Metaverse for mental health disorders:Opportunities and challenges

Several articles on the mental health impact of the metaverse and the need to balance its potential benefits with the risks of metaverse use has recently published.The metaverse consists of a combination of immersive technologies and artificial intelligence algorithms.The metaverse differs from the preceding digital psychiatric interventions due to its complex structure and interactions between components.The diverse functions of the metaverse ensure that it may have a substantial impact on mental health.However,the evidence for its efficacy in treating mental health disorders is limited to a few trials.The mental health benefits of immersive technologies are well-documented and suggest that metaverse-based psychiatric treatment may be similarly efficacious.The mental health risks of the metaverse are largely unknown,and it is not clear whether they will be greater than other digital psychiatric interventions.Much more research is needed to determine whether metaverse-based psychiatric treatment will meet the standards of appropriate mental healthcare.

Ultra‑High Sensitivity Anisotropic Piezoelectric Sensors for Structural Health Monitoring and Robotic Perception

Monitoring minuscule mechanical signals,both in magnitude and direction,is imperative in many application scenarios,e.g.,structural health monitoring and robotic sensing systems.However,the piezoelectric sensor struggles to satisfy the requirements for directional recognition due to the limited piezoelectric coefficient matrix,and achieving sensitivity for detecting micrometer-scale deformations is also challenging.Herein,we develop a vector sensor composed of lead zirconate titanate-electronic grade glass fiber composite filaments with oriented arrangement,capable of detecting minute anisotropic deformations.The as-prepared vector sensor can identify the deformation directions even when subjected to an unprecedented nominal strain of 0.06%,thereby enabling its utility in accurately discerning the 5μm-height wrinkles in thin films and in monitoring human pulse waves.The ultra-high sensitivity is attributed to the formation of porous ferroelectret and the efficient load transfer efficiency of continuous lead zirconate titanate phase.Additionally,when integrated with machine learning techniques,the sensor’s capability to recognize multi-signals enables it to differentiate between 10 types of fine textures with 100%accuracy.The structural design in piezoelectric devices enables a more comprehensive perception of mechanical stimuli,offering a novel perspective for enhancing recognition accuracy.

Low‑Temperature Fabrication of Stable Black‑Phase CsPbI_(3) Perovskite Flexible Photodetectors Toward Wearable Health Monitoring

Flexible wearable optoelectronic devices fabricated fromorganic–inorganic hybrid perovskites significantly accelerate the developmentof portable energy,biomedicine,and sensing fields,but their poor thermal stabilityhinders further applications.Conversely,all-inorganic perovskites possessexcellent thermal stability,but black-phase all-inorganic perovskite filmusually requires high-temperature annealing steps,which increases energy consumptionand is not conducive to the fabrication of flexible wearable devices.In this work,an unprecedented low-temperature fabrication of stable blackphaseCsPbI3perovskite films is demonstrated by the in situ hydrolysis reactionof diphenylphosphinic chloride additive.The released diphenyl phosphateand chloride ions during the hydrolysis reaction significantly lower the phasetransition temperature and effectively passivate the defects in the perovskitefilms,yielding high-performance photodetectors with a responsivity of 42.1 AW−1 and a detectivity of 1.3×10^(14)Jones.Furthermore,high-fidelity imageand photoplethysmography sensors are demonstrated based on the fabricated flexible wearable photodetectors.This work provides a newperspective for the low-temperature fabrication of large-area all-inorganic perovskite flexible optoelectronic devices.

Mediating role of health literacy in the relationship between social isolation and psychological distress among pre-frail older adults

BACKGROUND Frailty has become a significant public health issue.The recent increase in the number of frail older adults has led to increased attention being paid to psycho-logical care services in communities.The social isolation of pre-frail older adults can impact their psychological distress.AIM To explore the mediating effect of health literacy between social isolation and psychological distress among communitydwelling older adults with pre-frailty.METHODS This descriptive cross-sectional study was conducted with 254 pre-frail older individuals aged 60 years and over.Social isolation,health literacy,and psycholo-gical distress were comprehensively measured using the Lubben Social Network Scale-6,12-item Short-Form Health Literacy Questionnaire,and the Kessler Psy-chological Distress Scale-10.Data were evaluated using the SPSS 27.0 package program and the PROCESS macro tool.Descriptive statistical analyses,correlation analyses,and bootstrap mediation tests were used to assess associations between the variables.RESULTS The results showed that social isolation had an effect on health literacy among pre-frail older adults(β=0.240,P<0.001),social isolation impact on psycho-logical distress pre-frail older adults(β=-0.415,P<0.001);health literacy was identified effect on psychological distress among pre-frail older persons(β=-0.307,P<0.001).Health literacy partially mediated the relationship between social isolation and psychological distress among community-dwelling older adults with pre-frailty,with a mediation effect of-0.074,accounting for 17.83%of the total effect.

Decoding the nexus:branched-chain amino acids and their connection with sleep,circadian rhythms,and cardiometabolic health

The sleep-wake cycle stands as an integrative process essential for sustaining optimal brain function and,either directly or indirectly,overall body health,encompassing metabolic and cardiovascular well-being.Given the heightened metabolic activity of the brain,there exists a considerable demand for nutrients in comparison to other organs.Among these,the branched-chain amino acids,comprising leucine,isoleucine,and valine,display distinctive significance,from their contribution to protein structure to their involvement in overall metabolism,especially in cerebral processes.Among the first amino acids that are released into circulation post-food intake,branched-chain amino acids assume a pivotal role in the regulation of protein synthesis,modulating insulin secretion and the amino acid sensing pathway of target of rapamycin.Branched-chain amino acids are key players in influencing the brain's uptake of monoamine precursors,competing for a shared transporter.Beyond their involvement in protein synthesis,these amino acids contribute to the metabolic cycles ofγ-aminobutyric acid and glutamate,as well as energy metabolism.Notably,they impact GABAergic neurons and the excitation/inhibition balance.The rhythmicity of branchedchain amino acids in plasma concentrations,observed over a 24-hour cycle and conserved in rodent models,is under circadian clock control.The mechanisms underlying those rhythms and the physiological consequences of their disruption are not fully understood.Disturbed sleep,obesity,diabetes,and cardiovascular diseases can elevate branched-chain amino acid concentrations or modify their oscillatory dynamics.The mechanisms driving these effects are currently the focal point of ongoing research efforts,since normalizing branched-chain amino acid levels has the ability to alleviate the severity of these pathologies.In this context,the Drosophila model,though underutilized,holds promise in shedding new light on these mechanisms.Initial findings indicate its potential to introduce novel concepts,particularly in elucidating the intricate connections between the circadian clock,sleep/wake,and metabolism.Consequently,the use and transport of branched-chain amino acids emerge as critical components and orchestrators in the web of interactions across multiple organs throughout the sleep/wake cycle.They could represent one of the so far elusive mechanisms connecting sleep patterns to metabolic and cardiovascular health,paving the way for potential therapeutic interventions.

A Comprehensive Survey on Federated Learning Applications in Computational Mental Healthcare

Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing trend.This aims to alleviate the workload on healthcare professionals and aid individuals.Numerous applications have been developed to support the challenges in intelligent healthcare systems.However,because mental health data is sensitive,privacy concerns have emerged.Federated learning has gotten some attention.This research reviews the studies on federated learning and mental health related to solving the issue of intelligent healthcare systems.It explores various dimensions of federated learning in mental health,such as datasets(their types and sources),applications categorized based on mental health symptoms,federated mental health frameworks,federated machine learning,federated deep learning,and the benefits of federated learning in mental health applications.This research conducts surveys to evaluate the current state of mental health applications,mainly focusing on the role of Federated Learning(FL)and related privacy and data security concerns.The survey provides valuable insights into how these applications are emerging and evolving,specifically emphasizing FL’s impact.

Relationship between the use of smart medical services and mental health status

In this editorial,I comment on the article by Zhang et al.To emphasize the importance of the topic,I discuss the relationship between the use of smart medical devices and mental health.Smart medical services have the potential to positively influence mental health by providing monitoring,insights,and inter-ventions.However,they also come with challenges that need to be addressed.Understanding the primary purpose for which individuals use these smart tech-nologies is essential to tailoring them to specific mental health needs and prefe-rences.

Health anxiety and work loss in patients diagnosed with serrated polyposis syndrome:A cross sectional study

BACKGROUND Serrated polyposis syndrome(SPS)is a polyposis condition with neoplastic potential,but its psychological impact is not well understood.AIM To assess health anxiety prevalence in a regional Australian cohort of SPS patients and explore factors influencing it,including workforce impacts of regular surveillance.METHODS This cross-sectional study screened patients aged 18-65 undergoing colonoscopy in a regional gastroenterology practice between January 2015 and June 2022.Eligible SPS patients were invited to participate.Data included the Short Health Anxiety Inventory,employment status,and previous demographic and medical findings.RESULTS Health anxiety was found in 21.57%of SPS patients,with anxious patients being significantly more concerned about surveillance(OR=7.70).Patients lost an average of 11.04 work hours per colonoscopy.CONCLUSION Health anxiety in SPS patients aligns with rates in other gastroenterology populations.Identifying it may improve management,though further research is needed to better understand prevalence and care improvements.

Method for Estimating the State of Health of Lithium-ion Batteries Based on Differential Thermal Voltammetry and Sparrow Search Algorithm-Elman Neural Network

Precisely estimating the state of health(SOH)of lithium-ion batteries is essential for battery management systems(BMS),as it plays a key role in ensuring the safe and reliable operation of battery systems.However,current SOH estimation methods often overlook the valuable temperature information that can effectively characterize battery aging during capacity degradation.Additionally,the Elman neural network,which is commonly employed for SOH estimation,exhibits several drawbacks,including slow training speed,a tendency to become trapped in local minima,and the initialization of weights and thresholds using pseudo-random numbers,leading to unstable model performance.To address these issues,this study addresses the challenge of precise and effective SOH detection by proposing a method for estimating the SOH of lithium-ion batteries based on differential thermal voltammetry(DTV)and an SSA-Elman neural network.Firstly,two health features(HFs)considering temperature factors and battery voltage are extracted fromthe differential thermal voltammetry curves and incremental capacity curves.Next,the Sparrow Search Algorithm(SSA)is employed to optimize the initial weights and thresholds of the Elman neural network,forming the SSA-Elman neural network model.To validate the performance,various neural networks,including the proposed SSA-Elman network,are tested using the Oxford battery aging dataset.The experimental results demonstrate that the method developed in this study achieves superior accuracy and robustness,with a mean absolute error(MAE)of less than 0.9%and a rootmean square error(RMSE)below 1.4%.

Assessing healthcare workers’knowledge and confidence in the diagnosis,management and prevention of Monkeypox

BACKGROUND Monkeypox(Mpox),is a disease of global public health concern,as it does not affect only countries in western and central Africa.AIM To assess Burundi healthcare workers(HCWs)s’level of knowledge and confidence in the diagnosis and management of Mpox.METHODS We conducted a cross-sectional study via an online survey designed mainly from the World Health Organization course distributed among Burundi HCWs from June-July 2023.The questionnaire comprises 8 socioprofessional-related questions,22 questions about Mpox disease knowledge,and 3 questions to assess confidence in Mpox diagnosis and management.The data were analyzed via SPSS software version 25.0.A P value<0.05 was considered to indicate statistical significance.RESULTS The study sample comprised 471 HCWs who were mainly medical doctors(63.9%)and nurses(30.1%).None of the 22 questions concerning Mpox knowledge had at least 50%correct responses.A very low number of HCWs(17.4%)knew that Mpox has a vaccine.The confidence level to diagnose(21.20%),treat(18.00%)or prevent(23.30%)Mpox was low among HCWs.The confidence level in the diagnosis of Mpox was associated with the HCWs’age(P value=0.009),sex(P value<0.001),work experience(P value=0.002),and residence(P value<0.001).The confidence level to treat Mpox was significantly associated with the HCWs’age(P value=0.050),sex(P value<0.001),education(P value=0.033)and occupation(P value=0.005).The confidence level to prevent Mpox was associated with the HCWs’education(P value<0.001),work experience(P value=0.002),residence(P value<0.001)and type of work institution(P value=0.003).CONCLUSION This study revealed that HCWs have the lowest level of knowledge regarding Mpox and a lack of confidence in the ability to diagnose,treat or prevent it.There is an urgent need to organize continuing medical education programs on Mpox epidemiology and preparedness for Burundi HCWs.We encourage future researchers to assess potential hesitancy toward Mpox vaccination and its associated factors.

Posttraumatic stress symptoms among Chinese university students during the COVID-19 pandemic:Prevalence,correlates,and mental health help-seeking

BACKGROUND Revisiting the epidemiology of posttraumatic stress symptoms(PTSSs)among university students during the coronavirus disease 2019(COVID-19)pandemic as well as understanding the mental health help-seeking behavior of individuals with PTSSs has critical implications for public mental health strategies in future medical pandemics.AIM To investigate the prevalence and correlates of PTSSs among university students during the first wave of the COVID-19 pandemic in China and to examine mental health help-seeking behaviors among these students.METHODS A total of 2507 Chinese university students were recruited via snowball sampling.The students completed the Seven-item Screening Scale for Post-traumatic Stress Disorder during the first wave of the COVID-19 pandemic in China.Sociodemo-graphic characteristics,pandemic-related characteristics,and mental health help-seeking behaviors of students with PTSSs were also collected.RESULTS The prevalence of PTSSs among the participants was 28.0%.Seven significant correlates of PTSSs were identified(odds ratio=1.23-3.65,P≤0.024):Female sex,being 19 years old or older,living with others or alone,a low level of family economic status,fair or poor interpersonal relationships,severe or very severe local pandemic,and having family members diagnosed with COVID-19.However,only 3.28%of the students with PTSSs reported seeking help from mental health specialists.Among the 23 students who sought help from mental health specialists,13 opted for online or telephone-based psychological consultation.CONCLUSION Our data suggest that there was a high risk of PTSSs among university students and a high level of unmet mental health needs during the COVID-19 pandemic.The delivery of mental health services online or via telephone is a promising approach to address these unmet needs.

Impact of community public health care on treatment effect,health cognition,and self-management in patients with type 2 diabetes

BACKGROUND At present,China has become the country with the largest number of individuals with diabetes mellitus(DM)in the world,with a total of approximately 140 million patients,the majority of whom have type 2 DM(T2DM).Based on conven-tional nursing methods,community home care has important clinical significance in controlling blood sugar and disease progression.AIM To explore the impact of community public health nursing on treatment effect,health cognition,and self-management in patients with T2DM.METHODS One hundred patients with T2DM were selected as the research subjects.The patients were divided into either a conventional nursing group or community nursing(CN)group using the random number table method.The conventional nursing group(50 cases)received routine care,while the CN group(50 cases)received community public health care in addition to routine care as that for the conventional nursing group.The rate of excellent and good blood glucose control,fasting blood glucose before and after care,2-h postprandial blood glucose,health cognition,and self-management ability,and patient satisfaction were compared between the two groups.RESULTS The CN group had a higher rate of excellent blood sugar control than the conventional nursing group(88%vs 70%,P<0.05).Before care,there was no significant difference in fasting blood glucose or 2-h postprandial blood glucose between the two groups of patients(P>0.05).After nursing,fasting blood glucose and 2-h postprandial blood glucose were reduced to varying degrees in both groups,and both blood glucose levels in the CN group were lower than those of the conventional nursing group(P<0.05).Compared with the scores before care,the cognitive level score for diabetes and self-management ability score improved after care in both groups.The cognitive level and self-management ability of patients in the CN group were higher than those of the conventional nursing group(P<0.05).The overall satisfaction of the CN group was better than that of the conventional nursing group(98%vs 86%,P<0.05).CONCLUSION Community public health care based on conventional care of T2DM can achieve better blood sugar control,and improve patients’health cognitive level and self-management ability.

Reevaluating health metrics:Unraveling the limitations of disabilityadjusted life years as an indicator in disease burden assessment

In 1993,the World Bank released a global report on the efficacy of health promotion,introducing the disability-adjusted life years(DALY)as a novel indicator.The DALY,a composite metric incorporating temporal and qualitative data,is grounded in preferences regarding disability status.This review delineates the algorithm used to calculate the value of the proposed DALY synthetic indicator and elucidates key methodological challenges associated with its application.In contrast to the quality-adjusted life years approach,derived from multi-attribute utility theory,the DALY stands as an independent synthetic indicator that adopts the assumptions of the Time Trade Off utility technique to define Disability Weights.Claiming to rely on no mathematical or economic theory,DALY users appear to have exempted themselves from verifying whether this indicator meets the classical properties required of all indicators,notably content validity,reliability,specificity,and sensitivity.The DALY concept emerged primarily to facilitate comparisons of the health impacts of various diseases globally within the framework of the Global Burden of Disease initiative,leading to numerous publications in international literature.Despite widespread adoption,the DALY synthetic indicator has prompted significant methodological concerns since its inception,manifesting in inconsistent and non-reproducible results.Given the substantial diffusion of the DALY indicator and its critical role in health impact assessments,a reassessment is warranted.This reconsideration is imperative for enhancing the robustness and reliability of public health decisionmaking processes.

The Sequential Mediating Roles of Body Pain and Self-Reported Health Status in the Relationship between Sleep Duration and Life Satisfaction

Objective This study examines the sequential mediating roles of body pain and self-reported health in the association between sleep duration and self-reported life satisfaction among elderly Chinese adults.Methods Data from the fifth wave of the China Health and Retirement Longitudinal Survey(CHARLS)were used to analyse the relationships between sleep duration and body pain,self-reported health,and life satisfaction through logistic regression and Restricted Cubic Spline(RCS)analyses.The sequential mediation effects of body pain and self-reported health status were examined via chain mediation analysis.Results Logistic regression analysis showed that sleeping fewer than 6 hours or 6-7 hours was linked to higher risks of body pain,poor health,and dissatisfaction with life compared to sleeping 7-8 hours(all P<0.05).Additionally,those sleeping more than 9 hours also had increased risks of poor health and dissatisfaction with life compared to those sleeping 7-8 hours(all P<0.05).Chain mediation analysis showed that body pain and self-reported health status sequentially mediated 46.15%of the association between sleep duration and life satisfaction.Conclusion Body pain and self-reported health may shape the relationship between sleep duration and life satisfaction in elderly Chinese adults.

Cultural Adaptation of the Mental Health Literacy Scale

Background:Mental health literacy(MHL)refers to one’s knowledge and understanding of mental health disorders and their treatments.This literacy may be influenced by cultural norms and values that shape individuals’experiences,beliefs,attitudes,and behaviors regarding mental health.This study focuses on adapting the Mental health literacy scale(MHLS)for use in the multicultural context of Israel.Objectives include validating its construct,assessing its accuracy in measuring MHL in this diverse setting and examining and comparing levels of MHL across different cultural groups.Methods:The data collection included 1057 participants,representing all the ethnic groups of the Israeli population aged 18 and over.The tools included the MHLS and a demographic questionnaire.Confirmatory factor analysis(CFA)was employed to assess the original structure of the MHLS.Results:The results revealed that after evaluating the original MHLS,five items were excluded,leading to the validation of a modified version—Israeli mental health scale(IMHLS)with four factors and 25 items.CFA and reliability analyses supported an established and robust four-factor model.Significant ethnic differences in MHLS scores were identified,with Muslim participants showing the highest familiarity with mental disorders,followed by Druze and Christian participants,while Jewish participants had the lowest familiarity.Conclusion:The study concluded that the IMHLS is a valid and reliable tool for assessing MHL in Israel’s diverse and multicultural population.The revised scale better reflects the cultural nuances of the Israeli context.The significant ethnic differences that the study revealed in IMHLS emphasize the importance of culturally sensitive mental health interventions tailored to different ethnic groups in Israel.

Leveraging Artificial Intelligence to Achieve Sustainable Public Healthcare Services in Saudi Arabia: A Systematic Literature Review of Critical Success Factors

This review aims to analyze the development and impact of Artificial Intelligence(AI)in the context of Saudi Arabia’s public healthcare system to fulfill Vision 2030 objectives.It is extensively devoted to AI technology deployment relevant to disease management,healthcare delivery,epidemiology,and policy-making.However,its AI is culturally sensitive and ethically grounded in Islam.Based on the PRISMA framework,an SLR evaluated primary academic literature,cases,and practices of Saudi Arabia’s AI implementation in the public healthcare sector.Instead,it categorizes prior research based on how AI can work,the issues it poses,and its implications for the Kingdom’s healthcare system.The Saudi Arabian context analyses show that AI has increased the discreet prediction of diseases,resource management,and monitoring outbreaks during mass congregations such as hajj.Therefore,the study outlines critical areas for defining the potential for artificial intelligence and areas for enhancing digital development to support global healthcare progress.The key themes emerging from the review include Saudi Arabia:(i)the effectiveness of AI with human interaction for sustainable health services;(ii)conditions and quality control to enhance the quality of health care services using AI;(iii)environmental factors as influencing factors for public health care;(iv)Artificial Intelligence,and advanced decision-making technology for Middle Eastern health care systems.For policymakers,healthcare managers,and researchers who will engage with AI innovation,the review proclaims that AI applications should respect the country’s socio-cultural and ethical practices and pave the way for sustainable healthcare provision.More empirical research is needed on the implementation issues with AI,creating culturally appropriate models of AI,and finding new applications of AI to address the increasing demand for healthcare services in Saudi Arabia.

Physiology and health assessment,risk balance,and model for endstage liver disease scores:Postoperative outcome of liver transplantation

BACKGROUND Liver transplantation aims to increase the survival of patients with end-stage liver diseases and improve their quality of life.The number of organs available for transplantation is lower than the demand.To provide fair organ distribution,predictive mortality scores have been developed.AIM To compare the Acute Physiology and Chronic Health Evaluation IV(APACHE IV),balance of risk(BAR),and model for end-stage liver disease(MELD)scores as predictors of mortality.METHODS Retrospective cohort study,which included 283 adult patients in the postoperative period of deceased donor liver transplantation from 2014 to 2018.RESULTS The transplant recipients were mainly male,with a mean age of 58.1 years.Donors were mostly male,with a mean age of 41.6 years.The median cold ischemia time was 3.1 hours,and the median intensive care unit stay was 5 days.For APACHE IV,a mean of 59.6 was found,BAR 10.7,and MELD 24.2.The 28-day mortality rate was 9.5%,and at 90 days,it was 3.5%.The 28-day mortality prediction for APACHE IV was very good[area under the curve(AUC):0.85,P<0.001,95%CI:0.76-0.94],P<0.001,BAR(AUC:0.70,P<0.001,95%CI:0.58–0.81),and MELD(AUC:0.66,P<0.006,95%CI:0.55-0.78),P<0.008.At 90 days,the data for APACHE IV were very good(AUC:0.80,P<0.001,95%CI:0.71–0.90)and moderate for BAR and MELD,respectively,(AUC:0.66,P<0.004,95%CI:0.55–0.77),(AUC:0.62,P<0.026,95%CI:0.51–0.72).All showed good discrimination between deaths and survivors.As for the best value for liver transplantation,it was significant only for APACHE IV(P<0.001).CONCLUSION The APACHE IV assessment score was more accurate than BAR and MELD in predicting mortality in deceased donor liver transplant recipients.

Effectiveness of RESET care program: A real-world-evidence on managing non-alcoholic fatty liver disease through digital health interventions

BACKGROUND Non-alcoholic fatty liver disease(NAFLD)management requires sustainable lifestyle modifications.This study aimed to evaluate the effectiveness of the RESET care plan,a comprehensive program that is an integrated personalized diet,exercise,and cognitive behavior therapy,delivered via MyTatva’s digital health application enabled through a body composition analyzer(BCA)and smartwatch.AIM To evaluates the effectiveness of the comprehensive program delivered via My-Tatva’s digital health app enabled through internet of thing devices.METHODS This retrospective observational study analyzed deidentified data from 22 par-ticipants enrolled in the MyTatva RESET care program.Participants were divided into three groups:Group A,diet plan;Group B,diet+exercise plan;and Group C,diet+exercise+cognitive behavioral therapy plan.Participants were provided with a BCA and smartwatch for continuous monitoring of anthropometric para-meters.Statistical analysis,including one-way ANOVA and post-hoc Tukey’s Honest Significant Difference test,was conducted to compare mean changes in anthropometric parameters across the groups.INTRODUCTION Non-alcoholic fatty liver disease(NAFLD)has emerged as a global health burden,affecting approximately 1 in 4 in-dividuals worldwide.NAFLD ranges from simple steatosis(fat accumulation)to non-alcoholic steatohepatitis(NASH),and its global prevalence in the general population is estimated between 6.3%and 33%,with NASH affecting 3-5%[1,2].Obesity is a major risk factor of NAFLD,with studies showing that the likelihood of developing NAFLD increases 5-fold at a body mass index(BMI)of 30-32.5 kg/m²and up to 14-fold at BMI of 37.5-40 kg/m²compared to a BMI of 20-22.5 kg/m²[3,4].Effective NAFLD management requires both dietary and physical activity modifications.Healthy weight loss with sustained muscle mass plays a pivotal role,with a reduction of 3%-5%decreasing hepatic steatosis,5%-7%improving NASH conditions,and 10%or more needed to reverse hepatic fibrosis[5].Management also normalizes elevated liver enzymes(aspartate aminotransferase and alanine aminotransferase),enhances insulin sensitivity,and thereby reduces cardiovascular risk by improving endothelial function and increasing cardiorespiratory fitness[6].However,diet or exercise alone is often not as effective as a combined approach.Integrating both balanced dietary changes and increased physical activity yields more sustainable improvements in NAFLD and overall metabolic health[1,7].Traditional intervention methods usually involve in-person consultations,which often lack real-time and continuous patient monitoring.The recommendation of drastic changes in diet and exercise can also be overwhelming for patients,leading to low adherence rates.Many patients struggle to maintain these changes in the long-term due to a lack of con-tinuous motivational support[8,9].In recent years,the health ecosystem has witnessed a significant shift toward digital health platforms,which complement pharmacological treatments in chronic disease management,and increase scala-bility.These platforms provide continuous monitoring and personalized support,helping to bridge the gap between health care setups and patients[10].Recent digital advances enable internet of things(IoT)devices to be integrated into such management plans to track health metrics,to address the limitations of traditional methods[11,12].The MyTatva digital health application offers the RESET plan,a novel comprehensive approach for NAFLD mana-gement by integrating personalized support from nutrition,physiotherapy,and cognitive behavioral therapy(CBT)coaches.We aimed to evaluate the effectiveness of the RESET plan by analyzing the reduction in anthropometric para-meters across three different digital intervention groups.

全健康(One Health)视角下绿色健康社区景观设计体系构建

以全健康(OneHealth)视角为出发点,保障人、环境和动物的健康对于社区健康环境规划与设计具有重要意义。在现有健康社区景观设计体系中,人、环境、物种三者关系虽被提及,但未被系统探究,健康社区建设实践也需要系统性的景观设计体系支持。基于全健康理念中人、环境和物种的相互关系,结合生态系统服务功能、康复景观理论和园艺疗法,进一步探讨人、环境、物种健康之间的关联途径。通过对全健康理念视角下的3个层次进行演绎发展,归纳总结了人的健康、环境健康和物种健康的组成要素及内容,构建了以人的健康需求为导向的社区健康设计框架,提出了以人与人际、人与环境和人与物种为三大圈层的健康社区景观设计体系。全健康视角为社区健康环境设计提供了一个系统性、整合性且具有操作性的框架,未来应重视健康社区体系中各类疗愈空间的设计与使用,以增进社区居民共享的健康福祉。

Comprehensively Strengthen and Improve Students’ Mental Health System in the New Era

The incidence of mental health issues among children and adolescents is on the rise,positioning it as a critical public health concern that could impact the future of humanity(1).To address this challenge,on April 27,2023,17 government bodies including the Ministry of Education launched the“Special Action Plan for Comprehensively Strengthening and Improving the Mental Health Work of Students in the New Era(2023–2025)”(hereinafter referred to as the Special Action Plan)(2),signaling a significant advancement in China’s commitment to student mental health.The Special Action Plan delineates eight major initiatives,such as enhancing mental health education,standardizing mental health monitoring,and augmenting the support for psychological professionals.These coordinated efforts aim to establish an extensive,multi-layered mental health framework to enhance student well-being from various perspectives.As a holistic and strategically designed initiative,the policy holds substantial practical importance and serves an instrumental role in guiding advancements in this field.