Objective: We aimed to investigate the effectiveness of buspirone as an adjunctive therapy for alleviating anxiety symptoms in patients with depressive disorders who are already taking antidepressants. Methods: This was an open-label prospective multicenter non-interventional observational study conducted over 12 weeks. We enrolled 180 patients diagnosed with depressive disorders according to DSM-5 criteria and Hamilton Anxiety Rating Scale (HAMA) scores >= 18. Participants were already taking selective serotonin reuptake inhibitors or serotoninnorepinephrine reuptake inhibitors and were prescribed adjunctive buspirone. Efficacy was assessed using HAMA, Hamilton Depression Rating Scale (HAMD), Clinical Global Impression Scale-Improvement, Clinical Global Impression Scale-Severity, Sheehan Disability Scale (SDS), and WHO-5 Well-Being Index. Results: The efficacy analysis included 161 patients. HAMA scores decreased significantly from 25.2 +/- 6.7 at baseline to 15.4 +/- 8.6 at 12 weeks (p < 0.001), whereas HAMD scores decreased from 19.4 +/- 4.6 to 12.7 +/- 5.7 (p < 0.001). WHO-5 and SDS scores showed significant improvements. The HAMA response rate was 39.1% and the remission rate was 13.7% at 12 weeks. Adverse drug reactions were reported in 3.7% of participants. Subgroup analyses showed no significant differences in treatment response based on buspirone dosage, baseline anxiety/depression severity, or benzodiazepine use. Conclusion: Adjunctive buspirone therapy effectively improved anxiety symptoms in depressed patients taking antidepressants, regardless of baseline symptom severity or buspirone dosage. The treatment was well-tolerated with few adverse events. Future studies using a control group are needed.

The sixth edition of the Korean Medication Algorithm Project for Depressive Disorder (KMAP-DD) was published in 2025. This review compared KMAP-DD 2025 with four major international clinical practice guidelines: Canadian Network for Mood and Anxiety Treatments Clinical Guidelines for the Management of Major Depressive Disorders, National Institute for Health and Care Excellence Depression Guideline, Royal Australian and New Zealand College of Psychiatrists Clinical Practice Guidelines for Mood Disorders, and British Association for Psychopharmacology Guideline. While KMAP-DD is based on expert consensus, and others on evidence-based methods, overall treatment strategies for depressive episodes were fairly consistent. Especially, KMAP-DD 2025 offers more structured recommendations in areas lacking strong evidence, such as premenstrual dysphoric disorder, perinatal depression, and depression with medical comorbidities. KMAP-DD 2025 also reflected Korean clinical practice patterns emphasizing rapid symptom relief and early use of combination strategies. Despite limitations as a consensus-based guideline, KMAP-DD 2025 complements evidence-based approaches and provides practical, situation-specific guidance for real-world clinical decision-making in Korea.

Objective:Since its development in 2002 by the Korean College of Neuropsychopharmacology and the Korean Society for Affective Disorders, the Korean Medication Algorithm Project for Depressive Disorder (KMAP-DD) has undergone five revisions. Methods:To improve survey efficiency, reflect general clinical practice, and facilitate comparisons with previous KMAP-DD revisions, the overall structure of the questionnaire was retained. The six sections of the questionnaire were as follows: 1) pharmacological treatment strategies for major depressive disorder with and without psychotic features; 2) pharmacological treatment strategies for persistent depressive disorder and other depressive disorder subtypes; 3) consensus on treatment-resistant depression; 4) selection of an antidepressant in consideration of safety, adverse effects, and comorbid physical conditions; 5) treatment strategies for special populations (children/adolescents, elderly, and women); and 6) non-pharmacological biological therapies. First-, second-, and third-line treatment recommendations were statistically derived. Results:Compared to KMAP-DD 2021, only minor changes were noted, due to the limited introduction of new medications or treatment modalities. Nonetheless, notable shifts included an increased preference for atypical antipsychotics (AAPs), and higher preference of combination strategies involving AAPs and mood stabilizers, indicating a more proactive and intensive treatment trend in Korea. Conclusion:KMAP-DD is expected to serve as a valuable clinical resource by providing expert consensus-based recommendations on specific treatment strategies and pharmacological options for major depressive disorders, thereby supporting the integration of real-world clinical practice with evidence-based medicine.

Background: This study aimed to identify the prevalence of workplace hazards and organizational protection resources according to the size of the enterprise in the manufacturing industry of the Republic of Korea. Methods: We analyzed data of waged workers (weighted N 1/4 5,879) from the Fifth Korean Working Conditions Survey (2017). Enterprise sizes were categorized as "micro enterprises" (less than five employees), "small enterprises" (5-49 employees) and "medium-large enterprises" (50 or more employees). Self-reported exposure to 18 physical, chemical, ergonomic, and psychological hazards were measured. The presence of organizational protection resources such as a labor union, a safety delegate working at the company, designated spaces to deal with safety, and the provision of health and safety information was evaluated. Results: Compared to workers in medium-large enterprises, those in micro enterprises showed a higher proportion of exposure to most of physical, chemical, ergonomic, and psychological hazards, except for exposure to solvents, prolonged sitting, and experiencing a state of emotional unrest. On the other hand, workers in micro enterprises had the lowest proportion of access to organizational protection resources. Conclusion: Our study demonstrates that manufacturing workers at the micro enterprise in the Republic of Korea are exposed to the most hazardous work environment and yet have access to the fewest organizational protection resources. (c) 2024 Occupational Safety and Health Research Institute. Published by Elsevier B.V. on behalf of Institute, Occupational Safety and Health Research Institute, Korea Occupational Safety and Health Agency. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).

OBJECTIVES: This study investigated the relationship between night work, supervisor support, and depressive symptoms among full-time wage workers, with a focus on gender differences.METHODS: A nationwide sample of 22,422 full-time wage workers from the Sixth Korean Working Conditions Survey (2020-2021) was analyzed. Experiences of night work were categorized into 5 groups based on the number of night work days per month: 0, 1-5, 6-10, 11-15, and 16-31. Depressive symptoms were evaluated using the 5-item World Health Organization Well-Being Index. Supervisor support was assessed with 5 items.RESULTS: Workers who engaged in 1-5 days (prevalence ratio [PR], 1.23; 95% confidence interval [CI], 1.12 to 1.36) and 6-10 days (PR, 1.17; 95% CI, 1.06 to 1.30) of night work per month exhibited a higher prevalence of depressive symptoms than those without night work. After stratifying by supervisor support levels, workers with 1-5 days, 6-10 days, and 11-15 days of night work per month were more likely to experience depressive symptoms compared to those without night work in the low supervisor support group. In contrast, no association was found between night work (≥6 days) and depressive symptoms in the high supervisor support group. Furthermore, gender differences were notable: female workers with 6-10 days (PR, 1.45; 95% CI, 1.23 to 1.70), and 11-15 days (PR, 1.43; 95% CI, 1.08 to 1.90) of night work per month exhibited a higher prevalence of depressive symptoms, whereas their male counterparts did not. This pattern of gender difference was also found among those with low supervisor support.CONCLUSIONS: Supervisor support may mitigate the adverse effects of night work on depressive symptoms among full-time wage workers, with differences manifested across genders.

Objective We aimed to identify the expectations and preferences for medication and medical decision-making in patients with major psychiatric disorders.Methods A survey was conducted among patients with major psychiatric disorders who visited psychiatric outpatient clinics at 15 hospitals between 2016 and 2018 in Korea. The survey consisted of 12 questions about demographic variables and opinions on their expectations for medication, important medical decision-makers, and preferred drug type. The most preferred value in each category in the total population was identified, and differences in the preference ratio of each item among the disease groups were compared.Results A total of 707 participants were surveyed. In the total population, patients reported high efficacy (44.01%±21.44%) as the main wish for medication, themselves (37.39%±22.57%) and a doctor (35.27%±22.88%) as the main decision makers, and tablet/capsule (36.16%±30.69%) as the preferred type of drug. In the depressive disorders group, the preference ratio of high efficacy was significantly lower, and the preference ratio of a small amount was significantly higher than that of the psychotic disorder and bipolar disorder groups. The preference ratio of a doctor as an important decision maker in the bipolar disorder group was higher compared to the other groups.Conclusion This study revealed the preference for medications and showed differences among patients with psychiatric disorders. Providing personalized medicine that considers a patient’s preference for the drug may contribute to the improvement of drug compliance and outcomes.

O2O platform foodservice industry has been increased so quickly during the Pandemic era, this study was performed to identify the effect of O2O platform food service quality on behavior intention by verifying the sequential mediating effect of the review acceptance and customer satisfaction between the relationship of food service quality and behavior intention. The survey was conducted for those using O2O platform foodservice, and 364 valid responses were used for analysis. According to the analysis results, it has been confirmed that O2O platform foodservice quality, review acceptance, customer satisfaction and behavior intention were significantly related to each other. Second, review acceptance and customer satisfaction in the relationship between O2O platform foodservice quality and behavior intention have been found to be partial-mediated and sequential mediated. Specifically, the factors of ‘delivery’, ‘response’, ‘information’, ‘trust’ from O2O platform foodservice quality had the significant effect on the behavior intention. Therefore O2O platform foodservice business should be focused on this results so that they can use them for more effective service marketing strategies. Specially, the platform business must have more weight on the education and management for both(personal and technical service) sides.

The objective of this study was to compare recommendations of the Korean Medication Algorithm Project for Bipolar Disorder 2022 (KMAP-BP 2022) with other recently published guidelines for treating bipolar disorder. We reviewed a total of six recently published global treatment guidelines and compared treatment recommendation of the KMAP-BP 2022 with those of other guidelines. For initial treatment of mania, there were no significant differences across treatment guidelines. All guidelines recommended mood stabilizer (MS) or atypical antipsychotic (AAP) monotherapy or a combination of an MS with an AAP as a first-line treatment strategy in a same degree for mania. However, the KMAP-BP 2022 recommended MS + AAP combination therapy for psychotic mania, mixed mania and psychotic depression as treatment of choice. Aripiprazole, quetiapine and olanzapine were the first-line AAPs for nearly all phases of bipolar disorder across guidelines. Some guideline suggested olanzapine is a second-line options during maintenance treatment, related to concern about long-term tolerability. Most guidelines advocated newer AAPs (asenapine, cariprazine, long-acting injectable risperidone, and aripiprazole once monthly) as first-line treatment options for all phases while lamotrigine was recommended for depressive and maintenance phases. Lithium and valproic acid were commonly used as MSs in all phases of bipolar disorder. KMAP-BP 2022 guidelines were similar to other guidelines, reflecting current changes in prescription patterns for bipolar disorder based on accumulated research data. Strong preference for combination therapy was characteristic of KMAP-BP 2022, predominantly in the treatment of psychotic mania, mixed mania and psychotic depression.

BACKGROUND:The Korean Medication Algorithm Project for Depressive Disorder (KMAP-DD) is an expert consensus guideline for depressive disorder created in 2002, and since then, four revisions (2006, 2012, 2017, 2021) have been published. In this study, changes in the content of the KMAP-DD survey and recommendations for each period were examined.METHODS:The development process of the KMAP-DD was composed of two stages. First, opinions from experts with abundant clinical experience were gathered through surveys. Next, a final guideline was prepared through discussion within the working committee regarding the suitability of the results with reference to recent clinical studies or other guidelines.RESULTS:In mild depressive symptoms, antidepressant (AD) monotherapy was preferred, but when severe depression or when psychotic features were present, a combination of AD and atypical antipsychotics (AD + AAP) was preferred. AD monotherapy was preferred in most clinical subtypes. AD monotherapy was preferred for mild depressive symptoms, and AD + AAP was preferred for severe depression and depression with psychotic features in children, adolescents, and the elderly.CONCLUSIONS:This study identified the changes in the KMAP-DD treatment strategies and drug preferences in each period over the past 20 years. This work is expected to aid clinicians in establishing effective treatment strategies.

Background: Depression is a major public health concern, with an estimated 10.8% of adults experiencing depression.Depression can have a significant impact on an individual's quality of life, social function, and productivity.Early diagnosis of depression is important in preventing its progression.Several tools, such as the Patient Health Questionnaire-9 (PHQ-9) and Beck Depression Inventory, are used to screen patients for depression.We investigated the potential of machine learning in predicting the presence of depression using the results of a national survey.Methods: We collected the data of 5,420 patients from the 2020 Korea National Health and NutritionExamination.The presence of depression was defined as ≥5 PHQ-9.We categorized output variables into the presence of depression (PHQ-9, ≥5) and absence of depression (PHQ-9, <5).We used 20 variables related to sociodemographic characteristics, health behavior, and presence of chronic disease for the development of three machine learning algorithms [random forest, logistic regression, and deep neural network (DNN)].Eighty-seven decision trees were used for the random forest model.Linear regression algorithm shows a linear relationship between various input and output variables.For the DNN model, three layers with 16-32-64 neurons, Adam optimizer, and rectified linear unit (ReLU) activation were used.Of the included samples, 70% and 30% were randomly divided into the training and test sets, respectively. Results:The area under the curve (AUC) of the test dataset for the random forest model was 0.803 [95% confidence interval (CI), 0.776-0.829],0.812 (95% CI, 0.787-0.837)for the logistic regression model, and 0.805 (95% CI, 0.780-0.831)for the DNN model. Conclusions:Our study demonstrated the potential of machine learning for the development of models for predicting the presence of depression based of various health-related data.Machine learning models can potentially overcome the limitations of traditional diagnostic methods for depression by incorporating a wide range of objective variables to accurately identify patients with depression, thus avoiding the subjectivity and potential diagnostic errors associated with the subjective interpretation of symptoms observed by a clinician.Further efforts to increase the accuracy of machine learning models by utilizing more variables and data needed to detect depression.