Npj mental health research最新文献

{"title":"Behavioral health and generative AI: a perspective on future of therapies and patient care","authors":"Emre Sezgin, Ian McKay","doi":"10.1038/s44184-024-00067-w","DOIUrl":"10.1038/s44184-024-00067-w","url":null,"abstract":"There have been considerable advancements in artificial intelligence (AI), specifically with generative AI (GAI) models. GAI is a class of algorithms designed to create new data, such as text, images, and audio, that resembles the data on which they have been trained. These models have been recently investigated in medicine, yet the opportunity and utility of GAI in behavioral health are relatively underexplored. In this commentary, we explore the potential uses of GAI in the field of behavioral health, specifically focusing on image generation. We propose the application of GAI for creating personalized and contextually relevant therapeutic interventions and emphasize the need to integrate human feedback into the AI-assisted therapeutics and decision-making process. We report the use of GAI with a case study of behavioral therapy on emotional recognition and management with a three-step process. We illustrate image generation-specific GAI to recognize, express, and manage emotions, featuring personalized content and interactive experiences. Furthermore, we highlighted limitations, challenges, and considerations, including the elements of human emotions, the need for human-AI collaboration, transparency and accountability, potential bias, security, privacy and ethical issues, and operational considerations. Our commentary serves as a guide for practitioners and developers to envision the future of behavioral therapies and consider the benefits and limitations of GAI in improving behavioral health practices and patient outcomes.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44184-024-00067-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141287024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic learning of individual-level suicidal ideation trajectories to enhance mental health care","authors":"Mathew Varidel, Ian B. Hickie, Ante Prodan, Adam Skinner, Roman Marchant, Sally Cripps, Rafael Oliveria, Min K. Chong, Elizabeth Scott, Jan Scott, Frank Iorfino","doi":"10.1038/s44184-024-00071-0","DOIUrl":"10.1038/s44184-024-00071-0","url":null,"abstract":"There has recently been an increase in ongoing patient-report routine outcome monitoring for individuals within clinical care, which has corresponded to increased longitudinal information about an individual. However, many models that are aimed at clinical practice have difficulty fully incorporating this information. This is in part due to the difficulty in dealing with the irregularly time-spaced observations that are common in clinical data. Consequently, we built individual-level continuous-time trajectory models of suicidal ideation for a clinical population (N = 585) with data collected via a digital platform. We demonstrate how such models predict an individual’s level and variability of future suicide ideation, with implications for the frequency that individuals may need to be observed. These individual-level predictions provide a more personalised understanding than other predictive methods and have implications for enhanced measurement-based care.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44184-024-00071-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141287022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinical adoption of virtual reality in mental health is challenged by lack of high-quality research","authors":"Benjamin Selaskowski, Annika Wiebe, Kyra Kannen, Laura Asché, Julian Pakos, Alexandra Philipsen, Niclas Braun","doi":"10.1038/s44184-024-00069-8","DOIUrl":"10.1038/s44184-024-00069-8","url":null,"abstract":"Virtual reality has been found effective for some mental disorders, while for many others weak methodology prevents conclusive evidence. Similar to other digital technologies, the field has particular demands for conducting clinical research which currently remain poorly addressed. In this commentary, we discuss the unique issues associated with the incorporation of virtual reality in clinical research. In addition, we elaborate on the possibility that these challenges may also be consequences of current funding and publication schemes, and speculate on specific improvement approaches that might be more compatible with the characteristics of clinical virtual reality research.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44184-024-00069-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140949333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Guided and unguided internet-delivered psychodynamic therapy for social anxiety disorder: A randomized controlled trial","authors":"Jakob Mechler, Karin Lindqvist, Kristoffer Magnusson, Adrián Ringström, Johan Daun Krafman, Pär Alvinzi, Love Kassius, Josefine Sowa, Gerhard Andersson, Per Carlbring","doi":"10.1038/s44184-024-00063-0","DOIUrl":"10.1038/s44184-024-00063-0","url":null,"abstract":"Social Anxiety Disorder (SAD) is highly prevalent and debilitating disorder. Treatments exist but are not accessible and/or helpful for all patients, indicating a need for accessible treatment alternatives. The aim of the present trial was to evaluate internet-delivered psychodynamic therapy (IPDT) with and without therapist guidance, compared to a waitlist control condition, in the treatment of adults with SAD. In this randomized, clinical trial, we tested whether IPDT was superior to a waitlist control, and whether IPDT with therapeutic guidance was superior to unguided IPDT. Participants were recruited nationwide in Sweden. Eligible participants were ≥ 18 years old and scoring ≥ 60 on the Liebowitz Social Anxiety Scale self-report (LSAS-SR) whilst not fulfilling any of the exclusion criteria. Included participants were randomly assigned to IPDT with guidance (n = 60), IPDT without guidance (n = 61), or waitlist (n = 60). The IPDT intervention comprised eight self-help modules based on affect-focused dynamic therapy, delivered over 8 weeks on a secure online platform. The primary outcome was SAD symptoms severity measured weekly by the LSAS-SR. Primary analyses were calculated on an intention-to-treat sample including all participants randomly assigned. Secondary outcomes were depressive symptoms, generalized anxiety, quality of life, emotion regulation and defensive functioning. At post-treatment, both active treatments were superior to the waitlist condition with guided treatment exhibiting larger between group effects than unguided treatment (d = 1.07 95% CI [0.72, 1.43], p < .001 and d = 0.61, 95% CI [0.25, 0.98], p = .0018) on the LSAS-SR respectively. Guided IPDT lead to larger improvements than unguided IPDT (d = 0.46, 95% CI [0.11, 0.80], p < .01). At post-treatment, guided IPDT was superior to waitlist on all secondary outcome measures. Unguided IPDT was superior to waitlist on depressive symptoms and general anxiety, but not on emotion regulation, self-compassion or quality of life. Guided IPDT was superior to unguided PDT on depressive symptoms, with a trend towards superiority on a measure of generalized anxiety. At six and twelve month follow-up there were no significant differences between guided and unguided IPDT. In conclusion, IPDT shows promising effects in the treatment of SAD, with larger benefits from guided IPDT compared to non-guided, at least at post-treatment. This finding increases the range of accessible and effective treatment alternatives for adults suffering from SAD. The study was prospectively registered at ClinicalTrials (NCT05015166).","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44184-024-00063-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140902816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An exploration into the causal relationships between educational attainment, intelligence, and wellbeing: an observational and two-sample Mendelian randomisation study","authors":"J. M. Armitage, R. E. Wootton, O. S. P. Davis, C. M. A. Haworth","doi":"10.1038/s44184-024-00066-x","DOIUrl":"10.1038/s44184-024-00066-x","url":null,"abstract":"Educational attainment is associated with a range of positive outcomes, yet its impact on wellbeing is unclear, and complicated by high correlations with intelligence. We use genetic and observational data to investigate for the first time, whether educational attainment and intelligence are causally and independently related to wellbeing. Results from our multivariable Mendelian randomisation demonstrated a positive causal impact of a genetic predisposition to higher educational attainment on wellbeing that remained after accounting for intelligence, and a negative impact of intelligence that was independent of educational attainment. Observational analyses suggested that these associations may be subject to sex differences, with benefits to wellbeing greater for females who attend higher education compared to males. For intelligence, males scoring more highly on measures related to happiness were those with lower intelligence. Our findings demonstrate a unique benefit for wellbeing of staying in school, over and above improving cognitive abilities, with benefits likely to be greater for females compared to males.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-12"},"PeriodicalIF":0.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44184-024-00066-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140900496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neurocognitive consequences of adolescent sleep disruptions and their relationship to psychosis vulnerability: a longitudinal cohort study","authors":"Julien Ouellet, Roxane Assaf, Mohammad H. Afzali, Sima Nourbakhsh, Stéphane Potvin, Patricia Conrod","doi":"10.1038/s44184-024-00058-x","DOIUrl":"10.1038/s44184-024-00058-x","url":null,"abstract":"Adolescence is a key period for neurocognitive maturation where deviation from normal developmental trajectories may be tied to adverse mental health outcomes. Cognitive disruptions have been noted in populations at risk for psychosis and are known to accompany periods of sleep deprivation. This study aims to assess the role of cognition as a mediator between sleep disruptions and psychosis risk. A cohort of 3801 high school students (51% female, mean age = 12.8, SD = 0.45 years) was recruited from 31 Montreal high schools. Measures of sleep, psychotic-like experiences, inhibition, working memory, perceptual reasoning, and delayed recall were collected from participants on a yearly basis over the five years of their high school education. A multi-level model mediation analysis was performed controlling for sex and time squared. Response inhibition was shown to be associated with, and to mediate (B = −0.005, SD = 0.003, p = 0.005*) the relationship between sleep disruptions (B = −0.011, SD = 0.004, p < 0.001*) and psychotic-like experiences (B = 0.411, SD = 0.170, p = 0.005*). Spatial working memory deficits on a given year were associated with a higher frequency of psychotic-like experiences that same year (B = −0.046, SD = 0.018, p = 0.005*) and the following year (B = −0.051, SD = 0.023, p = 0.010*), but were not associated with sleep disturbances. No significant associations were found between our variables of interest and either delayed recall or perceptual reasoning at the within person level. Findings from this large longitudinal study provide evidence that the association between sleep disruptions and psychosis risk is specifically mediated by inhibitory rather than general cognitive impairments. The association of spatial working memory, response inhibition, and sleep disruptions with psychotic-like experiences suggests that these factors may represent potential targets for preventative interventions.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44184-024-00058-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140878134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prevalence of burnout among healthcare professionals: a survey at fort portal regional referral hospital","authors":"Ian Batanda","doi":"10.1038/s44184-024-00061-2","DOIUrl":"10.1038/s44184-024-00061-2","url":null,"abstract":"The work environment in most hospitals is characterised by activities that are strenuous both physically and mentally. These can result in physical and mental exhaustion, which can lead to burnout if not adequately addressed. Burnout among healthcare professionals can negatively affect their clinical decision-making, quality of communication with patients and colleagues as well as their ability to cope with work-related pressure, and ultimately affect the quality of care and patient outcomes. The inclusion of burnout in the 11th revision of the International Classification of Diseases (ICD-11) as an occupational phenomenon indicates that it is an issue of concern in the workplace for which people may need professional attention. This descriptive cross-sectional survey aimed to determine the point prevalence of burnout among healthcare professionals at Fort Portal Regional Referral Hospital and the factors contributing to burnout. The study also evaluated the linear relationship between the age of workers, their work duration at the hospital, and their burnout score, in addition to the possible impact on patient care. Participants were selected from the hospital WhatsApp group and invitations to participate were sent to their individual accounts. Burnout was assessed using the Copenhagen Burnout Inventory. Generally, burnout scores ranged from 16% to 86%, with an overall mean burnout score of 57.4%. The notable factors contributing to burnout included imbalances in duty allocation, physically strenuous work, and resource constraints. Burnout of varying levels was found to be prevalent across all carders in the hospital, although the results indicate that most healthcare professionals experience moderate burnout. Most of the factors contributing to burnout are within the scope of hospital leadership to address. The possible impact on staff performance and patients’ clinical outcomes is speculative, and additional studies are required.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44184-024-00061-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140842591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying autism spectrum disorder from multi-modal data with privacy-preserving","authors":"Haishuai Wang, Hezi Jing, Jianjun Yang, Chao Liu, Liwei Hu, Guangyu Tao, Ziping Zhao, Ning Shen","doi":"10.1038/s44184-023-00050-x","DOIUrl":"10.1038/s44184-023-00050-x","url":null,"abstract":"The application of deep learning models to precision medical diagnosis often requires the aggregation of large amounts of medical data to effectively train high-quality models. However, data privacy protection mechanisms make it difficult to perform medical data collection from different medical institutions. In autism spectrum disorder (ASD) diagnosis, automatic diagnosis using multimodal information from heterogeneous data has not yet achieved satisfactory performance. To address the privacy preservation issue as well as to improve ASD diagnosis, we propose a deep learning framework using multimodal feature fusion and hypergraph neural networks for disease prediction in federated learning (FedHNN). By introducing the federated learning strategy, each local model is trained and computed independently in a distributed manner without data sharing, allowing rapid scaling of medical datasets to achieve robust and scalable deep learning predictive models. To further improve the performance with privacy preservation, we improve the hypergraph model for multimodal fusion to make it suitable for autism spectrum disorder (ASD) diagnosis tasks by capturing the complementarity and correlation between modalities through a hypergraph fusion strategy. The results demonstrate that our proposed federated learning-based prediction model is superior to all local models and outperforms other deep learning models. Overall, our proposed FedHNN has good results in the work of using multi-site data to improve the performance of ASD identification.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44184-023-00050-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140819081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Measuring algorithmic bias to analyze the reliability of AI tools that predict depression risk using smartphone sensed-behavioral data","authors":"Daniel A. Adler, Caitlin A. Stamatis, Jonah Meyerhoff, David C. Mohr, Fei Wang, Gabriel J. Aranovich, Srijan Sen, Tanzeem Choudhury","doi":"10.1038/s44184-024-00057-y","DOIUrl":"10.1038/s44184-024-00057-y","url":null,"abstract":"AI tools intend to transform mental healthcare by providing remote estimates of depression risk using behavioral data collected by sensors embedded in smartphones. While these tools accurately predict elevated depression symptoms in small, homogenous populations, recent studies show that these tools are less accurate in larger, more diverse populations. In this work, we show that accuracy is reduced because sensed-behaviors are unreliable predictors of depression across individuals: sensed-behaviors that predict depression risk are inconsistent across demographic and socioeconomic subgroups. We first identified subgroups where a developed AI tool underperformed by measuring algorithmic bias, where subgroups with depression were incorrectly predicted to be at lower risk than healthier subgroups. We then found inconsistencies between sensed-behaviors predictive of depression across these subgroups. Our findings suggest that researchers developing AI tools predicting mental health from sensed-behaviors should think critically about the generalizability of these tools, and consider tailored solutions for targeted populations.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44184-024-00057-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140632091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Association of temporal discounting with transdiagnostic symptom dimensions","authors":"Kristof Keidel, Xiaping Lu, Shinsuke Suzuki, Carsten Murawski, Ulrich Ettinger","doi":"10.1038/s44184-024-00060-3","DOIUrl":"10.1038/s44184-024-00060-3","url":null,"abstract":"Temporal discounting (TD), the tendency to devalue future rewards as a function of delay until receipt, is aberrant in many mental disorders. Identifying symptom patterns and transdiagnostic dimensions associated with TD could elucidate mechanisms responsible for clinically impaired decision-making and facilitate identifying intervention targets. Here, we tested in a general population sample (N = 731) the extent to which TD was related to different symptom patterns and whether effects of time framing (dates/delay units) and monetary magnitude (large/small) had particularly strong effects in people scoring higher on specific symptom patterns. Analyses revealed that TD was related to symptom patterns loading on anxious-depression and inattention-impulsivity-overactivity dimensions. Moreover, TD was lower in the date than the delay version and with higher magnitudes, especially in people scoring higher on the inattention-impulsivity-overactivity dimension. Overall, this study provides evidence for TD as a transdiagnostic process across affective and impulsivity-related dimensions. Future studies should test framing interventions in clinical populations characterized by impulsivity. Preregistration: This research was preregistered at https://osf.io/fg9sc .","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44184-024-00060-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140556426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}