Jmir Mental Health最新文献

Background: Individuals with dementia, especially those in later stages, have difficulties with verbally reporting their experience of pain. This results in both underassessment and undertreatment of pain, signaling the need for better pain recognition in persons with dementia. A promising form of pain assessment is digital monitoring, which can concurrently and more objectively detect and use numerous relevant pain cues.

Objective: This review aimed to identify observable cues of pain, which could be used for digital pain monitoring. A total of 2 research questions (RQs) were formed as we set out to examine which digital cues offered a valid insight into pain in people with dementia (RQ1) and identify how these cues were originally measured (RQ2).

Methods: A standard methodological approach for scoping reviews was used. Relevant research papers were chosen based on SCOPUS and Web of Science databases, and relevant data on pain cues were extracted from all papers that satisfied the inclusion criteria. The gathered data were analyzed using a thematic analysis, which involved categorizing the observable cues into higher-order categories.

Results: Of the 3705 publications identified in the search, 34 satisfied the inclusion criteria and were closely examined. Addressing RQ1, we identified 7 categories of behavioral and physiological cues associated with pain, most frequently facial expressions (20/34, 59%) and body movements or expressions (15/34, 44%). Several subcategories for each main category of pain cues were also identified, each involving between 1 and 28 relevant specific pain cues. Addressing RQ2, 29/34 (85%) studies assessed pain cues via human observation only, while 5/34 (15%) combined human observation with either facial recognition software, PainChek app, or computer vision.

Conclusions: The review provides a comprehensive list of the most relevant cues that signify pain in persons with dementia and offers a foundation for the use of artificial intelligence and digital monitoring for the screening of pain in dementia.

Background: Mental health recovery typically continues after patients leave the hospital. However, hospital readmission in the 12 months after discharge is common and costly.

Objective: This study aimed to examine the effectiveness of supportive text messaging (hereinafter "SMS") and SMS with or without peer support service on hospital readmission and length of stay after discharge from inpatient psychiatric care.

Methods: A stepped-wedge cluster randomized trial was used to examine differences in the changes in the mean number of admissions and the mean duration of total length of stay in days, for patients discharged from psychiatric inpatient care, at 6 and 12 months pre- and post index admissions, for 2 intervention periods compared to a control period of treatment as usual.

Results: Overall, 1070 participants were assigned to 1 of 3 study arms: SMS (n=302), SMS with or without peer support service (n=342), or treatment as usual (n=426). Compared to treatment as usual, SMS with or without peer support service reduced hospital readmissions 6 months pre- and post index admission by an average of 0.26 admissions, and SMS alone reduced inpatient length of stays 6 months pre- and post index admission by an average of 7.28 days.

Conclusions: Our results demonstrate that simple, low-cost digital tools-either by themselves or paired with peer support-can help close gaps in postdischarge care. We anticipate that these findings may inform future service delivery models and policy development aimed at enhancing postdischarge mental health support. By supporting smoother transitions and reducing future hospital use, such approaches may offer a scalable way to build more sustainable and person-centered mental health systems.

Trial registration: ClinicalTrials.gov NCT05133726; https://clinicaltrials.gov/study/NCT05133726.

Background: Telemedicine has emerged as a promising tool to enhance adherence and monitoring in patients with eating disorders (EDs). Traditional face-to-face cognitive therapies remain the gold standard; however, integrating telemedicine may provide additional support and improve patient engagement and retention. Given the increasing use of digital health interventions, it is crucial to assess their safety and effectiveness in complementing conventional treatments.

Objective: We aimed to evaluate the safety and effectiveness of telemedicine as a complementary tool for cognitive face-to-face therapies to promote adherence and monitoring of patients with EDs.

Methods: We consulted the National Institute for Health and Care Excellence, the Canadian Agency for Drugs and Technologies in Health (now known as Canada's Drug Agency), MEDLINE (Ovid), Embase, Web of Science, Cochrane Library, international HTA database (International Network of Agencies for Health Technology Assessment), CINAHL (EBSCO), and PsycINFO (EBSCO) websites and databases in December 2024 to identify eligible systematic reviews, synthesis reports, or meta-analyses that address telemedicine as a complementary therapy to face-to-face care in patients with EDs. Two researchers performed an independent critical reading of the systematic reviews and assessed the risk of bias using AMSTAR-2 (A Measurement Tool to Assess Systematic Reviews, version 2).

Results: We initially identified 1004 studies, but only 5 (0.5%) systematic reviews met the inclusion criteria. Email, vodcasts, smartphone apps, and SMS text messaging were the principal telemedicine channels. Telemedicine interventions were safe, helpful, and motivating; improved retention rates and patient-physician communication; and reduced ED symptoms.

Conclusions: Telemedicine interventions showed promising, positive findings as a complementary tool for face-to-face ED treatment that must be interpreted cautiously. The limited number of systematic reviews selected and their moderate to critically low quality underscore the need for further research in this area.

Background: Alexithymia, defined as difficulty identifying and describing one's emotions, has been identified as a transdiagnostic emotional process that impacts the course, severity, and treatment outcomes of psychiatric conditions such as posttraumatic stress disorder (PTSD). As such, alexithymia is an important process to accurately measure and identify in clinical contexts. However, research identifying the association between the experience of alexithymia and psychopathology has been limited by an overreliance on self-report scales, which have restricted use for measuring constructs that involve deficits in self-awareness, such as alexithymia. Hence, more suitable and effective methods of measuring and identifying those experiencing alexithymia in clinical samples are needed.

Objective: In this cross-sectional study, we aimed to determine if facial, vocal, and language phenotypes extracted from 1-minute recordings of war veterans with PTSD describing a traumatic event could be used to identify those experiencing alexithymia.

Methods: A total of 96 participants were included in this cross-sectional study. Specialized software was used to extract facial, vocal, and language features from the recordings. These features were then integrated into machine learning (extreme gradient boosting [XGBoost]) classification models that were trained and tested within a 5-fold nested cross-validation pipeline for their capacity to classify veterans scoring above the cutoff for alexithymia on the Toronto Alexithymia Scale-20.

Results: The best performing XGBoost classification model trained in the nested cross-validation pipeline was able to classify those experiencing alexithymia with a good level of accuracy (average F₁-score=0.78, SD 0.07; average area under the curve score=0.87, SD 0.12). Consistent with theoretical models and past research into phenotypes of alexithymia, language, vocal, and facial features all contributed to the accuracy of the XGBoost classification model.

Conclusions: These findings indicate that facial, vocal, and language phenotypes incorporated in machine learning models could represent a promising alternative to identifying individuals with PTSD who are experiencing alexithymia. The further validation and use of this approach could facilitate more tailored and effective allocation of treatment resources to individuals experiencing alexithymia in clinical settings.

Background: Digital social activity, defined as interactions on social media and electronic communication platforms, has become increasingly important. Social factors impact mental health and can contribute to depression and anxiety. Therefore, incorporating digital social activity into routine mental health care has the potential to improve outcomes.

Objective: This study aimed to compare treatment augmented with an electronic dashboard of patient's digital social activity versus treatment-as-usual on patient-rated outcomes symptoms of depression in a randomized trial of patients with mood and anxiety disorders.

Methods: We developed a personalized electronic dashboard summarizing a participant's digital social activity. This dashboard, collaboratively discussed during mental health visits, was used to augment clinical care and tested in a randomized trial against treatment-as-usual. Clinicians and patients were recruited from outpatient psychiatry clinics. Patients were eligible if they were 12 years or older and were receiving treatment for a mood or anxiety disorder. Psychiatric symptoms measures for depression (primary outcome measure) and anxiety (secondary outcome measure) were obtained at each clinic visit as part of measurement-based standard of care. Baseline and 3-month follow-up assessments included a measure of mental health status and therapeutic alliance measure. Collateral information and clinical action scale were also collected at each visit.

Results: A total of 103 patients consented to participate, 97 of whom were randomized to the dashboard arm (n=49) or the treatment-as-usual arm (n=48). There were no differences in psychiatry symptom rating scores or mental health status between the two arms. However, there was a significant increase in the discussion of digital social activity with the intervention, and it did not appear to change patient therapeutic alliance.

Conclusions: The incorporation of a personalized electronic dashboard into clinical care was feasible and led to an increased discussion of digital social activity, but there was no impact on mental health outcomes.

Background: Artificial intelligence (AI), particularly large language models (LLMs), presents a significant opportunity to transform mental health care through scalable, on-demand support. While LLM-powered chatbots may help reduce barriers to care, their integration into clinical settings raises critical concerns regarding safety, reliability, and ethical oversight. A structured framework is needed to capture their benefits while addressing inherent risks. This paper introduces a conceptual model for prompt engineering, outlining core design principles for the responsible development of LLM-based mental health chatbots.

Objective: This paper proposes the Mental Well-Being Through Dialogue - Safeguarded and Adaptive Framework for Ethics (MIND-SAFE), a comprehensive, layered framework for prompt engineering that integrates evidence-based therapeutic models, adaptive technology, and ethical safeguards. The objective is to propose and outline a practical foundation for developing AI-driven mental health interventions that are safe, effective, and clinically relevant.

Methods: We outline a layered architecture for an LLM-based mental health chatbot. The design incorporates (1) an input layer with proactive risk detection; (2) a dialogue engine featuring a user state database for personalization and retrieval-augmented generation to ground responses in evidence-based therapies such as cognitive behavioral therapy, acceptance and commitment therapy, and dialectical behavior therapy; and (3) a multitiered safety system, including a postgeneration ethical filter and a continuous learning loop with therapist oversight.

Results: The primary contribution is the framework itself, which systematically embeds clinical principles and ethical safeguards into system design. We also propose a comparative validation strategy to evaluate the framework's added value against a baseline model. Its components are explicitly mapped to the Framework for AI Tool Assessment in Mental Health and Readiness Evaluation for AI-Mental Health Deployment and Implementation frameworks, ensuring alignment with current scholarly standards for responsible AI development.

Conclusions: The framework offers a practical foundation for the responsible development of LLM-based mental health support. By outlining a layered architecture and aligning it with established evaluation standards, this work offers guidance for developing AI tools that are technically capable, safe, effective, and ethically sound. Future research should prioritize empirical validation of the framework through the phased, comparative approach introduced in this paper.

Background: Digital conversational agents (or "chatbots") that can generate human-like conversations have recently been adapted as a means of administering mental health interventions. However, their development for youth seeking mental health services requires further investigation.

Objective: This youth-engaged scoping review synthesizes the recent research on digital conversational agents for youth seeking mental health or substance use services.

Methods: Studies were included if they were published between 2016 and 2025 and examined digital conversational agents for youth aged 11 to 24 years with mental health or substance use challenges in clinical settings. Systematic literature searches were conducted in February 2024 in multiple databases and updated in March 2025. Data were extracted using codeveloped forms and synthesized narratively.

Results: Ten studies were included, all focusing on mental health. Seven examined the acceptability and feasibility of digital conversational agents; others explored youth perceptions of use, design, and content, with some exploration of impact on mental health symptoms. Eight of ten studies reported high acceptability or positive user experiences. Three were randomized controlled trials that found potential reductions in depressive symptoms. Reporting on the ethical standards was limited. No studies focused on substance use alone.

Conclusions: Literature on digital conversational agents for treatment-seeking youth is emerging but limited. Future rigorous research is needed that prioritizes data security, safety measures, and youth co-design in the development of safe, engaging, digital conversational agents for youth with mental health conditions.