JMIR Mental Health

Detecting Pediatric Emergency Service Use for Suicide and Self-Harm: Multimodal Analysis of 3828 Encounters

2026-02-04T13:30:09-05:00

Background: Suicide is the second-leading cause of U.S. childhood mortality after age nine. Accurate measurement of pediatric emergency service use for self-injurious thoughts and behaviors (SITB) remains challenging, as diagnostic codes undercount children. This measurement gap impedes public health and prevention efforts. Current research has not established which combination of electronic health record (EHR) data elements achieves both high detection accuracy and consistent performance across youth populations. Objective: To 1) compare detection accuracy of EHR-based methods for identifying SITB-related pediatric emergency department (ED) visits: basic structured data (ICD-10-CM codes, chief concern), comprehensive structured data, clinical note text with natural language processing, and hybrid approaches combining structured data with notes; and 2) for each method, measure variability in detection by youth demographics and underlying mental health diagnosis. Methods: Multiple human experts reviewed clinical records of 3,828 pediatric mental health emergency visits (28,861 clinical notes) to a large health system with two EDs (June 2022-October 2024). Reviewers used the Columbia Classification Algorithm for Suicide Assessment (C-CASA) to label presence of SITB at the visit. Random forest classifiers were developed using three data modalities: 1) structured data (low-dimensional [ICD/CC], medium-dimensional [adding c-SSRS screening or mental health diagnoses], and high-dimensional [all structured data/aCS]); 2) text data (NLP-general, NLP-medical, and LLaMA-derived scores); and 3) hybrid data (combining aCS with each text approach). Model performance was evaluated using area under the receiver operating characteristic curve (AUROC). Results: Of 3,828 visits, 1,760 (46.0%) were SITB-related. Detection performance improved with dimensionality: low- (AUROC=0.865), medium- (AUROC=0.934-0.935), and high-dimensional (AUROC=0.965). Low-dimensional structured (ICD/CC) showed high variability in detection, with lower accuracy among preadolescents (AUROC=0.821 versus 0.880 for adolescents), males (AUROC=0.817 versus 0.902 for females), and patients with neurodevelopmental (AUROC=0.568-0.809), psychotic (AUROC=0.718), and disruptive disorders (AUROC=0.703). Hybrid modality (aCS+LLaMA) achieved optimal performance (AUROC=0.977), with AUROC ≥0.90 for all 20 demographic and 12/15 diagnostic subgroups. Conclusions: This cross-sectional retrospective study identified that, relative to diagnostic codes and chief concern alone, hybrid structured-text detection methods improved accuracy, and mitigated unwanted detection variability. Findings offer a scaffold for future clinical deployment of improved information retrieval of pediatric suicide and self-harm-related emergencies.

“It Felt Good to Be Able to Say That Out Loud”—Therapeutic Alliance and Processes in AVATAR Therapy for People Who Hear Distressing Voices: Peer-Led Qualitative Study

2026-01-28T18:00:20-05:00

Background: AVATAR therapy is a novel psychological therapy that aims to reduce distress associated with hearing voices. The approach involves a series of therapist-facilitated dialogues between a voice-hearer and a digital embodiment of their main distressing voice (the avatar), which aim to increase coping and self-empowerment. Objective: This study explored therapeutic processes that are distinctive to AVATAR therapy, including direct early work with voice content and the role of the therapist in dialogue enactment. Methods: People with lived experience relating to psychosis (peer researchers) contributed to each stage of the study. Peer researchers led semistructured interviews, which were conducted with 19 participants who received AVATAR therapy as part of the AVATAR2 trial, including 3 participants who dropped out of therapy. Data were analyzed using interpretative phenomenological analysis (n=5) and template analysis (n=14). Results: Participants described the initial challenges of experiential work with distressing voice content; however, most reported a meaningful increase in power and control over the course of dialogues and improvements with voices in daily life. A strong therapeutic alliance was experienced by all participants, including those who chose to discontinue therapy, often mitigating the discomfort associated with initial challenges by enhancing their sense of safety. Several important themes relating to individual engagement were highlighted, such as the emotional intensity of the experience and the importance of participants’ determination and open-minded attitudes despite initial doubts. Those who decided not to continue with therapy described challenges with the realism of working dialogically with a digital representation of their distressing voice. Conclusions: This study has provided a deeper understanding of the experience of engaging in AVATAR therapy, in particular the challenges and opportunities of direct work with voice content. The importance of therapeutic alliance and establishing a sense of voice presence has been emphasized. Implications for the planned optimization and wider implementation of AVATAR therapy in routine care settings are discussed. Trial Registration: ISRCTN Registry ISRCTN55682735; https://www.isrctn.com/ISRCTN55682735

Retention and Engagement in Culturally Adapted Digital Mental Health Interventions: Systematic Review of Dropout, Attrition, and Adherence in Non-Western, Educated, Industrialized, Rich, Democratic Settings

2026-01-28T11:00:04-05:00

Background: Digital mental health interventions (DMHIs) offer scalable and cost-effective support for mental health but are predominantly developed in WEIRD (Western, Educated, Industrialized, Rich, Democratic) contexts, raising questions about their global applicability. Dropout, attrition, and adherence rates critically influence DMHI effectiveness yet remain poorly characterized in culturally adapted formats. Objective: This systematic review aimed to (a) synthesize evidence on dropout, attrition, and adherence in culturally adapted DMHIs delivered to non-WEIRD adult populations and (b) assess the methodological quality of the included studies. Methods: PsycINFO, PubMed, and ScienceDirect were systematically searched for randomized controlled trials (RCTs) published in English between January 2014 and April 2024. Screening and data extraction followed PRISMA guidelines, and methodological quality was evaluated using the AXIS tool. Extracted variables included dropout, attrition, adherence, adaptation techniques, and clinical outcomes. Results: Twenty-three RCTs (N = 4,656) from diverse regions met inclusion criteria. Attrition ranged from 5.3% to 87% (median ≈18.4%), dropout from 0% to 66% (median ≈18.7%), and adherence from 26.3% to 100% (median ≈71%). Deep, participatory adaptations—such as language translation combined with culturally resonant content, stakeholder engagement, and iterative refinement—were consistently associated with lower dropout (<11%) and higher adherence (>75%). In contrast, surface-level adaptations (e.g., translation only) showed higher dropout (up to 56%). Studies that incorporated both cultural tailoring and human support reported the most favorable engagement and clinical outcomes (e.g., reductions in insomnia, depression, and anxiety). Most studies (91%) were rated as “Good” quality, although some lacked representative sampling or objective engagement metrics Conclusions: Comprehensive and participatory cultural adaptation is associated with engagement and effectiveness of DMHIs among non-WEIRD populations. Future research should integrate hybrid human-digital delivery models, objective engagement metrics, and larger multicenter trials to improve generalizability and scalability. Clinical Trial: PROSPERO (CRD42025641863)

Remote Measurement-Based Care Interventions for Mental Health: Systematic Review and Meta-Analysis

2026-01-28T10:00:11-05:00

Background: Poor management of mental health conditions leads to reduced adherence to treatment, prolonged illness, unnecessary rehospitalisation and significant financial burden to the health care system. Recognizing this, ecological momentary assessment (EMA) and remote measurement-based care (RMBC) interventions have emerged as promising strategies to address gaps in current care systems. They provide convenient means to continuously monitor patient-reported outcomes, thereby informing clinical decision-making and potentially improving outcomes such as psychopathology, relapse, and quality of life. Objective: This systematic review and meta-analysis aims to comprehensively appraise and analyse the existing evidence on the use of EMA and RMBC for people living with mental illness. Methods: The study was conducted according to PRISMA-P guidelines and pre-registered with PROSPERO. A comprehensive search was conducted in four online databases using MeSH terms related to mental disorders and digital technologies. Studies were included if they included adults with a formally diagnosed mental disorder and measured symptoms using ecological momentary assessment or remote measurement-based care. Studies were independently reviewed by subgroups of authors and data were extracted focusing on symptom-focused or disease-specific outcomes, relapse, recovery-focused outcomes, global functioning, quality of life and acceptability of the intervention. We performed a descriptive analysis of demographic variables and a meta-analysis of randomised controlled trials. Risk of bias was assessed using the Cochrane risk-of-bias tool for randomised trials version 2. Results: The systematic review included k = 103 studies, of which k = 15 used remote measurement-based care (RMBC). Of these, k = 9 were randomised controlled trials that were meta-analyzed. RMBC interventions varied in effectiveness, generally showing small but significant effects on symptom-specific outcomes, with notable effects on mania symptoms and empowerment. Adherence to all tracking items was 74.46 % (SD = 13.98, k = 38). More prompts per day, but not more items per prompt, was associated with lower adherence. Adverse effects were infrequently reported and included technical problems and psychological distress. Concerns about bias were raised, particularly regarding participants' awareness of the interventions and potential deviations from the intended protocols. Conclusions: Although RMBC shows growing potential in improving and tailoring psychiatric care to individual needs, the evidence of its clinical effectiveness is still limited. However, we found potential effects on mania symptoms and on empowerment. Overall, there were only a few RCTs with formal psychiatric diagnoses to be included in our analyses, and these had moderate risks of bias. Future studies assessing RMBCs effectiveness and long-term efficacy with larger populations are needed. Clinical Trial: Prospero CRD42022356176

Using Smartphone-Tracked Behavioral Markers to Recognize Depression and Anxiety Symptoms: Cross-Sectional Digital Phenotyping Study

2026-01-26T13:30:09-05:00

Background: Depression and anxiety are prevalent but commonly missed and misdiagnosed, an important concern because many patients do not experience spontaneous recovery and duration of untreated illness is associated with worse outcomes. Objective: This study explores the potential of using smartphone-tracked behavioral markers to support diagnostics and improve recognition of these disorders. Methods: We used the dedicated Behapp digital phenotyping platform to passively track location and app usage in 217 individuals, comprising symptomatic (n=109; depression/anxiety diagnosis or symptoms) and asymptomatic individuals (n=108; no diagnosis/symptoms). After quantifying 46 behavioural markers (e.g., % time at home), we applied a machine learning approach to (1) determine which markers are relevant for depression/anxiety recognition and (2) develop and evaluate diagnostic prediction models for doing so. Results: Our analysis identifies the total number of GPS-based trajectories as a potential marker of depression/anxiety, where individuals with fewer trajectories are more likely symptomatic. Models using this feature in combination with demographics or in isolation outperformed demographics-only models (AUROCMdn=0.60 vs 0.60 vs 0.51). Conclusions: Collectively, these findings indicate that smartphone-tracked behavioural markers have limited discriminant ability in our study but potential to support future depression/anxiety diagnostics.

Evaluating a Culturally Tailored Digital Storytelling Intervention to Improve Trauma Awareness in Conflict-Affected Eastern Congo: Quasi-Experimental Pilot Study

2026-01-26T13:00:41-05:00

Background: Posttraumatic stress disorder (PTSD) is highly prevalent in conflict-affected regions like eastern Democratic Republic of Congo; yet, cultural stigma and lack of psychoeducation limit public understanding and help-seeking behaviors. Objective: This study evaluates the effect of a short, culturally adapted animated video on mental health perception, knowledge, and attitudes toward trauma. Methods: A community-based quasi-experimental pre-post design was implemented among 239 participants from South Kivu. The intervention involved viewing a 3-minute animated psychoeducational video portraying locally relevant PTSD symptoms and resilience strategies. Perception, knowledge, and attitude scores were measured before and after the intervention, alongside PTSD prevalence and video appreciation. Results: Out of 239, 40% (n=96) of the participants screened positively for PTSD. Post intervention, significant improvements were observed in perception (P=.01), knowledge (P<.001), and attitudes (P=.001) toward trauma. Appreciation was high; 82% (n= 195) expressed empathy for the characters, and 74% (n= 176) were likely to share the video. Linear regression showed that having PTSD symptoms (β coefficient=3.29, SE=1.09; P=.003), years of education (β coefficient=0.54, SE=0.08; P<.001), empathy toward the portrayed situations (β coefficient=5.07, SE=0.56; P<.001), perceived acquisition of new knowledge (β coefficient=2.58, SE=0.59; P<.001) and willingness to share the video (β coefficient=1.75, SE=0.50; P=.001) predicted stronger positive effect. A multiple linear regression including all predictors revealed that PTSD symptoms (β coefficient=1.93, SE=0.90; P=.03), years of education (β coefficient=0.47, SE=0.07; P<.001), empathy toward the portrayed situations (β coefficient=3.50, SE=0.55; P<.001), and willingness to share the video (β coefficient=1.75, SE=0.50; P=.001) remained significant predictors of video impact. Age and perceived acquisition of new knowledge were not significant in the multivariate model. This model accounted for 44.6% of the variance in video impact scores (R²=0.446, F_6,231=30.99, P<.001). Conclusions: This study highlights the effectiveness of culturally grounded, low-cost digital media for improving mental health literacy in postconflict settings. Video-based tools may serve as scalable components of trauma-informed care and public health communication in low-resource, high-need areas. Trial Registration:

Examining the Acceptability and Effectiveness of a Self-Directed, Web-Based Resource for Stress and Coping in University: Randomized Controlled Trial

2026-01-23T16:00:14-05:00

Background: University students face high levels of stress with limited support for coping and well-being. Campus mental health services are increasingly using digital resources to support students’ stress-management and coping capacity. However, the effectiveness of providing this support through web-based, self-directed means remains unclear. Objective: Using a randomized-controlled design, this study examined the acceptability and effectiveness of a self-directed, web-based resource containing evidence-based strategies for stress-management and healthy coping for university students. The study additionally explored the potential benefits of screening and directing students to personalized resources aligned with their needs. Methods: Participants consisted of 242 university students (79.9% women; mean age 21.15), assigned to one of three groups (i.e., automatically directed to personalized resources, non-directed, and waitlist comparison), and completed pre, post (4 weeks), and follow-up (8 weeks) measures for stress, coping, and well-being. The resource groups also completed acceptability measures at 2, 4, and 8 weeks after the web-based resource access. Results: Results indicate high acceptability, reflecting students’ satisfaction with the resource. Furthermore, significant decreases in stress and unhealthy coping as well as significant increases in coping self-efficacy and healthy coping in the resource groups relative to the comparison group were found. Interestingly, the directed approach showed no added benefit over non-directed resource access. Conclusions: In summary, this study demonstrates the acceptability and effectiveness of a self-directed digital resource platform as a viable support option for university student stress and coping. Clinical Trial: ClinicalTrials.gov NCT07086001; https://clinicaltrials.gov/study/NCT07086001

Triaging Casual From Critical—Leveraging Machine Learning to Detect Self-Harm and Suicide Risks for Youth on Social Media: Algorithm Development and Validation Study

2026-01-23T15:30:03-05:00

Background: This study aims to detect self-harm or suicide (SH-S) ideation language used by youth (aged 13-21 y) in their private Instagram (Meta) conversations. While automated mental health tools have shown promise, there remains a gap in understanding how nuanced youth language around SH-S can be effectively identified. Objective: Our work aimed to develop interpretable models that go beyond binary classification to recognize the spectrum of SH-S expressions. Methods: We analyzed a dataset of Instagram private conversations donated by youth. A range of traditional machine learning models (support vector machine, random forest, Naive Bayes, and extreme gradient boosting) and transformer-based architectures (Bidirectional Encoder Representations from Transformers and Distilled Bidirectional Encoder Representations from Transformers) were trained and evaluated. In addition to raw text, we incorporated contextual, psycholinguistic (linguistic injury word count), sentiment (Valence Aware Dictionary and Sentiment Reasoner), and lexical (term frequency–inverse document frequency) features to improve detection accuracy. We further explored how increasing conversational context—from message-level to subconversation level—affected model performance. Results: Distilled Bidirectional Encoder Representations from Transformers demonstrated a good performance in identifying the presence of SH-S behaviors within individual messages, achieving an accuracy of 99%. However, when tasked with a more fine-grained classification—differentiating among “self” (personal accounts of SH-S), “other” (references to SH-S experiences involving others), and “hyperbole” (sarcastic, humorous, or exaggerated mentions not indicative of genuine risk)—the model’s accuracy declined to 89%. Notably, by expanding the input window to include a broader conversational context, the model’s performance on these granular categories improved to 91%, highlighting the importance of contextual understanding when distinguishing between subtle variations in SH-S discourse. Conclusions: Our findings underscore the importance of designing SH-S automatic detection systems sensitive to the dynamic language of youth and social media. Contextual and sentiment-aware models improve detection and provide a nuanced understanding of SH-S risk expression. This research lays the foundation for developing inclusive and ethically grounded interventions, while also calling for future work to validate these models across platforms and populations.

Prediction of 12-Week Remission in Patients With Depressive Disorder Using Reasoning-Based Large Language Models: Model Development and Validation Study

2026-01-23T14:45:07-05:00

Background: Depressive disorder affects over 300 million people globally, with only 30-40% of patients achieving remission with initial antidepressant monotherapy. This low response rate highlights the critical need for digital mental health tools that can identify treatment response early in the clinical pathway. Objective: This study aimed to evaluate whether reasoning-based large language models (LLMs) could accurately predict 12-week remission in patients with depressive disorder undergoing antidepressant monotherapy and to assess the clinical validity and interpretability of model-generated rationales for integration into digital mental health workflows. Methods: We analyzed data from 390 patients in the MAKE Biomarker Discovery study who were undergoing first-step antidepressant monotherapy with 12 different medications including escitalopram, paroxetine, sertraline, duloxetine, venlafaxine, desvenlafaxine, milnacipran, mirtazapine, bupropion, vortioxetine, tianeptine, and trazodone after excluding those with uncommon medications (n=9) or missing biomarker data (n=32). Three LLMs (ChatGPT o1, o3-mini, Claude 3.7 Sonnet) were tested using advanced prompting strategies including zero-shot chain-of-thought, atom-of-thoughts, and our novel referencing of deep research (RoD) prompt. Model performance was evaluated using balanced accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. Three psychiatrists independently assessed model outputs for clinical validity using 5-point Likert scales across multiple dimensions. Results: Claude 3.7 Sonnet with 32,000 reasoning tokens using the RoD prompt achieved the highest performance (balanced accuracy=0.6697, sensitivity=0.7183, specificity=0.6210). Medication-specific analysis revealed negative predictive values of 0.75 or higher across major antidepressants, indicating particular utility in identifying likely non-responders. Clinical evaluation by psychiatrists showed favorable ratings for correctness (mean, standard deviation [SD]; 4.3, [0.7]), consistency (4.2, [0.8]), specificity (4.2, [0.7]), helpfulness (4.2, [1.0]), and human-likeness (3.6, [1.7]) on 5-point scales. Conclusions: These findings demonstrate that reasoning-based LLMs, particularly when enhanced with research-informed prompting, show promise for predicting antidepressant response and could serve as interpretable adjunctive tools in depressive disorder treatment planning, though prospective validation in real-world clinical settings remains essential. Clinical Trial: Not applicable

Navigating the Digital Landscape for Potential Use of Mental Health Apps in Clinical Practice: Scoping Review

2026-01-15T17:30:23-05:00

Background: The global demand for mental health services has significantly increased over the past decade, exacerbated by the COVID-19 pandemic. Digital resources, particularly smartphone apps, offer a flexible and scalable means of addressing the research-to-practice gap in mental health care. Clinicians play a crucial role in integrating these apps into mental health care, although practitioner-guided digital interventions have traditionally been considered more effective than stand-alone apps. Objective: This scoping review explored mental health practitioners’ views on potential use or integration of smartphone apps into clinical practice. We asked, “What is known about how mental health practitioners view the integration of smartphone apps into their practice?” Further, this scoping review explored the factors that might influence integration of smartphone apps into practice, such as practitioner and client characteristics, app design and functionality, and practitioner views. Methods: We conducted a systematic search of 3 databases that yielded 38 studies published between 2018 and 2025, involving 1894 participants across various mental health disciplines, most predominantly psychologists and psychiatrists. Data were collected on practitioner and client characteristics, app functionality, and factors deemed important or influencing practitioners’ opinions about app integration. Results: The included studies were most likely to explore use of apps outside the clinical session and focused on self-management apps for mental health monitoring and tracking, and for collecting data from the patient. Fewer studies explored use of apps within-session, or practitioner-guided apps. Practitioners prioritized app features aligned with the American Psychological Association’s evaluation criteria, with practitioners prioritizing engagement and interoperability, but also noted the importance of training and resourcing to support integration. Conclusions: While practitioners recognize the potential of apps in mental health care, integration into clinical practice remains limited. This study highlights the need for further research on practical implementation, clinical effectiveness, and practitioner training to facilitate the transition from potential to actual use of apps in mental health care settings. Recommendations include evaluating effectiveness of app integration through experimental studies and developing training modules to develop practitioners’ digital competencies and confidence in app use.