Covariate-adaptive Randomization Research Articles

The impact of combined administration of surfactant and intratracheal budesonide compared to surfactant alone on bronchopulmonary dysplasia (BPD) and mortality rate in preterm infants with respiratory distress syndrome: a single-blind randomized clinical trial

BackgroundRespiratory distress syndrome (RDS) is one of the most important and common disorders among premature infants.ObjectiveThis study aimed to compare the effect of the combination of surfactant and budesonide with surfactant alone on Bronchopulmonary dysplasia (BPD) and mortality rate among premature infants with RDS.MethodAn outcome assessor-blind randomized clinical trial was conducted on 134 premature infants with RDS who were born in Ayatollah Mousavi Hospital, Zanjan, Iran in 2021. The covariate adaptive randomization method was utilized to allocate participants into two groups (surfactant alone and a combination of surfactant and budesonide). The primary outcomes were BPD and Mortality rate from admission to hospital discharge. The data in this study were analyzed using SPSS software version 18.ResultsOverall the comparison of mortality rate and BPD between the two groups did not show a significant difference(p > 0.05). The subgroup results showed that administering surfactant with budesonide to infants under 30 weeks of age significantly reduced the number of deaths compared to using surfactant alone (5 vs. 17). Similar positive effects were observed for the occurrence of Pulmonary Hemorrhage, the need for a second dose of surfactant, oxygen index, mean blood pressure and mean arterial pressure (MAP) in infants under 34 weeks of age compared to more than 34 weeks (p < 0.05).ConclusionThese findings suggest that the combination therapy of surfactant and budesonide may be beneficial, particularly in preterm infants with less than 34 weeks gestational age and 1500 birth weight. However, further studies with larger sample sizes and longer follow-up periods are needed to confirm these results and assess long-term outcomes.Trial registrationThe study was registered at the Iranian Registry of Clinical Trials website under the code IRCT20201222049802N1. https://en.irct.ir/user/trial/48117/view.Registration date28/02/2021.Public repository: Data setThis research data set link is displayed on the Zanjan-Iran Medical Sciences website: https://repository.zums.ac.ir/cgi/users/login? target=https%3 A%2 F/repository.zums.ac.ir/id/eprint.

Mathematical programming tools for randomization purposes in small two-arm clinical trials: A case study with real data.

Modern randomization methods in clinical trials are invariably adaptive, meaning that the assignment of the next subject to a treatment group uses the accumulated information in the trial. Some of the recent adaptive randomization methods use mathematical programming to construct attractive clinical trials that balance the group features, such as their sizes and covariate distributions of their subjects. We review some of these methods and compare their performance with common covariate-adaptive randomization methods for small clinical trials. We introduce an energy distance measure that compares the discrepancy between the two groups using the joint distribution of the subjects' covariates. This metric is more appealing than evaluating the discrepancy between the groups using their marginal covariate distributions. Using numerical experiments, we demonstrate the advantages of the mathematical programming methods under the new measure. In the supplementary material, we provide R codes to reproduce our study results and facilitate comparisons of different randomization procedures.

Covariate-adaptive randomization inference in matched designs

Abstract It is common to conduct causal inference in matched observational studies by proceeding as though treatment assignments within matched sets are assigned uniformly at random and using this distribution as the basis for inference. This approach ignores observed discrepancies in matched sets that may be consequential for the distribution of treatment, which are succinctly captured by within-set differences in the propensity score. We address this problem via covariate-adaptive randomization inference, which modifies the permutation probabilities to vary with estimated propensity score discrepancies and avoids requirements to exclude matched pairs or model an outcome variable. We show that the test achieves type I error control arbitrarily close to the nominal level when large samples are available for propensity score estimation. We characterize the large-sample behaviour of the new randomization test for a difference-in-means estimator of a constant additive effect. We also show that existing methods of sensitivity analysis generalize effectively to covariate-adaptive randomization inference. Finally, we evaluate the empirical value of combining matching and covariate-adaptive randomization procedures using simulations and analyses of genetic damage among welders and right-heart catheterization in surgical patients.

Optimal design for inference on the threshold of a biomarker.

Enrichment designs with a continuous biomarker require the estimation of a threshold to determine the subpopulation benefitting from the treatment. This article provides the optimal allocation for inference in a two-stage enrichment design for treatment comparisons when a continuous biomarker is suspected to affect patient response. Several design criteria, associated with different trial objectives, are optimized under balanced or Neyman allocation and under equality of the first two empirical biomarker's moments. Moreover, we propose a new covariate-adaptive randomization procedure that converges to the optimum with the fastest available rate. Theoretical and simulation results show that this strategy improves the efficiency of a two-stage enrichment clinical trial, especially with smaller sample sizes and under heterogeneous responses.

Comparison of Pocock and Simon’s covariate-adaptive randomization procedures in clinical trials

When multiple influential covariates need to be balanced during a clinical trial, stratified blocked randomization and covariate-adaptive randomization procedures are frequently used in trials to prevent bias and enhance the validity of data analysis results. The latter approach is increasingly used in practice for a study with multiple covariates and limited sample sizes. Among a group of these approaches, the covariate-adaptive procedures proposed by Pocock and Simon are straightforward to be utilized in practice. We aim to investigate the optimal design parameters for the patient treatment assignment probability of their developed three methods. In addition, we seek to answer the question related to the randomization performance when additional covariates are added to the existing randomization procedure. We conducted extensive simulation studies to address these practically important questions.

Validity of Tests for Time-to-Event Endpoints in Studies with the Pocock and Simon Covariate-Adaptive Randomization

Randomization procedures that enforce balance in prognostic factors, most commonly stratified randomization, are often employed in clinical trials. When the number of factors or factor levels is large, dynamic allocation procedures, such as the Pocock and Simon’s covariate-adaptive randomization (minimization) are preferred. In their ground-breaking work Ye and Shao identified two classes of covariate-adaptive randomization procedures. They have demonstrated theoretically that for these classes, when the model is misspecified, the robust score test (Lin and Wei) as well as the unstratified log-rank test used for analysis of time-to-event endpoints, are valid or conservative (Ye and Shao). This fact, however, was not established for minimization other than through simulations of survival endpoints. In this article, we point out that the results of Ye and Shao can be expanded to a more general class of randomization procedures. We show, in part theoretically, in part through simulations of the within-strata imbalances, that minimization belongs to this class. Along the way we describe the asymptotic correlation matrix of the normalized within-stratum imbalances following minimization with equal prevalence of all strata. We expand the robust tests proposed by Ye and Shao for stratified randomization to minimization and examine their performance through simulations.

Log-rank and stratified log-rank tests

In randomized clinical trials with right-censored time-to-event outcomes, the popular log-rank test without adjusting for baseline covariates is asymptotically valid for treatment effect under simple randomization of treatments but is too conservative under covariate-adaptive randomization. The stratified log-rank test, which adjusts baseline covariates in the test procedure by stratification, is asymptotically valid regardless of what treatment randomization is applied. In the literature, however, under simple randomization there is no affirmative conclusion about whether the stratified log-rank test is asymptotically more powerful than the unstratified log-rank test. In this article we show when the stratified and unstratified log-rank tests aim for the same null hypothesis and that, under simple randomization, the stratified log-rank test is asymptotically more powerful than the unstratified log-rank test in the region of alternative hypothesis that is specified by a Cox proportional hazards model. We also provide some discussion about why we do not have an affirmative conclusion in general.

Statistical Inference for Vaccine Efficacy: A Re-Randomization Procedure to Analyse Poisson Outcomes under Covariate-Adaptive Randomization

Re-randomization inference is used as an alternative approach to more traditional statistical methods. Free from parametric assumptions, its use is particularly suited for studies incorporating covariate-adaptive randomization, and can provide additional evaluation and confirmation of inferences drawn from the original analyses, for example, as a sensitivity analysis. We discuss methodological and computational aspects in the context of a Poisson regression and describe an approach to re-randomization inference. This is tested in a simulation study and then illustrated in a case study in which we evaluate vaccine efficacy data from a previously published influenza vaccine study. Our simulations indicate that re-randomization inference corrects for model misspecification. The case study, which accounted for the minimization factors, shows that the p-value and confidence limits from re-randomization inference agree with the original analysis. In conclusion re-randomization inference is a useful method that can be used to support vaccine clinical development.

Regression-based multiple treatment effect estimation under covariate-adaptive randomization.

Covariate-adaptive randomization methods are widely used in clinical trials to balance baseline covariates. Recent studies have shown the validity of using regression-based estimators for treatment effects without imposing functional form requirements on the true data generation model. These studies have had limitations in certain scenarios; for example, in the case of multiple treatment groups, these studies did not consider additional covariates or assumed that the allocation ratios were the same across strata. To address these limitations, we develop a stratum-common estimator and a stratum-specific estimator under multiple treatments. We derive the asymptotic behaviors of these estimators and propose consistent nonparametric estimators for asymptotic variances. To determine their efficiency, we compare the estimators with the stratified difference-in-means estimator as the benchmark. We find that the stratum-specific estimator guarantees efficiency gains, regardless of whether the allocation ratios across strata are the same or different. Our conclusions were also validated by simulation studies and a real clinical trialexample.

Inference under covariate-adaptive randomization with imperfect compliance

This paper studies inference in a randomized controlled trial (RCT) with covariate-adaptive randomization (CAR) and imperfect compliance of a binary treatment. In this context, we study inference on the local average treatment effect (LATE), i.e., the average treatment effect conditional on individuals that always comply with the assigned treatment. As in Bugni et al. (2018, 2019), CAR refers to randomization schemes that first stratify according to baseline covariates and then assign treatment status so as to achieve “balance” within each stratum. In contrast to these papers, however, we allow participants of the RCT to endogenously decide to comply or not with the assigned treatment status.We study the properties of an estimator of the LATE derived from a “fully saturated” instrumental variable (IV) linear regression, i.e., a linear regression of the outcome on all indicators for all strata and their interaction with the treatment decision, with the latter instrumented with the treatment assignment. We show that the proposed LATE estimator is asymptotically normal, and we characterize its asymptotic variance in terms of primitives of the problem. We provide consistent estimators of the standard errors and asymptotically exact hypothesis tests. In the special case when the target proportion of units assigned to each treatment does not vary across strata, we can also consider two other estimators of the LATE, including the one based on the “strata fixed effects” IV linear regression, i.e., a linear regression of the outcome on indicators for all strata and the treatment decision, with the latter instrumented with the treatment assignment.Our characterization of the asymptotic variance of the LATE estimators in terms of the primitives of the problem allows us to understand the influence of the parameters of the RCT. We use this to propose strategies to minimize their asymptotic variance in a hypothetical RCT based on data from a pilot study. We illustrate the practical relevance of these results using a simulation study and an empirical application based on Dupas et al. (2018).

Covariate-adjusted log-rank test: guaranteed efficiency gain and universal applicability

Summary Nonparametric covariate adjustment is considered for log-rank-type tests of the treatment effect with right-censored time-to-event data from clinical trials applying covariate-adaptive randomization. Our proposed covariate-adjusted log-rank test has a simple explicit formula and a guaranteed efficiency gain over the unadjusted test. We also show that our proposed test achieves universal applicability in the sense that the same formula of test can be universally applied to simple randomization and all commonly used covariate-adaptive randomization schemes such as the stratified permuted block and the Pocock–Simon minimization, which is not a property enjoyed by the unadjusted log-rank test. Our method is supported by novel asymptotic theory and empirical results for Type-I error and power of tests.

The effect of immersion on sense of presence and affect when experiencing an educational scenario in virtual reality: A randomized controlled study

In this paper, the objective was to investigate these outcomes in two VR conditions, immersive and desktop in a randomized controlled trial with a parallel design. The sample consisted of 134 university students (70 women, mean age 23 years, SD=2.99). These were randomized using a covariate-adaptive randomization procedure based on stratification by gender into two interventions; play out a VR scenario in either desktop (control group) or immersive VR (intervention group). The setting was a university lab. There was a significant within subject effect for positive affect and a significant between-group effect for the immersive compared to desktop VR groups. Positive affect was reduced after interacting with the VR scenario in both the immersive and desktop versions, however, positive affect was overall higher in the immersive, compared to the desktop version. The results show higher scores for sense of presence (d=0.90, p<0.001) and positive affect pre- and post-scenario in the immersive VR condition (d=0.42, p=0.017 and d=0.54, p=0.002) compared to the desktop condition. Immersive VR may be beneficial in higher education as it promotes high levels of sense of presence as well as positive emotions. When it comes to changing the immediate emotions of the students, type of VR does not seem to matter. The project was funded by the Norwegian Directorate for Higher Education and Skills.

MP32-14 COMPLETELY DRY DAYTIME CONTINENCE AND UNMODIFIED SEXUAL FUNCTION AS KEY ASPECTS OF HEALTH-RELATED QUALITY OF LIFE AFTER RADICAL CYSTECTOMY AND ORTHOTOPIC NEOBLADDER

You have accessJournal of UrologyCME1 Apr 2023MP32-14 COMPLETELY DRY DAYTIME CONTINENCE AND UNMODIFIED SEXUAL FUNCTION AS KEY ASPECTS OF HEALTH-RELATED QUALITY OF LIFE AFTER RADICAL CYSTECTOMY AND ORTHOTOPIC NEOBLADDER Riccardo Mastroianni, Gabriele Tuderti, Umberto Anceschi, Alfredo Maria Bove, Aldo Brassetti, Simone D'annunzio, Mariaconsiglia Ferriero, Leonardo Misuraca, Salvatore Guaglianone, and Giuseppe Simone Riccardo MastroianniRiccardo Mastroianni More articles by this author , Gabriele TudertiGabriele Tuderti More articles by this author , Umberto AnceschiUmberto Anceschi More articles by this author , Alfredo Maria BoveAlfredo Maria Bove More articles by this author , Aldo BrassettiAldo Brassetti More articles by this author , Simone D'annunzioSimone D'annunzio More articles by this author , Mariaconsiglia FerrieroMariaconsiglia Ferriero More articles by this author , Leonardo MisuracaLeonardo Misuraca More articles by this author , Salvatore GuaglianoneSalvatore Guaglianone More articles by this author , and Giuseppe SimoneGiuseppe Simone More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003265.14AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Survival outcomes are primary endpoints of oncologic surgery. However, health-related quality of life (HRQoL) is too often misevaluated. Radical cystectomy (RC) with urinary diversion has a significant impact on HRQoL. In this study, we evaluated predictors of unmodified 2yr-QoL after RC with orthotopic neobladder (ON) from an ongoing randomized controlled trial (RCT) comparing ORC and RARC with intracorporeal (i)-UD. METHODS: Patients were eligible with a cT2-4cN0cM0, or recurrent HG NMIBC and no anesthesiologic contraindications to robotic surgery. Between Jan 2018 and Sept 2020, 116 patients were enrolled with a covariate adaptive randomization process based on: BMI, ASA score, baseline haemoglobin, UD, neoadiuvant chemo and cT-stage. Data from self-assessed questionnaires (EORTC-QLQ-C30 and QLQ-BLM30), peri- and postoperative data were collected. Unmodified HRQoL was defined as a 2yr Global Health status/QoL increase≥0 (ΔQL≥0) from baseline. Continence status was evaluated trough 3-day voiding diaries; day-time continence was defined as “totally dryness” (0gr), night-time continence as pad wetness ≤50gr. Univariable and multivariable logistic regression (LR) analysis were performed to identify predictors of 2yr ΔQL≥0. RESULTS: Out of 116 patients enrolled, 88 received ON, and 71 with a minimum 2yr follow-up were included. Two (2) patients were lost to follow-up and 17 patients died within 2yr after surgery. Baseline clinical features were reported (Tab.1). On multivariable LR analysis, day-time urinary continence and unmodified sexual functioning were independent predictors of 2yrs unmodified global health status (Tab. 2). Particularly, 54%, 30%, 72% and 65% of patients experienced unmodified 2 yrs global health status, sexual, social and emotional functioning, respectively; 62% of patients reported a totally dryness day-time urinary continence (Fig. 1). CONCLUSIONS: Recovery of completely dry day-time continence status and equal to baseline sexual functioning scores are main drivers of patients' HRQoL 2yrs after RC. These data highlight the need for further assessing oncological safety of sex-sparing techniques and technical improvements to maximize the likelihood of daytime continence recovery in patients receiving RC and ON. Source of Funding: No © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e446 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Riccardo Mastroianni More articles by this author Gabriele Tuderti More articles by this author Umberto Anceschi More articles by this author Alfredo Maria Bove More articles by this author Aldo Brassetti More articles by this author Simone D'annunzio More articles by this author Mariaconsiglia Ferriero More articles by this author Leonardo Misuraca More articles by this author Salvatore Guaglianone More articles by this author Giuseppe Simone More articles by this author Expand All Advertisement PDF downloadLoading ...

Open vs robotic intracorporeal Padua ileal bladder: functional outcomes of a single-centre RCT.

Functional outcomes of robot-assisted (RA) radical cystectomy (RC) with intracorporeal orthotopic neobladder (i-ON) have been poorly investigated. The study aimed to report functional outcomes of a prospective randomized controlled trial (RCT) comparing open RC (ORC) and RARC with i-ON. Inclusion criteria were cT2-4/N0/M0, or BCG-failure high-grade urothelial carcinoma, candidate to RC with curative intent. A covariate adaptive randomization process was used, based on the following variables: BMI, ASA score, haemoglobin levels, cT-stage, neoadjuvant chemotherapy, urinary diversion. Day-time continence was defined as "totally dryness", nigh-time continence as pad wetness ≤ 50cc. Continence recovery probabilities were compared between arms with Kaplan-Meier method and Cox regression analysis was performed to identify predictors of continence recovery. HRQoL outcomes analysis was assessed with a generalized linear mixed effect regression (GLMER) model. Out of 116 patients randomized, 88 received ON. Quantitative analysis of functional outcomes reported similar results in terms of day continence, while a better night continence status in ORC cohort was observed. However, 1-yrday- and night-time continence recovery probabilities were comparable. Night-time micturition frequency < 3h was the only predictor of nigh-time continence recovery. At GLMER, 1-yr body image and sexual functioning were significantly better in RARC cohort, while urinary symptoms were comparable between arms. Despite superiority of ORC at quantitative night-time pad use analysis, we showed comparable day- and night-time continence recovery probabilities. At 1-yr analysis of HRQoL outcomes, urinary symptoms were comparable between arms, while RARC patients reported lower body image and sexual functioning worsening.

Examining the effects of an Educational Person-centered Intervention on Compensatory Strategies (EPICS) in older adults living with frailty: A mixed-methods pilot trial

This study aimed to explore effects of a two-session Educational Person-centered Intervention on Compensatory Strategies (EPICS) designed to increase knowledge of strategies, reduce barriers, and improve accomplishment of meaningful leisure activities (MLA) and well-being in older adults living with frailty. Using a double-blind concurrent mixed-methods design, 36 community-dwelling older adults were assigned to the experimental (EPICS) or control (friendly visits) group through a covariate adaptive randomization. Questionnaires were administered prior to the intervention and ± two months post-intervention. Individual semi-structured interviews conducted at the end of the study furthered the authors’ understanding of the effects of the intervention. Quantitative analysis revealed significant increase in knowledge of compensatory strategies and reduction of barriers for the experimental group only. Qualitative analysis (purposive sample, n = 8) showed enhanced well-being and self-activation. Discussions about barriers to accomplishment may be sufficient to trigger self-activation in someolder adults living with frailty to improve participation in MLA and well-being.

Regression analysis for covariate-adaptive randomization: Arobust and efficient inference perspective.

Linear regression is arguably the most fundamental statistical model; however, the validity of its use in randomized clinical trials, despite being common practice, has never been crystal clear, particularly when stratified or covariate-adaptive randomization is used. In this article, we investigate several of the most intuitive and commonly used regression models for estimating and inferring the treatment effect in randomized clinical trials. By allowing the regression model to be arbitrarily misspecified, we demonstrate that all these regression-based estimators robustly estimate the treatment effect, albeit with possibly different efficiency. We also propose consistent non-parametric variance estimators and compare their performances to those of the model-based variance estimators that are readily available in standard statistical software. Based on the results and taking into account both theoretical efficiency and practical feasibility, we make recommendations for the effective use of regression under various scenarios. For equal allocation, it suffices to use the regression adjustment for the stratum covariates and additional baseline covariates, if available, with the usual ordinary-least-squares variance estimator. For unequal allocation, regression with treatment-by-covariate interactions should be used, together with our proposed variance estimators. These recommendations apply to simple and stratified randomization, and minimization, among others. We hope this work helps to clarify and promote the usage of regression in randomized clinical trials.

A New and Unified Family of Covariate Adaptive Randomization Procedures and Their Properties

In clinical trials and other comparative studies, covariate balance is crucial for credible and efficient assessment of treatment effects. Covariate adaptive randomization (CAR) procedures are extensively used to reduce the likelihood of covariate imbalances occurring. In the literature, most studies have focused on balancing of discrete covariates. Applications of CAR with continuous covariates remain rare, especially when the interest goes beyond balancing only the first moment. In this article, we propose a family of CAR procedures that can balance general covariate features, such as quadratic and interaction terms. Our framework not only unifies many existing methods, but also introduces a much broader class of new and useful CAR procedures. We show that the proposed procedures have superior balancing properties; in particular, the convergence rate of imbalance vectors is O P ( n ϵ ) for any ϵ > 0 if all of the moments are finite for the covariate features, relative to O P ( n ) under complete randomization, where n is the sample size. Both the resulting convergence rate and its proof are novel. These favorable balancing properties lead to increased precision of treatment effect estimation in the presence of nonlinear covariate effects. The framework is applied to balance covariate means and covariance matrices simultaneously. Simulation and empirical studies demonstrate the excellent and robust performance of the proposed procedures. Supplementary materials for this article are available online.

FINDINGS AND FUTURE DIRECTIONS FROM A SMOKING CESSATION TRIAL UTILIZING A CLINICAL DECISION SUPPORT TOOL.

Tobacco smoking is the leading cause of disease, death, and disability in the United States. Dental practitioners are advised to provide evidence-based smoking cessation interventions to their patients, yet dental practitioners frequently fail to deliver brief smoking cessation advice. To test whether giving dental practitioners a clinical decisions support (CDS) system embedded in their electronic dental record would increase the rate at which patients who smoke (1) report receiving a brief intervention or referral to treatment during a recent dental visit, (2) taking action related to smoking cessation within 7 days of visit, and (3) stop smoking for 1 day or more or reduce the amount smoked by 50% within 6 months. Two-group, parallel arm, cluster-randomized trial. From March through December 2019, 15 nonacademic primary care dental clinics were randomized via covariate adaptive randomization to either a usual care arm or the CDS arm. Adult smokers completed an initial telephone survey within 7 days of their visit and another survey after 6 months. Forty-three patients from 5 CDS and 13 patients from 2 usual care clinics completed the 7-day survey. While the proportion of patients who reported receipt of a brief intervention or referral to treatment was significantly greater in the CDS arm than the usual care arm (84.3% vs 58.6%; P = .005), the differences in percentage of patients who took any action related to smoking cessation within 7 days (44.4% vs 22.3%; P = .077), or stopped smoking for one day or more and/or reduced amount smoked by 50% within 6 months (63.1% vs 46.2%; P = .405) were large but not statistically significant. Despite interruption by COVID-19, these results demonstrate a promising approach to assist dental practitioners in providing their patients with smoking cessation screening, brief intervention and referral to treatment.

Estimation of hurst exponent for sequential monitoring of clinical trials with covariate adaptive randomization

BackgroundClassical Brownian motion (BM) has been commonly used in monitoring clinical trials including those with covariate adaptive randomization (CAR). Independent increment property is commonly assumed in the sequential monitoring process of the clinical trials with CAR designs. However, in reality, correlation may exist in the error terms of the underlying model, resulting in dependent increment in the sequential monitoring process. MethodsWe conducted simulations for estimating the Hurst exponent to evaluate the stochastic property in the covariate adaptive randomized clinical trials under two scenarios: 1. CAR designs with independent and identically distributed error terms. 2. CAR designs with correlated error terms. The theoretical properties of covariate adaptive randomized clinical trials with correlated error structure were investigated. A test statistic including the covariance pattern of the error terms was proposed. ConclusionIn our study, the sequential test statistics under CAR procedure is shown to be asymptotically Brownian motion when the error structure is correctly specified. Further, Brownian motion is a special case of fractional Brownian motion when Hurst exponent equals to 0.5. Our simulations are consistent with the theoretical asymptotic results.

Effects of a Gamified, Behavior Change Technique-Based Mobile App on Increasing Physical Activity and Reducing Anxiety in Adults With Autism Spectrum Disorder: Feasibility Randomized Controlled Trial.

BackgroundPhysical activity (PA) has an impact on physical and mental health in neurotypical populations, and addressing these variables may improve the prevalent burden of anxiety in adults with autism spectrum disorder (ASD). Gamified mobile apps using behavior change techniques present a promising way of increasing PA and reducing sedentary time, thus reducing anxiety in adults with ASD.ObjectiveThis study aimed to compare the effectiveness of a gamified and behavior change technique–based mobile app, PuzzleWalk, versus a commercially available app, Google Fit, on increasing PA and reducing sedentary time as an adjunct anxiety treatment for this population.MethodsA total of 24 adults with ASD were assigned to either the PuzzleWalk or Google Fit group for 5 weeks using a covariate-adaptive randomization design. PA and anxiety were assessed over 7 days at 3 different data collection periods (ie, baseline, intervention start, and intervention end) using triaxial accelerometers and the Beck Anxiety Inventory. Group differences in outcome variables were assessed using repeated-measures analysis of covariance, adjusting for age, sex, and BMI.ResultsThe findings indicated that the PuzzleWalk group spent a significantly longer amount of time on app use compared with the Google Fit group (F2,38=5.07; P=.01; partial η2=0.21), whereas anxiety was unfavorably associated with increases in light PA and decreases in sedentary time after intervention (all P<.05).ConclusionsFurther research is needed to clarify the determinants of physical and mental health and their interrelationship in adults with ASD to identify the factors that facilitate the use and adoption of mobile health technologies in these individuals. Despite these mixed results, the small changes in PA or anxiety may be clinically significant for adults with ASD.Trial RegistrationClinicalTrials.gov NCT05466617; https://clinicaltrials.gov/show/NCT05466617