background: Despite the increasing proportion of older-aged individuals suffering from problematic gambling, research on gambling among this specific age cohort is still in its infancy. Chasing is a pathognomonic feature of disordered gambling and is considered one of the key risk factors in the transition from recreational to disordered gambling. Despite the increased research on chasing over the past decade, no previous study has ever examined the psychological determinants of chasing behavior among old-aged gamblers. Given the importance of chasing in facilitating and maintaining problem gambling, and the paucity of research examining gambling among older individuals, the present study is the first to empirically investigate the joint role of chasing behavior, negative affectivity, and mentalizing among older-aged gamblers.

Methods: The sample comprised 116 older-aged gamblers who were administered the South Oaks Gambling Screen (SOGS), the Depression Anxiety Stress Scale (DASS-21) and the Reflective Functioning Questionnaire (RFQ-8) to assess gambling severity, psychological distress, and mentalizing, respectively. Participants also performed the ChasIT, a computerized task that assesses chasing behavior, in which participants were randomly assigned to three different experimental conditions: loss, control, and win.

Results: No effect of the experimental conditions of ChasIT on chasing behavior was observed. Regression analyses indicated that heightened levels of gambling severity and lower levels of certainty about mental states (i.e., hypermentalizing) predicted both the decision to chase and chasing frequency. Along with problem gambling and hypermentalizing, chasing frequency was also predicted by high levels of depression.

Conclusions: The present study demonstrated the association between disordered gambling, depression, and hypermentalizing in chasing behavior among older-aged gamblers. The findings make an important contribution to providing insight regarding variables that are associated with chasing among older-aged gamblers, one of the least represented populations of gamblers in the literature. The results suggest that specific training on mentalizing abilities could help gamblers to reflect on their own behaviors in terms of mental states, rather than following the impulse to gamble in order to ameliorate poor mood.

[This retracts the article DOI: 10.3389/fpsyt.2022.836950.].

Background: With the advancement of transcranial electrical stimulation (tES) technology, an increasing number of stimulation devices and treatment protocols have emerged. However, safety and tolerability remain critical concerns before new strategies can be implemented. Particularly, the use of gel particle electrodes brings new challenges to the safety and tolerability of tES, which hinders its widespread adoption and further research.

Objective: Our study utilized a specially designed and validated transcranial electrical stimulation stimulator along with preconfigured gel particle electrodes placed at F3 and F4 in the prefrontal lobes. We aimed to assess the tolerance and safety of these electrodes in healthy subjects by administering different durations and types of tES.

Methods: Each participant underwent ten sessions of either transcranial direct current stimulation (tDCS) or transcranial alternating current stimulation (tACS), with session durations varying. In the experiment, we collected various measurement data from participants, including self-report questionnaire data and behavioral keystroke data. Tolerability was evaluated through adverse events (AEs), the relationship of adverse events with tES (AEs-rela), the Self-Rating Anxiety Scale (SAS), and the Visual Analog Mood Scale-Revised (VAMS-R). Safety was assessed using the Visual Analog Scale (VAS), the Skin Sensation Rating (SSR), Montreal Cognitive Assessment (MoCA), and Stroop task. These data were analyzed to determine the impact of different parameters on the tolerability and safety of tES.

Results: There were no significant changes in the results of the MoCA and SAS scales before and after the experiment. However, significant differences were observed in VAS, SSR, AEs, and AEs-rela between tDCS and tACS. Additionally, fatigue increased, and energy levels decreased on VAMS-R with longer durations. No significant differences were found in other neuropsychological tests.

Conclusion: Our study revealed significant differences in tolerability and safety between tDCS and tACS, underscoring the importance of considering the stimulation type when evaluating these factors. Although tolerance and safety did not vary significantly across different stimulation durations in this study, future research may benefit from exploring shorter durations to further assess tolerability and safety efficiently.