Senecal, Conor

Background: Digital health applications are increasingly ubiquitous, however the effectiveness remains mixed. Using a large working age cohort we sought to evaluate use of a digital health intervention (DHI) in people with uncontrolled hypertension and their changes in blood pressure control. Methods: We analyzed usage of DHI and change in blood pressure control and other CVD risk factors over one year in 9,724 participants of a work health program across 81 organizations in 42 states. Patients were selected from a larger cohort based on a previous ICD-9 diagnosis of hypertension. Patients with uncontrolled hypertension, systolic blood pressure (SBP) greater than 140 mmHg, at the initial visit were included. Application users, defined as at least one login, were compared to non-users. Changes in blood pressure, weight, and lipids were compared at one year. Results: Users and non-users were similar in age, weight, blood pressure and lipids at baseline, though users were more likely to be female. At one year users had 16.44 mmHg reduction in SBP and 7.16 mmHg reduction in diastolic blood pressure (DBP), both statistically significantly more than non-users (Table 1). Both users and non-users had an improvement in LDL from baseline, while non-users also had an improvement in BMI and TG from baseline. Discussion: These results show that the use of a DHI is associated with improved systolic and diastolic blood pressure control compared to a workplace health program alone.

Introduction Cardiovascular disease is the most common cause of morbidity and mortality in the United States. Patients are increasingly using internet search to find health-related information, including searches for cardiovascular diseases and risk factors. We sought to evaluate the change in the state by state correlation of cardiovascular disease and risk factors with Google Trends search volumes. Methods Data on cardiovascular disease hospitalizations and risk factor prevalence were obtained from the publically available Centers for Disease Control and Prevention website from 2006 to 2018. Google Trends data were obtained for matching conditions and time periods. Simple linear regression was performed to evaluate for an increase in correlation over time. Results Hospitalizations for six separate cardiovascular disease conditions showed moderate to strong correlation with online search data in the last period studied (heart failure (0.58, p < .001), atrial fibrillation (0.57, p < .001), coronary heart disease (0.58, p < .001), myocardial infarction (0.70, p < .001), stroke (0.62, p < .001), cardiac dysrhythmia (0.46, p < .001)) in the United States. All diseases studied showed a positive increase in correlation throughout the time period studied ( p < .05). All five of the cardiovascular risk factors studied showed strong correlation with online search data; diabetes ( R = 0.78, p < .001), cigarette use ( R = 0.79, p < .001), hypertension ( R = 0.81, p < .001), high cholesterol ( R = 0.59, p < .001), and obesity ( p = 0.80, p < .001) in the United States. Three of the five risk factors showed an increasing correlation over time. Conclusion The prevalence of and hospitalizations for cardiovascular conditions in the United States strongly correlate with online search volumes in the United States when analyzed by state. This relationship has progressively strengthened or been strong and stable over recent years for these conditions. Google Trends represents an increasingly valuable tool for evaluating the burden of cardiovascular disease and risk factors in the United States.

Objective: To identify an efficacious intervention on treating adolescents with overweight and obesity, this might result in health benefits. Methods: Adolescents with overweight or obesity aged 10–17 years with BMI percentile ≥85th were included in this historical observational analysis. Subjects used an entirely remote weight loss program combining mobile applications, frequent self-weighing, and calorie restriction with meal replacement. Body weight changes were evaluated at 42, 60, 90, and 120 days using different metrics including absolute body weight, BMI, and BMI z-score. Chi-square or Fisher exact tests (categorical variables) and Student's t -test (continuous variables) were used to compare subjects. Results: In total, 2,825 participants, mean age 14.4 ± 2.2 years, (54.8% girls), were included from October 27, 2016, to December 31, 2017, in mainland China; 1355 (48.0%) had a baseline BMI percentile ≥97th. Mean BMI and BMI z-score were 29.20 ± 4.44 kg/m 2 and 1.89 ± 0.42, respectively. At day 120, mean reduction in body weight, BMI, and BMI z-score was 8.6 ± 0.63 kg, 3.13 ± 0.21 kg/m 2 , and 0.42 ± 0.03; 71.4% had lost ≥5% body weight, 69.4% of boys and 73.2% of girls, respectively. Compared with boys, girls achieved greater reduction on BMI z-score at all intervals ( p < 0.004 for all comparisons). Higher BMI percentile at baseline and increased frequency of use of the mobile application were directly associated with more significant weight loss. Conclusions: An entirely remote digital weight loss program is effective in facilitating weight loss in adolescents with overweight or obesity in the short term and mid term.

Abstract Introduction: Cardiovascular disease is the most common cause of morbidity and mortality in the United States and in the world. Patients are increasingly using internet search to find health-related information, including searches for cardiovascular diseases and risk factors. We sought to evaluate the change in the state by state correlation of cardiovascular disease and risk factors with Google Trends search volumes. Methods: Data on cardiovascular disease hospitalizations and risk factor prevalence were obtained from the publically available CDC website from 2006-2018. Google Trends data were obtained for matching conditions and time periods. Simple linear regression was performed to evaluate for an increase in correlation over time. Results: Hospitalizations for six separate cardiovascular disease conditions showed moderate to strong correlation with online search data in the last period studied (heart failure (0.58, P<0.001), atrial fibrillation (0.57, P<0.001), coronary heart disease (0.58, P<0.001), myocardial infarction (0.70, P<0.001), stroke (0.62, P<0.001), cardiac dysrhythmia (0.46, P<0.001)). All diseases studied showed a positive increase in correlation throughout the time period studied (P<0.05). All five of the cardiovascular risk factors studied showed strong correlation with online search data; diabetes (R=0.78, P<0.001), cigarette use (R=0.79, P<0.001), hypertension (R=0.81, P<0.001), high cholesterol (R=0.59, P<0.001), obesity (R=0.80, P<0.001). Three of the five showed an increasing correlation over time. Conclusion: The prevalence of and hospitalizations for cardiovascular conditions in the United States strongly correlate with online search volumes nationwide and when analyzed by state. This relationship has progressively strengthened or been strong and stable over recent years for these conditions. Google Trends represents an increasingly valuable tool for evaluating the burden of cardiovascular disease and risk factors in the United States.

Importance. Obesity is a worsening epidemic worldwide. Effective and accessible weight loss programs to combat obesity on a large scale are warranted, but a need for frequent face-to-face care might impose a limitation. Objective. To evaluate whether individuals following a weight loss program based on a mobile application, wireless scale, and nutritional program but no face-to-face care can achieve clinically significant weight loss in a large cohort. Design. Retrospective observational analysis. Setting. China from October 2016 to December 2017. Participants. Mobile application users with a minimum of 2 weights (baseline and ≥35 days). Intervention. A commercial (Weijian Technologies) weight loss program consisting of a dietary replacement, self-monitoring using a wireless home scale, and frequent guidance via mobile application. Main Outcome. Mean weight change around 42, 60, 90, and 120 days after program initiation with subgroup analysis by gender, age, and frequency of use. Results. 251,718 individuals, with a mean age of 37.3 years (SD: 9.86) (79% female), were included with a mean weight loss of 4.3 kg (CI: ±0.02) and a mean follow-up of 120 days (SD: 76.8 days). Mean weight loss at 42, 60, 90, and 120 d was 4.1 kg (CI: ±0.02), 4.9 kg (CI: ±0.02), 5.6 kg (CI: ±0.03), and 5.4 kg (CI: ±0.04), respectively. At 120 d, 62.7% of participants had lost at least 5% of their initial weight. Both genders and all usage frequency tertiles showed statistically significant weight loss from baseline at each interval (P<0.001), and this loss was greater in men than in women (120 d: 6.5 vs. 5.2 kg; P<0.001). The frequency of recording (categorized as high-, medium-, or low-frequency users) was associated with greater weight loss when comparing high, medium, and low tertile use groups at all time intervals investigated (e.g., 120 d: −8.6, −5.6, and −2.2 kg, respectively; P<0.001). Conclusions. People following a commercially available hybrid weight loss program using a mobile application, wireless scale, and nutritional program without face-to-face interaction on average achieved clinically significant short- and midterm weight loss. These results support the implementation of comparable technologies for weight control in a large population.

Introduction Severe obesity is a growing epidemic that causes significant morbidity and mortality, and is particularly difficult to reverse. Efficacious and cost-effective interventions are needed to combat this epidemic. This study hypothesized that obese people (body mass index (BMI) ≥35 kg/m 2 ) using a remote weight-loss program combining a mobile application, wireless scales, and low-calorie meal replacement would experience clinically significant weight loss. Methods This study was a retrospective observational analysis of 8275 individuals with a baseline BMI ≥35 kg/m 2 who used a remote weight-loss program combining mobile applications, frequent self-weighing, and calorie restriction via meal replacement for a minimum of 35 days. Weight changes were evaluated at multiple intervals (42, 60, 90, and 120 days), and weight loss was evaluated for all and for pre-specified subgroups based on demographic features and frequency of self-weighing. Results Mean weight loss at 42 days ( N = 6781) was 8.1 kg (margin of error (MOE) = 0.126 kg) with 73.6% of users experiencing >5% total body weight loss. Both men (9.1 kg; MOE = 0.172 kg; 7.9% from baseline) and women (7.1 kg; MOE = 0.179 kg; 7.2% from baseline) experienced significant weight loss. At the 120-day interval ( N = 2914), mean weight loss was 14 kg (MOE = 0.340 kg), 13% total body weight loss from baseline, and 82.3% of participants had lost >5% of their initial body weight. The decrease in body-fat percent correlated well with weight loss ( R = 0.92; p < 0.001). Conclusions In a large cohort of individuals with class II or III obesity, a remote weight-loss program combining mobile applications, daily self-weighing, and calorie restriction via meal replacement resulted in dramatic weight loss among subjects who were active users when evaluated through a retrospective observational analysis.

Introduction: Metabolic Syndrome represents a constellation of findings known to increase cardiovascular risk and all-cause mortality and is becoming more frequent in China. Smartphone applications may provide a cost-effective method to engage in weight loss and improve metabolic syndrome components. Methods: Prospective study of changes in weight and metabolic syndrome risk factors at 6 weeks and 6 months using a smartphone application utilizing a wireless scale, meal replacement biscuits and e-coaching in China. All participants had or were at high risk for metabolic syndrome and all interaction was online, without face to face contact. Results: 218 individuals ,55% female, participated. Median weight loss at 6 weeks and 6 months was 7.3kg and 8.4 kg (P<0.001) respectively. At baseline, 60% met a diagnosis of metabolic syndrome this dropped to 38% at 6 weeks (P<0.001) and stayed at 38% at 6 months (P<0.001). At 6 week the prevalence of all components of metabolic syndrome were improved (P<0.05) except HDL. Conclusions: A smartphone weight loss platform that utilizes a wireless scale, meal replacement biscuits and e-coaching was associated with significant weight loss and improvement in several metabolic syndrome components at 6 weeks and 6 months. This program may provide a scalable intervention against metabolic syndrome, especially in developing countries such as China.