Jmir Mental Health最新文献_第8页

Child and Adolescent Virtual Mental Health Care and Duration of Treatment: Retrospective Cohort Study. 儿童和青少年虚拟心理健康护理和治疗时间：回顾性队列研究。

IF 5.8 2区医学 Q1 PSYCHIATRY

Jmir Mental Health

Pub Date : 2025-09-11 DOI: 10.2196/70650

Allyson Cruickshank, Pantelis Andreou, Debbie Johnson Emberly, Sandra Meier, Leslie Anne Campbell

Background: Due to public health restrictions, the COVID-19 pandemic required significant changes in the delivery of child and adolescent mental health services. The use of virtual care for balancing access with treatment needs requires a shared decision between clients, caregivers, and clinicians. One aspect for consideration is the length of treatment necessary to achieve desired outcomes and whether it differs by treatment modality. Insights gained from the comparison of treatment duration between modalities may improve our understanding of the effectiveness of virtual care and help to inform clinical decision-making and effective use of resources.Objective: We sought to improve our understanding of how treatment modality impacts treatment duration for children and adolescents accessing Community Mental Health and Addictions services at IWK Health following the rapid implementation of virtual care in March 2020. In this study, we aimed to compare the duration of treatment within episodes of care by treatment modality and determine whether client characteristics, system factors, or time period influenced any associations between treatment modality and treatment duration.Methods: Episodes of care were created using administrative data collected by the IWK Mental Health and Addictions program and used as the unit of analysis. A multilevel mixed-effects negative binomial model and time-to-event analysis were used to model the association between treatment modality and treatment duration, both in visits and days, adjusting for client and system characteristics.Results: Virtual episodes of care had more visits than in-person episodes between April 1, 2020, and March 31, 2021 (incidence rate ratio [IRR] 1.59, 95% CI 1.38-1.83), and April 1, 2021, and March 31, 2022 (IRR 1.22, 95% CI 1.10-1.35), whereas between April 1, 2022, and March 31, 2023, virtual episodes of care were associated with fewer visits (IRR 0.82, 95% CI 0.74-0.91). Comparable results were seen for treatment duration in days (2020-2021: hazard ratio [HR] 0.64, 95% CI 0.54-0.76; 2021-2022: HR 0.80, 95% CI 0.70-0.90; and 2022-2023: HR 1.10, 95% CI 0.97-1.25). These differences by time period relative to the onset of the COVID-19 pandemic and switch to virtual care were consistent after adjusting for client and system characteristics.Conclusions: To our knowledge, this is the first study to examine the association between virtual or in-person treatment modality and treatment duration. While initially longer than in-person episodes of care, both in numbers of visits and length in days, over time the average length of episodes conducted mainly virtually had attenuated. These findings may be due to growing comfort with the technology or client factors not adequately captured in administrative data. This information can be valuable to clinicians, clients, and their families reg

背景：由于公共卫生限制，COVID-19大流行要求儿童和青少年精神卫生服务的提供发生重大变化。使用虚拟护理来平衡可及性和治疗需求需要客户、护理人员和临床医生之间的共同决策。需要考虑的一个方面是达到预期结果所需的治疗时间，以及治疗方式是否不同。从不同治疗方式的治疗持续时间的比较中获得的见解可能会提高我们对虚拟治疗有效性的理解，并有助于为临床决策和有效利用资源提供信息。目的：在2020年3月快速实施虚拟护理后，我们试图提高我们对治疗方式如何影响在IWK Health获得社区心理健康和成瘾服务的儿童和青少年的治疗时间的理解。在本研究中，我们旨在比较不同治疗方式的治疗持续时间，并确定患者特征、系统因素或时间段是否影响治疗方式和治疗持续时间之间的关联。方法：使用IWK心理健康和成瘾项目收集的管理数据创建护理情节，并将其作为分析单元。采用多水平混合效应负二项模型和事件时间分析来模拟治疗方式和治疗持续时间之间的关联，包括就诊次数和天数，并根据客户和系统特征进行调整。结果：在2020年4月1日至2021年3月31日期间（发病率比[IRR] 1.59, 95% CI 1.38-1.83），以及2021年4月1日至2022年3月31日期间（IRR 1.22, 95% CI 1.10-1.35），虚拟护理发作的就诊次数多于现场就诊次数（IRR 0.82, 95% CI 0.74-0.91）。治疗持续时间（以天为单位）也有类似的结果（2020-2021年：风险比[HR] 0.64, 95% CI 0.54-0.76； 2021-2022年：风险比[HR] 0.80, 95% CI 0.70-0.90； 2022-2023年：风险比1.10,95% CI 0.97-1.25）。在针对客户和系统特征进行调整后，这些与COVID-19大流行发病和转向虚拟医疗相关的时间段差异是一致的。结论：据我们所知，这是第一个研究虚拟或面对面治疗方式与治疗时间之间关系的研究。虽然最初比面对面的护理时间更长，无论是访问次数还是天数，随着时间的推移，主要进行的平均时间实际上已经减少。这些发现可能是由于对技术或管理数据中未充分捕获的客户因素越来越满意。这些信息对于临床医生、客户和他们的家庭对于预期的治疗时间表和帮助告知服务计划是有价值的。

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引用次数: 0

Explainable AI for Depression Detection and Severity Classification From Activity Data: Development and Evaluation Study of an Interpretable Framework. 基于活动数据的抑郁症检测和严重程度分类的可解释AI：一个可解释框架的开发和评估研究。

IF 5.8 2区医学 Q1 PSYCHIATRY

Jmir Mental Health

Pub Date : 2025-09-11 DOI: 10.2196/72038

Iftikhar Ahmed, Anushree Brahmacharimayum, Raja Hashim Ali, Talha Ali Khan, Muhammad Ovais Ahmad

Background: Depression is one of the most prevalent mental health disorders globally, affecting approximately 280 million people and frequently going undiagnosed or misdiagnosed. The growing ubiquity of wearable devices enables continuous monitoring of activity levels, providing a new avenue for data-driven detection and severity assessment of depression. However, existing machine learning models often exhibit lower performance when distinguishing overlapping subtypes of depression and frequently lack explainability, an essential component for clinical acceptance.

Objective: This study aimed to develop and evaluate an interpretable machine learning framework for detecting depression and classifying its severity using wearable-actigraphy data, while addressing common challenges such as imbalanced datasets and limited model transparency.

Methods: We used the Depresjon dataset and applied Adaptive Synthetic Sampling (ADASYN) to mitigate class imbalance. We extracted multiple statistical features (eg, power spectral density mean and autocorrelation) and demographic attributes (eg, age) from the raw activity data. Five machine learning algorithms (logistic regression, support vector machines, random forest, XGBoost, and neural networks) were assessed via accuracy, precision, recall, F1-score, specificity, and Matthew correlation constant. We further used Shapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) to elucidate prediction drivers.

Results: XGBoost achieved the highest overall accuracy of 84.94% for binary classification and 85.91% for multiclass severity. SHAP and LIME revealed power spectral density mean, age, and autocorrelation as top predictors, highlighting circadian disruptions' role in depression.

Conclusions: Our interpretable framework reliably identifies depressed versus nondepressed individuals and differentiates mild from moderate depression. The inclusion of SHAP and LIME provides transparent, clinically meaningful insights, emphasizing the potential of explainable artificial intelligence to enhance early detection and intervention strategies in mental health care.

背景：抑郁症是全球最普遍的精神健康障碍之一，影响约2.8亿人，经常未被诊断或误诊。日益普及的可穿戴设备可以持续监测活动水平，为数据驱动的抑郁症检测和严重程度评估提供了新的途径。然而，现有的机器学习模型在区分重叠的抑郁症亚型时往往表现出较低的性能，并且经常缺乏可解释性，这是临床接受的重要组成部分。目的：本研究旨在开发和评估一个可解释的机器学习框架，用于使用可穿戴式活动记录仪数据检测抑郁症并对其严重程度进行分类，同时解决数据集不平衡和模型透明度有限等常见挑战。方法：利用depression数据集，采用自适应合成采样（ADASYN）来缓解类失衡。我们从原始活动数据中提取了多个统计特征（如功率谱密度平均值和自相关性）和人口统计属性（如年龄）。五种机器学习算法（逻辑回归、支持向量机、随机森林、XGBoost和神经网络）通过准确性、精密度、召回率、f1评分、特异性和马修相关常数进行评估。我们进一步使用Shapley加性解释（SHAP）和局部可解释模型不可知论解释（LIME）来阐明预测驱动因素。结果：XGBoost在二元分类和多类严重程度上的总体准确率最高，分别为84.94%和85.91%。SHAP和LIME显示，功率谱密度均值、年龄和自相关性是最重要的预测因子，突出了昼夜节律中断在抑郁症中的作用。结论：我们的可解释框架可靠地识别抑郁与非抑郁个体，并区分轻度和中度抑郁。SHAP和LIME的纳入提供了透明的、有临床意义的见解，强调了可解释的人工智能在加强精神卫生保健早期发现和干预策略方面的潜力。

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引用次数: 0

Principles of Industry-Academic Partnerships Informed by Digital Mental Health Collaboration: Mixed Methods Study. 数字化心理健康协作的产学研合作原则：混合方法研究。

IF 5.8 2区医学 Q1 PSYCHIATRY

Jmir Mental Health

Pub Date : 2025-09-10 DOI: 10.2196/77439

Sophie S Hall, Olivia Hastings, Kelly Marie Prentice, Beverley Brown, Jacob Andrews, Sonal Marner, Rebecca Woodcock, Jennifer Martin, Charlotte L Hall

Background: Cross-sector collaboration is increasingly recognized as essential for addressing complex health challenges, including those in mental health. Industry-academic partnerships play a vital role in advancing research and developing health solutions, yet differing priorities and perspectives can make collaboration complex.

Objective: This study aimed to identify key principles to support effective industry-academic partnerships, from the perspective of industry partners, and develop this into actionable guidance, which can be applied across sectors. Mental health served as a motivating example due to its urgent public health relevance and the growing role of digital innovation.

Methods: Using a 3-stage, mixed-methods approach, we conducted a web-based survey of UK-based digital mental health companies (N=22) to identify key barriers and facilitators to industry-academic partnerships. This was followed by 2 focus groups (n=5) that explored emerging themes from the survey using thematic analysis. Finally, we conducted a workshop with industry representatives, researchers, clinicians, and PPI members to co-develop the Principles of Industry-Academic Partnerships (PIP) guidance.

Results: Survey findings highlighted that industry partners valued academic collaboration for enhancing credibility, facilitating knowledge transfer, and gaining access to PPI networks. However, key barriers included high costs, slow academic timelines, and complex contracting processes. The 4 major themes that emerged from the focus groups were: advantages of collaboration, cultural differences between organizations, collaboration models, and structural barriers within universities. Through informed discussions in the workshop, these themes were explored, leading to the development of 14 actionable strategies. These strategies reflect industry perspectives and formed the PIP guidance, categorized under project initiation, defining the scope and agreements, project execution, and promoting sustainability.

Conclusions: The PIP guidance provides a practical framework to support more effective and mutually beneficial collaborations between industry and academia. Developed through the lens of mental health research, the strategies identified are broadly applicable across disciplines where cross-sector partnerships are essential. Industry partners valued academic collaborations for their credibility and scientific rigor, but highlighted persistent structural and cultural barriers within universities. Addressing these challenges by aligning expectations and timelines, adopting flexible collaboration models, and streamlining operational processes can help foster impactful and sustainable partnerships in mental health and beyond.

背景：人们日益认识到，跨部门协作对于应对复杂的卫生挑战，包括精神卫生挑战至关重要。产学研伙伴关系在推进研究和制定卫生解决方案方面发挥着至关重要的作用，但不同的优先事项和观点可能使合作变得复杂。目的：本研究旨在从行业合作伙伴的角度，确定支持有效的产学研合作的关键原则，并将其发展为可操作的指导方针，可跨部门应用。精神卫生是一个鼓舞人心的例子，因为它与公共卫生息息相关，而且数字创新的作用越来越大。方法：采用三阶段混合方法，我们对英国的数字心理健康公司（N=22）进行了一项基于网络的调查，以确定产学研合作的主要障碍和促进因素。随后是2个焦点小组（n=5），他们使用主题分析来探索调查中出现的主题。最后，我们与行业代表、研究人员、临床医生和PPI成员举行了一次研讨会，共同制定行业-学术伙伴关系原则（PIP）指南。结果：调查结果强调，行业合作伙伴重视学术合作，以提高可信度，促进知识转移，并获得PPI网络。然而，主要的障碍包括高成本、缓慢的学术时间表和复杂的合同流程。从焦点小组中出现的4个主要主题是：合作的优势、组织之间的文化差异、合作模式和大学内部的结构障碍。通过研讨会上知情的讨论，对这些主题进行了探讨，从而制定了14项可操作的战略。这些策略反映了行业的观点，形成了PIP指南，分为项目启动、定义范围和协议、项目执行和促进可持续性。结论：PIP指南提供了一个实用框架，以支持工业界和学术界之间更有效和互利的合作。所确定的战略是通过精神卫生研究制定的，在跨部门伙伴关系至关重要的领域广泛适用于各个学科。行业合作伙伴重视学术合作的可信性和科学严谨性，但强调了大学内部持续存在的结构和文化障碍。通过调整期望和时间表、采用灵活的协作模式以及精简业务流程来应对这些挑战，有助于在精神卫生及其他领域建立有影响力和可持续的伙伴关系。

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引用次数: 0

Directory of Public Datasets for Youth Mental Health to Enhance Research Through Data, Accessibility, and Artificial Intelligence: Scoping Review. 通过数据、可及性和人工智能加强研究的青少年心理健康公共数据集目录：范围审查。

IF 5.8 2区医学 Q1 PSYCHIATRY

Jmir Mental Health

Pub Date : 2025-09-08 DOI: 10.2196/73852

Hua Min, Xia Jing, Cui Tao, Joel E Williams, Sarah F Griffin, Christianne Esposito-Smythers, Bruce Chorpita

Background: Youth mental health issues have been recognized as a pressing crisis in the United States in recent years. Effective, evidence-based mental health research and interventions require access to integrated datasets that consolidate diverse and fragmented data sources. However, researchers face challenges due to the lack of centralized, publicly available datasets, limiting the potential for comprehensive analysis and data-driven decision-making.

Objective: This paper introduces a curated directory of publicly available datasets focused on youth mental health (less than 18 years old). The directory is designed to serve as critical infrastructure to enhance research, inform policymaking, and support the application of artificial intelligence and machine learning in youth mental health research.

Methods: Unlike a systematic review, this paper offers a brief overview of open data resources, addressing the challenges of fragmented health data in youth mental health research. We conducted a structured search using 3 approaches: targeted searches on reputable health organization websites (eg, National Institutes of Health [NIH] and Centers for Disease Control and Prevention [CDC]), librarian consultation to identify hard-to-find datasets, and expert knowledge from prior research. Identified datasets were curated with key details, including name, description, components, format, access information, and study type, with a focus on freely available resources.

Results: A curated list of publicly available datasets on youth mental health and school policies was compiled. While not exhaustive, it highlights key resources relevant to youth mental health research. Our findings identify major national survey series conducted by organizations such as the NIH, CDC, Substance Abuse and Mental Health Services Administration (SAMHSA), and the U.S. Census Bureau, which focus on youth mental health and substance use. In addition, we include data on state and school health policies, offering varying scopes and granularities. Valuable health data repositories such as ICPSR, Data.gov, Healthdata.gov, Data.CDC.gov, OpenFDA, and Data.CMS.gov host a wide range of research data, including surveys, longitudinal studies, and individual research projects.

Conclusions: Publicly accessible health data are essential for improving youth mental health outcomes. Compiling and centralizing these resources streamlines access, enhances research impact, and informs interventions and policies. By improving data integration and accessibility, it encourages interdisciplinary collaboration and supports evidence-based interventions.

背景：近年来，青少年心理健康问题已被认为是美国迫在眉睫的危机。有效的、以证据为基础的精神卫生研究和干预措施需要获得整合各种分散数据源的综合数据集。然而，由于缺乏集中的、公开的数据集，研究人员面临着挑战，限制了全面分析和数据驱动决策的潜力。目的：本文介绍了一个关于青少年心理健康（18岁以下）的公开数据集的策划目录。该目录旨在作为关键基础设施，加强研究，为决策提供信息，并支持人工智能和机器学习在青年心理健康研究中的应用。方法：与系统综述不同，本文提供了开放数据资源的简要概述，解决了青少年心理健康研究中碎片化健康数据的挑战。我们使用3种方法进行了结构化搜索：在知名的卫生组织网站上进行有针对性的搜索（例如，美国国立卫生研究院（NIH）和疾病控制与预防中心（CDC）），向图书管理员咨询以确定难以找到的数据集，以及从先前的研究中获得专家知识。确定的数据集以关键细节进行整理，包括名称、描述、组成、格式、访问信息和研究类型，重点是可免费获得的资源。结果：编制了一份关于青少年心理健康和学校政策的公开数据集的策划清单。虽然不是详尽无遗，但它突出了与青少年心理健康研究有关的关键资源。我们的发现确定了由NIH、CDC、药物滥用和心理健康服务管理局（SAMHSA）和美国人口普查局等组织进行的主要全国性调查系列，这些组织关注青少年心理健康和药物使用。此外，我们还包括州和学校卫生政策的数据，提供不同的范围和粒度。有价值的健康数据存储库，如ICPSR、data.gov、Healthdata.gov、data. cdc .gov、OpenFDA和data. cms .gov托管广泛的研究数据，包括调查、纵向研究和个人研究项目。结论：可公开获取的健康数据对于改善青少年心理健康结果至关重要。收集和集中这些资源可以简化获取途径，增强研究影响，并为干预措施和政策提供信息。通过改进数据整合和可获取性，它鼓励跨学科合作并支持基于证据的干预措施。

{"title":"Directory of Public Datasets for Youth Mental Health to Enhance Research Through Data, Accessibility, and Artificial Intelligence: Scoping Review.","authors":"Hua Min, Xia Jing, Cui Tao, Joel E Williams, Sarah F Griffin, Christianne Esposito-Smythers, Bruce Chorpita","doi":"10.2196/73852","DOIUrl":"10.2196/73852","url":null,"abstract":"Background: Youth mental health issues have been recognized as a pressing crisis in the United States in recent years. Effective, evidence-based mental health research and interventions require access to integrated datasets that consolidate diverse and fragmented data sources. However, researchers face challenges due to the lack of centralized, publicly available datasets, limiting the potential for comprehensive analysis and data-driven decision-making.Objective: This paper introduces a curated directory of publicly available datasets focused on youth mental health (less than 18 years old). The directory is designed to serve as critical infrastructure to enhance research, inform policymaking, and support the application of artificial intelligence and machine learning in youth mental health research.Methods: Unlike a systematic review, this paper offers a brief overview of open data resources, addressing the challenges of fragmented health data in youth mental health research. We conducted a structured search using 3 approaches: targeted searches on reputable health organization websites (eg, National Institutes of Health [NIH] and Centers for Disease Control and Prevention [CDC]), librarian consultation to identify hard-to-find datasets, and expert knowledge from prior research. Identified datasets were curated with key details, including name, description, components, format, access information, and study type, with a focus on freely available resources.Results: A curated list of publicly available datasets on youth mental health and school policies was compiled. While not exhaustive, it highlights key resources relevant to youth mental health research. Our findings identify major national survey series conducted by organizations such as the NIH, CDC, Substance Abuse and Mental Health Services Administration (SAMHSA), and the U.S. Census Bureau, which focus on youth mental health and substance use. In addition, we include data on state and school health policies, offering varying scopes and granularities. Valuable health data repositories such as ICPSR, Data.gov, Healthdata.gov, Data.CDC.gov, OpenFDA, and Data.CMS.gov host a wide range of research data, including surveys, longitudinal studies, and individual research projects.Conclusions: Publicly accessible health data are essential for improving youth mental health outcomes. Compiling and centralizing these resources streamlines access, enhances research impact, and informs interventions and policies. By improving data integration and accessibility, it encourages interdisciplinary collaboration and supports evidence-based interventions.","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"12 ","pages":"e73852"},"PeriodicalIF":5.8,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12422525/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145024450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Characteristics of Suicidal Patients Who Engaged in Suicide-Related Internet Use in the United Kingdom: Cross-Sectional Survey Findings. 在英国从事与自杀有关的互联网使用的自杀患者的特征：横断面调查结果。

IF 5.8 2区医学 Q1 PSYCHIATRY

Jmir Mental Health

Pub Date : 2025-09-05 DOI: 10.2196/73702

Lana Bojanić, Isabelle M Hunt, Saied Ibrahim, Pauline Turnbull, Sandra Flynn

Background: Suicide-related internet use encompasses various web-based behaviors, including searching for suicide methods, sharing suicidal thoughts, and seeking help. Research suggests that suicide-related internet use is prevalent among people experiencing suicidality, but its characteristics among mental health patients remain underexplored.

Objective: This study aimed to examine the sociodemographic, clinical, and suicidality-related characteristics of suicidal mental health patients who engage in suicide-related internet use compared with those who do not.

Methods: A cross-sectional survey was conducted from June to December 2023, recruiting participants aged 18 years and older with recent contact with secondary mental health services in the United Kingdom. The survey assessed sociodemographic characteristics, psychiatric diagnoses, suicidal thoughts and behaviors, and engagement in suicide-related internet use. Statistical analyses included chi-square tests, Wilcoxon tests, and multivariable logistic regression to identify predictors of engaging in suicide-related internet use.

Results: Of 696 participants, 75% (522) engaged in suicide-related internet use in the past 12 months. Those who engaged in suicide-related internet use were almost 3 times as likely to have attempted suicide in the past year (32.5% vs 9.2%, P<.001). They were more likely to have a diagnosis of personality disorder (34.4% vs 18.5%, P<.001) and to disclose suicidal thoughts to someone (87.8% vs 72.8%, P<.001). They also reported higher levels of suicidal ideation intensity (median =6.6 vs 5.1, P<.001). There were no significant sociodemographic differences between groups, including age.

Conclusions: The findings suggest that suicide-related internet use is a common behavior among suicidal mental health patients across various age groups, challenging the notion that it is primarily a concern for younger populations. The association between suicide-related internet use and increased suicidality highlights the need for clinicians to incorporate discussions about web-based behaviors in suicide risk assessments. Given the high rate of disclosure of suicidal thoughts among suicide-related internet users, clinicians may have an opportunity to engage in open, nonjudgmental discussions about their patients' internet use.

背景：与自杀相关的互联网使用包括各种基于网络的行为，包括搜索自杀方法、分享自杀想法和寻求帮助。研究表明，与自杀相关的互联网使用在有自杀倾向的人群中很普遍，但其在精神健康患者中的特点仍未得到充分探讨。目的：本研究旨在探讨与自杀相关的互联网使用的自杀性心理健康患者与不使用互联网的自杀性心理健康患者的社会人口学、临床和自杀相关特征。方法：从2023年6月至12月进行了一项横断面调查，招募了最近与英国二级精神卫生服务接触的18岁及以上的参与者。该调查评估了社会人口学特征、精神诊断、自杀想法和行为，以及与自杀有关的互联网使用情况。统计分析包括卡方检验、Wilcoxon检验和多变量逻辑回归，以确定与自杀相关的互联网使用的预测因素。结果：在696名参与者中，75%（522人）在过去12个月内使用过与自杀有关的互联网。在过去的一年中，那些从事与自杀有关的互联网使用的人试图自杀的可能性几乎是3倍（32.5%比9.2%）。结论：研究结果表明，与自杀有关的互联网使用是各个年龄段的自杀心理健康患者的常见行为，挑战了这主要是年轻人关注的概念。与自杀相关的互联网使用与自杀倾向增加之间的联系突出了临床医生在自杀风险评估中纳入关于网络行为的讨论的必要性。鉴于自杀相关互联网用户中自杀想法的高披露率，临床医生可能有机会就患者的互联网使用情况进行公开、非评判性的讨论。

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引用次数: 0

Digital Contingency Management for Substance Use Disorder Treatment: 12-Month Quasi-Experimental Design. 物质使用障碍治疗的数字应急管理：12个月的准实验设计。

IF 5.8 2区医学 Q1 PSYCHIATRY

Jmir Mental Health

Pub Date : 2025-09-02 DOI: 10.2196/73617

Xiaoni Zhang, Valerie Hardcastle

Background: Although contingency management has shown some efficacy in substance use disorder treatment, digital contingency management (DCM) needs more evidence supporting its value in treating substance misuse.

Objective: This study aimed to evaluate the effectiveness of DCM in treating substance use disorder by examining 2 key outcome variables-abstinence and appointment attendance.

Methods: A 12-month quasi-experimental design was conducted by enrolling patients into 2 groups using an alternating assignment process: one group receiving treatment-as-usual plus DCM and the other receiving treatment as usual with no contingency management. Propensity score matching was conducted to match groups on covariates. After matching, t tests were conducted to examine the difference between groups on urine abstinence and appointment attendance rates.

Results: Two cohorts of propensity-matched patients (66 interventions and 59 controls) were analyzed. Abstinence was significantly higher in the DCM group (mean 0.92, 95% CI 0.88-0.96) than in the treatment-as-usual group (mean 0.85, 95% CI 0.79-0.90; P<.01). Appointment attendance also demonstrated significant differences between the groups, with the DCM group achieving a mean rate of 0.69 (95% CI 0.65-0.74) compared with 0.50 (95% CI 0.45-0.55) in the treatment-as-usual group (P<.001). This notable increase highlights the role of DCM in fostering engagement with care, an essential factor for successful treatment outcomes.

Conclusions: The results suggest that DCM can be an effective treatment modality for substance use disorder.

背景：虽然应急管理在药物使用障碍治疗中显示出一定的疗效，但数字应急管理（DCM）在药物滥用治疗中的价值还需要更多的证据来支持。目的：本研究旨在通过检查戒断和预约出勤率两个关键结果变量来评估DCM治疗物质使用障碍的有效性。方法：采用12个月的准实验设计，采用交替分配过程将患者分为两组：一组接受常规治疗+ DCM，另一组接受常规治疗，不进行应急管理。采用倾向评分匹配方法对组间的协变量进行匹配。配对后，进行t检验，检验各组之间尿禁和预约出勤率的差异。结果：分析了两组倾向匹配的患者（66名干预组和59名对照组）。DCM组戒断率（平均0.92,95% CI 0.88-0.96）明显高于常规治疗组（平均0.85,95% CI 0.79-0.90）。结论：DCM可作为药物使用障碍的有效治疗方式。

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引用次数: 0

Web-Based Interactive Training for Managers (Managing Minds at Work) to Promote Mental Health at Work: Pilot Feasibility Cluster Randomized Controlled Trial. 基于网络的管理人员互动培训（管理工作中的思想）促进工作中的心理健康：试点可行性群随机对照试验。

IF 5.8 2区医学 Q1 PSYCHIATRY

Jmir Mental Health

Pub Date : 2025-09-02 DOI: 10.2196/76373

Juliet Hassard, Holly Blake, Teixiera Mishael Dulal-Arthur, Alexandra Frost, Craig Bartle, Joanna Yarker, Fehmidah Munir, Ben Vaughan, Guy Daly, Caroline Meyer, Sean Russell, Louise Thomson

Background: Line managers play a key role in preventing poor mental health but often lack necessary skills and knowledge. Existing interventions typically focus on mental health awareness rather than practical skills. The evidence-based Managing Minds at Work (MMW) web-based training program was developed to address this gap by enhancing line managers' confidence and competence in prevention.Objective: This study piloted the MMW intervention to assess its feasibility. Objectives included evaluating (1) uptake potential across small, medium, and large companies; (2) perceived suitability and effectiveness of the intervention; and (3) feasibility of outcome data collection methods.Methods: We conducted a 2-arm pilot cluster randomized controlled trial of a self-guided, web-based training intervention for line managers. Twenty-four organizations were randomly assigned to the MMW intervention or a 3-month waitlist. A total of 224 line managers completed baseline measures (intervention: n=141, 62.9%; control: n=83, 37.1%), along with 112 of their direct reports (intervention: n=74, 66.1%; control: n=38, 33.9%). Follow-up data were collected at 3 and 6 months. Semistructured interviews with line managers and stakeholders (n=20) explored experiences with the study and intervention, and qualitative data were analyzed thematically. Line managers also completed feedback forms after each of the 5 MMW modules.Results: The recruitment of organizations and line managers exceeded targets, and retention rates of line managers were good at 3 months (161/224, 71.9%) but not at the 6-month follow-up (55/224, 24.6%). Feedback on the intervention was very positive, indicating that line managers and organizational stakeholders found the intervention acceptable, usable, and useful. We observed significant improvements with moderate to large effect sizes for all trial outcomes for line managers in the intervention arm from baseline to the 3-month follow-up. Line managers completed a variety of questionnaires, which showed increased scores for confidence in creating a mentally healthy workplace (intervention group: mean change 3.8, SD 3.2; control group: mean change 0.6, SD 3.2), mental health knowledge (intervention group: mean change 1.9, SD 3.0; control group: mean change 0.2, SD 2.9), psychological well-being (intervention group: mean change 3.6, SD 8.3; control group: mean change -0.7, SD 7.7), and mental health literacy at work (intervention group: mean change 11.8, SD 8.9; control group: mean change 0.8, SD 6.2). Collecting data from direct reports in both study arms was challenging, with results inconclusive regarding observed changes in trial outcomes. Time constraints and workload were commonly cited barriers to completion of the intervention.Conclusions: This pilot feasibility trial provides strong evidence for the usability and acceptability of

背景：部门经理在预防不良心理健康方面发挥关键作用，但往往缺乏必要的技能和知识。现有的干预措施通常侧重于心理健康意识，而不是实用技能。基于证据的“工作中的管理思想”（MMW）网络培训项目旨在通过提高直线经理的信心和预防能力来解决这一差距。目的：初步探讨毫米波干预的可行性。目标包括评估(1)小型、中型和大型公司的吸收潜力；(2)感知干预的适宜性和有效性；(3)结局数据收集方法的可行性。方法：我们对直线管理人员进行了一项自我指导、基于网络的培训干预的两组随机对照试验。24个组织被随机分配到MMW干预组或3个月的等待组。共有224名直线经理完成了基线测量（干预：n=141, 62.9%；对照组：n=83, 37.1%），以及他们的112名直接下属（干预：n=74, 66.1%；对照组：n=38, 33.9%）。随访3个月和6个月。对部门经理和利益相关者的半结构化访谈（n=20）探讨了研究和干预的经验，并对定性数据进行了主题分析。生产线经理还在每个5mmw模块完成后填写了反馈表格。结果：组织和一线管理人员的招聘超额完成，一线管理人员的保留率在随访3个月时较好（161/224,71.9%），但在随访6个月时较差（55/224,24.6%）。对干预的反馈是非常积极的，表明部门经理和组织涉众发现干预是可接受的、可用的和有用的。我们观察到，从基线到3个月随访期间，干预组直线管理人员的所有试验结果均有中等到较大的显著改善。直线经理完成了各种问卷调查，结果显示，在创建心理健康工作场所的信心（干预组：平均变化3.8,SD 3.2；对照组：平均变化0.6,SD 3.2）、心理健康知识（干预组：平均变化1.9,SD 3.0；对照组：平均变化0.2,SD 2.9）、心理健康(干预组：平均变化3.6,SD 8.3；对照组：平均变化-0.7,SD 7.7)，以及工作时的心理健康素养（干预组：平均变化11.8,SD 8.9；对照组：平均变化0.8,SD 6.2）。从两个研究组的直接报告中收集数据具有挑战性，关于观察到的试验结果变化的结果尚无定论。时间限制和工作量是完成干预的常见障碍。结论：该试点可行性试验为毫米波数字化训练和研究设计的可用性和可接受性提供了有力的证据。MMW显示出提高直线经理在促进心理健康方面的信心和能力的潜力。该研究还确定了今后大规模实施和评价的关键考虑因素。试验注册：ClinicalTrials.gov NCT05154019；https://clinicaltrials.gov/study/NCT05154019。

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引用次数: 0

Cost-Effectiveness of Internet-Delivered Emotion Regulation Therapy for Adolescents With Nonsuicidal Self-Injury: Within-Trial Analysis of a Randomized Controlled Trial. 网络情绪调节治疗青少年非自杀性自伤的成本-效果：随机对照试验的试验内分析。

IF 5.8 2区医学 Q1 PSYCHIATRY

Jmir Mental Health

Pub Date : 2025-08-27 DOI: 10.2196/74303

Johan Bjureberg, Olivia Ojala, Björn Rasmusson, Jessica Malmgren, Clara Hellner, Filipa Sampaio, Oskar Flygare

Background: Nonsuicidal self-injury (NSSI) is common among adolescents and is associated with adverse clinical outcomes, as well as suicidal behavior. Current treatments are resource-intensive and may not be accessible to all adolescents with NSSI. Internet-delivered emotion regulation individual therapy for adolescents (IERITA) with NSSI disorder is a promising treatment option, but its cost-effectiveness is unknown.

Objective: This study aims to evaluate the cost-effectiveness of IERITA for adolescents with NSSI disorder.

Methods: Within-trial cost-effectiveness analysis of a randomized controlled trial at three child and adolescent mental health services in Sweden (n=166). A total of 12 weeks of IERITA plus treatment as usual (TAU) versus TAU only were compared. The primary outcome was the frequency of NSSI at 1-month posttreatment. Secondary outcomes were NSSI remission and quality-adjusted life years (QALYs).

Results: IERITA led to reductions in NSSI frequency, a higher proportion of participants with NSSI remission, and more QALYs at 1-month posttreatment, at additional health care costs of US $3663 (95% CI US $2182-$5002) and societal costs of US $4458 (95% CI US $-577 to $9509). The incremental cost of one additional NSSI remission at 1-month posttreatment was US $18,677, and the incremental cost per QALY gained was US $792,244 for IERITA+TAU relative to TAU. IERITA had an 8% probability of being cost-effective at a societal willingness-to-pay threshold of US $84,000 for one QALY at 1-month posttreatment, which increased to 18% at 3-months posttreatment.

Conclusions: IERITA delivered adjunctive to TAU led to improvements in NSSI frequency, remission, and QALYs, at additional costs compared to TAU only. This study provides an estimate of the additional cost of delivering IERITA; however, future studies should include longer follow-up periods to better assess the magnitude of the effects on QALYs and societal costs.

背景：非自杀性自伤（NSSI）在青少年中很常见，并且与不良临床结果和自杀行为相关。目前的治疗是资源密集型的，可能不是所有的青少年自伤都能获得。网络情绪调节个体治疗青少年自伤障碍（IERITA）是一种很有前途的治疗选择，但其成本效益尚不清楚。目的：本研究旨在评估IERITA治疗青少年自伤障碍的成本-效果。方法：对瑞典三家儿童和青少年精神卫生服务机构（n=166）的随机对照试验进行试验内成本-效果分析。总共12周的IERITA加常规治疗（TAU）与仅TAU进行比较。主要终点是治疗后1个月的自伤频率。次要结局是自伤缓解和质量调整生命年（QALYs）。结果：IERITA导致自伤频率降低，自伤缓解的参与者比例更高，治疗后1个月的QALYs更多，额外的医疗费用为3663美元（95% CI为2182- 5002美元），社会成本为4458美元（95% CI为-577 - 9509美元）。治疗后1个月一次额外的自伤缓解的增量成本为18,677美元，IERITA+TAU相对于TAU获得的每个QALY的增量成本为792,244美元。IERITA在治疗后1个月的一次QALY的社会支付意愿阈值为84,000美元时，具有成本效益的概率为8%，而在治疗后3个月时，这一概率增加到18%。结论：与单纯TAU相比，IERITA辅助TAU治疗可以改善自伤频率、缓解和QALYs，但需要额外的费用。这项研究估计了提供IERITA的额外费用；然而，未来的研究应该包括更长的随访期，以更好地评估对质量年和社会成本的影响程度。

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引用次数: 0

A Social Support Just-in-Time Adaptive Intervention for Individuals With Depressive Symptoms: Feasibility Study With a Microrandomized Trial Design. 抑郁症状个体的社会支持即时适应性干预：微随机试验设计的可行性研究

IF 5.8 2区医学 Q1 PSYCHIATRY

Jmir Mental Health

Pub Date : 2025-08-26 DOI: 10.2196/74103

Timon Elmer, Markus Wolf, Evelien Snippe, Urte Scholz

Background: Just-in-time adaptive interventions (JITAIs) aim to provide psychological support during critical moments in daily life.

Objective: This preregistered study aims to evaluate the feasibility of a social support JITAI for individuals with subclinical and clinical levels of depressive symptoms awaiting psychotherapy. Triggered by ecological momentary assessment (EMA) reports, the intervention encouraged participants to activate their (digital) social support networks.

Methods: A total of 25 participants completed 2689 EMA surveys and received 377 JITAIs over an 18-day intervention period, including a microrandomized trial, to compare 4 strategies to trigger an intervention: fixed cutoff points of distress variables, personalized thresholds (through Shewhart control charts) of distress variables, momentary support need, and no intervention.

Results: The results showed high feasibility, with participants completing 85.37% (2689/3150) of the EMA surveys, exhibiting a low study-related attrition rate (7%; total attrition rate was 17%), and reporting minimal technical issues. Engagement and perceived helpfulness were heterogeneous and moderate, with participants seeking support in one-third of the instances after an intervention was triggered instances. JITAIs triggered by self-reported need for support were rated as more appropriately timed, helpful, and effective for promoting support-seeking behavior compared to those based on distress indicators, despite being triggered less frequently. Barriers, such as time constraints and perceived unavailability of support providers, likely affected support-seeking behavior, as indicated by additional qualitative analyses. Exploratory effectiveness analyses indicated Cohen d effect sizes between 0.06 and 0.14 in reducing distress after JITAIs were received.

Conclusions: The findings of this study demonstrate that a social support JITAI is feasible to implement, with high compliance and minimal technical issues. However, further research is needed to evaluate the JITAI's effectiveness and optimize trigger strategies in addressing individual needs for and barriers to engagement.

背景：即时适应干预（JITAIs）旨在为日常生活中的关键时刻提供心理支持。目的：本预注册研究旨在评估社会支持JITAI对等待心理治疗的亚临床和临床抑郁症状个体的可行性。由生态瞬时评估（EMA）报告引发的干预措施鼓励参与者激活他们的（数字）社会支持网络。方法：共有25名参与者在18天的干预期内完成了2689份EMA调查，并接受了377份jitai，其中包括一项微随机试验，以比较4种触发干预的策略：固定的痛苦变量截止点、痛苦变量的个性化阈值（通过Shewhart控制图）、瞬间支持需求和不干预。结果：结果显示高可行性，参与者完成了85.37%（2689/3150）的EMA调查，显示出较低的研究相关损失率（7%，总损失率为17%），并且报告了最小的技术问题。参与和感知的帮助是异质性的和适度的，在干预被触发后，参与者在三分之一的情况下寻求支持。尽管触发的频率较低，但与基于痛苦指标的jitai相比，由自我报告的支持需求触发的jitai在促进寻求支持行为方面被评为更适时、更有帮助、更有效。额外的定性分析表明，诸如时间限制和认为无法获得支持提供者等障碍可能会影响寻求支持的行为。探索性有效性分析表明，在接受jitai后减少痛苦的Cohen效应值在0.06至0.14之间。结论：本研究结果表明社会支持JITAI是可行的，具有较高的依从性和最小的技术问题。然而，需要进一步的研究来评估JITAI的有效性，并优化触发策略，以解决个人需求和参与障碍。

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引用次数: 0

"I Believe That AI Will Recognize the Problem Before It Happens": Qualitative Study Exploring Young Adults' Perceptions of AI in Mental Health Care. “我相信人工智能将在问题发生之前识别问题”：探索年轻人对人工智能在精神卫生保健中的看法的定性研究。

IF 5.8 2区医学 Q1 PSYCHIATRY

Jmir Mental Health

Pub Date : 2025-08-25 DOI: 10.2196/76973

Lena Petersson, Mikael G Ahlborg, Katrin Häggström Westberg

Background: Globally, young adults with mental health problems struggle to access appropriate and timely care, which may lead to a poorer future prognosis. Artificial intelligence (AI) is suggested to improve the quality of mental health care through increased capacities in diagnostics, monitoring, access, advanced decision-making, and digital consultations. Within mental health care, the design and application of AI solutions should elucidate the patient perspective on AI.Objective: The aim was to explore the perceptions of AI in mental health care from the viewpoint of young adults with experience of seeking help for common mental health problems.Methods: This was an interview study with 25 young adults aged between 18 and 30 years that applied a qualitative inductive design, with content analysis, to explore how AI-based technology can be used in mental health care.Results: Three categories were derived from the analysis, representing the participants' perceptions of how AI-based technology can be used in care for mental health problems. The first category entailed perceptions of AI-based technology as a digital companion, supporting individuals at difficult times, reminding and suggesting self-care activities, suggesting sources of information, and generally being receptive to changes in behavior or mood. The second category revolved around AI enabling more effective care and functioning as a tool, both for the patient and health care professionals (HCPs). Young adults expressed confidence in AI to improve triage, screening, identification, and diagnosis. The third category concerned risks and skepticism toward AI as a product developed by humans with limitations. Young adults voiced concerns about security and integrity, and about AI being autonomous, incapable of human empathy but with strong predictive capabilities.Conclusions: Young adults recognize the potential of AI to serve as personalized support and its function as a digital guide and companion between mental health care consultations. It was believed that AI would function as a support in navigating the help-seeking process, ensuring that they avoid the "missing middle" service gap. They also voiced that AI will improve efficiency in health care, through monitoring, diagnostic accuracy, and reduction of the workload of HCPs, while simultaneously reducing the need for young adults to repeatedly tell their stories. Young adults express an ambivalence toward the use of AI in health care and voice risks of data integrity and bias. They consider AI to be more rational and objective than HCPs but do not want to forsake personal interaction with humans. Based on the results of this study and young adults' perceptions of the monitoring capabilities of AI, future studies should define the boundaries regarding information collection responsibilities of the health care system ve

背景：在全球范围内，有精神健康问题的年轻人难以获得适当和及时的护理，这可能导致未来预后较差。建议利用人工智能（AI）提高诊断、监测、获取、高级决策和数字咨询方面的能力，从而提高精神卫生保健的质量。在精神卫生保健领域，人工智能解决方案的设计和应用应该阐明患者对人工智能的看法。目的：从有常见心理健康问题求助经历的年轻人的角度，探讨人工智能在心理卫生保健中的认知。方法：对25名年龄在18岁至30岁之间的年轻人进行访谈研究，采用定性归纳设计和内容分析，探讨如何将基于人工智能的技术应用于精神卫生保健。结果：从分析中得出了三个类别，代表了参与者对如何使用基于人工智能的技术来治疗精神健康问题的看法。第一类包括将基于人工智能的技术视为数字伴侣，在困难时期支持个人，提醒和建议自我保健活动，建议信息来源，以及通常接受行为或情绪的变化。第二类围绕人工智能实现更有效的护理和作为工具的功能，无论是对患者还是医疗保健专业人员（HCPs）。年轻人对人工智能改善分诊、筛查、识别和诊断充满信心。第三类是对人工智能的风险和怀疑，认为人工智能是有局限性的人类开发的产品。年轻人表达了对安全和诚信的担忧，并担心人工智能是自主的，没有人类的同理心，但具有很强的预测能力。结论：年轻人认识到人工智能作为个性化支持的潜力，以及它作为精神卫生保健咨询之间的数字指南和伴侣的功能。据信，人工智能将在寻求帮助的过程中发挥支持作用，确保他们避免“中间缺失”的服务差距。他们还表示，人工智能将通过监测、诊断准确性和减少医护人员的工作量来提高医疗保健的效率，同时减少年轻人反复讲述他们的故事的需要。年轻人对在医疗保健中使用人工智能表达了矛盾的态度，并提出了数据完整性和偏见的风险。他们认为人工智能比hcp更理性和客观，但不想放弃与人类的个人互动。基于这项研究的结果和年轻人对人工智能监测能力的看法，未来的研究应该界定卫生保健系统的信息收集责任与个人自我保健责任之间的界限。

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