Psychotherapy schools often describe overlapping process phenomena in non-interoperable vocabularies. This pluralism is clinically valuable but computationally costly: datasets become difficult to compare, clinically load-bearing distinctions are collapsed into convenience labels, and artificial intelligence (AI) systems inherit annotation schemes rather than a clinically interpretable intermediate representation. Building on the Perceive–Assess–Dose–Safeguard (PAD-S) framework and the Conflict-Square Algorithm (CSA), this theory article asks a narrower question than the prior PAD-S and CSA papers: can the same variables be formulated as a candidate shared representation layer between heterogeneous observation models and school-specific intervention policies? The proposed layer projects a high-dimensional biopsychosocial state into four clinically observable process coordinates—defensive/avoidant organization (DEF), anxiety/arousal and tolerance (ANX), progression toward direct experience and action (PRO), and self-attack/shame processes (SUP)—plus a safety threshold that constrains admissible intervention intensity. The contribution is architectural rather than empirical: it isolates the representational role from earlier decision-grammar and transcript-coding roles; clarifies the distinction between observations, representation, and policy; specifies a minimal falsifiable family of state-transition models; illustrates translation across four pragmatic therapy families; and defines a staged validation order from reliability and function linkage to transcript-level predictive operationalization and only then sparse equation discovery. The framework should therefore be read as a candidate shared representation layer for computational psychotherapy and computational psychiatry rather than as a therapy protocol, a fitted predictive model, a complete generative theory, or an autonomous decision system. No new dataset, fitted classifier, transcript-level predictive result, or discovered equation is reported here. The article aims instead to state what would count for or against PAD-S/CSA as a clinically interpretable interface for later empirical modeling.
Prevalence of Cognitive Distortion Markers in a Suicide Prevention Chat Service: Mixed Methods Study
Background: Suicide helplines increasingly employ chat services to aid those in urgent need, but the wording and structure of text-driven exchanges may affect their effectiveness. Objective: Given the association of cognitive distortions with depression and anxiety, this study investigated their prevalence in the language of individuals seeking help from the Dutch 113 suicide helpline. Methods: We observed the prevalence of cognitive distortions for both help seekers and counselors in a large volume of chat sessions (N=71,148) of the Dutch 113 suicide chat helpline using natural language processing. The results were compared to 2 large collections of online text data from Dutch social media and web content. Results: We found that nearly all types of cognitive distortions are more prevalent in the language of help seekers compared to the control group of helpline counselors. Distortions of the personalizing, emotional reasoning, and mental filtering types were, respectively, 20.22, 7.87, and 4.53 times more prevalent among help seekers, revealing a distinct pattern of thought and language among individuals affected by suicidality. Conclusions: Our results raise the prospect of improving the effectiveness of online therapeutic interventions that target cognitive distortions through lexical analysis that detects the cognitive and lexical markers of suicidality.
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Safety and preliminary efficacy of Aurora: a pilot, non-randomized clinical trial of a culturally adapted digital cognitive behavioral therapy intervention for anxiety and depression in Mexico
Background/objectiveAnxiety and depressive disorders are leading causes of disability worldwide, and access to evidence-based psychological treatment remains limited in many middle-income countries. Digital cognitive–behavioral therapy (CBT) interventions have emerged as scalable tools to address this treatment gap, yet few have undergone clinical evaluation in Latin American populations. This study aimed to assess the safety and preliminary efficacy of Aurora, a Spanish-language, culturally adapted digital CBT program, when used as an adjunct to pharmacotherapy in adults with generalized anxiety disorder.MethodsIn a multicenter, open-label, non-randomized pilot study, 34 adults diagnosed with generalized anxiety disorder receiving stable pharmacological treatment were assigned through pragmatic, convenience-based allocation either to an experimental group (Aurora plus medication; n = 24) or to a control group receiving medication alone (n = 10). The sample had a mean age of 39.85 ± 12.88 years, with a predominance of women (22/34). Participants were followed for 12 weeks with assessments at baseline and weeks 4, 8, and 12. Clinical outcomes included anxiety severity measured by the Generalized Anxiety Disorder-7 (GAD-7), pathological worry assessed by the Penn State Worry Questionnaire (PSWQ), and depressive symptoms evaluated using the Patient Health Questionnaire-9 (PHQ-9). Safety was monitored through structured adverse-event reporting. Statistical analyses included linear mixed-effects models for longitudinal outcomes, ordinal logistic regression for severity transitions, and negative binomial regression and Fisher’s exact test for adverse events, with false discovery rate correction applied where appropriate.ResultsAurora demonstrated a favorable safety profile, with no serious adverse events and comparable adverse-event incidence between groups under structured clinical monitoring at weeks 4, 8, and 12. Anxiety symptoms (GAD-7) showed a significant effect of time (F3,96 = 169.65; p < 0.001), indicating reductions across both groups. Pathological worry (PSWQ) demonstrated significant group (F1,31.12 = 6.96; p = 0.013) and group × time interaction effects (F3,93.4 = 7.86; p < 0.001), with greater reductions in the Aurora group, particularly at weeks 8 and 12. At week 12, ordinal analyses indicated higher odds of lower worry severity in the intervention group (β = 2.53; p = 0.004; OR = 12.5). Depressive symptoms decreased similarly in both groups. Positive effect increased progressively across intervention modules, and module-embedded cognitive measures of anxiety and depression showed significant reductions over time.ConclusionThis pilot study provides preliminary, hypothesis-generating evidence that a culturally adapted digital CBT intervention can be safely integrated with pharmacotherapy and may be associated with enhanced improvements in anxiety-related outcomes, particularly pathological worry, in a Mexican clinical population. However, the non-randomized design, small sample size, and baseline imbalances limit causal inference and generalizability, and findings should be interpreted with caution. Larger randomized controlled trials are needed to confirm efficacy, determine long-term clinical impact, and guide the implementation of digital therapeutics in Latin American mental health systems.
Real-world effectiveness of medication-assisted treatment and psychotherapy for opioid use disorder: a national multi–health care organization analysis
BackgroundHarm reduction strategies for opioid use disorder (OUD) emphasize pragmatic, evidence-based approaches that reduce overdose risk, relapse, and other adverse outcomes without requiring abstinence. Medication for opioid use disorder (MOUD) and structured psychotherapy represent core harm-reduction modalities, yet their real-world comparative effectiveness, alone and in combination, remains underexplored at scale.MethodsA retrospective cohort study was conducted using the TriNetX Research Network, comprising de-identified electronic health records from 112 U.S. health systems. 18,047 adults aged 18–45 were identified with a diagnosis of opioid dependence (ICD-10 F11.20) between 2016 and 2025. Subjects were assigned to eight mutually exclusive treatment cohorts: no treatment (Cohort 1); buprenorphine alone (Cohort 2); methadone alone (Cohort 3); psychotherapy alone (30 minutes (Cohort 4), 45 minutes (Cohort 5), or 60 minutes (Cohort 6)); buprenorphine + psychotherapy (Cohort 7); and methadone + psychotherapy (Cohort 8), with combination treatments defined within a ±30-day window. Cox proportional hazards models estimated adjusted hazard ratios (aHRs) for remission (F11.21, F11.11) within 12 months.ResultsBuprenorphine (aHR = 2.33; 95% CI: 1.85–2.94), methadone (aHR = 2.50; 95% CI: 2.05–3.04), and psychotherapy (30 min: aHR = 2.18; 45 min: aHR = 2.38) were each independently associated with significantly higher remission compared to no treatment. The combination of buprenorphine + psychotherapy yielded the strongest effect (aHR = 5.26; 95% CI: 2.68–10.32). Anxiety diagnoses and gabapentinoid prescriptions were positively associated with remission; benzodiazepine co-prescription was negatively associated.ConclusionsIn this first national-scale, multi–health-care-organization analysis, both pharmacologic and psychosocial harm-reduction interventions were independently associated with improved OUD remission, with additive benefit when integrated. These findings underscore the value of embedding comprehensive, multimodal harm-reduction services within routine care and support policies promoting equitable access to both MOUD and behavioral health supports across diverse health systems.
Sociodemographic factors, anxiety and attitudes toward generative artificial intelligence among nurses
BackgroundAlthough generative artificial intelligence offers substantial potential benefits in healthcare, negative attitudes and elevated anxiety among nurses may hinder its effective integration into clinical practice. Evidence regarding the psychological impact of generative artificial intelligence on nurses remains limited.ObjectiveThis study examined the relationships among sociodemographic characteristics, anxiety, and attitudes toward generative artificial intelligence among nurses.MethodsA cross-sectional correlational design was employed. Data were collected from 312 hospital nurses using online questionnaires assessing sociodemographic characteristics, attitudes toward artificial intelligence, and artificial intelligence-related anxiety. Data were analyzed using IBM Statistical Package for the Social Sciences (SPSS) Statistics software version 28.ResultsHigher levels of artificial intelligence-related anxiety were associated with less favorable attitudes toward artificial intelligence. Sociodemographic characteristics and anxiety scores collectively explained 49.4% of the total variance in attitudes toward artificial intelligence. Gender, experience with artificial intelligence, use of artificial intelligence in nursing care, awareness of artificial intelligence applications in healthcare, hours spent on the internet, age, and professional experience accounted for 24.7% of the variance in negative attitudes toward generative artificial intelligence.ConclusionAnxiety and experiential factors play a central role in shaping nurses’ attitudes toward generative artificial intelligence. Increasing nurses’ exposure to and awareness of artificial intelligence in nursing practice may reduce anxiety and support its acceptance and appropriate use.
Enhancing Sleep and Mental Health: Longitudinal, Observational, Real-World Study From a Digital Mental Health Platform
Background: Poor sleep is closely linked to mental health challenges and workplace burnout. Mental health and workplace stressors can impair sleep, while good sleep quality supports cognitive and emotional resources to cope with daily challenges. Despite positive outcomes of maintaining good sleep, many people struggle to get enough restorative sleep at night. Given the bidirectional relationship between sleep and mental health, evidence-based digital mental health solutions may offer an accessible and scalable approach to improving sleep quality. Objective: This study examines whether engagement with an employer-sponsored, multimodal digital mental health platform is associated with improvements in sleep quality over time, and whether changes in sleep quality are associated with concurrent changes in mental health and burnout outcomes. Methods: This 12-month prospective, observational study followed working adults who were newly registered to an employer-sponsored digital mental health platform (Modern Health). The platform leveraged technology (mobile and web) to connect employees with comprehensive provider-led and self-guided care through therapy, coaching, on-demand digital resources, and group psychoeducational sessions. Participants [N=578; 61.1% (n=353) women; mean age 33.88, SD 8.73 years; 40.3% (n=233) people of color] completed measures of self-rated sleep quality, depression, anxiety, and burnout (exhaustion, cynicism, and professional efficacy) at baseline and after 3 and 12 months of accessing the platform. Upon registering for the platform, participants were given an initial care recommendation, but could flexibly engage in any combination of services. Participants in this study engaged with at least one care modality, including therapy, coaching, psychoeducation sessions, and self-guided mental health resources. We examined perceived sleep quality and associations with other study variables at baseline, changes in perceived sleep quality over time, and whether changes in sleep quality correlated with concurrent changes in mental health and burnout. Results: At baseline, 42% (243/578) reported poor sleep quality and were more likely to have higher levels of depression, anxiety, and burnout. A generalized linear mixed-effects model showed that each additional month of platform access was related to an increased odds of having good sleep quality by 3.7% (=.02). Linear mixed-effects models found that higher sleep quality over time was associated with lower depression, anxiety, exhaustion, cynicism, and efficacy (all <.001). Among participants reporting poor sleep quality at baseline, 44% (62/141) reported good sleep quality at 12 months. Within this subgroup, paired sample tests showed significant reductions in depression (−48.3%) and anxiety (−38.3%), and increased cynicism, burnout, though cynicism levels remained below the cutoff for high burnout (23.9%; all <.01). Conclusions: Use of an employer-sponsored digital mental health platform was associated with meaningful improvements in self-reported sleep quality over 12 months. These gains were associated with significant reductions in depression, anxiety, and burnout symptoms, highlighting broader well-being benefits of comprehensive mental health care.
Comparing Perceptions of ChatGPT Use in Health Attitude Contexts Among Users and Nonusers: Cross-Sectional Study
Background: In light of the growing use of artificial intelligence (AI) in health care, individuals’ access to and use of health information are transforming. ChatGPT, an AI chatbot, provides immediate responses to health queries, with the potential to influence health-related attitudes, thereby raising concerns related to privacy, reliability, and security. Objective: This study aimed to investigate the perceived usefulness, risks, anxiety, and social influence of ChatGPT on health attitudes among users and nonusers in Saudi Arabia. Methods: A cross-sectional study was conducted using an online survey based on a validated tool. In total, 337 participants aged 18 years and older responded to questions assessing their perceptions of ChatGPT on health-related attitudes. Results: Data showed that 76.1% (194/255) of the respondents used ChatGPT, with the majority being younger and more highly educated. The main uses for health-related purposes were health education (43/194, 22.2%) and physical activity guidance (31/194, 16%). The analysis showed that users considered ChatGPT useful for health-related decisions, with 45.9% (89/194) finding it easy to learn and use, but concerns about privacy (106/194, 54.7%) and reliability (87/194, 44.9%) remained. Among nonusers, security risks (39/61, 63.9%) were the major barrier to using AI-based tools for health purposes, and 68.9% (42/61) found such tools attractive and engaging. There were no statistically significant differences between users and nonusers across all examined sociodemographic characteristics (>.05). Conclusions: The study established the potential of ChatGPT in improving health decision-making and revealed cultural, privacy, and trust issues that may affect its implementation. These findings underscore the importance of strengthening the security of AI-based applications to enhance public acceptability of related health policies and to support the safe integration of tools such as ChatGPT into the health care system.
The Effectiveness and Mechanisms of Action of App-Based Interventions for Improving Mental Health and Workplace Well-Being: Randomized Controlled Trial
Background: Depression is the most common mental health disorder worldwide and frequently leads to workplace absence. As face-to-face treatment can be difficult to access, app-based interventions are a popular solution, although their effectiveness in working populations and their mechanisms of action are unclear. Deficits in executive function may contribute to the onset and maintenance of depression, and executive function training is proposed to improve symptoms by enhancing executive function. Responders to cognitive behavioral therapy (CBT) show improvements in executive function, suggesting that this may be one mechanism of action. Objective: This study investigated the effectiveness of app-based interventions (executive function or CBT-based) for reducing depressive and anxiety symptoms and improving workplace well-being, and assessed whether changes in executive function mediated improvements. Methods: A total of 228 participants (147 female participants) with mild-to-moderate symptoms of depression and anxiety were recruited online and randomly assigned to a waitlist control group, an executive function training group (NeuroNation app, Synaptikon GmbH), or a self-guided CBT group (Moodfit app, Roble Ridge LLC) for a 4-week intervention period. Participants assigned to the active intervention groups were asked to use their apps a minimum of 21 times during the intervention. Participants completed measures of depressive symptoms, anxiety symptoms, and workplace well-being, and a working memory task at baseline, postintervention, and follow-up (12 weeks). Results: Executive function training reduced anxiety (β=−2.79; =.004) and depressive (β=−2.77; =.02) symptoms at follow-up but not at postintervention, and it did not affect workplace well-being. There were no reductions in depressive or anxiety symptoms in the self-guided CBT group, though workplace well-being was improved at postintervention (β=3.72; =.02) and follow-up (β=4.46; =.02). Improvements in executive function did not mediate intervention-related changes in symptoms or workplace well-being. Self-reported adherence rates were high (executive function training: 48/54, 89%; self-guided CBT: 52/54, 96%), although attrition was high at follow-up (58% missing). Conclusions: These results suggest that app-based executive function training may be effective at managing symptoms of anxiety and depression in a working population, while self-guided CBT apps may improve workplace well-being. However, improving executive function did not appear to be a mechanism of action of either intervention. Trial Registration: ISRCTN 12730006; https://www.isrctn.com/ISRCTN12730006
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Emotional Training via Telerehabilitation After Surgical Treatment for Facial Palsy: Prospective, Assessor-Blinded, 2-Arm Pilot Cohort Study
Background: Peripheral facial nerve palsy is a debilitating condition that may necessitate surgical intervention. Although motor rehabilitation is considered essential, the most effective approach has not yet been determined. Objective: This study aimed to evaluate the feasibility and effectiveness of emotional training, a novel telerehabilitation-based treatment, on motor, functional, and psychological outcomes in patients with unilateral facial palsy following triple innervation surgery. Methods: A prospective, assessor-blinded, 2-arm pilot cohort study was conducted at the rehabilitation unit at University Hospital San Paolo, Milan, Italy, from January to October 2024. Participants (N=16) received 1 treatment session every 2 weeks over 20 weeks, each lasting 45 minutes, according to standard clinical procedures in place at the rehabilitation unit. Participants were nonrandomly assigned to either an in-person group (n=8) or an online group (ie, telerehabilitation; n=8) based on their ability to attend in-person sessions. The primary outcomes assessed at baseline (T0) and after treatment (T1) included facial symmetry (Sunnybrook Facial Grading System; SFGS), facial disability (Facial Disability Index; FDI), and anxiety levels (Beck Anxiety Inventory). Results: Statistical analysis revealed significant improvements at T1 for both groups in the FDI social and well-being function subscale, Beck Anxiety Inventory, SFGS resting symmetry score, SFGS symmetry of voluntary movement score, SFGS composite score, SFGS with bilateral masseter contraction symmetry of voluntary movement score, and SFGS with bilateral masseter contraction composite score (<.001 for all). Only the FDI physical function subscale showed a differential improvement at T1 for the in-person group treatment (ANOVA for time × treatment: =14.356; =.002; Holm-Bonferroni post hoc test: <.001). Finally, a strong positive correlation was observed between the time elapsed from surgery to rehabilitation and SFGS composite score improvement at T1 (=0.94; =.005). Conclusions: These results suggest that the online emotional training protocol is as feasible and effective as the in-person emotional training protocol in improving facial motor function, reducing anxiety, and enhancing facial expression spontaneity in patients who had undergone surgery for peripheral facial palsy. These findings support the validity of telerehabilitation approaches as a feasible, accessible, and sustainable alternative to conventional in-person therapy for facial nerve recovery.
<![CDATA[DT120, a pharmaceutical-grade formulation of LSD, shows rapid, lasting anxiety relief—single dose, no therapy—with 48% remission at 12 weeks.]]>