Search Results (7)

Background: Although forest therapy services in South Korea have demonstrated mental and physical effects, there is no established system for forest therapy prescriptions. To this end, it is necessary to devise a systematic model for the introduction of forest therapy prescriptions by linking the existing forest therapy infrastructure and medical services. Therefore, this study aimed to derive the components and application plans needed to devise a forest therapy prescription model for the spread of medical-linked forest therapy services and to secure a forest therapy prescription infrastructure. Methods: To this end, Korean and foreign cases of prescription models and healthcare service provision systems were analyzed to derive the necessary components for prescription models. Subsequently, a Focus Group Interview (FGI) was conducted with eight experts in the fields of forest therapy and welfare, psychiatry, and health and nursing, and opinions were derived regarding the conception and empirical application of the forest therapy prescription model through content analysis. Results: As a result of the study, five components (clear role-sharing and a collaboration system, a continuous system, customized service provision, various technologies and content, and a database-based prescription system) were derived from cases of prescription models and healthcare service provision systems according to field. Furthermore, the FGI identified three primary topics: stakeholders’ scope and role, procedures and effectiveness, and additional considerations. Each was categorized into eight sub-categories relevant to the design of the forest therapy prescription model. Conclusions: These results can be used as basic data for devising a systematic Korean forest therapy prescription model in which forest therapy and medical services are linked, providing a foundation for personalized forest therapy prescriptions to be implemented. Full article

The objective of this research is to investigate the awareness and opinions concerning forest therapy within a public health institution, specifically as a green prescription for patients with stable chronic disease. Through qualitative preliminary analysis, this study compared the responses of a group to gather physical activity and wood frequentation insights, as well as forest therapy patients’ attitudes. The results underline a general predisposition among respondents to engage in moderate physical activity and visit natural environments for relaxation purposes. Emerging parallelly is how forest therapy is largely an unknown practice, although it draws considerable interest and a general predisposition to participate. This research outlines the ideal conditions that emerge for participating in forest therapy sessions, including the availability to pay, pointing toward environmentally and socio-economically sustainable reflections. Further studies should extend this preliminary investigation using appropriate statistical methodologies on larger samples, involving different regions and medical conditions. Full article

Background: Psychotic disorders are major psychiatric disorders that can impact multiple domains including physical, social, and psychological functioning within individuals with these conditions. Being able to better predict the outcomes of psychotic disorders will allow clinicians to identify illness subgroups and optimize treatment strategies in a timely manner. Objective: In this scoping review, we aimed to examine the accuracy of the use of artificial intelligence (AI) methods in predicting the clinical outcomes of patients with psychotic disorders as well as determine the relevant predictors of these outcomes. Methods: This review was guided by the PRISMA Guidelines for Scoping Reviews. Seven electronic databases were searched for relevant published articles in English until 1 February 2024. Results: Thirty articles were included in this review. These studies were mainly conducted in the West (63%) and Asia (37%) and published within the last 5 years (83.3%). The clinical outcomes included symptomatic improvements, illness course, and social functioning. The machine learning models utilized data from various sources including clinical, cognitive, and biological variables such as genetic, neuroimaging measures. In terms of main machine learning models used, the most common approaches were support vector machine, random forest, logistic regression, and linear regression models. No specific machine learning approach outperformed the other approaches consistently across the studies, and an overall range of predictive accuracy was observed with an AUC from 0.58 to 0.95. Specific predictors of clinical outcomes included demographic characteristics (gender, socioeconomic status, accommodation, education, and employment); social factors (activity level and interpersonal relationships); illness features (number of relapses, duration of relapses, hospitalization rates, cognitive impairments, and negative and disorganization symptoms); treatment (prescription of first-generation antipsychotics, high antipsychotic doses, clozapine, use of electroconvulsive therapy, and presence of metabolic syndrome); and structural and functional neuroimaging abnormalities, especially involving the temporal and frontal brain regions. Conclusions: The current review highlights the potential and need to further refine AI and machine learning models in parsing out the complex interplay of specific variables that contribute to the clinical outcome prediction of psychotic disorders. Full article

Background: Liver transplantation (LT) is a routine therapeutic approach for patients with acute liver failure, end-stage liver disease and/or early-stage liver cancer. While 5-year survival rates have increased to over 80%, long-term outcomes are critically influenced by extrahepatic sequelae of LT and immunosuppressive therapy, including diabetes mellitus (DM). In this study, we used machine learning (ML) to predict the probability of new-onset DM following LT. Methods: A cohort of 216 LT patients was identified from the Disease Analyzer (DA) database (IQVIA) between 2005 and 2020. Three ML models comprising random forest (RF), logistic regression (LR), and eXtreme Gradient Boosting (XGBoost) were tested as predictors of new-onset DM within 12 months after LT. Results: 18 out of 216 LT patients (8.3%) were diagnosed with DM within 12 months after the index date. The performance of the RF model in predicting the development of DM was the highest (accuracy = 79.5%, AUC 77.5%). It correctly identified 75.0% of the DM patients and 80.0% of the non-DM patients in the testing dataset. In terms of predictive variables, patients’ age, frequency and time of proton pump inhibitor prescription as well as prescriptions of analgesics, immunosuppressants, vitamin D, and two antibiotic drugs (broad spectrum penicillins, fluocinolone) were identified. Conclusions: Pending external validation, our data suggest that ML models can be used to predict the occurrence of new-onset DM following LT. Such tools could help to identify LT patients at risk of unfavorable outcomes and to implement respective clinical strategies of prevention. Full article

The augmentation of clozapine with electroconvulsive therapy (ECT) has been an optimal treatment option for patients with treatment- or clozapine-resistant schizophrenia. Using data from the Research on Asian Psychotropic Prescription Patterns for Antipsychotics survey, which was the largest international psychiatry research collaboration in Asia, our study aimed to develop a machine learning algorithm-based substantial prediction model for the augmented use of clozapine with ECT in patients with schizophrenia in terms of precision medicine. A random forest model and least absolute shrinkage and selection operator (LASSO) model were used to develop a substantial prediction model for the augmented use of clozapine with ECT. Among the 3744 Asian patients with schizophrenia, those treated with a combination of clozapine and ECT were characterized by significantly greater proportions of females and inpatients, a longer duration of illness, and a greater prevalence of negative symptoms and social or occupational dysfunction than those not treated. In the random forest model, the area under the curve (AUC), which was the most preferred indicator of the prediction model, was 0.774. The overall accuracy was 0.817 (95% confidence interval, 0.793–0.839). Inpatient status was the most important variable in the substantial prediction model, followed by BMI, age, social or occupational dysfunction, persistent symptoms, illness duration > 20 years, and others. Furthermore, the AUC and overall accuracy of the LASSO model were 0.831 and 0.644 (95% CI, 0.615–0.672), respectively. Despite the subtle differences in both AUC and overall accuracy of the random forest model and LASSO model, the important variables were commonly shared by the two models. Using the machine learning algorithm, our findings allow the development of a substantial prediction model for the augmented use of clozapine with ECT in Asian patients with schizophrenia. This substantial prediction model can support further studies to develop a substantial prediction model for the augmented use of clozapine with ECT in patients with schizophrenia in a strict epidemiological context. Full article

The boundary between mindfulness and forest bathing, two conceptually related therapies, is unclear. Accordingly, this study reports the strengths and challenges, similarities and differences, and barriers and facilitators for both. Semi-structured interviews were conducted with seven trained and experienced practitioners of both mindfulness and forest bathing. Reflexive thematic analysis revealed four main themes: (i) differences between the approaches; (ii) the benefits of forest bathing; (iii) biophilia through forest bathing; and (iv) inward versus outward attentional focus as a distinction between the approaches. Both practices were found to benefit well-being, but practitioners revealed key barriers to mindfulness. For vulnerable groups experiencing mental health challenges or difficulties achieving a meditative state, mindfulness may introduce well-being risks. By offering a gentler, more intuitive approach that encourages outward attentional focus, forest bathing was found to overcome this barrier. Forest bathing is suitable for all groups, but adaptations are recommended for those expressing fear or discomfort in forested environments. The findings inform how to position both approaches in practice, as a first step towards social prescribing recommendations. Wider implications concern forest bathing’s potential to impact environmental well-being. Future research must garner comparative data, involve young people, and explore the feasibility of a forest bathing social prescription. Full article

Engagement with nature is an important part of many people’s lives, and the health and wellbeing benefits of nature–based activities are becoming increasingly recognised across disciplines from city planning to medicine. Despite this, urbanisation, challenges of modern life and environmental degradation are leading to a reduction in both the quantity and the quality of nature experiences. Nature–based health interventions (NBIs) can facilitate behavioural change through a somewhat structured promotion of nature–based experiences and, in doing so, promote improved physical, mental and social health and wellbeing. We conducted a Delphi expert elicitation process with 19 experts from seven countries (all named authors on this paper) to identify the different forms that such interventions take, the potential health outcomes and the target beneficiaries. In total, 27 NBIs were identified, aiming to prevent illness, promote wellbeing and treat specific physical, mental or social health and wellbeing conditions. These interventions were broadly categorized into those that change the environment in which people live, work, learn, recreate or heal (for example, the provision of gardens in hospitals or parks in cities) and those that change behaviour (for example, engaging people through organized programmes or other activities). We also noted the range of factors (such as socioeconomic variation) that will inevitably influence the extent to which these interventions succeed. We conclude with a call for research to identify the drivers influencing the effectiveness of NBIs in enhancing health and wellbeing. Full article