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Background: Aerobic exercise has shown inconsistent cognitive effects in older adults with Alzheimer's disease (AD) dementia.Objective: To examine the immediate and longitudinal effects of 6-month cycling on cognition in older adults with AD dementia.Methods: This randomized controlled trial randomized 96 participants (64 to cycling and 32 to stretching for six months) and followed them for another six months. The intervention was supervised, moderate-intensity cycling for 20-50 minutes, 3 times a week for six months. The control was light-intensity stretching. Cognition was assessed at baseline, 3, 6, 9, and 12 months using the AD Assessment Scale-Cognition (ADAS-Cog). Discrete cognitive domains were measured using the AD Uniform Data Set battery.Results: The participants were 77.4±6.8 years old with 15.6±2.9 years of education, and 55% were male. The 6-month change in ADAS-Cog was 1.0±4.6 (cycling) and 0.1±4.1 (stretching), which were both significantly less than the natural 3.2±6.3-point increase observed naturally with disease progression. The 12-month change was 2.4±5.2 (cycling) and 2.2±5.7 (control). ADAS-Cog did not differ between groups at 6 (p = 0.386) and 12 months (p = 0.856). There were no differences in the 12-month rate of change in ADAS-Cog (0.192 versus 0.197, p = 0.967), memory (-0.012 versus -0.019, p = 0.373), executive function (-0.020 versus -0.012, p = 0.383), attention (-0.035 versus -0.033, p = 0.908), or language (-0.028 versus -0.026, p = 0.756).Conclusion: Exercise may reduce decline in global cognition in older adults with mild-to-moderate AD dementia. Aerobic exercise did not show superior cognitive effects to stretching in our pilot trial, possibly due to the lack of power. [ABSTRACT FROM AUTHOR]

Introduction: Participant noncompliance, in which participants do not follow their assigned treatment protocol, has long complicated the interpretation of randomized clinical trials. No gold standard has been identified for detecting noncompliance, but in some trials participants' biomarkers can provide objective information that suggests exposure to non-study treatments. However, existing methods are limited to retrospectively detecting noncompliance at a single time point based on a single biomarker measurement. We propose a novel method that can leverage participants' full biomarker history to detect noncompliance across multiple time points. Conditional on longitudinal biomarker data, our method can estimate the probability of compliance at (1) a single time point of the trial, (2) all time points, and (3) a future time point. Methods: Across time points, we model the biomarker as a mixture density with (latent) components corresponding to longitudinal patterns of compliance. To estimate the mixture density, we fit mixed effects models for both compliance and the biomarker. We use the mixture density to derive compliance probabilities that condition on the longitudinal biomarker data. We evaluate our compliance probabilities by simulation and apply them to a trial in which current smokers were asked to only smoke low nicotine study cigarettes (Center for the Evaluation of Nicotine in Cigarettes Project 1 Study 2). In the simulation, we investigated three different effects of compliance on the biomarker, as well as the effect of misspecification of the covariance structures. We compared probability estimators (1) and (2) to those that ignore the longitudinal correlation in the data according to area under the receiver operating characteristic curve. We evaluated estimator (3) by plotting its calibration lines. For Center for the Evaluation of Nicotine in Cigarettes Project 1 Study 2, we compared estimators (1) and (3) to a probability estimator of compliance at the last time point that ignores the longitudinal correlation. Results: In the simulation, for both compliance at the last time point and at all time points, conditioning on the longitudinal biomarker data uniformly raised area under the receiver operating characteristic curve across all three compliance effect scenarios. The gains in area under the receiver operating characteristic curve were smaller under misspecification. The calibration lines for the prediction of compliance closely followed 45°, though with additional variability under misspecification. For compliance at the last time point of Center for the Evaluation of Nicotine in Cigarettes Project 1 Study 2, conditioning on participants' full biomarker history boosted area under the receiver operating characteristic curve by three percentage points. The prediction probabilities somewhat accurately approximated the non-longitudinal compliance probabilities. Discussion: Compared to existing methods that only use a single biomarker measurement, our method can account for the longitudinal correlation in the biomarker and compliance to more accurately identify noncompliant participants. Our method can also use participants' biomarker history to predict compliance at a future time point. [ABSTRACT FROM AUTHOR]

Objectives: In this study, we evaluated the classification accuracy of biomarkers of nicotine exposure for identifying noncompliance in randomized controlled trials (RCTs) of very low nicotine content (VLNC) cigarettes. Methods: We combined data from 2 studies to evaluate the classification accuracy of biomarkers of nicotine exposure for identifying noncompliance in RCTs of VLNC cigarettes. Using a novel approach that did not require knowledge of each participant's compliance status, we modeled the distributions of total nicotine equivalents (TNE), total cotinine, and anatabine in compliant and noncompliant participants using a mixture model. Estimates of the classification accuracy were derived from the estimated densities. Results: TNE and total cotinine had near-perfect classification accuracy, but TNE had better classification accuracy than total cotinine (p = .03) and anatabine (p = .014). The classification accuracy of TNE and total cotinine decreased as self-reported study cigarettes increased; anatabine was similar, but the results were more statistically variable. Conclusions: TNE and total cotinine are better classifiers of compliance than anatabine. These results will be useful in determining biomarker thresholds for identifying noncompliance and will aid in the interpretation of future RCTs of VLNC cigarettes. [ABSTRACT FROM AUTHOR]