The COVID-19 pandemic has the potential to accelerate another pandemic: physical inactivity. Daily steps, a proxy of physical activity, are closely related to health. Recent studies indicate that over 7000 steps per day is the critical physical activity standard for minimizing the risk of all-cause mortality. Moreover, the risk of cardiovascular events has been found to increase by 8% for every 2000 steps per day decrement.To quantify the impact of the COVID-19 pandemic on daily steps in the general adult population.This study follows the guidelines of the MOOSE  checklist. PubMed, EMBASE, and Web of Science were searched from inception to February 11, 2023. Eligible studies were observational studies reporting monitor-assessed daily steps before and during the confinement period of the COVID-19 pandemic in the general adult population. Two reviewers performed study selection and data extraction independently. The modified Newcastle-Ottawa Scale was used to assess the study quality. A random effects meta-analysis was conducted. The primary outcome of interest was the number of daily steps before  and during  the confinement period of COVID-19. Publication bias was assessed with a funnel plot and further evaluated with the Egger test. Sensitivity analyses were performed by excluding studies with low methodological quality or small sample sizes to test the robustness of the findings. Other outcomes included subgroup analyses by geographic location and gender.A total of 20 studies  were included. The proportion of studies with subjects with optimal daily steps  declined from 70% before the pandemic to 25% during the confinement period. The change in daily steps between the 2 periods ranged from –5771 to –683 across studies, and the pooled mean difference was –2012 (95% CI –2805 to –1218). The asymmetry in the funnel plot and Egger test results did not indicate any significant publication bias. Results remained stable in sensitivity analyses, suggesting that the observed differences were robust. Subgroup analyses revealed that the decline in daily steps clearly varied by region worldwide but that there was no apparent difference between men and women.Our findings indicate that daily steps declined substantially during the confinement period of the COVID-19 pandemic. The pandemic further exacerbated the ever-increasing prevalence of low levels of physical activity, emphasizing the necessity of adopting appropriate measures to reverse this trend. Further research is required to monitor the consequence of long-term physical inactivity.PROSPERO CRD42021291684; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=291684 Physical activity has been crucial for preventing and treating multiple chronic health conditions ,2 and prAlthough global responses were far from homogeneous, most countries adopted restriction measures in varying forms to limit the transmission of COVID-19, such as physical distancing, working from home, and closing schools . These mTo fill this knowledge gap, we conducted a meta-analysis of observational studies to quantify both global and regional impacts of confinement during the COVID-19 pandemic on daily steps in the general adult population, and we also examined whether the decline in daily steps differed between men and women.The MOOSE   and PRISA preliminary search was conducted on PubMed, EMBASE, and Web of Science to retrieve studies that reported daily step data both before and during the confinement period of the COVID-19 pandemic in the general adult population published before November 22, 2021. Further, an updated search was performed on February 11, 2023, to add recently published articles. The detailed search strategy is listed in We included observational studies that reported daily steps before and during the confinement period of the COVID-19 pandemic in the general adult population. The reference period was restricted to ≤2 years before the time points when the government implemented the confinement measures. Further eligibility criteria are listed in Inclusion criteriaStudy type: Observational studiesParticipants: General adult population (average age over 20 years)Outcomes: Daily steps, measured using monitoring devices including wearable physical activity trackers  and smartphones mobile apps Other criteria: Daily step data collected both before and during the confinement period of COVID-19. The reference period was restricted to ≤2 years before the time points when the government implemented the confinement measuresExclusion criteriaStudy type: Any non–observational study , conference abstractsParticipants: Children, adolescents, athletes, specific patient groups, participants with particular conditions Outcomes: Data other than daily steps Other criteria: Studies offering financial incentives that may promote physical activity , studies lacking available data for meta-analysisTwo independent reviewers (LS and WR) screened all titles and abstracts. The full text of potentially relevant literature was further evaluated. A third reviewer (ZW) arbitrated the decision if a consensus was not reached.Two authors (LS and WR) extracted the following data independently: study design, country and continent  where the study was conducted, the sample size used for daily step analysis, participant characteristics , monitoring devices used to measure daily steps, the algorithm used to analyze daily steps, data collection duration, and COVID-19 restriction measures by the government at the time of data collection. The outcomes of interest were the total number of daily steps before and during the confinement period of COVID-19 and the percentage change between the 2 periods. If not reported, the mean (SD) of the outcome was estimated based on the methods provided by the Cochrane Handbook . If dataselection (maximum 3 stars), comparability (maximum 2 stars), and outcome (maximum 2 stars). Therefore, 1 study can be awarded a maximum of 7 stars. Studies with 6 to 7 stars were rated as high quality, those with 5 stars were rated as moderate quality, and those with 4 or fewer stars were rated as low quality [The modified Newcastle-Ottawa Scale (NOS) for applicable observational studies was used to assess the methodological quality and risk of bias in each study -30. Spec quality . Two revI2) was used to test statistical heterogeneity across studies [I2 value of more than 75% indicated high heterogeneity [A random effects meta-analysis was conducted using Stata software . The change in daily steps was calculated using the mean difference (MD) with the 95% CI between the mean (SD) of daily steps before and during the confinement period of COVID-19. The Cochran Q statistic of the inconsistency index , 14 did not provide necessary data for meta-analysis, 13 focused on irrelevant populations, 5 were not observational studies, 3 were conducted with financial incentives, 2 reported duplicated data, and 1 was a conference abstract. The remaining 20 studies were included in the meta-analysis ,20,37-54The included studies were conducted in Australia , Canada selection dimension, 30% received 2 stars in the comparability dimension, and 60% received 2 stars in the outcome dimension.Thirteen studies (65%) were rated as having moderate to high methodological quality . The funnel plot shows a symmetrical horizontal distribution for each study relative to the vertical line, suggesting the absence of reporting bias  declined from 70% before the pandemic to 25% during the confinement period, and the average number of daily steps was reduced by 2000. Our results also suggest a possible inequality in physical activity across countries, whether before or during the pandemic. However, given that most included studies lacked sufficient nationwide representativeness, this difference should be interpreted cautiously. Furthermore, there was no apparent gender difference in the decline in daily steps before and during the confinement period of COVID-19.The population-level trends in daily steps during the confinement period of COVID-19 may reflect public panic about the virus and adherence to confinement measures  ,56. Our Daily steps are strongly associated with health outcomes -16. ReceOur finding of a reduction in daily steps during the confinement period of the pandemic is consistent with findings from previous studies using different physical activity metrics ,68. An eBased on data collected from 187 countries and districts by a smartphone app, Tison and colleagues  found thSeveral limitations should be acknowledged. First, daily step data are lacking from Africa and South America, limiting the generalizability of our findings. Second, when comparing regional differences across countries, we could not rule out potential sampling bias, because most of the included studies lacked sufficient nationwide representativeness. Therefore, future studies are needed to verify our findings. Third, since long-term data from the period that confinement was lifted are not suitable for inclusion in a meta-analysis, we could not directly assess the long-term impact of COVID-19 on physical activity levels using meta-analysis techniques. As more published data become available ,63-66, tIn summary, we found that daily steps declined substantially during the COVID-19 confinement period. Our findings indicate that COVID-19 further exacerbated the ever-increasing prevalence of low levels of physical activity, emphasizing the necessity of adopting appropriate measures to reverse this trend. Further research is required to monitor the consequence of long-term physical inactivity.


Background: The outbreak of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) resulted in the global COVID-19 pandemic. The urgency for an effective SARS-CoV-2 vaccine has led to the development of the first series of vaccines at unprecedented speed. The discovery of SARS-CoV-2 spike-glycoprotein mutants, however, and consequentially the potential to escape vaccine-induced protection and increased infectivity, demonstrates the persisting importance of monitoring SARS-CoV-2 mutations to enable early detection and tracking of genomic variants of concern. Results: We developed the CoVigator tool with three components: (1) a knowledge base that collects new SARS-CoV-2 genomic data, processes it and stores its results; (2) a comprehensive variant calling pipeline; (3) an interactive dashboard highlighting the most relevant findings. The knowledge base routinely downloads and processes virus genome assemblies or raw sequencing data from the COVID-19 Data Portal (C19DP) and the European Nucleotide Archive (ENA), respectively. The results of variant calling are visualized through the dashboard in the form of tables and customizable graphs, making it a versatile tool for tracking SARS-CoV-2 variants. We put a special emphasis on the identification of intrahost mutations and make available to the community what is, to the best of our knowledge, the largest dataset on SARS-CoV-2 intrahost mutations. In the spirit of open data, all CoVigator results are available for download. The CoVigator dashboard is accessible via covigator.tron-mainz.de. Conclusions: With increasing demand worldwide in genome surveillance for tracking the spread of SARS-CoV-2, CoVigator will be a valuable resource of an up-to-date list of mutations, which can be incorporated into global efforts. The identification, characterization and monitoring of the pathogen responsible for a novel emerging disease is crucial for the development of a timely public health response. This includes rapid and open sharing of data , which hNevertheless, the discovery of SARS-CoV-2 spike-glycoprotein mutants, associated with the potential to escape vaccine-induced protection, demonstrates the importance of monitoring SARS-CoV-2 genomic sequences to enable early detection of these genomic variants of concern. In a first study, we analyzed 1,036,030 genomic assemblies from the Global Initiative on Sharing Avian Influenza Data (GISAID) ,11,12 anThere are multiple initiatives to monitor SARS-CoV-2 mutations based on the GISAID dataset: NextStrain , CoV-GLUEach dataset has its own advantages. While genomic assemblies are easier to share and interpret, raw reads provide granular information about the mutations through access to the pileup of reads supporting each mutation, also allowing the characterization of intrahost mutations. Analyzing both datasets together may support the identification of potential false positives in the data and the confirmation of trends.To enable monitoring of SARS-CoV-2 sequences from both sources, we have developed CoVigator, an NGS pipeline and dashboard that allows geographical and temporal navigation through SARS-CoV-2 genomic variants. We automatically download and analyze genomic assemblies from the COVID-19 Data Portal and raw reads from the European Nucleotide Archive (ENA). Furthermore, we screen the literature for studies on SARS-CoV-2 intrahost mutations ,43,44,45The CoVigator knowledge base is implemented in Python version 3.8 and the database for storing data is PostgreSQL (version 13.4).The CoVigator pipeline (version 0.14.0) is implemented in the Nextflow framework version 19.10.0. All dependencies are managed within conda (version 4.9) environments  see Tab. The pipThe CoVigator dashboard is also implemented in Python using the visualization framework Dash (version 2.1.0). The computation is distributed through a high-performance computing cluster with a library that provides advanced parallelism, Dask (version 2022.9.2).The CoVigator system  has threCoVigator operates via interaction with external systems: a high-performance computing (HPC) cluster and the ENA and COVID-19 Data Portal Application Programming Interfaces (APIs). Samples between both original datasets (raw reads and genomic assemblies) may overlap. As recommended, some data providers might automatically upload both data formats. The results presented in the dashboard are stratified by dataset.The CoVigator knowledge base collects data from both genomic assemblies and raw reads, orchestrates its processing through the variant calling pipeline and stores all the metadata, raw data and processed results in a relational database.The data for both datatypes are fetched via the corresponding API hosted by the European Bioinformatics Institute ; the metFurthermore, the knowledge base iteratively builds a variant co-occurrence matrix (only for the raw read dataset) and precomputes analyses of the data  that ensure low-latency responses.In general, the CoVigator pipeline processes FASTQ and FASTA files into annotated and normalized analysis-ready VCF files via two independent workflows . We implThe dashboard is the user interface to CoVigator. There are two separate views for the raw reads and genomic assembly datasets. Each view provides a set of tabs that allows the user to explore different aspects of the data held in the database. Each tab provides some interactive visualizations, described below. When applicable, the tabs provide a set of filters on the left side. These have been excluded from the screenshots for the purpose of clarity.The most relevant tabs are described below, and some notable findings are highlighted. The data shown here include 137,025 samples downloaded from ENA on 21 October 2022 and 6,165,681 samples downloaded from the COVID-19 Data Portal on 18 November 2022.The samples tab  enables The lineages tab enables the user to explore the different lineages through time and geography . Both thThe mutation statistics tab provides insights into the variant calling results on the different datasets and genomic regions . ExpecteThe median number of SNVs per sample in the raw read dataset is 32, with an interquartile range (IQR) of 30 A. AdditiWe observe that the base substitution C > T is by and large the most frequent, followed by G > T and A > G; the deletion TA > T and the MNV GG > AA is the most frequent in both datasets C,D.In The recurrent mutations tab allows the user to explore the most recurrent mutations by the total count of observations through time within their genomic context . In FiguAdditionally, the mutation statistics tab provides a co-occurrence analysis that points to clusters of co-occurring mutations and their correspondence with virus lineages; or in the case of mutations shared between lineages, these clusters may contain a mixture of different but related lineages. Due to performance limitations, this analysis is only available in the raw read dataset and at the gene level. In The FASTQ files provide the pile-up of reads across the genome, and this gives detailed information into the called variants. In particular, we can count the number of reads supporting each variant, and this allows us to identify subclonal variants supported using only a fraction of the reads. These variants likely emerged within the host and are referred to as intrahost variants. The identification of intrahost variants is not possible on the genomic assemblies.We consider high-quality clonal mutations as those with a VAF greater than or equal to 80%, and those with a VAF greater than or equal to 50% and lower than 80% as low-confidence clonal mutations. Only high-confidence clonal mutations are used to determine a consensus sequence and assign a SARS-CoV-2 lineage .The remaining dataset of mutations poses a different technical challenge due to the difficulty of separating true low VAF mutations from noise. We first determine those mutations with a VAF below 50% as raw candidate intrahost mutations.We observed a large number of low-frequency mutations among SARS-CoV-2 genomes. In order to establish a high-quality set of intrahost mutations for studying viral evolution, we screened and compared the literature on SARS-CoV-2 intrahost mutations for different filtering approaches and implemented a conservative approach .The persistently increasing amount of publicly available SARS-CoV-2 sequencing data calls for robust platforms that allow constant monitoring of genomic SARS-CoV-2 variants in heterogeneous data sets. Our CoVigator pipeline covers the essential steps of preparing the data and calling variants from SARS-CoV-2 raw sequencing data from ENA and genome assemblies from the COVID-19 Data Portal. The pipeline is integrated within the CoVigator knowledge base that orchestrates the download, processing and storage of the underlying samples and results. The CoVigator dashboard provides different visualizations and features for selecting clonal variants across all genes from the SARS-CoV-2 genome in a selected period. The dashboard also provides a comprehensive analysis of intrahost variants observed across detected mutations in the raw read dataset. To this end, we propose a conservative filtering approach based on filtering samples and mutations. The dataset of intrahost mutations derived from public data that we make available through CoVigator is, to the best of our knowledge, the largest published dataset of SARS-CoV-2 intrahost mutations. The main strength of CoVigator is the combination of a software pipeline with a dashboard, which ensures both processing of the data and its interpretation. Uniquely, CoVigator processes genome assemblies and raw sequencing data types, making it open-data-friendly and allowing it to be adopted to other SARS-CoV-2 data sources. A brief comparison of the important features of CoVigator with other pipelines is tabulated in The identification of mutations over such heterogeneous datasets obtained with different sequencing protocols is challenging. With CoVigator, we observed VAF dilution on mutations identified via targeted amplicon sequencing with overlapping primers, genome edge effects and read edge effects. We aim to address these challenges in the future, e.g., through inferring the primers used in an arbitrary sample. Additionally, we implemented a simplistic phasing of clonal mutations occurring in the same amino acid to ensure their correct annotation. However, we identified the need for a phasing method for low-VAF mutations that existing germline phasing tools do not cover. CoVigator is currently limited to processing Illumina sequencing data, while the majority of SARS-CoV-2 sequencing projects  and pipelines use Oxford Nanopore sequencing. SARS-CoV-2 Nanopore data processing will be implemented in subsequent releases of CoVigator.Future versions of CoVigator can be broadened to other use cases, such as other infectious organisms or co-existing infections during the pandemic (see


The free vibration of functionally graded porous cylindrical shell panels reinforced with graphene platelets (GPLs) was numerically investigated. The free vibration problem was formulated using the first-order shear deformation shell theory in the framework of the 2-D natural element method (NEM). The effective material properties of the GPL-reinforced shell panel were evaluated by employing the Halpin–Tsai model and the rule of mixtures and were modified by considering the porosity distribution. The cylindrical shell surface was transformed into the 2-D planar NEM grid to avoid complex computation, and the concept of the MITC3+shell element was employed to suppress shear locking. The numerical method was validated through benchmark experiments, and the free vibration characteristics of FG-GPLRC porous cylindrical shell panels were investigated. The numerical results are presented for four GPL distribution patterns  and three porosity distributions (center- and outer-biased and uniform). The effects of GPL weight, porosity amount, length–thickness and length–radius ratios, and the aspect ratio of the shell panel and boundary condition on the free vibration characteristics are discussed in detail. It is found from the numerical results that the proposed numerical method accurately predicts the natural frequencies of FG-GPLRC porous cylindrical shell panels. Moreover, the free vibration of FG-GPLRC porous cylindrical shell panels is significantly influenced by the distribution pattern as well as the amount of GPLs and the porosity. Graphene platelets (GPLs) have been widely used as advanced nanofiller materials to improve the physical properties of composites due to their extraordinary physical properties ,2. The eMore recently, the GPL-reinforced composite has been developed into a closed-cell porous reinforced foam due to rapidly developed nanotechnology ,10,11. AAccording to the literature survey on FG-GPLRC porous structures, Kitipornchai et al.  presenteAs revealed in the relevant literature survey, most of the studies on FG-GPLRC porous structures have been confined to beam and plate structures by analytical approaches based on the shear deformation theory or by numerical approaches using FEM. The studies on cylindrical structures have been poorly presented and mostly restricted to the buckling behavior. In this context, the purpose of this study was to thoroughly examine the free vibration of FG-CNTRC porous cylindrical shell panels by developing a 2-D effective and locking-free meshfree-based numerical method. The numerical method was developed in the framework of the 2-D planar natural element method (NEM), a previously introduced meshfree method characterized by high smooth Laplace interpolation functions ,27, in wThe numerical method was verified through experiments of benchmark examples, and the free vibration responses of FG-GPLRC porous cylindrical shell panels were investigated with respect to the GPL- and porosity-associated parameters, shell geometry dimensions, and boundary conditions. Following the introduction, the FG-GPLRC porous cylindrical shell panel and its displacement, strain and stress fields, and effective material properties are described in Letting In which the GPL volume fraction The GPLs are assumed to be uniformly dispersed within the matrix and act as an effective rectangular solid fiber with length al model , which gHere, Meanwhile, the effective mass density property . By lettBy using the first-order shear deformation shell theory, the displacement field For the free vibration analysis of the FG-GPLRC cylindrical shell panel by 2-D NEM, the mid-surface ns ψJx,y ,32 and tThe definition of the Laplace interpolation function and its manipulation on the cylindrical surface are complex and painstaking. To relax this difficulty, a geometry transformation Then, Laplace interpolation functions The partial derivatives Introducing Equation (25) into Equations (18) and (19) results in Then, the NEM approximations of the in-plane strains The direct approximation (20) of transverse shear strain  locking ,33,34. T locking  with coounctions , the appThen, referring to Meanwhile, the standard Galerkin weak form for the free vibration analysis of FG-GPLRC cylindrical shell panels can be derived from the dynamic form of the energy principle (30)∫−h/Here, TDB dϖdzKse=∫−h/2arameter . The valA Fortran program was coded according to the numerical formulae presented in The present method was compared with the other methods with two isotropic, one FG-GPLRC, and one FG-GPLRC porous cylindrical panels.The first example is a clamped isotropic cylindrical panel with the geometry dimensions given by The second example is the simply supported isotropic cylindrical panels with four different aspect ratios and Shen  and Kobaand Shen  in Tableand Shen  adopted and Shen  and 1.70and Shen  and 43]s/𝓁. The The third example is the functionally non-porous cylindrical panels reinforced with graphene platelets with different ratios of d Daniel  and RafiIt can be seen in Next, the normalized fundamental frequency Next, the free vibration of the FG-GPLRC porous cylindrical panel was investigated by considering three porosity distributions shown in The numerical method accurately analyzes the free vibration of FG-GPLRC porous cylindrical shell panels, without causing shear locking, with the maximum relative difference of 4.213% even for coarse and 2-D planar NEM grids.The normalized natural frequency The distribution patterns of both the GPL and porosity significantly affect the variations in The increase in the slope of This paper presents a free vibration analysis of FG-GPLRC porous cylindrical shell panels using a NEM-based 2-D numerical method. In the framework of 2-D planar NEM, the numerical method was developed by integrating a geometry transformation between the shell surface and the 2-D planar NEM grid and the MITC3+shell element. Benchmark and parametric experiments were carried out to validate the proposed numerical method and to thoroughly investigate the free vibration characteristics of FG-GPLRC porous cylindrical panels with respect to the associated parameters. The numerical results reveal the following main observations:


Background: Autosomal recessive osteopetrosis (ARO) is a rare genetic disorder of bone metabolism, primarily affecting the remodelling function of osteoclasts. Haematopoietic stem cell transplantation (HSCT) is the first-line treatment for ARO. Traditional tools for the assessment of therapeutic response, such as measuring donor chimerism, do not provide information on bone remodelling. The use of bone turnover markers (BTMs) might be ideal. Here, we report a case of a paediatric ARO patient undergoing successful HSCT. Methods: For the evaluation of donor-derived osteoclast activity and skeletal remodelling throughout the transplantation, the bone resorption marker β-CTX  was used. Results: The low baseline level of β-CTX markedly increased after transplantation and remained in the elevated range even after 3 months. Donor-derived osteoclast activity reached its new baseline level around the 50th percentile range after 5 months and proved to be stable during the 15-month follow-up time. The apparent increase of the baseline osteoclast activity after HSCT was in consonance with the radiographic improvement of the disease phenotype and the correction of bone metabolic parameters. Despite the successful donor-derived osteoclast recovery, craniosynostosis developed, and reconstructive surgery had to be performed. Conclusions: The use of β-CTX may be of aid in assessing osteoclast activity throughout the transplantation. Further studies could help to establish the extended BTM profile of ARO patients using the available osteoclast- and osteoblast-specific markers. Several rare genetic disorders are referred to as osteopetrosis, which have the common ground of skeletal sclerosis and abnormal osteoclast function . The impSince osteoclasts are derived from granulocyte-monocyte progenitor cells , HSCT haUsing bone turnover markers (BTMs) for the evaluation of therapeutic response and success during HSCT is an unexplored possibility. BTMs are secreted molecules or by-products of bone formation and resorption . MonitorIn our case report, the BTM C-terminal telopeptide of type I collagen (CTX) was used to monitor bone remodelling throughout the transplantation. CTX is located on the carboxy-terminal telopeptide region of the most abundant bone-forming protein . The telA six-month-old male patient diagnosed with ARO was admitted to our transplantation unit. The patient’s clinical presentation was characteristic of the disease: deformed sclerotic skull, multiple fractures, optic nerve atrophy with progressive visual impairment, and thrombocytopaenia with hepatosplenomegaly as the sign of bone marrow involvement. The auditory system is often affected in ARO, but the patient showed no sign for otologic involvement of the disease at this time. Diagnostic radiography of the chest and cranial MRI can be seen in Sanger sequencing revealed the pathognomonic mutation of the osteoclast-specific gene TCIRG1, which accounts for approximately half of the ARO cases with identified genetic origin . The pat2 once daily between days –6 and –3), and anti-thymocyte globulin . Upon completing the conditioning protocol, peripheral stem cell graft was administered from an HLA (human leukocyte antigen)-matched unrelated donor. An additional stem cell boost was given 30 days after the transplantation. An absolute neutrophil cell count of >500/μL was reached on day 20, and significant improvement of the haematopoietic recovery was observed after the stem cell boost. Transplantation-associated complications characteristic of ARO, such as rejection, veno-occlusive disease, pulmonary hypertension, and severe hypercalcaemia, were not detected. HSCT-related toxicity was well tolerated. Chimerism analyses showed that >95% of peripheral blood cells were derived from the donor.Due to the severity and fast progression of the disease, urgent HSCT was decided. The patient’s transplantation was performed following the guidelines of the ESID (European Society for Immunodeficiencies) and EBMT (European Society for Blood and Marrow Transplantation) working party of inborn errors . The myeResolution of osteopetrosis could be seen on MR imaging that took place 6 months after the transplantation, and radiography also demonstrated the recovery of the disease phenotype A.The vision loss consequent to optic nerve compression was deemed irreversible, but the auditory system was unaffected based on brainstem auditory evoked potential (BAEP) analysis and tympanometry. The patient’s neuromotor development was not delayed compared to his age group.However, radiological normalisation was not apparent in one region, the calvaria: neurocranial sutures progressed to ossify even though osteoclast resorptive function had been initiated in other parts of the skull and the skeletal system B. Neuros® electro-chemiluminescence immunoassay . The sample collection had started before the conditioning protocol was initiated and continued through the early and late post-transplantation period. β-CTX serum concentration curves are presented in β-CTX measurements were made from serum samples using β-CrossLapsPaediatric reference intervals of BTMs were used for the evaluation . Basic pBefore conditioning, baseline β-CTX concentration was below the 3rd percentile, indicating a low level of osteoclast bone resorptive activity. From day 28, a progressive increase could be interpreted: the β-CTX concentration markedly increased compared to the baseline and rose above the 97th percentile. On day 30, an additional stem cell boost was given. Osteoclast activity and β-CTX levels continued to remain in a highly elevated range even after 50 days of transplantation, and the continuously improving haematopoiesis demonstrated the successful remodelling of the bone marrow. After 2 months, β-CTX levels started to gradually decrease below the 97th percentile range. Even after 100 days, at the time of reaching the cell count of CD4+ T-lymphocytes 200/μL, β-CTX levels remained elevated. Donor-derived osteoclast activity reached its steady state after 5 months. Around this time, the conclusion of radiologic improvement could be made based on skull radiography and MR imaging. β-CTX levels remained stable around the 50th percentile margin from that onward. The influence of donor-derived osteoclasts on the β-CTX kinetics was demonstrated by the patient’s chimerism analysis, which indicated 100% donor presence throughout the follow-up period.Pre-transplantation metabolic parameters were shown to be characteristic of the laboratory profile of ARO: minor hypocalcaemia and hypophosphatemia throughout the aplastic period with increased levels of ALP and BAP. The total calcium level increased to the physiologic range parallel to the β-CTX increment, but abnormally high calcium levels were not observed. Hypophosphatemia was also gradually corrected. Both ALP and BAP remained elevated in the first weeks but normalised gradually within three months. The osteocalcin level was within normal range throughout the course of HSCT apart from a transitory decrease in the first month.The process of skeletal remodelling and changes in bone turnover induced by HSCT is not well documented . RegardiThe use of BTMs as diagnostic or therapy-monitoring tools in metabolic bone disorders has been receiving more attention in recent years ,5,6. TheOur observations might provide further examples for this. The patient’s initial β-CTX concentration and other metabolic parameters indicated deprived bone resorptive activity. High ALP and BAP levels can be attributed to the osteopetrotic cells and the increased synthetic activity of bones. In the case of ALP, accompanying liver damage is also a contributing factor. The transitory decrease of osteocalcin can probably be related to the damage of osteoblasts following conditioning. Donor-derived osteoclasts initiate bone remodelling during the early period of HSCT, as displayed by the rise of β-CTX levels. This primary remodelling makes the niche of the sclerotic bone marrow a more hospitable environment for haematopoietic activity and upcoming stem cells. Due to the narrowed intratrabecular space, delayed haematopoietic reconstitution is a chief concern in ARO. Therefore, to ensure engraftment, an additional stem cell boost is given . Highly It should also be noted that decreased bone density is a known complication of HSCT, possibly caused by the impairment of bone formation and the boost of osteoclastic activity. Polgreen et al.  observedIn the case of the neurocranium, however, an irreversible bony fusion of cranial sutures  was observed. Paradox craniosynostosis after HSCT is not unknown but is an extremely rare phenomenon remaining obscure to this day ,19. In tTraditional tools for establishing the success of HSCT and long-term remission, such as measuring donor chimerism, are constrained in the case of ARO, for they provide no information on osteoclast function. The use of β-CTX and other BTMs may be of aid in assessing osteoclast activity throughout the transplantation to monitor the effect of the stem cell boost and the resolution of ARO in a simple, fast way. Further studies in this field might have a potential for expanding the diagnostic and follow-up tools for clinicians ,24. TablOur report is to be viewed with the limitations of featuring a single case. These data must also be interpreted with caution, as the bone metabolic profile did not include relevant hormones or other commercially available BTMs, and the β-CTX values prior to the HSCT are not available.


Traditional cardiac rehabilitation programs are centre-based and clinically supervised, with their safety and effectiveness well established. Notwithstanding the established benefits, cardiac rehabilitation remains underutilised. A possible alternative would be a hybrid approach where both centre-based and tele-based methods are combined to deliver cardiac rehabilitation to eligible patients. The objective of this study was to determine the long-term cost-effectiveness of a hybrid cardiac telerehabilitation and if it should be recommended to be implemented in the Australian context.Following a comprehensive literature search, we chose the Telerehab III trial intervention that investigated the effectiveness of a long-term hybrid cardiac telerehabilitation program. We developed a decision analytic model to estimate the cost-effectiveness of the Telerehab III trial using a Markov process. The model included stable cardiac disease and hospitalisation health states and simulations were run using one-month cycles over a five-year time horizon. The threshold for cost-effectiveness was set at $AU 28,000 per quality-adjusted life-year (QALY). For the base analysis, we assumed that 80% completed the programme. We tested the robustness of the results using probabilistic sensitivity and scenario analyses.Telerehab III intervention was more effective but more costly and was not cost-effective, at a threshold of $28,000 per QALY. For every 1,000 patients who undergo cardiac rehabilitation, employing the telerehabilitation intervention would cost $650,000 more, and 5.7 QALYs would be gained, over five years, compared to current practice. Under probabilistic sensitivity analysis, the intervention was cost-effective in only 18% of simulations. Similarly, if the intervention compliance was increased to 90%, it was still unlikely to be cost-effective.Hybrid cardiac telerehabilitation is highly unlikely to be cost-effective compared to the current practice in Australia. Exploration of alternative models of delivering cardiac telerehabilitation is still required. The results presented in this study are useful for policymakers wanting to make informed decisions about investment in hybrid cardiac telerehabilitation programs.The online version contains supplementary material available at 10.1186/s12913-023-09546-w. Cardiac rehabilitation is a holistic multidisciplinary program tailored to provide patients with cardiovascular disease a range of supports including education, risk factor management, psychosocial care, eating healthy and exercise training . ConventLow referral and participation rates can be attributed to several issues that occur across patient, provider, program and system levels. These include gender, socio-economic factors , travel A number of different cardiac rehabilitation models have been trialled to improve uptake. In particular, telerehabilitation, or the delivery of services via telecommunication networks or the internet, has garnered much attention for its potential to overcome some of the barriers of conventional centre-based cardiac rehabilitation. Additionally, telerehabilitation has demonstrated effectiveness , may redIn the absence of randomised controlled trials with sustained follow-up, there has been increasing use of decision analytic models to determine the long-term costs and benefits of a health intervention. Decision analytic models integrate information from various sources into a single analytical framework and capture the variation of economic outcomes over a long period of time. Consequently, model-based economic evaluations provide better evidence for decision making than trial-based economic evaluations. Even though model-based economic evaluations are vital in identifying cost-effective interventions, none of the systematic reviews to date have assessed the long-term cost-effectiveness of a hybrid cardiac telerehabilitation program. Therefore, the objective of this study was to determine the long-term cost-effectiveness of a hybrid cardiac telerehabilitation in the context of implementation in the Australian setting.As cost-effectiveness studies require detailed costing information such as the intervention cost, cost of the health service utilisation, and long-term effectiveness measures, the first aim was to find a study that met the above requirements. To do this, previously published systematic reviews examining the cost-effectiveness of cardiac rehabilitation were used , 21 to iThis process identified several potential randomised controlled trials which measured costs of telerehabilitation for cardiovascular disease  compared to those attending centre-based cardiac rehabilitation.The Telerehab III trial comprised of 140 patients, who had (i) coronary heart disease (94% in the intervention group and 93% in the control group), treated with coronary artery bypass grafting or percutaneous coronary intervention or (ii) chronic heart failure (6% in the intervention group and 7% in the control group) with reduced or preserved ejection fraction and defined by the New York Heart Association (NYHA) functional class system I to III only .n = 70) were enrolled in a 24-week cardiac telerehabilitation program after receiving the first 6 weeks of a standard 12-week centre-based cardiac rehabilitation program. During the first 6 weeks of the cardiac telerehabilitation program, the intervention group continued to receive centre-based cardiac rehabilitation but were also taught how to use the computer-based software and accelerometer. Upon initiation of the telerehabilitation only phase, they were provided with customised exercise programs and were asked to self-monitor their activities using an accelerometer. They were asked to upload their activity data to a secure webpage every two weeks. A semi-automatic tele-coaching system provided feedback on their physical activity performance, and smoking and dietary modification advice weekly via email or text message. In essence, while utilising a hybrid cardiac rehabilitation format, the intervention focussed more on sustaining behaviour change beyond attendance at a centre-based program.The intervention group . This is consistent with cardiac rehabilitation eligibility criteria reported by Australian programs .Several studies, such as an audit of South Australian cardiac rehabilitation programs, have reported a high percentage of participants successfully completing telerehabilitation programs, with proportions exceeding 85% , 29. HowA Markov model was developed on Tree Age Pro 2022 to estimate the costs and quality-adjusted life-years (QALYs) of people who had undergone hybrid cardiac telerehabilitation compared to those who underwent centre-based cardiac rehabilitation. The Markov model has four health states Fig. : stable The data used for the model was from the Telerehab III study  and was Effectiveness of the Telerehab III intervention was only available for two years after the intervention ended and there was a lack of data supporting longer term effectiveness. Therefore, the decision-analytic model was simulated only for a 5-year time horizon, transitioning in 1-month cycles through the health states described earlier. Quality-adjusted life-years were used to evaluate effectiveness where one QALY is equivalent to one year in perfect health. A healthcare payer perspective was taken for this analysis. An annual rate of 5% was used to discount costs and QALYs in the base case and probabilistic sensitivity analyses.All costs are reported in 2022 Australian dollars for the current analysis. The Campbell & Cochrane Economics Methods Group—Evidence for Policy and Practice Information—Centre (CCEMG-EPPI) Cost Converter was used when required . The cosThe cost of the resources used in the cardiac telerehabilitation program was based on the Telerehab III study  and inclTo estimate the cost of managing a stable cardiac patient in Australia, we assumed that a patient with cardiovascular disease has four visits to the general practitioner and one specialist visit per year, based on expert opinion of the clinician members from the study team. The cost of a general practitioner visit was estimated using Medical Benefit Schedule (MBS) Item 23 and the cost of an annual specialist visit using MBS Item 116 . A cardiThe utility weights used in the model were sourced from previously published information . This stHospitalisation for a cardiac event Fig. ; P1Hospitalisation for a non-cardiac event Fig. ; P2Death after a cardiac-related hospitalisation Fig. ; P3Death after a non-cardiac-related hospitalisation Fig. ; P4Death among stable cardiac patients Fig. ; P5We calculated five transition probabilities in the model. Monthly probability of:The probability of hospitalisation for a cardiac event was based on the Telerehab III study , which wWe used the VCOR-30-day rehospitalisation data to estimate the probability of hospitalisation for a non-cardiac event Fig. ; P2 37]37]. We aIn 2018–2019, there were 11.5 million hospitalisations in Australia where 10,372,469 were for people aged 20 years and over  and 160,We used a similar approach to estimate the probability of death during a non-coronary heart disease related hospitalisation using data from the Australian Institute of Health and Welfare and the Australian Bureau of Statistics. We deducted the age specific coronary heart disease related hospitalisations 160,787) from age-specific total hospitalisations  to estimate the number of non-coronary heart disease related hospitalisations. Mortality data was sourced from the Australian Bureau of Statistics , where i60,787 frThe Australian Institute of Health and Welfare reports that in Australia an estimated 571,000 Australians aged 18 and over (2.9% of the adult population) have coronary heart disease . In 2019The model was run over a 5-year period with monthly cycles. The transition probabilities dictate the proportion of patients who move between the health states during each cycle. As patients transition between health states, they accumulate costs and utilities (QALYs). The accumulated costs and utilities of the Telerehab III intervention and the current centre-based cardiac rehabilitation were compared, and an Incremental Cost-Effectiveness Ratio (ICER) was estimated using marginal QALYs and costs. The study used a cost-effectiveness threshold of AU$28,000 per QALY gained, suggested by Edney et al., which reflects the opportunity cost of introducing new health system investment into a constrained budget in Australia . This thScenario analysis is critical in the economic evaluation of healthcare interventions to aid decision-makers in understanding the impact of uncertainty parameter estimates. An alternative scenario of 90% completion rate of the intervention was considered in the scenario analysis.Probabilistic sensitivity analysis (PSA) was used to assess the uncertainty across all relevant parameters used in the model, and their effect on the cost-effectiveness results. To reflect the full range of uncertainty in the input parameters, each parameter for which there is uncertainty is assigned an appropriate probability distribution. In this study, the transition probabilities were assigned beta distributions as they are most suitable for binomial parameters. Costs parameters were assigned gamma distributions as they are able to reflect the skew that is often associated with healthcare costs. We performed Monte Carlo simulations with 10,000 iterations, sampling from the distributions described in Table To avoid the problems associated with interpreting negative ICERs, we used Incremental Net monetary benefit (iNMB) to evaluate the probabilistic sensitivity analyses.The equation is:Incremental net monetary benefit (INMB) was used to summarise uncertainty in the cost-effectiveness results in this study. The expected maximum and minimum cost-saving, QALY gain, and net monetary benefit were estimated from the 10,000 iterations of the probabilistic sensitivity analyses.Base case analysis found the telerehabilitation intervention was more effective but more costly than centre-based cardiac rehabilitation and was not cost-effective at a cost-effectiveness threshold of $28,000 per QALY , the probability of the hybrid option being cost-effective was low (30%). While it would be unrealistic to assume a higher than 90% completion rate, a recent systematic review and a meta-analysis did indicate that exercise adherence is significantly higher in telerehabilitation compared to centre-based cardiac rehabilitation  . SeveralThe COVID-19 pandemic necessitated the delivery of cardiac rehabilitation by entirely virtual means for many programs, providing a potential catalyst for the adoption of such models in practice. However, uptake of telerehabilitation during this time was largely confined to phone and email modalities. A shift towards telerehabilitation interventions as described in successful trials will require significant organisational, clinical, and cultural change, particularly as issues remain around technological equity and literacy, reimbursement models, patient privacy and safety, quality of social interactions and staff capacity .Additionally, recent research suggests that ongoing implementation of stand-alone cardiac telerehabilitation programs is not the preferred model of care. Rather, clinicians both in Australia and abroad have reported a desire to deliver hybrid cardiac rehabilitation programs combining in-person visits for assessments, provider consultation and initial exercise sessions with virtually delivered exercise training and education . This enFurther research about the feasibility, effectiveness, equitability, service-level impacts and costs of telerehabilitation in Australian practice is warranted to understand the potential benefits of implementing hybrid models. Importantly, consideration of how these virtual models of care may impact underserved populations is required. While telerehabilitation may benefit those living in regional areas by improving access, it can also increase disparity for those with poor digital or health literacy, limited English proficiency, or from low socio-economic backgrounds . MoreoveThe current study has several limitations. Firstly, as we were not able to find trial data for an Australian cohort, the transition probabilities used in the model were derived from a range of published data sources. However, we believe that our study uses the best currently available information from published data sources representing the Australian population. Additionally, our analysis only applies to a specific application of telerehabilitation that we modelled in the study and does not imply that hybrid telerehabilitation will not be cost-effective with a different intervention or in a different context. It is hoped that two Australian telerehabilitation studies currently underway , 52 may Our study is the first to evaluate long-term cost-effectiveness of a hybrid cardiac telerehabilitation intervention in the Australian context. The results indicate that over the long-term, the Telerehab III model of hybrid cardiac telerehabilitation is highly unlikely to be cost-effective compared to current centre-based cardiac rehabilitation practice in Australia. Exploration of other models of delivering cardiac telerehabilitation and their implications for the diverse population groups they are intended to serve is still required. Our findings will assist policymakers and researchers to make informed decisions when designing future trials and planning for implementation of hybrid cardiac telerehabilitation programs.Additional file 1: Supplementary table 1. The studies which were considered for the economic evaluation.


Shadow coaching, a type of one-on-one provider counseling by trained peers, is an effective strategy for improving provider behaviors and patient interactions, but its effects on improving patient experience for English- and Spanish-preferring patients is unknown.Assess effects of shadow coaching on patient experience for English- and for Spanish-preferring patients.n=46,089) from an urban Federally Qualified Health Center with 44 primary care practices and 320 providers. One-third  were Spanish-preferring patients. We fit mixed-effects regression models with random effects for provider (the level of treatment assignment) and fixed effects for time (a linear spline for time with a knot and “jump” at coaching date), patient characteristics, and site indicators, stratified by preferred language.We analyzed 2012–2019 Clinician and Group Consumer Assessment of Healthcare Providers and Systems (CG-CAHPS) data  were shadow coached. Similar percentages of English-preferring (45%) and Spanish-preferring patients (43%) were seen by coached providers.Trained providers observed patient care by colleagues and provided suggestions for improvement. Verbal feedback was provided immediately after the observation and the participant received a written report summarizing the comments and recommendations from the coaching session.CG-CAHPS Visit Survey 2.0 provider communication composite and overall provider rating (0–100 scoring).We found a statistically significant 2-point  jump in CAHPS provider communication and overall provider rating among English-preferring patients of coached providers. There was no evidence of a coaching effect on patient experience for Spanish-preferring patients.Coaching improved care experiences for English-preferring patients but may not have improved patient experience for Spanish-preferring patients. Selection and training of providers to communicate effectively with Spanish-preferring patients is needed to extend the benefits of shadow coaching to Spanish-preferring patients. This avenue is chosen because good provider communication is crucial to the doctor-patient relationship7 Pursuing health equity necessitates analyzing data to understand differential effects of interventions and improvement efforts. Consumer Assessment of Healthcare Providers and Systems (CAHPS®) patient experience measures, the national standard for collecting, tracking, and benchmarking patient care experiences across settings including ambulatory care,11 are typically used to monitor and target modifiable provider behaviors to improve patient experience.13 CAHPS data can also be used to analyze the experiences of different patient subgroups.16Healthcare organizations also increasingly pursue health equity, “striving for equal opportunities for all social groups to be as healthy as possible, with selective focus on improving conditions for those groups who have had fewer opportunities.”17 The US population is estimated to reach 106 million by 2050.18 In addition, the US census indicated that 28% of Hispanics in the US had limited English proficiency, 42% spoke English very well, and 28% spoke only English.19 Therefore, incorporating effective communication and interaction strategies to serve the growing Hispanic, Spanish-preferring patient population is crucial for most health care providers. Many health-related settings, including primary care, provide interpreter services for their non-English speaking patients; however, there is mounting evidence that patients have language needs that go beyond simply needing an interpreter for them to receive the best medical care and care experience.22With the US Hispanic population increasing, and one-third having limited English proficiency, ensuring that providers have effective communication strategies for all their patients is critical. As of 2020, 18.7% (62.1 million) of the US population is Hispanic, an increase of 16.3% from 2010.24 that uses peers as coaches, who enter into an equal, noncompetitive voluntary relationship with those they coach, observe providers in real-time encounters at a point-of-care, and provide individualized structured, specific feedback to improve task performance and support positive changes.30 Sessions usually occur in dyads31 during a half- or full-day to observe several patient encounters.33 Mutual trust between recipients and coaches is essential for successful peer-coaching.37 Traditional medical professional mentorship is a long-term process through which an experienced person (the mentor) guides another (the mentee) in developing skills and knowledge for their professional development.38 The aim of mentorship is to enhance the abilities of the mentee, building their capacity to produce the desired career outcomes. Shadow coaching differs from mentorship in that it involves a half-to-full day of observation of direct patient care and provides specific oral and written feedback about how to improve provider-patient interactions; in contrast, mentorship is a long-term relationship with feedback about a large array of topics related to career and professional development.Healthcare organizations often use training to change or improve physician behavior and interactions with patients. Some groups and practices use individualized feedback or one-on-one provider counseling, known as “shadow coaching.” This is a type of collaborative learning39 The authors coded 1082 recommendations found in 92 shadow coaching reports. About half of the recommendations encouraged consistency of existing behaviors and half encouraged new behaviors. Most recommendations related to behaviors of the provider rather than support staff and targeted actions within the exam room rather than other spaces . The most-common recommendations were about behavioral aspects of provider communication and targeted verbal rather than non-verbal communication. In addition, most recommendations were deemed actionable  and encouraged new behaviors rather than encouraged existing actions. Recommendations to providers aimed at improving their interactions with patients need to not only suggest the exact behaviors assessed directly by patient experience surveys but also include actions indirectly associated with those measured behaviors.Additionally, the recommendations from coaches to providers that were supplied via written feedback reports highlight the content and actionability of the coach-provider interchange in shadow coaching.42 Quigley et al. examined patient experience scores before and after coaching that incorporated features consistent with the literature on successful behavior change: a learner-centered approach, immediate feedback, written recommendations on what skills to practice and suggested behavior change.43 They found significant immediate improvement in patient experience CAHPS measures of provider communication and overall provider rating following coaching with the gains for coached providers eroding and disappearing after 2.5 years. However, it is not known whether the effectiveness of shadow coaching differs for patients with limited English proficiency and prefer Spanish. We examine whether shadow coaching was similarly effective for Spanish-preferring and English-preferring patients.Shadow coaching has proven to be effective, with some studies finding that coaching helps build and maintain competencies among physicians, nurses, and other staff, and increases compliance with practice guidelines.9 Provider communication was selected, as it is the CAHPS composite with the highest correlation with the overall rating of care,3 meaning provider communication is the strongest “driver” of a patient’s overall rating.The study was conducted in a large, urban Federally Qualified Health Center (FQHC) in California that had a quality monitoring system based on the Clinician and Group CAHPS (CG-CAHPS) Visit Survey 2.0 overall provider rating and provider communication composite completed by adult patients or parents of pediatric patients.45Shadow coaching was part of the FQHC’s quality monitoring and improving of patient care experiences. Every 6 months, in January and July since 2015, the FQHC calculated every provider’s average 6-month score on the CG-CAHPS overall provider rating . The 74 providers with a 6-month average top-box score below 90 in the 6 months prior to calculation were selected for coaching. Details of the coaching intervention and its evaluation are described elsewhere.48 Provider assignments were based on geography; coaches were assigned regions to minimize their commuting time. Medical director coaches were not permitted to coach providers who reported to them. The shadow coaches observed providers, and, after the observation, provided verbal feedback about strengths and areas of improvement with a focus on patient-provider interactions. Coach feedback was based on their own experiences as high-performing physicians and broader insights into what makes for high-quality patient-provider communication derived from the coaching seminar. This initial feedback was followed by a written coaching report from the coach to the provider summarizing the comments and recommendations from the coaching session.32 The primary goal of the shadow coaching session was to identify and target areas of patient-provider interaction that a provider could improve when interacting and caring for their patients, with a focus on provider communication. Coaching occurred from March 2015 to August 2018.Eight full-time, high-performing providers (identified on the basis of patient experience and other performance indicators) were selected to shadow other providers for 4 or more patient encounters during a half-to-full day. Coaches attended a one-day coaching seminar by the SullivanLuallin group.t-tests and chi-square tests.The analytic sample consisted of 46,452 patients who completed the CG-CAHPS Visit Survey 2.0 assessing care from 320 providers from 2012 to 2019; 363 had missing information for provider rating and 12 missing information for provider communication. We compared provider and patient characteristics for the 46,089 respondents with data on both outcomes, who completed the survey in English vs. Spanish  and fixed effects for time (represented with a linear spline for time with a knot at coaching date), patient characteristics , and indicators for practice. This spline model allows for a change in slope  and for a “jump”  at the date of the intervention and a hypothesized change in scores (the “knot”). Allowing the trajectory to change at the time of coaching independently for the coached and uncoached groups addressed the possible threat of regression to the mean associated with performance-based treatment assignment. Uncoached providers were compared with coached providers by evaluating score changes at coaching and the slope following coaching. These two main effects in the model allow us to assess whether patients of providers who were coached had a significant change or jump in scores immediately following coaching and whether this jump declined after coaching over the remaining study period.We fit separate models for each of the two language groups corresponding to the stratification variable  for both outcomes, for a total of four models. A two-sided, 0.05 significance level was used. All analyses were conducted using the statistical software R and SAS. Study protocols were approved by RAND’s Human Subjects Protection Committee .n=14,631) were Spanish-preferring and 68% were English-preferring patients. Forty-three percent of Spanish-preferring patients (n=6355) and 45%  of English-preferring patients were seen by coached providers. For both languages, 82% were for adult patient visits. Spanish-preferring patients tended to be older, with a mean age of 46 years versus 37 years of age for English-preferring patients. Having a 4-year college degree was higher among English-preferring patients versus Spanish-preferring patients, 22% vs. 5% respectively. Also, as expected, a much higher percentage of Spanish-preferring patients were Hispanic (91% versus 59% for English language surveys). Tests of patient characteristic differences were all significant, except for adult versus child visits, with p-values < 0.001. Models adjusted for patient age, sex, general health status, and education and random effect indicators for practice.One-third  were Spanish qualified , including 12 of the 74 coached providers (16%)  versus 2.2 for English-preferring patients with coached providers  . For Spanish-preferring patients of coached providers, a decline was not observed.In the preferred-language stratified models, the coefficient for the immediate change at coaching  for Spanish-preferring patients was non-significant and less than half the magnitude of the jump for English-preferring patients: 0.9 for Spanish-preferring patients with coached providers  versus 2.0 for English-preferring patients with coached providers  . For Spanish-preferring patients of coached providers, a decline was not observed.For the provider communication composite, the trends are similar. The coefficient for the jump in provider communication for Spanish-preferring patients was non-significant and less than half the magnitude of the jump for English-preferring patients: 0.9 for Spanish-preferring patients with coached providers  for Spanish-preferring patients.The apparent difference in coaching effect for English- and Spanish-preferring patients suggests that the content of current coaching protocols should be revisited and refined with the input of providers who are especially effective with Spanish-preferring patients. Notably, only one-third of Spanish-preferring patients in the coached provider group of the study were seen by a Spanish-qualified coached provider. Coaching for providers with significant Spanish-preferring patient volume could also be arranged to include observation of interactions with both English-preferring and Spanish-preferring patients to ensure that the coach is able to recommend improvements unique to care for Spanish-preferring patients. Revision of protocols might include examination of and improvement in the provision of patient materials , linguistic support In addition, these findings also highlight the need to investigate the potential impact of provider-patient language concordance on patient experience and how language concordance impacts patient-provider interactions. Specifically for the shadow coaching program, selection and training of providers caring for Spanish-preferring patients could be refined to extend benefits of shadow coaching to Spanish-preferring patients. In particular, it may be challenging for providers to fully confer the lessons of shadow coaching unless they have mastery of a patient’s preferred language.Our study has limitations. First, this work is based on a single health care organization that is a large FQHC, so our findings may not be generalizable to other medical care settings. Additionally, two providers who were coached were not included in the analysis because of missing patient experience surveys either before or after their coaching data. Also, coached providers might unrepresentatively present only their best behaviors when being observed but observing a provider for a half to full day should still allow sufficient patient interactions for the coach to provide input and feedback on how a provider can improve their care for patients. Lastly, we defined Spanish-preferring patients based on the survey language of the completed CG-CAHPS survey, a proxy of actual language preference. Finally, the confidence intervals of treatment effects for English-preferring and Spanish-preferring patients overlap, so while there is clear evidence of a treatment effect for English-preferring patients and no such evidence for Spanish-preferring patients, there is not statistically significant evidence that the treatment effect differs by language preference (i.e., an interaction of treatment with language preference.Health care organization dually strive to improve patient care experience and health equity. Patient-provider interactions can be improved through one-on-one provider counseling that includes patient-care observation and individualized recommendations from trained peers, known as shadow coaching. Such coaching improved care experiences, as measured by the CAHPS overall provider rating and provider communication composite, overall and for English-preferring patients but may not have improved Spanish-preferring patient experiences. Targeted refinement to shadow-coaching, targeted coaching of language-concordant providers, and direct assistance of Spanish-preferring patients may broaden the benefits of shadowing and increase its contributions to improving health equity.


Adversarial attacks have become one of the most serious security issues in widely used deep neural networks. Even though real-world datasets usually have large intra-variations or multiple modes, most adversarial defense methods, such as adversarial training, which is currently one of the most effective defense methods, mainly focus on the single-mode setting and thus fail to capture the full data representation to defend against adversarial attacks. To confront this challenge, we propose a novel multi-prototype metric learning regularization for adversarial training which can effectively enhance the defense capability of adversarial training by preventing the latent representation of the adversarial example changing a lot from its clean one. With extensive experiments on CIFAR10, CIFAR100, MNIST, and Tiny ImageNet, the evaluation results show the proposed method improves the performance of different state-of-the-art adversarial training methods without additional computational cost. Furthermore, besides Tiny ImageNet, in the multi-prototype CIFAR10 and CIFAR100 where we reorganize the whole datasets of CIFAR10 and CIFAR100 into two and ten classes, respectively, the proposed method outperforms the state-of-the-art approach by 2.22% and 1.65%, respectively. Furthermore, the proposed multi-prototype method also outperforms its single-prototype version and other commonly used deep metric learning approaches as regularization for adversarial training and thus further demonstrates its effectiveness. Deep neural networks (DNNs) have achieved superior performance in a wide range of applications in computer vision ,2, incluadversarial training [One idea to defend deep neural networks (DNNs) from such adversarial attacks is through training the models that perform robustly on both clean and adversarially perturbed data. To attain a satisfactory performance, several defense mechanisms have been developed to alleviate the problem and increase the robustness of DNNs, where the most dominant defense approach is the technique called training .In adversarial training, adversarial examples are generated on the fly using adversarial attacks, such as the Fast Gradient Sign Method (FGSM) and projected gradient descent (PGD), and used to train a more robust model. Since adversarial training is one of the most effective defense methods in the literature, it becomes the foundation of many other defense algorithms. Besides adversarial training, recent works in adversarial defense also used metric learning as a regularization mechanism. Deep metric learning ,23,24,25Adversarial training serves as the foundation for various defense methods, including those employing strong data augmentation , auxiliaBased on the observation, we consider that samples of each class have multiple local clusters. The adversarial examples of the same class are misclassified into different classes. To tackle the problem of multi-modes, we propose an adversarial defense framework using adversarial training with multi-mode loss to accommodate the multiple centers of data. To verify the effectiveness of the proposed approach, we perform extensive experiments in both normal single-mode and multi-mode settings. We evaluate the proposed method on four publicly available benchmarks: MNIST , CIFAR10The experimental results show that the proposed method can effectively improve the robustness of PGD , Free 3, and Fas1.As per our knowledge, this is the first work to introduce a multi-prototype in adversarial training and consider the multi-mode nature of real-world data.2.The proposed framework leverages adversarial training and uses multiple centers for each class to train robust classification models on multi-mode datasets and achieves superior performance compared to existing approaches.The main contributions of this work are as follows:In recent years, there have been a large number of related works focusing on various approaches to adversarial attacks and defenses. Here we give an overview of relevant adversarial attack and defense techniques.Adversarial perturbations can mislead well-trained deep neural networks and were first observed in computer vision , which c restart . It can  restart , DeepFoo restart ; all fal restart  introduc restart ,47.Adversarial attacks have encouraged researchers to develop strong defense methods against such attacks ,49,50. AExtensive evaluations demonstrate that PGD is probably the most effective adversarial attack. Based on this conjecture, PGD-based adversarial training  was propFree [Free method takes FGSM steps with full step size N iteration on the same mini-batch (referred to as mini-batch replays). The perturbation of one mini-batch is passed to the next mini-batch as an initial value of perturbation. The total number of epochs is reduced by a factor of N; the bigger the N value the fewer epochs needed to get the same results as PGD-based adversarial training. We refer interested readers to [While PGD-based adversarial training is highly effective against adversarial attacks, it is not an efficient method due to the significant computational cost associated with generating PGD adversarial examples. To illustrate, training a ResNet model for CIFAR10 using PGD-based adversarial training can take up to three days on a single GPU. Furthermore, the first position model in the Competition on Adversarial Attacks and Defenses (CAADs) required 52 hours on 128 V100 GPUs. Free  proposedaders to  for moreFree training is faster than PGD adversarial training, it is not fast enough. Fast [While gh. Fast  developeng. Fast  effectivClass-wise Calibrated Fair Adversarial training (CFA)  adapts atraining  uses auxDeep metric learning using triplet loss aims to learn the similarity measure from raw materials (images) in the embedded space ,30,56. TIn this section, we present the motivation behind the proposed method, which is followed by a detailed discussion of the proposed algorithm. The adversarial training will be positioned as a baseline method. We further describe the training settings for the proposed method.C, from an image x to its corresponding softmax output C with argmax of softmax output. The true label for an image x is denoted as y. When C is denoted by x and untargeted attack, where the objective of the adversarial attack is to generate x and In this study, we consider x. The set of adversarial samples can be defined as follows:x can be perturbed. For the white-box settings, the adversary has full access to the model parameters and architecture.The main focus of this work is to improve the performance and robustness of deep neural networks against adversarial examples. The goal of an adversary is to mislead the classification model. We restrict l, perturbation Adversarial training (AT) is one of the state-of-the-art defense methods against adversarial attacks. Adversarial examples generated using untargeted attacks are commonly used in adversarial training. The untargeted adversarial attacks change the predicted output label to other labels without a specific target. Thus, AT tries to solve an optimization problem for a given network Equation .(2)minθThe threat model is to take The Projected Gradient Decent (PGD) is one of the most powerful adversarial attacks that has been used to create adversarial samples during adversarial training . AdversaClassification models usually treat data in the single-mode setting for each class. Similarly, adversarial training does the same without considering that there may be more than one mode for each class when each class has large intra-class variations. Training neural networks without considering the multi-mode nature in the real-world dataset could not effectively capture full data distributions. Our findings reveal that modeling the multi-mode nature using multiple prototypes for each class improves the performance and robustness of both clean and adversarially perturbed images.Multi-Prototype Adversarial Loss: To train a robust model for real-world settings, we adopt adversarial training with metric learning for adversarial robustness [bustness . For thebustness  loss froSoftTriple loss is defined in Equation .(3)LSofM, C, K are the numbers of training samples, classes, and prototypes for each class, respectively. To accommodate multi-mode settings for each class, SoftTriple loss has a regularization term. The following equation represents the objective function of the SoftTriple loss.The final loss (Equation .(5)LallTo be specific, SoftTriple loss employs multiple prototypes for each class that can effectively capture the hidden distribution of multi-mode data better since they help to reduce the intra-class variance. While training with multiple prototypes, this property is crucial to reserve the triple constraints over original examples. Compared with existing deep metric learning methods, the number of triplets in SoftTriple grows linearly with the number of original examples. SoftTriple loss encodes the prototypes in a fully connected layer and hence does not need to sample triplets while optimizing. The number of prototypes for each class has to be determined by SoftTriple loss during training. To overcome this issue, a large number of prototypes for each class has been used at the beginning, and we then gradually merge the closest prototypes according to their Free [N iterations on the same mini-batch (referred to as mini-batch replays). The perturbation is not reset between mini-batches, but the previous perturbation is continually used for the next mini-batch to recycle the gradient information. To account for the additional iterations of mini-batch replay, the total number of epochs is reduced by a factor of N to make the total iterations equivalent to T epochs of standard training. This algorithm is faster than standard PGD adversarial training and takes less time to train the same model. To further improve the training speed over the Free method, the Fast [Although PGD adversarial training  is curreFree  method tthe Fast  method dThe proposed method is evaluated to verify the robustness against state-of-the-art adversarial attacks and compare the results with existing methods. We conduct experiments on benchmark datasets: MNIST, CIFAR10, CIFAR100, and Tiny-ImageNet. We follow the same setting as ,30,37,38We conducted experiments based on widely used datasets for classification to demonstrate the effectiveness of the proposed method, including CIFAR10 , CIFAR10MNIST: This dataset consists of handwritten digits. It contains 60,000 training images and 10,000 testing images. MNIST is a ten-class dataset with gray-scale images with dimensions of CIFAR10: This is a ten-class dataset with RGB images with dimensions of CIFAR100: The CIFAR100 dataset has 50,000 training and 10,000 testing images of 100 classes. Each image is an RGB image with a size of Tiny ImageNet: This has 200 classes and each image is RGB with dimension CIFAR10-2. Similarly, the CIFAR100 dataset is converted into a 10-class dataset and uses the notation CIFAR100-10. The numbers of images in the CIFAR10-2 and CIFAR100-10 datasets are the same, and only the number of classes has been reduced. Unless specified, CIFAR10 and CIFAR100 are the original datasets. In addition, since there are more intra-variations for the Tiny ImageNet dataset, we do not reorganize the dataset as for the CIFAR10 and CIFAR100 datasets and directly use it for the multi-mode evaluation.For traditional single-mode settings, we mainly compare the proposed method with other approaches on the MNIST, CIFAR10, and CIFAR100 datasets. To verify the effectiveness of multi-mode settings, we further customize the CIFAR10 and CIFAR100 datasets to create multi-mode data for this experiment. The CIFAR10 dataset is converted into a 2-class dataset and denoted as d-dimensional embedding features, the output of layer 4 of the model is used with 512 hidden units using ResNet18 and 2048 for ResNet50. In all experiments, the model uses Stochastic Gradient Descent (SGD) with mini-batch size 32 for Tiny ImageNet and 128 for all other datasets, momentum 0.9, and weight decay The proposed method is implemented based on PyTorch and uses ResNet50 for Tiny ImageNet and ResNet18  for CIFAAWNBench  to trainAWNBench ,61, PGD-AWNBench , Free [3AWNBench , and FasAWNBench  across aStandard Training: We train the CIFAR10, CIFAR100, and Tiny ImageNet models for 120 epochs with an initial learning rate of 0.1 and reduce the learning rate by a factor of 10 after every 30 epochs during training. MNIST models were trained for 50 epochs using 0.01 as the initial learning rate, and the rate is reduced one time by a factor of 10.DAWNBench: This competition has shown that the classifiers can be trained at significantly quicker times and at much lower cost than traditional training methods. A cyclic learning rate with a minimum of 0.001 and a maximum of 0.2 was utilized and it reduces the number of epochs needed to train the models. All the models were trained for 15 epochs.N iterations on the same minibatch  where N) as 8 for CIFAR10, CIFAR10-2, CIFAR100, CIFAR100-10, and 4 for the Tiny ImageNet and MNIST datasets. Fast adversarial training uses FGSM with step size PGD7 (7 steps of PGD attack) is used for PGD-based adversarial training. The number of modes for MNIST is 5, while for CIFAR10, CIFAR10-2, CIFAR100, CIFAR100-10 is 10, and for Tiny ImageNet is 20. If two modes are similar in the latent feature space, they are merged into one mode in the multi-mode settings. The number of modes is set to 1 in single-mode experiments. Free adversarial training takes FGSM steps with full step sizes All training times are measured using a single GeForce GTX 1080Ti GPU. The proposed method has been tested against a variety of adversarial attacks including PGD, BIM, FGSM, and CW. PGD10 means 10 iterations of PGD adversarial attack with one random restart following . The λ1 Equation  are 0.5 To demonstrate the effectiveness of the proposed method, we use different epsilon and alpha values as shown in each experiment setting. The accuracies of clean examples and adversarial examples are measured to evaluate the performance of the proposed approach. For the MNIST dataset, a variant of the LeNet CNN architecture having batch normalization  is used As explained in In the following experiments, we evaluate the proposed method on the MNIST, CIFAR10, and CIFAR100 datasets and compare the performance with other state-of-art defenses. To begin with, we compare the proposed method trained in different adversarial settings on CIFAR10, including standard PGD and fast adversarial training approaches, where Ours (PGD), Ours (Free), and Ours (Fast) indicate that to train the proposed method in the corresponding normal or fast adversarial training settings. For each method shown in With standard adversarial training procedure, Ours (PGD) produces the best adversarial robustness against various adversarial attacks. In the subsequent experiments, we primarily focus on reporting the performance of Ours (PGD). For Then, we further evaluate the robustness of the proposed method on the multi-mode datasets, CIFAR10-2, CIFAR100-10, and Tiny ImageNet. The details of the customized datasets are mentioned in The experimental results show that the proposed method is not only better suited for the single-mode datasets but also further improves the performance on multi-mode datasets. Additional results of different hyper-parameters and settings are shown in The Deep Metric Learning algorithm learns similarity measures from raw images in embedding space. Triplet Loss Adversarial (TLA)  proves tIn this section, we also perform additional analyses to evaluate the effectiveness of different parameters under various attacks. We propose a multi-prototype adversarial defense method for multi-mode data. Our method builds upon recent advances in adversarial training ,19,30 anWe have thoroughly investigated the combination of the Adversarial Contrastive Learning (ACL) approach with other methods mentioned, such as PGD, Free and Fast, and TLA. In the case of combining ACL with PGD, it essentially duplicates the process ACL already performs during fine-tuning after self-supervision. As for Free and Fast, their training algorithm differs significantly from ACL, and incorporating ACL weights did not yield improved performance. Regarding TLA, utilizing ACL weights made the training process unstable, as TLA relies on triplet mining. Hence, the combination with ACL weights did not contribute to enhanced performance.U denote the dataset size, adversarial steps, classes, batches per epoch, and proxies of each class, respectively. We use the same training settings, including the number of epochs, batch sizes, etc, for all methods. The training time complexity of proxy-based losses  and unseen  adversarial attacks, shown in roxy-NCA  and Proxroxy-NCA ) is in groxy-NCA  and N-Paroxy-NCA  losses).(M3N/B2) . SoftTriOn the other hand, to better understand the effects of multiple modes in adversarial training, we also conduct experiments to explore the effects of changing the number of prototypes for each class for CIFAR10-2 under different cases. Thus, we confirm that the number of prototypes plays an important role when training a robust classifier using adversarial training. Hyper-parameters also affect performance. Unlike TLA, our method does not depend too much on the batch size because we did not use negative sampling. Equation  also affIn this experiment, we compare our method with AT, TLA, and RoCL for the retrieval of nearest neighbors in the presence of adversarial attacks in a multi-mode setting. We only show the top four retrievals for this experiment. The results of the CIFAR10-2 dataset, as shown in We present a multi-prototype metric learning regularization for image classification. Our method outperforms the current state-of-the-art method including TLA and PGD-AT, especially in the multi-mode settings. To the best of our knowledge, the proposed approach is the first work to take the multi-mode nature of each class of real-world datasets into consideration in adversarial training. The experimental results show that the proposed method also outperforms other deep metric learning regularizations for adversarial training. In the future, we will keep working in the direction of multi-stage loss, where the loss function takes input from the intermediate layers. Furthermore, future work will also concentrate on optimizing the number of prototypes more effectively.