Background: Sacubitril/valsartan plays a key role in improving left ventricular remodeling and prognosis in patients with heart failure with a reduced ejection fraction (HFrEF). Moreover, some data support its role in preserving renal function. In order to better clarify the effects of sacubitril/valsartan in cardiorenal syndrome, this study evaluated its effects on the renal resistance index (RRI). Methods: A group of patients with HFrEF was enrolled. The RRI was assessed with renal echo-color Doppler at enrollment and again after at least six months of sacubitril/valsartan treatment. In a subgroup of patients, the RRI was also evaluated at least six months before enrollment. The variations in echocardiographic parameters reflecting the left and right ventricular function, as well as creatinine and the estimated glomerular filtration rate, were also evaluated. Results: After treatment with sacubitril/valsartan, significant improvements in the left ventricular ejection fraction, and a decrease in the left atrial and ventricular volumes were observed. The RRI also showed a significant decrease. No relationship was found between the improvements in the parameters reflecting cardiac function and changes in the RRI. Conclusions: Treatment with sacubitril/valsartan is associated with improvements in both left ventricular function and renal perfusion, through decreasing the renal resistance. These data help to clarify the effects of the drug on cardiorenal syndrome progression.  In patients with heart failure with a reduced left ventricular ejection fraction (HFrEF), sacubitril/valsartan has been demonstrated to be superior to ACE inhibitors (ACEi) in decreasing the risk of heart failure hospitalization and death , and in Some data have also demonstrated the ability of sacubitril/valsartan to slow the progression of renal dysfunction ,7,8,9. AIn order to better clarify the effects on the renal blood flow, the aim of this study was to evaluate the variation in the renal resistance index (RRI) after treatment with sacubitril/valsartan.We evaluated patients referred to the Heart Failure Unit of the University Policlinic Hospital of Bari from 2016 and 2019, and to the Heart Failure Unit of the University Policlinic Hospital of Foggia from 2019 and 2021 for HFrEF (ESC criteria), who had been prescribed sacubitril/valsartan. Patients from Bari were enrolled in a study aimed at evaluating the predictors of cardiorenal syndrome progression whose main results have already been published . Patient2; and serum potassium of <5.2 mmol/L. Patients taking an ACEi before study enrollment underwent a 36 h washout before the start of treatment with sacubitril/valsartan. The starting dose of sacubitril/valsartan was 24/26 mg b.i.d. or 49/51 mg b.i.d. depending on arterial pressure, renal function, and the previous ARNi/ARB dose. The dose was then up-titrated, when tolerated, to 97/103 mg b.i.d.Study design. Patients for whom sacubitril/valsartan was prescribed were evaluated. According to the indications of the Italian Ministry of Health, sacubitril/valsartan was prescribed to the patients with the New York Heart Association (NYHA) class II–IV; left ventricular ejection fraction (LVEF) of ≤ 35%; prior treatment with ACEi or angiotensin II receptor blockers (ARB) for at least 6 months; no history of angioedema; systolic arterial pressure of >95 mm Hg; estimated GFR of >30 mL/min/1.73 mBaseline evaluation (T0) was considered to be the time in which the sacubitril/valsartan therapy was started. Between 6 and 12 months after beginning ARNI, a new complete evaluation was performed (T1). Moreover, an evaluation was performed at 6 and 12 months before ARNI therapy was started (T-1). -Medical examination and ECG. Records were documented, including ischemic heart disease, arterial hypertension, diabetes mellitus, history of ventricular arrhythmic events, NYHA class, arterial pressure, heart rhythm and heart rate at ECG;-Echocardiographic examinations. Left ventricular end-diastolic volume (LVEDV), end-systolic volume (LVESV), and LVEF were calculated with Simpson’s rule. The peak of the E wave (E), through mitral pulsed Doppler at the level of the mitral leaflets, and early diastolic velocity peak (e’) at the level of the septal and lateral mitral annulus, through tissue Doppler imaging, were measured. The E/e’ ratio was then calculated as the ratio between E and the mean value of septal and lateral e’. The central venous pressure was determined through the assessment of the inferior vena cava diameter and respiratory excursion. The mitral regurgitation (MR) was evaluated and quantified in arbitrary units (a.u. range from 0 to 4). The systolic pulmonary artery pressure (PAP) was estimated by the measurement of the RV–right atrium gradient from the peak velocity of the tricuspid valve regurgitation (TR) with the simplified Bernoulli equation; this value was added to an estimate of the mean right atrium pressure. The RV systolic function was evaluated according to tricuspid annular plane systolic excursion (TAPSE);-Doppler of interlobular renal arteries. The method to assess the RRI was described previously ,13. The At T-1, T0, and T1, the following evaluations were performed:RRI is a parameter with a high inter-operator and intra-operator reproducibility, as previously demonstrated . To avoi-2) [Blood sample analyses. Blood samples were collected to evaluate NT-proBNP  and creatinine (mg/dL). The glomerular filtration rate was calculated with the abbreviated CKD-EPI formula  .Study end-points. The primary end-point of the study was to evaluate the changes in the RRI between the evaluations before and the evaluation after the introduction of sacubitril/valsartan therapy. As the secondary end-point we evaluated the changes in echocardiographic parameters after sacubitril/valsartan therapy and their relationship with those of the RRI. Reverse remodeling was defined as a relative change in LVESVI of >15% [p value of <0.05 was considered statistically significant.Statistical analysis. Continuous variables are expressed as mean values ± standard deviation. Discrete variables were summarized as frequencies and percentages. Spearman analysis was used to evaluate the relationship between the changes in the RRI and changes in the other studied parameters. To study the effect of sacubitril/valsartan therapy, we applied a linear regression mixed model on the values obtained at the different time points (before and after the therapy), with patients fitted as subject-specific random intercepts. The effects of sacubitril/valsartan therapy and the interaction at different time points were considered. If the overall effect was significant in the linear model, then pairwise differences were examined. The trends over timing points were displayed by plotting the mean values with standard error. Statistical analyses were performed in STATA software, version 12  or Statistica 6.1 software . A A total of 80 consecutive patients for whom sacubitril/valsartan was prescribed were evaluated, and 14 patients were excluded as follows: eight because of sacubitril/valsartan intolerance (seven for hypotension and one for muscular pain) and six because of a missing T1 evaluation. The clinical characteristics of the remaining 66 patients are shown in As shown in When the RRI was analyzed, a significant reduction was demonstrated after the sacubitril/valsartan treatment, whereas no differences were found between the T-1 and T0 evaluations in the 41 patients in whom it was available  (p 0.911) and E/e’ . Analogously, no correlation was found with the absolute changes in TAPSE  and PAP . In addition, the changes in the RRI were not correlated with the absolute and relative changes in creatinine  and with the relative changes in GFR . Finally, the changes in the RRI were not correlated with those of the parameters reflecting the diastolic function, i.e., LAV (Spearman’s R 0.014, The main finding of this study was that, after treatment with sacubitril/valsartan, significant improvements in renal resistances were observed and were not associated with the improvements in the left ventricular function.Sacubitril/valsartan plays a key role in the treatment of patients with HFrEF ,16. The However, the beneficial effects of sacubitril/valsartan are mediated not only by cardiac protection but also by renal protection, thus slowing the progression of cardiorenal syndrome  in patieOur results support the hypothesis that sacubitril/valsartan exerts specific nephroprotective effects that decrease arterial renal resistance and are associated with the progression of renal dysfunction. In fact, the RRI reflects arterial renal resistance ,19,20, wInterestingly, the changes in the RRI that have we observed were not related to those reflecting reverse remodeling, improvements in LVEF, or decreased left ventricular filling pressure and right pressure. As a consequence, sacubitril/valsartan might be hypothesized to have additive and direct renal effects independent from the improvements in cardiac function. Our study did not include a control group. In order to overcome this limitation and to strengthen the evidence of a causal relationship between sacubitril/valsartan therapy and RRI variation, we evaluated the parameters not only at the time of prescription and after the treatment with sacubitril/valsartan, but also at least six months before the start of the treatment. Interestingly, the RRI, as well as the left ventricular atrial and ventricular volumes and the left ventricular ejection fraction, were similar at T0 and T-1, but changed significantly after the administration of sacubitril/valsartan. These findings provide further support for the drug’s role in the observed changes.Finally, no relationship was found between the RRI changes and the changes in creatinine serum levels and estimated GFR. As previously demonstrated , an alteHowever, our study presents several limitations. It was not a randomized study, and we did not have data to compare the effects of sacubitril/valsartan with those of ACE-inhibitors or angiotensin II receptor blockers. However, all of our patients were taking one of these two classes of drugs before sacubitril/valsartan and no change was observed between T-1 and T0 evaluations, whereas the changes were observed after the initiation of sacubitril/valsartan. The changes in RRI were statistically significant but small. This could be due to the short follow-up. However, it is worth noting that no significant changes in GFR were observed during the same period. Consequently, despite the above-mentioned limitations, our results could be useful to generate hypotheses about the effects of sacubitril/valsartan on renal resistances that could occur earlier than those of GFR. Future studies should confirm our results and evaluate the RRI changes in a larger population with a longer follow-up. Sacubitril/valsartan appears to have favorable effects on arterial renal resistances and kidney function. These variations are likely to be related to the combined renal effects of the inhibition of the renin-angiotensin system and the inhibition of neprylisin. Moreover, these effects are not related to reverse remodeling, changes in creatinine serum levels, or the estimated GFR. In this sense, more evidence is needed in order to better characterize the effects on renal function and hemodynamics.


Patients with sleep apnea (SA) and coronary artery disease (CAD) are at higher risk of atrial fibrillation (AF) than the general population. Our objectives were: to evaluate the role of CAD and SA in determining AF risk through cluster and survival analysis, and to develop a risk model for predicting AF.Electronic medical record (EMR) database from 22,302 individuals including 10,202 individuals with AF, CAD, and SA, and 12,100 individuals without these diseases were analyzed using K-means clustering technique; k-nearest neighbor (kNN) algorithm and survival analysis. Age, sex, and diseases developed for each individual during 9 years were used for cluster and survival analysis.p = 0.0041; HR: 1.46, 95% CI: 1.09–1.96, p = 0.01; HR: 1.54, 95% CI: 1.22–1.94, p = 0.0039, respectively] and individuals with chronic kidney disease (CKD) developed AF approximately 50% earlier than patients without these comorbidities in a period of 7 years . Comorbidities that contributed to develop AF earlier in females compared to males in the group of 50–64 years were HBP  CAD and SA in the group of 60–75 years were .The risk models for AF, CAD, and SA were identified with high accuracy and sensitivity (0.98). Cluster analysis showed that CAD and high blood pressure (HBP) are the most prevalent diseases in the AF group, HBP is the most prevalent disease in CAD; and HBP and CAD are the most prevalent diseases in the SA group. Survival analysis demonstrated that individuals with HBP, CAD, and SA had a 1.5-fold increased risk of developing AF [hazard ratio (HR): 1.49, 95% CI: 1.18–1.87, Machine learning based algorithms demonstrated that CAD, SA, HBP, and CKD are significant risk factors for developing AF in a Latin–American population. Atrial fibrillation (AF) is the most common heart rhythm disorder, however, frequently remains undiagnosed, or manifests sub-clinically. The prevalence of AF increases with age and is approximately 15% in individuals older than 80 years . The worScreening for detection of subclinical AF has been recommended by multiple cardiovascular and stroke guidelines. However, significant questions regarding the best technology and the duration of monitoring have been raised. Additionally, routine use of technology for AF detection is pragmatically limited . IncremeElectronic medical records (EMR) are a valuable source for research since predictive variables can be extracted to develop these models. EMR are being used to classify, diagnose and predict future hospitalization through machine learning methods , 16. LimMachine learning methods can reduce bias caused by human intervention resulting in more accurate prognostic models and have been useful to establish clinical phenotyping, risk stratification and treatment outcomes , 20. How+ channel alterations that reduce the atrial refractory period and increase dispersion of refractoriness promoting re-entry while decreasing automaticity . The sample included a database from 22,302 individuals . The total of records  of these individuals during 2010–2019 were 177,656  . The infElectronic medical record information was cleaned to process data with well-defined criteria and context and patients with missing data were removed. Only data until the first diagnosis of the disease of interest is considered. First, the k-nearest neighbor (kNN) algorithm was applied to classify patients into groups to perform risk models for AF, CAD, or OSA. Next, the elbow method was applied to the data to identify the optimal number of groups in the data. The k-means method was used to group the sample in clusters before the event of interest  occurred. In the final step, survival analysis was performed using Cox proportional hazards (PH) models to evaluate the relationship between comorbidities and time to develop the diseases AF, CAD, or OSA.The experiment aims to identify clusters for AF, CAD, and OSA. The variables selected to perform the cluster for each disease included sex, age, and the time (in days) until the diagnosis of AF, CAD, or OSA. The Elbow method was applied to identify the ideal number of groups. The K-means clustering technique was used to perform the clustering.Survival analysis using multivariable Cox proportional hazard and Kaplan–Meier models was performed to identify the risk and time involved until the development of AF, OSA, or CAD. The start condition was age ≥ 18 years, and the time until diagnostic (T variable) is more than 0. The metrics used to measure the risk was hazard ratio (HR), and the model concordance was C-index.i = 1,⋯, n) in the study, are represented, in general, by the pair  where ti is the time of breakdown and δi is an indicator variable of breakdown. In the presence of co-variables measured at the same individual level, such as xi = , the data are represented by . There is no special case of interval data, yet there is a representation  where li and ui are, respectively, the lower and upper limits of the comparison range for the i-th individual.The survival data for the individual i  = P(T ≥ t), and the cumulative distribution function represents the probability of no survival on time t is F(t) = 1−S(t) and the density function of no survival can be obtained by The survival function is used to represent the probability of the event of interest, i.e., patient survival, during an interval of time h(t) = h0(t) ⋅ g(βX′).The formula used for Cox PH model was: g is a function such that g(0) = 1.where Therefore, Cox proportional-hazards analysis with a time-dependent definition for the AF, CAD, or OSA apparition were used . HR = exY = {AF, CAD, OSA} given the patient was diagnosed according to an ICD code X, where X is derived from the EMR and the kNN method applied. Machine learning through kNN was employed to identify the risk of developing AF, OSA, or CAD, based only on EMR and without the information of the diagnostic. To build prognostic models by machine learning, the parameter K varied between 3 and 30. For each one of these values, it carried out ten experiments and, in each experiment, the sample was randomly split into a training cohort (70% of the patients), and a validation cohort (30% of patients)  . The valThe present analysis included 10,202 individuals represented by 1,686 with AF, 7,879 with CAD, and 1,032 with OSA. The comparison group include 12,100 individuals without these diseases. The prevalence of AF, OSA, and CAD in groups classified by sex and age are shown in Seven clusters were identified for each disease AF, CAD, and OSA. The clusters that grouped the majority number of patients are described in p = 0.0039; CAD = HR: 1.49, 95% CI: 1.18–1.87, p = 0.0041; CKD = HR: 3.36, 95% CI: 1.46–7.73, p = 0.0023 and OSA = HR: 1.46 95% CI: 1.09–1.96, p = 0.01). Subsequently, patients with CAD develop AF earlier when CAD is associated with CKD . The survival analysis for the risk of AF comparing men and women by decades in patients older than 50 years showed that women develop AF earlier between 50 and 64 years when associated with HBP , in patients between 60 and 75 years with CAD  and OSA , and in women older than 75 years with HBP  and CAD .The findings of survival analysis are shown in The results of the risk models for AF in individuals with CAD and OSA are shown in via clusters  with very high rates (> 95%) of sensitivity, specificity, AUC and ACC; (3) On average a 1.5-fold increase in developing AF was observed in individuals with HBP, CAD, and OSA and a threefold increase with CKD. AF was developed approximately 50% earlier than patients without these comorbidities in a period of 7 years (the median time to develop AF was 943 days); (4) Individuals with CAD and CKD develop AF significantly earlier compared to those with CAD and preserved kidney function; (5) Finally, women show a risk biggest of up to 67% to develop AF in a period of 7 years compared to when HBP and CAD are present, this finding was primarily related to age.The present investigation applied conventional statistical and machine learning techniques on a cohort of 22,302 EMR records from Latin American patients to build risk models and determine the effect of demographic and comorbid predictors on AF during a follow-up of approximately 7 years. The relationship with CAD and OSA was also explored; the main findings were: (1) Cluster analysis confirmed that comorbidities associated with AF, CAD, and OSA were HBP, CKD, hypertensive heart disease, mitral regurgitation, having a cardiac pacemaker and obesity; (2) kNN Machine learning was useful to classify each disease associated to its comorbidities independently The most frequent comorbidities grouped in clustering were explored in the survival analysis to identify the role of CAD and OSA to determine the risk of developing AF, but also, the relationship between AF, CAD, and OSA with other comorbidities. As expected, the prevalence of AF, OSA, and CAD increased in individuals older than 50 years, therefore patients were divided into a study and control cohort accordingly. Three findings derived from the survival analysis were relevant: (1) the inflection time to evaluate the risk of AF is clearly 50 years and HBP, CAD, OSA, and CKD were significantly associated with AF risk within a time frame of almost 7 years , (2) comorbidities significantly associated with CAD risk in patients over 50 years old during the same time frame were HBP, hypertensive heart disease and having a pacemaker previously implanted. Finally, the lifetime risk to develop AF in women was the 7th decade, however there is an almost twofold increase in the risk of developing AF when associated HBP and CAD. The pathophysiologic mechanisms involved in the differential risk for men and women per decade are unclear and may include increased pregnancies due to repeated hormonal exposure and other metabolic factors ; includiThe relationship between AF and CAD may be explained by the following facts: AF and CAD share multiple comorbidities, and the most significant comorbidity for AF risk was HBP followed by CAD while the most significant comorbidities for CAD risk were HBP and Hypertensive heart disease. This two-way relationship between AF and CAD shares a common pathway of inflammation and co-existent risk factors. Similarly, AF is present in 17–47% of patients with CAD while the prevalence of CAD in patients with AF has been reported from 0.2 to 5% . AF has Obstructive sleep apnea increased by one and a half fold the risk of developing AF. This finding is in keeping with previous studies. OSA, is a sleep disorder that has been recognized as a risk factor for CAD and more recently for AF reportedly having a fourfold risk of developing AF compared to non-SA patients . HoweverThe results of our cluster analyses identified seven phenotypes for each group of study  and showed that HBP was the most prevalent comorbidity associated to CAD, OSA, and AF. Cluster analytic techniques used in several studies have proposed phenotypic groups for CVD risk and include HBP, AF, CAD and renal disfunction as comorbidities with the highest risk for heart failure and death . The rolOur kNN analysis indicates that an effective risk prediction model for AF derived from EMR derived comorbidities is feasible. Considering the increasing prevalence of AF in the population it is necessary to maximize the detection of AF cases and a potentially cost-effective method may be machine learning methods and artificial intelligence to appropriately apply precision medicine for diagnosis and personalized treatment. Hill et al.  evaluateOther studies have reported the use of machine learning for the identification of patients with AF derived from physical examination findings or documented rhythm alterations detected by a smartwatch. Lown et al.  designedIncorporating machine learning systems to EMR for AF may be useful to determine the behavior of physiological data and the temporal relationships associated with risk factors. Cox proportional hazard regression and survival analysis have been employed to predict the response of pharmacological and electrical cardioversion therapies for AF , 59, howSome limitations should be considered; EMR had to undergo a significant data cleaning process prior to analysis, similarly we cannot rule out that a significant proportion of patients over 65 years may have had subclinical AF and therefore not identified in this study. Finally, patients diagnosed in a secondary care clinic may have more comorbidities than patients in primary care or in the general population. Our study focused on comorbidities as risk factors for AF and future studies should include genomic, socioeconomic status, and family history for model risks.Machine learning identified risk factors for AF and other comorbidities in a large cohort of EMR derived from Latin American patients. Future prospective studies based on machine learning methods should be performed and include phenotype and genotype risk variables and comparing different populations. The identification of risk factors associated with AF may potentially provide better therapeutic results and tools for prevention policies in public health.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the Ethics Committee at the Pontificia Universidad Javeriana (OD-0249). The patients/participants provided their written informed consent to participate in this study.CS performed the machine learning analysis, clustering, risk prediction, and survival analysis. CL-C, MB, and RG-O assisted in the data collection and supervision of the machine learning analysis. LO, CS, CL-C, MB, RG-O, and CM conceived and designed the study. CM, JC, RG, RG-O, and LO reviewed and edited the manuscript. All authors contributed to the article and approved the submitted version.


Study objectives: The aim of the study was to evaluate of the quality of life, depression, anxiety levels, and physical activity in the groups after the implantation of an ICD or CRT-D. Methods: All subjects (111 CHF patients) underwent tests to assess the quality of life (NHP), the level of physical activity (IPAQ), the level of perceived stress (PSS), and the incidence of depression (BDI). Results: After the implantation, physical activity (PA) of the patients from the primary prevention (PP) group remains unchanged, whereas in the secondary prevention (SP) group, it decreases noticeably. Physical activity is lower in the SP group in comparison with the PP group. There are no statistically significant differences in the level of depression. The scores in the second part of the NHP questionnaire indicate that the SP group significantly more often declare problems with housework and with social life than the PP group. Conclusions: 1. The type of prevention does not have an influence on the level of anxiety, stress, or depression. 2. The patients after implantation as SP are physically less active; lower PA is associated additionally with the higher NYHA class and chronic kidney disease. 3. The quality of life of the patients from SP group is at a lower level than patients from PP group. Congestive heart failure is one of the most common reasons for hospitalization of people aged over 65 years of age, and is responsible for 11% of all hospital stays. In high-income European countries, 1–2% of people (and approximately 10% of those over 70 years of age) live with heart failure [The main cause of mortality in moderate congestive heart failure is sudden cardiac death. Insertion of an implantable cardioverter-defibrillator (ICD) or a cardiac resynchronization therapy defibrillator (CRT-D) can reduce the risk of sudden cardiac death and may be used in primary or secondary  prevention regimens. In addition to preventing sudden cardiac death, the insertion of a CRT-D can improve general prognosis, reduce hospitalization and mortality, increase exercise tolerance, enhance quality of life, lower the New York Heart Association (NYHA) functional class, and improve heart function in patients with intraventricular conduction abnormalities  ,3,4.Patients with congestive heart failure often face psychological challenges. Many tools are available to evaluate psychological challenges associated with different arrhythmic problems . FollowiWe hypothesized that, due to their traumatic experience, patients receiving an ICD or CRT-D might adapt more easily to life with a defibrillator and, therefore, report better quality of life and lower levels of anxiety and depression compared with patients receiving an ICD or CRT-D for primary prevention. We also hypothesized that higher levels of physical activity would be associated with improved quality of life and reduced anxiety and depression among patients receiving an ICD or CRT-D.This study aims to compare quality of life, depression and anxiety levels, and physical activity in patients undergoing implantation of an ICD or a CRT-D for primary versus secondary prevention.This prospective observational cohort study included consecutive patients with congestive heart failure aged over 18 years with an inserted ICD or CRT-D recruited from a single center between January 2017 and December 2019. Study was approved by the local Bioethics Committee . Patients gave written, informed consent to be included in the study.Inactive—participants are insufficiently active ;Minimally active—(a) 3 or more days of vigorous physical activity for 20 min per day; or (b) 5 or more days of moderate-intensity physical activity and/or a short walk for at least 30 min per day; (c) or 5 or more days of any combination, reaching at least 600 MET-min/week;HEPA —highly active—(a) vigorous physical activity for at least 3 days, with a minimum of at least 1500 MET-min/week; (b) 7 or more days of any combination achieving a minimum of 3000 MET-min/week ,12.All participants underwent tests to assess psychological variables, quality of life, and level of physical activity at a minimum of 3 months following implantation. Paper forms were filled in personally by study participants, who were supported by investigators in reading, discussing, and understanding the form any time it was needed. Physical activity was scored according to the International Physical Activity Questionnaire , which contains seven questions concerning levels of exertion within the past 7 days , as well as sitting. Participants specified whether they had performed certain activities, the number of days per week they participated in those activities, and the duration of particular physical activities within 24 h (in min) ,12. PartAdopted metabolic equivalent of task (MET) values: walking 3.3 MET, moderate-intensity activity 4 MET, vigorous-intensity activity 8 MET. Calculating the level of the whole-week physical activity: MET (min/week) = walking (MET * minutes * number of days) + moderate activity (MET * minutes * number of days) + vigorous activity (MET * minutes * number of days).Quality of life was tested with the Polish version of the Nottingham Health Profile questionnaire, adapted into native study participants’ language (Polish) by K. Wrześniewski. It consisted of two parts. The first contains 38 statements concerning energy, pain, motor limitations, and the psychosocial sphere. The second refers to the influence of current health status on the respondent’s paid work, housework , social life , family life , sex life, interests and hobbies , and free time . Participants responded to each statement by agreeing or disagreeing that this is a particular area of concern (‘yes’ or ‘no’ answers). Scores were calculated separately for each of the six areas in the first and the second parts of the questionnaire. For part one, statistical analysis was conducted with the use of a weighted average for each question to calculate the intensity of particular features  ,14.The level of perceived stress was assessed with the Perceived Stress Scale (PSS-10), which consists of ten questions related to subjective feelings connected with problems, personal experiences, and behaviors, as well as ways of coping with them. It is used to assess the intensity of stress connected with a patient’s life situation within the past month. The score measures chronic stress and mental wellbeing connected with the ability to cope with challenges. The range of values is 0–40 points, which are expressed in sten scores. The higher the score on the sten scale, the higher intensity of perceived stress: 1–4 stens is considered a low level of perceived stress, 5–6 stens a moderate level, and 7–10 stens a high level.The incidence of depression and its intensity were assessed on the basis of the Polish language version of Beck’s Depression Inventory. The questionnaire consists of 21 statements concerning various symptoms of affective disorders. The participants responded to each statement by choosing the answer that—in their view—most accurately depicted their condition in the previous week. Particular questions in the Beck Depression Inventory assess both mental and somatic aspects of depression. The total score shows the severity of symptoms of depression. To analyze the obtained results, German norms were used: scores of 0–11 indicated no depression; scores of 12–19 mild depression; scores of 20–25 moderate depression; and scores of 26 or more severe depression.Additionally, patients completed a patient report form containing 61 questions concerning personal data, medical history, type of treatment, anxiety scale (0–10 scores), disorders concerning attention span and memory, sleep disorders, and physical activity.p = 0.043)  with an inserted ICD or CRT-D were recruited between January 2017 and December 2019. Of these, 74 patients received the ICD or CRT-D for primary prevention and 37 received the ICD or CRT-D for secondary prevention. No statistically significant differences are observed between the tested groups with regard to demographic and clinical characteristics, with the exception of age, which is higher in the secondary prevention group  .p = 0.585). After implantation, physical activity decreases in the secondary prevention group, but remains unchanged in the primary prevention group . In both groups, the preferred activities are walking and cycling; however, following implantation, the percentage of participants involved in these activities is significantly lower in the secondary prevention group . Additionally, 64.9% of participants in the secondary prevention group versus 42.5% of participants in the primary prevention group (p = 0.026) admit that they have limited their physical activity due to fear of electrical shock, which leads to a reduction in physical activity after implantation in 73.0% of participants in the secondary prevention group versus 38.4% in the primary prevention group (p = 0.002)  .p = 0.717) (Although the median whole-week activity in the primary prevention group (4065 MET * minutes * number of days) is 25% higher than in the secondary prevention group (3252 MET * minutes * number of days), this difference is not statistically significant (= 0.717) .It is found that a low level of physical activity is associated with a higher NYHA classes  and with the occurrence of chronic kidney diseases .p = 0.057). The groups also do not differ in terms of the severity of chronic stress connected with participants’ circumstances  and social life , significantly more often than respondents in the primary prevention group. Although problems connected with family life are identified in twice as many cases in the secondary prevention group, the difference is not statistically significant (9.5% vs. 18.9%)  .Psychological effects in patients with heart disease are broad ranging, and evaluation must cover different areas . In thisp = 0.008) and have an inferior quality of life connected with vitality compared with patients in the secondary prevention group. No significant differences in the level of depression are identified between groups, similar to the findings of our study. Similar results are obtained in a study by Berg et al. [Symptoms of depression are not observed in either group, the level of anxiety in both groups is moderate, and the level of chronic stress is between moderate and high. Anxiety levels are numerically, but not statistically significantly, higher in the secondary prevention group. In a study by Rahmawati et al. , in whicg et al. , in whicg et al. , anxietyg et al. .p = 0.007) and social life (p = 0.004) than in the primary prevention group, which indicates a lower quality of life in this group. These results differ from those of an analysis of five studies by Pedersen et al. [Among all patients, health status appears to be related to self-reported quality of life. Nearly half of participants declare problems with paid work (47.3% in the primary prevention group vs. 46.0% in the secondary prevention group) and spending free time (45.9% in the primary prevention group vs. 45.9% in the secondary prevention group). Significantly more patients in the secondary prevention group have problems with household duties (n et al.  in 2009,n et al.  In our sStudy limitationsThis was an observational single-center study with a modest sample size and non-consecutive recruitment in terms of intention-to-treat. Further, there was a significant difference in the average age of the tested subpopulations. We did not collect data on electrical shocks, which could have affected quality of life.Clinical implicationsOur study identifies areas of inferior post-treatment adaptation in patients receiving an ICD or CRT-D for primary versus secondary prevention, highlighting areas for therapeutic intervention, including application of methods used in clinical psychology and psychotherapy. These results suggest the need for psychological well-being or pre-procedure counseling for psychological assessment.The type of prevention: primary vs. secondary, as an indication for an implantable cardioverter-defibrillator or cardiac resynchronization therapy defibrillator does not affect levels of anxiety, stress, nor depression;Patients with implantation for secondary prevention are significantly less physically active after implantation than those for primary prevention. Lower physical activity is associated additionally with the higher NYHA class and the incidence of chronic kidney disease;Quality of life of patients after implantation for secondary prevention is lower than in subjects with primary prevention.


The phenomenon of multilingualism and its impact on child development are in the spotlight of latter-day psychology, and of utmost importance both for theory and practice. Language development is a strong predictor of psychological readiness for school and further academic success. At the same time, children’s mastery of written and oral speech in school education in a multilingual environment has several distinctive features. This study was dedicated to examining the influence of executive functions on the development of the vocabulary aspects of speech (both active and passive vocabulary) of mono- and bilingual children growing up in a bilingual environment.We aimed to analyze the relationship between bilingualism and language development  and determine which executive functions may help overcome the resulting difficulties at preschool age.Both monolingual and bilingual children participated in the study . The children’s ages ranged from 6 to 7 years . Two independent General Linear Models (GLM) were built to define which executive functions influenced the vocabulary and verbal fluency of the mono- and bilingual subjects .The results confirmed that bilingualism is negatively related to language development, but showed that verbal working memory significantly helps bilinguals compensate for difficulties in developing vocabulary and verbal fluency.The study demonstrated that the ability to preserve and reproduce verbal information was of more significance for children’s vocabulary and verbal fluency than their language group . Bi- or multilingual education and parenting is gaining popularity internationally and becoming more and more common. In many countries, children learn and use two or more languages from their early childhood.The phenomenon of multilingualism and its impact on child development are in the spotlight of latter-day psychology, and of utmost importance for both theory and practice. According to the definition given by the U.S. Department of Health and Human Services, 2008, a dual language learner is a person who acquires two or more languages simultaneously, The experience of parallel mastery of two languages to a certain extent affects the process of language mastery itself, and is tightly connected to cognitive systems of language application, reflection on the world, and self-regulation. The current study is dedicated to examining the impact of executive functions on the development of vocabulary aspects of speech (active and passive vocabulary) of mono- and bilingual children growing up in a bilingual environment.Language development is a complex phenomenon, defined by a whole number of mental processes related to children’s mastering of written and oral speech. Speech not only executes its main function of communication; it also provides for the development and functioning of other mental functions. Language development at preschool age has a significant impact on the development of cognitive skills  and emoti.e., lexical items) and is reflected in the richness of the child’s vocabulary. Syntactic and grammar development is linked to the child’s mastery of language rules, necessary for constructing sentences. And last but not least, the symbolic aspect of language development covers the child’s mastery of graphic representations of sounds (letters) and the development of basic writing and reading skills.A 2008 report by the U.S. National Institute for Literacy suggests distinguishing the following criteria when analyzing speech development: phonetic, vocabulary, syntactic, and symbolic. The phonetic criterion includes the perception of oral speech and independent sounds and words through the development of articulatory skills. The vocabulary aspect characterizes the success in the child’s mastery of words’ meanings , was confirmed by research on children’s atypical speech development. Vygotsky suggested considering inner speech “not as speech minus sound, but as a speech function completely special and peculiar in its structure and mode of functioning, which /…/ is in an inextricable dynamic unity of transitions from one plan to another” , p. 275.It was to be expected that the development of bilingual children’s executive and language functions would have some sort of unique and complex dynamic by contrast with monolingual subjects. That would be explained by the fact that a bilingual child has to master two language systems simultaneously . Yet, there are clearly insufficient studies on the relationship between bilingual children’s language and executive functions, while there are quite a few for monolingual children  . TherefoTwo hundred and eighteen 6–7-year-old children participated in the current study , 50.1% of them girls). All the subjects lived in the Republic of Sakha (Yakutia), and attended public kindergartens. The children were divided into groups  in accordance with their preschool educators’ answers to the following questions: a) which language does the child speak most of the time in the classroom? and b) which language is used for child-parents communication? The results of the t-test for independence allowed us to be sure that the mono-and bilingual groups did not differ age-wise (p > 0.05). Assessment of the children’s executive functions was conducted individually by specially trained assistants. The assessment was performed in Russian in a separate quiet room provided by the kindergarten.The Peabody Picture Vocabulary Test 4th Edition (PPVTTM-4) was used for the assessment of the subjects’ passive vocabulary, i.e., the volume of words that they could understand when perceived audibly  , the interviewer assigned 1 point for the whole row.In order to evaluate the children’s verbal fluency, we applied the st (VFT) . This toThe Dimensional Change Card Sort (DCCS) (t (DCCS)  is an exInhibition  (s (CMPM)  was chosDescriptive statistics and Pearson’s r correlation were run for all the variables, to examine the data structure. For the main analysis, two independent General Linear Models (GLM) were built to define which executive functions influenced the vocabulary and verbal fluency of the mono- and bilingual subjects . Jamovi software, version 1.0.7.0 (by The Jamovi Project) was used for the statistical analyses required for the current study.Table 1 for descriptive statistics and Pearson’s r correlation for all study variables, including the subjects’ vocabulary, verbal fluency, non-verbal intelligence, and four components of executive functions.See Table 2 separately for mono- and bilingual children.The outcome of Pearson’s r correlation analyses confirmed that bilingualism had a significantly negative relationship with the subjects’ vocabulary (r (217) = –.262, p < .01) and verbal fluency (r (217) = -.222, p < .01). It was positively related to their inhibitory control, though (r (217) = .135, p < .05). Apparently, respondents with a larger vocabulary performed better in all tasks focused on execution functions except cognitive flexibility (p > 0.05). Thus, they more often got higher scores on the visual working memory test (r (217) = .152, p < .01), the verbal working memory test (r (217) = .387, p < .001), and the inhibitory control test (r (217) = .134, p < .01). Children with well-developed verbal fluency often obtained higher scores for cognitive flexibility (r (217) = .230, p < .01), and verbal working memory (r (217) = .376, p < .001). Assessment results for executive functions and speech development are detailed in We built two independent General Linear Models (GLM) with the purpose of examining what levels of vocabulary and verbal fluency (dependent variable) were demonstrated by the subjects, depending upon their executive functions (independent variable). The following factors were controlled as well: language group , age (continuous in months), gender , and non-verbal intelligence (continuous).2p = .506. According to Fixed Effects Parameter Estimates, only verbal working memory had a significant effect on the children’s vocabulary: F (1) = 9.366, p = .004, ɳ2p = .182. No significant effects of language group, gender, age, non-verbal intelligence, cognitive flexibility, visual working memory, and inhibition were registered. The vocabulary test score of the respondents from both language groups  was higher in correspondence with the development of their verbal working memory.One GLM (Vocabulary (PPVT-4) ~ 1 + `Language group` + Gender + `Age` + `Non-verbal Intelligence` + `Cognitive Flexibility` + `Visual Working Memory` + `Inhibition Combined` + `Verbal Working Memory`) was built to find out which executive functions influenced the subjects’ vocabulary . [Model with Adj. R-squared = 0.506.] An ANOVA Omnibus test revealed that the model described the data correctly: F (8) = 5.369, p < .001, ɳ2p = .116. Language group, age, gender, and nonverbal intelligence were controlled. No significant effects of language group, gender, age, non-verbal intelligence, cognitive flexibility, visual working memory, and inhibition were discovered.Then we built a GLM , to see which executive functions affected the subjects’ verbal fluency. [Model with Adj. R-squared = 0.316.] An ANOVA Omnibus test indicated that the model described the data well: F (9) = 1.59, p < .0161, ɳp = .316. According to Fixed Effects Parameter Estimates, only verbal working memory had a significant effect on the children’s verbal fluency: F (1) = 5.975, p = .020, ɳThe key goal set for our study was to analyze the relationship between bilingualism and language development  and determine which executive functions may help overcome difficulties in language development at preschool age, while controlling for age, gender, and non-verbal intelligence. First, we explored the relationship among vocabulary, verbal fluency, executive functions, and bilingualism. It was revealed that bilingualism had a significant negative correlation with the subjects’ inhibitory control, and a positive correlation with their inhibitory control. Vocabulary size turned out to be related positively to all executive functions except cognitive flexibility.At the next stage, we studied the influence of executive functions on vocabulary and verbal fluency of bi- and monolingual children . The outcome of that analysis proved that the size of vocabulary and the level of verbal fluency are determined to a major extent by verbal working memory. As per our study results, other executive functions do not have any significant effect on the vocabulary and verbal fluency of bi- and monolingual children . Besides, the language group  was of no significance in predicting vocabulary size and verbal fluency, if the models included verbal working memory.The positive impact of verbal working memory on verbal fluency among bi- and monolingual children could possibly be understood as linked to the growth of the child’s cognitive abilities due to the development of his or her capability to remember verbal information . It has i.e., it begins to fulfill the function of forming a plan for solving the problem arising in behavior” . If we desire further research to replicate these results, a more diverse sample will be needed, which will be more representative. Another limitation was related to the age range of the respondents (only 6–7-year-olds participated in the study). This, unfortunately, was due to the constraints of existing organizational resources. If we want to explore the age dynamic in relation to vocabulary and verbal fluency, and its association with the regulatory functioning of mono- and bilingual children, a new study with a broader age range of the sample is required.Yet, despite the indicated limitations, this study provides an important contribution to the research field focused on language development of bilingual children. This is due to the evidence we obtained of the positive effect of verbal working memory on children’s vocabulary and verbal fluency .This study’s results confirmed that bilingualism in preschool years is negatively related to language development and demonstrated that the ability to preserve and reproduce verbal information is more significant for children’s vocabulary and verbal fluency than the child’s language group. The results obtained may be helpful in the elaboration of programs aimed at reducing learning difficulties for bilingual children.


CC showed a relatively high predictive capacity of sarcopenia (area under the curve: AUC = 0.82). Furthermore, CC could be a valuable tool to predict sarcopenia risk compared with the currently used screening tools, SARC-F and SARC-CalF . More studies are needed to validate these findings in external study populations.Sarcopenia is an important risk factor for hip fracture in older people. Nevertheless, this condition is overlooked in clinical practice. This study aimed to explore the factors associated with sarcopenia among older patients hospitalized for hip fracture, to identify a predictive model of sarcopenia based on variables related to this condition, and to evaluate the performance of screening tools in order to choose the most suitable to be adopted in routine care of older people with hip fracture. A cross-sectional study was undertaken with 90 patients (mean age 83.4 ± 7.2 years), by assessing sociodemographic and clinical characteristics, anthropometric measures, such as body mass index (BMI) and calf circumference (CC), the functional status (Barthel Index), the nutritional status (MNA-SF), and the adherence to the Mediterranean Diet (MEDAS). Diagnosis of sarcopenia was established according to the criteria of the European Working Group on Sarcopenia in Older People (EWGSOP2). The analysis of variables associated with sarcopenia was performed using multivariate logistic regression models. Clusters of sarcopenia were explored with heatmaps and predictive risk models were estimated. Sarcopenia was confirmed in 30% of hip fracture patients. Variables with the strongest association with sarcopenia were BMI (OR = 0.79 [0.68–0.91],  Sarcopenia is a skeletal muscle disease  that involves a progressive and generalized decrease in muscle strength and muscle mass ,4,5,6,7.In Spain, it is estimated that there are between 40,000 and 45,000 hip fractures in older people per year . This nuThere is no single diagnostic criterion for the establishment of sarcopenia. The consensus published by the European Working Group on Sarcopenia in Older People (EWGSOP) is the most widely used one. In its latest update in 2019 (EWGSOP2), the use of screening tests to identify potential cases of sarcopenia was reinforced. Moreover, it was recommended to measure muscle strength by handgrip using dynamometry, and muscle mass mainly by dual-energy X-ray absorptiometry or bioimpedance analysis. However, EWGSOP2 also expanded the possibility of detecting this condition by suggesting other techniques and tools to assess muscle properties, included but not restricted to mid-thigh muscle measurement and ultrasound assessment of muscle mass . In this study, sarcopenia was defined according to the Japan Society of Hepatology . LikewisWhen comparing the CC with the commonly used screening tests, we observed that CC presented higher sensitivity and specificity to predict sarcopenia than SARC-F and SARC-CalF . In line with our results, Chen et al. showed that CC had a higher accuracy compared to SARC-CalF, and SARC-F in older assisted living subjects (AUC = 0.819 vs. 0.734 vs. 0.576), as well as a higher sensitivity and specificity for sarcopenia . However, regarding screening tools our findings were different. The other authors reported a sensitivity/specificity for SARC-CalF and SARC-F of 38.0%/80.0% and 10.9%/91.8%, whereas we estimated a different trend with higher sensitivity but lower specificity values (85.1%/41.2% vs. 88.9%/28.6%) . Again, Finally, it is important to note that in our study, we did not find any significant relationship between BI, CCI, MEDAS, and sarcopenia. While this is an unexpected finding, we believe that these results can be explained by the fact that the study sample comprised mostly older patients with high dependency and mortality risk. Silva et al. (2018) in a systematic review and meta-analysis concluded that adherence to the MedDiet could be protective for frailty and functional disability, but not for sarcopenia . By contThis study has some limitations to note. First, the sample size was probably not large enough to observe significant associations between the study variables and sarcopenia. Second, it was unfeasible to measure weight and height of the patients, even though previous studies have shown high inter/intraclass correlation coefficients for self-reported and measured values of weight and height in older adults . DespiteThere are also some important strengths to highlight. To our knowledge, this is the first study in Europe to evaluate the aforementioned performance of CC to predict sarcopenia in older patients hospitalized and having suffered a hip fracture. All potential variables related with sarcopenia were collected from these patients by using valid questionnaires, face-to-face interviews and physical measures of body composition and anthropometry (except weight and height). Our results suggest, for the first time, that CC is a good screening tool to predict sarcopenic individuals among hospitalized older adult with hip fracture. In addition, we used comprehensive statistical methods and several approaches to corroborate our findings. For instance, in cluster analyses, we could verify that the characteristics of sarcopenic patients differ from those of non-sarcopenic patients and that a low CC was present in almost all sarcopenic patients with hip fracture. Our findings further reinforce the importance of including CC as a simple, rapid, noninvasive measurement to be included in the comprehensive evaluation of all older patients admitted with hip fracture. This may initiate awareness of sarcopenia and may help begin to design more specific nutritional interventions and treatment protocols for the improvement of post-surgical rehabilitation and prevention disability, as well as mortality, in these patients.In conclusion, the prevalence of sarcopenia in older adults with hip fractures was 30% and the main factors associated with this condition were CC and BMI. CC proved to be a potential valuable tool to predict sarcopenia, with a relatively high sensitivity and specificity to identify sarcopenia among these patients compared to other screening tools . Its incorporation into clinical practice should be evaluated and validated in large and external study populations. Thus, further studies are necessary to confirm these findings, i.e., to demonstrate the validity of CC for sarcopenia prediction and its association with sarcopenia and related factors in patients with hip fracture.


Fibromyalgia is a common pain condition that often leads to significant disability. Unfortunately, the effectiveness of most medications for fibromyalgia is limited, and there is a need for alternative, non-pharmacological therapies. Yoga and aerobic exercise are both evidence-based non-pharmacological treatments for fibromyalgia. However, no prior studies have directly compared the effectiveness of yoga vs. exercise.This article describes the study design and recruitment outcomes of the Pain Outcomes comparing Yoga vs. Structured Exercise (POYSE) Trial, a two-arm randomized comparative effectiveness trial.Veterans with fibromyalgia, defined by the 2010 American College of Rheumatology diagnostic criteria, who also experienced at least moderate pain severity were enrolled. The participants were randomized to a 12-week yoga-based or a structured exercise program (SEP) and will undergo comprehensive outcome assessments at baseline, 1, 3, 6, and 9 months by interviewers blinded to treatment assignment. The primary outcome will be the overall severity of fibromyalgia as measured by the total Fibromyalgia Impact Questionnaire-Revised. Secondary outcomes included depression, anxiety, health-related quality of life, pain beliefs, fatigue, sleep, and self-efficacy.n = 129) or the SEP (n = 127) arm of the trial.A total of 2,671 recruitment letters were sent to potential participants with fibromyalgia. Of the potential participants, 623 (23.3%) were able to be contacted by telephone and had their eligibility assessed. Three hundred seventy-one of those interviewed were found to be eligible (59.6%) and 256 (69.0%) agreed to participate and were randomized to the YOGA ; Trial registration:  Fibromyalgia affects at least 3–5% of the general population , 2 or ~1Medications are the most common treatment for fibromyalgia . HoweverClinicalTrials.gov NCT01797263] is a two-arm parallel group, randomized clinical trial. The POYSE trial enrolled 256 veterans with fibromyalgia and compared the effectiveness of a yoga-based intervention (YOGA) with that of a structured exercise program (SEP). The trial will last for 9-months, and the participants will undergo comprehensive outcome assessments at baseline and 1, 3, 6, and 9 months after the first intervention session.While there is trial evidence supporting both yoga and structured exercise, these approaches have not been compared against each other. The Pain Outcomes comparing Yoga vs. Structured Exercise (POYSE) trial . Blood pressure will be assessed at the beginning of each session, and heart rate will be checked at 5-min intervals. Heart rate will be measured with the study-issued heart rate monitor. Ratings of perceived exertion (RPE) will be evaluated according to the Borg scale , which rThis combination of RPE and HR data with the aerobic exercise will allow for the veterans to better gauge their “comfort zone” and select physical activities based on how they feel and/or how their body is responding to their physical activity on any given day. Both methods provide useful information for the clinicians and veterans to set their exercise intensity based on different levels of exercise while specifically providing a mechanism for veterans who have an attenuated cardiovascular response to exercise.The general exercise portion of SEP includes strengthening and flexibility exercises focused especially on trunk and proximal extremity muscles, which are body areas most frequently affected by fibromyalgia. The exercises will be taught with a focus on body awareness and biomechanically correct positioning and movement. The exercises will be developed in three progressive sections with increasing task complexity to facilitate a more intense exercise session. The exercises in the first section (weeks 1–4) will be basic one limb/joint movements in a stable posture and will be advanced to multi-limb/joint movements in more challenging postures in the third section (weeks 9–12). The progressions will include use of therapy balls and whole-body exercises such as lunges and squats with use of medicine balls and kettle bells. The exercises will remain consistent in each section and will be graded down as needed for the target body part and as needed for veterans struggling with maintaining biomechanically correct movements. For strengthening exercises, repetitions will be maintained at 8–15 throughout all levels of the program, while the level of resistance and position  will vary to accommodate intensity needs at each level. The strengthening and stretching exercises were organized into 4-week sections with incrementally increasing difficulty/intensity while ensuring that the participants were exercising in their “comfort zone.” The time interval between sets of exercises used was set at 5 min. The flexibility exercises will remain consistent throughout the program with increased hold time of each stretch by 10 s at each progression. Eventually participants will reach 30 s stretch holds by the third section (weeks 9–12). Each section of the SEP program will last 4 weeks.In addition to the schedule of supervised group exercise sessions shown in The exercise tracking sheets will be reviewed each week by the PT. After reviewing the tracking sheets, the PT will follow up with participants as needed about their exercise activity and any barriers listed on the sheets. The PT will encourage the veterans to continue their exercise activities and will offer suggestions if a veteran is struggling. The veterans will be provided a DVD with suggested home exercises. Attendance in the exercise group sessions will be recorded.Strenuous exercise, especially if done too vigorously, can exacerbate fibromyalgia symptoms and adversely affect exercise adherence . ExercisTo minimize muscle micro-trauma and exercise-induced muscle soreness, we will employ the following safety precautions for both intervention groups: (1) the participants will be advised not to exceed the prescribed exercise  during a “good day,” (2) the participants will be forewarned that even an exercise performed at the appropriate intensity might result in short-term increases in exercise-induced pain and fatigue that should dissipate within a few days (up to 2 weeks), (3) the participants will be allowed to self-adjust their exercise intensity, especially during a flare, (4) participants who develop intolerable exercise-induced pain (or fatigue) will be instructed to avoid exercise for 48 h restart the program at 5% below the previous exercise intensity  and continue on with the original exercise prescription, (5) should a participant develop an increase in regional body pain  as a result of the aerobic exercise, they will be asked to do the following: rest, apply ice, compress the injured tissue, and elevate during the first 24 h; switch to a different type of exercise , (6) If the participants are unable to exercise for >2 weeks, they will be instructed to resume their exercise at 65% of their previous exercise duration (in minutes) and 5% below the previous exercise intensity; if they fail to exercise for <2 weeks, they will restart at the level  from where they left; then, they will follow the original recommendation of gradually increasing the exercise duration (in minutes) each week and the exercise intensity each month.The participants will continue to be followed by their treating physicians/providers for all medical care unrelated to the trial. This includes continuation of medications as prescribed, clinic visits, and other care as usual. Specifically, use of medications and specialist consultations for fibromyalgia will be permitted and assessed, both to adjust for co-intervention differences between arms in the analyses and to assess as secondary outcomes.The schedule of outcomes and key variables to evaluate the effectiveness of the POYSE interventions are listed in To minimize the potential for ascertainment bias, the baseline and follow-up assessments will be conducted by a research assistant blinded to the treatment assignment. The baseline interview will take ~45 min and the 1-, 3-, 6-, and 9-month interviews about 30 min. The assessments will be completed by our research assistant and generally conducted in person unless telephone interviews are preferred for veteran convenience.If participants cannot be scheduled for an in-person interview or reached by phone, we will employ two strategies to capture all outcome assessments: (1) send a mailed questionnaire to the veterans with postage paid, self-addressed envelope to our office, and (2) conduct a face-to-face interview in conjunction with a scheduled clinic visit. Veterans will occasionally lack transportation to the in-person interviews. In this situation, we can arrange for taxi-cab rides to and from our center.The primary outcome measure will be the total score from the Fibromyalgia Impact Questionnaire Revised (FIQR). The FIQR is a brief instrument to assess the overall impact and severity of fibromyalgia . The FIQIn addition to our main outcome measure, we will measure several other secondary outcomes recommended by OMERACT guidelines , 49 and Baseline patient characteristics will include socio-demographics, disability compensation, comorbid medical and psychiatric disorders, and prior treatments for fibromyalgia. We will also regularly assess use of other treatments for fibromyalgia  and prescription medication use. For medications, we will record prescribed doses, administration schedule, and number of pills prescribed, especially opioid analgesics, antidepressants, and benzodiazepines.Pain severity will be assessed by the Brief Pain Inventory (BPI). The BPI is an 11-item, multidimensional pain measurement tool with demonstrated reliability in patients with arthritis as well as other pain conditions , 51. ThePsychological symptoms are frequently associated with fibromyalgia, and several common mental health conditions will be assessed. The Patient Health Questionnaire-9 (PHQ-9) will be used to assess depression severity. Several studies have validated the PHQ-9 as a diagnostic measure with excellent psychometric properties . The GenFinally, PTSD symptoms will be assessed. The Primary Care PTSD Screen (PC-PTSD) has been validated for use in VA primary care settings . SensitiWe will assess for fatigue and sleep disturbances associated with fibromyalgia. The Multidimensional Fatigue Inventory (MFI) will be used to assess fatigue . The MFISeveral measures will assess health-related quality of life, physical activity, and balance. The primary measure to assess generic health-related quality of life and functional status will be the Medical Outcomes Study Short Form Questionnaire (SF-12) . The SF-Several other measures and potential predictors of treatment response will be assessed. The Self-Efficacy for Managing Chronic Disease 6-item Scale will be used to assess self-efficacy . The PatThe participants in both groups were encouraged to practice yoga or exercise, respectively, between their separate sessions. These activities were recorded by the participants in a quantitative manner and will be included in the final analysis to determine whether these differed between the groups and may be impacting outcome measures.t-test, we needed 107 subjects in each arm to detect such a difference with >80% power and 5% type I standard error rate. Assuming a small and equal intraclass correlation coefficient (ICC) of 0.02 for both treatment groups with eight subjects per treatment group/class or cluster, the design effect is 1+ (8–1) × 0.02 =1.14. Therefore, the sample size was inflated by 14% to accommodate the potential clustering effects. We then need 122 evaluable subjects in each arm. With a conservative 20% attrition, we sought to enroll (122*2)/0.8 = 306 participants (153 in each arm).Pain Outcomes Comparing Yoga vs. Structured Exercise is a two-arm, parallel group, randomized comparative effectiveness trial. The sample size was based on the estimated mean difference of intervention effects between the two arms on the primary outcome of Fibromyalgia Impact Questionnaire Revised (FIQR) total score at 3 months. Based on prior studies, we estimated the difference between YOGA and SEP to be ~17%. In a published yoga trial for patients with fibromyalgia, Carson et al.  found a t-tests and Wilcoxon rank-sum tests will be conducted for continuous variables depending on if the data are skewed or not, and Chi-Square tests will be conducted for categorical variables.Baseline characteristics and survey results will be compared between the Yoga and the SEP groups to ensure that randomization achieved balanced groups and to determine if any variables should be considered in the main analyses. Student's Longitudinal analyses will be performed using the constrained longitudinal data analysis (cLDA) approach , 77. ThiResults that will be presented will include raw means (standard deviations) for both treatment groups at each time point the outcome is assessed, and adjusted means and associated 95% confidence intervals for contrasts of interest obtained from the model. All analyses will be conducted under an intent-to-treat approach with the primary end point at 3 months and evaluation of “early” response at 1 month and “sustained” response at 6- and 9-months post-intervention. Šidák adjustments will be conducted to maintain a type I error rate of 0.05 for the three FIQR subscales  at a given time point and for the secondary outcomes at each time point.Based on previous literature , patientAll analytic assumptions will be verified, and all analyses will be performed using SAS V9.4 .We will conduct a cost effectiveness evaluation to determine if changes in health care utilization may offset the intervention costs and relative gains in both treatment arms. To conduct this analysis, we will use established methods and best practices –82 to esn = 129) or the SEP (n = 127) arm of the trial.Due to time and budget constraints, POYSE did not meet its recruitment goal of 306 participants. Rather, 256 participants  were recruited and enrolled. This trial will be the first to directly compare a yoga-based intervention to a standardized exercise intervention including strength and aerobic training for patients with fibromyalgia. Prior clinical trials have demonstrated the effectiveness of aerobic and strength training –13 as weOur research team thoroughly discussed and debated study design issues. In the end, we agreed to frame POYSE as a fully powered, two-arm comparative effectiveness trial. However, we strongly considered a three-arm study to compare yoga vs. structured exercise vs. usual care control. However, we rejected this design for the following reasons. The first was pragmatic: it would be difficult to enroll a 50% greater sample size from a single site, and this design would add substantially to the trial costs. Second, findings from our prior pain trials have demonstrated the ineffectiveness of usual care for chronic pain management, making a comparative effectiveness trial more relevant , 46. FurThe POYSE trial involves two multi-component interventions that will primarily test the “bundled” effect on reduction of overall fibromyalgia severity and associated symptoms. The yoga intervention combines yoga poses, relaxation, and meditation practices, and the SEP combines aerobic exercise, physical activity education, and activity monitoring and feedback. Determining the relative effects of the individual treatment components will be difficult but not impossible. While we considered a study of less complex interventions, multi-component interventions are best suited to handle the complexities of fibromyalgia management based on the biopsychosocial model.To elucidate the relative effects and “unbundle” the individual treatment components, we will conduct post-study, in-depth interviews of a subsample of study participants who receive each intervention. Interview questions will focus on perceptions of which treatment component was most or least helpful, which exercises did participants use most (or least) at home, and participant recommendations to create a “treatment toolbox,” i.e., exercises viewed most effective to reduce fibromyalgia symptoms. Based on our experience with similar qualitative studies, we expect to conduct approximately 25 interviews to improve our understanding of the relative value participants place on the different treatment components. We will not control for differential use of components in our main analysis, because our main goal is to determine the overall effectiveness of the multi-component interventions.Prior studies on yoga and other treatments for fibromyalgia have primarily included civilian female participants. For this study, we will compare two non-pharmacological treatments for veterans with fibromyalgia. We have enrolled 256 veterans, of whom 31.2% were identified as women and 68.8% were identified as men. This study is important in that it emphasizes two unique and understudied populations in fibromyalgia research: veterans and, more broadly, men. The vast majority of participants in prior fibromyalgia trials has been women despite the fact that almost 5% of men may in fact meet fibromyalgia diagnostic criteria , 92.The results of this study will be subject to several limitations that may impact generalizability and implications. First, our enrollment total was modestly lower than our initial target, which reduces our statistical power to detect between treatment differences. Second, this is a single center study located in the Midwest. Third, all the participants are veterans. Veterans have unique employment and exposure history and may disproportionately have comorbidities such as PTSD and anxiety; thus, the results may not generalize to a larger civilian sample. Fourth, the study will not use any quantitative instrument to measure the participants' pain, such as a pressure algometer. However, the subjective report of a given pain stimulus may vary between individual participants with fibromyalgia. Finally, individuals with fibromyalgia were identified for the purpose of this study with ICD-9 codes and a review of ACR 2010 Criteria Diagnosis was performed, but diagnoses were not confirmed by a rheumatologist or other experts.Despite the study limitations, the POYSE trial has a number of strengths including: (1) testing the comparative effectiveness of two unique interventions designed to improve the management of fibromyalgia, (2) non-pharmacological treatments that challenge existing treatment paradigms for fibromyalgia that rely significantly on medication management; (3) focus on a significantly understudied fibromyalgia population (men and veterans), (4) a clinical condition that has become frustrating to providers, (5) a randomized clinical trial design, (6) statistical power to detect meaningful differences (8.1 points or 0.46 SD) in the primary outcome despite not meeting our initially planned recruitment goal, (7) a large sample size relative to previous trials on yoga and exercise, and (8) an economic evaluation that may provide administrators, clinical managers, and policy makers with data to inform budget decisions to invest in these interventions.In sum, clinicians are faced with numerous challenges in treating patients with fibromyalgia. The interventions being tested in the POYSE trial have the potential to provide primary care settings with new treatment options for clinicians while simultaneously providing a much needed relief for patients suffering from fibromyalgia.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Indiana University Institutional Review Board. The patients/participants provided their written informed consent to participate in this study.MB: had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis and obtained funding. MB, KM, NS, AF, MK, and CS: study supervision. VA, AF, MK, and CS: administrative, technical, or material support. MB, JS, and JD: statistical analysis. MB, VA, AS, KM, JS, JD, DA, DF, and MVP: critical revision of the manuscript for important intellectual content. MB, VA, AS, KM, JS, JD, DA, DF, and MVP: drafting of the manuscript. MB, AS, KM, JS, JD, DA, DF, and MVP: analysis and interpretation of data and study concept and design. MB, VA, AF, CS, MK, KM, and NS: acquisition of data. All authors contributed to the article and approved the submitted version.Funded by VA Rehabilitation Research and Development and U.S. Department of Veteran Affairs. The Department of Veterans Affairs had no role in the design and conduct of the study, collection, management, analysis, and interpretation of the data, preparation, review, or approval of the manuscript, and decision to submit the manuscript for publication. Grant number: D1100-R.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.