Bayesian structural time series for biomedical sensor data: A flexible modeling framework for evaluating interventions

Abstract

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.

Urges to Move and Other Motivation States for Physical Activity in Clinical and Healthy Populations: A Scoping Review Protocol

“…Interventions might incorporate aspects of mindfulness, such as meditation or exercises that are inherently mindful, such as martial arts (Naves-Bittencourt et al, 2015 ; Stults-Kolehmainen et al, 2015 ). For those at risk of disease and not physically active, this review may help to map out just-in-time adaptive interventions, perhaps mobile-based, to identify ideal time points for action, which we call “CRAVE moments”, and to modify the environment or lead people to environments that both: (a) produce desire-promoting stimuli for movement and (b) reveal opportunities to act on these desires (Hardeman et al, 2019 ; Ash et al, 2021 ; Liu et al, 2021 ). Additionally, understanding the factors that drive a person to engage in exercise, their natural experience of movement desires, and their activity preferences, could lead to flexible and personalized exercise prescriptions, leading to better exercise initiation, engagement, further adherence and less drop out.…”