No abstract

Cardiovascular disease remains the leading cause of death and disease worldwide. As demands on an already resource-constrained healthcare system intensify, disease prevention in the future will likely depend on out-of-office monitoring of cardiovascular risk factors. Mobile health tracking devices that can track blood pressure and heart rate, in addition to new cardiac vital signs, such as physical activity level and pulse wave velocity (PWV), offer a promising solution. An initial barrier is the development of accurate and easily-scalable platforms. In this study, we made a customized smartphone app and used mobile health devices to track PWV, blood pressure, heart rate, physical activity, sleep duration, and multiple lifestyle risk factors in ≈250 adults for 17 continual weeks. Eligible participants were identified by a company database and then were consented and enrolled using only a smartphone app, without any special training given. Study participants reported high overall satisfaction, and 73% of participants were able to measure blood pressure and PWV, <1 hour apart, for at least 14 of 17 weeks. The study population's blood pressure, PWV, heart rate, activity levels, sleep duration, and the interrelationships among these measurements were found to closely match either population averages or values obtained from studies performed in a controlled setting. As a proof-of-concept, we demonstrated the accuracy and ease, as well as many challenges, of using mHealth technology to accurately track PWV and new cardiovascular vital signs at home.

Poor sleep quality is associated with greater visceral adiposity and leptin secretion. Further research is needed to probe potential cause and effect relationships among visceral adipose tissue, leptin, and sleep quality.

BackgroundDigital self-monitoring, particularly of weight, is increasingly prevalent. The associated data could be reused for clinical and research purposes.ObjectiveThe aim was to compare participants who use connected smart scale technologies with the general population and explore how use of smart scale technology affects, or is affected by, weight change.MethodsThis was a retrospective study comparing 2 databases: (1) the longitudinal height and weight measurement database of smart scale users and (2) the Health Survey for England, a cross-sectional survey of the general population in England. Baseline comparison was of body mass index (BMI) in the 2 databases via a regression model. For exploring engagement with the technology, two analyses were performed: (1) a regression model of BMI change predicted by measures of engagement and (2) a recurrent event survival analysis with instantaneous probability of a subsequent self-weighing predicted by previous BMI change.ResultsAmong women, users of self-weighing technology had a mean BMI of 1.62 kg/m2 (95% CI 1.03-2.22) lower than the general population (of the same age and height) (P<.001). Among men, users had a mean BMI of 1.26 kg/m2 (95% CI 0.84-1.69) greater than the general population (of the same age and height) (P<.001). Reduction in BMI was independently associated with greater engagement with self-weighing. Self-weighing events were more likely when users had recently reduced their BMI.ConclusionsUsers of self-weighing technology are a selected sample of the general population and this must be accounted for in studies that employ these data. Engagement with self-weighing is associated with recent weight change; more research is needed to understand the extent to which weight change encourages closer monitoring versus closer monitoring driving the weight change. The concept of isolated measures needs to give way to one of connected health metrics.

The digital revolution of information and technology in late 20th century has led to emergence of devices that help people monitor their weight in a long-term manner. Investigation of population-level variations of body mass using smart connected weight scales enabled the health coaches acquire deeper insights about the models of people's behavior as a function of time. Typically, body mass varies when the seasons change. That is, during the warmer seasons people's body mass tend to decrease while in colder seasons it usually moves up. In this paper we study the seasonal variations of body mass in seven countries by utilization of linear regression. Deviation of monthly weight values from the starting point of astronomical years (beginning of spring) were modeled by fitting orthogonal polynomials in each country. The distinction of weight variations in southern and northern hemispheres were then investigated. The studied population involves 6429 anonymous weight scale users from:(1) Australia, (2) Brazil, (3) France, (4) Germany, (5) Great Britain, (6) Japan, and (7) United States of America. The results suggest that there are statistically significant differences between the models of weight variation in southern and northern hemispheres. In both northern and southern hemispheres the lowest weight values were observed in the summer. However, the highest weight values were noticed in the winter and in the spring for northern and southern hemispheres, respectively.