Jmir Mental Health最新文献

Background: Intrusive memories are a core symptom of posttraumatic stress disorder (PTSD), yet their retrospective assessment is prone to biases, making real-time methods such as e-diaries essential. While trauma-focused treatments target intrusive symptoms, their efficacy has not yet been evaluated using real-time assessments.

Objective: This study aimed to use e-diaries to assess and compare the effects of dialectical behavior therapy for PTSD (DBT-PTSD) and cognitive processing therapy (CPT) on intrusive memories and related inner tension in a large sample of women with childhood abuse-related PTSD and co-occurring borderline personality disorder (BPD) symptoms.

Methods: In a multicenter randomized controlled trial, 193 women with PTSD related to childhood sexual or physical abuse and at least 3 BPD criteria were randomized to receive either DBT-PTSD or CPT. e-Diary assessments were conducted at 3 time points: before treatment, after 6 months, and after 12 months of therapy. At each time point, participants reported intrusive memories and related inner tension over 5 consecutive days using an event-based design.

Results: Both intrusive memories and related inner tension decreased significantly over time (intrusions: ß=-0.53, P<.001; inner tension: ß=-0.15, P<.001). While reductions in intrusion frequency did not differ significantly between treatment groups (ß=0.05, P=.45), DBT-PTSD was associated with significantly greater reductions in intrusion-related inner tension compared with CPT (ß=-0.16, P<.001).

Conclusions: This study provides the first real-time evaluation of trauma-focused PTSD treatments using e-diaries in daily life. Both interventions were associated with reduced intrusion frequency, while DBT-PTSD showed greater reductions in associated emotional distress-potentially reflecting its emphasis on emotion-regulation strategies and distress tolerance, which may be particularly relevant for individuals with comorbid BPD symptoms. These findings highlight the value of e-diaries for capturing treatment-related symptom change in ecologically valid contexts.

Background: Innovative, scalable mental health tools are needed to address systemic provider shortages and accessibility barriers. Large language model-based tools can provide real-time, tailored feedback to help users engage in cognitive reappraisal outside traditional therapy sessions. Socrates 2.0 (Rush University Medical Center) is a multiagent artificial intelligence tool that guides users through Socratic dialogue.

Objective: The study aimed to examine the feasibility, acceptability, and potential for symptom reduction of Socrates 2.0.

Methods: A total of 61 adult participants enrolled in a 4-week mixed methods preclinical feasibility study. The participants used Socrates 2.0 as desired and completed the self-report measures of depression, social anxiety, posttraumatic stress, and obsessive-compulsive symptoms at baseline and 1-month follow-up. Feasibility, acceptability, and appropriateness, along with usability and working alliance, were assessed via validated measures. The semistructured interviews explored user experiences and perceptions.

Results: Participants engaged with Socrates 2.0 an average of 6.70 (SD 4.57) times over 4 weeks. Feasibility (mean 4.26, SD 0.67), acceptability (mean 4.16, SD 0.84), and usability ratings were high. Participants reported small-to-moderate reductions in depression (effect size d=0.30), social anxiety (d=0.25), obsessive-compulsive (d=0.33), and posttraumatic stress (d=0.28) symptoms. Working alliance scores suggested a moderately strong perceived bond with the artificial intelligence tool. Qualitative feedback indicated that the nonjudgmental, on-demand nature of Socrates 2.0 encouraged self-reflection and exploration. Some users critiqued the repeated questioning style and limited conversation depth.

Conclusions: Socrates 2.0 was perceived as feasible, acceptable, and moderately helpful for self-guided cognitive reappraisal, demonstrating potential as an adjunct to traditional therapy. Further research, including randomized trials, is needed to determine effectiveness across different populations, optimize personalization, and address the repetitive conversational nature.

Background: Stopping smoking can improve mental health, with effect sizes similar to antidepressant treatment. Internet-based cognitive behavioral therapy (iCBT) provides evidence-based treatment for depression and anxiety, and digital interventions can support smoking cessation. However, combined digital smoking and mental health support is not currently available in UK health services.

Objective: This feasibility trial aimed to investigate the acceptability and feasibility of a digital tailored smoking cessation intervention delivered alongside usual iCBT, and test trial procedures.

Methods: The study design was a 2-armed, parallel groups, pragmatic, feasibility randomized controlled trial. Eligible participants were adult (18 years and older), regular smokers referred to iCBT from National Health Service Talking Therapies services in England. Participants were screened, consented, and randomized via a web-based platform and allocated to intervention (integrated smoking cessation support) or control (usual care) arms. Fully automated processes ensured allocation concealment. It was not possible to blind participants or clinicians to the behavioral intervention. Follow-ups via web-based questionnaires were completed at 3- and 6-months. Prespecified progression criteria, to determine the feasibility of the integrated intervention and trial procedures for a definitive trial, were enrolment of eligible clients (≥20%); recruitment to the target (≥80%); outcome data completeness (≥70%); and self-reported quit attempts in the intervention arm (≥8%).

Results: A total of 309 participants were randomized: 154 to the intervention arm and 155 to the control arm. The proportion of eligible clients enrolled (309/1484, 21%) met the criteria for progression; however, the number randomized was below target (309/500, 62%). In the intervention arm, 18% (27/154) self-reported at least one quit attempt, which exceeded the progression criteria but was comparable to the control arm (32/155, 21%). High loss to follow-up meant data completeness was low (<30% across 6 key pilot clinical outcomes).

Conclusions: Integrating smoking cessation within digital mental health treatment and using automated procedures to enroll and randomize participants appears feasible. Adjustments to site recruitment could improve participant recruitment; however, a large loss to follow-up undermines the feasibility of progression.

Trial registration: ISRCTN Registry ISRCTN10612149; https://www.isrctn.com/ISRCTN10612149.

International registered report identifier (irrid): RR2-10.1016/j.cct.2024.107541.

Unlabelled: Artificial intelligence (AI) applications in mental health have expanded rapidly, and consumers are already using freely available generative AI models for self-guided mental health support despite limited clinical validation. In August 2025, Illinois enacted Public Act 104-0054, the first state statute in the United States to explicitly define and regulate the use of AI in psychotherapy services, establishing boundaries around administrative support, supplementary support, and therapeutic communication. While the Act clarifies several aspects of AI use in therapy, it also leaves important gray areas, such as whether AI-generated session summaries, psychoeducation, or risk-flagging functions should be considered therapeutic communication. Drawing on the history of empirically supported treatments in psychology, we argue that a framework of evidence, safety, fidelity, and legal compliance could help determine when AI tools should be integrated into clinical care. This approach provides a concrete pathway for balancing patient protection with responsible innovation in the rapidly evolving field of mental health AI tools.

Background: Distinguishing pediatric bipolar disorder (BD) from attention-deficit/hyperactivity disorder (ADHD) is challenging due to overlapping fluctuations in mood, energy, and activity. Combining objective actigraphy with self-reported mood and energy data may aid differential diagnosis and risk monitoring.

Objective: This study aimed to test same-day associations between actigraphy-derived activity extremes and self-reported mood and energy, and to evaluate whether these measures predict same-day and next-day severe mood in adolescents with BD, ADHD, and other diagnoses.

Methods: We analyzed 209 inpatients (2148 patient-days) across 4 groups (ADHD without BD: n=54; BD with ADHD: n=42; BD without ADHD: n=34; other diagnoses: n=79). Actigraphy data (Philips Actiwatch 2) were summarized into daily maximum and minimum quartiles (Max1-Max4 and Min1-Min4). Mood and Energy Thermometer (-10 to +10) ratings were categorized as follows: OK (<3), mild (3-4), moderate (5-6), and severe (>6). Group differences used Kruskal-Wallis and Mann-Whitney U tests with Bonferroni correction (P<.004). Associations used chi-square tests with Cramér V. Leak-safe machine learning (patient-wise GroupKFold) classified SevereDay (same day) and SevereTomorrow (next day) using actigraphy, sleep, energy, and demographic data.

Results: BD without ADHD showed the tightest coupling of extreme activity with negative mood and energy (Cramér V of up to 0.24; P<.004). ADHD without BD showed stronger links between activity and positive energy. Machine learning achieved a receiver operating characteristic area under the curve (ROC-AUC) of 0.85, an accuracy of 0.79, and an F1-score of 0.67 for SevereDay. SevereTomorrow performance was moderate (ROC-AUC=0.80; accuracy=0.79; F1-score=0.60). Energy variability and actigraphy averages/peaks were the top predictors.

Conclusions: Integrating actigraphy, sleep, and daily energy ratings identifies severe mood days and provides early next-day risk signals in hospitalized adolescents. The findings support wearable-based phenotyping for precision monitoring, with external validation needed in outpatients.

Background: Cognitive behavioral therapy (CBT)-based chatbots, many of which incorporate artificial intelligence (AI) techniques, such as natural language processing and machine learning, are increasingly evaluated as scalable solutions for addressing mental health issues, such as depression and anxiety. These fully automated or minimally supported interventions offer novel pathways for psychological support, especially for individuals with limited access to traditional therapy.

Objective: This narrative review synthesized evidence on the clinical efficacy, therapeutic mechanisms, and technological features of CBT-based chatbots designed to alleviate depressive and anxiety symptoms.

Methods: Fourteen peer-reviewed studies published between January 2015 and March 2025 were identified through systematic searches and met predefined inclusion criteria. The studies were analyzed to extract information on intervention structure, therapeutic components, outcomes, and implementation characteristics.

Results: Across the included studies, CBT-based chatbots consistently demonstrated short-term reductions in depressive symptoms, whereas findings for anxiety outcomes were mixed, with some studies reporting improvements and others showing nonsignificant or unreported effects. Moderate effect sizes were observed for depression. Reported therapeutic features included cognitive restructuring, behavioral activation, relaxation and mindfulness strategies, emotional support, self-monitoring and feedback, and therapeutic alliance. Technological characteristics such as real-time feedback and adaptive goal tracking were associated with enhanced engagement and adherence.

Conclusions: CBT-based chatbots appear to be a promising and scalable modality for delivering psychological support, particularly for underserved populations. However, variability in study designs, heterogeneity of outcome reporting, and limited long-term evidence pose challenges for generalizability. Emerging evidence from generative AI chatbots (eg, Therabot and Limbic Care) highlights both opportunities and risks. Future work should examine long-term efficacy, adaptive personalization, cross-cultural adaptation, and rigorous ethical oversight.

Unlabelled: Digital mental health tools such as apps, virtual reality, and artificial intelligence (AI) hold great promise but continue to face barriers to widespread clinical adoption. The Society of Digital Psychiatry, in partnership with JMIR Mental Health, presents a 3-pronged road map to accelerate their safe, effective, and equitable implementation. First, education: integrate digital psychiatry into core training and professional development through a global webinar series, annual symposium, newsletter, and an updated open-access curriculum addressing AI and the evolving digital navigator role. Second, AI standards: develop transparent, actionable benchmarks and consensus guidance through initiatives like MindBench.ai to assess reasoning, safety, and representativeness across populations. Third, digital navigators: expand structured, train-the-trainer programs that enhance digital literacy, engagement, and workflow integration across diverse care settings, including low- and middle-income countries. Together, these pillars bridge research and practice, advancing digital psychiatry grounded in inclusivity, accountability, and measurable clinical impact.

Background: Generative artificial intelligence (GenAI) models have emerged as a promising yet controversial tool for mental health.

Objective: The purpose of this study is to understand the experiences of individuals who repeatedly used ChatGPT (GenAI) for emotional and mental health support (EMS).

Methods: We recruited 270 adult participants across 29 countries who regularly used ChatGPT (OpenAI) for EMS during April 2024. Participants responded to quantitative survey questions on the frequency and helpfulness of using ChatGPT for EMS, and qualitative questions regarding their therapeutic purposes, emotional experiences of using, and perceived helpfulness and rationales. Thematic analysis was used to analyze qualitative data.

Results: Most participants reported using ChatGPT for EMS at least 1-2 times per month for purposes spanning traditional mental health needs (diagnosis, treatment, and psychoeducation) and general psychosocial needs (companionship, relational guidance, well-being improvement, and decision-making). Users reported various emotional experiences during and after use for EMS (eg, connected, relieved, curious, embarrassed, or disappointed). Almost all users found it at least somewhat helpful. The rationales for perceived helpfulness include perceived changes after use, emotional support, professionalism, information quality, and free expression, whereas the unhelpful aspects include superficial emotional engagement, limited information quality, and lack of professionalism.

Conclusions: Despite the absence of ethical regulations for EMS use, GenAI is becoming an increasingly popular self-help tool for emotional and mental health support. These results highlight the blurring boundary between formal mental health care and informal self-help and underscore the importance of understanding the relational and emotional dynamics of human-GenAI interaction. There is an urgent need to promote AI literacy and ethical awareness among community users and health care providers and to clarify the conditions under which GenAI use for mental health promotes well-being or poses risk.