Personalized Activity Intelligence (PAI) for Prevention of Cardiovascular Disease and Promotion of Physical Activity.

Purpose: To derive and validate a single metric of activity tracking that associates with lower risk of cardiovascular disease mortality.

Methods: We derived an algorithm, Personalized Activity Intelligence (PAI), using the HUNT Fitness Study (n = 4631), and validated it in the general HUNT population (n = 39,298) aged 20-74 years. The PAI was divided into three sex-specific groups (≤50, 51-99, and ≥100), and the inactive group (0 PAI) was used as the referent.

Sample size requirement for comparison of decompression outcomes using ultrasonically detected venous gas emboli (VGE): power calculations using Monte Carlo resampling from real data.

Introduction: In studies of decompression procedures, ultrasonically detected venous gas emboli (VGE) are commonly used as a surrogate outcome if decompression sickness (DCS) is unlikely to be observed. There is substantial variability in observed VGE grades, and studies should be designed with sufficient power to detect an important effect.

Methods: Data for estimating sample size requirements for studies using VGE as an outcome is provided by a comparison of two decompression schedules that found corresponding differences in DCS incidence (3/192 [DCS/dives] vs.

Use of heart rate monitoring for an individualized and time-variant decompression model.

Individual differences, physiological pre-conditions and in-dive conditions like workload and body temperature have been known to influence bubble formation and risk of decompression sickness in diving. Despite this fact, such effects are currently omitted from the decompression algorithms and tables that are aiding the divers. There is an apparent need to expand the modeling beyond depth and time to increase safety and efficiency of diving.

Parameter estimation of the copernicus decompression model with venous gas emboli in human divers.

Decompression Sickness (DCS) may occur when divers decompress from a hyperbaric environment. To prevent this, decompression procedures are used to get safely back to the surface. The models whose procedures are calculated from, are traditionally validated using clinical symptoms as an endpoint.

A dynamic two-phase model for vascular bubble formation during decompression of divers.

Accumulated inert gas during a dive and subsequent reduction of ambient pressure may lead to formation of gas bubbles, which is the initial cause of decompression sickness (DCS). Decompression procedures are used to get divers safely up from depth, and traditionally, the algorithms are evaluated against clinical symptoms of DCS. However, this approach has several weaknesses.