Deep learning models, such as AlphaFold2 and RosettaFold, enable high-accuracy protein structure prediction. However, large protein complexes are still challenging to predict due to their size and the complexity of interactions between multiple subunits. Here we present CombFold, a combinatorial and hierarchical assembly algorithm for predicting structures of large protein complexes utilizing pairwise interactions between subunits predicted by AlphaFold2. CombFold accurately predicted (TM-score > 0.7) 72% of the complexes among the Top-10 predictions in two datasets of 60 large, asymmetric assemblies. Moreover, the structural coverage of predicted complexes was 20% higher compared to corresponding PDB entries. We applied the method on complexes from Complex Portal with known stoichiometry but without known structure and obtained high-confidence predictions. CombFold supports the integration of distance restraints based on crosslinking mass spectrometry and fast enumeration of possible complex stoichiometries. CombFold’s high accuracy makes it a promising tool for expanding structural coverage beyond monomeric proteins. These assemblies perform important functions, such as energy transduction4, transport5, and signal transduction6. The determination of the 3D structures of these assemblies is critical for understanding their function and evolution, interpreting the effects of mutations, and potential applications in drug discovery. The large size of some assemblies and conformational heterogeneity poses challenges for traditional structural characterization techniques, such as X-ray crystallography and NMR spectroscopy. While progress has been made using cryo-Electron Microscopy (cryo-EM), high-throughput structure determination of large assemblies is still challenging.Most proteins function as multimolecular assemblies in the cells. There are on average a few dozen interactions per protein7 and RosettaFold8. While AlphaFold2 was designed to predict single-chain proteins, it can also apply to predict protein complexes using the same architecture. Soon after its release, several techniques were developed to use AlphaFold2 to predict multi-chain protein complexes - first by using a linker9 and later by offsetting the residue index10. Similar techniques were used for the training of AlphaFold-Multimer (AFM)11 which is able to predict multimeric complexes with high accuracy using paired and padded multiple sequence alignment (MSA). On several pairwise protein-protein docking benchmarks AFM achieves a success rate of 40–70% for complexes consisting of two to nine chains up to 1,536 in total length13.Recently deep learning techniques significantly advanced our ability to predict high-accuracy protein structures. One of the most significant advancements was the release of AlphaFold213. The first difficulty is the requirement for significant resources, such as a graphical processing unit (GPU) with a large memory size. Currently, common GPUs have no more than 20 gigabytes of memory, enabling the prediction of complexes up to 1,800 and 3,000 amino acids for AFMv2 and AFMv3, respectively. Also, as AFM memory usage increases roughly quadratically with the number of amino acids7, any potential hardware advancements in the future are unlikely to have a significant impact. This limits the practical capability of many researchers to predict structures of large size, leaving many macromolecular complexes without a structure prediction. The second difficulty is sampling with a large number of restraints: as the number of chains and amino acids increases, the number of residue-residue contacts and distance restraints to optimize increases as well, making it harder for the model to converge to accurate structures. Large, multimolecular complex prediction is an out-of-domain inference setup for AFM since it was trained only on cropped regions and thus is not expected to perform well. The third difficulty is that AFM converges to a single (sometimes incorrect) structure (for each of the five available trained models) and it is highly challenging to obtain a diverse set of predictions for the same target14.However, AFM application for predicting structures of large assemblies is still challenging16, and the second is docking-based methods that rely on pairwise protein-protein docking19. Integrative modeling methods rely on information from multiple sources, such as crosslinking mass spectrometry, Förster resonance energy transfer, co-evolution, cryo-EM, and small angle x-ray scattering to compute models. This information is converted into spatial restraints and combined into an integrative modeling approach21, using specialized software packages23 to generate a set of structural models that are consistent with it. The generation of candidate models is often performed by a Monte Carlo search of the conformational space. Docking-based methods significantly rely on pairwise protein-protein docking for the prediction of complexes26 and do not require additional input information. In pairwise docking, the two input proteins are docked to one another using geometric shape and physicochemical complementarity. The main problem is that they sample thousands of docked configurations. While the correct ones are usually sampled, it is difficult to rank them as top-scoring. Typically, pairwise docking methods succeed in ranking a correct model among the Top-10 best scoring in 25–40% of the cases28. This low accuracy further complicates the multi-protein assembly stage, where methods have to consider a large number of pairwise protein-protein docking models. For example, Multi-LZerD18 builds the multimolecular assembly by applying a stochastic search driven by a genetic algorithm. Kuzu et al.19 construct the multimolecular complex iteratively, where a single subunit is added to the subassembly in each iteration. The CombDock method is hierarchical and combinatorial29. The complexes are constructed hierarchically by generating subassemblies of two or more subunits. At each stage, subassemblies are connected using pairwise docking configurations between subunits. Due to multiple possible hierarchical assembly pathways, the algorithm combinatorially enumerates assembly trees. Since the algorithms used for docking and scoring pairwise interactions have low accuracy, it is difficult to reach high accuracy in multi-subunit docking.Prior to the deep learning revolution, methods developed for the assembly of multi-protein complexes could be divided into two main categories. The first category is integrative modeling methods that mainly rely on experimental data30. However, the approach is applicable mainly to homomeric complexes with a success rate of ~30%. Inspired by this work, here we combine AlphaFold2 with a deterministic combinatorial assembly algorithm29. Our new method, CombFold, uses a small number of pairwise subunit interactions generated by AlphaFold2 for assembly instead of thousands generated by docking. The hierarchical and combinatorial assembly stage exhaustively enumerates possible assembly trees, maximizing the probability of correctly assembling the complex based on pairwise AlphaFold2 interactions. We validate our approach on two benchmarks of large heteromeric assemblies  and obtain a Top-1 success rate of 62% and Top-10 success rate of 72% (TM-score > 0.7). Moreover, CombFold is able to increase the structural coverage by 20% relative to experimental structures in our benchmarks. We also test the method on the benchmark of homomeric complexes used for MoLPC validation and obtain a Top-1 success rate of 57%. CombFold successfully assembles six out of seven CASP15 targets with over 3,000 amino acids. We apply the method on a set of complexes with known stoichiometry and without known structure from Complex Portal31 and obtain confident predictions.The recently developed MoLPC method relies on AlphaFold2 to produce configurations for pairs and triplets of chains and assemble them using Monte Carlo Tree SearchThe input to CombFold is the subunit sequences and optionally distance restraints, the output is a set of assembled structures. A subunit can be a single chain or a domain. The approach is based on combinatorial and hierarchical assembly via pairwise interactions. In principle, there is no limitation on complex size, as the complex can be divided into subunits suited for the GPU memory limit, and our current implementation supports up to 64 subunits. CombFold works in three major stages: (i) generation of pairwise subunit interactions by AFM, (ii) creation of a unified representation of subunits and interactions, and (iii) combinatorial assembly of subunits .In the first stage, we apply AFM to all possible subunit pairings. Following this, we create three additional AFM models for each subunit, ranging in size from three to five subunits, that include subunits with which the given subunit had the highest confidence-scored predicted pairwise interactions (Methods). The underlying concept is that some groups of more than two subunits form intertwined structures and therefore all of them should be predicted as a single model by AFM (Methods).In the second stage, to prepare input for the third assembly stage, a single representative structure for each subunit is selected and the transformations between representative subunits are calculated. This is required since there are multiple AFM structures for each subunit from pairwise AFM runs and their enumeration during the assembly stage is intractable. The representative subunit structures are extracted from the predicted modeled subcomplexes according to the maximal average plDDT score for this subunit. Next, we use all interacting subunit pairs (Cα-Cα distance < 8Å) from AFM models to extract pairwise transformations (rotation and translation in 3D) between their representative structures in the global reference frame. The representation of the input by representative subunit structures and transformations between them enables us to apply the combinatorial assembly algorithm with AFM interactions instead of docking-based ones. Each transformation is coupled with a score based on AFM’s PAE score (Methods).35. If a protein chain is divided into subunits (eg. domains), distance constraints are added to enforce sequence connectivity. This combinatorial assembly stage consists of i-th iteration we construct 3 and In the third stage, we use The model confidence score produced by our method is based on the AFM PAE score. Each pairwise interaction (represented by a transformation) has a PAE-based score (Methods). The confidence of an assembled structure is a weighted score of the transformations that were used for assembly, where the weight is proportional to the sizes of the subunit subsets that were merged by each transformation.30. It contains 153 complexes ranging between 500 and 10,000 amino acids with 10 to 30 chains per complex. This dataset contains mainly symmetric homomers (98 complexes consisting of one unique chain and 27 consisting of two unique chains). Finally, Benchmark 4 dataset contains seven CASP15 targets with more than 3,000 amino acids.We tested the method on four benchmark datasets . We gene36 which assesses the global accuracy of the complex, similar to CASP and MoLPC30. Similarly to CAPRI assessment37, a model is considered Acceptable-quality if the TM-score is above 0.7 and High-quality if the TM-score is above 0.8. The success rate is measured as a fraction of the benchmark complexes with Acceptable- or High-quality models among the Top-N best-scoring predictions.To evaluate the accuracy of the modeled structures we rely on the TM-scoreWe obtain a Top-1 success rate of 60% for CombFold on this benchmark, accurately modeling 21 out of 35 complexes  with TM-We compare CombFold to an end-to-end AFM on all the Benchmark 1 complexes using the A100 GPU card with 40-gigabyte memory. AFM succeeded in producing at least one result for 17 out of 35 complexes with up to 3,700 amino acids. Of these, 10 complexes were modeled with Acceptable- or High-quality, resulting in success rates of 26% and 29% for Top-1 and Top-5 results respectively , d.The largest complex assembled by CombFold was eIF2B:eIF2 . The performance on this dataset is comparable to Benchmark 1 , with To41. The information is used for scoring and sampling models to produce structures that are consistent with the available data. Here, we add to CombFold support for integrating information about known interactions between subunits and distance restraints that originate from crosslinking mass spectrometry. This type of information can be obtained for individual complexes in vitro or for multiple assemblies identified from in situ experiments45. AFM does not currently support the integration of this type of data. Recently, AlphaLink46 was developed to add distance restraints support to AlphaFold2/OpenFold as a bias to residue-residue contacts, similar to template support in AlphaFold2. This method requires subsampling of MSA to give more weight to distance restraints and is currently not applicable for complex structure prediction. The advantage of CombFold is that it can integrate additional information during the assembly stage (Methods).Integrative structure modeling is often used to determine the structures of large macromolecular assemblies using information from a variety of sources, such as crosslinking mass spectrometry, cryo-Electron Microscopy, or bioinformatics analysis47 to compile a set of 12 distance restraints. We also divided the chains into two groups for assembly (Methods), based on a known structure of a subcomplex of TIM9 and TIM10 (PDB 2BSK). The resulting model is of High-quality with a TM-score of 0.85 , a Benchmark 1 case, for which both CombFold and AFM failed to produce an accurate prediction . We used crosslinking mass spectrometry experiment for this complex of 0.85 .We obtain a Top-1 success rate of 57% on this benchmark, accurately modeling 87 out of 153 complexes , Table 113. In contrast, in the assembly of heteromeric structures, multiple pairwise interactions are considered, and pairwise interaction can form indirectly even if it is not predicted correctly by AFM. Therefore, the success rate of CombFold on heteromeric complexes is higher , a partial subcomplex of two HucS and two HucL subunits could be assembled . The oth31. We queried the database for all complexes with over 5,000 amino acids, known stoichiometry, and without homology to any experimentally determined structure (Methods) to obtain 28 complexes from three organisms . High-confidence structures were found for seven complexes  that is mainly applicable to homomeric complexes, our combinatorial algorithm doubles the success rate from ~30% to ~60% . Further, we only kept the entries where the numbering and residue type of ATOM records was consistent with the SEQRES records to enable simple and accurate model validation. This filtering protocol resulted in 35 structures that were used as the test dataset for the pipeline  and retained only those that contain at least 5 unique (distinct) chains, resulting in 4,997 entries. Next, we removed entries containing DNA or RNA molecules because they can cause conformational changes upon binding to the protein complex, as well as, entries with short (<50 amino acids) or long chains , resulting in 1,383 entries. We also filtered entries that contained multiple source organisms, such as host-pathogen interactions, as AFM performance for complexes often depends on the availability of paired alignment, leaving 682 entries. To remove redundancy, the complexes were clustered based on sequence identity. An entry was removed if it had a matching entry with an earlier release date and at least 80% of the chains matching with 70% sequence identity, leaving 213 entries. We also removed all entries released before May 2018 because they were part of the AFM training datasetThis benchmark was prepared following a protocol used for Benchmark 1 with several changes. First, the PDB entries used are all entries up to March 2023. Second, instead of filtering entries released before May 2018 (AFMv2 cutoff date), we used September 2021 (AFMv3 cutoff date). Also, we did not filter complexes with chains over 1,500 amino acids. This resulted in 25 complexes .30 that contains 175 complexes. 17 complexes were removed due to incorrectly annotated biological units in the PDB . In addition, 5 structures of amyloid fibrils were also removed  due to the expected compositional and conformational heterogeneity.We relied on a dataset by Bryant et al.31 is a database that curates information from literature on stable, macromolecular complexes. We queried all complexes listed with over 5,000 amino acids with known stoichiometry. Entries with a homologous PDB structure were removed using a stricter threshold for homology to focus on entries that are more likely to be novel. Therefore, we defined a PDB entry as homologous to the Complex Portal entry if at least 66% of the chains were matching with at least 30% sequence identity.The Complex Portal48 which were used to identify linkers between domains. The linkers connecting the domains were not included in the prediction. For Complex Portal predictions, the UniProt sequences were divided until each pair of sequences was under 1,800 amino acids.A subunit is a sequence that can either be an independent chain of the complex or a part of a chain (for example a certain domain). Sometimes it is necessary to divide a chain into a number of subunits - either because the chain is too long to be predicted by AFM or because domains are connected by a long linker and are not in spatial proximity. For Benchmarks 1 and 3 each subunit was a full single chain as defined in the SEQRES segment of the PDB entry. In two cases , the longest chain was too long to be predicted as part of a pair  so it was divided into two subunits, one containing the first 1,000 amino acids and the other with the rest. For Benchmark 2, long chains were divided into subunits evenly until every pair could be predicted by AFMv3. This affected five complexes . In the CASP15 benchmark, the chains were divided into subunits according to IUPred3 domains68. To account for this, AFM prediction is applied for larger subsets of three to five subunits as follows. For each subunit, we select the most likely interacting subunits based on the pairwise PAE interaction score and use them to build larger subsets (Methods). Here, we limit our calculations to the total length of input sequences of 1,800 which can be run on standard GPUs.In the first stage, we run AFM on each possible pairing of the subunits. Proteins, both homomers and heteromers, have the ability to create intertwined structures where the interacting chains exchange small segments or compact protein substructures. These interactions can result in a wide range of quaternary arrangements, including dimers, or higher-order oligomers69 with default parameters (without templates), producing five structures per run. Subunits were inputted as separate chains. For Benchmarks 1 and 3, we used AFMv2 and AlphaFold-ptm to obtain 10 structural models. For comparison to CombFold on Benchmark 1, only end-to-end AFMv2 was used. For Benchmark 2, CASP15, and Complex Portal predictions, we used AFMv3 only, as it was not trained on these targets. CombFold predictions on Benchmark 2 were compared to end-to-end AFMv3.AlphaFold2 runs were performed using ColabFoldEach subunit structure from AFM predictions is ranked based on the mean plDDT score using all predicted structures from AFM runs for pairs and larger subsets. The structure with the maximal score is selected as the “representative subunit structure” for the assembly stage. Additional criteria were examined as possible ranking scores including, the average PAE score for the structure, the maximal plDDT, or the interaction score with other subunits in AFM prediction. There were no significant differences between the described possibilities, the mean plDDT which is easy to calculate and more widely used was chosen.71. Similarly, the transformation The method computes for each pair of subunits a list of possible transformations between them based on their interaction models from AlphaFold2. All pairs of subunits are extracted from multi-subunit predictions. For each pair, if it is interacting (Cα-Cα distance < 8Å), the transformation between the subunits is calculated. We can mark the predicted interacting structure for two subunits Each transformation is scored using the PAE score of the two subunits. PAE score is computed by AFM for any two amino acids in the structure, predicting their alignment error relating to each other. The PAE score values are between 0 to 30 with lower values corresponding to a lower predicted error. The transformation score is calculated and normalized to be between 1 to 100 by the equation: 72 was also a good indicator for RMSD from correct transformation however not as good as the average PAE . In the case of ambiguity of crosslinked residues due to multiple copies of the same subunit, we require that one of the possible distances restrained by the crosslink is below the distance threshold.CombFold runtime is dominated by the AFM prediction runs for subunit pairs and larger subsets. On Benchmark 1, the average GPU time for AFM predictions was 548 and 1,164 seconds for subunit pairs and larger subsets, respectively, running on NVIDIA A30 with 24GB of memory. However, since our method requires 73 score relative to the transformation in the target complex is at an acceptable level (DockQ > 0.23). We calculate the connected components of this graph. The pairwise connectivity ratio is defined as the ratio between the number of amino acids in the largest connected component and the total number of amino acids in the complex. A single connected component in the graph (pairwise connectivity = 1.0) indicates that there are pairwise transformations that can lead to the assembly of the complex. In contrast, multiple connected components indicate that accurate assembly is not possible with available transformations.Given a set of pairwise transformations and a target complex structure, this metric measures how many of the pairwise transformations between subunits from the target complex are present in the set. A graph is built, where each node is a subunit in the target complex and an edge is present if there exists a transformation in the set between those subunits for which the DockQ74. Graphs were created using Matplotlib75.Protein complexes were visualized using ChimeraXSupplement 1


DOP2, ABCB1, and COMT were genotyped and correlated with the AE to find candidate genes. Furthermore, a review of all medications assessed in a clinical trial for adults with autism was performed to enrich the search for potential pharmacogenetic markers, keeping in mind the usual medications. Results: The majority of the study population were men (75%) with multiple comorbidities and polypharmacy, the most frequently prescribed drugs were antipsychotics (69%); 21% of the participants had four or more AEs related to psychotropic drugs. The most common were “Neurological” and” Psychiatric” (both 41%). Statistical analysis results suggested a significant correlation between the neurological symptoms and the DOP2 genotype, given that they are not equally distributed among its allelic variants. The final review considered 19 manuscripts of medications for adults with ASD, and the confirmed genetic markers for those medications were consulted in databases. Conclusion: A possible correlation between neurologic AEs and polymorphisms of DOP2 was observed; therefore, studying this gene could contribute to the safety of this population’s prescriptions. The following studies are underway to maximize statistical power and have a better representation of the population. Introduction: Up to 73% of individuals with autism spectrum disorder (ASD) and intellectual disability (ID) currently have prescriptions for psychotropic drugs. This is explained by a higher prevalence of medical and psychiatric chronic comorbidities, which favors polypharmacy, increasing the probability of the appearance of adverse events (AEs). These could be a preventable cause of harm to patients with ASD and an unnecessary waste of healthcare resources. Objective: To study the impact of pharmacogenetic markers on the prevention of AE appearance in a population with ASD and ID. Methods: This is a cross-sectional, observational study  in the ASD population. Sociodemographic and pharmacological data were gathered. The Udvalg for Kliniske Undersøgelser Scale  was used to identify AEs related to the use of psychotropic medication. Polymorphisms of  Autism spectrum disorder (ASD) is a lifelong neurodevelopmental disorder that involves deficits in social interactions and repetitive/restricted behaviors . The estThese individuals usually have prescriptions for psychotropic drugs in the context of symptomatic treatment for irritability or behavior disorders . Those pDOP2, which codes for the dopamine receptor D2, is involved in the action mechanism of the above-mentioned medications, e.g., risperidone ) AND  AND (humans [Filter]) AND ).The procedures to select manuscripts included in this work were all clinical trials performed for (a) adults (age > 17 years old) (b) with ASD demonstrated by criteria published in the Diagnostic and Statistical Manual of Mental Disorders (DSM-51 and DSM-IV14) and confirmed using the Autism Diagnostic Interview Revised (ADI-R12) and/or Autism Diagnostic Observational Schedule (ADOS-2), (c) randomized with a control group (either placebo or standard treatment) against the intervention drug, (d) with the efficacy of their pharmacological response producing a change at the neurobiological and behavioral level in ASD evaluated, and (e) achieving a threshold of a 50% score in the requirements of the Consolidated Standards of Reporting Trials (CONSORT15). All works not compliant with these criteria were considered excluded manuscripts in the present work.The evaluation process of the articles retrieved through searches began by examining their titles and keywords. Following that, abstracts of selected articles were screened. The final step involved reading the full text of potential manuscripts and assessing them using the CONSORT guidelines.To assess the potential risk of bias in the selected studies, we used the Cochrane tool   for randAn optimal sample size was not determined for this study. All study participants were patients at the hospital where the research was conducted, and the size of the sample was in line with previous studies pursuing similar goals. The symptomatology scale  was categorized as a dichotomous variable (presents symptoms yes/no) for each symptomatic group. Each gene’s allele was categorized as a qualitative variable of 3 categories . This gives us a resulting statistical analysis when combining each dichotomous variable of the symptomatic groups with qualitative variables (3 categories) of allele variants.To prove if there are significant differences in a binary variable between more than 3 independent groups, it is common to use the Chi-Square test; however, in our model, there are only n < 30 subjects obtaining expected values lower than 5; therefore, the Chi-Square test is not viable . Given the above, the Freeman Halton extension of Fisher’s Exact Test with Yates correction was used to calculate the probability (two-tailed) to obtain a value distribution in a contingency table, given the observations on each cell.DOP2, COMT, ABCB1.1, and ABCB1.2) = 16 hypothesis contrasts. This analysis was performed only to explore relevant relationships between genes and AEs. Hardy–Weinberg Equilibrium was assessed using the equation and considering the final Chi-Square value with a degree of freedom. All analyses were carried out in the software R studio 3.3.0; significant differences were considered when p-value was <0.05.This results in a series of statistical analyses (4 symptomatic groups yes/no) × 4 genes (DOP2 genotype, given that the alleles were not equally distributed. Follow up studies are underway to maximize statistical power with a bigger sample size, which we expect will corroborate these results or suggest new hypotheses. Furthermore, they would contribute to a differential ASD prescription and tolerability. Considering these potential interactions plus subsequent monitoring could help us to understand the interindividual variability in autistic real-world subjects.Our data evidenced that most of the study population were men with multiple comorbidities, amongst which the most prevalent was dyslipidemia, being under two or three drugs co-prescription, mostly antipsychotics. These findings are supported by previous studies with similar results and should be analyzed through a sex/gender perspective. The highest percentage of a third of the subjects had no AEs, but nearly a quarter presented four or more, mostly related to neurological and psychiatric areas, specifically epileptic seizures and restlessness. This different safety profile should be analyzed more deeply through personalized medicine. Statistical analysis results suggested a significant correlation between the neurological symptoms and the


Aim2Be is a gamified lifestyle app designed to promote lifestyle behavior changes among Canadian adolescents and their families.Z score [zBMI]) and improve lifestyle behaviors among adolescents with overweight and obesity and their parents versus a waitlist control group over 3 months. The secondary aim was to compare health trajectories among waitlist control participants over 6 months (before and after receiving access to the app), assess whether support from a live coach enhanced intervention impact, and evaluate whether the app use influenced changes among intervention participants.The primary aim was to test the efficacy of the Aim2Be app with support from a live coach to reduce weight outcomes  or a waitlist control group . Adolescents’ assessments at baseline and at 3 and 6 months included measured height and weight, 24-hour dietary recalls, and daily step counts measured with a Fitbit. Data on self-reported physical activity, screen time, fruit and vegetable intake, and sugary beverage intake of adolescents and parents were also collected.P=.02), discretionary calories (P=.03), and physical activity outside of school (P=.001) declined, whereas daily screen time increased (P<.001) after receiving access to the app compared with before receiving app access. Adolescents randomized to Aim2Be with live coaching reported more time being active outside of school compared with adolescents who used Aim2Be with no coaching over 3 months (P=.001). App use did not modify any changes in outcomes among adolescents in the intervention group.A total of 214 parent-child participants were randomized. In our primary analyses, there were no significant differences in zBMI or any of the health behaviors between the intervention and control groups at 3 months. In our secondary analyses, among waitlist control participants, zBMI (The Aim2Be intervention did not improve zBMI and lifestyle behaviors in adolescents with overweight and obesity compared with the waitlist control group over 3 months. Future studies should explore the potential mediators of changes in zBMI and lifestyle behaviors as well as predictors of engagement.ClinicalTrials.gov NCT03651284; https://clinicaltrials.gov/ct2/show/study/NCT03651284RR2-10.1186/s13063-020-4080-2 Although prevalence levels have stabilized over the past decade in Canada, 1 out of 5 and 1 out of 7 children have overweight and obesity, respectively -5. GivenCurrent standards of care for childhood obesity management involve family-based interventions that target multiple behaviors associated with obesity  ,10. AlthZ score [zBMI]) and improve lifestyle behaviors among adolescents with overweight and obesity and their parents versus a waitlist control group over 3 months (primary aim), (2) compare health trajectories among waitlist control participants over 6 months (before and after receiving access to the app), (3) assess whether support from a live coach enhanced the Aim2Be app impact, and (4) evaluate whether app use influenced changes in health outcomes among intervention participants from baseline to 3 months and then from 3 to 6 months. We hypothesized that participants (child-parent dyads) randomized to the intervention group who had access to the Aim2Be app with live coaching would improve their weight and health behaviors compared with participants who were randomized to a waitlist control group.To fill this knowledge gap, an mHealth lifestyle behavior intervention (the Aim2Be intervention) was developed to promote healthy behaviors related to nutrition, PA, and screen time among Canadian families . In thisThe trial was prospectively registered in August 2018   and has The evaluation protocol for the study was approved by the Children’s and Women’s Research Ethics Board at the University of British Columbia (H16-03090/H17-02032), the Health Research Ethics Board at the University of Alberta (Pro00076869), the Hospital for Sick Children Research Ethics Board (REB1000059362), the Hamilton Integrated Research Ethics Board (Project #4250), and the Children’s Hospital of Eastern Ontario Research Ethics Board (18/01E). A detailed protocol has been published , which iFamilies randomized to the intervention group also received tailored messages from a health coach, with the option of scheduled and unscheduled text support. The health coach was a registered dietitian trained in motivational interviewing and had experience in both pediatric lifestyle management and working with families. This health coach communicated with participants through the in-app text feature, and participants had the option of scheduling a web-based appointment if they wished to do so. The health coach sent an initial contact to all participants enrolled in the intervention group and sent follow-up supportive messages within the app on a regular basis.Waitlist control participants were put on a waitlist for 3 months, during which they received a brochure with Canadian health recommendations about PA , diet 2, screen Throughout this manuscript, the Aim2Be intervention included access to the Aim2Be app and the live coach. The theoretical framework guiding the development of the Aim2Be app has been previously published and summarized below . Aim2Be Aim2Be was developed iteratively to reflect the current recommendations and best clinical practices for pediatric obesity management. Aim2Be became a gamified app that supports youth and their families in initiating sustainable behaviors in 4 primary areas: healthy eating, active living, reducing screen time, and healthy sleeping habits. It retained its focus on linking behaviors with health and living green, as well as adding emphasis on healthy body image and self-esteem. The Aim2Be app uses strategies to strengthen self-regulatory skills through self-guided goals, as well as both planning and self-monitoring of lifestyle behaviors. Strategies used by the various app features were grounded in the behavior change taxonomy by Michie et al , which sThe first version of the app was field-tested for 4.5 months among 301 teens  . The quaThe following 3 versions of the improved version of the app were used in this RCT: a preteen version (for adolescents aged 10-13 years), a teen version (14-17 years), and a companion parent app, with some variations in app features depending on the user type. Once they enrolled in the app, all preteen and teen users were asked to select a personalized animal avatar as part of the onboarding process to personalize their profile. After enrolling in the app, users were offered a selection of aims to address  and then provided with tasks to help them set incremental goals, plan, and self-monitor their behaviors. Users then progress along their journey by completing quick wins and quizzes. In the preteen and teen versions of the app, users received currency that they could draw on to unlock items (collectibles) and purchase their own adventure stories. All users  were provided with tools within the app, including self–check-ins and articles, to further support their journey. In the teen and parent versions of the app, users were able to interact with one another through a moderated social wall, where they could post, comment, and react to each other’s posts. Participants who were randomized to the intervention arm of the trial received an initial in-app text message from the live coach and were sent follow-up supportive messages within the app on a regular basis.Participants were recruited from November 2018 to July 2019, and a 6-month follow-up that ended in June 2020. We screened the participants for the trial and later collected the baseline data. Participants were only randomized once they completed all baseline data, so there was a gap in starting the trial from initial recruitment, which explains the longer study period than anticipated in the protocol paper [Interested participants were directed to provide their contact information through an electronic form and were screened by telephone. Families were eligible for inclusion if they had a child between 10 and 17 years old who was overweight or obese , were cOnce eligibility was confirmed, parents and adolescents completed baseline measures, and all participants received a package containing a scale, measuring tape, activity tracker (Fitbit), and brochure with current health recommendations. Once baseline measures were completed, participants were randomized and provided with a CAD $60 (US $46) incentive for e-transfer. A computer-generated randomization schedule was used to allocate participants into blocks of 4, 6, or 8 participants with a randomization ratio of 1:1 .The allocation schedule was concealed in the randomization module of REDCap and only assigned after informed consent and baseline assessments were obtained. Research team members did not enter or modify the allocation schedule; they were exclusively computer generated. Participants were not blinded to their allocation conditions, and allocation assignments were not concealed from researchers at the analysis stage.Parents were mailed a digital scale (Active Era) and a measuring tape (HDX Corp) with instructions (using the Centers for Disease Control and Prevention home protocol ) to accuPA in adolescents was measured using objective and self-reported data at baseline and at follow-up. The child PA questions were modeled after the International PA and Environment Network questions  and inquDietary behaviors were measured using a 7-item diet screener (for both adolescents and parents) adapted from the 2016 Canadian Community Health Survey , with soSedentary behaviors were measured with an adapted version of the assessment of screen time by French et al , which hSociodemographic variables were self-reported, and included parental age, sex, race or ethnicity, parental education, marital status, total household income, and recruitment site and method .Intervention use and retention or adherence were assessed using web-based data internally collected within the Aim2Be app for parents and adolescents. Intervention use was evaluated using the following indicators: (1) the proportion of participants who downloaded and used the app, (2) total time (in minutes) spent in the app over 3 months, (3) weekly proportion of participants who accessed the app and weekly mean minutes spent on the app over 3 months, and (4) the proportion of participants who used an app feature at least once over 1 month. These analyses included the whole sample, from both the Aim2Be intervention and waitlist control groups (who accessed the app from 3 to 6 months). These measures were computed separately for parents, preteens, and teens because a slightly different app version was developed for preteens .t test (1-tailed), 77 families were needed in each group. Additionally, to account for both missing data and attrition, we projected the need for at least 80 families in each group. As the waitlist control group received the intervention after 3 months and further attrition was expected, we required ~100 participants enrolled in each group . Power calculations were conducted using nQuery software (Statsols).On the basis of previously published data , a samplP<.05. Intention-to-treat principles were used, with all participants analyzed in the group to which they were randomized, regardless of whether they attended all data collection time points or completed the intervention.All analyses were performed using Stata version 15.1 (StataCorp LLC) and the significance level of all statistical analyses was set at t tests  and chi-square tests  were used to compare baseline differences between the groups.Descriptive statistics were generated to examine the participant characteristics and app use. Student P value associated with the interaction between group and time was used to determine the statistical significance of any difference between groups over time. Because the between-group differences at baseline were found for 2 outcomes (intake of unsaturated fats and frequency of sugary beverages), we ran models with and without baseline values for these outcomes to determine whether they impacted the results.The primary analyses for intervention outcomes were performed using linear mixed-effect models to examine changes in child anthropometry, health behaviors, and parental health behaviors, expressed as differences in the means and 95% CI between baseline and 3 months (Aim 1). The P value associated with the interaction between group and time was used to determine the statistical significance of any difference over time between groups. Finally, to examine whether app engagement  influenced 0 to 3 months and 3 to 6 months changes in health outcomes among intervention participants (aim 4), linear mixed-effect regression models were used. The P value associated with the interaction between total app use and time was used to determine the statistical significance of any difference over time between participants who used the app for less than 30 minutes over a 3-month period (“low app users”) and participants who used the app for at least 30 minutes over a 3-month period (“high app users”). The 30-minute cutoff was based on previous dose-response analyses from the formative evaluation phase of the app [Linear mixed-effects models were used to examine changes in health behavior trajectories among participants randomized to the waitlisted control condition before and after being given access to the Aim2Be app (Aim 2). This aim was originally not included in our protocol paper ; however the app . All theSensitivity analyses were conducted to evaluate differences in outcomes between the control and intervention groups to examine the stability of the results when the intervention sample included only those who used the app for at least 30 minutes in total (from baseline to 3 months). Posttest differences in outcome measures at follow-up between the groups were examined using analysis of covariance. The results are presented for both complete case analyses and multiple imputations. Finally, a portion of the 6-month data (n=32 parent-child dyads) was collected after March 2020 (the beginning of lockdown measures enforced in most Canadian provinces to stop the spread of the COVID-19 virus). Therefore, we conducted exploratory analyses to examine the stability of our results when participants were excluded from our analyses.The baseline characteristics of participants in the intervention arm are shown in P=.51). There were no between-group differences in health behaviors (coprimary outcomes) from baseline to follow-up, except in the control group (mean 3-month change=−42 min/day) in which the screen time was reduced significantly more than the intervention group . In our sensitivity analyses, no significant differences in any outcome emerged between the control group (n=105 at 3 months) and the reduced sample of intervention participants at 3 months . However, waitlist control participants reported, on average, fewer minutes of PA outside of school  and more screen time  after accessing the app than before accessing the app. We examined whether these findings changed after dropping participants who had the 6-month data collected during the COVID-19 pandemic and found no differences, except for the proportion of discretionary calories (P=.06) and total weekly PA (P=.004).The longitudinal analyses comparing pre- and postintervention changes within the waitlist control participants found a significant decrease in zBMI, meaning that participants experienced a greater decline in zBMI after receiving access to the app as compared with before receiving access to the app  and out-of-school PA (P=.001). Participants who accessed the app with coaching increased their out-of-school PA (mean difference: +41 min/week from baseline to 3 months), whereas participants who accessed the app without coaching decreased their out-of-school PA (mean difference: −14 min/week from 3 to 6 months). Participants with coaching also increased their total PA (mean difference: +28 min/week from baseline to 3 months), whereas participants who accessed the app without coaching decreased their total PA . Removing control participants who completed their 6-month follow-up during the pandemic did not change the significance of these findings.No significant between-group differences were found for most outcomes in the analyses that evaluated the effect of additional support from a live coach , except P=.04) and screen time (P=.005). From baseline to 3 months, parents reported a substantial increase in the time spent walking, which then decreased during the 3- to 6-month period. Among parents who reported using the app for at least 30 minutes, the decrease in time spent walking was smaller than that of parents who used the app for less than 30 minutes. From baseline to 3 months, all parents reported reductions in screen time. However, parents who were more engaged with the app reported the smallest reduction in screen time from baseline to 3 months, whereas parents who were less engaged with the app overall reported the largest decline in screen time in this period. Parents across all levels of app use reported similar increases in screen time  from 3 to 6 months. These findings remained largely unchanged after dropping participants who provided data after the beginning of the pandemic. However, the time-by-app use interaction for time spent walking by parents was no longer significant (P=.06).App use (minutes spent in the app) did not modify any of the changes in health outcomes for adolescents within the intervention group from baseline to 3 months and then from 3 months to 6 months . HoweverAmong the 214 parents randomized to either the intervention or waitlist intervention groups, 190 (88.7%) were enrolled in the app. Most parents accessed the app via an iOS or Android system, but 1021 out of 4567 sessions  were accessed via a web-based (computer) platform. Among the 190 enrolled parents, 182 (96%) logged into the app for at least one session.Of the potential 99 preteens, 87 (88%) were enrolled in the app and among the 87 enrolled, 85 (98%) logged for at least 1 session in the app. Out of the potential 115 teens, 102 (89%) enrolled in the app, and among those 102 enrolled, 91 (89%) logged for at least one session in the app. Most adolescents accessed the app via an iOS or Android system, but 582 out of 4877 sessions  were accessed via a web-based platform.By week 2, the percentage of participants who used the app at least once had decreased to <60% for both preteens and parents and <50% for teens . RetentiOur study is the first RCT to assess the efficacy of an app-based lifestyle intervention on zBMI and lifestyle behaviors among overweight and obese Canadian youth that was mostly smartphone-based with no clinical in-person component. This study found no evidence of an effect on zBMI scores or any coprimary outcomes  for intervention participants compared with those of a control group over a 3-month period. Our primary analyses revealed no significant differences in zBMI or any of the health behaviors between the intervention and control groups. Secondary exploratory analyses revealed the following mixed findings: among waitlist control participants, zBMI, discretionary calories, and PA outside of school declined, whereas screen time increased after receiving app access compared with before receiving app access. Overall, we did not find evidence of additional benefits in terms of giving participants access to a live coach, and app use did not modify any outcomes among adolescents in the intervention group.In contrast with 2 recent systematic reviews and meta-analyses that pooled findings from eHealth interventions targeting children and adolescents ,44, we dWhen we corrected for app use in our RCT analyses, our findings related to the efficacy of the app did not change, suggesting that the total number of minutes spent in the app over 3 months did not predict changes in health behaviors over time. However, it is worth noting that a crude measure of engagement  might not provide a nuanced and comprehensive picture of what aspects of the intervention “works” or leads to changes in health outcomes. Factor mixture modeling of web-analytics data from a previous version of the app among 301 teens who used the app for 4.5 months revealed distinct engagement profiles ranging from “uninvolved” teens to “dabblers,” “engaged,” and finally “keeners” . “KeenerP<.05 from paired t tests for both outcomes), indicating that these participants had already begun to change some of their dietary behaviors before receiving access to the app. These findings reiterate the importance of considering motivation and readiness for change before engaging in lifestyle interventions.Our findings suggest that the Aim2Be app might have had some effects on the waitlisted control participants. Although initially challenging to understand, there may be some plausible explanations for these findings. There could be seasonality effects, as the randomization of participants for this RCT could not be spread over the course of a full year. It is also possible that participants randomized to the waitlist intervention arm (control participants), who remained in the study to receive the intervention from 3 to 6 months, had greater motivation or readiness for change compared with participants randomized to the intervention arm. In a separate exploratory analyses, we found that adolescents in the waitlisted control group reported decreased total daily calories and energy from solid fats (saturated fats and trans fats) during the waitlist period  PA compared with waitlisted intervention participants, these differences could be attributed to seasonality effects, as the randomization of participants for this RCT could not be spread over the course of a full year.Previous research has shown the key role of parents in supporting health behavior changes through the household environment ,50. In aThis study had several limitations. The first and likely the most relevant being the generalizability of the findings given who were eligible to participate in this study (parent-adolescent dyads in Canada with either overweight or obesity). Therefore, these results may not apply to other populations (children without overweight or obesity) and adolescents outside Canada. Second, we found that preteens and teens predominantly spent time in the gamified components of the app instead of using its active ingredients (such as setting aims and completing tasks). There was an effort to strengthen access to the gamified or “fun” elements of the revised version of the app for this trial, which might explain why there was no association in the evaluation phase as compared with an earlier formative evaluation phase. Third, we could not control for seasonality effects, as the recruitment of participants for this trial could not occur over a year because of limited funds. Fourth, participants in both conditions were provided with their Fitbit at baseline (shortly after randomization), which could have resulted in an early PA intervention effect among the waitlisted control participants. Fifth, this study presented only the findings related to intervention efficacy, and there is a growing consensus on the need to explore metrics beyond efficacy, such as cost-effectiveness, reach, and engagement, which could allow a broader examination of the impact of such interventions .In summary, the Aim2Be trial was not effective in improving zBMI, PA, diet, or screen time over a 3-month period among overweight and obese Canadian children compared with those of a waitlist control group. However, secondary analyses revealed some beneficial effects of the intervention among waitlisted control participants who experienced a decline in zBMI and discretionary calories after receiving app access compared with those from before receiving app access. Future studies should explore the mediators of changes in lifestyle behaviors and identify strategies to increase app user engagement with the “active ingredients” of interventions.


This vicious pattern leads to further clinical deterioration, impaired functioning, and reduced quality of life 3. There is a plethora of evidence supporting the fact that long-acting injectable and depot antipsychotics can increase adherence to treatment, reduce the risk of discontinuation and hospital admissions4. It is also known that low fluctuations between peaks and lows in plasma drug levels could be related to a better tolerability profiles5. A new paliperidone palmitate prolonged release formulation, which is administered twice annually, has been approved as maintenance treatment for patients with schizophrenia who are already stable on the monthly or quarterly prolonged release paliperidone palmitate6.The lack of insight can be present among patients with a diagnosis of schizophrenia, which often results in lack of adherence to pharmacological treatmentsWe aimed to evaluate the transition of monthly and quarterly paliperidone palmitate to the new six-monthly formulation and patients’ satisfaction with it in a real-world clinical setting.We collected a basic epidemiologic questionnaire, responses to a query about local pain after administration, and the Drugs Attitude Inventory (DAI).A total of 21 patients from an outpatient clinic for severe mental disorders with a long evolution of their disease in Salamanca, Spain, were included. All of them had a DSM5-TR diagnosis of Schizophrenia. Sixteen were male and 5 female. The mean age was 42.6 years. 14 were receiving quartlery paliperidone palmitate (10 with high doses (525 mg) and 4 with moderate doses (350 mg)) and 7 were on monthly injections (6 with high doses (150 mg) and 1 with a moderate dose (100 mg)). Those receiving moderate doses of quarterly or monthly paliperidone palmitate were administered 700 mg of six-monthly paliperidone palmitate; 1000 mg were injected to those with higher doses. The mean score on the DAI scale was 8. Only one patient reported an increase in local pain after the injection, and another reported dissatisfaction with the administration in the gluteus instead of the deltoid muscle. The first administration of the new formulation in our site was on June 26th; to date none of these patients have required hospital admission due to relapse.Six-monthly prolonged release paliperidone palmitate seems to be an effective maintenance treatment for schizophrenia. In addition, this new formulation is well received and tolerated by patients previously on monthly or quarterly formulations of the same drug.None Declared


The median duration out of competition following a concussion for able-bodied athletes was 16 (80 athletes) compared to 51 days (8 athletes) in para-cyclists, which was not statistically different . Conclusions: This is the first study to report SRC concussion recovery times in elite cycling, including para-athletes. Between January 2017 and September 2022, there were 88 concussions diagnosed at BC and the median duration for time out of competition for all concussions was 16 days. There was no statistically significant difference in recovery times between male and females and para- and able-bodied athletes. This data should be used to help establish minimum withdrawal times post-SRC for elite cycling participation and we call on the UCI to review this data when establishing SRC protocols for cycling, with further research required in para-cyclists.Introduction: A concussion or sports-related concussion (SRC) is a traumatic brain injury induced by biomechanical forces. After a SRC diagnosis is made, a concussed individual must undergo a period away from competition while they return to their baseline level of functioning. The Union Cycliste Internationale (UCI) currently recommend a minimum of 6 days restriction from competitive cycling following a SRC but there is a growing feeling amongst those involved in brain injury research that this period is too short. Therefore, how much time should cyclists be removed from competitive sporting action following a SRC? Aims: To review the time out of competition following the diagnosis of a SRC for elite cyclists within British Cycling (BC). Methods: All medical records for elite cyclists within BC were audited for diagnoses of “concussion” or “sports-related concussions” from January 2017 until September 2022. The days out of competition following the concussion until ready to compete again  was then calculated. All diagnoses and management of SRC were undertaken by the medical team at BC and in-keeping with current international guidelines. Results: Between January 2017 and September 2022, there were 88 concussions diagnosed, 54 being males and 8 in para-athletes. The median duration for time out of competition for all concussions was 16 days. There was no statistical difference between males (median 15.5 days) and females (median 17.5 days) for time out of competition ( A concussion or sports-related concussion (SRC), referred to as SRC throughout this article, “is a traumatic brain injury induced by biomechanical forces” . The terConcussions carry potential acute and long-term consequences. Acute symptoms of concussion can be split into somatic, cognitive, mood and sleep effects . Acute sSome individuals can experience long-term effects following SRC and these include physical and cognitive deficits as well as mood disturbances . The morThe Sports Concussion Assessment Tool 5 (SCAT5) can be used by health care professionals in the acute evaluation of SRC in adults , with thThe 1st international conference on concussion on sport used expert consensus to propose a GRTP protocol that permitted a return to competition as soon as 6 days following injury for some adult individuals. It was, and still is, recognised that age affects return to play time, with younger, high school athletes taking longer to recover than older, professional athletes . Return The English Football Association state that the earliest a player aged 20 years or older can return to play is 6 days in an ‘enhanced care setting’  and thisTo review the time out of competition following the diagnosis of a concussion for all elite cyclists within the Great British Cycling Team.p < 0.05.Since 2017, all medical records within British Cycling have been kept on an electronic system (Performance Date Management System—PDMS). This allows the medical team to record diagnoses and then input appropriate medical notes. For this project, we audited all episodes of “concussion” (and “sports-related concussion”) recorded in the medical records for our elite cyclists from January 2017 until September 2022. Training and competition-related injuries were included for analysis but we did not differentiate between the two. All elite cyclists within the GBCT are full-time athletes, representing their country at the highest level of cycling sport, including international events such as the Olympic and Paralympic Games. There are approximately 120 riders each year included within the GBCT pool of riders. For each recorded episode of concussion, we calculated the number of days taken (from the day of injury) for the athlete to be medically cleared for a return to competition. The medical record for each concussion episode was created and updated by two medical doctors caring for the GBCT athletes, NJ and CG, with the SRCs diagnosed and managed as per international guidelines . Data wep-value 0.25). Meanwhile, the median duration out of competition following a concussion for able-bodied athletes was 16 days (80 athletes) compared to 51 days (8 athletes) in para-cyclists. However, there was no statistical difference between these two groups when compared using a 2-sample Mann–Whitney test, with the p-value being 0.39. A total of 18 riders suffered more than one concussion over the study period, with 1 rider having suffered 4 concussion episodes, 7 riders having 3 episodes and 10 having 2 episodes. Due to the small number of athletes involved, no further analysis was undertaken on riders with multiple concussions.Between January 2017 and September 2022, there were 88 concussions diagnosed and recorded . The ageThis is the first study to report SRC concussion recovery times in elite cycling, including para-athletes. Between January 2017 and September 2022, there were 88 concussions diagnosed in elite GBCT cyclists and the median duration for time out of competition for all concussions was 16 days. There was no statistically significant difference between males (median 15.5 days) and females (median 17.5 days) for time out of competition following a concussion. Additionally, there was no statistically significant difference between able-bodied athletes (median days 16 in 80 athletes) and para-cyclists (median 51 days in 8 athletes) for time out of competition following a concussion, albeit the numbers were low.Wasserman et al.  undertoop = 0.001) there was no difference in return to sport time between males and females. Indeed the retrospective cohort study by Chand et al. [Do females have a prolonged recovery compared to males following a concussion? The evidence for sex affecting concussion recovery times appears mixed. In the study by Iverson et al. , they und et al.  followedd et al.  states td et al. . Thus, tThe first position statement of the Concussion in Para Sport (CIPS) group was published in 2021 . This coWe have not documented the impact of SRC symptom severity and compared this to length of time to recover from the SRC. We are aware that higher initial symptom severity in the first 72 h post-SRC will cause a more prolonged recovery time from the SRC and would have influenced our results . AdditioThis is the first study to report SRC concussion recovery times in elite cycling, including para-athletes. Between January 2017 and September 2022, there were 88 concussions diagnosed at BC and the median duration for time out of competition for all concussions was 16 days. There was no statistically significant difference in recovery times between male and females and para and able-bodied athletes. These data should be used to inform minimum withdrawal times post-SRC for elite cycling participation. We call on the UCI to review these data when establishing SRC protocols for cycling and increase the minimum duration out of competition following SRC from the current minimum of 6 days to at least 14 days. Further research is particularly needed in para-cyclists to gain further understanding of their SRC recovery times.


The aim of this study was to evaluate the frequency of hypoglycemia and the treatment satisfaction in patients with type 1 diabetes (T1D) using insulin analogues.This observational retrospective study included 516 adult patients with T1D from 38 cities in Southern Brazil. Demographics and clinical data were collected using a self-report questionnaire. Hypoglycemia was defined as an event based on either symptoms or self-monitored blood glucose < 70 mg/dL. Treatment satisfaction was evaluated using the Diabetes Treatment Satisfaction Questionnaire status version (DTSQs) and with a specific question with scores ranging from 0–10. Common mental disorders were assessed using the General Health Questionnaire (GHQ-12).Overall, the mean age was 38 ± 14 years and 52% of the participants were women. The median diabetes duration was 18 years. The scores for insulin analogue treatment satisfaction were higher than those for previous treatments. DTSQ scores had a median value of 32 (interquartile range 29–35) and remained unchanged over time. The percentage of patients with hypoglycemia  was comparable across groups divided according to duration of use of insulin analogues. Most patients  screened positive for common mental disorders.Patient satisfaction with insulin analogue treatment was high and remained unchanged with time. Episodes of hypoglycemia also remained unchanged over time among patients using insulin analogues. Type 1 diabetes mellitus is a chronic and progressive disease with an increasing incidence over the past decades. Estimates project that 5–10% of all 12 million patients with diabetes in Brazil have type 1 diabetes .The Diabetes Control and Complications Trial (DCCT) has shown that strict glycemic control in patients with diabetes significantly decreases the risk of chronic complications . After tBecause of their pharmacological profile, insulin analogues can better mimic endogenous insulin production compared with human insulin, thus contributing to a decreased frequency of hypoglycemia and improved treatment satisfaction . Some stAnother limitation in interpreting the results of studies that have associated the use of insulin analogues with better patient satisfaction with treatment is that other parameters such as depression and anxiety can also impact patient satisfaction . Indeed,In addition to the concerns regarding the potential advantages of therapy with insulin analogues, it is unclear whether patient satisfaction with this specific therapy could reduce over time, as observed with other interventions in chronic diseases . We hypoHospital de Clínicas de Porto Alegre (Certificado de Apresentação para Apreciação Ética –CAAE-1.283.728).This was an observational study carried out from April 2016 to December 2017. The protocol of the study was approved by the research ethics committee of The primary outcomes were the frequency of hypoglycemia and treatment satisfaction of patients with type 1 diabetes mellitus after starting treatment with insulin analogues provided by the government, seeking possible predictors of greater satisfaction and fewer hypoglycemic events. A secondary outcome was the impact of the duration of treatment with insulin analogues on the primary outcomes.The public health system in the state of Rio Grande do Sul is geographically divided into 19 regional health coordinating units. These 19 units comprehend different numbers of municipalities, totaling 498 in the entire state. To determine the locations for data collection, we first selected 19 municipalities to represent each coordinating unit. Other 21 municipalities were randomly selected to complete the number of patients necessary to represent the regional health coordination units that received insulin analogues in the state.Patients were invited to participate in the study upon their arrival at the pharmacy to pick up short- or long-acting insulin analogues dispensed by administrative or judicial procedures. A written informed consent was signed by the patients included in the study or their legal guardians.The eligible population comprised patients aged 18 years or more, with type 1 diabetes mellitus treated with short-acting  and/or long-acting  insulin analogues supplied by the Health Secretariat of Rio Grande do Sul (SES-RS) via an administrative or judicial procedure. Patients with impaired cognition or communication barriers were excluded.i.e., type 1 diabetes mellitus, use of human insulin for at least 6 months, HbA1c level < 12%, at least two severe hypoglycemic events over 6 months, and follow-up with an endocrinologist for at least 6 months. Patients who did not meet all the criteria to receive insulin analogue via an administrative procedure appealed to the judiciary to receive the medication via a judicial procedure.To receive insulin analogues via an administrative procedure, a patient is required to have certain inclusion criteria, Clinical and sociodemographic characteristics were obtained using a self-report questionnaire. For the hypoglycemic outcomes, the questionnaire referred to the month before study enrollment. Hypoglycemia was defined as an event based on either symptoms or self-monitored blood glucose < 70 mg/dL. Severe hypoglycemia was defined according to the American Diabetes Association as any hypoglycemic event requiring assistance of another person to actively administer carbohydrate, glucagon, or other resuscitative actions . Nocturni.e., 0 represents “never”, and 6 represents “most of the time”. Scores for all DTSQs items except for items two and three were used for the total DTSQs score, which ranged from 0 to 36. Higher scores indicated greater treatment satisfaction.Treatment satisfaction was analyzed using the Portuguese version of the Diabetes Treatment Satisfaction Questionnaire status version (DTSQs), an 8-item inventory assessing therapy for type 1 diabetes mellitus in the previous weeks. This survey measures general satisfaction, adequacy, flexibility, diabetes understanding, and willingness to recommend the current treatment to other people and maintain such treatment . Each itFor mental health screening, the participants filled out the 12-item General Health Questionnaire (GHQ-12) translated into Brazilian Portuguese and validated for the Brazilian population . This seExperience with the current and previous treatments was also evaluated using a specific question with scores ranging from 0 to 10, obtained through a self-report questionnaire. Other relevant survey information, including the patients’ weight (kg), height (cm), and insulin dispensing process , were collected from the SES-RS computerized drug delivery system by a trained and authorized researcher.Considering an alpha error of 5% and a confidence interval range of 8%, the sample size calculation yielded 527 individuals.Data were described using measures of central tendency  and dispersion (standard deviations and interquartile range [IQR]) for continuous variables and absolute numbers and proportions for categorical variables. Analysis of variance (ANOVA) was used for comparing numerical variables with normal distribution, and the Kruskal-Wallis test for data without normal distribution. The Mann-Whitney and Wilcoxon tests were used for paired samples, and the chi-square test was used to compare categorical variables. P values < 0.05 were considered statistically significant. All analyses were performed using the statistical software SPSS v.16.0 .From 4124 patients with type 1 diabetes mellitus older than 18 years receiving insulin analogues from SES-RS, we selected 566 potential participants. Of these, 50 were excluded for not meeting the inclusion criteria, yielding a final sample of 516 patients. The patients in the final sample belonged to 38 of the 40 municipalities included in the sample selection. The collection of data from some municipalities was difficult and we were unable to achieve 100% representation for each coordinating unit.2  was 25 ± 5 kg/m2 . The med2 . RegardiAmong all patients included in the study, 350 (74%) reported hypoglycemia the month before data collection. Regarding the type of hypoglycemia experienced, 113 patients (22%) described severe hypoglycemia and 211 (41%) reported nocturnal hypoglycemia. A total of 144 patients (28%) reported more than four hypoglycemic events in the previous month. There were no differences among the percentages of patients reporting hypoglycemia when analyzed by groups divided according to duration of use of insulin analogues  or type The median DTSQs score in the overall sample was 32 (IQR 29–35) . The medThe satisfaction score with the experience with current treatments  was obtained with a specific question with scores ranging from 0 to 10. The score obtained  was higher than the score obtained for previous treatments (NPH and/or regular human insulin), which was 5 , reflecting increased satisfaction over time (p < 0.001). Patients without prior use of NPH or regular human insulin were not included in these analyses.Most patients  had a positive screening  for common mental disorders . The ratStrict glucose control in type 1 diabetes mellitus reduces the risk of chronic disease complications at the expense of an increased number of hypoglycemic events ,18. The Hypoglycemia is one of the main problems in patients with type 1 diabetes mellitus. These patients have an indeterminable number of episodes of asymptomatic hypoglycemia and an average of two events of symptomatic hypoglycemia per week . In the An interesting finding of our study was that the rate of hypoglycemia remained unchanged over time in patients who already used insulin analogues. In contrast, data from the literature indicate that the risk of hypoglycemia increases markedly with the duration of the disease .As a group with prior risk of severe hypoglycemia, our study patients who received insulin analogues via administrative proceedings abided to the requirement of the dispensing protocol, in which the presence of at least two events of severe hypoglycemia within 6 months was one of the criteria. These patients receive insulin free of charge from the state, but bear the expense of the needles that are necessary for insulin injection, thereby promoting, in many cases, excessive needle reuse .Similar to previously described in other self-report studies, the patients in the present study identified the episodes of hypoglycemia by the occurrence of symptoms, blood glucose testing alone, or a combination of both ,22. ThisAnother important finding of our study was that the patients were considerably more satisfied with the current diabetes treatment with insulin analogues compared with prior treatment with NPH and regular human insulin, as evaluated using a specific question with scores ranging from 0 to 10. This may be an important result since, according to the literature, treatment satisfaction is associated with better glucose control . PreviouIn our evaluation of the DTSQs per individual item, satisfaction was high in the six subscales of the questionnaire. Factors such as the similarity of insulin analogues to endogenous insulin with regard to pharmacokinetics, regimen complexity reduction, and dose frequency may have contributed to greater treatment satisfaction among our patients ,29. The Our study found a high rate of patients screening positive for common mental disorders (77%) compared with a previous study in 358 patients with diabetes, in which 29% screened positive for common mental disorders . HoweverOn analysis stratified by duration of use of insulin analogues, patients using insulin analogues for less than 1 year were older than those using insulin analogues for more than 5 years. This finding of patients newly enrolled in the protocol for the acquirement of insulin analogues being older at diagnosis may be related to a more stringent technical criteria for access to insulin analogues implemented after updates in the SES-RS dispensing protocol.e.g., HbA1c), which were not available in the SES-RS computerized drug delivery system. Also, we did not evaluate if hypoglycemia unawareness could have influenced our results. However, the characteristics of our study enabled the inclusion of more patients, driving significant observations in regard to the real-life impact of patient satisfaction with treatment. The sample represented more than 50% of most regional health coordinating units, and due to factors including temporary shortages of insulin analogues and difficulty of applying the questionnaire in some cities, did not include the entire planned sample but was overall representative of the population.Limitations of the present study include the fact that patients who are dissatisfied with the therapy or with the paperwork involved for acquiring insulin analogues in Brazil have the option to switch back to human insulin, which is also offered free of charge. Other limitations include the observational and retrospective design of the study, and the use of self-report data (which may not have reflected accurately the current blood glucose level of the participants in cases of hypoglycemia), and the absence of measurements of glycemic control (In conclusion, despite high rates of hypoglycemia and positive screening for common mental disorders, patients with type 1 diabetes mellitus using insulin analogues maintained great satisfaction with their diabetes treatment, which remained unchanged in the long term, unlike reports from other interventions in chronic diseases. Due to our study design, we were unable to confirm that the hypoglycemia rates were higher with treatment received prior to insulin analogues, as hypothesized. Observational studies, such as the present one, are essential since they are conducted in a real-world environment and provide valuable data regarding the use of a drug in clinical practice without the strict supervision of a randomized controlled trial. Additional studies and analyses are needed to investigate in each health macroregion of the state a potential association between hypoglycemia and conventional predictive factors of hypoglycemia including ethnic, cultural, and health-related factors.


Patients with systemic juvenile idiopathic arthritis (sJIA) and adult-onset Still’s disease (AOSD) have been under-represented in studies about safety of the COVID-19 immunization. We aimed to inquire about the safety and tolerability of COVID-19 immunization in this population.An anonymous online survey on closed Facebook groups for patients and parents with self-reported sJIA/AOSD was posted from June 27th until August 30th, 2022. Continuous variables were analyzed using t-tests or the Mann-Whitney U test if non-normally distributed. Fisher`s tests were used for categorical variables.Of a total of 167 responses, 17 were excluded. Ninety-nine patients received the COVID-19 immunization, and 51 patients did not. Patients in both immunized and unimmunized groups had a similar history of disease complications such as macrophage activation syndrome (50% vs. 49%), lung disease (17% vs. 29%), arthritis (51% vs. 50%), and pericarditis/myocarditis (10% vs. 8%). Unimmunized patients were younger  and had a higher incidence of a history of disease flare or severe side effects with other immunizations . Thirty-nine patients reported mostly mild immunization side effects. Severe side effects included 6 reports of disease flare and 2 reports of cardiac side effects . Seven patients reported side effects lasting ≥ 8 days. Three patients developed AOSD following COVID-19 immunization, and 2 of them had the only hospital admissions for immunization side effects. Regarding COVID-19 infection, 46 patients were infected without full immunization, and 33 were infected after 2 doses of immunization. There was one hospitalization in the immunized group, compared to one ICU admission leading to death in the non-immunized group. There was a trend (p > 0.05) toward a higher risk of disease flare after COVID-19 infection among non-immunized patients (43%), compared to immunized patients (24%).The COVID-19 immunization was well tolerated by sJIA/AOSD patients even in this group of patients with severe disease. There was a low incidence of disease flare with immunization. Most immunization side effects were mild and lasted < 7 days. The only ICU admission and death from COVID-19 infection occurred in unimmunized subjects.The online version contains supplementary material available at 10.1186/s12969-023-00911-x. It has been established that immunization is one of the most important tools for the control of the COVID-19 pandemic , 3. HoweData were generated via an anonymous online survey using the SurveyMonkey platform with anonymous data collection. The survey was posted on closed Facebook groups from June 27th, 2022, until August 30th, 2022. The sJIA Arthritis Parents Network Facebook group has 1900 members and the Adult Onset Still’s disease group has 3300 members. Membership in these groups is curated by group coordinators and is limited to patients with sJIA or AOSD and their family members. The survey consisted of 27 close-ended questions in English and a final open-ended question that allowed respondents to make any further comments. Inclusion criteria included the age of respondents over 18 years and a reported diagnosis of sJIA or AOSD, regardless of immunization status and country of origin. If the patient was older than 18 years old, they could answer the survey for themselves otherwise it could be answered by their caregiver. The survey was terminated if respondents did not report a diagnosis of sJIA or AOSD or reported an age less than 18. Additionally, responses that failed to indicate whether the subject had received at least one dose of the COVID-19 immunization (immunization status) were excluded from further analysis. This study was conducted in compliance with the Helsinki Declaration and was exempted from review by the NIAMS IRB, (IRB# 000920) with a waiver of written informed consent. Consent to participate was requested at the beginning of the survey.Continuous variables were analyzed as medians and interquartile ranges (IQR). Categorical variables were analyzed as absolute numbers and percentages. Continuous variables were analyzed using t-tests or the Mann-Whitney U test when data were not normally distributed. Categorical variables were analyzed using Fisher’s exact test. Logistic regression was used to identify factors associated with flares with COVID-19 infection.The survey received 167 responses. Seventeen responses were excluded from the analysis; 2 respondents were younger than 18 years old and 14 failed to answer the question about immunization status. One respondent had attested that they were older than 18 years old, but later marked that they were the patient, and selected a patient’s age younger than 18 years old. It was unclear which response was marked incorrectly, so we decided to exclude the patient from further analysis. Of the included patients, 131 had sJIA and 19 had AOSD. Among the 150 analyzed responses, 19 were from the affected subjects and 131 were from legal guardians responding on behalf of the patient.Patient demographics are displayed in Table Non-immunized sJIA participants were younger than the fully immunized  and had a younger age of onset of disease . There was a higher proportion of female participants in the immunized sJIA group compared to the unimmunized . About 3/4 of the respondents were from the United States, and most others were from English-speaking countries. The rate of immunization among AOSD was 95% vs. 62% in sJIA. Patients in both unimmunized and immunized sJIA groups had a similar history of disease complications such as macrophage activation syndrome (50% vs. 56%), arthritis (52% vs. 53%), and pericarditis/myocarditis (8% vs. 10%). The incidence of lung disease trended higher in the unimmunized group, although it was not statistically significant . The unimmunized group reported more disease flares and severe side effects from other immunizations compared with the immunized group . Table On average, unimmunized sJIA subjects had treatment histories that included a higher number of different medications, excluding non-steroidal anti-inflammatory medications, than immunized patients .Table The full list of medications paused for the immunization is listed in Supplementary Table 1 in Additional File 1. Twelve patients out of 99 who were immunized paused medication before immunization, including 6 who withheld methotrexate (60% of sJIA patients on methotrexate at the time of immunization), and one patient each who withheld oral glucocorticoids (6%), anakinra (20%), tofacitinib (11%), tacrolimus (20%), adalimumab (100%), and other JAK inhibitors (25%). One patient paused both leflunomide and tacrolimus for the immunization. Among the subjects who were on multiple treatments and who held some medications, several chose not to interrupt treatment with other medications before immunization, including tocilizumab (3 patients), NSAIDs (5 patients), hydroxychloroquine (2 patients), infliximab (1 patient), canakinumab (1 patient), and cyclosporine (1 patient).Thirty-nine patients (39%) reported immunization side effects, which are listed in detail in Table One 12-year-old male patient required an emergency department visit without the need for hospital admission following Pfizer immunization. He reported having had pericarditis as a side effect of the immunization. He did not specify the timing of this complication relative to the immunization. This patient had a prior history of pericarditis/myocarditis as a complication of his disease. His disease had been inactive at the time of the immunization, and he was only being treated with NSAIDs at the time.A total of 6 patients (4 JIA and 2 AOSD) reported disease flares associated with the COVID-19 immunization. They are all listed in detail in Supplementary Table 2 on Additional File 1. Three patients marked “disease flare” as a side effect of the immunization. Three other patients reported disease flares as part of other comments of the survey. One patient (Patient 3), who did not report any side effects of the immunization, wrote in the comments that their child came out of remission 5 days after receiving the second dose of the COVID-19 immunization. The child is still having disease activity 7 months after the immunization. One patient reported a large flare 2 weeks after the second dose of the immunization. One patient reported a flare of Crohn`s disease after the immunization. Five out of the 6 patients who reported some type of flare with the immunization were on inactive disease at the time of immunization, and the majority of them did not hold medications for the immunization. Only the patient who had a flare of Crohn`s disease held methotrexate for the immunization but continued on infliximab. All the other 5 patients who flared did not hold their medications. Four of the 6 patients who flared with the immunization  had a COVID-19 infection . All 4 of them also had a disease flare following the infection.Three patients reported to have had COVID-19 immunization as the trigger for their disease onset, and two of them required hospitalization. These were the only hospitalizations for immunization side effects. They are described in more detail in Supplementary Table 3 in the Additional File 1. One 63-year-old male patient reported having developed a multisystem inflammatory disorder akin to AOSD following the administration of the Oxford/ Astra Zeneca immunization. He required an ICU admission and he also reported to have developed atrial fibrillation and multisystem inflammatory syndrome in adults (MIS-A). It is unclear if the diagnosis was MIS-A or the new onset of AOSD. This patient did not specify which dose of the immunization triggered the reaction. However, he did receive a total of 4 doses of the immunization, including Pfizer/ BioNTech, and he reported having used anakinra at the time of the boosters, and he reported that he did not have the disease before the first dose. He did not specify if subsequent doses of the immunization triggered flares. One 35-year-old female patient developed symptoms of AOSD a few days after her immunization with Oxford/ Astra Zeneca immunization. She required hospitalization for her symptoms. One 60-year-old female patient developed symptoms of AOSD 10 days after receiving a booster with Moderna immunization. The patients did not specify if they had flares or side effects with subsequent doses of the immunization.Reasons cited by participants who decided not to receive a COVID-19 immunization are listed in Table Six patients were told not to get the immunization by a medical provider. Four of these patients had a history of flares or severe side effects with other immunizations. Five of these 6 patients had a history of MAS, and 3 of the 5 also had a history of sJIA-LD. All of these 5 patients reported a history of multiple different immunomodulators, including IL-1 inhibitors, IL-6 inhibitors, tacrolimus, cyclosporin, and JAK inhibitors. Three of those patients had used emapalumab, and one of them had received a stem cell transplant. The patient who had no history of MAS was advised not to get the immunization due to long COVID-19 following infection.Thirteen patients reported that the immunization was not available to the age group, and 1 patient reported an allergy to an immunization component. Eight patients reported other reasons for not receiving the immunization, which included one patient who reported a history of many severe medication allergies. One patient reported that they were told that they would need to stop the JAK inhibitors for one week before and one week after the immunization, and they were concerned with disease flare, so opted not to get the immunization. One patient believed that the benefit of the immunization did not outweigh the risk, citing the low risk of severe COVID-19 infection in pediatric patients. Two patients reported that the immunization had just become available to the age group and had not had a chance to get it yet.Of 75 patients who reported a history of MAS, 50 received at least one dose of the COVID-19 immunization. Sixteen patients had active disease at the time of the immunization, 29 patients had inactive disease, 4 patients were in medicated remission, and one patient had abnormal labs. The survey did not specify if the patient had MAS at the time of the immunization. Nineteen patients reported side effects from the immunization, which were mostly mild and lasted 7 days or less. Three patients reported some type of flare associated with the immunization, which are described in more detail in Supplementary Table 2 , and two patients had side effects lasting 8 days or more (patients 7 and 10). Of 33 patients with a history of sJIA-LD, 27 of them also had MAS. Characteristics of patients with a history of sJIA-LD are described in more detail in Supplementary Tables 4, stratified by immunization status. Of note, two children with sJIA- LD patients reported a history of a bone marrow transplant. There was only one AOSD patient who reported to have lung disease. This adult patient added as a free text that they had been diagnosed with a rare primary immune dysregulation disease, and also had immunologic changes as part of the presentation.Amongst the patients who answered the question about a COVID-19 infection, more patients tested positive for COVID-19 or had a diagnosis made by a medical provider in the unimmunized group compared with the fully immunized group . Three patients only received one dose of the immunization, and none of them had a COVID-19 infection. Some immunized patients contracted COVID-19 before receiving the immunization. Therefore, characteristics of the COVID-19 infection are listed in Table Eight sJIA patients reported developing MIS-C/MIS-A following a COVID-19 infection  The survey did not specify the dates of the infection, so it is not possible to determine the strain that caused it. These patients had a median age of 7.5 yo (IQR 2.5yo). One outlier patient was a 22 yo male. Seven of these patients had a history of MAS, and 4 of them had a history of lung disease. Seven of these 8 patients also reported having flared of the disease with the COVID-19 infection. Two patients (a 6 and a 4-year-old patient) reported that COVID-19 triggered the development of sJIA. One of these patients specified that the disease was diagnosed 6 weeks after the COVID-19 infection.Of the 79 patients who reported a history of COVID-19, we noticed a trend toward a higher risk of flare with infection amongst the un/incompletely immunized patients (20/46 patients − 43%) versus patients who had COVID-19 after at least 2 doses of the immunization (8/33 − 24%). This difference was not statistically significant (p > 0.05). Further confirming this, in a logistic regression including age, sex, age of onset of disease, diagnosis (AOSD vs. sJIA), immunization status, and history of immunization flare or severe side effects with other immunizations, only a history of flare or severe side effects with prior immunization had a statistically significant (p = 0.019) association with the risk of flaring with a COVID-19 infection. Only one of the patients who flared had held their medication for the immunization.This paper reported on the safety and tolerability of COVID-19 immunization in patients with sJIA and AOSD. The main finding of this study was that the immunization was well tolerated in this population, with very few severe side effects . The majority of the side effects reported were mild and lasted less than 7 days. This was true even for this population of patients who had a severe phenotype, with a high incidence of MAS (50%) and lung disease (17%), including many patients who had active disease at the time of the immunization (37%).Our respondents had a similar incidence of systemic side effects than the general population and other autoinflammatory diseases –8. The mThere were 6 reports of some type of disease flare with the immunization, out of 99 patients who received the immunization. Of those, all the patients who later contracted COVID-19 also flared with the infection. This suggests that the flare might have been specific to a component of the virus, and not specific to the immunization.We noticed a trend toward an increased rate of flares of sJIA/AOSD in patients who contracted COVID-19 while unimmunized, which could be a sign that the immunization might have prevented flares with subsequent infection. This finding should be interpreted with caution, since this was not the goal of the study design, and the immunized and unimmunized groups had significant differences at baseline, which could have accounted for the increased rate of flare, including younger age, history of flares with other immunizations, and higher number of medications used in unimmunized patients. In fact, in a logistic regression evaluating risk factors for flaring with a COVID-19 infection, having a history of flare or severe side effects with prior immunization was the only significant association. This suggests that these patients have a disease that is more susceptible to these types of triggers. Nonetheless, further studies should evaluate if COVID-19 immunization prevents disease flare with infection in sJIA.In our study of sJIA patients, the risk of flare with COVID-19 infection  was much higher than the risk of flare with the immunization (6%). This is in contrast with the findings of a recent cohort of pediatric rheumatology patients, in which the risk of flare with the vaccine was higher than the risk of flare with the infection  [Another important finding of this study was the evaluation of the reasons for not receiving the immunization. The main reasons for not receiving the COVID-19 immunization were due to concerns with the side effects of the immunization, both specific to sJIA/AOSD and not. There was also significant concern about interaction of the immunization with medications. Of note, some patients reported that the immunization had been contraindicated by a medical provider. Those patients tended to have severe disease, with a history of disease flares with other immunizations.Although this was not the intention of this survey, we identified 3 patients who reported to have developed AOSD following the COVD-19 immunization, and they were the only patients with hospital admissions for immunization side effects. The diagnosis of AOSD following COVID-19 immunization has been reported in the literature , howeverOne of the strengths of this study was the large number of patients, and the significant representation of patients with more severe spectrum of disease, which are normally under-represented in studies about immunization safety. We also reported the tolerability of the medication on patients who were using a diverse set of medications, including newer medications, such as emapalumab.The limitations of this study include the fact that this was an anonymous online survey, therefore all the information was self-reported, including the history of sJIA and AOSD. However, given that this survey was posted on closed Facebook groups, in which there is a review of the history of the patients before they are accepted, it is very likely that the population listed in this study indeed corresponded to patients with the correct diagnosis. Studies have also shown that the clinical characteristics of patients recruited through social media are similar to the patients recruited in traditional cohort studies, with some skewing towards patients with rarer conditions . While wFurthermore, the survey was also finalized shortly after the COVID-19 immunization was approved for children younger than 5 years of age, so many patients were not eligible for the immunization at the time of the survey, and this population is under-represented in our cohort. It is also notable that the approval of the immunization in the pediatric population, in general, occurred much later than in the adult population. This likely contributed to the significant difference in the age of the immunized and unimmunized groups in this study.This study shows that COVID-19 immunization was overall well tolerated in patients with sJIA and AOSD, across all spectrums of severity. This study also is a proof of concept that studies can be reliably done with the use of social media for research in patients with sJIA and AOSD, and allow for the capture of patients with severe phenotypes, which are normally under-represented in clinical studies.The COVID-19 immunization was well tolerated by sJIA and AOSD patients even in this severe group of patients. There was a low incidence of disease flare with immunization. Most immunization side effects were mild and lasted less than 7 days. The only ICU admission and death from COVID-19 infection occurred in unimmunized subjects.Below is the link to the electronic supplementary material.Supplementary Material 1