Background: Digital mental health technologies (DMHTs) are playing an increasing role in mental health services. The quality of evidence for DMHTs is variable, and there are concerns that evidence is not sufficient to support decision-making. Objective: This study used a cross-sectional analysis of evidence supporting DMHTs included in National Institute for Health and Care Excellence (NICE) evaluations to examine the strength of evidence available for decision-making. Methods: We identified all NICE evaluations relating to DMHTs by reviewing details of published NICE evaluations on the NICE website. From each of these evaluations, we identified included DMHTs and reviewed committee documentation to identify studies that provided supporting evidence for each of these technologies. We extracted information on a series of items relating to study quality and summarized the characteristics of evidence both at the level of individual studies and across the package of evidence from multiple studies supporting DMHTs. We also identified key evidence gaps in available evidence. Results: We included nine NICE evaluations relating to anxiety, depression, psychosis, insomnia, attention deficit hyperactivity disorder (ADHD), and tic disorders. These evaluations included 30 DMHTs and referenced 78 supporting studies. We identified common evidence gaps relating to effectiveness compared to relevant comparators, use of appropriate outcomes, including health-related quality of life, cost of delivery, and impact on resource use, and reporting of adverse events. Conclusions: Our study highlights that some DMHTs have been supported by high-quality studies and that evidence to support DMHTs is likely to be developed across a series of studies. However, there are often key evidence gaps that need to be addressed to provide a stronger case for adoption. Developers should ensure that they consider these gaps while planning evidence generation, and where possible, address them earlier in the product lifecycle.
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Medtech OEMs face a rare but closing window of opportunity
This is a manufacturing decision you can’t defer in 2026. Mark Freitas, Alvarez & Marsal Private-equity-backed CDMO platforms are aging into exits. OEMs who know what they want will move first. The 2022-2024 structural reset is over and the financing gap is narrowing. The sector has emerged from a period of value depression and as…
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A Gamified Pain Management Intervention for Adults With Chronic Pain in Mainland China: Single-Arm Pre-Post Pilot Study With Machine Learning Predictive Modeling
Background: The widespread prevalence of chronic pain (CP) significantly impacts daily functioning worldwide. In mainland China, maintaining engagement in biopsychosocial interventions remains challenging. Gamification, designed based on self-determination theory, can enhance motivation, while machine learning (ML) algorithms can assist clinicians in dynamically optimizing pain management. Objective: This study aimed to (1) evaluate the preliminary effectiveness of a gamified pain management (GPM) program on CP and psychological outcomes and (2) identify key factors of significant pain improvements through the application of ML to guide intervention adjustments. Methods: A single-arm, pre-post study was conducted with 16 participants with CP in mainland China, recruited via social media using convenience sampling. Participants engaged in a 10-week web-based GPM intervention consisting of education, physical activities, and gamified elements, including points, avatars, and feedback. Primary outcomes were pain intensity and interference measured by the Brief Pain Inventory. Secondary outcomes included anxiety, depression, and quality of life. Analysis included paired tests, and ML models were trained to predict clinically meaningful pain reductions. Shapley additive explanations, least absolute shrinkage and selection operator regression, association rule mining, and Kaplan-Meier survival analysis were used to identify key predictors and optimal sessions and intervention durations across subgroups. Results: A total of 16 participants were engaged, with a mean age of 27.63 (SD 9.584) years. Results from paired tests reported significant improvements in pain intensity (decreased by 27.3%, 95% CI 1.061 to 3.064; =.001), pain interference (decreased by 27.3%, 95% CI 8.159-17.216; <.001), and psychological distress, including anxiety (=3.538, 95% CI 0.969 to 3.906; =.003) and depression (=4.559, 95% CI 2.230 to 6.145; <.001). The gradient boosting model demonstrated the highest predictive accuracy (area under the curve=0.89 and accuracy=0.82). Least absolute shrinkage and selection operator regression identified session 3 (β=−0.45, 95% CI −0.68 to −0.22; <.001) and session 5 (β=−0.32, 95% CI −0.59 to −0.05; =.02) as most predictive of clinical success, while association rule mining revealed effective session combinations for different patient subgroups. Time-to-event analyses indicated that individuals with low back pain and higher baseline severity required longer intervention durations for improvement (5 wk; =.03). Conclusions: This pilot study presents an innovative method that combines ML with dynamic engagement data from a GPM program during interventions, rather than relying on static baseline data in prior studies. The results show preliminary efficacy and identify specific optimal session combinations and personalized treatment durations for different pain subgroups. These exploratory findings contribute to the field by providing a data-driven method for adaptive, personalized digital health interventions that move beyond one-size-fits-all strategies, potentially enabling clinicians to modify content and dosage to improve engagement and outcomes if validated in larger sample trials. Trial Registration: Chinese Clinical Trial Registry ChiCTR2400094247; https://www.chictr.org.cn/showprojEN.html?proj=245138
Synaptic remodeling and the female depression exposome: a mini-review of neuroendocrine, epigenetic, and social determinants
Depression is a multifactorial, chronic disorder and represents a leading cause of disability, with women exhibiting nearly twice the lifetime prevalence compared to men. Growing evidence indicates that this disparity cannot be explained by hormonal or psychosocial factors, but rather by dynamic interactions between environmental exposures, neuroendocrine signaling, and epigenetic regulation across development. This mini-narrative review aimed to examine how sex-specific exposome components interact with epigenetic mechanisms and synaptic remodeling processes to influence vulnerability to Major Depressive Disorder in women. The reviewed evidence demonstrates that fluctuations in ovarian hormones modulate HPA axis responsivity, neuroinflammatory signaling, and glutamatergic transmission through epigenetic regulation of stress-responsive genes such as NR3C1, SLC6A4, and BDNF, consequently influencing synaptic remodeling within corticolimbic circuits. Environmental and social exposures, particularly early-life adversity and psychosocial stressors, further interact with microglial activation and chromatin remodeling to produce long-lasting alterations in hippocampal and prefrontal plasticity. Collectively, these findings support a model in which sex-dependent neuroendocrine sensitivity amplifies exposome-driven epigenetic programming across the lifespan. Future research directions emerging from this synthesis include longitudinal life-course studies integrating multi-omic biomarkers, quantitative exposome assessment, and neuroimaging approaches to identify modifiable environmental targets and advance precision, sex-informed preventive and therapeutic strategies in depression.
Stage-specific ERP correlates of audiovisual facial emotion processing across depressive tendencies
Emotional dysregulation can emerge as early as the initial stages of depression. This study aimed to examine event-related potential characteristics during the perception of negative, positive, and neutral facial expressions in healthy individuals across depressive tendencies. Twenty-six healthy participants underwent ERP measurements during emotion recognition using a facial emotion recognition task in visual and audiovisual modalities. The Emotion Regulation Questionnaire (ERQ), the Difficulties in Emotion Regulation Scale (DERS-16), and the Beck Depression Inventory II (BDI-II) assessed cognitive strategy, emotion regulation difficulties, and depression severity, respectively. Facial affect elicited larger amplitudes compared to neutral faces, from the N170 and early posterior negativity (EPN) in the temporo-occipital region to the late positive potential (LPP) in the centroparietal region. Under audiovisual conditions, P1 peak latency to negative stimuli in the temporal region exhibited significant negative correlations with DERS-16 and BDI-II scores. N170 peak latency to positive stimuli also demonstrated a significant negative correlation with BDI-II scores. Under visual conditions, EPN amplitude to negative stimuli in the occipital region exhibited a significant positive correlation with BDI-II scores. P1 and N170 latencies, or neural response speeds, and EPN amplitude, which represents emotional reaction strength, correlate with depressive tendencies in healthy individuals. These early components function as initial neural signals that may serve as electrophysiological markers of abnormal emotional processing within neuropsychological functions prior to clinical depression.
Analysis of the prevalence of dyslipidemia in early-onset schizophrenia patients and its correlation with clinical characteristics
ObjectiveTo analyze the prevalence of dyslipidemia and related influencing factors in patients with early-onset schizophrenia (EOS).MethodsWe recruited 289 pediatric and adolescent EOS patients from October 2021 to June 2024 in the Third People’s Hospital of Fuyang. Researchers gathered comprehensive demographic and clinical records. Utilizing the 2023 Chinese Guidelines for Lipid Management, they calculated dyslipidemia prevalence and the incidence of irregularities in total cholesterol, triglycerides, LDL cholesterol, HDL cholesterol, and non-HDL cholesterol. Subsequently, differences in dyslipidemia among different genders, body mass index, and antipsychotic medication groups were analyzed. Finally, independent influencing factors of dyslipidemia in EOS patients were explored.ResultsThe overall prevalence of dyslipidemia was 24.9% (72/289), with abnormal rates of TG, TC, HDL-C, LDL-C, and non-HDL-C being 15.9%, 6.6%, 6.6%, 4.2%, and 7.3%, respectively. Male patients, those who were overweight or obese, and those taking two antipsychotic drugs had significantly higher rates of dyslipidemia. Regression analysis showed that male gender (OR = 2.04, P = 0.016), overweight/obesity (OR = 4.55, P < 0.001), body roundness index (OR = 1.53, P = 0.005), and the use of two antipsychotic drugs (OR = 1.90, P = 0.030) were risk factors for dyslipidemia in EOS patients.ConclusionThe prevalence of dyslipidemia in EOS patients is relatively high. When monitoring lipid levels in clinical practice, particular attention should be paid to male patients, those who are overweight or obese, and those receiving combined drug therapy.
Biomarkers for complex post-traumatic stress disorder: translational and evolutionary perspectives
Esketamine ameliorates depression-like behavior in mice via modulation of the NRG1–ErbB4 pathway
BackgroundEsketamine has a significant and rapid antidepressant effect. Although studies have shown that Neuregulin 1 (NRG1) and it’s signaling pathway are associated with depression, the possible regulatory relationship of esketamine on the NRG1-ErbB4 pathway is not yet clear.MethodsTo induce depressive-like behavior in mice, a Chronic Social Defeat Stress (CSDS) model was established. Behavioral indicators were then employed to assess depression in these mice, categorized into control, susceptible, and resilient groups. Following intraperitoneal injection of a subanesthetic dose of esketamine, behavioral tests were conducted at 30 minutes and 24 hours post-injection to observe any improvements in depressive-like behavior. Additionally, changes in immunofluorescence and protein expression levels of NRG1-ErbB4 and GAD67 in the prefrontal cortex were evaluated.ResultsCompared with the control group, the CSDS susceptible group mice showed decreases in social interaction ratio in the contact area, sucrose preference ratio, NRG1 immunofluorescence protein expression in the prefrontal cortex and NRG1 expression in tissue homogenate; showed significant increases in immobility time; the expression of NRG1 decreased;no significant change in GAD67 and ErbB4 expression level. in After 30 minutes of intraperitoneal injection of esketamine, the expression of NRG1 in the prefrontal cortex of susceptible mice increased significantly. no significant change in GAD67 and ErbB4 expression level. After 30 minutes and 24 hours of intraperitoneal injection of esketamine, the social interaction ratio of susceptible group improved compared to the control group, and the duration of forced swimming immobility was significantly shortened.ConclusionThe subanesthetic dose of esketamine may regulate the NRG1-ErbB4 signaling pathway and improve depressive like behavior in mice.
Vitamin A status is associated with sleep, clock genes, and symptoms in children with autism spectrum disorder
BackgroundVitamin A signals through retinoic acid receptors and may influence neurodevelopment and the expression of clock genes. However, the biological pathway linking vitamin A status to sleep disturbance in ASD remains insufficiently defined. This study aimed to examine associations between vitamin A status and sleep problems, core symptoms, and clock genes in children with ASD, and to explore the mechanistic role of RARβ in regulating core clock genes.MethodsThis observational study included 361 children with ASD. Clinical symptoms were assessed using the Children’s Sleep Habits Questionnaire (CSHQ); the Childhood Autism Rating Scale (CARS) and the Social Responsiveness Scale (SRS). Peripheral blood mononuclear cell (PBMC) mRNA levels of RARβ and clock genes (BMAL1 and CLOCK) were quantified by qPCR. RARβ expression was knocked down in mice by stereotaxic injection of adeno-associated virus.ResultsChildren with lower vitamin A levels exhibited more severe sleep problems and autistic symptoms. Vitamin A levels showed a weak positive correlation with the expression of RARβ and BMAL1. RARβ knockdown reduced the expression of RARβ and clock genes in mouse brain tissue. Chromatin immunoprecipitation quantitative PCR (ChIP-qPCR) confirmed RARβ occupancy at a predicted CLOCK regulatory region.ConclusionThis study provided evidence that vitamin A status was linked to sleep problems, symptom severity, and expression of clock genes in the morning in ASD. We also found that RARβ signaling may regulate the expression of clock genes. This finding provides new insights into the mechanisms underlying sleep disturbances in ASD, but further functional studies are needed to confirm these findings.
Methylome-Wide Association Study of Obsessive-Compulsive Disorder
Obsessive-compulsive disorder (OCD) is a debilitating psychiatric condition influenced by both genetic and environmental risk factors. Epigenetic modifications, such as DNA methylation, may offer insights into biologically meaningful differences associated with the disorder.