Men's Weight Loss Outcomes, Behaviors, and Perceptions in a Self-Directed Commercial Mobile Program: Retrospective Analysis.

BACKGROUND: There is little understanding of men's weight loss outcomes and behaviors in self-directed contexts, such as digital commercial mobile weight management programs. This is an especially pressing question given that men often express disinterest in weight management programs and it is unknown how that manifests in self-directed environments. Aims. Two studies fill this gap by retrospectively observing how men lose weight and engage in weight loss behaviors (Study 1) and their perceptions of improvements and gained knowledge (Study 2) when participating in the full length of a commercial mobile behavior change program called Noom. METHOD: In Study 1, repeated-measures linear mixed modeling was used to examine whether weight loss was statistically significant from baseline to 16 weeks and how engagement behaviors predicted weight in a sample of 7,495 male Noom users. In Study 2, 971 male Noom users completed an exploratory survey on the impact of the behavior change education in the program. RESULTS: In Study 1, men who remained in the full length of the program lost statistically significant weight from baseline to 16 weeks. 63% achieved clinically meaningful (5% or more) weight loss. Engagement in weight loss behaviors on the program predicted the amount of weight lost. In Study 2, men reported learning most about practical application and psychological aspects relating to food and psychology. DISCUSSION AND CONCLUSION: This is the first study to observe men's weight loss outcomes, behaviors, and perceptions of what they learned in a self-directed behavior change program. Our findings have important implications for more effective health promotion for the many men who choose to self-direct their weight loss.

The Relationship Between Weight Loss Outcomes and Engagement in a Mobile Behavioral Change Intervention: Retrospective Analysis.

BACKGROUND: There is large variance in weight loss outcomes of digital behavior change interventions (DBCIs). It has been suggested that different patterns of engagement in the program could be responsible for this variance in outcomes. Previous studies have found that the amount of engagement on DBCIs, such as the number of meals logged or articles read, is positively associated with weight loss. OBJECTIVE: This retrospective study extends previous research by observing how important weight loss outcomes (high weight loss: 10% or greater body weight loss; moderate weight loss: between 5% to 10%; stable weight: 0 plus or minus 1%) are associated with engagement on a publicly available mobile DBCI (Noom) from 9 to 52 weeks. METHODS: Engagement and weight data for eligible participants (N=11,252) were extracted from the Noom database. Engagement measures included the number of articles read, meals logged, steps recorded, messages to coach, exercise logged, weigh-ins, and days with 1 meal logged per week. Weight was self-reported on the program. Multiple linear regressions examined how weight loss outcome (moderate and high vs stable) was associated with each engagement measure across 3 study time periods: 9-16 weeks, 17-32 weeks, and 33-52 weeks. RESULTS: At 9-16 weeks, among the 11,252 participants, 2594 (23.05%) had stable weight, 6440 (57.23%) had moderate weight loss, and 2218 (19.71%) had high weight loss. By 33-52 weeks, 525 (18.21%) had stable weight, 1214 (42.11%) had moderate weight loss, and 1144 (39.68%) had high weight loss. Regression results showed that moderate weight loss and high weight loss outcomes were associated with all engagement measures to a significantly greater degree than was stable weight (all P values <.001). These differences held across all time periods with the exception of exercise for the moderate weight loss category at 1 time period of 33-52 weeks. Exercise logging increased from 9 to 52 weeks regardless of the weight loss group. CONCLUSIONS: Our results suggest that these clinically important weight loss outcomes are related to the number of articles read, meals logged, steps recorded, messages to coach, exercise logged, weigh-ins, and days with 1 meal logged per week both in the short-term and long-term (ie, 1 year) on Noom. This provides valuable data on engagement patterns over time on a self-directed mobile DBCI, can help inform how interventions tailor recommendations for engagement depending on how much weight individuals have lost, and raises important questions for future research on engagement in DBCIs.

How Do Emotions during Goal Pursuit in Weight Change over Time? Retrospective Computational Text Analysis of Goal Setting and Striving Conversations with a Coach during a Mobile Weight Loss Program.

During behavioral weight management, individuals reflect on their progress and barriers through goal pursuit (goal setting and goal striving). Emotions during goal pursuit are largely unknown, and previous investigations of emotions in weight management have primarily relied on self-report. In this retrospective study, we used a well-validated computational text analysis approach to explore how emotion words changed over time during goal setting and striving conversations with a coach in a mobile weight loss program. Linear mixed models examined changes in emotion words each month from baseline to program end and compared emotion words between individuals who set an overall concrete goal for the program (concrete goal setters) and those who set an overall abstract goal (abstract goal setters). Contrary to findings using self-report, positive emotion words were stable and negative emotion words significantly increased over time. There was a marginal trend towards greater negative emotion word use being associated with greater weight loss. Concrete goal setters used more positive words than abstract goal setters, with no differences in negative emotion words and weight loss. Implications include the possibility that individuals may need increasing support over time for negative emotions expressed during goal setting and striving, and concrete goals could boost positive emotion. Future research should investigate these possibilities.

Self-Reported Nutritional Factors Are Associated with Weight Loss at 18 Months in a Self-Managed Commercial Program with Food Categorization System: Observational Study.

Little is known about nutritional factors during weight loss on digital commercial weight loss programs. We examined how nutritional factors relate to weight loss for individuals after 4 and 18 months on a mobile commercial program with a food categorization system based on energy density (Noom). This is a two-part (retrospective and cross-sectional) cohort study. Two time points were used for analysis: 4 months and 18 months. For 4-month analyses, current Noom users who met inclusion criteria (n = 9880) were split into 5% or more body weight loss and stable weight loss (0 ± 1%) groups. Individuals who fell into one of these groups were analyzed at 4 months (n = 3261). For 18-month analyses, individuals from 4-month analyses who were still on Noom 18 months later were invited to take a one-time survey (n = 803). At 18 months 148 participants were analyzed. Noom has a system categorizing foods as low-, medium-, and high-energy-dense. Measures were self-reported proportions of low-, medium-, and high-energy-dense foods, and self-reported nutritional factors (fruit and vegetable intake, dietary quality, nutrition knowledge, and food choice). Nutritional factors were derived from validated survey measures, and food choice from a novel validated computerized task in which participants chose a food they would want to eat right now. ANOVAs compared participants with 5% or more body weight loss and participants with stable weight (0 ± 1%) at 4 months on energy density proportions. Analyses at 18 months compared nutritional factors across participants with >10% (high weight loss), 5-10% (moderate weight loss), and less than 5% body weight loss (low weight loss), and then assessed associations between nutritional factors and weight loss. Individuals with greater weight loss reported consuming higher proportions of low-energy-dense foods and lower proportions of high-energy-dense foods than individuals with less weight loss at 4 months and 18 months (all ps < 0.02). Individuals with greater weight loss had higher fruit and vegetable intake (p = 0.03), dietary quality (p = 0.02), nutrition knowledge (p < 0.001), and healthier food choice (p = 0.003) at 18 months. Only nutrition knowledge and food choice were associated with weight loss at 18 months (B = -19.44, 95% CI: -33.19 to -5.69, p = 0.006; B = -5.49, 95% CI: -8.87 to -2.11, p = 0.002, respectively). Our results highlight the potential influence of nutrition knowledge and food choice in weight loss on a self-managed commercial program. We also found for the first time that in-the-moment inclination towards food even when just depicted is associated with long-term weight loss.

Cross-National Outcomes of a Digital Weight Loss Intervention in the United States, Canada, United Kingdom and Ireland, and Australia and New Zealand: A Retrospective Analysis.

Mobile health (mHealth) interventions are ubiquitous and effective treatment options for obesity. There is a widespread assumption that the mHealth interventions will be equally effective in other locations. In an initial test of this assumption, this retrospective study assesses weight loss and engagement with an mHealth behavior change weight loss intervention developed in the United States (US) in four English-speaking regions: the US, Australia and New Zealand (AU/NZ), Canada (CA), and the United Kingdom and Ireland (UK/IE). Data for 18,459 participants were extracted from the database of Noom's Healthy Weight Program. Self-reported weight was collected every week until program end (week 16). Engagement was measured using user-logged and automatically recorded actions. Linear mixed models were used to evaluate change in weight over time, and ANOVAs evaluated differences in engagement. In all regions, 27.2-33.2% of participants achieved at least 5% weight loss by week 16, with an average of 3-3.7% weight loss. Linear mixed models revealed similar weight outcomes in each region compared to the US, with a few differences. Engagement, however, significantly differed across regions (P < 0.001 on 5 of 6 factors). Depending on the level of engagement, the rate of weight loss over time differed for AU/NZ and UK/IE compared to the US. Our findings have important implications for the use and understanding of digital weight loss interventions worldwide. Future research should investigate the determinants of cross-country engagement differences and their long-term effects on intervention outcomes.

Adherence to healthy food choices during the COVID-19 pandemic in a U.S. population attempting to lose weight.

BACKGROUND AND AIMS: Food preferences are often modified in populations during stressful, unanticipated events. We examined how a U.S. population's food choices changed during the beginning of the COVID-19 stay-at-home orders, specifically during the spring of 2020. METHODS AND RESULTS: Daily dietary intake data from a digital behavior change weight loss program, which includes an interface for logging meals, beverages, and snacks, were analyzed to assess self-reported food choices from March 5-March 11, 2020 ("Start-COVID") and during the first week of the COVID-19 lockdown (March 12-March 18, 2020; "during-COVID"). The final sample consisted of 381,564 participants: 318,076 (83.4%) females, the majority who were aged 45-65 years (45.2%). Results indicate that self-reported servings of fresh fruit and vegetable intake decreased from start-to during-COVID, while intake of red meat and starchy vegetables increased. More men than women increased their intake of red meat and processed meat. Less overall change in fruit and vegetable consumption was seen in those 66 and older, compared to aged 18-35. Lean meat and starchy vegetable intake increased in older participants, but the change was negligible in younger subjects. More subjects aged 18-35 years reduced their intake of caffeine, desserts, lean meat, and salads compared to older participants. No changes were observed in snack or alcohol intake logged. CONCLUSION: This study revealed that particular food groups were altered according to age and gender during the first weeks of COVID lockdown. Understanding changes in food choices during a crisis may be useful for preparing supply chains and public health responses.

Psychosocial Characteristics by Weight Loss and Engagement in a Digital Intervention Supporting Self-Management of Weight.

There is substantial variability in weight loss outcomes. Psychosocial characteristics underlying outcomes require better understanding, particularly on self-managed digital programs. This cross-sectional study examines differences in psychosocial characteristics by weight loss and engagement outcome, and which characteristics are most associated with weight loss, on a self-managed digital weight loss program. Some underexplored psychosocial characteristics are included, such as flourishing, or a sense of meaning and purpose in life. A questionnaire was emailed to a random sample of 10,000 current users at week 5 in the program and 10,000 current users at week 17. The questionnaire was completed by 2225 users, and their self-reported weight and recorded program engagement data were extracted from the program's database. Multiple comparison tests indicated that mental health quality of life, depression, anxiety, work-life balance, and flourishing differed by weight loss outcome at program end (week 17; ≥5%, 2-5%, below 2%) and by engagement tertile at program beginning and end (weeks 5 and 17). Only anxiety was associated with weight loss in a backward stepwise regression controlling for engagement and sociodemographic characteristics. Flourishing did not predict weight loss overall but predicted the weight loss outcome group. Our findings have implications for creating more effective interventions for individuals based on psychosocial characteristics and highlight the potential importance of anxiety in underexplored self-managed digital programs.

Postpartum Weight Retention: A Retrospective Data Analysis Measuring Weight Loss and Program Engagement with a Mobile Health Program.

Background: Mobile health (mHealth) technology can circumvent barriers to participation in weight loss programs faced by new mothers. The objective of this study was to assess weight change and program engagement in postpartum women (n = 130) participating in a 24-week behavior change mHealth weight-loss intervention. Materials and Methods: Participants were recruited through a program offered on a commercial mHealth application that provided evidence-based lifestyle interventions. To meet inclusion criteria, women had to be 18-45 years of age, and given birth within 2 years before the start of the study. Participants signed up for the Noom Healthy Weight program between January and March of 2019 and were offered the program free of charge. Linear mixed models were conducted; the primary outcome was weight change from baseline at 16 and 24 weeks. Secondary outcomes were program engagement and their relationship with completion status. Results: Results showed that time was a significant predictor of weight at week 16 [t(-3.94) = -9.40; p < 0.001] and week 24 [t(-4.08) = -9.74; p < 0.001]; users lost 3.94 kgs at week 16 and 4.08 kgs at week 24, compared with baseline. In addition, body mass index significantly decreased at week 24 [t(112) = 7.33, p < 0.0001] with the majority of participants (80%) experiencing reductions by more than 2 units. On average, subjects who completed the program (completers) lost more weight compared with those who did not complete the program [t(-5.09) = -2.94; p = 0.004], losing 5.09 kgs (95% CI -8.48 to -1.69) throughout the 24 weeks. Conclusion: This cohort study shows that a uniquely mobile, behavior change intervention for weight management is effective at producing significant weight loss with potential to address postpartum weight retention.

Relationship Between Age and Weight Loss in Noom: Quasi-Experimental Study.

BACKGROUND: The prevalence of obesity and diabetes among middle-aged and older adults is on the rise, and with an increase in the world population of adults aged 60 years and older, the demand for health interventions across age groups is growing. Noom is an mHealth behavior change lifestyle intervention that provides users with tracking features for food and exercise logging and weighing-in as well as access to a virtual 1:1 behavior change coach, support group, and daily curriculum that includes diet-, exercise-, and psychology-based content. Limited research has observed the effect of age on a mobile health (mHealth) lifestyle intervention. OBJECTIVE: The goal of the research was to analyze engagement of middle-aged and older adults using a mobile lifestyle or diabetes prevention intervention. METHODS: A total of 14,767 adults (aged 35 to 85 years) received one of two curricula via an mHealth intervention in a quasi-experimental study: the Healthy Weight program (HW) by Noom (84%) or the Noom-developed Diabetes Prevention Program (DPP), recognized by the US Centers for Disease Control and Prevention (CDC). The main outcome measure was weight over time, observed at baseline and weeks 16 and 52. RESULTS: Linear mixed modeling found age to be a significant predictor of weight at week 16 (F2,1398.4=9.20; P<.001; baseline vs week 16: ß=-.12, 95% CI -0.18 to -0.07), suggesting that as age increases by 1 year, weight decreased by 0.12 kg. An interaction between engagement and age was also found at week 52 (F1,14680.51=6.70; P=.01) such that engagement was more strongly associated with weight for younger versus older adults (age × engagement: ß=.02, 95% CI 0.01 to 0.04). HW users lost 6.24 (SD 6.73) kg or 5.2% of their body weight and DPP users lost 5.66 (SD 7.16) kg or 8.1% of their body weight at week 52, meeting the CDC standards for weight loss effects on health. CONCLUSIONS: Age and engagement are significant predictors of weight. Older adults lost more weight using an mHealth evidence-based lifestyle intervention compared with younger adults, despite their engagement. These preliminary findings suggest further clinical implications for adapting the program to older adults' needs.

BRInging the Diabetes prevention program to GEriatric populations (BRIDGE): a feasibility study.

BACKGROUND: The purpose of this 6-week intervention was to test the feasibility and acceptability of implementing a telehealth-adapted Diabetes Prevention Program (DPP) at a senior center. METHODS: Older adults (n = 16) attended weekly interactive webinars. At each measurement time point, participants completed questionnaires covering lifestyle, physical activity, quality of life, and food records and wore physical activity trackers. Qualitative data were gathered from 2 focus groups inviting all 16 participants with 13 and 10 participants attending, respectively. RESULTS: Over 2000 senior center members were contacted, approximately 2% (n = 39) responded to the recruitment email, and 16 were recruited into the study. Retention was 75%, and attendance rates averaged 80% across the six intervention sessions. The focus group participants provided positive opinions for most program components, especially the webinar group interaction and using physical activity trackers. Suggestions for improvement included a greater focus on specific needs of older adults (i.e., adapting activities) and placing a greater emphasis on dietary strategies to prevent diabetes. Mean weight loss was 2.9% (2.7 kg [95% CI 1.6, 3.7]; p value = 0.001). CONCLUSION: The feasibility of providing DPP via webinar appears to be high based on the retention and attendance rates. Similar to other behavioral interventions engaging older adults, recruitment rates were low. Acceptability was evidenced by high attendance at the intervention sessions and feedback from participants during focus group sessions. The intervention efficacy should be evaluated based on CDC criteria for program recognition in a larger scale randomized trial. TRIAL REGISTRATION: NCT03524404. Registered 14 May 2018-retrospectively registered. Trial protocol will be provided by the corresponding author upon request.

Determinants of microvascular damage recovery after acute myocardial infarction: results from the acute myocardial infarction contrast imaging (AMICI) multi-centre study.

AIMS: Microvascular damage (MD) occurring soon after primary percutaneous coronary intervention (PPCI) may reverse or remain sustained within the first week after ST-elevation myocardial infarction (STEMI). We investigated the incidence, determinants, and long-term clinical relevance of MD reversal after PPCI. METHODS AND RESULTS: Serial two-dimensional echocardiograms (2DE) and a myocardial contrast study were obtained within 24 h of PPCI (T1) and at pre-discharge (T2) in 110 successfully re-perfused STEMI patients. Six months 2DE and 2-year clinical follow-up were obtained. After PPCI myocardial re-perfusion was normal at T1 only in 40 patients (36%, 'normal reflow'), recovered at T2 in 33 (30%, 'reversible MD'), and remained abnormal in 37 (34%, 'sustained MD'). At follow-up, normal reflow and reversible MD were coupled with a significant reduction in the infarct area, decrease in cardiac volumes, and a slight non-significant improvement in systolic function. Conversely, in the sustained MD group, the infarct area did not change and cardiac volumes significantly increased with a parallel worsening in systolic function. By multivariate analysis, independent predictors of reversible MD were: absence of family history of coronary artery disease (CAD), younger age, shorter time to re-perfusion, and absence of diabetes. The 2-year combined events rate was significantly lower in reversible MD (log-rank test P= 0.03) compared with sustained MD patients. CONCLUSIONS: In STEMI patients treated according to the current guidelines, MD frequently occurs soon after re-perfusion but it is reversible in ~50% of cases and it is associated with a favourable functional and clinical outcome. Family history of CAD, aging, time to re-perfusion, and diabetes are independent predictors of MD reversibility.

Adopting Cultivation to Remain Pastoralists: The Diversification of Maasai Livelihoods in Northern Tanzania.

Over the past four decades, Maasai pastoralists in Tanzania have adopted agriculture, integrating it with their traditional pastoralism. This livelihood diversification has complex origins and profound implications for Maasai social organization, culture, and demography, and ultimately for their health and well being and for the local and regional environment. In this paper, we examine the process by which this engagement with, and increasing dependence upon, agriculture came about in Ngorongoro District, northern Tanzania. The process there was more complex and influenced by a wider variety of factors than has been reported by previous descriptions of Maasai livelihood diversification. It generally involved two stages: planting a garden first, and later expanding the garden to a farm. We found that some households adopted cultivation out of necessity, but far more did so by choice. Among the latter, some adopted cultivation to reduce risk, while for others it was a reflection of changing cultural and social norms. Motivations for adopting cultivation differed among people of different wealth categories. Diversification was part of wider cultural changes, and was also influenced by power differentials among Maasai age sets and by government policies.