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Routine Online Psychological Therapy in an Insurance-Based Care Setting: Retrospective Service Evaluation of Real-World Outcomes

<strong>Background:</strong> Depression, anxiety, and stress-related difficulties represent a major global health burden. Online psychological therapy has emerged as a promising approach to increasing access to care, yet evidence from routine, real-world clinical settings, particularly for services delivered without standardized treatment protocols, remains limited. <strong>Objective:</strong> This study aimed to describe symptom change and patient-reported satisfaction associated with routine online psychological therapy delivered within an insurance-based care setting. <strong>Methods:</strong> A retrospective observational service evaluation was conducted using deidentified routine care data. Clients aged 15 years and older who initiated online psychological therapy and completed baseline and end-of-treatment assessments on all 3 outcome measures were included. Symptoms of depression, anxiety, and perceived stress were measured using the 9-item Patient Health Questionnaire (PHQ-9), 7-item Generalized Anxiety Disorder (GAD-7), and 10-item Perceived Stress Scale (PSS-10), respectively. Changes in symptoms were examined using paired sample <i>t</i> tests, within-sample effect sizes (Cohen <i>d</i> and Cohen <i>d<sub>z</sub></i> with 95% CIs), and reliable change indices. Robustness was assessed with therapist-clustered mixed models and attrition sensitivity analyses. A ≥50% reduction in symptom scores was reported as a descriptive response criterion. Patient satisfaction was assessed with single-item ratings at the end-of-treatment assessment. <strong>Results:</strong> A total of 1221 clients were included. Clients completed a mean of 4.7 (SD 1.67) therapy sessions over an average treatment duration of 62.7 (SD 40.4) days. Significant reductions with large within-sample effect sizes were observed for depression (Cohen <i>d</i>=1.25, 95% CI 1.17-1.32), anxiety (Cohen <i>d</i>=1.49, 95% CI 1.41-1.57), and perceived stress (Cohen <i>d</i>=1.46, 95% CI 1.38-1.54). A ≥50% symptom reduction was observed in 60.7% (741/1220) of clients for depression, 67.5% (824/1221) for anxiety, and 30.8% (376/1221) for perceived stress. At the end-of-treatment assessment, 80.7% (985/1221) scored below the clinical cutoff for depression (PHQ-9&lt;10) and 79.2% (967/1221) for anxiety (GAD-7&lt;8). Reliable improvement was observed in 56% (688/1221) to 65% (794/1221) of clients, with deterioration below 2% on all measures. Mean patient-reported satisfaction scores ranged from 8.55 (SD 1.71) to 8.96 (SD 1.45) on a 10-point scale, and mean recommendation likelihood was 8.61 (SD 1.88). Therapist-level intraclass correlation coefficients were ≤0.029; effect sizes were 1.27-1.50 under inverse-probability weighting and 0.41-0.50 under zero-change imputation for baseline-screened clients without end-of-treatment assessments. <strong>Conclusions:</strong> Routine online psychological therapy delivered within an insurance-based care setting was associated with substantial pre-post symptom reductions, high proportions of clients meeting a descriptive ≥50% reduction criterion and showing reliable improvement, and high patient-reported satisfaction. Because the evaluation used an uncontrolled pre-post design restricted to clients with paired assessments, observed changes cannot be attributed causally to treatment. High satisfaction among responding clients is consistent with acceptability of the model to those clients; feasibility was not formally assessed, and inferences about comparative effectiveness require controlled designs.
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Perinatal Mental Health Detection and Prediction Using Mobile Sensing Data: Systematic Review

Background: Perinatal mental health disorders affect approximately 20% of pregnant and postpartum individuals, and are associated with substantial maternal and infant morbidity. Traditional assessment relies on infrequent, subjective self-reports. Mobile devices, including smartphones and wearables, offer opportunities for continuous and objective measurement, but evidence on their assessment utility in perinatal populations remains fragmented. Objective: This review aimed to examine the application of wearable devices and smartphones for detecting and predicting perinatal mental health outcomes, with emphasis on predictive performance, informative features, and methodological rigor. Methods: We conducted a systematic review following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines (PROSPERO CRD420251249218). Six databases (PubMed, Web of Science, Scopus, PsycINFO, IEEE Xplore, and ACM Digital Library) were searched initially in January 2026 and supplemented by an amended search in June 2026, with no publication date restrictions. Evidence was synthesized narratively, and the risk of bias was assessed using PROBAST+AI (Prediction Model Risk of Bias Assessment Tool With AI extension). Results: The initial and supplementary searches yielded 1952 unique records after deduplication, of which 10 studies met the inclusion criteria. The included studies covered postpartum depression, prenatal stress, discrete emotions during pregnancy (eg, happiness, anxiety, and sadness), and maternal loneliness. High discrimination metrics were reported for postpartum depression in individual studies, including a multiclass area under the curve of 0.85, a binary area under the curve of 0.871, and an -score of 0.9872. Heart rate variability, GPS-derived mobility, physical activity, and sleep features were most frequently reported as useful, and their interpretation requires perinatal-specific contextualization. Methodological quality was limited, with 80% (12/15) of PROBAST+AI assessment units rated as having high overall quality concern or risk of bias, mainly due to small samples, limited validation, inadequate handling of missing data, and potential overfitting in the analysis domain. Conclusions: Mobile sensing shows preliminary potential for perinatal mental health assessment, but current evidence does not yet support clinical screening or decision-making, and independent external validation in perinatal populations is currently lacking. Progress toward clinical utility requires broader mental health outcome coverage, larger longitudinal cohorts, standardized analytical and reporting practices, adoption of modeling approaches better suited to perinatal trajectories, independent external validation, and human-centered monitoring designs.
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Possible Role and Function of AI Conversational Agents in Dialectical Behavior Therapy for Borderline Personality Disorder: Qualitative Interview Study

Background: Borderline personality disorder (BPD) is associated with substantial distress and a high risk for suicide. Individuals with BPD may be unable to access evidence-based treatments like dialectical behavior therapy (DBT). Artificial intelligence conversational agents (AI-CA) are increasingly discussed as scalable tools for mental health support, but little is known about how DBT clinicians understand the possible role of AI-CA in treatment. Objective: This study aimed to explore DBT psychologists’ perspectives on integrating AI-CA into DBT for BPD in the future. Methods: Seventeen psychologists in Sweden, each with at least 1 year of clinical DBT experience (mean 6.4 years, SD 5.6), participated in semistructured interviews as part of this qualitative study. Interviews were conducted in Swedish, transcribed verbatim, and analyzed using reflexive thematic analysis within a constructivist framework. Participants did not test a specific AI-CA. Results: Three main themes were developed from the data. The first main theme, “Who Are We in Therapy?” explored how participants defined AI-CA relationally, positioning it variously as a tool, team member, or supervisor, and a sometimes harmful competitor. How these positionings were configured shaped what AI-CA was seen as allowed to do. The second main theme, “The Stoic Helper,” captured how AI-CA was constructed as an extension of the ideal helper: available, competent, adaptable, and tireless, able to provide support in moments when human therapists could not or preferred not to be present. Participants’ hopes for what AI-CA could become often mirrored qualities they found difficult to sustain in their own clinical work. The third main theme, “The Well-Intended Accommodator,” captured concerns that AI-CA may reinforce dependency and function as a safety behavior by supporting reassurance-seeking rather than autonomy. A central concern was not whether AI-CA could generate validating responses, but whether it could know when validation supports change and when it becomes maladaptive accommodation (AI functional ambiguity). Conclusions: Perceived benefits mainly centered on accessibility and support for DBT skills generalization, whereas key concerns involved alliance disruption, reinforcement of behaviors that would ideally be targeted for change, dependency, and questions regarding responsibility in high-risk situations. Integrating AI-CA into DBT is not only a technical question but a relational and ethical one. How AI-CA is positioned in relation to the therapist, person in treatment, and team shapes which tasks are considered acceptable and what form integration can take. The findings highlight the need for implementation frameworks that account for relational dynamics, treatment-specific considerations, and AI functional ambiguity that may arise when AI-CA operates in complex therapeutic contexts.
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Assessing the Need for Mental Health Support From Free-Text Responses: Development and Validation of Language-Based Assessments in Adults With Internalizing Symptoms

Background: Machine learning and natural language processing have demonstrated significant potential for mental health assessment: describing your mental health in your own words can offer a more ecologically valid approach than traditional rating scales. However, most models focus on specific diagnoses, conditions, or symptoms, which may prematurely assign labels and potentially reinforce stigma in the context of early-stage mental health screening. Objective: This study develops a language-based assessment model that assesses the need for mental health support based on probed natural language and validates it against best-estimate assessments from multiple experienced psychotherapists. Methods: We analyzed an enriched online sample (n=600 for development and n=212 for validation), in which about half reported experiencing internalizing symptoms (depression or anxiety). Participants described their mental health using open-ended responses regarding (1) mental health, (2) suicidal thoughts, (3) medical history, and (4) depression. The responses were converted into contextual word embeddings using a large language model and entered as predictors in a ridge regression using nested cross-validation. Two to three experienced psychotherapists assessed each participant’s need for mental health support on a scale from 1 (no support needed) to 5 (potential crisis). Their assessments were based on longitudinal clinical data (natural language, validated scales, clinical interview, sociodemographics, and clinical history) and were averaged into a best-estimate assessment for model validation. We used the Sequential Evaluation With Model Preregistration framework, which separates model development from validation in a held-out set to support robust estimations and generalizability. Results: The language-based assessments closely aligned with the best-estimate assessments (=0.82) and showed strong correlations with established clinical rating scales for depression (Patient Health Questionnaire-9), anxiety (Generalized Anxiety Disorder 7-Item Scale), stress (Perceived Stress Scale 10), and suicidality (Inventory of Depression and Anxiety Symptoms; =0.62-0.77). Language-based visualizations of topics and word embeddings showed that low need for support assessments was associated with mentioning well-being and good health, while high assessments were related to depression, anxiety, and suicidality. Conclusions: This study demonstrates that natural language responses analyzed through large language models and machine learning can be used to assess individuals’ need for mental health support in close alignment with best-estimate assessments from experienced psychotherapists. Using less than 5 minutes of respondent time, this approach offers a practical tool for early-stage mental health screening in both clinical and self-guided screening contexts.
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Biological hierarchy from genotype to phenotype in syndromic and non-syndromic autism spectrum disorder

Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition whose pathophysiology is thought to arise from complex interactions among genetic variation, molecular and cellular perturbations, and neural circuit dysfunction. Although the distinction between syndromic and non-syndromic ASD was introduced as a pragmatic clinical classification, these clinically defined subgroups may exhibit overlapping yet distinct patterns of genetic liability and molecular dysregulation while also exhibiting mechanistic convergence. This narrative review examines the molecular mechanisms underlying synaptic development, structure, and function, with particular emphasis on the roles of chromatin remodeling and epigenetic regulation in neurodevelopment. It further considers how molecular and cellular alterations may contribute to impaired functional integration at the neural circuit level. By comparing the shared and unique genes and pathways associated with syndromic and non-syndromic ASD, the review identifies potential points of mechanistic convergence across multiple levels of biological organization. To integrate these findings, the review proposes a three-layer hierarchical framework that links gene function (Layer 1), core biological pathways (Layer 2), and clinical phenotypes (Layer 3). By mapping ASD-associated genes onto a functional continuum ranging from local synaptic effectors to global regulators of chromatin and gene expression, this framework illustrates how subtype-enriched genetic factors may give rise to both shared and unique molecular, cellular, and circuit-level alterations. The proposed framework may help identify common and subtype-enriched biological mechanisms, inform biomarker discovery, and provide a conceptual basis for mechanism-informed ASD research and biologically grounded clinical stratification.
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Non-motor correlates of anxiety in de novo Parkinson’s disease: a cross-sectional study with exploratory subgroup analyses

ObjectiveAnxiety is a prevalent early non-motor symptom of Parkinson’s disease (PD), but its clinical correlates remain poorly defined in de novo, drug-naïve patients. This retrospective cross-sectional study enrolled 284 medication-naïve de novo PD patients and 147 age- and sex-matched healthy controls to identify non-motor correlates of anxiety after adjustment for demographic and motor covariates, and explore potential subgroup heterogeneity.MethodsAnxiety was assessed with the Hamilton Anxiety Rating Scale; multivariate logistic regression and prespecified stratified analyses by age, sex, disease duration, Hoehn-Yahr stage and motor severity were performed.ResultsClinically significant anxiety was detected in 44.0% of PD patients versus 8.1% of controls (p < 0.001), with psychic anxiety as the dominant subtype. After multivariate adjustment, higher depressive symptom scores were positively associated with anxiety (OR = 1.386), whereas better sleep quality (OR = 0.965) and intact olfactory identification (OR = 0.815) were negatively associated with anxiety; motor severity showed no significant association after multivariable adjustment. Exploratory subgroup analyses revealed heterogeneous patterns of sleep and olfactory correlates across clinical strata. The three-factor predictive model yielded an overall classification accuracy of 84.81%.ConclusionThese findings indicate that non-motor abnormalities, rather than motor impairment, are associated with anxiety in early PD after adjustment for demographic and motor covariates. The distinct correlative patterns across subgroups provide preliminary clinical clues for the heterogeneous neurodegenerative processes underlying PD-related anxiety, and support routine screening of depressive, sleep and olfactory symptoms to facilitate anxiety risk stratification at initial diagnosis.
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Metabolic dysfunction associates with neuroimaging measures of brain structure and connectivity

IntroductionMetabolic syndrome (MetS) and insulin resistance are established risk factors for Alzheimer’s disease (AD) and have been linked to alterations in brain structure and function, including within the Default Mode Network (DMN).MethodsHere, we examined associations between metabolic dysfunction, brain structure, and DMN functional connectivity using data from the Health and Aging Brain Study: Health Disparities (HABS-HD) cohort. Participants were classified as being cognitively unimpaired (CU; n = 1,045; mean age = 66.1 years; 65% female), or having mild cognitive impairment (MCI; n = 186; mean age = 66.5 years; 52% female) or dementia (n = 76; mean age = 69.0 years; 47% female).ResultsAcross diagnostic groups, cortical thickness in AD-vulnerable regions varied by both cognitive status and HbA1c grouping, with the strongest effects observed in the CU cohort. HbA1c grouping was also associated with white matter microstructure, including stepwise reduc tions in fractional anisotropy of the anterior corona radiata across glycemic cat egories (normoglycemia > prediabetes > diabetes) in CU individuals. Functional connectivity of the posterior DMN (pDMN) showed main effects of both cognitive status and HbA1c grouping. Using a continuous index of cardiometabolic risk (MetS-Z), pDMN connectivity decreased linearly with increasing cardiometabolic burden in the CU cohort, whereas other DMN subnetworks exhibited positive associations with MetS-Z.DiscussionThese findings suggest network-specific associations between cardiometabolic risk and DMN organization that are detectable in indi viduals with MCI, a clinical state preceding dementia, as well as in some degree within cognitively unimpaired individuals. Longitudinal analyses are needed to determine whether metabolic dysfunction predicts divergent trajectories of DMN connectivity over time.
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Trauma-informed psychological support alongside long-acting injectable buprenorphine: clients’ perspectives on the Buvidal psychological support service (BPSS) pilot in Wales

BackgroundOpioid use disorder (OUD) is associated with persistent drug use, high relapse rates, and significant morbidity, mortality, and psychosocial harm. While Long-Acting Injectable Buprenorphine (LAIB), marketed as Buvidal, offers improved pharmacological management over traditional opioid substitution therapy (OST), the mental clarity provided by LAIB often surfaces unresolved trauma and mental health needs. Integrating trauma-informed psychological care alongside pharmacological intervention is increasingly recognized as critical to optimize recovery outcomes for individuals with co-occurring psychiatric co-morbidities and substance use disorder.ObjectiveThis evaluation aimed to explore client experience of the Buvidal Psychological Support Service, a tiered trauma-informed psychological service, piloted to support individuals with OUD receiving LAIB in Wales, UK.MethodsQualitative data were derived from nine semi-structured interviews with BPSS service users, exploring their experiences, perceived impacts, and recovery journeys through reflexive thematic analysis.ResultsFindings indicated the transition to LAIB was often transformative but exposed trauma and emotional difficulties previously masked by substance use, making the availability of timely psychological support critical for maintaining abstinence and wellbeing. Clients valued the BPSS’s rapid access, continuity, person-centered care and non-judgmental ethos, reporting enhanced agency, emotional regulation, and social reintegration. Relapse prevention was strongly linked to psychological support alongside pharmacological treatment.ConclusionThe findings suggest that participants experienced BPSS as a valuable adjunct to LAIB, particularly where increased mental clarity brought trauma-related and emotional difficulties into focus. The pilot highlights the potential value of integrating trauma-informed psychological support within LAIB pathways, while further evaluation is needed to assess transferability and longer-term outcomes.