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. Hazard ratios for all-cause and cardiovascular disease mortality were estimated using Cox proportional hazard regressions. RESULTS: After >1 million person-years of observations during a mean follow-up time of 26.2 (SD 5.9) years, there were 10,062 deaths, including 3867 deaths (2207 men and 1660 women) from cardiovascular disease. Men and women with a PAI level ≥100 had 17% (95% confidence interval [CI], 7%-27%) and 23% (95% CI, 4%-38%) reduced risk of cardiovascular disease mortality, respectively, compared with the inactive groups. Obtaining ≥100 PAI was associated with significantly lower risk for cardiovascular disease mortality in all prespecified age groups, and in participants with known cardiovascular disease risk factors (all P-trends <.01). Participants who did not obtain ≥100 PAI had increased risk of dying regardless of meeting the physical activity recommendations. CONCLUSION: PAI may have a huge potential to motivate people to become and stay physically active, as it is an easily understandable and scientifically proven metric that could inform potential users of how much physical activity is needed to reduce the risk of premature cardiovascular disease death.

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. 10/198) and median maximum VGE grade (2 vs. 3, P < 0.0001, Wilcoxon test). Sixty-two subjects dived each schedule at least once, accounting for 183 and 180 man-dives on each schedule. From these data, the frequency with which 10,000 randomly resampled, paired samples of maximum VGE grade were significantly different (paired Wilcoxon test, one-sided P ⋜ 0.05 or 0.025) in the same direction as the VGE grades of the full data set were counted (estimated power). Resampling was also used to estimate power of a Bayesian method that ranks two samples based on DCS risks estimated from the VGE grades. RESULTS: Paired sample sizes of 50 subjects yielded about 80% power, but the power dropped to less than 50% with fewer than 30 subjects. CONCLUSIONS: Comparisons of VGE grades that fail to find a difference between paired sample sizes of 30 or fewer must be interpreted cautiously. Studies can be considered well powered if the sample size is 50 even if only a one-grade difference in median VGE grade is of interest.

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. The present paper outlines a mathematical model for how heart rate monitoring in combination with individual parameters can be used to obtain a customized and time-variant decompression model. We suggest that this can cover some of the individual differences and dive conditions that are affecting bubble formation. The model is demonstrated in combination with the previously published Copernicus decompression model, and is suitable for implementation in dive computers and post dive simulation software for more accurate risk analysis.

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. However, DCS is an uncommon phenomenon and the wide variation in individual response to decompression stress is poorly understood. And generally, using clinical examination alone for validation is disadvantageous from a modeling perspective. Currently, the only objective and quantitative measure of decompression stress is Venous Gas Emboli (VGE), measured by either ultrasonic imaging or Doppler. VGE has been shown to be statistically correlated with DCS, and is now widely used in science to evaluate decompression stress from a dive. Until recently no mathematical model has existed to predict VGE from a dive, which motivated the development of the Copernicus model. The present article compiles a selection experimental dives and field data containing computer recorded depth profiles associated with ultrasound measurements of VGE. It describes a parameter estimation problem to fit the model with these data. A total of 185 square bounce dives from DCIEM, Canada, 188 recreational dives with a mix of single, repetitive and multi-day exposures from DAN USA and 84 experimentally designed decompression dives from Split Croatia were used, giving a total of 457 dives. Five selected parameters in the Copernicus bubble model were assigned for estimation and a non-linear optimization problem was formalized with a weighted least square cost function. A bias factor to the DCIEM chamber dives was also included. A Quasi-Newton algorithm (BFGS) from the TOMLAB numerical package solved the problem which was proved to be convex. With the parameter set presented in this article, Copernicus can be implemented in any programming language to estimate VGE from an air dive.

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. The symptomatology of DCS is very diffuse and there are ethical concerns evaluating procedures through provoking DCS on the test subjects. In recent decades ultrasonic Doppler and imaging to detect venous gas emboli (VGE) have been used as additional tools to evaluate decompression procedures. A statistical correlation between VGE and DCS has been shown and the method is more sensitive than clinical manifestation. This paper suggests a dynamic mathematical model for VGE. We have used a physiological approach in the model derivation with VGE as a measurable endpoint. We propose that the underlying physiological and physical mechanisms of the model can be better validated with such an objective quantitative measurement method. Two simulation examples are given to illustrate the properties of the model and why there is a potential of improving the consistency of controlling bubble formation, and consequently, the risk of getting DCS.