Metabolic Syndrome among Patients with Polycystic Ovarian Syndrome Presenting to a Tertiary Care Hospital: A Descriptive Cross-Sectional Study.

INTRODUCTION: Metabolic syndrome in polycystic ovarian syndrome is associated with a long-term risk of developing type 2 diabetes mellitus and cardiovascular disease. This study aims to find the prevalence of metabolic syndrome among patients with polycystic ovarian syndrome presenting to a tertiary care hospital. METHODS: A descriptive cross-sectional study was done on women attending the obstetrics and gynecology outpatient department of a tertiary care hospital from June 2020 to May 2021. A total of 106 women diagnosed with polycystic ovarian syndrome using Rotterdam criteria 2003 were recruited for the study and cases of metabolic syndrome was defined according to the modified American Heart Association/National Heart Lung and Blood Institute. Ethical approval was taken from the Institutional Review Committee of Nepal Medical College and Teaching Hospital (Reference number: 001-077/078). Convenience sampling was done. The collected data was entered and analyzed in Statistical Package for the Social Sciences version 21. Calculation of point estimate at 95% confidence interval was done along with frequency and proportion for binary data. RESULTS: Among 106 women with polycystic ovarian syndrome, 50 (47.1%) had metabolic syndrome (37.59-56.60 at 95% Confidence Interval). The most common component of metabolic syndrome was low high-density lipoprotein cholesterol in 90 (84.9%) followed by central obesity in 60 (56.6%), hypertriglyceridemia in 47 (44.33%), high fasting sugar in 34 (32.07%), and high blood pressure in 14 (13.2%). CONCLUSIONS: The prevalence of metabolic syndrome among patients with the polycystic ovarian syndrome was similar to other studies done in similar settings.

A Bayesian-bandit adaptive design for N-of-1 clinical trials.

N-of-1 trials, which are randomized, double-blinded, controlled, multiperiod, crossover trials on a single subject, have been applied to determine the heterogeneity of the individual's treatment effect in precision medicine settings. An aggregated N-of-1 design, which can estimate the population effect from these individual trials, is a pragmatic alternative when a randomized controlled trial (RCT) is infeasible. We propose a Bayesian adaptive design for both the individual and aggregated N-of-1 trials using a multiarmed bandit framework that is estimated via efficient Markov chain Monte Carlo. A Bayesian hierarchical structure is used to jointly model the individual and population treatment effects. Our proposed adaptive trial design is based on Thompson sampling, which randomly allocates individuals to treatments based on the Bayesian posterior probability of each treatment being optimal. While we use a subject-specific treatment effect and Bayesian posterior probability estimates to determine an individual's treatment allocation, our hierarchical model facilitates these individual estimates to borrow strength from the population estimates via shrinkage to the population mean. We present the design's operating characteristics and performance via a simulation study motivated by a recently completed N-of-1 clinical trial. We demonstrate that from a patient-centered perspective, subjects are likely to benefit from our adaptive design, in particular, for those individuals that deviate from the overall population effect.

Goal setting and achievement for walking: A series of N-of-1 digital interventions.

OBJECTIVE: Despite evidence that goal setting is valuable for physical activity promotion, recent studies highlighted a potential oversimplification in the application of this behavior change technique. While more difficult performance goals might trigger higher physical activity levels, higher performance goals might concurrently be more difficult to achieve, which could reduce long-term motivation. This study examined (a) the association between performance goal difficulty and physical activity and (b) the association between performance goal difficulty and goal achievement. METHOD: This study used data from an e-Health intervention among inactive overweight adults (n = 20). The study duration included a 2-week baseline period and an intervention phase of 80 days. During the intervention, participants received a daily step goal experimentally manipulated by taking participants' baseline physical activity median (i.e., number of steps) multiplied by a pseudorandom factor ranging from 1 to 2.6. A continuous measure of goal achievement was inferred for each day by dividing the daily number of steps by the goal prescribed that day. Linear and generalized additive models were fit for each participant. RESULTS: The results confirm that, for a majority of the participants involved in the study, performance goal difficulty was positively and significantly associated with physical activity (n = 14), but, concurrently, negatively and significantly associated with goal achievement (n = 19). These associations were mainly linear. CONCLUSION: At the daily level, setting a higher physical activity goal leads to engaging in higher physical activity levels, but concurrently lower goal achievement. (PsycInfo Database Record (c) 2020 APA, all rights reserved).