Pub Date : 2023-09-27DOI: 10.1038/s44184-023-00035-w
Yuqi Wu, Kaining Mao, Liz Dennett, Yanbo Zhang, Jie Chen
Post-traumatic stress disorder (PTSD) is frequently underdiagnosed due to its clinical and biological heterogeneity. Worldwide, many people face barriers to accessing accurate and timely diagnoses. Machine learning (ML) techniques have been utilized for early assessments and outcome prediction to address these challenges. This paper aims to conduct a systematic review to investigate if ML is a promising approach for PTSD diagnosis. In this review, statistical methods were employed to synthesize the outcomes of the included research and provide guidance on critical considerations for ML task implementation. These included (a) selection of the most appropriate ML model for the available dataset, (b) identification of optimal ML features based on the chosen diagnostic method, (c) determination of appropriate sample size based on the distribution of the data, and (d) implementation of suitable validation tools to assess the performance of the selected ML models. We screened 3186 studies and included 41 articles based on eligibility criteria in the final synthesis. Here we report that the analysis of the included studies highlights the potential of artificial intelligence (AI) in PTSD diagnosis. However, implementing AI-based diagnostic systems in real clinical settings requires addressing several limitations, including appropriate regulation, ethical considerations, and protection of patient privacy.
由于创伤后应激障碍（PTSD）的临床和生物学异质性，它经常被诊断不足。在世界范围内，许多人在获得准确及时的诊断方面面临障碍。机器学习（ML）技术已被用于早期评估和结果预测，以应对这些挑战。本文旨在开展一项系统性综述，研究 ML 是否是诊断创伤后应激障碍的有效方法。在这篇综述中，我们采用了统计方法来综合所包含的研究成果，并就实施 ML 任务的关键注意事项提供指导。这些考虑因素包括：(a) 为可用数据集选择最合适的 ML 模型；(b) 根据所选诊断方法确定最佳 ML 特征；(c) 根据数据分布确定适当的样本大小；以及 (d) 使用合适的验证工具来评估所选 ML 模型的性能。我们筛选了 3186 项研究，并根据资格标准将 41 篇文章纳入最终综述。我们在此报告，对所纳入研究的分析凸显了人工智能（AI）在创伤后应激障碍诊断中的潜力。然而，在实际临床环境中实施基于人工智能的诊断系统需要解决几个限制因素，包括适当的监管、伦理考虑和患者隐私保护。
{"title":"Systematic review of machine learning in PTSD studies for automated diagnosis evaluation","authors":"Yuqi Wu, Kaining Mao, Liz Dennett, Yanbo Zhang, Jie Chen","doi":"10.1038/s44184-023-00035-w","DOIUrl":"10.1038/s44184-023-00035-w","url":null,"abstract":"Post-traumatic stress disorder (PTSD) is frequently underdiagnosed due to its clinical and biological heterogeneity. Worldwide, many people face barriers to accessing accurate and timely diagnoses. Machine learning (ML) techniques have been utilized for early assessments and outcome prediction to address these challenges. This paper aims to conduct a systematic review to investigate if ML is a promising approach for PTSD diagnosis. In this review, statistical methods were employed to synthesize the outcomes of the included research and provide guidance on critical considerations for ML task implementation. These included (a) selection of the most appropriate ML model for the available dataset, (b) identification of optimal ML features based on the chosen diagnostic method, (c) determination of appropriate sample size based on the distribution of the data, and (d) implementation of suitable validation tools to assess the performance of the selected ML models. We screened 3186 studies and included 41 articles based on eligibility criteria in the final synthesis. Here we report that the analysis of the included studies highlights the potential of artificial intelligence (AI) in PTSD diagnosis. However, implementing AI-based diagnostic systems in real clinical settings requires addressing several limitations, including appropriate regulation, ethical considerations, and protection of patient privacy.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44184-023-00035-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135537104","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}
Pub Date : 2023-09-13DOI: 10.1038/s44184-023-00036-9
Amanda M. Y. Chu, Benson S. Y. Lam, Jenny T. Y. Tsang, Agnes Tiwari, Helina Yuk, Jacky N. L. Chan, Mike K. P. So
The stress burden generated from family caregiving makes caregivers particularly prone to developing psychosocial health issues; however, with early diagnosis and intervention, disease progression and long-term disability can be prevented. We developed an automatic speech analytics program (ASAP) for the detection of psychosocial health issues based on clients’ speech. One hundred Cantonese-speaking family caregivers were recruited with the results suggesting that the ASAP can identify family caregivers with low or high stress burden levels with an accuracy rate of 72%. The findings indicate that digital health technology can be used to assist in the psychosocial health assessment. While the conventional method requires rigorous assessments by specialists with multiple rounds of questioning, the ASAP can provide a cost-effective and immediate initial assessment to identify high levels of stress among family caregivers so they can be referred to social workers and healthcare professionals for further assessments and treatments.
{"title":"An automatic speech analytics program for digital assessment of stress burden and psychosocial health","authors":"Amanda M. Y. Chu, Benson S. Y. Lam, Jenny T. Y. Tsang, Agnes Tiwari, Helina Yuk, Jacky N. L. Chan, Mike K. P. So","doi":"10.1038/s44184-023-00036-9","DOIUrl":"10.1038/s44184-023-00036-9","url":null,"abstract":"The stress burden generated from family caregiving makes caregivers particularly prone to developing psychosocial health issues; however, with early diagnosis and intervention, disease progression and long-term disability can be prevented. We developed an automatic speech analytics program (ASAP) for the detection of psychosocial health issues based on clients’ speech. One hundred Cantonese-speaking family caregivers were recruited with the results suggesting that the ASAP can identify family caregivers with low or high stress burden levels with an accuracy rate of 72%. The findings indicate that digital health technology can be used to assist in the psychosocial health assessment. While the conventional method requires rigorous assessments by specialists with multiple rounds of questioning, the ASAP can provide a cost-effective and immediate initial assessment to identify high levels of stress among family caregivers so they can be referred to social workers and healthcare professionals for further assessments and treatments.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44184-023-00036-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135741762","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}
Pub Date : 2023-08-30DOI: 10.1038/s44184-023-00034-x
Jiyeong Kim, Eleni Linos, Melanie S. Dove, Jeffrey S. Hoch, Theresa H. Keegan
Poor mental health has been found to be more prevalent among those with cancer and is considered a public health crisis since COVID-19. This study assessed the impact of COVID-19 and cancer survivorship on mental health and investigated factors, including online patient-provider communications (OPPC; email/internet/tablet/smartphone), associated with poor mental health prior to and during the early COVID-19. Nationally representative Health Information National Trends Survey data during 2017–2020 (n = 15,871) was used. While the prevalence of poor mental health was high (40–42%), Difference-In-Difference analyses revealed that cancer survivorship and COVID-19 were not associated with poor mental health. However, individuals that used OPPC had 40% higher odds of poor mental health. Low socioeconomic status (low education/income), younger age (18–64 years), and female birth gender were also associated with poor mental health. Findings highlight the persistence of long-standing mental health inequities and identify that OPPC users might be those who need mental health support.
{"title":"Impact of COVID-19, cancer survivorship and patient-provider communication on mental health in the US Difference-In-Difference","authors":"Jiyeong Kim, Eleni Linos, Melanie S. Dove, Jeffrey S. Hoch, Theresa H. Keegan","doi":"10.1038/s44184-023-00034-x","DOIUrl":"10.1038/s44184-023-00034-x","url":null,"abstract":"Poor mental health has been found to be more prevalent among those with cancer and is considered a public health crisis since COVID-19. This study assessed the impact of COVID-19 and cancer survivorship on mental health and investigated factors, including online patient-provider communications (OPPC; email/internet/tablet/smartphone), associated with poor mental health prior to and during the early COVID-19. Nationally representative Health Information National Trends Survey data during 2017–2020 (n = 15,871) was used. While the prevalence of poor mental health was high (40–42%), Difference-In-Difference analyses revealed that cancer survivorship and COVID-19 were not associated with poor mental health. However, individuals that used OPPC had 40% higher odds of poor mental health. Low socioeconomic status (low education/income), younger age (18–64 years), and female birth gender were also associated with poor mental health. Findings highlight the persistence of long-standing mental health inequities and identify that OPPC users might be those who need mental health support.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44184-023-00034-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41356943","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}
Pub Date : 2023-08-22DOI: 10.1038/s44184-023-00033-y
Raymond R. Bond, Maurice D. Mulvenna, Courtney Potts, Siobhan O’Neill, Edel Ennis, John Torous
This paper makes a case for digital mental health and provides insights into how digital technologies can enhance (but not replace) existing mental health services. We describe digital mental health by presenting a suite of digital technologies (from digital interventions to the application of artificial intelligence). We discuss the benefits of digital mental health, for example, a digital intervention can be an accessible stepping-stone to receiving support. The paper does, however, present less-discussed benefits with new concepts such as ‘poly-digital’, where many different apps/features (e.g. a sleep app, mood logging app and a mindfulness app, etc.) can each address different factors of wellbeing, perhaps resulting in an aggregation of marginal gains. Another benefit is that digital mental health offers the ability to collect high-resolution real-world client data and provide client monitoring outside of therapy sessions. These data can be collected using digital phenotyping and ecological momentary assessment techniques (i.e. repeated mood or scale measures via an app). This allows digital mental health tools and real-world data to inform therapists and enrich face-to-face sessions. This can be referred to as blended care/adjunctive therapy where service users can engage in ‘channel switching’ between digital and non-digital (face-to-face) interventions providing a more integrated service. This digital integration can be referred to as a kind of ‘digital glue’ that helps join up the in-person sessions with the real world. The paper presents the challenges, for example, the majority of mental health apps are maybe of inadequate quality and there is a lack of user retention. There are also ethical challenges, for example, with the perceived ‘over-promotion’ of screen-time and the perceived reduction in care when replacing humans with ‘computers’, and the trap of ‘technological solutionism’ whereby technology can be naively presumed to solve all problems. Finally, we argue for the need to take an evidence-based, systems thinking and co-production approach in the form of stakeholder-centred design when developing digital mental health services based on technologies. The main contribution of this paper is the integration of ideas from many different disciplines as well as the framework for blended care using ‘channel switching’ to showcase how digital data and technology can enrich physical services. Another contribution is the emergence of ‘poly-digital’ and a discussion on the challenges of digital mental health, specifically ‘digital ethics’.
{"title":"Digital transformation of mental health services","authors":"Raymond R. Bond, Maurice D. Mulvenna, Courtney Potts, Siobhan O’Neill, Edel Ennis, John Torous","doi":"10.1038/s44184-023-00033-y","DOIUrl":"10.1038/s44184-023-00033-y","url":null,"abstract":"This paper makes a case for digital mental health and provides insights into how digital technologies can enhance (but not replace) existing mental health services. We describe digital mental health by presenting a suite of digital technologies (from digital interventions to the application of artificial intelligence). We discuss the benefits of digital mental health, for example, a digital intervention can be an accessible stepping-stone to receiving support. The paper does, however, present less-discussed benefits with new concepts such as ‘poly-digital’, where many different apps/features (e.g. a sleep app, mood logging app and a mindfulness app, etc.) can each address different factors of wellbeing, perhaps resulting in an aggregation of marginal gains. Another benefit is that digital mental health offers the ability to collect high-resolution real-world client data and provide client monitoring outside of therapy sessions. These data can be collected using digital phenotyping and ecological momentary assessment techniques (i.e. repeated mood or scale measures via an app). This allows digital mental health tools and real-world data to inform therapists and enrich face-to-face sessions. This can be referred to as blended care/adjunctive therapy where service users can engage in ‘channel switching’ between digital and non-digital (face-to-face) interventions providing a more integrated service. This digital integration can be referred to as a kind of ‘digital glue’ that helps join up the in-person sessions with the real world. The paper presents the challenges, for example, the majority of mental health apps are maybe of inadequate quality and there is a lack of user retention. There are also ethical challenges, for example, with the perceived ‘over-promotion’ of screen-time and the perceived reduction in care when replacing humans with ‘computers’, and the trap of ‘technological solutionism’ whereby technology can be naively presumed to solve all problems. Finally, we argue for the need to take an evidence-based, systems thinking and co-production approach in the form of stakeholder-centred design when developing digital mental health services based on technologies. The main contribution of this paper is the integration of ideas from many different disciplines as well as the framework for blended care using ‘channel switching’ to showcase how digital data and technology can enrich physical services. Another contribution is the emergence of ‘poly-digital’ and a discussion on the challenges of digital mental health, specifically ‘digital ethics’.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2023-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44184-023-00033-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47635174","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}
Pub Date : 2023-08-10DOI: 10.1038/s44184-023-00031-0
Peng Zhou, Huimin Ma, Bochao Zou, Xiaowen Zhang, Shuyan Zhao, Yuxin Lin, Yidong Wang, Lei Feng, Gang Wang
To explore the minds of others, which is traditionally referred to as Theory of Mind (ToM), is perhaps the most fundamental ability of humans as social beings. Impairments in ToM could lead to difficulties or even deficits in social interaction. The present study focuses on two core components of ToM, the ability to infer others’ beliefs and the ability to infer others’ emotions, which we refer to as cognitive and affective ToM respectively. Charting both typical and atypical trajectories underlying the cognitive-affective ToM promises to shed light on the precision identification of mental disorders, such as depressive disorders (DD) and autism spectrum disorder (ASD). However, most prior studies failed to capture the underlying processes involved in the cognitive-affective ToM in a fine-grained manner. To address this problem, we propose an innovative conceptual framework, referred to as visual theory of mind (V-ToM), by constructing visual scenes with emotional and cognitive meanings and by depicting explicitly a four-stage process of how humans make inferences about the beliefs and emotions of others. Through recording individuals’ eye movements while looking at the visual scenes, our model enables us to accurately measure each stage involved in the computation of cognitive-affective ToM, thereby allowing us to infer about potential difficulties that might occur in each stage. Our model is based on a large sample size (n > 700) and a novel audio-visual paradigm using visual scenes containing cognitive-emotional meanings. Here we report the obtained differential features among healthy controls, DD and ASD individuals that overcome the subjectivity of conventional questionnaire-based assessment, and therefore could serve as valuable references for mental health applications based on AI-aided digital medicine.
探索他人的思想，即传统上所说的心智理论（ToM），也许是人类作为社会人最基本的能力。心智图式（ToM）的缺陷可能会导致社会交往的困难甚至缺陷。本研究的重点是心智理论的两个核心组成部分，即推断他人信念的能力和推断他人情感的能力，我们分别称之为认知心智理论和情感心智理论。绘制认知-情感ToM的典型和非典型轨迹有望为精神障碍（如抑郁障碍（DD）和自闭症谱系障碍（ASD））的精确识别提供启示。然而，之前的大多数研究都未能以精细的方式捕捉到认知-情感ToM所涉及的潜在过程。为了解决这个问题，我们提出了一个创新的概念框架，即视觉心智理论（V-ToM），通过构建具有情感和认知意义的视觉场景，明确描述人类如何对他人的信念和情感做出推断的四个阶段过程。通过记录个体在观看视觉场景时的眼球运动，我们的模型使我们能够精确测量认知-情感 ToM 计算过程中的每个阶段，从而推断出每个阶段可能出现的困难。我们的模型基于一个大样本量（700 人）和一个新颖的视听范式，使用的是包含认知-情感意义的视觉场景。在此，我们报告了在健康对照组、DD 和 ASD 患者中获得的差异特征，这些特征克服了传统问卷评估的主观性，因此可作为基于人工智能辅助数字医学的心理健康应用的宝贵参考。
{"title":"A conceptual framework of cognitive-affective theory of mind: towards a precision identification of mental disorders","authors":"Peng Zhou, Huimin Ma, Bochao Zou, Xiaowen Zhang, Shuyan Zhao, Yuxin Lin, Yidong Wang, Lei Feng, Gang Wang","doi":"10.1038/s44184-023-00031-0","DOIUrl":"10.1038/s44184-023-00031-0","url":null,"abstract":"To explore the minds of others, which is traditionally referred to as Theory of Mind (ToM), is perhaps the most fundamental ability of humans as social beings. Impairments in ToM could lead to difficulties or even deficits in social interaction. The present study focuses on two core components of ToM, the ability to infer others’ beliefs and the ability to infer others’ emotions, which we refer to as cognitive and affective ToM respectively. Charting both typical and atypical trajectories underlying the cognitive-affective ToM promises to shed light on the precision identification of mental disorders, such as depressive disorders (DD) and autism spectrum disorder (ASD). However, most prior studies failed to capture the underlying processes involved in the cognitive-affective ToM in a fine-grained manner. To address this problem, we propose an innovative conceptual framework, referred to as visual theory of mind (V-ToM), by constructing visual scenes with emotional and cognitive meanings and by depicting explicitly a four-stage process of how humans make inferences about the beliefs and emotions of others. Through recording individuals’ eye movements while looking at the visual scenes, our model enables us to accurately measure each stage involved in the computation of cognitive-affective ToM, thereby allowing us to infer about potential difficulties that might occur in each stage. Our model is based on a large sample size (n > 700) and a novel audio-visual paradigm using visual scenes containing cognitive-emotional meanings. Here we report the obtained differential features among healthy controls, DD and ASD individuals that overcome the subjectivity of conventional questionnaire-based assessment, and therefore could serve as valuable references for mental health applications based on AI-aided digital medicine.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44184-023-00031-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48221564","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}
Pub Date : 2023-07-24DOI: 10.1038/s44184-023-00032-z
Sverker Sikström, Bleona Kelmendi, Ninni Persson
Middle aged adults experience depression and anxiety differently than younger adults. Age may affect life circumstances, depending on accessibility of social connections, jobs, physical health, etc, as these factors influence the prevalence and symptomatology. Depression and anxiety are typically measured using rating scales; however, recent research suggests that such symptoms can be assessed by open-ended questions that are analysed by question-based computational language assessments (QCLA). Here, we study middle aged and younger adults’ responses about their mental health using open-ended questions and rating scales about their mental health. We then analyse their responses with computational methods based on natural language processing (NLP). The results demonstrate that: (1) middle aged adults describe their mental health differently compared to younger adults; (2) where, for example, middle aged adults emphasise depression and loneliness whereas young adults list anxiety and financial concerns; (3) different semantic models are warranted for younger and middle aged adults; (4) compared to young participants, the middle aged participants described their mental health more accurately with words; (5) middle-aged adults have better mental health than younger adults as measured by semantic measures. In conclusion, NLP combined with machine learning methods may provide new opportunities to identify, model, and describe mental health in middle aged and younger adults and could possibly be applied to the older adults in future research. These semantic measures may provide ecological validity and aid the assessment of mental health.
{"title":"Assessment of depression and anxiety in young and old with a question-based computational language approach","authors":"Sverker Sikström, Bleona Kelmendi, Ninni Persson","doi":"10.1038/s44184-023-00032-z","DOIUrl":"10.1038/s44184-023-00032-z","url":null,"abstract":"Middle aged adults experience depression and anxiety differently than younger adults. Age may affect life circumstances, depending on accessibility of social connections, jobs, physical health, etc, as these factors influence the prevalence and symptomatology. Depression and anxiety are typically measured using rating scales; however, recent research suggests that such symptoms can be assessed by open-ended questions that are analysed by question-based computational language assessments (QCLA). Here, we study middle aged and younger adults’ responses about their mental health using open-ended questions and rating scales about their mental health. We then analyse their responses with computational methods based on natural language processing (NLP). The results demonstrate that: (1) middle aged adults describe their mental health differently compared to younger adults; (2) where, for example, middle aged adults emphasise depression and loneliness whereas young adults list anxiety and financial concerns; (3) different semantic models are warranted for younger and middle aged adults; (4) compared to young participants, the middle aged participants described their mental health more accurately with words; (5) middle-aged adults have better mental health than younger adults as measured by semantic measures. In conclusion, NLP combined with machine learning methods may provide new opportunities to identify, model, and describe mental health in middle aged and younger adults and could possibly be applied to the older adults in future research. These semantic measures may provide ecological validity and aid the assessment of mental health.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2023-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44184-023-00032-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47748579","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}
Melissa K. Holt, Katharine B. Parodi, Frank J. Elgar, Abra Vigna, L. B. Moore, Brian Koenig
{"title":"","authors":"Melissa K. Holt, Katharine B. Parodi, Frank J. Elgar, Abra Vigna, L. B. Moore, Brian Koenig","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"-"},"PeriodicalIF":0.0,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44184-023-00029-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138867586","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}
Pub Date : 2023-07-04DOI: 10.1038/s44184-023-00029-8
Melissa K Holt, Katharine B Parodi, Frank J Elgar, Abra Vigna, L B Moore, Brian Koenig
Few studies have disentangled differences in victimization exposures and mental health symptoms among gender diverse subgroups, nor considered the role of potential protective factors in ameliorating the impact of victimization on gender diverse youths' mental health. Here we report findings from a secondary data analysis, in which we address this gap by analyzing cross-sectional survey data (N = 11,264 in the final analytic sample) from a population-based survey of youth in participating school districts in a large Midwestern U.S. county. Relative to cisgender youth with gender conforming expression, transgender youth and cisgender youth with nonconforming gender expression are more likely to experience victimization and severe mental health concerns. Additionally, school-connectedness moderates the association between bias-based harassment and depression for cisgender youth with gender nonconforming expression, and family support/monitoring buffers the association of peer victimization with suicide attempts among transgender youth. Findings highlight the need to better understand factors which may confer protection among gender diverse adolescents, so that in turn appropriate supports across key contexts can be implemented.
{"title":"Identifying protective factors for gender diverse adolescents' mental health.","authors":"Melissa K Holt, Katharine B Parodi, Frank J Elgar, Abra Vigna, L B Moore, Brian Koenig","doi":"10.1038/s44184-023-00029-8","DOIUrl":"10.1038/s44184-023-00029-8","url":null,"abstract":"<p><p>Few studies have disentangled differences in victimization exposures and mental health symptoms among gender diverse subgroups, nor considered the role of potential protective factors in ameliorating the impact of victimization on gender diverse youths' mental health. Here we report findings from a secondary data analysis, in which we address this gap by analyzing cross-sectional survey data (N = 11,264 in the final analytic sample) from a population-based survey of youth in participating school districts in a large Midwestern U.S. county. Relative to cisgender youth with gender conforming expression, transgender youth and cisgender youth with nonconforming gender expression are more likely to experience victimization and severe mental health concerns. Additionally, school-connectedness moderates the association between bias-based harassment and depression for cisgender youth with gender nonconforming expression, and family support/monitoring buffers the association of peer victimization with suicide attempts among transgender youth. Findings highlight the need to better understand factors which may confer protection among gender diverse adolescents, so that in turn appropriate supports across key contexts can be implemented.</p>","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"10"},"PeriodicalIF":0.0,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10955934/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44712725","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}
Pub Date : 2023-06-27DOI: 10.1038/s44184-023-00030-1
Colleen Stiles-Shields, Giovanni Ramos, Adrian Ortega, Alexandra M. Psihogios
Youth in the United States are facing an unprecedented mental health crisis. Yet, brick-and-mortar mental healthcare, such as face-to-face therapy, is overwhelmingly inaccessible to youth despite research advances in youth mental health. Digital Mental Health tools (DMH), the use of technologies to deliver mental health assessments and interventions, may help to increase mental healthcare accessibility. However, for a variety of reasons, evidence-based DMH have not been successful in reaching youth in real-world settings, particularly those who are most encumbered with access barriers to mental healthcare. This Comment therefore focuses on increasing DMH reach and uptake by young people, particularly among minoritized youth, by engaging in community-based youth partnerships. This idea recognizes and grows from decades’ worth of community-based participatory research and youth partnerships successfully conducted by other disciplines (e.g., social work, public health, urban planning, education). Increasing uptake and engagement is an issue that is unlikely to be solved by adult-driven theory and design. As such, we emphasize the necessity of reframing youth input into DMH design and deployment from one-time participants to integral community-based partners. Indeed, recognizing and valuing their expertise to equitably address DMH implementation challenges, youth should help to pose the very questions that they will help to answer throughout the design and implementation planning for DMH moving forward.
{"title":"Increasing digital mental health reach and uptake via youth partnerships","authors":"Colleen Stiles-Shields, Giovanni Ramos, Adrian Ortega, Alexandra M. Psihogios","doi":"10.1038/s44184-023-00030-1","DOIUrl":"10.1038/s44184-023-00030-1","url":null,"abstract":"Youth in the United States are facing an unprecedented mental health crisis. Yet, brick-and-mortar mental healthcare, such as face-to-face therapy, is overwhelmingly inaccessible to youth despite research advances in youth mental health. Digital Mental Health tools (DMH), the use of technologies to deliver mental health assessments and interventions, may help to increase mental healthcare accessibility. However, for a variety of reasons, evidence-based DMH have not been successful in reaching youth in real-world settings, particularly those who are most encumbered with access barriers to mental healthcare. This Comment therefore focuses on increasing DMH reach and uptake by young people, particularly among minoritized youth, by engaging in community-based youth partnerships. This idea recognizes and grows from decades’ worth of community-based participatory research and youth partnerships successfully conducted by other disciplines (e.g., social work, public health, urban planning, education). Increasing uptake and engagement is an issue that is unlikely to be solved by adult-driven theory and design. As such, we emphasize the necessity of reframing youth input into DMH design and deployment from one-time participants to integral community-based partners. Indeed, recognizing and valuing their expertise to equitably address DMH implementation challenges, youth should help to pose the very questions that they will help to answer throughout the design and implementation planning for DMH moving forward.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10361731/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10240093","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}
Pub Date : 2023-06-05DOI: 10.1038/s44184-023-00027-w
Macarena S. Aloi, Guillermo F. Poblete, John Oldham, Michelle A. Patriquin, David A. Nielsen, Thomas R. Kosten, Ramiro Salas
Borderline personality disorder (BPD) is characterized by patterns of unstable affect, unstable interpersonal relationships, and chronic suicidal tendencies. Research on the genetics, epigenetics, and brain function of BPD is lacking. MicroRNA-124-3p (miR-124-3p) was recently identified in a Genome-Wide Association Study as likely associated with BPD. Here, we identified the anatomical brain expression of genes likely modulated by miR-124-3p and compared morphometry in those brain regions in BPD inpatients vs. controls matched for psychiatric comorbidities. We isolated lists of targets likely modulated by miR-124-3p from TargetScan (v 8.0) by their preferentially conserved targeting (Aggregate PCT > 0.99, see Supplementary Table 1). We applied Process Genes List (PGL) to identify regions of interest associated with the co-expression of miR-124-3p target genes. We compared the gray matter volume of the top region of interest co-expressing those genes between BPD inpatients (n = 111, 46% female) and psychiatric controls (n = 111, 54% female) at The Menninger Clinic in Houston, Texas. We then correlated personality measures, suicidal ideation intensity, and recovery from suicidal ideation with volumetrics. Gene targets of miR-124-3p were significantly co-expressed in the left Globus Pallidus (GP), which was smaller in BPD than in psychiatric controls. Smaller GP volume was negatively correlated with agreeableness and with recovery from suicidal ideation post-treatment. In BPD, GP volume may be reduced through miR-124-3p regulation and suppression of its target genes. Importantly, we identified that a reduction of the GP in BPD could serve as a potential biomarker for recovery from suicidal ideation.
{"title":"miR-124-3p target genes identify globus pallidus role in suicide ideation recovery in borderline personality disorder","authors":"Macarena S. Aloi, Guillermo F. Poblete, John Oldham, Michelle A. Patriquin, David A. Nielsen, Thomas R. Kosten, Ramiro Salas","doi":"10.1038/s44184-023-00027-w","DOIUrl":"10.1038/s44184-023-00027-w","url":null,"abstract":"Borderline personality disorder (BPD) is characterized by patterns of unstable affect, unstable interpersonal relationships, and chronic suicidal tendencies. Research on the genetics, epigenetics, and brain function of BPD is lacking. MicroRNA-124-3p (miR-124-3p) was recently identified in a Genome-Wide Association Study as likely associated with BPD. Here, we identified the anatomical brain expression of genes likely modulated by miR-124-3p and compared morphometry in those brain regions in BPD inpatients vs. controls matched for psychiatric comorbidities. We isolated lists of targets likely modulated by miR-124-3p from TargetScan (v 8.0) by their preferentially conserved targeting (Aggregate PCT > 0.99, see Supplementary Table 1). We applied Process Genes List (PGL) to identify regions of interest associated with the co-expression of miR-124-3p target genes. We compared the gray matter volume of the top region of interest co-expressing those genes between BPD inpatients (n = 111, 46% female) and psychiatric controls (n = 111, 54% female) at The Menninger Clinic in Houston, Texas. We then correlated personality measures, suicidal ideation intensity, and recovery from suicidal ideation with volumetrics. Gene targets of miR-124-3p were significantly co-expressed in the left Globus Pallidus (GP), which was smaller in BPD than in psychiatric controls. Smaller GP volume was negatively correlated with agreeableness and with recovery from suicidal ideation post-treatment. In BPD, GP volume may be reduced through miR-124-3p regulation and suppression of its target genes. Importantly, we identified that a reduction of the GP in BPD could serve as a potential biomarker for recovery from suicidal ideation.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10500603/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10653948","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：