Noom (United States)

Abstract: During the COVID-19 pandemic, a wide ideological gap frustrated an effective response to the health crisis. Whereas most liberals supported protective measures, many conservatives opposed them. Five studies ( Ntotal = 3,090) demonstrate that American and German conservatives’ opposition to COVID-19 measures arose partially from nostalgic emotions. We show that framing COVID-19 measures as a return to the past reduces conservatives’ opposition to face masks and vaccinations. An internal meta-analysis shows that although the overall effect of temporal framing is significant, it is small. This research identifies conservatives’ focus on the past as a theoretically relevant antecedent to their opposition to COVID-19 measures and introduces temporal framing as a small but practically feasible strategy to reduce such opposition.

Sleep is hypothesized to interact with weight gain and loss; however, modelling this relationship remains elusive. Poor sleep perpetuates a cascade of cardiovascular and metabolic consequences that may not only increase risk of adiposity, but also confound weight loss efforts. We conducted a scoping review to assess the research on sleep and weight loss interventions. We searched six databases for studies of behavioural weight loss interventions that included assessments of sleep in the general, non-clinical adult human population. Our synthesis focused on dimensions of Population, Intervention, Control, and Outcomes (PICO) to identify research and knowledge gaps. We identified 35 studies that fell into one of four categories: (a) sleep at baseline as a predictor of subsequent weight loss during an intervention, (b) sleep assessments after a history of successful weight loss, (c) concomitant changes in sleep associated with weight loss and (d) experimental manipulation of sleep and resulting weight loss. There was some evidence of improvements in sleep in response to weight-loss interventions; however, randomized controlled trials of weight loss interventions tended not to report improvements in sleep when compared to controls. We conclude that baseline sleep characteristics may predict weight loss in studies of dietary interventions and that sleep does not improve because of weight loss alone. Future studies should enrol large and diverse, normal, overweight and obese short sleepers in trials to assess the efficacy of sleep as a behavioural weight loss treatment.

12070 Background: Post-diagnosis weight gain is common among women with early-stage breast cancer and is associated with increased risk of recurrence, breast cancer death, and all-cause mortality. Intentional weight loss following diagnosis is difficult to maintain. Targeted lifestyle interventions focusing on behavioral changes and education that can be implemented long-term are needed. Methods: This was a prospective single-arm pilot study in patients with history of early-stage breast cancer (NCT04753268). Key eligibility criteria included body mass index (BMI) ≥27.5 kg/m2, stage I-III breast cancer, and completion of active cancer treatment ≥6 months prior to study enrollment. Patients were recruited at the Dempsey Center in Lewiston, ME and via social media. Participants were given access to the Noom mobile application, which utilizes cognitive behavioral therapy, motivational interviewing, and self-determination theory to induce behavior change using an educational curriculum of daily articles, virtual health coaching, social networking, food records, and physical activity logs. The primary endpoint was change in self-reported weight from baseline to 26 weeks. Secondary endpoints included change in physical activity measured using step count and the Global Physical Activity Questionnaire (GPAQ), change in diet patterns analyzed using the Nutrition Data System for Research, patient-reported outcome (PRO) measures, and metrics for engagement with the mobile application. Results: 31 patients were enrolled; 24 at the Dempsey Center and 7 via social media. Mean age was 56.8 and mean baseline BMI was 33.5 kg/m2. Mean weight change was -4.8 kg (range +0.6 to -19.9 kg, p < 0.001) and mean percent weight change was -5.6%; 11/31 patients (35.5%) lost ≥5% of their initial weight. Average daily step count increased by 54.7% (p = 0.004). Mean GPAQ score for physical activity increased from 990 to 1773 (p = 0.01), and the proportion of patients who met physical activity guidelines increased from 45.1% to 74.2%. Mean energy intake did not change significantly over 26 weeks (p = 0.779). Improvements were observed in PROMIS-Physical Functioning T-scores and Body Image Scale scores (p = 0.04, p < 0.001 respectively). Metrics of engagement with Noom that were predictive of weight loss ≥5% included total articles read (p = 0.012), total weights logged (p = 0.006), total food records logged (p = 0.001), messages sent to coach (p = 0.001), and number of times the application was opened (p = 0.014). Conclusions: In this pilot study, breast cancer survivors lost weight and became more physically active using a commercially-available mobile application. This digital weight loss program is a scalable intervention, and these findings support future studies investigating impact on breast cancer outcomes. Clinical trial information: NCT04753268 .

At present, most machine learning research on customer optimization focuses on short term success of the customers by addressing questions such as - which users have a higher propensity to click? Where to place one ad/multiple contents on a web page? What is the most appropriate time to show content? There has been less/little thought put into building a coherent system for the long term/end-end customer optimization from acquisition by understanding a user's propensity to convert to a particular product at a certain time, to user's ability to be successful long term on a platform as measured by CLV (Customer Lifetime Value), to users' ability to buy more products (cross sell) on the same platform, and finally users propensity to churn. Currently, such models and algorithms are built in isolation to serve a single purpose which leads to inefficiencies in modeling and data pipelines. Also, most of the time the customer is not looked at as a single entity - but each product/subgroup within an organization (marketing, sales, product growth, go-to-market, product) considers the customer independently. This workshop aims to connect academic researchers and industrial practitioners who are working on, or interested in building holistic systems and solutions in the field of end to end customer journey optimization.

ABSTRACT Patient-centered communication (PCC) by healthcare professionals can contribute to enacting and facilitating patients’ self-management of chronic health conditions. This study investigates the emerging patterns of PCC that occur in an mHealth-based diabetes prevention program for older adults. The analysis of user-coach communication data during the 16-week period of the program revealed four PCC strategies employed by coaches: (a) triggering reflections on users’ routinized habits, (b) jointly determining a measurable health goal, (c) facilitating self-evaluations on recent behavior change, and (d) tailoring programs to adapt to users’ lifestyle and health status. To advance these strategies, coaches utilized various mHealth features that helped them (a) engage in data-driven coaching, (b) increase situational awareness of users’ health conditions and routines, (c) provide continuous support to users through regular and spontaneous in-app chats, and (d) foster user autonomy and engagement. The findings extend implications for developing technology-enabled healthcare practice to enhance self-management of chronic illness.

There are inconsistent results for the effectiveness of using smartphone applications (apps) or websites on weight loss. We investigated the efficacy of a smartphone intervention using a designated app that utilizes a lifestyle intervention-focused approach, including a human coaching element, toward weight loss in overweight or obese Korean adults. One hundred four adults aged 20-60 years with a body mass index ≥23 kg/m2, who signed up for a smartphone program for weight loss (using the Noom app), were recruited. Participants received an in-person orientation about the study and app use, and a baseline blood sample was obtained. The in-app intervention with daily behavior and nutrition education content and coaching lasted 15 weeks. The primary endpoint of the study was a change in weight. The secondary endpoints were changes in metabolic risk factors such as blood pressure, waist circumference, and glucose and lipid profiles. Body composition changes were also assessed, and body weight at 52 weeks was measured to ascertain long-term effects. Participants showed a clinically significant weight loss effect of -7.5% at the end of the 15-week program (P < 0.001), and at a 52-week follow-up, a weight loss effect of -5.2% was maintained. At 15 weeks, percent body fat and visceral fat decreased by -6.0 ± 5.4% and -3.4 ± 2.7 kg, respectively (both P < 0.001). Fasting glucose level also decreased significantly by -5.7 ± 14.6 mg/dL at 15 weeks. Lipid parameters showed significant improvements, except for high-density lipoprotein cholesterol. The frequency of logging meals and exercise was associated with body fat loss. This advanced smartphone app was a useful tool to maintain weight loss in overweight or obese people.