Background: Women living in rural agrarian reform communities face intersecting challenges related to social, economic, racial, and gender vulnerabilities, which significantly increase their likelihood of developing physical and mental health problems. Despite the potential of telephone-based interventions to promote mental health, there is a lack of studies assessing their feasibility and effectiveness among underserved populations in Brazil. Objective: This study aimed to assess the feasibility and effectiveness of a telephone-based intervention on mental health outcomes among women living in a rural agrarian reform community in Brazil. Methods: We conducted a descriptive, prospective pilot study with a pretest and posttest design. Data were collected at 3 time points: baseline, 1 week, and 1 month after the intervention. The outcomes assessed included quality of life, social support, self-efficacy, and common mental disorder symptoms. Nonparametric tests were used to analyze the data. The intervention consisted of 3 phone calls supported by a workbook, with content based on cognitive behavioral and psychiatric nursing principles. Results: Of the 31 women enrolled, 23 (74.2%) completed all 3 phone-based sessions. There was a significant reduction in common mental disorder symptoms (Kendall =0.280; =.002), particularly in the somatic domain (=.02). Moreover, participants reported improved perceptions of the physical domain of quality of life (Kendall =0.131; =.049). All women rated the intervention positively, with more than half emphasizing its practical usefulness. Conclusions: The telephone-based intervention was feasible and showed promising results in improving mental health outcomes among women in a rural setting. These findings support integrating low-intensity, remote psychosocial strategies into primary health care, especially those led by nurses, to increase access to mental health promotion for vulnerable populations.
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Willingness of Patients With Mental Disorders to Engage in Online Psychotherapy: Multicenter Cross-Sectional Survey
<strong>Background:</strong> China faces a high prevalence of mental disorders but low treatment uptake, a gap driven by limited awareness and unevenly distributed mental health resources. While online psychotherapy has the potential to expand access, patient willingness remains insufficiently explored. <strong>Objective:</strong> This study aimed to investigate the willingness of Chinese patients with mental disorders to engage in online psychotherapy and to identify associated factors. <strong>Methods:</strong> A multicenter, cross-sectional survey was conducted using a structured questionnaire to assess the attitudes and willingness of patients with mental disorders in China to engage in online psychotherapy. Willingness to engage in online psychotherapy was assessed using a 0 to 100 rating scale, with higher scores indicating greater willingness. Univariate analysis, correlation analysis, and multivariate linear regression analyses were used to identify factors influencing willingness. <strong>Results:</strong> Among 361 eligible participants, the mean willingness score for online psychotherapy was 70 (SD 28.56). In total, 86.4% (n=312) of participants preferred short-term therapy (1 to 10 sessions), while 92.5% (n=334) expected the cost per session to remain less than CNY ¥400 (US $55.50). Participants most preferred therapist-guided online individual therapy (n=142, 39.3%). Convenience (124/361, 34.3%) and perceived anonymity (“no one will know about the illness”; 119/361, 33.0%) were the 2 most commonly reported perceived benefits of online psychotherapy. The leading barrier was concerns about data security and privacy (108/303, 35.6%), followed by difficulty in establishing therapeutic rapport (60/303, 19.8%). The regression analysis revealed that age, self-stigma, satisfaction with current psychiatric medications, and satisfaction with previous online psychotherapy significantly influenced patients’ willingness to seek online psychotherapy. <strong>Conclusions:</strong> This multicenter study reveals a high level of willingness to engage in online psychotherapy among Chinese patients, with self-stigma as a key barrier. These findings support the development of tailored services, stigma reduction interventions, and infrastructure investment to enhance mental health care delivery.
Strength of Evidence to Support Decision-Making on the Use of Digital Mental Health Technologies in NICE Evaluations: Cross-Sectional Analysis of Studies
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.
Biomarkers for complex post-traumatic stress disorder: translational and evolutionary perspectives
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.
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.