Out-of-hospital cardiac arrest (OHCA) is a major public health problem and a leading cause of death worldwide. Previous studies have focused on improving the survival of people who have had OHCA by analyzing short-term survival outcomes, such as the return of spontaneous circulation, 30-day survival, and survival to discharge. Research has been conducted on prehospital prognostic factors to improve the survival of patients with OHCA, among which the association between socioeconomic status (SES) and survival has been reported. SES could affect bystander cardiopulmonary resuscitation rates and whether OHCA is witnessed, and low cardiopulmonary resuscitation education rates are associated with low SES. It has been reported that areas with high SES have shorter hospital transfer times and more public defibrillators per person. Previous studies have shown the impact of SES disparities on the short-term survival of patients with OHCA. However, understanding the impact of SES on the long-term prognosis of OHCA survivors remains limited. As long-term outcomes are more indicative of a patient’s ongoing health care needs and the burden on public health than short-term outcomes, understanding the long-term prognosis of OHCA survivors is important.This study aimed to identify whether SES influenced the long-term outcomes of OHCA.Using health claims data obtained from the National Health Insurance (NHI) service in Korea, we included OHCA survivors who were hospitalized between January 2005 and December 2015. The patients were divided into 2 groups: NHI and Medical Aid (MA) groups, with the MA group defined as having a low SES. Cumulative mortality was estimated using the Kaplan-Meier method, and a Cox proportional hazards model was used to evaluate the impact of SES on long-term mortality. A subgroup analysis was performed based on whether cardiac procedures were performed.We followed 4873 OHCA survivors for up to 14 years (median of 3.3 years). The Kaplan-Meier survival curve showed that the MA group had a significantly decreased long-term survival rate compared to the NHI group. With an adjusted hazard ratio (aHR) of 1.52 (95% CI 1.35-1.72), low SES was associated with increased long-term mortality. The overall mortality rate of the patients who underwent cardiac procedures in the MA group was significantly higher than that of the NHI group . The overall mortality rate of patients without cardiac procedures was also increased in the MA group compared to the NHI group .OHCA survivors with low SES had an increased risk of poor long-term outcomes compared with those with higher SES. OHCA survivors with low SES who have undergone cardiac procedures need considerable care for long-term survival. Out-of-hospital cardiac arrest (OHCA) is a leading cause of death worldwide ,2. With To achieve the best possible outcomes for adults who have had OHCA, a multifaceted approach is required. This includes early detection of cardiac arrest, activation of an emergency response, prompt administration of high-quality cardiopulmonary resuscitation (CPR), early defibrillation for shockable rhythms, and post–return of spontaneous circulation (ROSC) care . HoweverSince 1990, the Utstein Resuscitation Registry, an international standard guideline for data collection and analysis aimed at improving the outcomes of patients with OHCA, has primarily focused on short-term outcome measures: ROSC, 30-day survival, and survival to discharge . HoweverTherefore, this nationwide study aimed to explore the potential effect of SES on long-term outcomes among patients who survived 30 days or more after OHCA. In addition, the association between SES and long-term outcomes among OHCA survivors with and without cardiac procedures was evaluated.We conducted a population-based study using health claims data obtained from the National Health Insurance (NHI) service. The NHI service database includes inpatient and outpatient medical information, such as patient demographics, diagnoses, procedures, drug prescriptions, date of death, and type of patient insurance . The diagnoses were classified based on International Classification of Diseases, 10th Revision (ICD-10) codes. All patients who visit the emergency department (ED) in South Korea for emergency symptoms, such as cardiac arrest, are charged an ED management fee. The NHI service covers a portion of the ED management fee for patients with health insurance and pays the entire fee for those with MA.This was a retrospective longitudinal cohort study to analyze long-term mortality. Patients who had a primary diagnosis of cardiac arrest (ICD-10 code I46.x) and were hospitalized on the day of the initial diagnosis between January 2005 and December 2015 were included. To exclude patients with in-hospital cardiac arrest, those without a primary diagnosis code for cardiac arrest or a code for ED management fees were excluded. To validate the definition of OHCA, we investigated the medical records of 252 patients who visited a tertiary hospital and had a primary diagnosis code for cardiac arrest (I46.x) and a code for ED management fees. This OHCA definition had a 92.1% (232/252) positive predictive value . This stDue to the absence of medical records in NHI service data, we were unable to determine whether the cause of cardiac arrest was cardiac in origin. However, OHCA survivors who had undergone cardiac procedures could be assumed to have cardiac arrest due to a cardiac origin. Therefore, we examined whether OHCA survivors had undergone cardiac procedures such as percutaneous coronary intervention (PCI), pacemaker placement, implantable cardioverter defibrillator placement, cardiac resynchronization therapy with pacemakers, cardiac resynchronization therapy with defibrillators, and coronary artery bypass grafting during hospitalization. According to this definition, patients with OHCA were divided into cardiac procedure and no cardiac procedure groups.In South Korea, the NHI system was introduced in 1977, and by July 1989, the entire population was covered . In the The NHI service data include patient demographics, general specifications , in-hospital treatment , out-of-hospital prescriptions, diagnoses, death records, and socioeconomic variables such as income decile ,28. CosmIndividual-level indicators of SES are generally defined by income, education, and occupation . The proThe follow-up data obtained for up to 14 years (until 2018) were analyzed, and the primary outcome was long-term cumulative mortality according to SES. The subgroups were divided into OHCA with a cardiac procedure and OHCA without cardiac procedure, and the long-term cumulative mortality for each group was analyzed. Follow-up was initiated on the index date and continued until the date of death or December 31, 2018 (the last day of the study), whichever occurred first. In addition, 1-year, 3-year, 5-year, 10-year, and overall mortality were analyzed for each group. In addition, we performed an analysis by dividing the NHI group into 4 quartiles based on the insurance premium level.This study was approved by the Institutional Review Board of Hanyang University Hospital (HYUH-2023-01-021) and the Health Insurance Review and Assessment Service (NHIS-2023-1-122). The need for written informed consent was waived because data analyses were performed retrospectively using anonymized data derived from the NHI service. The study was conducted in accordance with the Helsinki Declaration.t test or Mann-Whitney U test as appropriate. Cumulative mortality was estimated by the Kaplan-Meier method. A Cox proportional hazards model was used to identify the impact of SES on long-term mortality, and the results are presented as hazard ratios and 95% CIs. The covariables included age, sex, and Charlson Comorbidity Index (CCI) score. The CCI score was calculated at the time of diagnosis within 1 year before the index date using the Quan algorithm [P value <.05. All statistical analyses were conducted using R software  and SAS software .Continuous variables are presented as the mean and SD for variables with a normal distribution and median and IQR for variables with a nonnormal distribution. Normality was evaluated using the Shapiro-Wilk test. Categorical variables are presented as frequencies and percentages. The categorical variables of 2 groups were compared using the Fisher exact test, and continuous variables were compared using the 2-tailed lgorithm . By corrWe enrolled 4937 patients with OHCA aged 18 years or older who had survived for 30 days or more. Finally, a total of 4873 patients were selected as the study population after excluding 4 patients whose insurance type information was missing and 60 patients whose insurance eligibility and date of death information was missing . The median follow-up duration was 1214 (IQR 140-2112) days, and the maximum follow-up duration was 5059 days. The number of patients with OHCA who underwent cardiac procedures during hospitalization was 1121 (23%) . The carP<.001).The Kaplan-Meier curve showed that the MA group had significantly decreased long-term survival compared with the NHI group . The resP=.03) and no cardiac procedure groups . The Kaplan-Meier curves for patients with OHCA with cardiac procedures and without cardiac procedures are presented in In subgroups  of OHCA survivors using data from the NHI service. In the past, the focus was on the survival of patients with OHCA, but now both the European Resuscitation Council guidelines and the American Heart Association guidelines also emphasize the importance of survivors’ recovery and rehabilitation ,43. FromIn public health research, SES is typically measured using individual-level or area-level indicators depending on the study objectives ,45. AreaRecent studies have noted the lack of research measuring SES at the individual level ,46,49. IIt has been reported that a low SES is associated with poor long-term outcomes in a variety of diseases, such as cardiovascular disease, stroke, and surgery -53. HoweAlthough the exact etiology explaining why survivors with low SES have poor long-term outcomes after OHCA is not well understood, several hypotheses can be proposed. The usual explanation for health disparities is differences in lifestyle . IndividIt is also possible that patients with low SES receive less adequate hospital care. Post–cardiac arrest care, including critical care interventions, is another important link in the chain of survival, and post arrest interventions such as targeted temperature management and PCI are associated with improved cardiac arrest survival and neurologic recovery ,58. CaseIt is well known that individuals with a low SES have poor medication adherence . Among pRecent studies have reported the need for national financial assistance to help cardiovascular disease patients with a low SES participate in CR ,67. LikeOur study has several limitations. First, the diagnosis of OHCA was defined using ICD-10 codes, and diagnostic inaccuracies could not be ruled out. Defining patients with OHCA and in-hospital cardiac arrest can be particularly imprecise. We validated the definition of OHCA, but it was based on a small sample size. Second, although this study included patients who survived 30 days or more after OHCA to minimize the influence of factors that could have affected the survival of patients with OHCA, such as initial cardiac arrest rhythm, witnessed status, bystander CPR, and duration of cardiac arrest, the potential impact could not be completely discounted. Third, because our study was based on data from a single nation, it is difficult to generalize the findings to other populations. Fourth, we only analyzed mortality as a long-term prognosis for OHCA survivors. Although mortality is an important issue in patients’ long-term outcomes, health-related quality of life (HRQOL) also has a substantial impact on long-term survival . HRQOL iThis nationwide population-based study showed an association between SES and long-term outcomes among survivors after OHCA. Survivors with low SES, especially vulnerable individuals such as those eligible for MA, were at higher risk of unfavorable long-term outcomes after OHCA. Particularly, OHCA survivors with low SES who have undergone cardiac procedures need greater attention for long-term survival.


During the COVID-19 pandemic, remote working was pervasively implemented, causing an increase in technology-related job demands. Concurrently, there was an increase in psychological problems in the occupational population. This study on remote workers tested a moderated mediation model positing burnout, conceptualized according to the Burnout Assessment Tool, as the mediator between techno-stressors and psychological health outcomes and e-work self-efficacy as a protective personal resource. A sample of 225 remote workers filled out anonymous questionnaires measuring techno-stressors, e-work self-efficacy, burnout, and psychological health symptoms . The data were analyzed using structural equation mediation and moderated mediation models, adopting a parceling technique. The results showed that burnout totally mediated the relationship between techno-stressors and depressive mood, while partially mediating the association between techno-stressors and anxiety symptoms. Moreover, e-work self-efficacy buffered the positive effects of techno-stressors on depressive mood and anxiety symptoms through burnout. The present research attested to the relevance of techno-stressors for the psychological health of remote workers and supported burnout as a mediator of this process, although anxiety symptoms were also directly related to techno-stressors. Moreover, the protective role of domain-specific self-efficacy was confirmed in the realm of remote working. Limitations and practical implications are discussed. Over the last decade, the diffusion of Information and Communication Technology (ICT) within organizational contexts has exponentially increased, especially following the COVID-19 outburst ,3, whichAlthough working remotely is often associated with a greater sense of autonomy, flexibility, and job satisfaction ,6,7, it At the same time, the pandemic context was also characterized by an increase in several psychological health symptoms related to anxiety and depression ,20,21. N(1)To explore whether techno-stressors are related to burnout, measured using the Burnout Assessment Tool ;(2)To examine whether burnout mediates the relationship between techno-stressors and psychological health outcomes ;(3)To investigate whether the detrimental effects of techno-stressors on anxiety symptoms and depressive mood through burnout are buffered by e-work self-efficacy levels.In the present study, we refer to the Conservation of Resources Theory (COR-Theory)  as our mThe organizational advantages of new working arrangements  are well known nowadays ,34,35. NBurnout, which is the most studied occupational stress-related syndrome, refers to individuals’ negative responses to chronic work-related stress . This phIn line with the Conservation of Resources Theory , workingHypothesis 1:Techno-stress creators will be positively related to burnout.Based on the Burnout Assessment Tool , burnoutConsistent with this line of research and according to COR theory principles , when peHypothesis 2a:Burnout will be positively related to depressive mood.Hypothesis 2b:Burnout will be positively related to anxiety symptoms.Previous research has empirically demonstrated that techno-stressors are negatively related to mental health . Thus, wHypothesis 3a:Burnout will mediate the relationship between techno-stress creators and depressive mood.Hypothesis 3b:Burnout will mediate the relationship between techno-stress creators and anxiety symptoms.Drawing on the COR theory , people Hypothesis 4:E-work self-efficacy will moderate the relationship between technostress creators and burnout so that when facing technostress creators, employees with higher e-work self-efficacy will be less likely to experience burnout symptoms and consequently depressive mood and anxiety symptoms than those with lower e-work self-efficacy.Moreover, to ascertain the distinctive contribution of e-work self-efficacy as a protective individual resource for remote workers, we are also interested in comparing its moderating effect with that played by another personal resource, namely resilience. Resilience represents the capacity to bounce back in the face of adversities, failures, and crises to regain personal balance . ResilieThis study was conducted in Italy between June and October 2020 during the COVID-19 pandemic. The survey was carried out through the compilation of an anonymous self-report questionnaire using the Qualtrics platform. The link to invite participants to complete the survey was distributed through social networks  and personal contacts. All procedures performed in this study were in accordance with the ethical standards of the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Data storage met current Data Protection regulations. The questionnaire’s cover sheet informed the participants about the study’s goal and ensured both the voluntariness of their participation and the confidentiality of the responses. The inclusion criteria were as follows: to be at least 18 years of age, to be working remotely, and to provide an explanation of the research goals and an informed consent form. Participation in the study was voluntary, and the team guaranteed complete anonymity to all respondents. No personal data were collected for the research. The resulting sample was composed of 349 remote workers. We excluded 124 subjects because of incomplete responses . Thus, the final sample consisted of 225 remote workers from several organizations and sectors. Most of them were women (60%), aged between 26 and 35 years (35.6%), followed by workers 46–55 years old (20%), 18–25 years old (16.9%), 36–45 years old (16.9%), and over 55 years old (10.7). Regarding the job, 32.9% of the respondents were office workers, followed by highly specialized professionals (28.4%). More than half of them had a permanent contract (56.4%). Most of the respondents had a university degree (48.9%), followed by a post-graduate degree (23.1%) and a high school diploma (27.6%). Most of the participants did not have any children (61.3%), followed by those who had two children (19.1%), one child (13.3%), three children (5.3%), and more than three children (0.9%).Techno-stressors were adapted by the Technostress Creators scale , which mBurnout was assessed using the Italian version of the short version of the Burnout Assessment Tool ,103,104.Depressive mood and anxiety symptoms were measured using the short version of the Depression Anxiety Stress Scales (DASS-21) . ParticiE-work self-efficacy was measured by adapting five items from an e-work self-efficacy scale  for the Resilience was assessed by adapting four items taken from the resiliency scale of the Psychological Capital Questionnaire , specifiUsing IBM SPSS Statistics 25, we first examined the psychometric properties of the study scales. We considered values of the variance inflation factor and tolerance below the recommended threshold of 10 as indicators of no sign of multicollinearity. The normality of the data was considered acceptable when skewness < |3.0| and kurtosis < |8.0| . To test2 statistic to the respective degrees of freedom  with the maximum likelihood (ML) method to compare our expected measurement model with a series of alternative models in Mplus Version 8. In line with the technostress literature, e.g., ,99, we tple data , the roople data ; the staple data ; the comple data ; and theple data . After eple data  to assesple data .p-value was less than or equal to 0.05. Lower AIC and BIC values indicated a better fit, so that the best-fitting model was that with the lowest AIC and BIC values. Finally, to plot the statistically significant interaction effects, we used specific Excel worksheets that allowed us to calculate these effects , SRMR = 0.08; see p < 0.001, 95% CI [0.34, 0.61) which, in turn, was positively related to depressive mood  and to anxiety symptoms , confirming Hypotheses 1, 2a and 2b. Techno-stressors were positively and directly related to anxiety symptoms  but not to depressive mood . Then, burnout totally mediated the relationship between techno-stressors and depressive mood , while partially mediated the relationship between techno-stressors and anxiety symptoms . The mediating effects remained statistically significant independently of the exclusion of covariates. Note that the reverse mediation model yielded a poor fit, and burnout did not mediate the reversed relationships from depressive mood and anxiety symptoms to techno-stressors, supporting the directionality of our hypothesized associations  and depressive mood , while being female was positively associated with depressive mood  and anxiety symptoms .In our expected mediation model , suggesting that e-work self-efficacy protected against the negative effects of techno-stressors in terms of burnout and then both depressive mood and anxiety symptoms. When experiencing techno-stressors, remote workers who scored low  and moderate  on e-work self-efficacy were more likely to experience burnout and then depressive mood. Conversely, workers with high e-work self-efficacy levels did not suffer from the negative effects of techno-stressors in terms of depressive mood, as indicated by the non-significant indirect effect . Likewise, when exposed to techno-stressors, remote workers who scored low  and moderate  on e-work self-efficacy were more likely to develop burnout and then anxiety symptoms. The negative effects of techno-stressors in terms of anxiety symptoms were drastically lower for those who had high e-work self-efficacy levels . An examination of the interaction plot , while being female was positively associated with depressive mood  and anxiety symptoms .The results of the moderated mediation model see  indicateplot see  revealedThe present study aimed to investigate the effects of techno-stressors on burnout and psychological health outcomes, along with the protective role of e-work self-efficacy, in a sample of remote workers. Our findings provided support for our hypotheses. Specifically, our results showed that techno-stressors were positively related to burnout and, in turn, to depressive mood and anxiety symptoms. As such, remote workers who experienced higher levels of techno-stressors were more likely to have higher levels of burnout and to present poorer psychological health. These results confirm prior research on this topic ,47,123, From a practical perspective, our results offer relevant insights into how to promote remote workers’ psychological health. Specifically, we found that if workers have high levels of e-work self-efficacy, they will be less susceptible to experiencing negative psychological outcomes. Therefore, organizations could invest in strengthening work self-efficacy beliefs through targeted interventions, following Bandura’s recommendations and strategies . In thisThe present study has some limitations that should be addressed. First, the self-rated and cross-sectional nature of our data prevented us from making causal inferences about the hypothesized relationships. However, following methodologists’ recommendations , we usedThe present study highlighted the process by which techno-stressors impact remote workers’ psychological health during the COVID-19 pandemic. Through a moderated mediation model, we found that burnout is a key variable in totally mediating the effects of techno-stress creators on remote workers’ depressed mood and partially mediating the effects of techno-stressors on their anxiety symptoms. Furthermore, we found that e-work self-efficacy acts as a buffer in this relationship, suggesting the importance of this personal resource in managing remote work effectively. These results provide meaningful insights into the promotion of organizational interventions aimed at empowering personal resources and protecting remote workers’ well-being.


R = 0.214, P = .006; R = 0.268, P < .001; R = 0.182, P = .019;R = 0.197, P = .011; R = 0.211, P = .006; R = 0.159, P < .041). Clinical stage, triglycerides (TG) in endocrine therapy progression-free-survival (ePFS) and low-density lipoprotein cholesterol (LDL-C) in ePFS were independent predictors of endocrine resistance . Moreover, in clinical stage III, the ePFS was worse in patients with in the high-risk and extremely high-risk group the median ePFS time was 8.0 months . Clinical stage, TG in ePFS and LDL-C in ePFS may act as a new predictive biomarker for endocrine resistance in BC. The lipid levels of BC patients should be closely monitored throughout the treatment process, and patients with dyslipidemia should receive treatment immediately.Lipid metabolism may be involved in the development of endocrine drug resistance in ER-positive (ER+) breast cancer (BC). This study aimed to investigate the relationship between serum lipid levels, risk stratification of dyslipidemia, and endocrine resistance. We collected the data from 166 ER + breast cancer patients who received endocrine therapy (ET). 73 of 166 patients (44.0%)developed endocrine resistance. Univariate and multivariate COX regression were conducted to explore the potential factors affecting endocrine resistance in BC. The clinical T stage, mean serum lipid levels in ET progression-free-survival  were correlated with endocrine resistance ( ER-positive (ER+) breast cancer has the highest incidence rate and approximately 80% of all cases are present. Endocrine therapy (ET) is one of the standard treatments for estrogen receptor-positive BC and is mainly divided into selective estrogen receptor modulators and aromatase inhibitors. While this form of breast cancer is often successfully targeted with endocrine therapies against ER + receptors, roughly 20% of cases are initially resistant to ET, with another 30 to 40% of cases developing resistance over time.In the world, breast cancer (BC) is the most common cancer and the leading cause of death among women.,6 Lipid metabolic reprogramming is a significant characteristic of numerous malignant tumors. This process encompasses several aspects, including the dynamic equilibrium between the augmentation of de novo fatty acid synthesis and fatty acid oxidation, alterations in the lipid composition of biofilm to facilitate proliferation, and modifications in lipid signaling molecules that promote tumor progression. Furthermore, the up-regulation of lipid metabolism-related proteins in tumors can stimulate the emergence of malignant phenotypes. Previous studies have reported the correlation between lipids and breast cancer prognosis. Total cholesterol is an independent predictor of breast cancer risk. Good control of serum lipid metabolism may improve the tumor immune microenvironment and prognosis in postmenopausal Hormone receptor-positive/HER2-negative breast cancer patients. The cholesterol metabolite 27-hydroxycholesterol was found to induce the proliferation of estrogen receptor-positive breast cancer cells and facilitate metastasis.,14 In BC endocrine therapy, Tamoxifen reduced total cholesterol (TC) and low-density lipoprotein cholesterol (LDL-C) levels. Tamoxifen binds with high affinity to the microsomal antiestrogen binding site and inhibits cholesterol esterification at therapeutic doses. There have also been some reports of lipid metabolism involved in ET, lipid metabolism is involved in tamoxifen resistance and cholesterol metabolism plays a role in tamoxifen resistance.,17 Basic research on the relation between lipid reprogramming and endocrine resistance in BC is currently being studied by many researchers. But there no relevant clinical study has reported the relation of lipid reprogramming and endocrine resistance in BC. Most of these previous studies focus on the serum lipid level at a certain cutoff time, while ignoring the impact of the full-course lipid level on the prognosis of endocrine drug resistance in breast cancer.The mechanism of endocrine resistance is still under discussion, and it can be linked to lipid reprogramming in BC.Hence, our study aims to explore the relationship between lipid levels and endocrine resistance in ER + breast cancer patients and to provide a basis for clinical judgment on whether early intervention of lipid levels is needed to reduce the occurrence of endocrine resistance and improve the quality of life of patients.In this retrospective study, we recruited 166 women who received ET at Fujian Provincial Hospital with histologically proven ER + breast cancer from January 2014 to July 2022. These patients met the following inclusion criteria: regular follow-up without loss of follow-up or death; invasive ductal carcinoma of the breast confirmed by pathology, estrogen receptor-positive in immunohistochemistry; patients receiving regular adjuvant ET; head, chest, and abdomen computed tomography, magnetic resonance imaging, whole body bone scan, or whole body PET-CT is one of the tests to determine the disease No distant metastasis occurred; and regular reexamination and fasting blood lipid test in the morning, with relatively complete biochemical test results. The exclusion criteria were: patients with poor treatment compliance; patients with a history of other malignant tumors; and patients without standard treatment. The TNM staging method was referred to by the American Joint Committee on Cancer 8th edition TNM staging system for breast cancer. The study is approved by the Institutional Research Ethics Committee of Fujian Provincial Hospital . Endocrine therapy progression-free-survival (ePFS) is defined as the time from the start of ET to the onset of endocrine resistance, and ePFS is measured in months. Mean serum lipid levels in ePFS are defined as the mean of serum lipids from the onset of endocrine medication until the onset of endocrine resistance, and the serum lipid index is averaged 5 or more times per sample. The automatic biochemical analyzer in our hospital was used to measure the concentration of TC, triglyceride (TG), LDL-C, high-density lipoprotein cholesterol (HDL-C), apolipoprotein A (Apo A), apolipoprotein B (Apo B), and Apo B/Apo A in the serum of the patients. The criteria for determining hypercholesterolemia were referred from those prescribed by the Joint Committee for the Development of Chinese Adult Dyslipidemia Prevention and Control Guidelines, as follows: TC > 6.1 mmol/L, LDL-C > 3.12 mmol/L any indicator exceeds the upper limit; lipid ratio AI = (TC-HDL-C)/HDLC, THR = TG/HDL-C, LHR = LDL-C/HDL-C. According to Chinese expert consensus on lipid management in patients with malignancy (2021 edition), considering the influence of 10-year risk factors of Atherosclerotic Cardiovascular Disease events, patients undergoing early treatment will continue to be divided into low-risk, medium-risk, high-risk, and extremely high-risk. Considering that the interventions of low-risk and medium-risk were the same, and the interventions of high-risk and extremely high-risk were consistent, we divided them into 2 groups according to the intervention: low-risk and medium-risk, high-risk and extremely high-risk.The data on the clinicopathological features were collected, which included the age, height, weight, menstrual status, T stage, N stage, M stage, Ki67, imaging data, the baseline serum lipid level, and the serum lipid levels in ET progression-free-survival. Among these, the cutoff value for Ki-67 is 30%, with greater than or equal to the cutoff value indicating strong expression and less than the cutoff value indicating low expression. According to the 5th ESO-ESMO international consensus guidelines for advanced breast cancer (ABC 5), endocrine resistance is described as either primary or secondary endocrine resistant breast cancer. Primary endocrine resistance is defined as relapse while on the first 2 years of adjuvant ET, or disease progression within the first 6 months of first-line ET for advanced breast cancer (ABC), while on ET. Secondary endocrine resistance is defined as relapse while on adjuvant ET but after the first 2 years, or relapse within 12 months of completing adjuvant ET, or disease progression ≥6 months after initiating ET for ABC, while on ET. According to the definition, 73 of 166 patients (44.0%) developed endocrine resistance.The criteria for recurrence and metastasis were based on new response evaluation criteria in solid tumors: revised RECIST guideline (version 1.1).U test was conducted for all continuous variables and the χ2-test or Fisher exact test for categorical variables, as appropriate, to identify candidate covariates. The relationship between endocrine resistance and the factors with a P value <.05 in univariate analysis were analyzed by the Spearman correlation test, the point-biserial correlation, the Mantel-Haenszel chi-squared test. Then, the independent risk factors influencing prognosis were analyzed with the occurrence of endocrine resistance as the dependent variable and the factors with a P value <.15 in the univariate analysis as the independent variable by multivariate COX regression. Factors with P < .05 in the COX regression analysis were considered independent factors. The survival curve showed that the risk grade of dyslipidemia affected the drug-resistant survival of ER+ breast cancer patients under different cancer stages. (P < .05 was considered a significant difference).SPSS 25.0 was used for statistical analyses. Firstly, continuous variables were presented as mean ± standard deviation or median with interquartile range, as appropriate. Categorical variables were presented as proportions. The 2-tailed t-test or Mann–Whitney R = 0.214, P < .05, Tables A total of 166 patients with ER + breast cancer without distant metastasis were enrolled in this study. Among them, 73 patients (44.0%) developed endocrine resistance. The comparison of clinicopathological characteristics between the 2 groups showed that with the increase of T stage, the patients were more prone to endocrine resistance . Details are shown in Tables There were no significant differences in baseline serum lipids between the nonresistant group and resistant groups. Endocrine resistance is also the lack of association with risk stratification of dyslipidemia and hypercholesteremia. But in the comparison of mean serum lipid levels in ePFS, TG, TC, LDL, Apo A, and THR levels are positively associated with endocrine resistance  and mean serum lipid levels in ePFS  were candidate predictors of therapeutic effect. The COX regression analysis suggested that the clinical stage and mean serum lipid levels in ePFS  could serve as the independent predictor of therapeutic effect (P < .05). Details are shown in Table The independent risk factors influencing prognosis were analyzed with the occurrence of endocrine resistance as the dependent variable and the factors with a http://links.lww.com/MD/J877.Patients were assigned to 2 groups according to the dyslipidemia risk assessment criteria: low-risk and medium-risk groups (n = 142), high-risk and extremely high-risk groups (n = 24). The proportion of deletion in low-risk and medium-risk groups, high-risk and extremely high-risk groups were 54.9% and 62.5%, respectively. See Supplemental Digital Content (Table S1), The Kaplan–Meier survival curve showed that the risk stratification of dyslipidemia had no significant effect on the ePFS Fig. A. To fur The mechanism of endocrine resistance is still under discussion, and it may be related to lipid reprogramming in breast cancer.ET is the primary treatment modality in patients with ER + breast cancer. However, a large proportion of patients develop drug resistance during ET, which is also the main cause of disease recurrence and metastasis, and ultimately death.,23 And activation of cholesterol and fatty acid metabolism is considered as drivers of endocrine resistance. However, in BC endocrine therapy, Tamoxifen reduced TC and LDL-C levels. Tamoxifen binds to microsomal antiestrogen binding sites with high affinity and inhibits cholesterol esterification at therapeutic doses. On the basis that ET is beneficial to controlling serum lipid levels, but lipid-related components are the factors driving endocrine resistance, we wanted to explore whether serum lipids have good predictive indicators for early screening of endocrine resistance. Therefore, we collected clinical pathology data of patients receiving ET and conducted a series of analyses on the relationship between endocrine resistance and clinical pathology characteristics, the relationship between serum lipid indicators and endocrine resistance, the influence of dyslipidemia risk grade on endocrine resistance, and the relationship between blood lipid interventions and endocrine resistance.Previous clinical studies have reported that dyslipidemia is a high risk factor for the poor prognosis of BC. and also reported that baseline dyslipidemia is a good prognostic factor for breast cancer.,25 It is unclear whether there is a correlation between the above 2 findings. In conclusion, current studies on the role of serum lipids in breast cancer prognosis are still controversial. Our results showed no significant association between baseline serum lipid levels and endocrine resistance in breast cancer patients, but a significant association between mean serum lipid levels in ePFS and endocrine resistance. Our study further divided the serum lipid levels into baseline serum lipid levels and mean serum lipid levels in ePFS. Unlike previous research on breast cancer prognosis, there may be differences in the results with baseline serum lipid levels, possibly due to the unstable nature of lipid levels at a single time point. Mean serum lipid levels in ePFS can better represent the overall lipid levels during the entire treatment period compared to a single time point. Additionally, we found a significant correlation between average blood lipid levels during ET resistance and endocrine resistance, providing further support for fatty acid metabolism is drivers of endocrine resistance.Previous clinical studies have reported that dyslipidemia is a high-risk factor for poor prognosis in BC, considering the influence of ASCVD event 1 0 years of risk factors and intervention measures, patients continue to be divided into low-risk and medium-risk groups and high-risk and extremely high-risk group, to explore the correlation with the ePFS of the patient, the results showed no significant difference .Subsequently, according to Chinese expert consensus on lipid management in patients with malignancy 2021 edition), edition,,27 but the mechanisms by which statins improve cancer prognosis are unclear. We collected data on statin samples (including irregular doses), the study results did not show an association with endocrine resistance, and the lack of statistical difference here may be due to the small sample size.Some studies have reported that statins can improve prognosis in breast cancer,The strength of this study is that we collected and analyzed the whole-process blood lipid data. However, this study has some limitations, such as the retrospective study design. The short follow-up period may affect the results of the prognosis analysis. In addition, our data were collected from a single center.According to this study, clinical stage, TG in ePFS and LDL-C in ePFS act as new predictive biomarkers for endocrine resistance in breast cancer. The lipid levels of breast cancer patients should be closely monitored throughout the treatment process, and patients with dyslipidemia should receive treatment immediately. To demonstrate these possible mechanisms, more research is needed.We would like to thank the patients who participated in this study.Conceptualization: Hong Sun.Data curation: Congting Hu.Formal analysis: Congting Hu, Xiaohan Zheng.Funding acquisition: Hong Sun, Jiaqin Cai, Jie Zhuang, Xiaoxia Wei.Investigation: Hong Sun, Congting Hu.Methodology: Hong Sun.Writing – original draft: Congting Hu, Xiaohan Zheng.Writing – review & editing: Jie Zhuang, Xiaoxia Wei, Jiaqin Cai.


Advanced melanoma is an aggressive form of skin cancer characterized by low survival rates. Less than 50% of advanced melanoma patients respond to current therapies, and of those patients that do respond, many present with tumor recurrence due to resistance. The immunosuppressive tumor-immune microenvironment (TIME) remains a major obstacle in melanoma therapy. Adjuvant treatment modalities that enhance anti-tumor immune cell function are associated with improved patient response. One potential mechanism to stimulate the anti-tumor immune response is by inducing immunogenic cell death (ICD) in tumors. ICD leads to the release of damage-associated molecular patterns within the TIME, subsequently promoting antigen presentation and anti-tumor immunity. This review summarizes relevant concepts and mechanisms underlying ICD and introduces the potential of non-ablative low-intensity focused ultrasound (LOFU) as an immune-priming therapy that can be combined with ICD-inducing focal ablative therapies to promote an anti-melanoma immune response. Melanoma is a rare and aggressive form of skin cancer that is responsible for 75% of skin cancer-related deaths ,2. When CI treatment efficacy has been shown to correlate with the number of tumor-infiltrating lymphocytes, indicating that an endogenous anti-tumor T-cell immunity is associated with increased response . HoweverBiomarkers are a potential tool to guide personalized treatment plans for melanoma patients. The programmed death-ligand 1 (PD-L1), expressed on tumor cells, binds to T-cell surface PD-1, thereby attenuating the anti-tumor T-cell response. Although PD-L1 expression, measured by immunohistochemistry, has been approved by the Food and Drug Administration (FDA) as a biomarker to predict responsiveness to immunotherapy in certain cancers , the utility of this biomarker is still under investigation ,26. HoweA major challenge in cancer immunotherapy is the immunosuppressive tumor-immune microenvironment (TIME). The generation of a robust anti-tumor response relies on dendritic cells (DCs), professional antigen-presenting cells which must first recognize tumor antigens and damage-associated molecular patterns (DAMP) within the TIME via pattern-recognition receptors, and then process the antigens and migrate to the draining lymph node, where they present tumor antigen peptides to both CD4+ and CD8+ T-cells . HoweverFocal therapies that induce ICD in tumor cells have the potential to overcome immunosuppression in the TIME and to improve patient responses to current immunotherapies . Tumor-cBioRender.com).ICD, immunogenic cell death; ER, endoplasmic reticulum; UPR, unfolded protein response; PERK, protein kinase RNA-like ER kinase; ATF4, activating transcription factor 4; DAMP, damage-associated molecular pattern; HMGB1, high-mobility group box protein 1; ATP, adenosine triphosphate . HMGB1 is normally found within the nucleus, but as cells are damaged, it can translocate to the cytosol and be secreted into the extracellular space, where it acts as a DAMP that subsequently activates the immune response . HMGB1 dEnhancing ICD in melanoma patients may lead to a better patient prognosis. Ren et al. identified a group of three differentially expressed genes  in melanoma patients that are related to ICD and can be utilized to predict patient responses to immunotherapies . FurtherTo better identify potential targets that can be utilized to promote an anti-tumor immune response, it is important to first understand the mechanisms underlying melanoma cell growth and development. Melanoma stems from the pigment-producing cells of the skin, called melanocytes. One of the transcription factors that plays an important role in melanocyte development, proliferation, and survival is the microphthalmia-associated transcription factor (MITF) . High leAnother potential target to increase melanoma tumor immunogenicity is the transcriptional activator SOX10, which also regulates MITF ,63. SOX1In addition to histone deacetylase inhibitors, which have been shown to improve melanoma cell responsiveness to therapy, other epigenetic modulations have also been shown to promote immunogenicity within the TIME. For example, a dual inhibitor of histone and DNA methyltransferases was shown to induce apoptosis in B16-F10 melanoma and subsequently resulted in increased activation of CD4+ and CD8+ T-cells within the TIME and tumor growth delay . The chrICD is associated with the accumulation of misfolded and unfolded proteins in the ER, resulting in the induction of ER stress and the UPR. The protein kinase RNA-like ER kinase (PERK) arm of the UPR plays a role in ICD ,70. PERKOther types of cell death have also been associated with ICD and an increased susceptibility to CI therapy ,74. FerrICD can also be induced via innate immune system pathways. For example, retinoic acid-inducible gene I (RIG-I) is a cytosolic RNA sensor that, when stimulated, can induce ICD via the interferon-dependent apoptosis of melanoma cells ,80,81. RTLRs are an example of pattern-recognition receptors that recognize pathogen-associated molecular patterns and DAMPs. They also play a major role in initiating the innate immune response and, as a result, are an attractive target for increasing susceptibility to current CI therapies ,83,84. IThe direct targeting of molecules and mechanisms known to play a role in ICD  may enhaSome chemotherapeutics, including doxorubicin and paclitaxel, promote anti-tumor immunity by promoting ICD . DoxorubOther ICD-inducers under investigation for use in melanoma include chromomycins A5-8, antibiotics that induce tumor cell ICD, autophagy, apoptosis, and ecto-CRT ; and polHowever, one of the challenges with chemical inducers of ICD is the potential for severe side effects, including cardiotoxicity and neurotoxicity . TargeteOncolytic viruses are of interest due to their ability to induce and secrete DAMPs and tumor antigens, while reprogramming and immune-activating the stromal cells in the TIME . These vOther oncolytic viruses have also been evaluated as promising immunomodulatory tools to create a “hot” pro-immunogenic tumor microenvironment ,116,117.Carbon ion radiotherapy (CIRT), also called heavy ion therapy, is an emerging form of radiation therapy that uses carbon ion/nuclei beam radiation to primarily treat solid tumors. One of the advantages of CIRT is its increased precision in targeting the tumor site, thus mitigating off-target adverse effects due to the characteristic energy distribution of particles, known as the “Bragg Peak” . CIRT alPhototherapy relies on the application of a photosensitizer, typically a chemical compound or nanoparticle, that is excited by a light source to generate either local hyperthermia  or reactive oxygen species (photodynamic therapy) at a desired target location. These stressors subsequently affect cellular homeostasis and activate cellular stress responses and ICD . RecentlStudies to enhance photothermal therapy effects have also investigated other photosensitizing mechanisms. Optical droplet vaporization is a strategy that was previously used to improve ultrasound imaging, in which perfluorocarbon droplets are superheated and vaporized to a gas phase . This prPhotodynamic therapy of B16 melanoma leads to enhanced ecto-CRT and an increased release of DAMPs, including HMGB1 and IFN1 . Phototh2, has already been applied in clinical trials for various solid cancers. HIFU primarily acts by inducing thermal coagulative necrosis, which is associated with non-specific tissue destruction and inflammation, and the induction of cellular stress responses [Therapeutic focused ultrasounds (FUSs) can be used as a non-invasive adjuvant therapy to induce thermal and/or mechanical stress focally at the tumor site. A piezoelectric transducer is used to convert an electrical signal into ultrasonic waves, characterized by periods of compression and periods of rarefaction, measured by the peak positive pressure and peak negative pressure, respectively. High-intensity FUS (HIFU), where the delivered total energy intensity is typically greater than 1000 Watts/cmesponses ,135,136.esponses ; increasesponses ,139. In In addition to promoting immunogenicity at the tumor site, FUS has also been combined with nanobubbles to enhance drug delivery. The nanobubbles contain inert gas, which, upon application of FUS, begin to oscillate, expanding and collapsing, generating a phenomenon called the cavitation effect . The delFUS has also gained interest within the context of sonodynamic therapy, which refers to a combination therapy using FUS with an intra-tumoral injection of a sensitizing agent, such as a nickel ferrite/carbon nanocomposite . This le3O4 nanoparticles has been shown to induce necrotic ICD and has been used both in preclinical mouse studies and in preliminary clinical trials in patients with advanced metastatic melanoma [Magnetic hyperthermia is a form of nanovesicle-mediated, localized hyperthermia that induces tumor cell death. Magnetic hyperthermia delivered via Femelanoma ,148. In melanoma ,148. In Nanosecond pulsed electric fields (nsPEFs) are another potential adjuvant therapy that can be applied to ablate melanoma tumors. However, studies have shown a high variation in tumor response, likely due to differences in the electrical field parameters among research groups ,150,153.Partially ionized gas or plasma has been used for biological and medical applications for several decades. For example, a hot argon plasma coagulator is used to cauterize telangiectasia to stop rectal bleeding from radiation proctitis. In contrast, cold physical plasma generates a multitude of reactive oxygen and nitrogen species (ROS/RNS) that can cause mitochondrial and ER stress and ICD in tumor cells, such as melanoma. When chemotherapeutics such as doxorubicin, epirubicin, and oxaliplatin are combined with cold plasma-derived oxidants, they upregulate organic cationic transporter SLC22A16 that facilitates cytotoxic drug uptake, increases the secretion of ICD-associated DAMPs such as ATP, and enhances the tumoricidal effects of chemotherapeutics . Non-theMany of the above therapeutic modalities have been shown to promote the adaptive immune response by enhancing the pro-immunogenic ICD. This is especially important within the TIME, which is typically characterized by immunosuppressive cell phenotypes. For example, a major mechanism underlying T-cell tolerance in the TIME involves immature DCs, which have a low expression of co-stimulatory molecules and can lead to the establishment of T-cell anergy . AnergicWhile ICD-inducing, ablative therapies have been shown to promote tumor immunogenicity, they are still primarily used as focal therapies for the local ablation of tumors. The true test of their efficacy will be in their ability to reprogram the immunosuppressive features of the TIME and to sustain an immune-activating TIME that will facilitate tumor antigen presentation and the cross-priming of both CD4+ and CD8+ T cells. A sustained anti-tumoral immune response that can achieve control of systemic metastases after the induction of ICD in tumor cells by focal ablative therapies has not been commonly observed . To date2. Unlike HIFU, which is already being applied as a form of ablative cancer therapy for certain solid malignancies in the United States, LOFU is non-invasive and primarily induces a thermal and mechanical cellular stress response, activating the pre-apoptotic phase of ICD and increasing the expression of heat shock proteins [LOFUs are performed by applying a lower-intensity ultrasound to tissue, with a maximal intensity limit of 800 Watts/cmproteins ,158. Wheproteins . The mosproteins . All theproteins , thus suThe current gold standard therapies for melanoma are not sufficient to overcome the poor survival rates seen in advanced-stage melanoma patients. Considering the low response rate, the potential for tumor recurrence following therapy, and the immune-related adverse effects associated with CI therapies, there remains a need for adjuvant therapies to further improve patient survival and quality of life. CD4+ and CD8+ T-cells are considered the primary effectors of the anti-tumor response, but in the setting of melanoma, they often enter an anergic and exhausted state. An attractive approach to overcome the tolerant TIME is to promote an endogenous immune response by introducing DAMPs and activating pro-immunogenic signaling pathways. ICD has therefore been garnering more attention as a vital mechanism to promote the anti-tumor response. Therapies that induce ICD stressors directly at the tumor site have the potential to simultaneously enhance the endogenous anti-tumor immune response while mitigating off-target adverse effects. Of the discussed ICD-inducing treatment modalities, LOFU especially shows promise in its ability to reprogram the TIME and promote long-term, pro-immunogenic anti-tumor effects due to its potential to reverse T-cell anergy. Furthermore, LOFU can be combined as an immune-priming therapy prior to traditional, focal ablative therapies. Future research to better understand how these various therapies induce ICD is needed to continue enhancing tumor immunogenicity and to provide novel targets for future treatment strategies.


To our knowledge, no studies have examined the association of diet quality and plant-based diets (PBD) with inflammatory-related mortality in obesity. Therefore, this study aimed to determine the joint associations of Healthy Eating Index-2015 (HEI-2015), plant-based dietary index (PDI), healthy PDI (hPDI), unhealthy PDI (uPDI), pro-vegetarian dietary index (PVD), and systemic inflammation with all-cause, cardiovascular disease (CVD), and cancer mortality risks by obesity status.N = 27,915, cycle 1999–2010, 2015–2018) and longitudinal analysis . HEI-2015, PDI, hPDI, uPDI, and PVD were constructed based on the 24-h recall dietary interview. The grade of inflammation  was determined based on C-reactive protein (CRP) values and multivariable ordinal logistic regression was used to determine the association. Cox proportional hazard models were used to determine the joint associations of diet and inflammation with mortality.Participants from NHANES were included in cross-sectional , PDI , hPDI , and PVD  were associated with lower systemic inflammation. In contrast, uPDI was associated with higher systemic inflammation . Severe inflammation was associated with a 25% increase in all-cause mortality . No association was found between PDI, hPDI, uPDI, and PVD with mortality. The joint association, between HEI-2015, levels of systemic inflammation, and all-cause, CVD and cancer mortality, was not significant. However, a greater reduction in mortality risk with an increase in HEI-2015 scores was observed in individuals with low and moderate inflammation, especially those with obesity.In the fully adjusted model, HEI-2015 (ORHigher scores of HEI-2015 and increased intake of a healthy plant-based diet were associated with lower inflammation, while an unhealthy plant-based diet was associated with higher inflammation. A greater adherence to the 2015 dietary guidelines may reduce the risk of mortality associated with inflammation and may also benefit individuals with obesity who had low and moderate inflammation.The online version contains supplementary material available at 10.1007/s00394-023-03191-z. More than 50% of global mortality in 2019 was attributed to chronic conditions, such as cardiovascular diseases (CVD), diabetes, and certain types of cancer . This beAdherence to a high diet quality is associated with lower inflammation and mortality risk , 11. In A global shift toward eating plant-based diets (PBD) has been increasingly promoted given the inverse association with CVD, cancer, type 2 diabetes, and other cardiometabolic risks –15, as wTo our knowledge, no studies have examined the joint association between diet quality, PBD, and inflammation with all-cause, CVD, and cancer mortality risks. Therefore, this study aimed to determine: (1) the association between HEI-2015, PDI, hPDI, uPDI, and PVD with C-reactive protein (CRP) and mortality risks; and (2) the joint associations between these indices and systemic inflammation with all-cause, CVD and cancer mortality risks. We hypothesized that adherence to a high diet quality or increased consumption of a PBD is associated with reduced inflammation, and mitigation of inflammatory-associated mortality risks in obesity.This study is reported based on STROBE-nut guidelines (Supplementary Table 1).  in the UFig. . A totalAssessment of dietary intake data in NHANES has been described previously . In brieThe overall diet quality of the population was assessed using the HEI-2015 score which reflects adherence to the 2015–2020 DGA , 26. TheWe constructed PVD using a method described previously . In briePDI, hPDI, and uPDI were constructed following a previously described method . In brieFood groups were divided into deciles and each decile was given a score 1–10. For PDI, a score of 10 was given to participants for each plant food group, if they were above the highest decile of consumption, a score of 9 if they were above the second highest decile but below the highest decile, and so on. A score of 1 was allocated for consumption below the lowest decile. For each animal food group, reverse scores were given to the participants. For hPDI, positive scores were allocated to healthy plant food groups while reverse scores were given to less healthy plant and animal food groups. Conversely, positive scores were given to less healthy plant food groups, and reverse scores to healthy plant and animal food groups, for uPDI. Scores for the 18 food groups were summed for each participant to obtain the indices. The possible score range was 18 to 180.The CRP (high sensitivity) values were examined using an immunoturbidimetric system from the collected blood samples. The detailed method for each cycle has been described previously . For theAscertainment of mortality for the included participants were obtained from National Center for Health Statistics Public-Use Linked Mortality Files with a probabilistic matching algorithm to the National Death Index . We cons. Those covariates include sociodemographic factors , behavioral factors , chronic conditions , and body mass index (BMI). Measurement of the covariates has been described in the NHANES survey methods and analytics guidelines [We selected covariates based on previous literature and summarized in a directed acyclic diagram idelines  and NHANidelines . A summaA complete case analysis was used. HEI-2015, PDI, hPDI, uPDI, and PVD were categorized into tertiles based on participant intake scores. Categorization was undertaken to facilitate interpretation of association estimates in relevance to clinical settings and public health practice and to allow comparison with other studies.Descriptive analysis of covariates was performed across tertiles of dietary indices. For continuous and normally distributed variables, mean, median and standard deviation were calculated. For categorical variables, proportions were used. Significant differences across dietary indices tertiles were determined using ANOVA and Chi-square tests. Multivariable logistic regression was used to determine the odds ratio of the association between dietary measures and the grade of inflammation. Cox proportional hazard models were used to examine the association between the dietary indices and all-cause, CVD and cancer mortality.Four models were developed for the cross-sectional and longitudinal analysis. Model one was adjusted for sociodemographic factors. Model two was additionally adjusted for behavioral factors. Model three was additionally adjusted for chronic conditions. Model four was additionally adjusted for BMI and total energy intake (PBD and PVD models only). The p value for trend was determined using tertiles as a continuous variable.We developed additional Cox proportional hazard models, with exposure variables that combine dietary measure tertiles and inflammation levels to determine the joint association of HEI-2015, systemic inflammation and mortality. We derived a joint variable that combined the HEI-2015 score and inflammation levels. The participants in the highest tertile of HEI-2015, the low inflammation group, served as the reference.2 defined as obesity and BMI < 30 kg/m2 defined as non-obesity [Subgroup analysis based on obesity status was performed in both cross-sectional analysis on the association between HEI-2015, PDI, hPDI, uPDI, and PVD with systemic inflammation, and the joint association between HEI-2015, systemic inflammation, and all-cause mortality. Obesity status was determined based on BMI following the World Health Organization definition, with BMI ≥ 30 km/m-obesity . Stratifggplot2 [The statistical analyses were performed with STATA/SE version 17 . Data visualization was performed using RStudio version 2022.02.3  and R paggplot2 .2. HEI-2015 scores of the participants ranged between 8 and 100 with a mean score of 50.4, suggesting a poor diet quality [Characteristics of participants for the cross-sectional analysis are presented in Table  quality . The mea (details for the other models are provided in Supplementary Table 6–8). HEI-2015 , PDI , hPDI , and PVD  were associated with lower systemic inflammation in the fully adjusted model, in all participants. In contrast, uPDI was associated with higher systemic inflammation . The association between all dietary indices and systemic inflammation remained strong in participants with or without obesity with no marked changes in the effect size, except for uPDI in participants without obesity .The odds ratio of the association between dietary indices and inflammation in the fully adjusted model is presented in Fig. 2. In the fully adjusted model, the highest tertile of CRP was associated with all-cause mortality risk in all participants . The highest tertile of HEI-2015 was also associated with reduced all-cause mortality risk in all participants. Association between severe inflammation and all-cause mortality was stronger in people with obesity compared to those without obesity. HEI-2015 was associated with reduced CVD and cancer mortality. No association was observed between CRP with CVD or cancer-associated mortality risk. We also explored the association between PBD indices and PVD with mortality; however, no association was found in the fully adjusted model for all participants (Supplementary Table 9). The joint associations between HEI-2015 and the grade of inflammation with all-cause, CVD, and cancer mortality are presented in Fig. T3vsT1 = 1.45, 95% CI 1.02–2.05) of the fully adjusted model. A greater reduction of mortality risk with an increase of HEI-2015 scores (tertile 1 to tertile 2) was observed in individuals with low and moderate inflammation. When comparing people with and without obesity, adherence to higher scores of HEI-2015 was not associated with decreased all-cause mortality in participants with obesity who had severe inflammation.The association between HEI-2015, CRP, and mortality risks is presented in Table 2This study is the first, to our knowledge, to examine the joint association of diet quality and inflammation with all-cause, CVD, and cancer mortality, in healthy and people with obesity. We found that higher adherence to HEI-2015, overall/healthy PDI, or PVD was associated with lower inflammation. In the longitudinal analysis, higher HEI-2015 scores were associated with a reduced CVD mortality risk. A greater adherence to the dietary guidelines may mitigate inflammation-associated all-cause, CVD, and cancer mortality risks, particularly for participants with moderate levels of inflammation.Our results relating to diet quality and inflammation are consistent with previous cross-sectional studies using HEI-2015. The Mitchelstown cohort of the Cork and Kerry Diabetes and Health Diseases Study in Ireland reported that levels of inflammatory biomarkers  were lower in individuals with higher HEI-2015 scores. A studyOur study is one among only a few that have explored the association between PBD and inflammation. We found that adherence to a healthy PDI was associated with a 15–21% reduction in hs-CRP. This is in accordance with the Nurse’s Health Study (NHS), where healthy PDIs were associated with a reduction in hs-CRP levels in cross-sectional (13.6%) and longitudinal (17.8%) analysis . In addiWe also found that the PVD was associated with a 12–15% reduction in hs-CRP, consistent with the results of the overall PDI. PVD has been associated with a lower risk of metabolic syndromes , 43 and When comparing between HEI-2015, overall PDI, and PVD in the association with hs-CRP, we found the association was stronger for HEI-2015, followed by the overall PDI and PVD. This suggests that HEI-2015 could be a better predictor of inflammation. HEI-2015 is constructed based on a combination of food groups and nutrient components . This isFurthermore, subgroup analysis by obesity status revealed a greater reduction of hs-CRP in individuals with obesity compared to ones without obesity who adhere to high diet quality and overall PBD . Altogether, our cross-sectional results suggest that higher diet quality and higher intake of healthy PBD were associated with lower systemic inflammation with a greater effect in participants with obesity.We showed that HEI-2015 and hs-CRP are independent predictors of mortality. Adherence to HEI-2015 was associated with a reduction in CVD mortality. Likewise, HEI-2015 was associated with all-cause and cancer mortality. These findings are in line with several cohort studies in the general US population , 47 and We found no association between higher intake and the quality of PBDs with all-cause mortality. Although this is surprising, given PBDs have been associated with lower risks of CVD, cancer, and type 2 diabetes –51. PrevThe joint association suggests that a greater adherence to the dietary guidelines may reduce all-cause, CVD, and cancer mortality risk in all grades of inflammation. This protective effect is biologically plausible since a healthier diet, or high diet quality, is characterized by intake of vegetables, fruits, nuts, and legumes, which compose the healthy plant-based foods. These food groups contain anti-inflammatory and antioxidants components  which reduce inflammation –7. ConveWe also observed a weak or null association in the joint associations. This may be due to the scoring of protein intake in the index. In the last two versions, HEI-2010  and HEI-The study strengths include the large number of participants, representative of the general US adult population, with a mean of 11.2-year follow-up. The study is one among the few that has used the general adult population data to examine the association between diet quality, PBDs, inflammation, and mortality in prospective cohort design , 63, 64.There are some limitations which should be considered. Given dietary intake data were only obtained at baseline with repetitive assessment unavailable with the NHANES data, it is possible that participants changed their diets during the follow-up period which may impact on the associations. The percentage of missing data that were excluded in this study was quite high . This might impact on the strength of association. The use of 24-h dietary recall may not fully reflect habitual dietary intake of the participants since it is unable to account day-to-day variation. Furthermore, the grade of inflammation was assessed solely based on hs-CRP levels. Nonetheless, CRP is widely used as a clinical biomarker of inflammation , is a prWe found that higher scores for HEI-2015 and healthy PDI were associated with lower inflammation, while an unhealthy plant-based diet was associated with higher inflammation. A greater adherence to the dietary guidelines may reduce all-cause, CVD, and cancer mortality risk in all grades of inflammation. Adherence to the dietary guidelines may also benefit individuals with obesity where the inflammatory status falls into the low and moderate range. Our findings support dietary guidelines, recommendations, and health promotion activities that highlight adherence to high diet quality, increased intake of healthy PBDs, and reduced consumption of animal-sourced diets for better health. Importantly, they suggest the need for improving dietary guidelines or recommendations that are more focused on addressing inflammation by (1) emphasizing plant-based diet quality, (2) considering pro/anti-inflammatory nutrient components, and 3) tailoring the recommendations for population subgroups based on obesity and inflammatory status. Further studies that include different inflammatory biomarkers, repeated assessment of dietary intake data, and different populations are also warranted to further examine the benefit of high diet quality and plant-based diet consumption in reducing inflammation and mortality.YBW is supported by an Australia Awards Scholarship from The Department of Foreign Affairs and Trade, The Government of Australia. YAM is supported by a National Health and Medical Research Council of Australia (NHMRC) an Investigator Grant (2009776).Supplementary file1 (DOCX 5580 KB)Below is the link to the electronic supplementary material.


In the field of machine intelligence and ubiquitous computing, there has been a growing interest in human activity recognition using wearable sensors. Over the past few decades, researchers have extensively explored learning-based methods to develop effective models for identifying human behaviors. Deep learning algorithms, known for their powerful feature extraction capabilities, have played a prominent role in this area. These algorithms can conveniently extract features that enable excellent recognition performance. However, many successful deep learning approaches have been built upon complex models with multiple hyperparameters. This paper examines the current research on human activity recognition using deep learning techniques and discusses appropriate recognition strategies. Initially, we employed multiple convolutional neural networks to determine an effective architecture for human activity recognition. Subsequently, we developed a hybrid convolutional neural network that incorporates a channel attention mechanism. This mechanism enables the network to capture deep spatio-temporal characteristics in a hierarchical manner and distinguish between different human movements in everyday life. Our investigations, using the UCI-HAR, WISDM, and IM-WSHA datasets, demonstrated that our proposed model, which includes cross-channel multi-size convolution transformations, outperformed previous deep learning architectures with accuracy rates of 98.92%, 98.80%, and 98.45% respectively. These results indicate that the suggested model surpasses state-of-the-art approaches in terms of overall accuracy, as supported by the research findings. These sensors are commonly embedded in smart devices like smartphones and smartwatches. Given the ubiquity and indispensability of such devices in our daily lives, sensor-based approaches offer an immediate solution for HAR research3. The adoption of sensor-based HAR holds immense potential across numerous practical domains. For instance, it can be employed to develop advanced movement tracking systems in healthcare settings4, benefiting elderly individuals and disabled persons. Furthermore, it can facilitate automatic interpretation of player actions in sports5, enabling more streamlined analysis. Additionally, sensor-based HAR enables user identification and verification in surveillance systems by analyzing gait characteristics6. Lastly, it contributes to human-robot interactions through gesture recognition7. Harnessing the power of sensor-based HAR can bring significant advantages to these diverse sectors.Advances in sensor technology have led to a surge of interest in recognizing human activities based on sensor data, owing to its wide-ranging applications in everyday life, such as medical care, movement analysis, intelligent monitoring systems, and smart homes. Human activity recognition (HAR) aims to study the details of human behavior in order to understand and predict specific actions. Behavior data can be obtained through various means, including accelerometers, infrared sensors, RFID, and video recordings. Currently, HAR can be categorized into two main classes: video-based and sensor-based HAR. Video-based HAR systems primarily rely on cameras to capture videos and images, utilizing computer vision technology to identify human actions and behaviors. While these techniques often yield satisfactory results, they are susceptible to environmental factors such as lighting conditions, occlusion, and privacy concerns. In contrast, sensor-based systems employ environmental or wearable sensors to determine human actions8. While conventional machine learning (ML) algorithms like Naive Bayes, decision trees, and support vector machines have been successful in categorizing various human behaviors9, manual feature extraction requires specialized knowledge or expertise, limiting its practicality. Consequently, the use of mathematical methods for learning fails to capture distinct characteristics that can effectively differentiate complex actions. Fortunately, the introduction of convolutional layers in deep learning (DL) models has revolutionized the field by automating the feature extraction process2. This breakthrough empowers HAR with the capabilities of DL techniques, opening new possibilities for advancement in the field.The utilization of wearable sensors in HAR has traditionally presented a complex challenge due to the classification of time-series data with multiple variables. A crucial aspect of overcoming this challenge lies in the extraction of relevant features, which can be achieved by employing mathematical methods in both the temporal and spectral domains11. The effectiveness of CNN in extracting features and achieving accuracy is influenced by the depth and width of the network. A typical CNN comprises convolutional layers and pooling layers, which play a critical role in extracting feature maps essential for categorization. However, not all feature maps contribute significantly to accurately identifying targeted actions. CNN excels in capturing spatial representation from sensor data, while Recurrent Neural Networks (RNN) excel in capturing temporal representation. Therefore, combining CNN and RNN allows for a comprehensive representation of spatial and temporal features from sensor input. In a previous work by Ordonez et al.12, both CNN and RNN were employed for HAR. To further enhance the effectiveness of HAR, it is recommended to prioritize valuable feature maps while suppressing unreliable ones. This is addressed by the squeeze-and-excitation (SE) block13, which acts as a channel-attention mechanism. The SE block recalibrates each feature map by assigning a weight proportional to its significance in the identification process. Zhongkai et al.2 report the implementation of the SE block in CNN and/or RNN models, resulting in an improved efficacy of HAR.The convolutional neural network (CNN) model is known for its local connectivity and weight distribution mechanisms, resulting in a reduced number of parameters and faster training. Consequently, numerous studies have been published on sensor-based HAR utilizing CNNWe developed a hybrid CNN embedded with a channel attention mechanism, called ResNet-BiGRU-SE, to extract deep spatio-temporal features hierarchically and distinguish human activities in daily living.Various CNN architectures have been employed as the underlying models for sensor-based HAR. To evaluate the performance of the ResNet-BiGRU-SE model, we compared its effectiveness with that of other CNN-based models on the HAR dataset. Additionally, we conducted a comparative analysis between our proposed approach and state-of-the-art models using three benchmark HAR datasets  for a fair assessment.The remaining sections of the study are arranged as follows: Section “The existing literature provides valuable inspiration for understanding how individual actions occur in spatial and temporal aspects. By leveraging this knowledge, we can analyze data from wearable sensors using abstract features to identify human behaviors. In this study, we propose a novel approach called ResNet-BiGRU-SE, which combines a hybrid CNN with a channel attention system, to recognize human activities based on sensor data. We conducted multiple experiments using different standard datasets for HAR to assess the effectiveness of our model. Our hybrid model surpasses previous DL models in terms of accuracy, as evidenced by its performance on evaluation metrics. Therefore, this study emphasizes the following key contributions: 15.HAR poses challenges as a time series classification problem, involving the prediction of an individual’s movements using sensory input. Typically, it necessitates extensive domain knowledge and signal processing techniques to extract appropriate features from raw data that align with a machine learning algorithm. DL methods, such as CNNs and Long Short-Term Memory Neural Networks (LSTMs), have demonstrated their effectiveness by automatically learning relevant features from raw sensory input, thereby achieving state-of-the-art performance17 as it has the potential to revolutionize how humans interact with technology in the present era. The objectives of HAR can be categorized into five main areas: identifying fundamental movements, detecting everyday motions, recognizing unique events, forecasting caloric expenditure, and performing individual biometric recognition18. To achieve these goals, a variety of sensors can be utilized, including environmental sensors and wearable video cameras. In practice, wearable sensors often take the form of smartphones or sensors integrated into wearable devices.HAR aims to collect and recognize real-world actions performed by individuals or groups while considering the surrounding environmental factors. This field holds significant promise in the study of Human-Computer Interaction19. An example of a camera-based indoor human motion tracking system is presented by Zhou et al.20, showcasing continuous video monitoring and advanced video processing capabilities. Another benefit of camera sensors is their ability to provide accurate data for human motion identification systems.While camera sensors can provide unique information not obtainable from other sensor types, they come with certain drawbacks. Camera-based systems require constant monitoring of individuals, resulting in the need for significant storage capacity and computational capabilities. Additionally, continuous surveillance by camera systems may lead to discomfort or unease among individuals21, wireless Bluetooth acceleration and gyroscope sensors were employed to capture situational components and demonstrate their usage. Furthermore, room-side wired microphone arrays were utilized to detect ambient sound, while Reed switches were placed on doors, drawers, and shelves to detect their operation and generate contextual information. However, it should be noted that environmental sensors are limited to specific conditions and architectural configurations, rendering the HAR system non-universal. A well-designed and trained HAR system cannot be directly applied to a different environmental setting. Additionally, the implementation cost associated with these sensors tends to be relatively high.Ambient sensors offer the ability to monitor and record an individual’s interactions with their environment. In the experimental context of Zhan et al.’s study22 introduced a wearable sensor network designed for HAR. Additionally, Curone et al.23 utilize a tri-axial accelerometer worn on the body for action recognition.Wearable technologies worn on the human body have the capability to recognize the physical aspects and characteristics of individuals’ everyday tasks. Inertial sensors such as accelerometers and gyroscopes, along with GPS and magnetic field sensors, are commonly used in applications for action identification. In specific studies, action identification is achieved by utilizing one or more accelerometers attached to various regions of the human body. Dong and Biawas24 conducted a comprehensive review of 56 publications from 2011 that utilized DL techniques, including deep neural networks, CNNs, RNNs, auto-encoders, and limited Boltzmann machines, for sensor-based HAR. They found that no single model outperforms all others in every scenario, emphasizing the importance of selecting a model based on the specific application requirements. Additionally, they compared three benchmark datasets for HAR: the Opportunity dataset25, the Skoda dataset26, and the UCI-HAR dataset27 . Among these datasets, they identified studies30 as representing the state-of-the-art in DL for HAR utilizing inertial measurement units (IMUs).Given the significant advancements made by DL across various ML applications, and considering the inherent multi-class nature of DL techniques, our systematic review begins with a concise overview of DL for human activity detection. Wang et al.Sophisticated HAR models benefit from complex and deeper structures, leading to improved accuracy compared to previous feature learning methods. These models utilize CNNs for automatic feature extraction. In the context of object identification, the CNN feature extractor is often referred to as the backbone. This term emphasizes that the architecture of the feature extractor and the overall model construction are evaluated separately and independently.31 introduced a combination of Hierarchical Cross-Filtering (HCF) and an inception module. Long et al.32 proposed a method of independently learning large-scale and small-scale networks and subsequently joining them. This approach incorporates two different sizes of residual blocks as crucial components. Tuncer et al.33 suggested utilizing a ResNet structure with multiple layers as feature extractors, with the extracted features cascaded to serve as the backbone. Ronald et al.34 presented the iSPLInception backbone, which is based on Inception-ResNet and utilizes a multichannel-residual hybrid architecture for HAR research. Mehmood et al.35 employed DenseNet as the backbone and leveraged dense connections for HAR purposes.Instead of relying on basic models, researchers have developed sophisticated backbone models to enhance performance. Dong et al.This research investigated sensor-based HARs using DL techniques to extract abstract characteristics from raw sensor data. As shown in Fig. This section highlights the HAR datasets utilized in the evaluation of this study. For assessment purposes, three public datasets were included: UCI-HAR, WISDM, and IM-WSHA. These datasets consist of inertial data collected from smartphone sensors, with each dataset capturing information from a group of individuals as they performed their daily activities. Table 27 as the public activity dataset for the proposed approach. The UCI-HAR dataset comprises action data collected from a diverse group of 30 individuals with varying ages (19 to 48 years), genders, heights, and weights. Participants wore a smartphone at their waist position while performing everyday activities. Each individual engaged in six different activities. The smartphone’s tri-axial accelerometer and gyroscope captured sensor data during the execution of these six predetermined tasks. Data on triaxial linear acceleration and angular velocity were collected at a consistent rate of 50 Hz.This paper utilizes the ”UCI Human Activity Recognition Using Smartphone Dataset (UCI-HAR)”36 serves as a fundamental HAR dataset derived from the Wireless Sensor Data Mining Laboratory. It consists of 1,098,207 samples and captures the action recognition patterns of 36 individuals, encompassing activities such as strolling and seating. The data was collected by participants who carried an Android smartphone in their front leg pocket, utilizing the device’s built-in accelerometer sensor at a sampling rate of 20 Hz.The WISDM dataset37 is a comprehensive collection of signal data specifically designed to serve as a standard dataset for HAR. This dataset features three wearable Inertial Measurement Unit (IMU) sensors that capture three-axis accelerometer, gyroscope, and magnetometer data. The sampling frequency of the dataset is 100 Hz. To accurately capture individuals’ movement patterns during their daily activities, the IMU sensors were strategically positioned on different body parts, namely the thorax, femur, and wrist. The study involved ten participants, with an equal distribution of males and females, who performed a total of eleven distinct physical tasks within an indoor environment. These tasks encompassed various common activities such as walking, exercising, cooking, drinking, talking on the phone, doing laundry, watching television, studying, brushing hair, using a laptop, and vacuum-cleaning.The IM-Wearable Smart Home Activities (IM-WSHA) dataset39. In our work, we employed a third-order low-pass Butterworth filter with a cutoff frequency of 20 Hz across all three dimensions of the accelerometer, gyroscope, and magnetometer sensors for effective signal denoising. This choice of filter parameters is suitable for recording human motion since the energy content below 15 Hz accounts for 99.9% of the signal, making it an appropriate resolution.The acquired raw data from sensors often contains measurement noise and additional unforeseen noise caused by the participant’s dynamic movements during data collection. The presence of noise in the signal distorts the usable information it carries. Therefore, it becomes crucial to reduce the influence of noise and extract valuable information from the signal for further processing. Common filtering techniques used to address this issue include mean, Low-pass, and Wavelet filteringOnce the noise was removed, the filtered sensor data underwent a transformation to prepare them for further analysis. In this phase, a Min-Max normalization approach was employed to adjust each dataset’s values within the range of . This normalization is advantageous for learning techniques aiming to assess the effects of various factors.During the data segmentation phase, the normalized data from all sensors is divided into equal-sized portions using fixed-size sliding windows. In this study, we chose a sliding window of 2.56 seconds, which resulted in sequences of sensory data with a specific length. These segmented portions are then used for model training.This research proposes an effective biometric recognition model called ResNet-BiGRU-SE for utilizing motion signal data captured from smartphone sensors. The proposed method automatically generates identifying characteristics based on the sensor data inputs. ResNet-BiGRU-SE consists of a convolutional block and eight hybrid residual blocks, which extract standard spatial features. The model also includes a global average-pooling (GAP) layer, a flattened layer, and a fully connected layer, as illustrated in Fig. 40. In CNNs, pooling is often used for dimension reduction. Both maximum and average pooling functions, referred to as max-pooling and average-pooling, are commonly utilized.CNNs typically employ a predefined set of elements and are commonly utilized for supervised learning. In these neural networks, each neuron is connected to every other neuron in the subsequent layers. The activation function of the neural network converts the input value of the neurons into their output value. The effectiveness of the activation function is influenced by two important factors: sparsity and the neural network’s ability to handle reduced gradient flow to its lower layersIn this study, we utilized a convolutional block (ConvB) to process the raw sensor data and extract low-level features. The ConvB, as depicted in Fig. 41. The GRU is a straightforward variation of the LSTM in which individual memory cells are omitted from its design42. As seen in Fig. t based on the output of the update gate Gate recurrent unite (GRU) was developed as a new RNN-based approach to prevent the exploding/vanishing gradient issue; nevertheless, the design’s memory cells result in a higher memory consumption43 introduced a bidirectional RNN (BiRNN) in 1997 in order to address the drawback of a conventional  RNN. In addition to the present input, the output at a given period also incorporates past and future data. This is performed by concurrently training the network in the forward and reverse directions. A normal RNN does this by dividing its neurons into a portion responsible for the forward direction and a portion responsible for the reverse direction. Positive neuron output is not linked to negative neuron output, and vice versa. This results in the general structure depicted in Fig. Schuster and PaliwalCommonly, simple DL algorithms employ convolution layers followed by fully connected layers for classification tasks, without incorporating shortcut connections. These architectures are known as sequential networks, where each layer passes data to the next layer. However, as the size of the sequential network increases, a challenge arises in the form of vanishing or exploding gradients. This can pose difficulties for the effective training of such networks.To overcome this problem, ResNet utilizes residual blocks, which allow for skip connections between blocks of convolutional layers. These skip connections enhance gradient propagation and facilitate the training of increasingly deeper CNNs, mitigating the issue of gradient vanishing. A residual layer can be represented as follows:x denotes the input, f(x) denotes the layer’s output, ELU(x) denotes the exponential linear unit function, and R(x) denotes the residual block’s output. The residual element f(x) is generated in this block as two consecutive repetitions of a trio of operational processes: convolution with a filter of size 3f(x) feature map is then concatenated with the input x, and the ELU activation function is then applied to the combined characteristics.Where 44. As depicted in Fig. In order to extract hybrid features hierarchically by incorporating both spatio-temporal and channel-wise data, we introduced the SEResidual block based on previous work by Muqeet et al.R(x) is the residual block’s output. The residual element f(x) is generated in this block as two consecutive repetitions of a trio of operational processes: convolution with a filter of size 3f(x) feature map is then concatenated with the input x. The ELU activation function is then applied to the combined characteristics.Figure 45. During this step, a scalar value reflecting a global channel is established. The procedure indicated by Eq.  learning rate (k-CV) technique is a valuable method for estimating the performance of a classification model using multiple data subsets49. This approach involves randomly dividing a dataset, obtained from either a single individual or multiple participants, into k non-overlapping subsets of approximately equal size. Each subset is then used to evaluate the classification model trained on the remaining k - 1 subsets. The overall effectiveness of the model is determined by computing the mean value of performance measures such as accuracy, precision, recall, and F-measure, obtained from the k-CV50. It’s worth noting that this approach can be computationally demanding, particularly when dealing with large sample sizes or high values of k. In this study, we applied the k-CV technique with k set to 5, as depicted in Fig. The k-fold cross-validation  was utilized as a module for sampling and data production in investigations.TensorFlow, Keras, and TensorBoard were operated to produce and train models using DL.This research utilized Google Colab Pro+ with a Tesla V100-SXM2-16GB graphics processor module to accelerate the training of DL models. The ResNet-BiGRU-SE and other primary DL models were developed in a Python library with TensorFlow and CUDA backends. These studies focused on the following Python libraries:In this study, we assessed the proposed framework by comparing it to baseline DL algorithms using three publicly available datasets: UCI-HAR, WISDM, and IM-WSHA. The following subsections present the experimental findings of these DL methods trained on smartphone sensing data from these benchmark datasets.In the first experiment, we evaluated the performance of the proposed ResNet-BiGRU-SE model on the UCI-HAR dataset. The results are summarized in Table The results presented in Table The findings from the third investigation, which utilized the IM-WSHA dataset, are summarized in Table 56, Bidir-LSTM57, CNN-LSTM58, SDAE59, and CNN-GRU60. Each of these models was developed in accordance with its respective study descriptions.We conducted a comprehensive comparison of our proposed model with state-of-the-art DL models in the field of sensor-based HAR. In Table Notably, our suggested ResNet-BiGRU-SE model achieved an outstanding success rate of 98.92% on the UCI-HAR dataset, surpassing the performance of all the other models. The comparative results are presented in Table 61, CNN with statistical features14, U-Net62, and CNN-GRU60. Each of these models was implemented based on the descriptions provided in their respective publications.In our evaluation using the WISDM dataset, we compared our proposed model with state-of-the-art DL algorithms for sensor-based HAR. Table Remarkably, our suggested ResNet-BiGRU-SE model achieved an impressive accuracy of 98.80% on the WISDM dataset, outperforming all the other models. This outstanding performance further highlights the superiority of our proposed ResNet-BiGRU-SE model in accurately classifying activities based on the WISDM dataset.The findings from our study strongly support our hypothesis that our hybrid DL model, which combines local spatio-temporal characteristics with long-term contextual understanding, improves the comprehension of sensor data and ultimately enhances the performance of activity classification. Additionally, the results suggest that deep residual models exhibit favorable performance when applied to raw signals. However, the inclusion of BiGRU and SE modules further enhances the effectiveness of HAR for real-life human motion detection. These findings highlight the significance of incorporating both architectural enhancements and feature extraction techniques in order to achieve optimal results in HAR applications.63, resulting in an accuracy of 81.92%. Another approach involved the utilization of a random forest model with stochastic gradient descent (SGD) optimization, denoted as64, which achieved a recognition accuracy rate of 90.18% in order to enhance the effectiveness of HAR. Remarkably, when applied to the IM-WSHA dataset, the ResNet-BiGRU-SE model achieved an impressive identification accuracy of 98.45%. These results highlight the superiority of our proposed model over other advanced techniques in accurately identifying human activities based on the IM-WSHA dataset.Table 65, k-CV, and Leave-One-Subject-Out cross-validation (LOSO)50. Hold-out validation involves dividing the dataset into a training set  and a test set (30% of the data). On the other hand, k-CV repeatedly partitions the dataset into k subsets for training and testing, evaluating the algorithm’s performance k times. LOSO validation creates a training set with n - p samples and a testing set with p samples, where p represents all data from a single subject. This approach ensures that there is no overlap between subjects in the training and testing sets.Sensor-based HAR studies commonly employ three validation techniques: hold-out validationTo assess the impact of these validation techniques, we conducted supplementary investigations using three HAR datasets: UCI-HAR, WISDM, and IM-WSHA. We evaluated the effectiveness of the ResNet-BiGRU-SE model and presented the results in Fig. k-CV, achieved the highest levels of accuracy. However, it is important to note that this result may be influenced by the fact that the k-CV method does not consider scenarios where all samples come from a single study participant. This issue arises due to the time series segmentation used in the pre-processing phase. In a generalized HAR implementation, independently dividing the dataset can lead to instances where a participant’s data appears in both the training and test sets simultaneously, causing data leakage that artificially inflates the classifier’s accuracy.The findings presented in Fig. On the other hand, when adopting the LOSO approach, which takes into account individual-specific data , the accuracy of the classification model tends to decrease. The implementation of this improved assessment approach resulted in a 12% reduction in accuracy, indicating a preliminary overestimation of the results. It is important to consider these factors when selecting a validation approach to ensure accurate and reliable performance evaluation in HAR tasks.To analyze the misclassification patterns of the suggested model, we conducted a comprehensive examination of the confusion matrices generated by the ResNet-BiGRU-SE model on three different HAR datasets: UCI-HAR, WISDM, and IM-WSHA. These datasets contain activity data collected from a variety of sensor categories, as summarized in Table 66. However, the ResNet-BiGRU-SE model performed well in accurately categorizing the other four activities, as shown in Fig. 67. Lastly, the confusion matrix depicted in Fig. Regarding the UCI-HAR dataset, the categories of ”sitting” and ”standing” exhibited the highest frequency of misclassifications. This can be attributed to the similarity in linear acceleration patterns observed in these static actionsThis study focuses on examining the recognition performance of CNN-based classifiers with diverse topologies for sensor-based HAR. We conducted experiments using three widely-used HAR datasets: UCI-HAR, WISDM, and IM-WSHA, to thoroughly investigate the effectiveness of CNN-based models. The findings indicate that the ResNet architecture stands out as a suitable choice for HAR compared to other backbone architectures. In particular, we introduce a lightweight residual network, named ResNet-BiGRU-SE, specifically designed for sensor-based HAR. The proposed model’s efficiency was evaluated using the three datasets. By employing the 5-CV technique, our results demonstrate the superiority of our proposed model, achieving an impressive average recognition performance of 98.92% for UCI-HAR, 98.80% for WISDM, and 98.45% for IM-WSHA. Furthermore, our suggested model exhibits a reduced number of training parameters compared to previous HAR models. Moving forward, our future work aims to expand the scope of our research by including scenarios with larger populations, diverse locations, various sensor types, and activities of daily living (ADLs).