Positive emotion in distress as a potentially effective emotion regulation strategy for depression: A preliminary investigation

Differential effects of stress-related and stress-unrelated humor in remitted depression

Enhancing emotion regulation among previously depressed people is crucial for improving their resilience and reducing relapse. Therefore, emphasis is placed on determining effective regulation strategies, particularly those that, besides down-regulating negative emotions, also up-regulate positive emotions. One promising strategy, with great potential in both these respects, is humor. It is unclear, however, what type of humor is most adaptive in remitted depression. This study compared two distinct humor-based strategies: stress-related humor and stress-unrelated humor. Outpatients with remitted depression (N = 94) participated in a randomized experiment evoking personal stress and the subsequent application of stress-related humor, stress-unrelated humor, or a non-humorous regulation. They repeatedly reported positive and negative emotions (at four time points) and experienced distress (at three time points). There were also assessments of selective attention, subsequent performance, effort, and intrusive thoughts. Unlike non-humorous regulation, humor-based strategies had adaptive consequences, both immediately and after a delay; however, stress-unrelated humor was most beneficial and was the only effective strategy when attention deficits were present. Humor, especially if unrelated to stressors, might broaden the repertoire of powerful emotion regulation strategies in remitted depression. Humorous focusing on distress can be detrimental for patients with attention impairment.

Clinical trial registration: The study was registered under the number ISRCTN86314628 (20/09/2021).

A machine-learning model to predict suicide risk in Japan based on national survey data

OBJECTIVE

Several prognostic models of suicide risk have been published; however, few have been implemented in Japan using longitudinal cohort data. The aim of this study was to identify suicide risk factors for suicidal ideation in the Japanese population and to develop a machine-learning model to predict suicide risk in Japan.

MATERIALS AND METHODS

Data was obtained from Wave1 Time 1 (November 2016) and Time 2 (March 2017) of the National Survey for Stress and Health in Japan, were incorporated into a suicide risk prediction machine-learning model, trained using 65 items related to trauma and stress. The study included 3,090 and 2,163 survey respondents >18 years old at Time 1 and Time 2, respectively. The mean (standard deviation, SD) age was 44.9 (10.9) years at Time 1 and 46.0 (10.7) years at Time 2. We analyzed the participants with increased suicide risk at Time 2 survey. Model performance, including the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity, were also analyzed.

RESULTS

The model showed a good performance (AUC = 0.830, 95% confidence interval = 0.795-0.866). Overall, the model achieved an accuracy of 78.8%, sensitivity of 75.4%, specificity of 80.4%, positive predictive value of 63.4%, and negative predictive value of 87.9%. The most important risk factor for suicide risk was the participants' Suicidal Ideation Attributes Scale score, followed by the Sheehan Disability Scale score, Patient Health Questionnaire-9 scores, Cross-Cutting Symptom Measure (CCSM-suicidal ideation domain, Dissociation Experience Scale score, history of self-harm, Generalized Anxiety Disorder-7 score, Post-Traumatic Stress Disorder check list-5 score, CCSM-dissociation domain, and Impact of Event Scale-Revised scores at Time 1.

CONCLUSIONS

This prognostic study suggests the ability to identify patients at a high risk of suicide using an online survey method. In addition to confirming several well-known risk factors of suicide, new risk measures related to trauma and trauma-related experiences were also identified, which may help guide future clinical assessments and early intervention approaches.

Depressive Symptoms and Associated Factors in Female Students in Fukushima Four Years after the Fukushima Nuclear Power Plant Disaster

Young women in their late teens and early 20s are at the highest risk for depression onset. The present study aimed to assess depressive symptoms among female college students in Fukushima. More specifically, it aimed to clarify factors predicting possible symptom profiles, with an emphasis on determining how nuclear radiation risks affect the reporting of depression symptoms. A cross-sectional survey was conducted of 310 female students at a college in the Fukushima prefecture, Japan, in December 2015, and 288 participants submitted valid questionnaires. In total, 222 (77.1%) participants lived in Fukushima at the time of the Great East Japan Earthquake. The measures included the World Health Organization-Five Well-Being Index, the Fukushima Future Parents Attitude Measure, and risk perception of radiation health effects. A total of 46.5% of participants reported depressive symptoms. Path analysis revealed that higher radiation risk perceptions and reduced efficacy with reproduction related to a decline in self-esteem and self-efficacy, which was subsequently associated with increased depressive symptoms. These findings highlight the importance of radiation education among children and young adults, both after a nuclear accident and during disaster preparation, particularly in the context of reproductive and mental health.