Incentivizing the Use of Quantified Self Devices: The Cases of Digital Occupational Health Programs and Data-Driven Health Insurance Plans

Initially designed for a use in private settings, smartwatches, activity trackers and other quantified self devices are receiving a growing attention from the organizational environment. Firms and health insurance companies, in particular, are developing digital occupational health programs and data-driven health insurance plans centered around these systems, in the hope of exploiting their potential to improve individual health management, but also to gather large quantities of data. As individual participation in such organizational programs is voluntary, organizations often rely on motivational incentives to prompt engagement. Yet, little is known about the mechanisms employed in organizational settings to incentivize the use of quantified self devices. We therefore seek, in this exploratory paper, to offer a first structured overview of this topic and identify the main motivational incentives in two emblematical cases: digital occupational health programs and data-driven health insurance plans. By doing so, we aim to specify the nature of this new dynamic around the use of quantified self devices and define some of the key lines for further investigation

Physical principles of membrane remodelling during cell mechanoadaptation

Biological processes in any physiological environment involve changes in cell shape, which must be accommodated by their physical envelope—the bilayer membrane. However, the fundamental biophysical principles by which the cell membrane allows for and responds to shape changes remain unclear. Here we show that the 3D remodelling of the membrane in response to a broad diversity of physiological perturbations can be explained by a purely mechanical process. This process is passive, local, almost instantaneous, before any active remodelling and generates different types of membrane invaginations that can repeatedly store and release large fractions of the cell membrane. We further demonstrate that the shape of those invaginations is determined by the minimum elastic and adhesive energy required to store both membrane area and liquid volume at the cell–substrate interface. Once formed, cells reabsorb the invaginations through an active process with duration of the order of minutes

Guidelines for the analysis of free energy calculations

Free energy calculations based on molecular dynamics (MD) simulations show considerable promise for applications ranging from drug discovery to prediction of physical properties and structure-function studies. But these calculations are still difficult and tedious to analyze, and best practices for analysis are not well defined or propagated. Essentially, each group analyzing these calculations needs to decide how to conduct the analysis and, usually, develop its own analysis tools. Here, we review and recommend best practices for analysis yielding reliable free energies from molecular simulations. Additionally, we provide a Python tool, alchemical–analysis.py, freely available on GitHub at https://github.com/choderalab/pymbar–examples, that implements the analysis practices reviewed here for several reference simulation packages, which can be adapted to handle data from other packages. Both this review and the tool covers analysis of alchemical calculations generally, including free energy estimates via both thermodynamic integration and free energy perturbation-based estimators. Our Python tool also handles output from multiple types of free energy calculations, including expanded ensemble and Hamiltonian replica exchange, as well as standard fixed ensemble calculations. We also survey a range of statistical and graphical ways of assessing the quality of the data and free energy estimates, and provide prototypes of these in our tool. We hope these tools and discussion will serve as a foundation for more standardization of and agreement on best practices for analysis of free energy calculations