Jmir Mental Health最新文献

Background: Access to evidence-based interventions is urgently required, especially for individuals of minoritized identities who experience unique barriers to mental health care. Digital mental health interventions have the potential to increase accessibility. Previous pilot studies testing HabitWorks, a smartphone app providing an interpretation bias intervention, have found strong engagement and adherence for HabitWorks; however, previous trials' samples consisted of predominantly non-Hispanic, White individuals.

Objective: This study conducted an open trial of HabitWorks in a community sample of adults who identified as Black, Hispanic or Latinx, or both. This study aims to test safety, acceptability, and engagement with the HabitWorks app for Black and Latinx adults.

Methods: Black, Hispanic or Latinx adults (mean age 32.83, SD 11.06 y; 22/31, 71% women) who endorsed symptoms of anxiety or depression were asked to complete interpretation modification exercises via HabitWorks 3 times per week for 1 month. Interpretation bias and anxiety and depression symptoms were assessed at baseline and posttreatment assessments. Participants completed qualitative interviews to assess overall perceptions of HabitWorks.

Results: Of the 31 participants that downloaded the app, 27 (87%) used HabitWorks all 4 weeks. On average, participants completed 15.74 (SD 7.43) exercises out of the 12 prescribed, demonstrating high engagement. Acceptability ratings met all a priori benchmarks except for relevancy. Qualitative interviews also demonstrated high acceptability and few negative experiences. Significant improvements were found in interpretation style (t₃₀=2.29; P<.001), with a large effect size (Cohen d=1.53); anxiety symptoms (t₃₀=2.29; P=.03), with a small effect size (Cohen d=0.41); and depression symptoms (t₃₀=3.065; P=.005), with a medium effect size (Cohen d=0.55).

Conclusions: This study adds to the literature evaluating digital mental health interventions in Black and Latinx adults. Preliminary results further support a future controlled trial testing the effectiveness of HabitWorks as an intervention.

Unlabelled: Global rates of mental health concerns are rising, and there is increasing realization that existing models of mental health care will not adequately expand to meet the demand. With the emergence of large language models (LLMs) has come great optimism regarding their promise to create novel, large-scale solutions to support mental health. Despite their nascence, LLMs have already been applied to mental health-related tasks. In this paper, we summarize the extant literature on efforts to use LLMs to provide mental health education, assessment, and intervention and highlight key opportunities for positive impact in each area. We then highlight risks associated with LLMs' application to mental health and encourage the adoption of strategies to mitigate these risks. The urgent need for mental health support must be balanced with responsible development, testing, and deployment of mental health LLMs. It is especially critical to ensure that mental health LLMs are fine-tuned for mental health, enhance mental health equity, and adhere to ethical standards and that people, including those with lived experience with mental health concerns, are involved in all stages from development through deployment. Prioritizing these efforts will minimize potential harms to mental health and maximize the likelihood that LLMs will positively impact mental health globally.

Background: Digital exclusion, characterized by a lack of access to digital technology, connectivity, or digital skills, disproportionally affects marginalized groups. An important domain impacted by digital exclusion is access to health care. During COVID-19, health care services had to restrict face-to-face contact to limit the spread of the virus. The subsequent shift toward remote delivery of mental health care exacerbated the digital divide, with limited access to remote mental health care delivery. In response, Camden and Islington National Health Service Foundation Trust launched the innovative Digital Inclusion Scheme (DIS).

Objective: This study aimed to examine the impact of facilitating digital inclusion in mental health access. Camden and Islington National Health Service Foundation Trust implemented the trust-wide DIS for service users who were digitally excluded, that is, were without devices or connectivity or reported poor digital skills. The scheme provided access to a loan digital device (a tablet), internet connectivity devices, and mobile data, as well as personalized digital skills support.

Methods: The DIS went live in October 2021 and received 106 referrals by June 2022. Semistructured interviews were conducted with 12 service users to ask about their experience of accessing the DIS. A thematic analysis identified themes and subthemes relating to the extent of their digital exclusion before engaging with the scheme and the impact of accessing a scheme on their ability to engage with digital technology and well-being.

Results: There were 10 major themes. A total of 6 themes were related to factors impacting the engagement with the scheme, including digital exclusion, relationship to the trust, the importance of personalized digital support, partnership working, device usability and accessibility, and personal circumstances. The remaining 4 themes spoke to the impact of accessing the scheme, including improved access to services, impact on well-being, financial implications, and a greater sense of empowerment.

Conclusions: Participants reported an increased reliance on technology driving the need for digital inclusion; however, differences in motivation for engaging with the scheme were noted, as well as potential barriers, including lack of awareness, disability, and age. Overall, the experience of accessing the DIS was reported as positive, with participants feeling supported to access the digital world. The consequences of engaging with the scheme included greater perceived access to and control of physical and mental health care, improved well-being, and a greater sense of empowerment. An overview of the lessons learned are provided along with suggestions for other health care settings that are looking to implement similar schemes.

Background: Comprehensive session summaries enable effective continuity in mental health counseling, facilitating informed therapy planning. However, manual summarization presents a significant challenge, diverting experts' attention from the core counseling process. Leveraging advances in automatic summarization to streamline the summarization process addresses this issue because this enables mental health professionals to access concise summaries of lengthy therapy sessions, thereby increasing their efficiency. However, existing approaches often overlook the nuanced intricacies inherent in counseling interactions.

Objective: This study evaluates the effectiveness of state-of-the-art large language models (LLMs) in selectively summarizing various components of therapy sessions through aspect-based summarization, aiming to benchmark their performance.

Methods: We first created Mental Health Counseling-Component-Guided Dialogue Summaries, a benchmarking data set that consists of 191 counseling sessions with summaries focused on 3 distinct counseling components (also known as counseling aspects). Next, we assessed the capabilities of 11 state-of-the-art LLMs in addressing the task of counseling-component-guided summarization. The generated summaries were evaluated quantitatively using standard summarization metrics and verified qualitatively by mental health professionals.

Results: Our findings demonstrated the superior performance of task-specific LLMs such as MentalLlama, Mistral, and MentalBART evaluated using standard quantitative metrics such as Recall-Oriented Understudy for Gisting Evaluation (ROUGE)-1, ROUGE-2, ROUGE-L, and Bidirectional Encoder Representations from Transformers Score across all aspects of the counseling components. Furthermore, expert evaluation revealed that Mistral superseded both MentalLlama and MentalBART across 6 parameters: affective attitude, burden, ethicality, coherence, opportunity costs, and perceived effectiveness. However, these models exhibit a common weakness in terms of room for improvement in the opportunity costs and perceived effectiveness metrics.

Conclusions: While LLMs fine-tuned specifically on mental health domain data display better performance based on automatic evaluation scores, expert assessments indicate that these models are not yet reliable for clinical application. Further refinement and validation are necessary before their implementation in practice.

Background: Fetal alcohol spectrum disorder (FASD) is a common developmental disability that requires lifelong and ongoing support but is often difficult to find due to the lack of trained professionals, funding, and support available. Technology could provide cost-effective, accessible, and effective support to those living with FASD and their caregivers.

Objective: In this review, we aimed to explore the use of technology available for supporting people living with FASD and their caregivers.

Methods: We conducted a scoping review to identify studies that included technology for people with FASD or their caregivers; focused on FASD; used an empirical study design; were published since 2005; and used technology for assessment, diagnosis, monitoring, or support for people with FASD. We searched MEDLINE, Web of Science, Scopus, Embase, APA PsycINFO, ACM Digital Library, JMIR Publications journals, the Cochrane Library, EBSCOhost, IEEE, study references, and gray literature to find studies. Searches were conducted in November 2022 and updated in January 2024. Two reviewers (CZC and HW) independently completed study selection and data extraction.

Results: In total, 17 studies exploring technology available for people with FASD showed that technology could be effective at teaching skills, supporting caregivers, and helping people with FASD develop skills.

Conclusions: Technology could provide support for people affected by FASD; however, currently there is limited technology available, and the potential benefits are largely unexplored.