Yaguang Zheng

Current Science on Consumer Use of Mobile Health for Cardiovascular Disease Prevention A Scientific Statement From the American Heart Association

Although 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.3 In 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, 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 …

Self-weighing in weight management: a systematic literature review.

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.

The Use of mHealth to Deliver Tailored Messages Reduces Reported Energy and Fat Intake

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:The aim of this study was to examine the impact of daily feedback (DFB) messages, delivered remotely, on changes in dietary intake. Methods:This was a secondary analysis of the Self- Monitoring And Recording using Technology (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 DFB group (DFB group; n = 70) with the self-monitoring without DFB group (no-DFB group, 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 and at 6, 12, 18, and 24 months. Linear mixed modeling was used to examine percentage changes in dietary intake from baseline. Results:Compared with the no-DFB group, the DFB group achieved a larger reduction in energy (−22.8% vs −14.0%; P = .02) and saturated fat (−11.3% vs −0.5%; P = .03) intake and a trend toward a greater decrease in total fat intake (−10.4% vs −4.7%; P = .09). There were significant improvements over time in carbohydrate intake and total fat intake for both groups (P values < .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.

Socio-demographic, anthropometric, and psychosocial predictors of attrition across behavioral weight-loss trials.

Preventing attrition is a major concern in behavioral weight loss intervention studies. The purpose of this analysis was to identify baseline and six-month predictors associated with participant attrition across three independent clinical trials of behavioral weight loss interventions (PREFER, SELF, and SMART) that were conducted over 10 years. Baseline measures included body mass index, Barriers to Healthy Eating, Beck Depression Inventory-II (BDI), Hunger Satiety Scale (HSS), Binge Eating Scale (BES), Medical Outcome Study Short Form (MOS SF-36 v2) and Weight Efficacy Lifestyle Questionnaire (WEL). We also examined early weight loss and attendance at group sessions during the first 6 months. Attrition was recorded at the end of the trials. Participants included 504 overweight and obese adults seeking weight loss treatment. The sample was 84.92% female and 73.61% white, with a mean (± SD) age of 47.35 ± 9.75 years. After controlling for the specific trial, for every one unit increase in BMI, the odds of attrition increased by 11%. For every year increase in education, the odds of attrition decreased by 10%. Additional predictors of attrition included previous attempts to lose 50-79 lbs, age, not possessing health insurance, and BES, BDI, and HSS scores. At 6 months, the odds of attrition increased by 10% with reduced group session attendance. There was also an interaction between percent weight change and trial (p<.001). Multivariate analysis of the three trials showed education, age, BMI, and BES scores were independently associated with attrition (ps ≤ .01). These findings may inform the development of more robust strategies for reducing attrition.

Association between Self-Weighing and Percent Weight Change: Mediation Effects of Adherence to Energy Intake and Expenditure Goals.

BACKGROUND To date, no investigators have examined electronically recorded self-weighing behavior beyond 9 months or the underlying mechanisms of how self-weighing might impact weight change. OBJECTIVE Our aims were to examine electronically recorded self-weighing behavior in a weight-loss study and examine the possible mediating effects of adherence to energy intake and energy expenditure (EE) goals on the association between self-weighing and weight change. DESIGN This was a secondary analysis of the self-efficacy enhancement arm of the Self Efficacy Lifestyle Focus (SELF) trial, an 18-month randomized clinical trial. PARTICIPANTS/SETTING The study was conducted at the University of Pittsburgh (2008-2013). Overweight or obese adults with at least one additional cardiovascular risk factor were eligible. INTERVENTION Participants in the self-efficacy enhancement arm were given a scale (Carematix, Inc) and instructed to weigh themselves at least 3 days per week or every other day. The scale date- and time-stamped each weighing episode, storing up to 100 readings. MAIN OUTCOME MEASURES Weight was assessed every 6 months. Adherence to energy intake and EE goals was calculated on a weekly basis using paper diary data. STATISTICAL ANALYSES PERFORMED Linear mixed modeling and mediation analyses were used. RESULTS The sample (n=55) was 80% female, 69% non-Hispanic white, mean (standard deviation) age was 55.0 (9.6) years and body mass index (calculated as kg/m2) was 33.1 (3.7). Adherence to self-weighing declined over time (P<0.001). From baseline to 6 months, there was a significant mediation effect of adherence to energy intake (P=0.02) and EE goals (P=0.02) on the association between adherence to self-weighing and percent weight change. Mediation effects were not significant during the second and third 6-month periods of the study. CONCLUSIONS Objectively measured adherence to self-weighing declined over 18 months. During the first 6 months, self-weighing directly impacted weight change and indirectly impacted weight change through changes in energy intake and EE.

Patterns of self-weighing behavior and weight change in a weight loss trial

Background/Objectives:Regular self-weighing has been associated with weight loss and maintenance in adults enrolled in a behavioral weight loss intervention; however, few studies have examined the patterns of adherence to a self-weighing protocol. The study aims were to (1) identify patterns of self-weighing behavior; and (2) examine adherence to energy intake and step goals and weight change by self-weighing patterns.Subjects/Methods:This was a secondary analysis of self-monitoring and assessment weight data from a 12-month behavioral weight loss intervention study. Each participant was given a scale that was Wi-Fi-enabled and transmitted the date-stamped weight data to a central server. Group-based trajectory modeling was used to identify distinct classes of trajectories based on the number of days participants self-weighed over 51 weeks.Results:The sample (N=148) was 90.5% female, 81.1% non-Hispanic white, with a mean (s.d.) age of 51.3 (10.1) years, had completed an average of 16.4 (2.8) years of education and had mean body mass index of 34.1 (4.6) kg m−2. Three patterns of self-weighing were identified: high/consistent (n=111, 75.0% self-weighed over 6 days per week regularly); moderate/declined (n=24, 16.2% declined from 4–5 to 2 days per week gradually); and minimal/declined (n=13, 8.8% declined from 5–6 to 0 days per week after week 33). The high/consistent group achieved greater weight loss than either the moderate/declined and minimal/declined groups at 6 months (−10.19%±5.78%, −5.45%±4.73% and −2.00%±4.58%) and 12 months (−9.90%±8.16%, −5.62%±6.28% and 0.65%±3.58%), respectively (P<0.001). The high/consistent group had a greater mean number days per week of adherence to calorie intake goal or step goal but not higher than the moderate/declined group.Conclusions:This is the first study to reveal distinct temporal patterns of self-weighing behavior. The majority of participants were able to sustain a habit of daily self-weighing with regular self-weighing leading to weight loss and maintenance as well as adherence to energy intake and step goals.

The SELF Trial: A self-efficacy based behavioral intervention trial for weight-loss maintenance

The SELF Trial examined the effect of adding individual self‐efficacy (SE) enhancement sessions to standard behavioral weight loss treatment (SBT).

Impact of Perceived Barriers to Healthy Eating on Diet and Weight in a 24-Month Behavioral Weight Loss Trial

OBJECTIVE To examine longitudinal changes in perceptions of barriers to healthy eating and its impact on dietary intake and weight loss in a 24-month trial. METHODS A secondary analysis was conducted using data from a behavioral weight loss trial (n = 210). The Barriers to Healthy Eating (BHE) scale was used to measure perceived barriers to healthy eating. Weight, total energy, and fat intake were measured. Longitudinal mixed regression modeling was used for data analysis. RESULTS The BHE total score decreased from baseline to 6 months and increased slightly from 6 to 24 months (P < .001). Changes in BHE total and subscale scores were positively associated with changes in total energy and fat intake (P < .05) as well as weight (P < .01). CONCLUSIONS AND IMPLICATIONS Reducing barriers could lead to improved short-term dietary changes and weight loss. Innovative strategies need to be developed to prevent barriers from increasing when intervention intensity begins to decrease.

Neighborhood factors and six-month weight change among overweight individuals in a weight loss intervention

The purpose of this study was to examine the neighborhood environment and the association with weight change among overweight/obese individuals in the first six months of a 12-month weight loss intervention, EMPOWER, from 2011 to 2015. Measures of the neighborhood environment included neighborhood racial composition, neighborhood income, and neighborhood food retail stores density (e.g., grocery stores). Weight was measured at baseline and 6 months and calculated as the percent weight change from baseline to 6 months. The analytic sample (N = 127) was 91% female and 81% white with a mean age of 51 (± 10.4) years. At 6 months, the mean weight loss was 8.0 kg (± 5.7), which was equivalent to 8.8% (± 6%) of baseline weight. Participants living in neighborhoods in which 25–75% of the residents identified as black had the greatest percentage of weight loss compared to those living in neighborhoods with < 25% or > 75% black residents. No other neighborhood measures were associated with weight loss. Future studies testing individual-level behavioral weight loss interventions need to consider the influence of neighborhood factors, and how neighborhood-level interventions could be enhanced with individual-level interventions that address behaviors and lifestyle changes.

The SMARTER pilot study: Testing feasibility of real-time feedback for dietary self-monitoring

Self-monitoring (SM) of food intake is central to weight loss treatment. Technology makes it possible to reinforce this behavior change strategy by providing real-time feedback (FB) tailored to the diary entry. To test the feasibility of providing 1–4 daily FB messages tailored to dietary recordings via a smartphone, we conducted a 12-week pilot randomized clinical trial in Pittsburgh, PA in US in 2015. We compared 3 groups: SM using the Lose It! smartphone app (Group 1); SM + FB (Group 2); and SM + FB + attending three in-person group sessions (Group 3). The sample (N = 39) was mostly white and female with a mean body mass index of 33.76 kg/m2. Adherence to dietary SM was recorded daily, weight was assessed at baseline and 12 weeks. The mean percentage of days adherent to dietary SM was similar among Groups 1, 2, and 3 (p = 0.66) at 53.50% vs. 55.86% vs. 65.33%, respectively. At 12 weeks, all groups had a significant percent weight loss (p < 0.05), with no differences among groups (− 2.85% vs. − 3.14% vs. − 3.37%) (p = 0.95); 26% of the participants lost ≥ 5% of their baseline weight. Mean retention was 74% with no differences among groups (p = 0.37). All groups adhered to SM at levels comparable to or better than other weight loss studies and lost acceptable amounts of weight, with minimal intervention contact over 12 weeks. These preliminary findings suggest this 3-group approach testing SM alone vs. SM with real-time FB messages alone or supplemented with limited in-person group sessions warrants further testing in a larger, more diverse sample and for a longer intervention period.