Systemic corticosteroids are a leading cause of drug-related complications, yet little has been done to quantify the dose-response relationship between systemic corticosteroid exposure and complications in patients with severe asthma.To (a) evaluate the risk of developing systemic corticosteroid-related complications by corticosteroid exposure in severe asthma and (b) quantify the associated health care resource utilization and costs.This is a retrospective study using administrative claims data from a large commercial database between 2003 and 2014. Multivariate generalized estimating equation models were used to compare corticosteroid-related complications in patients continuously exposed to at least 5 mg of prednisone or equivalent for ≥ 6 months with a 1:1 ratio of propensity score-matched patients with asthma who did not use corticosteroids.P values <0.05) for low (defined as < 5 mg/day), medium (≥ 5-10 mg/day), and high (>10 mg/day) exposure, respectively, relative to no exposure. Health care resource utilization increased significantly with levels of systemic corticosteroid exposure. Hence, incidence rate ratios for inpatient visits with low, medium, and high exposure relative to none were estimated to be 1.86, 2.40, and 3.37, respectively (P < 0.05).A total of 12,697 corticosteroid users and as many matched nonusers were identified. The odds of developing associated complications increased significantly in a dose-dependent manner with systemic corticosteroid exposure: odds ratios were 2.50, 2.95, and 3.32 (A significant dose-response relationship was found between the long-term use of systemic corticosteroids and the risk of developing systemic corticosteroid-related complications in patients with severe asthma, resulting in increased burden and costs on the health care system that intensified with systemic corticosteroid exposure. What is already known about this subjectLong-term use of systemic corticosteroids is a leading cause of drug-related complications.Asthma patients on maintenance corticosteroids have higher health care costs and notably higher nonmedication and nonasthma-related medication costs.What this study addsThis study shows that patients with severe asthma had a significantly higher risk of developing systemic corticosteroid-related complications compared with those not exposed to systemic corticosteroids.The identified risk significantly increased with systemic corticosteroid exposure, resulting in an incremental burden on health care resources, although the benefit of using systemic corticosteroid for asthma control was not assessed in the current study.3 They are also recommended for treatment of severe asthma (evidence level D) when other treatments have failed to control the disease.4 However, long-term use of corticosteroids is associated with short-and long-term complications such as fractures, susceptibility to infections, obesity, stroke, cataract, and skin thinning.5 According to data from the Healthcare Cost and Utilization Project, corticosteroids were the most common cause of drug-related complications in 2004, accounting for 10% of all drug-related complications and 141,000 hospital stays in the United States.6 However, as reported in a recent literature review, evidence for the risks and costs associated with SCS therapy is sparse and inconsistent, particularly in patients with asthma.7 Very few studies have considered the cumulative dose-response relationship between SCSs and their related complications, and no study has measured the association between the magnitude of SCS exposure and health care resource utilization.Systemic corticosteroids (SCSs) are widely used as frontline treatment in a variety of chronic inflammatory and autoimmune diseases, such as rheumatoid arthritis, systemic lupus erythematosus, or Crohn’s disease because of their potent anti-inflammatory and immunosuppressive properties.In this study, we investigated the risk of developing SCS-related complications for different degrees of SCS exposure compared with no SCS exposure and quantified the associated health care resource utilization and costs in severe asthma patients from a large U.S. commercially insured population.The Truven Health MarketScan Research Databases contain claims from > 300 contributing employers and 25 contributing health plans across all regions of the United States. The present study used a subset of asthma patients from 2 core Truven databases: the Commercial Claims and Encounters database (2003-2014) and the Medicare Supplemental and Coordination of Benefits database (2006-2013). These databases include information on enrollment history, patient demographic characteristics , claims for medical and pharmacy services, and complete payment information. For the purpose of this study, the total gross payment, including covered payer costs and patients’ copays and deductables, was used. The databases were de-identified and complied with the Health Insurance Portability and Accountability Act of 1996 to preserve patient anonymity and confidentiality.. SCS users were required to have ≥ 6 months of continuous chronic SCS use .9 These first 6 months constituted the baseline period, and the index date was defined as the first day with a daily dose of ≥ 5 mg prednisone equivalent following this period. Among asthma patients never exposed to SCS, those with ≥ 1 prescription for an asthma medication other than SCS were eligible for the control group (SCS nonusers). The index date was defined as the initiation of this medication. All patients were required to be continuously covered by their health plans for ≥ 6 months (baseline period) before their index dates. The follow-up period spanned from the index date to the earliest of disenrollment from health plan or data cutoff. Patients with conditions other than asthma that are commonly treated with SCS were excluded from the study . Patients with a dose per claim higher than the 99th percentile were excluded to avoid the effects of outliers. A retrospective, longitudinal, open-cohort study was conducted using de-identified claims from the Truven Health MarketScan Research Databases. Eligible patients were aged ≥ 12 years and had ≥ 2 administrative charges associated with a diagnosis for asthma To appropriately account for the fact that a patient’s asthma severity and SCS exposure may change over time, follow-up periods were divided into quarterly time intervals over which patient level of SCS exposure was categorized according to the cumulative SCS dose intensity . These quarters were also used to assess the occurrence of SCS-related complications, complication-related health care resource utilization, and complication-related health care costs.10The study outcomes were the risk of developing acute and chronic SCS-related complications and associated health care resource utilization and costs. Acute complications included infections and gastrointestinal complications, whereas chronic complications included cardiovascular, metabolic, bone- and muscle-related, psychiatric, ocular, skin-related, adrenal, and other  conditions  or (b) p11 and allcause and asthma-related health care costs. Frequency counts and percentages were used to summarize categorical variables, while means and standard deviations were used for continuous variables.Descriptive statistics were generated to summarize the patient baseline characteristics at the index date. Baseline characteristics included age, gender, region, calendar year of index date, pre-existing conditions that may influence the occurrence of SCS-related complications , history of SCS-related complications, Charlson Comorbidity Index (CCI),12 were used to compare the baseline characteristics between SCS users and SCS nonusers. Baseline characteristics with standardized differences below 10% were considered well balanced, as commonly found in the literature.12 SCS users and SCS nonusers were matched exactly with a ratio of 1:1 based on the propensity score. The propensity score modeled the probability of SCS use given demographic  and clinical (CCI and pre-existing conditions) covariates, as well as baseline asthma-related health care costs.Standardized differences, which are the commonly accepted metrics to evaluate balance in propensity score matching,The association between SCS exposure and outcomes was estimated through multivariate generalized estimating equation (GEE) models. This approach was chosen to account for the longitudinal and correlated nature of repeated quarterly data for the same patient on SCS exposure and outcomes and for the potential progression of confounders over time. The GEE models further controlled for key baseline characteristics –to account for potential differences in the characteristics of patients with different degrees of SCS exposure—and time-dependent variables .P values. In addition to the aforementioned key baseline characteristics and time-dependent variables, the GEE models controlled for the baseline CCI, the year of index date, and the presence of an SCS-related complication of interest or pre-existing conditions at baseline, where applicable (see Odds ratios (ORs), estimated with a GEE model using a binomial distribution with logit link function and exchangeable correlation structure, were used to assess the risk of developing SCS-related complications for patients with low, medium, and high SCS exposure relative to patients without exposure to SCS. The GEE model used to estimate the incidence rate ratios of health care resource utilization because of SCS-related complications was based on a Poisson distribution with independent correlation structure. Adjusted cost differences between patients with low, medium, and high SCS exposure and SCS nonusers were assessed with a GEE model using a normal distribution with exchangeable correlation structure. Nonparametric bootstrap procedures with 999 replications were used to estimate 95% confidence intervals (CIs) and All analyses were conducted using SAS software version 9.3 of the SAS System for Windows . Statistical significance was declared at a 2-sided test at α-level of 0.05 or less.The initial unmatched asthma population included 12,697 SCS users and 590,450 SCS nonusers , and botP < 0.0001). Similar results were observed for the risks of developing acute and chronic complications, although the OR for chronic complications was not significant for low SCS exposure. The odds of developing infections ; bone/muscle-related complications ; and skin diseases  increased by at least 36%, 42%, and 66% for low, medium, and high SCS exposure, respectively, compared with no exposure. For medium and high SCS exposure compared with no SCS exposure, the odds for cardiovascular complications  and psychiatric complications  increased by at least 73%, whereas they increased by at least 96% for gastrointestinal complications  and 32% for metabolic complications , 2.95 (95% CI = 2.60-3.35), and 3.32 (95% CI = 2.90-3.80) for low, medium, and high SCS exposure, respectively, compared with no exposure A. A signP < 0.0001). This was also reflected in inpatient visits  and emergency room visits .Exposure to SCSs was associated with significant increases in health care resource utilization because of SCS-related complications, and these increases were more pronounced with higher degrees of SCS exposure . In factP < 0.0001), and annual health care costs of SCS-related complications were estimated to be $2,670, $4,639, and $9,162 higher for patients with low, medium, and high SCS exposure, respectively, than for SCS nonusers. These cost differences were mainly driven by incremental pharmacy costs and inpatient visit costs.In this large retrospective open-cohort study of severe asthma patients, we found a significant dose-response relationship between degrees of SCS cumulative dose intensity and the risk of developing SCS-related complications. As expected, the magnitude of the dose-response relationship differed across categories of complications.14 Nevertheless, many previous studies found a dose and time relationship for serious long-term corticosteroid-related complications.18 In particular, fractures were often studied in the literature.23 The present study found that patients with low, medium, and high SCS exposure had a 1.36-fold, 2.28-fold, and 2.42-fold risk, respectively, of developing bone- and muscle-related complications, such as fractures, relative to patients without exposure to SCSs. De Vries et al. (2007) reported a significantly increased risk of developing fractures among obstructive airway disease patients exposed to an SCS compared with those who were never exposed to an SCS.19 Depending on the type of fracture, they estimated a relative risk varying from 1.37 to 5.65 for patients with a cumulative dose of SCS between 1 gm and 5 gm, and from 3.00 to 10.61 for patients with a cumulative dose > 5 gm.19Previous asthma studies focusing on SCS-related complications were found either to be conducted on small sample sizes, to be of short duration in SCS exposure or in follow-up, or to examine a limited number of complications.14 They found that costs for patients on maintenance oral corticosteroids were 43% higher than for those not receiving maintenance oral corticosteroids. For those patients on maintenance steroids, asthma-related medications were more expensive, but notably, their nonmedication costs and those for nonasthma-related medications were significantly higher. Interestingly, nonasthma medication included proton pump inhibitors and bisphosphonates, which are examples of therapies used to manage side effects of oral corticosteroid-induced morbidity. Recurrent exacerbations were also a significant driver of costs; costs for patients with > 2 exacerbations requiring oral corticosteroids were approximately 31% higher than for patients with < 2 courses of rescue oral corticosteroids.In their report, O’Neill et al. (2015) estimated the direct health care treatment costs from a National Health Service perspective using data from the British Thoracic Society Difficult Asthma Registry (n = 596) and examined factors that explain variations in costs.14 These results regarding health care utilization and costs are important for health care policy makers and payers, since they suggest that chronic SCS use, particularly at a high dose, imposes a large burden on health care systems. The report by Sarnes et al. (2011), which evaluated the burden of complications associated with corticosteroid use across different diseases, provided a similar conclusion.5 The authors estimated that reducing daily doses of oral and parenteral corticosteroids may result in a cost reduction of $1.76 million per 10,000 persons by avoiding 96 fractures per 10,000 persons. This finding is in line with our results, since we estimated that a reduction from high to medium exposure and from medium to low exposure may result in a cost reduction of $968,579 and $3.93 million per 10,000 persons, respectively, by avoiding bone- and muscle-related complications.In the current study, higher cumulative doses of SCS significantly increased all types of SCS-related health care resource utilization with a significant dose-response relationship. Patients with low, medium, and high SCS exposure were estimated to have $2,670, $4,639, and $9,162, respectively, annual incremental costs attributable to SCS-related complications relative to those without SCS exposure. These are comparable with the annual mean treatment costs among severe refractory asthma patients reported by O’Neill et al.  after conversion to U.S. dollars.26 Of note, patients with rheumatoid arthritis rarely receive any other source of exogenous corticosteroid, whereas patients with severe asthma generally additionally receive daily doses of moderate- to high-dose inhaled corticosteroids. Although several large retrospective studies have shown that long-term corticosteroid use, even at daily doses < 5 mg prednisone equivalent, is a significant independent predictor of SCS-related complications,27 no clinical trial in a rheumatoid arthritis setting has found statistically significant evidence of this relationship in low-dose SCS.26 However, in the current study, we found that low exposure significantly increased the risk of developing 3 types of SCS-related complications compared with no SCS exposure . Furthermore, the present study found a significant increase in health care resource use and costs associated with SCS-related complications compared with no SCS use for SCS doses < 5 mg. One explanation is that this significant increase in the health care resource utilization and cost burden could stem from follow-up visits or treatments for SCS-related complication episodes that may have happened before the low SCS exposure period, since all SCS users were continuously exposed to doses ≥ 5 mg prednisone equivalent during at least 6 months before their index dates. These results may indicate a lasting impact of SCS-related complications on health care resource utilization and costs associated with doses of SCS ≥5 mg, thereby supporting the need to develop corticosteroid-sparing therapies.There is an ongoing debate on the toxicity and safety of SCS use at daily doses ≤ 10 mg prednisone equivalent, in particular in the treatment of chronic diseases such as rheumatoid arthritis.28 Similarly, we identified complications and health care resource utilization using claims with a diagnosis of SCS-related complications. It is possible that these services were associated with other conditions. For example, although gastrointestinal conditions have been reported in the literature as potential complications of SCS use, some gastrointestinal disorders considered in the current study may not be associated with SCS use. The same limitation may apply to the psychiatric disorders assessed in this study. Also, it is possible that some of the differences estimated between the 2 groups may be partly attributable to asthma severity. Unfortunately, health care claims do not provide detailed clinical information related to asthma severity to control for potential confounding that may remain between the 2 groups.There are limitations in this study that are commonly associated with the use of administrative claims data, including coding errors and reliance on ICD-9-CM codes to identify SCS-related complications that might not have reflected confirmed clinical diagnoses. Moreover, it was assumed that a prescription claim for SCS indicated use of SCS. However, patients might not have adhered to the SCS treatment regimen as prescribed. Additionally, pharmacy costs because of SCS-related complications were identified using the claims for medications used to treat each category of SCS-related complications. But, these medications could have been used for reasons other than treating complications because of SCS use. For example, antidepressants such as selective serotonin reuptake inhibitors, which were used to identify psychiatric SCS-related complications in the database, can sometimes be used to treat menopausal symptoms instead of depression.5 Therefore, it is possible that SCS exposure was underestimated in the current study. Furthermore, patients with SCS-related complications before SCS initiation were not excluded. Consequently, the diagnosis of a complication occurring during the follow-up period may actually have referred to a follow-up visit for a condition that had developed before the use of an SCS. Finally, the study compared observable SCS-related complications associated with resource use and costs between SCS users and nonusers; however, the potential benefit of corticosteroids in terms of asthma control and related morbidity was not assessed.This study was also subject to limitations related to the study design. Inhaled corticosteroids were not included in the list of SCSs, consistent with previous literature excluding inhaled corticosteroids from a systematic literature review of corticosteroid-associated adverse event costs, based on the assumption that they would lack significant systemic absorption.We found that among a large U.S. commercially insured population, patients with severe asthma who had been treated for at least 6 months with a daily dose of at least 5 mg of prednisone had a significantly higher risk of developing SCS-related complications compared with those not exposed to SCSs and that this risk significantly increased with SCS exposure. We also found a significant incremental burden on health care resources and costs associated with SCS-related complications for SCS users compared with SCS nonusers and that this effect was significant even at daily doses < 5 mg prednisone equivalent.


Digital tools assessing momentary parameters and offering interventions in people’s daily lives play an increasingly important role in mental health research and treatment. Ecological momentary assessment (EMA) makes it possible to assess transient mental health states and their parameters. Ecological momentary interventions (EMIs) offer mental health interventions that fit well into individuals’ daily lives and routines. Self-efficacy is a transdiagnostic construct that is commonly associated with positive mental health outcomes.The aim of our study assessing mood, specific self-efficacy, and other parameters using EMA was 2-fold. First, we wanted to determine the effects of daily assessed moods and dissatisfaction with social contacts as well as the effects of baseline variables, such as depression, on specific self-efficacy in the training group (TG). Second, we aimed to explore which variables influenced both groups’ positive and negative moods during the 7-day study period.In this randomized controlled trial, we applied digital self-efficacy training (EMI) to 93 university students with elevated self-reported stress levels and daily collected different parameters, such as mood, dissatisfaction with social contacts, and specific self-efficacy, using EMA. Participants were randomized to either the TG, where they completed the self-efficacy training combined with EMA, or the control group, where they completed EMA only.b=0.15, SE 0.05, P=.005). Higher self-efficacy at baseline was associated with reduced negative mood during study participation , while we could not determine an effect on positive mood. Baseline depression severity was significantly associated with lower specific self-efficacy over the week of the training . Associations between higher baseline anxiety with higher mean negative mood  and lower mean positive mood  during study participation were found. Emotional flexibility was significantly enhanced in the TG. Additionally, dissatisfaction with social contacts was associated with both a decreased positive mood  and an increased negative mood .In total, 93 university students participated in the trial. Positive momentary mood was associated with higher specific self-efficacy in the evening of the same day (This study showed several significant associations between mood and self-efficacy as well as those between mood and anxiety in students with elevated stress levels, for example, suggesting that improving mood in people with low mood could enhance the effects of digital self-efficacy training. In addition, engaging in 1-week self-efficacy training was associated with increased emotional flexibility. Future work is needed to replicate and investigate the training’s effects in other groups and settings.ClinicalTrials.gov NCT05617248; https://clinicaltrials.gov/study/NCT05617248 Technological development and the ubiquitous presence of smartphones entail that digital assessments and interventions gain presence and importance in mental health. These novel digital methods show great potential to advance mental health research, and there is sound evidence for their feasibility and high compliance . EcologiSelf-efficacy refers to a person’s belief in their ability to succeed in a particular situation and is commonly associated with positive mental health outcomes ,6. It poA growing body of research has shown that perceptions of self-efficacy can be experimentally manipulated , which may result in clinically relevant improvements in cognitive, affective, and decision-making processes -18. FurtBased on these findings, we developed an EMI providing a 1-week digital, that is, smartphone-based, self-efficacy training based on the recall of autobiographical self-efficacy memories. We combined this training with EMA of mood, specific self-efficacy, the social context of the current situation, and web-based contact with others. In this study, situation-dependent specific self-efficacy was assessed in the training group (TG) only and refers to the participants’ belief in completing and benefiting from the study’s tasks. The investigation of the effects of the training on the outcomes of self-efficacy, anxiety, hopelessness, perceived stress, positive and negative affect, and hope was part of a different manuscript. We found significant reductions in anxiety and hopelessness from baseline to postintervention and a significant increase in self-efficacy when controlling for baseline self-efficacy .This study focuses on the daily measures we captured using EMA. Based on previously found associations between mood and self-efficacy, we hypothesized that the daily captured mood was associated with the daily captured specific self-efficacy. Additionally, we wanted to explore how the other daily captured mental health–related aspects and baseline depression and anxiety interacted with mood and specific self-efficacy, aiming to gain a more nuanced picture of the effects of the training and to investigate relationships between these parameters.We conducted a randomized controlled trial with 93 student participants who had reported elevated levels of stress . BecParticipants were recruited from universities in Switzerland using mailing lists and postings on university websites and social media. Participants received detailed information about the study, its procedures, duration, potential risks, and benefits, but no specific information on the different groups. For example, they were informed about a 1-week smartphone app that assessed thoughts and emotions daily, which applied to both groups and secured blinding. All participants provided informed consent. They were offered monetary compensation, depending on compliance or course credits.Interested students were asked to complete a web-based screening questionnaire assessing basic demographic information and their current stress level using the PSS. Inclusion criteria were (1) being a student at a Swiss university, (2) being 18-29 years old, (3) being a smartphone owner, (4) having fluent German language skills, and (5) having a score of ≥13 on the PSS. Exclusion criteria were a current psychiatric disorder.In this study, we used 2 apps. One providing EMA and EMI (TG) and one providing EMA only (CG). Consecutively, both groups received EMA  10 times3 (Melbourne eResearch Group) platform, an open-source software application for delivering advanced smartphone surveys )Using EMA, we measured mood, social contacts, and web-based contacts 10 times per day . The first 10 questions focused on mood and were rated on a 7-point Likert scale . We measured positive mood using 3 items  and negative mood using 7 items . The following section explores social contacts. Two options indicated dissatisfaction with social contacts: (1) being with nobody and feeling excluded or wanting to be with someone; or (2) being with someone instead of wanting to be alone. We combined these into 1 variable. The last section of the EMA focused on web-based contacts. We asked who the person was in web-based contact with at that moment and how they felt about it.The TG received 8 additional EMA questions on specific self-efficacy every evening. These questions did not measure general self-efficacy but specific situation-dependent self-efficacy, which referred to participation in the self-efficacy trainings. For example, participants were asked about their level of agreement with statements such as “Right now, I think I will be able to do what is necessary to make this training successful.”The analysis focuses on the daily EMA, which captures mental health aspects such as mood, satisfaction with social and web-based contacts, and specific self-efficacy. In addition, we investigated how these variables collected as additional outcomes interacted with each other and with baseline variables. The investigation of the changes in the primary (self-efficacy) and secondary outcomes  was part of a different manuscript .sjstats package. For α=.05, power 80%, and an effect size of d=0.6, the required sample size was 94 participants, which is in line with our recent review summarizing findings from mobile health EMI studies [Power analysis was conducted in RStudio using the Descriptive statistics  were used to present the baseline characteristics of the sample. We present our findings in text, tables, and figures. We applied a type I error rate of α=.05 in all analyses.First, we were interested in whether we could predict self-efficacy through mood and other EMA variables as well as through baseline variables. For this purpose, we used multilevel modeling for repeated measures to assess which variables influenced specific self-efficacy during the self-efficacy training in the TG. With measurement occasions on level 1 nested in participants on level 2, we assessed the effects of mood and dissatisfaction with social contacts on specific self-efficacy during the self-efficacy training within (level 1) and the effects of baseline variables on specific self-efficacy during the training between (level 2) participants.Second, we used a multivariate multilevel model to assess the effects of the experimental group (EMA and EMI vs EMA only) and dissatisfaction with social contacts on positive and negative mood, including autoregressive mood effects. As before, level 1 was defined by the measurement occasions and level 2 by participants (see note 2 for access to complete data tables and codes).All analyses were conducted in R  and Mplus (version 8.6)  using thP=.14). There were 42 female participants in the TG and 31 female participants in the CG (group difference: P=.84). The total sample consisted of 93 (54 in the TG and 39 in the CG) participants. The mean age was 23.72 (SE 0.44) years in the TG and 22.64 (SE 0.61) years in the CG  were associated with lower specific self-efficacy (mean score over 1 week) during the self-efficacy training  and negative  mood in both groups. The effect of a positive mood on the following day’s mood was independent of group allocation. In contrast, the effect of negative mood was reduced in the TG . Dissatisfaction with social contacts was associated with both a decreased positive mood  and an increased negative mood  in both groups, with no significant group difference.Within participants, we found autoregressive effects for positive , while positive affect at baseline was associated with increased positive mood during study participation . More depressive symptoms at baseline (BDI-II) were associated with reduced negative mood , while high state and trait anxiety  were associated with increased negative mood . Higher self-efficacy at baseline (GSE) was associated with reduced negative mood during study participation ; we observed no effect on positive mood. The other variables, including group membership, showed no significant effect on mood. For detailed results, see Between participants, trait anxiety at baseline  was associated with lower positive mood  a positive association between momentary positive mood and specific self-efficacy in the evening of the same day, (2) an association between higher baseline self-efficacy and lower mean negative mood during study participation, and (3) an association between higher baseline depressiveness and lower mean specific self-efficacy during study participation. Additionally, we found associations between anxiety and mood: (1) higher baseline trait and state anxiety were associated with a higher mean negative mood during study participation, and (2) higher baseline trait anxiety was associated with a lower mean positive mood during study participation. Moreover, we found autoregressive mood effects in both groups, but the autoregressive effect of negative mood was reduced in the TG.Our results confirm previous studies that show an association between positive mood and self-efficacy ,19. The The relationship between anxiety and mood is in line with literature widely investigating and discussing that depression and anxiety share some aspects and are correlated . Within The training may enhance mood and enable participants to manage difficulties better when faced with challenging situations. The training may also have contributed to higher emotional flexibility , which iSpecific self-efficacy correlates with self-efficacy ,60. PrevThis study is not without limitations. First, the part of our analyses this study focuses on was exploratory. Aiming to gain more knowledge on specific self-efficacy and a more nuanced picture of the potential effects of the self-efficacy training, we examined the prediction of specific self-efficacy in the TG and associations of mood with other EMA and baseline parameters in addition to our main research questions focusing on the effects of the training, which we described in another publication . Such anThe study was conducted during the second wave of the COVID-19 pandemic, and we included participants with elevated stress levels detected by using the PSS, which measures the degree to which life has been perceived as unpredictable, uncontrollable, and overwhelming ,23. ThusOur study has several strengths. First, we showed multiple associations between mood and self-efficacy, respectively, specific self-efficacy leading to the conclusion that participants with lower baseline depression scores benefit more from the training than participants with higher baseline depression scores, giving the practical implication that enhancing self-efficacy might be more effective if individuals additionally receive training on how to elicit positive emotions or learn antidepressant strategies. The self-efficacy training showed a positive effect on the emotional flexibility needed to counterbalance the carryover effects of low mood.EMA data is extremely powerful in describing and explaining momentary symptoms and momentary symptom fluctuations. In this study, we focused on the within- and between-person relationships between various symptom measures, mood, and self-efficacy. It would be an interesting avenue for future research to also explore the time-varying effects of how the constructs impact each other.This study showed several significant associations between mood and self-efficacy as well as between mood and anxiety in students with elevated stress levels. Improving mood in participants with low mood could enhance the effects of self-efficacy training. The self-efficacy training could be especially beneficial for individuals with low emotional flexibility and contribute to mental health improvements. Additionally, analyses could focus on time-varying effects in which the constructs impact each other. Learning more about when and in which time frames changes in symptoms, mood, and self-efficacy occur might support practitioners in developing new psychological interventions.


Many adults use e-cigarettes to help them quit cigarette smoking. However, the impact of self-selected use of e-cigarettes on cigarette smoking cessation, particularly when concurrently receiving app-based behavioral interventions, remains unexplored.This study used data from a randomized trial of 2 smartphone apps to compare 12-month cigarette smoking cessation rates between participants who used e-cigarettes on their own  versus those who did not .The study population included all participants who did not use e-cigarettes at baseline. “Adopters” were those who self-reported the use of e-cigarettes at either 3- or 6-month follow-ups. “Nonadopters” were those who self-reported no use of e-cigarettes at either follow-up time point. The primary cessation outcome was self-reported, complete-case, 30-day point prevalence abstinence from cigarette smoking at 12 months. Secondary outcomes were missing-as-smoking and multiple imputation analyses of the primary outcome, prolonged abstinence, and cessation of all nicotine and tobacco products at 12 months. In logistic regression models, we first examined the potential interaction between e-cigarette use and treatment arm (iCanQuit vs QuitGuide) on the primary cessation outcome. Subsequently, we compared 12-month cigarette smoking cessation rates between adopters and nonadopters separately for each app.P=.05). In the iCanQuit arm, 12-month cigarette smoking cessation rates were significantly lower among e-cigarette adopters compared with nonadopters . In contrast, in the QuitGuide arm, 12-month cigarette smoking cessation rates did not differ between adopters and nonadopters .There was suggestive evidence for an interaction between e-cigarette use and treatment arm on cessation (The use of e-cigarettes while concurrently receiving an app-based smoking cessation intervention was associated with either a lower or an unimproved likelihood of quitting cigarette smoking compared to no use. Future behavioral treatments for cigarette smoking cessation should consider including information on the potential consequences of e-cigarette use.ClinicalTrials.gov NCT02724462; https://clinicaltrials.gov/study/NCT02724462 Electronic nicotine delivery systems or vaping (referred to hereafter as “e-cigarettes”) are battery-powered devices that simulate the experience of smoking by heating a liquid solution into an aerosol, which is then inhaled by the user . This maAccording to a survey conducted in the United States (US) by the Centers for Disease Control and Prevention in 2020, among adult e-cigarette users, 34.7% reported using e-cigarettes to quit smoking cigarettes, and 29.8% reported using e-cigarettes to reduce their cigarette smoking . While tIn-person traditional behavioral interventions such as counseling, support groups, and pharmacotherapy  have been used to aid in cigarette smoking cessation . HoweverAmong smoking cessation apps, iCanQuit is the only app that has demonstrated efficacy for cigarette smoking cessation in a full-scale RCT with long-term follow-up . The iCaP<.001), and (3) higher engagement with iCanQuit compared to QuitGuide. Although the iCanQuit app has shown promising results in helping individuals quit smoking, it is unknown to what extent concurrent use of e-cigarettes might impact their cigarette smoking cessation rates. Understanding the impact of e-cigarettes on the efficacy of app-based cessation interventions is crucial as it can inform the development of content for users who intend to combine e-cigarettes with app-based interventions.The results from the iCanQuit parent trial showed (1) potential for high reach nationwide, (2) higher efficacy for cigarette smoking cessation of iCanQuit relative to QuitGuide  on the primary cessation outcome. Subsequently, 12-month rates of cigarette smoking cessation were compared between adopters and nonadopters separately for each app. Finally, to gain a deeper understanding of the potential mechanisms underlying the impact of e-cigarettes on combustible cigarette smoking cessation, we conducted post hoc exploratory analyses to compare the following aspects between adopters and nonadopters: (1) self-selected use of cessation pharmacotherapy, (2) rates of cigarette smoking cessation for participants indicating they adopted e-cigarettes specifically as a cessation aid versus those who did not ,27, (3) Data from this secondary analysis were from participants (aged 18 years or older) in the iCanQuit parent RCT who reported no e-cigarette use at baseline. Details of the iCanQuit RCT have been previously published . BrieflyStudy procedures were approved by the Fred Hutchinson Cancer Center Institutional Review Board (reference number: 8317). All participants were provided with information about the study and signed informed consent forms before participation. This study is registered in the ClinicalTrials.gov (NCT02724462).Eligibility criteria included daily cigarette smoking, smartphone access, wanting to quit combustible cigarette smoking within the next 30 days, and wanting to quit within the next 30 days any other concurrently used nicotine or tobacco products. Exclusion criteria included being unable to read English, receiving smoking cessation treatment, having used either iCanQuit or QuitGuide in the past, or having a household member already enrolled in the study. Any use of e-cigarettes or other noncigarette nicotine or tobacco products was not an exclusion criterion, and data on their use were collected at each data collection time point. The approved study protocol was published alongside the main outcomes paper and is readily accessible for review .Facebook ads were the primary source of recruitment for all trial participants , followed by a survey sampling company , friends or family , and search engine results . The period of recruitment of all trial participants was May 2017 through September 2018. Participants completed a web-based screening survey and were notified of their eligibility via email. They then clicked on a secured emailed link to the study website, where they provided consent and completed the baseline survey. Consent to participate included collecting app use data via Google Analytics . Activation of the assigned app on the participant’s smartphone was conducted by entering the login code sent to them in their trial enrollment email.At each enrollment step, the study was presented as a comparison of 2 smartphone apps for cigarette smoking cessation. After completing the baseline survey, participants were then randomized 1:1 to receive iCanQuit or QuitGuide, both of which were accessible for 12 months. Randomization by permuted blocks of size 2, 4, and 6 was stratified by positive screening for depression , daily smoking frequency (≤20 vs ≥21 cigarettes per day), minoritized race or ethnicity backgrounds, and education level (less than high school vs some college or higher education). Neither research staff nor study participants had access to upcoming randomized study group assignments. For blinding, each app was branded as “iCanQuit” and did not mention either ACT or QuitGuide. Contamination between apps was avoided with a unique username and password provided only to the individual user and by having an eligibility criterion of not having other household members participating in the study.Data collection occurred between August 2017 through December 2019 via web-based study surveys at the 3-, 6-, and 12-month follow-ups. For trial integrity, the follow-up data were collected by the survey research unit that was blinded to random assignments, and cessation outcomes data were collected outside of the intervention apps. Participants were compensated for completed data collection. For each follow-up time point, participants received US $25 for completing the follow-up survey. Participants would receive an additional US $10 bonus if the encrypted web-based survey was completed within 24 hours of the initial email invitation to complete the survey, with up to US $105 in total compensation per participant.The data for this secondary analysis included a subsample of trial participants from the iCanQuit parent RCT who had available data on e-cigarette use after randomization. This subset comprised 1562 participants, which accounted for 64.7% (1562/2415) of the total sample. Within this subsample, we categorized participants into two distinct groups based on their self-reported use of e-cigarettes after randomization: (1) “adopters,” defined as individuals who reported no e-cigarette use in the past 30 days at baseline and reported using e-cigarettes within the past 30 days at either the 3- or 6-month follow-ups (465 adopters); and (2) “nonadopters,” defined as individuals who reported no e-cigarette use in the past 30 days at baseline, 3-month, and 6-month follow-ups (1097 nonadopters). Participants who reported using e-cigarettes at baseline (n=575) were excluded from the analysis, as were those with missing e-cigarette use data (n=278). It is important to emphasize that the use of e-cigarettes during the 12-month intervention period was entirely self-selected by the participants.Details of the iCanQuit app have been previously published . BrieflyDetails of the QuitGuide app have been previously published . BrieflyBoth apps provide education and skills for preparing to quit cigarette smoking and for preventing relapse after quitting; actionable plans for quitting cigarette smoking; skills for coping with cravings to smoke; and education on common triggers to smoke, barriers to cessation, and how to seek support for smoking cessation. Both apps also provided education on US Food and Drug Administration–approved medications for smoking cessation. However, neither app provided e-cigarettes as part of the behavioral interventions, encouraged their use, or provided information on the potential risks or benefits of using e-cigarettes to quit combustible cigarette smoking. The details on the features and functionalities of the apps as well as a comparison have been previously published .The baseline survey collected information on sociodemographic factors such as age, gender, race, education level, and household income. In addition, participants were screened for depression using the Center for Epidemiological Studies Depression Scale-20 and panic disorder and posttraumatic stress disorder (PTSD) using self-report measures -33. QuesStudy questionnaires at the 3-, 6-, and 12-month follow-ups asked participants whether they abstained from combustible cigarette smoking for the past 30 days and the date of their last cigarette. Consistent with the parent trial, the primary smoking cessation outcome was self-reported, complete-case, and 30-day point prevalence abstinence (PPA) from cigarette smoking at 12 months. Secondary outcomes at 12 months included 30-day PPA missing-as-smoking and multiple imputation; 30-day PPA from all nicotine and tobacco products ; prolonged abstinence (between 3 and 12 months); and harm reduction, defined as a substantial reduction (≥50%) in average daily cigarette consumption between baseline and 12 months ,36.Participants were asked via web-based study questionnaires at baseline and 3-, 6-, and 12-month follow-ups: “In the last 30 days, how often did you use any kind of e-cigarette or vaping?” Response options included (1) not at all, (2) less than once a month, (3) once a month or more but less than once a week, (4) once a week or more but not daily, and (5) at least daily. To assess self-selected use of cessation pharmacotherapy, participants were asked: “Since the date you joined the study, did you ever use any of the following nicotine replacement therapies or medications to help you quit smoking?” Response options included (1) nicotine gum, (2) nicotine patches, (3) e-cigarettes, (4) Chantix, (5) Zyban or Wellbutrin, or (6) “free text.” The response to this question was also used to assess self-selected use of cessation pharmacotherapy and whether e-cigarettes were intentionally used to aid quitting.The engagement was measured via Google Analytics and included the number of times users interacted with their assigned app  by 6 months after randomization.A number of quit attempts were assessed at each time point using the survey question, “About how many times since the start of the study did you quit smoking for at least 24 hours?t tests for continuous variables and chi-square tests for categorical variables. To test whether there was an interaction between adopting e-cigarettes and app assignment (iCanQuit vs QuitGuide) on the primary cessation outcome, we evaluated the interaction term in a logistic regression model. To compare 12-month cigarette smoking cessation rates between adopters and nonadopters, logistic regression models were used. For the multiple imputation of missing 30-day PPA from cigarette smoking, multivariate imputation by chained equations in the R package mice [MASS [Sociodemographic characteristics and smoking behaviors of participants were compared between adopters and nonadopters using 2-tailed age mice  was usedage mice . Differece [MASS . We usedce [MASS . These bce [MASS ,42. The ce [MASS .Participants included in this analysis  were on average 38.9 years old, 72.9% (1139/1562) female, and 32.8% (512/1562) were from minoritized racial groups .Overall, 29.8% (465/1562) of participants adopted e-cigarette use between baseline and the 6-month follow-up, while 70.2% (1097/1562) reported no use of e-cigarettes between baseline and the 6-month follow-up. A descriptively higher, but nonsignificantly different, proportion of QuitGuide participants adopted e-cigarettes compared with iCanQuit participants .Adopters were younger, more likely to be White, and screened positive for depression and PTSD compared with nonadopters. Adopters were also more dependent on nicotine than nonadopters  and more likely to report having their first cigarette within 5 minutes of waking and to live with other people who smoke. Outcome data retention rates were high and did not differ between adopters and nonadopters  that the association between adopting e-cigarettes and 12-month combustible cigarette smoking cessation was moderated by treatment arm assignment (iCanQuit vs QuitGuide). Therefore, the comparison between cigarette smoking cessation rates between adopters and nonadopters was conducted separately for each arm.There was suggestive evidence , multiple imputation , and 30-day PPA from all nicotine and tobacco products . Furthermore, prolonged abstinence from cigarette smoking between 3- and 12-month follow-ups was about 3 times lower among adopters as compared with nonadopters . Finally, there was no difference in daily cigarette reduction rates  between adopters and nonadopters (P=.99).Specifically, results showed that 12-month smoking cessation rates in the iCanQuit arm were lower among adopters as compared to nonadopters  with similar results for the secondary outcomes of missing-as-smoking and multiple imputation. There were no significant differences between adopters and nonadopters in 30-day PPA from all nicotine or tobacco products or in daily cigarette reduction. However, the odds of prolonged abstinence from cigarette smoking were roughly 3 times lower among adopters as compared with nonadopters .In the QuitGuide arm, adopting e-cigarettes was not associated with 12-month combustible cigarette smoking cessation  of all participants reported using another form of nicotine replacement therapy  or medications  to help them quit smoking at 3 and 6 months. We found no evidence of an interaction between the use of pharmacotherapy and treatment arm assignment on the primary cessation outcome (P=.91). We also observed no difference in the rate of pharmacotherapy usage between adopters and nonadopters in either treatment arm .Over a third  or the QuitGuide arm .Nearly half  of adopters reported using e-cigarettes as an aid to cessation. However, there was no difference in cigarette smoking cessation rates between those reporting they adopted e-cigarettes as an aid to cessation of cigarette smoking versus those who did not—in either the iCanQuit arm . Participants engaged less with the QuitGuide app overall, and in this treatment arm, there was no difference in app engagement between adopters compared with nonadopters (P=.27).P=.02) but not at 12 months (P=.44). In the QuitGuide arm, adopters reported a significantly higher number of quit attempts than nonadopters at 6 months (P=.001) and 12 months (P<.001). Given the higher number of quit attempts among adopters in both treatment arms, we further evaluated whether the number of quit attempts at the 3- and 6-month follow-ups was associated with cigarette smoking cessation rates at 12 months. We found a very small negative association between 12-month cigarette smoking cessation and number of quit attempts by 3 months  and 6 months .Adopters in both arms reported a significantly higher number of quit attempts compared with nonadopters . In the P=.01; P=.69; OR 0.99, 95% CI 0.92-1.06). Similar observations emerged when assessing the cessation rates of cigarette smoking at 12 months, comparing individuals with low to moderate nicotine dependence (FTCD scores below 6.0) and those with high nicotine dependence (FTCD scores of 6.0 or higher) at baseline .Regarding the baseline difference in FTCD scores between adopters and nonadopters . In contrast, in the QuitGuide arm, 12-month combustible cigarette smoking cessation rates did not differ between adopters and nonadopters .This study used data from an RCT to compare combustible cigarette smoking cessation rates between participants who used e-cigarettes on their own  versus those who did not . For the first time, this study provides empirical evidence that adults who adopted e-cigarettes while concurrently receiving an app-delivered cigarette smoking cessation intervention had either a lower or an unimproved likelihood of quitting combustible cigarette smoking—despite their strong motivation to quit. In the iCanQuit arm, 12-month combustible cigarette smoking cessation rates were significantly lower among adopters compared with nonadopters . The second study used data from a large RCT (n=2637) among adults who smoked combustible cigarettes daily and were assigned to receive 12-month web-based behavioral interventions for cigarette smoking cessation [P=.006). In contrast to these studies, observational data suggest that e-cigarette use may facilitate smoking cessation under “free living conditions” [Our results are consistent with 2 previous prospective RCT studies for smoking cessation. Both studies suggested that e-cigarette use impeded cigarette smoking cessation among individuals concurrently receiving a behavioral intervention for smoking cessation ,16. The ditions” -13. Moreditions” ,44,45. TThere are unclear reasons why e-cigarette adopters in this study had either a lower or an unimproved likelihood of quitting combustible cigarette smoking. Notably, we found no evidence that the self-selected use of cessation pharmacotherapy differed between adopters and nonadopters. We also examined whether other factors such as adopting e-cigarettes specifically to aid cessation had any impact on the 12-month cessation outcomes. Our findings revealed no significant associations in these regards. Regarding app engagement, however, we found that adopters in the iCanQuit arm used their assigned app less than nonadopters, albeit nonsignificantly, suggesting that adopting e-cigarettes may have been perceived as a method for quitting smoking—which could have thereby replaced app use. Given that engagement with an app-based intervention is a strong predictor of smoking cessation -51, it iFinally, the number of quit attempts at 3-, 6-, and 12-month follow-ups between adopters and nonadopters was compared. Interestingly, we discovered that adopters had notably higher instances of quit attempts in comparison to nonadopters at the 6-month follow-up in the iCanQuit arm and at 6- and 12-month follow-ups in the QuitGuide arm. These results suggest that, in contrast to nonadopters, adopters encountered more difficulty with quitting cigarette smoking. Notably, these results could not be attributed to differences in levels of nicotine dependence between adopters and nonadopters in this study, which agrees with previous studies with mixed results ,52-55. TThis study has several strengths. First, this is the only known study to examine the impact of e-cigarette use among adults receiving an app-delivered behavioral intervention to help them quit smoking. Second, the original iCanQuit RCT enrolled a racially and geographically diverse large sample (n=2415) of adults from all 50 US states , increasThere are also limitations of this study. As with any secondary analysis, the study was not specifically designed to examine the causal relationship between adopting e-cigarettes and cigarette smoking cessation; as such, a causal relationship may not be inferred from the associations found. Additionally, participants self-selected into using or not using e-cigarettes . This means that both measured and unmeasured confounding likely impact any associations seen. Nonetheless, we adjusted for any baseline characteristics that were significantly different between adopters and nonadopters and were associated with the cessation outcomes. Second, the study did not assess for type of e-cigarette used or its nicotine content. Third, self-reported data are subject to bias and inaccuracies, including in reporting smoking behavior and use of e-cigarettes. In this trial, the self-reported outcome was prespecified due to methodological issues with remote biochemical verification ,57. AlthAdults who adopted e-cigarettes while simultaneously receiving an app-delivered smoking cessation intervention had either a lower or an unimproved likelihood of quitting cigarette smoking. This is concerning, given the increasing popularity of e-cigarette use. The study can inform future app-based cessation interventions and best practices to mitigate harm from cigarette smoking. Future studies and interventions may include education on the potential negative or unhelpful impact of using e-cigarettes alongside app-delivered behavioral interventions for cigarette smoking cessation.


To summarize the available evidence from systematic reviews with meta-analysis on the effects of music-based interventions in adults diagnosed with cancer.An overview of systematic reviews was conducted. CINHAL, Embase, PEDro, PubMed, Scopus, the Cochrane Library and Web of Science were searched from inception until November 2022. Systematic reviews with meta-analysis in individuals with cancer (any type), any comparator, and outcomes of cancer-related pain, fatigue, and psychosocial symptoms were eligible. The methodological quality of systematic reviews and the amount of spin of information in the abstract were assessed. The Graphical Representation of Overlap for OVErviews tool (GROOVE) was used to explore the overlap of primary studies among systematic reviews.Thirteen systematic reviews, with over 9000 participants, containing 119 randomized trials and 34 meta-analyses of interest, were included. Music-based interventions involved passive music listening or patients’ active engagement. Most systematic reviews lacked a comprehensive search strategy, did not assess the certainty in the evidence and discussed their findings without considering the risk of bias of primary studies. The degree of overlap was moderate (5.81%). Overall, combining music-based interventions and standard care seems to be more effective than standard care to reduce cancer-related pain, fatigue, and distress. Mixed findings were found for other psychosocial measures.Music-based interventions could be an interesting approach to modulate cancer-related pain, fatigue, and distress in adults with cancer. The variability among interventions, together with important methodological biases, detract from the clinical relevance of these findings.The online version contains supplementary material available at 10.1007/s00520-023-07938-6. Nearly 18 million individuals are diagnosed with cancer every year . PatientNon-pharmacological therapies are of interest for the clinical management of long-term diseases, as considered to be safe, low-cost, and with minor side effects . Among thttps://doi.org/10.17605/OSF.IO/Y67BU). This overview has followed the preferred reporting items for overviews of reviews (PRIO) statement and the PRISMA for abstracts [The overview protocol was prospectively registered at the Open Science Framework  carried out an electronic search from inception to November 2022 in the following databases: CINHAL, Embase, PEDro, PubMed, Scopus, the Cochrane Library, and Web of Science. Medical Subject Headings (MeSH) terms associated with the intervention (music) and the medical condition  were combined. A comprehensive search strategy was first constructed for PubMed and then adapted for other databases. The lists of references of previous overviews were manually checked. The detailed search strategies are listed as Supplementary file TM carrieP: Adults diagnosed with cancer without restrictions in body location/system or the cancer stage.I: music-based interventions, used alone or as adjuvant to usual or standard careC: no restrictions regarding the control intervention.O: physical , and psychosocial measures .S: systematic reviews with meta-analysis .The eligibility criteria were established following the PICOs framework :Systematic reviews were not included when: a) the publication was written in a language other than Spanish or English; b) there were not meta-analyses for the condition of interest; c) music-based interventions were meta-analyzed together with other experimental treatments; and d) meta-analyses included non-adult participants, population without cancer, or non-randomized controlled trials. Possible outcomes of interest that were not analyzed in at least two systematic reviews were not considered. Congress proceedings, thesis dissertations, and network meta-analyses were also excluded.Duplicate records were removed using the Mendeley desktop software, v2.72.0. and manually checked. One researcher (ATM) screened the remaining records based on the title and the abstract. The full text of eligible studies and those lacking an abstract were then revised. A consensus was achieved for three studies with a second researcher (AMHR) who independently double-checked the entire selection process.Data were extracted with a standardized form that included: a) first author plus et al., the year of publication, and the number of clinical trials of interest; b) sample size ; c) the characteristics of participants ; d) description of the experimental and control interventions; e) music style used; f) outcome measures; and g) main results from meta-analysis. We aimed to extract the overall effect size from each meta-analysis. When this was not reported, results from sub analyses were included. Two corresponding authors were contacted by e-mail to clarify some information , 40. A rTwo independent reviewers (ATM and MJCH) evaluated the methodological quality of systematic reviews using the AMSTAR-2 tool . As recoThe abstracts of the systematic reviews were assessed in isolation to quantify the occurrence of spin of information. Two independent reviewers (ATM and PGG) utilized a 7-item checklist , where eFindings have been narratively described based on the outcomes of interest. To identify the most relevant key terms across systematic reviews, the VOSViewer software, v. 1.6.18  was used to conduct a co-occurrence analysis and bibliometric mapping. The degree of overlap of primary studies among included systematic reviews was evaluated with the Graphical Representation of Overlap for OVErviews (GROOVE). The GROn = 29) is described in the Supplementary file Search strategies retrieved a total of 926 eligible records. After removing duplicates, 466 records were screened. We eventually included 13 systematic reviews and 34 meta-analyses in the qualitative synthesis , and ‘selective reporting or overemphasis on the beneficial effect of music-based intervention’ (n = 5). No spin of information was found in three abstracts [The overall spin-abstract score was 21, with a mean value of 1.6 ± 1.3 points . The most common forms of spin were ‘concluding a positive effect despite high risk of bias of primary trials’  and this remained moderate  even after adjusting for chronological structural missingness. The citation matrix and the CCA calculation can be found in Supplementary file n = 6) concluded that music-based interventions plus usual or standard care were more effective than control interventions  to reduce cancer-related pain [All systematic reviews measuring pain as an outcome Supplementary file2 (DOCX 36 KB)Supplementary file3 (DOCX 15 KB)Supplementary file4 (DOCX 52 KB)Supplementary file5 (DOCX 18 KB)Supplementary file6 (DOCX 25 KB)Below is the link to the electronic supplementary material.


Endovascular thrombectomy (ET) has previously been reserved for patients with small to medium acute ischemic strokes. Three recent randomized control trials (RCTs) have demonstrated functional benefit and risk profiles for ET in large volume ischemic strokes.The primary objective of the meta-analysis was to determine the combined benefit of ET in adult patients with large volume acute ischemic strokes and to better determine the risk of adverse events following ET.We systematically searched MEDLINE, EMBASE, SCOPUS, the Cochrane Central Register of Controlled, and Google Scholar for all RCTs published in English language between January 1, 2010, to February 19, 2023.We included only RCTs specifically comparing ET to medical therapy in patients with acute ischemic stroke with large volume infarctions as defined by Alberta Stroke Program Early Computed Tomography Score (ASPECTS) 3–5 or calculated infarct volume of > 50–70mL. Two independent reviewers screened potential studies for full text review and meta-analysis inclusion with conflicts being resolved by consensus or third reviewer.Data was extracted based on pre-specified variables on study methods and design, participant characteristics, analysis approach, as well as efficacy and safety outcomes. Results were combined using a restricted maximum-likelihood estimation random-effects model. Studies were assessed for potential bias and quality of evidence.The prespecified primary outcome was an overall ordinal shift across the range of modified Rankin scale scores toward a better outcome at 90 days following either ET or medical management for patients with large volume ischemic strokes.2 = 0.01, I2 = 42.84%]. There was a trend toward increased risk of symptomatic ICH in the ET group, relative risk 1.85 .A total of 3044 studies were screened, and 29 underwent full text review. 3 RCTs (1011 patients) were included in the analysis. The pooled random effects model for the primary outcome of mRS improvement favored ET over medical management, generalized odds ratio 1.55 [95% CI 1.25 – 1.91, τIn patients with large volume ischemic strokes, ET has a clear functional benefit and does not confer increased risk of significant complications compared to medical management alone. Numerous randomized control trials (RCTs) have demonstrated significant benefit in functional outcome ) compared to medical management alone.11 These multicenter RCTs have all have demonstrated differing margins of benefit in functional outcome following ET in large-volume ischemic strokes, and they have also reported differing rates of ICH.11 We sought to perform a systematic review of the literature for any recent RCT that includes large-volume ischemic strokes and to subsequently perform a meta-analysis of these results. This study’s aims were to (1) determine if ET leads to improved outcomes measured via mRS in adult patients with large ischemic strokes when compared to medical management and (2) understand the safety of ET in patients with large ischemic strokes by comparing rates of symptomatic ICH and other adverse events. In analyzing these major trials, we can better understand the true benefit in mRS and risk profile for patients low ASPECTS receiving ET.In the past year, three multicenter RCTs have been published specifically investigating the benefits of ET in patients with large vessel occlusions and ASPECTS 3–5 while excluding those with very low ASPECTS of 0–2. The RESCUE-Japan LIMIT, ANGEL-ASPECT, and SELECT2 trials were conducted in Japan, China, and an international conglomerate , respectively.12We conducted a systematic review and meta-analysis according to a prespecified protocol registered at the International Platform of Registered Systematic Review and Meta-analysis Protocols (INPLASY) . Clinical and observational studies, case series with available abstracts and published as full-scale original articles, brief reports in peer-reviewed academic journals, pilot reports, opinion pieces, theses, conference proceedings, letters, editorials, meta-analysis, reviews, surgical technique papers, case reports, abstracts, presentations, and any non-English language publications without translations were excluded.13 The search strategy included broad key words to identify studies on patients with ischemic stroke, large vessel occlusion, endovascular treatment, endovascular therapy, and thrombectomy with filters to limit time range (since 2010) and in some instances filters to identify RCTs. The details of the search strategy employed on each database are included in We systematically searched MEDLINE, EMBASE, SCOPUS, the Cochrane Central Register of Controlled Trials (CENTRAL), and Google Scholar. Since the first thrombectomy trials were published nearly two decades ago, we limited our search article published between January 1, 2010, to February 19, 2023.www.covidence.org.)14After exclusion of duplicates, two reviewers (TJA and BET) independently screened articles for relevance first based on titles and abstracts and subsequently through full-text review to identify articles meeting eligibility criteria. Disagreements between reviewers were resolved in both phases by either consensus or with a third reviewer (DEO). For the selection process, we relied on the Covidence systematic review software  or middle cerebral artery (MCA), infarct core volume, and ASPECTS.Two investigators (TJA and BET) independently extracted data from each trial meeting eligibility criteria using a standardized excel collection form. Data was extracted according to the protocol and included variables on study characteristics, design, demographic data of enrolled participants, analysis approach (intention-to-treat vs per-protocol), efficacy and safety outcomes of interest as outlined in the protocol, and adherence to reporting guidelines.The primary outcome for the meta-analysis was an overall ordinal shift across the range of mRS scores toward a better outcome at 90 days. Secondary outcomes included functional independence defined as an mRS score of 0–2 at 90 days and independent ambulation defined as an mRS score of 0–3 at 90 days. Safety outcomes included symptomatic ICH, any ICH, death at 90 days, and need for decompressive HC.17 Disagreements were resolved by consensus or consulting with a third reviewer (TJA).Two investigators (NMBL and DEO) with no affiliation to the studies meeting eligibility criteria independently assessed risk of bias for each study using the Cochrane Risk-of-Bias in randomized trials (RoB 2) tool.Results of the included trials were combined through meta-analysis to obtain summary estimates of effect sized for each of the pre-specified primary and secondary outcomes. Generalized Odds Ratio (OR) for ordinal shift in mRS scores toward a better outcome were combined by using a random‐effect model with restricted maximum-likelihood estimation. All other outcomes were binary, and relative risks (RR) ratios were estimated by combining data from all studies also using a random‐effect model with restricted maximum-likelihood estimation. Subgroup analysis was performed only on the primary outcome. Trials with missing data were not used in the meta-analysis. This only occurred in two instances for safety outcomes alone.2 .19 Alpha was set at 0.05 and all test of significance were 2-sided. To reduce the potential for type I error due to multiple testing, we used Bonferroni corrections to adjust p-values. Data and syntax used for the analysis have been made publicly available through GitHub (https://github.com/estevezdo/Thrombectomy-in-Large-Volume-Strokes-SysRev-Metan).We also performed sensitivity analyses by exploring how global effect sizes and p-values were affected by adjusting to the between-study variance parameter τ2 . Statistel plots .18 Stati20 The combined outcomes were assigned an overall grade of high, moderate, low, or insufficient. This process was completed initially by a single investigator (NMBL) and with consensus after being reviewed by all investigators.We relied on the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach to assess the evidence that thrombectomy compared standard care improves outcomes as measured with the mRS.17 Using the GRADE approach, all evidence from the include studies was assigned a high grade  and failure to specifically investigate patients with ASPECTS 3–5 or volumes >50–70mL. gh grade .20Among the 3 studies, 1011 patients were included in the meta-analysis. Of these, 510 patient were randomized to ET, and 501 to medical management. Of the 757 patients with available data, 687 (90.7%) had ASPECTS of 3–5. 2 = 0.01, I2 = 42.84%]. For the outcome of functional independence (mRS 0–2) at 90 days, the combined results favored ET over medical management with RR 2.53 . For the secondary outcome of independent ambulation (mRS 0–3) at 90 days, the combined study results also favored ET over medical management with RR 1.78 . The forest plots of these random-effects models are shown in 2 = 0.00, I2 = 0.00%]. There was an increased risk of any ICH in the ET group in the Yoshimura et al. and Huo et al. studies, RR 2.30 . There were no significant differences between death at 90 days and need for decompressive hemicraniectomy (HC) for the two treatment arms of the combined studies with RR 0.95  and RR 1.22 , respectively. The forest plots of the random-effects models for the safety outcomes are shown in The generalized OR for ordinal improvement in mRS from the combined studies was 1.55 [95% CI 1.25 – 1.91, τ11 The benefit of ET was also confirmed in terms of proportions of 90-day functional independence and independent ambulation. Regarding safety of ET in this population, analysis of data from all three trials found no significant difference in proportions of symptomatic intracerebral hemorrhage and mortality.This combined analysis of the RESCUE-Japan LIMIT, SELECT2, and ANGEL-ASPECT trials, which enrolled patients with large core anterior circulation ischemic strokes within 24 hours of last seen well and randomly assigned to ET or medical care only, confirms the benefit of thrombectomy in terms of 90-day mRS improvements.23 These trials were designed as explanatory trials; the investigators sought to maximize the chance of detecting a benefit with ET by excluding patient subgroups, such as those with large cores, for whom benefit seemed less certain. Large core infarctions have been excluded largely out of concern that post-thrombectomy brain hemorrhage risk would be significant or that the functional benefit would be minimal. However, a meta-analysis of observational studies found favorable results after thrombectomy for patients with acute ischemic stroke and large ischemic cores, typically defined as an ASPECTS score of <6, without significant increased risk of hemorrhage.24 The three trials included in the present meta-analysis are the first multicenter RCT of ET for patients with large core infarctions or ASPECTS 3–5.The MR CLEAN, ESCAPE, REVASCAT, SWIFT PRIME, and EXTEND-IA trials, all published in 2015, provided the first high-level medical evidence supporting the use of ET in selected patients with anterior circulation large-artery occlusion ischemic stroke.This study detected a significant benefit with ET for two subgroups that were not found to be significant in the individual studies. Whereas patients with MCA occlusions benefited from thrombectomy in the SELECT2, and ANGEL-ASPECT trials, patients with ICA occlusions were not found to benefit in the individual trials. In the combined analysis, patients with ICA occlusions were found to benefit; this likely reflects the greater severity of ICA occlusions versus MCA occlusions, as well as a greater clot burden and number of needed thrombectomy passes, and therefore a slimmer margin of benefit with ET. Similarly, neither the SELECT2 nor the ANGEL-ASPECT trial found a benefit with ET for patients ASPECTS <3 while the combined analysis did find a benefit for this small subgroup. This likely reflects the greater severity of very low ASPECTS strokes, possibly reduced collateral circulation to the affected territory, and a narrower margin of benefit with ET.Limitations of this study include limitations of the trials themselves and limitations of the meta-analysis. Regarding the trials themselves, the RESCUE-Japan LIMIT relied only on ASPECTS for identification of patients with a large core, whereas the SELECT2 and ANGEL-ASPECT trials also employed perfusion imaging, which may have improved accuracy in patient selection in those trials compared to the RESCUE-Japan LIMIT trial. Enrollment in the SELECT2 and ANGEL-ASPECT trials was halted early because of efficacy, raising the possibility of overestimation of treatment effect. This also limited the statistical power of the subgroup analyses. Although the RESCUE-Japan LIMIT and ANGEL-ASPECT trials enrolled only Japanese and Chinese patients, raising questions about the generalizability of the findings to non-Asian populations, the SELECT2 trial was largely comprised of non-Asian study subjects and provides evidence that benefit of ET for large core infarctions extends across ethnic groups.26 However, the SITS-MOST symptomatic ICH criteria was developed before ET devices were being utilized, which may have limited the SELECT2 and RESUE-Japan LIMIT trials from capturing all hemorrhagic complication effects from device recanalization. Additionally, symptoms associated with a hemorrhagic complication are variable and are not clearly represented by just an increase in NIHSS ≥4 shortly after hemorrhage. Thus, the true severity of hemorrhagic complications in the context of a large ischemic stroke also may have been underestimated in these studies.All 3 RCTs incorporate a radiological-symptomatic classification scheme to determine symptomatic ICH. SELECT2 and RESUE-Japan LIMIT trials defined symptomatic ICH according to Safe Implementation of Thrombolysis in Stroke–Monitoring Study criteria (SITS-MOST), while ANGEL-ASPECT defined this outcome by the Heidelberg bleeding classification.This meta-analysis was limited by the fact that RESCUE-Japan LIMIT had a different primary endpoint than the other two trials, which constrained combined subgroup analyses only to the SELECT2 and ANGEL-ASPECT trials. This meta-analysis was also limited by the pooled data available from the included studies; a meta-analysis including individual patient data would permit a more granular and precise assessment of treatment effect.The findings of this study strengthen the evidence for benefit of ET over medical management for patients with acute ischemic stroke and large core infarctions. There was no increased risk of symptomatic ICH or other adverse events in this group compared to medical management. ET should not be withheld for patients with ASPECTS 3–5 or large ischemic cores based on perfusion imaging.Supplement 1