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The development of mobile-health technology has the potential to revolutionize personalized medicine. Biomedical sensors (e.g. wearables) can assist with determining treatment plans for individuals, provide quantitative information to healthcare providers, and give objective measurements of health, leading to the goal of precise phenotypic correlates for genotypes. Even though treatments and interventions are becoming more specific and datasets more abundant, measuring the causal impact of health interventions requires careful considerations of complex covariate structures, as well as knowledge of the temporal and spatial properties of the data. Thus, interpreting biomedical sensor data needs to make use of specialized statistical models. Here, we show how the Bayesian structural time series framework, widely used in economics, can be applied to these data. This framework corrects for covariates to provide accurate assessments of the significance of interventions. Furthermore, it allows for a time-dependent confidence interval of impact, which is useful for considering individualized assessments of intervention efficacy. We provide a customized biomedical adaptor tool, MhealthCI, around a specific implementation of the Bayesian structural time series framework that uniformly processes, prepares, and registers diverse biomedical data. We apply the software implementation of MhealthCI to a structured set of examples in biomedicine to showcase the ability of the framework to evaluate interventions with varying levels of data richness and covariate complexity and also compare the performance to other models. Specifically, we show how the framework is able to evaluate an exercise intervention's effect on stabilizing blood glucose in a diabetes dataset. We also provide a future-anticipating illustration from a behavioral dataset showcasing how the framework integrates complex spatial covariates. Overall, we show the robustness of the Bayesian structural time series framework when applied to biomedical sensor data, highlighting its increasing value for current and future datasets. Author summary: In this paper, we propose and describe a robust and flexible modelling framework called MhealthCI based on the Bayesian structural time series, for which we have found to excel at analyzing diverse biosensor data. While Bayesian modelling is often employed in various fields such as finance, marketing, and weather forecasting, it is rarely used in biomedicine, specifically for biosensor and wearable data relating to human health and behavior. We use and apply this framework with the goal of interpreting and quantifying the causal impact of an intervention, a widespread goal of biomedicine. We describe the diversity of data types to which it could apply, provide intuition to its mechanics, collect relevant data in various fields, provide a wrapper tool around well-known R packages that prepares and registers diverse biosensor data to be analyzed, and finally apply the method to showcase its strength in quantifying the impact of interventions. [ABSTRACT FROM AUTHOR]

Given the increased use of air cleaners as a prevention measure in classrooms during the COVID-19 pandemic, this study aimed to investigate the effects of portable air cleaners with HEPA filters and window A/C fans on real-time (1 minute) concentrations of PM less than 2.5 microns (PM2.5) or less than 1 microns (PM1.0) in two classrooms in a non-urban elementary school in Rhode Island. For half of each school day, settings were randomized to "high" or "low" for the air cleaner and "on" or "off" for the fan. Descriptive statistics and linear mixed models were used to evaluate the impacts of each set of conditions on PM2.5 and PM1.0 concentrations. The mean half-day concentrations ranged from 3.4–4.1 μg/m3 for PM2.5 and 3.4–3.9 μg/m3 for PM1.0. On average, use of the fan when the air cleaner was on the low setting decreased PM2.5 by 0.53 μg/m3 [95% CI: -0.64, -0.42] and use of the filter on high (compared to low) when the fan was off decreased PM2.5 by 0.10 μg/m3 [95% CI: -0.20, 0.005]. For PM1.0, use of the fan when the air cleaner was on low decreased concentrations by 0.18 μg/m3 [95% CI: -0.36, -0.01] and use of the filter on high (compared to low) when the fan was off decreased concentrations by 0.38 μg/m3 [95% CI: -0.55, -0.21]. In general, simultaneous use of the fan and filter on high did not result in additional decreases in PM concentrations compared to the simple addition of each appliance's individual effect estimates. Our study suggests that concurrent or separate use of an A/C fan and air cleaner in non-urban classrooms with low background PM may reduce classroom PM concentrations. [ABSTRACT FROM AUTHOR]