A lthough mortality for cardiovascular disease (CVD) has declined for several decades, heart disease and stroke continue to be the leading causes of death, disability, and high healthcare costs. Unhealthy behaviors related to CVD risk (eg, smoking, sedentary lifestyle, and unhealthful eating habits) remain highly prevalent. The high rates of overweight, obesity, and type 2 diabetes mellitus (T2DM); the persistent presence of uncontrolled hypertension; lipid levels not at target; and the ≈18% of adults who continue to smoke cigarettes pose formidable challenges for achieving improved cardiovascular health.1,2 It is apparent that the performance of healthful behaviors related to the management of CVD risk factors has become an increasingly important facet of the prevention and management of CVD. 3In 2010, the American Heart Association (AHA) made a transformative shift in its strategic plan and added the concept of cardiovascular health.2 To operationalize this concept, the AHA targeted 4 health behaviors in the 2020 Strategic Impact Goals: reduction in smoking and weight, healthful eating, and promotion of regular physical activity. Three health indicators also were included: glucose, blood pressure (BP), and cholesterol. On the basis of the AHA Life's Simple 7 metrics for improved cardiovascular health, <1% of adults in the United States follow a healthful eating plan, only 32% have a normal body mass index, and > 30% have not reached the target levels for lipids or BP. National Health and Nutrition Examination Survey (NHANES) data revealed that people who met ≥6 of the cardiovascular health metrics had a significantly better risk profile (hazard ratio for all-cause mortality, 0.49) compared with individuals who had achieved only 1 metric or none.2 The studies reviewed in this statement targeted these behaviors (ie, smoking, physical activity, healthful eating, and maintaining a healthful weight) and cardiovascular health indicators (ie, blood glucose, lipids, BP, body mass index) as the primary outcomes in the clinical trials testing mobile health (mHealth) interventions.eHealth, or digital health, is the use of emerging communication and information technologies, especially the Internet, to improve health and health care 4 (Table 1). mHealth, a subsegment of eHealth, is the use of mobile computing and communication technologies (eg, mobile phones, wearable sensors) for health services and information.4,5 mHealth technology uses techniques and advanced concepts from an array of disciplines, for example, computer science, electrical and

Objective: Regular self-weighing, which in this article is defined as weighing oneself regularly over a period of time (e.g., daily, weekly), is recommended as a weight loss strategy. However, the published literature lacks a review of the recent evidence provided by prospective, longitudinal studies. Moreover, no paper has reviewed the psychological effects of self-weighing. Therefore, the objective is to review the literature related to longitudinal associations between self-weighing and weight change as well as the psychological outcomes. Methods: Electronic literature searches in PubMed, Ovid PsycINFO, and Ebscohost CINAHL were conducted. Keywords included overweight, obesity, self-weighing, etc. Inclusion criteria included trials that were published in the past 25 years in English; participants were adults seeking weight loss treatment; results were based on longitudinal data. Results: The results (N 5 17 studies) revealed that regular self-weighing was associated with more weight loss and not with adverse psychological outcomes (e.g., depression, anxiety). Findings demonstrated that the effect sizes of association between self-weighing and weight change varied across studies and also that the reported frequency of self-weighing varied across studies. Conclusions: The findings from prospective, longitudinal studies provide evidence that regular selfweighing has been associated with weight loss and not with negative psychological outcomes.

The metabolism of soyasaponin I (3-O-[alpha-L-rhamnopyranosyl-beta-D-galactopyranosyl-beta-Dglucuronopyranosyl]olean-12-ene-3beta,22beta,24-triol) by human fecal microorganisms was investigated. Fresh feces were collected from 15 healthy women and incubated anaerobically with 10 mmol soyasaponin I/ g feces at 37 degrees C for 48 h. The disappearance of soyasaponin I in this in vitro fermentation system displayed apparent first-order rate loss kinetics. Two distinct soyasaponin I degradation phenotypes were observed among the subjects: rapid soyasaponin degraders with a rate constant k = 0.24 +/-0.04 h(-)(1) and slow degraders with a k = 0.07 +/-0.02 h(-)(1). There were no significant differences in the body mass index, fecal moisture, gut transit time, and soy consumption frequency between the two soyasaponin degradation phenotypes. Two primary gut microbial metabolites of soyasaponin I were identified as soyasaponin III (3-O-[beta-D-galactopyranosyl-beta-D-glucuronopyranosyl]olean-12-ene-3beta,22beta,24-triol) and soyasapogenol B (olean-12-ene-3beta,22beta,24-triol) by NMR and electrospray ionized mass spectroscopy. Soyasaponin III appeared within the first 24 h and disappeared by 48 h. Soyasapogenol B seemed to be the final metabolic product during the 48 h anaerobic incubation. These results indicate that dietary soyasaponins can be metabolized by human gut microorganisms. The sugar moieties of soyasaponins seem to be hydrolyzed sequentially to yield smaller and more hydrophobic metabolites. KeywordsCollege of Veterinary Medicine, Soyasaponin; metabolism; human gut microorganisms The metabolism of soyasaponinolean-12-ene-3 ,22 ,24-triol) by human fecal microorganisms was investigated. Fresh feces were collected from 15 healthy women and incubated anaerobically with 10 mmol soyasaponin I/g feces at 37°C for 48 h. The disappearance of soyasaponin I in this in vitro fermentation system displayed apparent first-order rate loss kinetics. Two distinct soyasaponin I degradation phenotypes were observed among the subjects: rapid soyasaponin degraders with a rate constant k ) 0.24 ( 0.04 h -1 and slow degraders with a k ) 0.07 ( 0.02 h -1 . There were no significant differences in the body mass index, fecal moisture, gut transit time, and soy consumption frequency between the two soyasaponin degradation phenotypes. Two primary gut microbial metabolites of soyasaponin I were identified as soyasaponin III (3-O-[ -D-galactopyranosyl--D-glucuronopyranosyl]olean-12-ene-3 ,-22 ,24-triol) and soyasapogenol B (olean-12-ene-3 ,22 ,24-triol) by NMR and electrospray ionized mass spectroscopy. Soyasaponin III appeared within the first 24 h and disappeared by 48 h. Soyasapogenol B seemed to be the final metabolic product during the 48 h anaerobic incubation. These results indicate that dietary soyasaponins can be metabolized by human gut microorganisms. The sugar moieties of soyasaponins seem to be hydrolyzed sequentially to yield smaller and more hydrophobic metabolites.

Background Evidence supports the role of feedback in reinforcing motivation for behavior change. Feedback that provides reinforcement has the potential to increase dietary self-monitoring and enhance attainment of recommended dietary intake. Objective To examine the impact of daily feedback (DFB) messages, delivered remotely, on changes in dietary intake. Methods A secondary analysis of the SMART trial, a single-center, 24-month randomized clinical trial of behavioral treatment for weight loss. Participants included 210 obese adults (mean body mass index=34.0 kg/m2) who were randomized to either a paper diary (PD), personal digital assistant (PDA), or PDA plus daily, tailored feedback messages (PDA+FB). To determine the role of daily tailored feedback in dietary intake, we compared the self-monitoring with daily feedback group (DFB, n=70) to the self-monitoring without daily feedback group (No-DFB, n=140). All participants received a standard behavioral intervention for weight loss. Self-reported changes in dietary intake were compared between the DFB and No-DFB groups and were measured at baseline, 6, 12, 18, and 24 months. Linear mixed modeling was used to examine percent changes in dietary intake from baseline. Results Compared to the No-DFB group, the DFB group achieved a larger reduction in energy (−22.8% vs. −14.0%, p=0.02) and saturated fat (−11.3% vs. −0.5%, p=0.03) intake, and a trend toward a greater decrease in total fat intake (−10.4% vs. −4.7%, p=0.09). There were significant improvements over time in carbohydrate intake and total fat intake for both groups (p’s<0.05). Conclusion Daily, tailored feedback messages, designed to target energy and fat intake and delivered remotely in real-time using mobile devices, may play an important role in the reduction of energy and fat intake.