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

Comparison of concurrent, resistance, or aerobic training on body fat loss: a systematic review and meta-analysis

BACKGROUND

This systematic review and meta-analysis compared the differential effects of resistance training (RT), aerobic training (AT), and concurrent training (CT) on body mass and body fat loss in metabolically healthy individuals.

METHODS

A systematic search of PubMed, SportDiscus, and Web of Science databases for randomized controlled trials published between January 1980 and January 2023, comparing RT, AT, and CT in healthy adults was conducted. Primary outcomes of interest included changes in fat mass and body fat percentage; secondary outcomes were body mass and fat-free mass (FFM). Sub-analyses on intervention duration (< or ≥ 10 weeks), CT timing (aerobic and resistance exercises done on the same day versus different days within a week), and workload matching (equating workloads between AT, RT, and CT), were conducted. Study protocols followed PRISMA 2020 guidelines and were pre-registered on PROSPERO (CRD42023396530).

RESULTS

In total, 36 studies with 1564 participants were included in the systematic review, with only 31 studies included in the meta-analysis due to missing data. For studies lasting at least 10 weeks, AT outperformed RT in reducing body mass (mean difference (MD) = -1.82 kg [95% CI = -2.72 to -0.93]; p < 0.001) and fat mass (MD = -1.06 kg [95% CI = -1.88 to -0.24]; p = 0.01) but led to less FFM retention (MD = - 0.88 kg [95% CI = -1.73 to -0.03], p = 0.04). CT reduced significantly more fat mass compared to RT (MD: -1.09 kg [95% CI = -0.27 to -1.91]; p = 0.009). No significant differences were found between CT, AT, and RT in altering body fat percentage (p > 0.05). For studies shorter than 10 weeks, no significant differences were noted across exercise modalities (p > 0.05). Under conditions where AT, RT, and CT workloads were matched, similar fat mass, body mass, body fat percentage, and FFM changes were observed between exercise modalities (p > 0.05). Similar body mass and body fat percentage loss was observed between same-day and different-day CT (p > 0.05); body fat mass loss only differed in a single study (n = 1) when comparing RT to different-day CT (aerobic and resistance exercises done on different days within a week).

CONCLUSIONS

While there are no differences in percent body fat loss between exercise modes, AT and CT are more effective than RT alone in reducing absolute fat mass; however, RT neither improved nor impeded fat mass loss when incorporated into CT. Combining aerobic and resistance exercises on the same-day or different-day does not appear to influence the effectiveness of CT. When exercise interventions are short in duration (<10 weeks), there does not appear to be a difference in fat loss between exercise modalities. These results support the concurrent use of aerobic and resistance exercises for fat mass reduction, as well as an emphasis on workload and duration when programming exercise for fat loss.

Practical Awareness-Based Strategies for Eating (PASE): A Pilot and Feasibility Randomized Trial

Background

Few prior interventions for obesity have focused on reward-related eating. Researchers developed a mobile health mindfulness-based intervention, Eat Right Now (ERN), for improving reward related eating; ERN has not yet been feasibility tested as a weight loss intervention.

Objective

To obtain 6-month pilot and feasibility data in patients using the ERN intervention alone or with asynchronous coaching.

Methods

A 6-month, two-arm pilot and feasibility randomized trial was conducted involving 20 adults with a BMI ≥ 25 kg/m2 recruited from Kaiser Permanente Washington. Participants were randomized to ERN alone or ERN plus individualized coaching. Weight loss was assessed via a Wi-Fi-enabled digital scale.

Results

Among the 20 randomized participants, 17 (85%) started the intervention and remained enrolled in six months. Among these, 82% (14/17) had weight data captured by a Wi-Fi scale and 70.5% (12/17) completed follow-up questionnaires at the 6-month time point. The coached ERN participants had 4.3% weight loss (95% confidence interval (CI): 2.3%, 6.3%) and uncoached participants had 3.6% weight loss (95% CI: 1.2%, 5.9%). Participants in both approaches reported reductions in reward-related eating.

Conclusions

This pilot randomized trial of ERN demonstrates the feasibility and acceptability of the intervention for the majority of both the coached and uncoached groups. Further efforts are needed to ensure high retention and data capture in future studies.

Walking away from depression: the mediating role of walking activity in depression impacting blood glucose levels of people with diabetes or prediabetes

Introduction

Depression can exacerbate diabetes by impairing self-care behaviors and increasing the risk of complication; however, the underlying mechanism is still unclear. Given the suggested associations between walking activity, depression status, and blood glucose levels this study explores the intricate relationship between depression and blood glucose (BG) control, with a focus on walking activity as a behavioral mediator. The purpose of this study is to examine walking activity's mediating role in depression's impact on BG levels, investigating and validating the non-linear association between BG levels and walking activity. This retrospective real-world study demonstrates the potential of regular walking activity as a simple and accessible intervention to mitigate the negative effects of depression on BG levels in T2D and prediabetes.

Methods

A cohort of 989 users with T2D and prediabetes, who regularly tracked their steps levels and BG levels for 12 months using the Dario digital health platform was evaluated. The mediating role of the monthly average number of steps on the relationship between the self-reported depression status and lagged monthly average BG was assessed. Additionally, the association between monthly walking activity and monthly average BG was tested using a piecewise linear mixed effects model.

Results

Users with self-reported depression demonstrated increased BG levels compared to users without depression (B=8.00, P=.01). The association between depression and monthly average number of steps was significant (B=-.27, P<.005) and monthly average number of steps significantly predicted the following months' average BG (B=-.81, P=.001), adjusting for depression. The monthly average number of steps significantly mediated the effect of self-reported depression on the following month's average BG (M=.22, P<.005). Further sensitivity analysis demonstrated model robustness over various periods. Finally, non-linear dynamics of walking activity over time was validated using unseen data showing a decrease in monthly average BG for users with over an average of 400 steps per day (B=-1.87, P<.01).

Discussion

This study shows how regular walking may reduce the negative impact of depression on BG levels in people with T2D. Our findings advocate for the integration of walking activity into treatment protocols as a cost-effective, accessible intervention strategy to improve glycemic management and depressive symptoms in this population.

Feasibility and acceptability of an online multicomponent very low-carbohydrate intervention in young adult women with obesity: a pilot study

BACKGROUND

Approximately one-third of US young adults (18-25 years) have obesity, and there are calls to help young adults lose weight to prevent weight-related chronic conditions. This pilot trial tested the feasibility and acceptability of a very low-carbohydrate (VLC) eating pattern, with supportive positive affect and mindful eating skills, for weight management among young females with obesity.

METHODS

In a single-arm trial, women (N = 17), aged 19-23, with obesity participated in a 4-month diet and lifestyle intervention. Participants were taught how to follow a VLC eating pattern with the help of a coach and 16 weekly web-based sessions. We assessed feasibility and acceptability through session participation, outcome collection, intervention satisfaction, and adverse events.

RESULTS

Seventeen participants enrolled and 14 (82%) reported body weight at 4 months. Fifteen participants (94% of those beginning the intervention) viewed at least one session, and 8/15 (53%) of these participants were active in the intervention, viewing at least half of the sessions. Among the nine participants who provided 4-month self-report information, intervention satisfaction was high (mean 5.89/7, 95% CI 4.59 to 7.19). Among participants with a 4-month body weight, 7/14 (50%) lost ≥ 5% of their body weight, and of those who were also active in the intervention, 6/7 (86%) lost ≥ 5% of their body weight. There were no serious adverse events.

CONCLUSIONS

The results of this pilot study suggest that a VLC eating pattern may be a feasible and acceptable approach for weight loss in some young women with obesity.

TRIAL REGISTRATION

This trial was registered with ClinicalTrials.gov on August 18, 2021. The trial number is NCT05010083.

Quantity and specificity of action-plans as predictors of weight loss: analysis of data from the Norfolk Diabetes Prevention Study (NDPS)

OBJECTIVE

Investigate associations between quantity, content and specificity of action-plans and weight loss in a diabetes prevention study.

DESIGN

Prospective cohort study nested within a randomised controlled trial. Participants completed action-planning worksheets during intervention sessions.

MAIN OUTCOME MEASURES

Action-plans were coded in terms of: number of plans set, their content, and specificity. Multivariate regression analyses assessed associations with weight loss at four-months.

RESULTS

890 planning-worksheets from 106 participants were analysed. Participants wrote a mean of 2.12 (SD = 1.20) action-plans per worksheet, using a mean of 2.20 (SD = 0.68) specificity components per action-plan. Quantity of action-plans per worksheet decreased over time (r = -0.137, p < 0.001) and increased quantity was associated with reduced specificity [r = -.215, p < 0.001]. Walking (34.9% of action-plans) and reducing high fat/sugar snacks (26.1%) were the most commonly planned lifestyle actions. In multivariate modelling, increased quantity of action-plans was associated with greater weight loss (R2 = 0.135, Unstandardised Beta = 0.144, p = 0.002). Specificity was not significantly associated with weight-loss (p = 0.096).

CONCLUSION

Producing more action-plans was associated with greater weight loss. Further research should directly compare more versus less specific action-plans and explore ways to sustain engagement in action-planning. Our findings imply that participants should freely set numerous action-plans, rather than being encouraged to focus on specificity.

Supplemental data for this article is available online at https://doi.org/10.1080/08870446.2022.2055026 .

Utilization of smart devices and the evolution of customized healthcare services focusing on big data: a systematic review

Background

Currently, smart devices can prevent diseases by continuously collecting user information and providing health-related feedback. Smart devices big data provide personalized, faster, and more accurate health care. By examining existing studies, we suggest a new healthcare evolution and health promotion through information technology (IT) convergence. A big data systematic review examined the evolution of new health care and their potential for health promotion by monitoring physical activities, preventing diseases, and analyzing health data smart devices.

Methods

Therefore, this evaluates whether a new healthcare industry combining smart devices and big-data-based customized health care services can promote health. This study searched PubMed, Google Scholar, Scopus, and Research Information Sharing Service (RISS) for keywords related to big data, smart devices, healthcare, customized health services, health apps, and mobile health. This study comprised 43 of 453 publications from 2007 to 2023. Among them, a total of 43 articles were successfully completed in this study using the PRISMA flowchart in the final stage.

Results

Smart devices centered on big data enable personalized health care, and app technologies that promote well-being to prepare for aging society have many applications in clinical, prevention, public health, and rehabilitation settings. Smart devices and tailored healthcare services using big data to inform individuals about exercise, health status, diagnosis, and health information will expand into major sectors. By reviewing previous studies, the convergence of the IT technology field, which allows you to easily identify individual health and receive faster and more accurate medical services through customized health care services, has future-oriented values as, new health care services evolve. The systematic review of big data herein can monitor physical activity and prevent diseases using smart devices, thus promoting a healthy lifestyle.

Conclusions

Smart devices that analyze data to provide personal exercise and health conditions, checkups, and information, are making our lives easier. The information service using big data will continue to evolve into a personalized management service and provide basic healthcare data as it grows into an expected industry in the future.

Behavioral and Psychological Factors Affecting Weight Loss Success

PURPOSE OF REVIEW

There is a large variability between individuals in the weight loss response to any given diet treatment, which fuels interest into personalized or precision nutrition. Although most efforts are directed toward identifying biological or metabolic factors, several behavioral and psychological factors can also be responsible for some of this interindividual variability.

RECENT FINDINGS

There are many factors that can influence the response to dietary weight loss interventions, including factors related to eating behavior (emotional eating, disinhibition, restraint, perceived stress), behaviors and societal norms related to age and sex, psychological and personal factors (motivation, self-efficacy, locus of control, self-concept), and major life events. The success of a weight loss intervention can be influenced by many psychological and behavioral constructs and not merely by physiological factors such as biology and genetics. These factors are difficult to capture accurately and are often overlooked. Future weight loss studies should consider assessing such factors to better understand the underlying reasons for the large interindividual variability to weight loss therapy.

The Impact of a Digital Weight Loss Intervention on Health Care Resource Utilization and Costs Compared Between Users and Nonusers With Overweight and Obesity: Retrospective Analysis Study

BACKGROUND

The Noom Weight program is a smartphone-based weight management program that uses cognitive behavioral therapy techniques to motivate users to achieve weight loss through a comprehensive lifestyle intervention.

OBJECTIVE

This retrospective database analysis aimed to evaluate the impact of Noom Weight use on health care resource utilization (HRU) and health care costs among individuals with overweight and obesity.

METHODS

Electronic health record data, insurance claims data, and Noom Weight program data were used to conduct the analysis. The study included 43,047 Noom Weight users and 14,555 non-Noom Weight users aged between 18 and 80 years with a BMI of ≥25 kg/m² and residing in the United States. The index date was defined as the first day of a 3-month treatment window during which Noom Weight was used at least once per week on average. Inverse probability treatment weighting was used to balance sociodemographic covariates between the 2 cohorts. HRU and costs for inpatient visits, outpatient visits, telehealth visits, surgeries, and prescriptions were analyzed.

RESULTS

Within 12 months after the index date, Noom Weight users had less inpatient costs (mean difference [MD] -US $20.10, 95% CI -US $30.08 to -US $10.12), less outpatient costs (MD -US $124.33, 95% CI -US $159.76 to -US $88.89), less overall prescription costs (MD -US $313.82, 95% CI -US $565.42 to -US $62.21), and less overall health care costs (MD -US $450.39, 95% CI -US $706.28 to -US $194.50) per user than non-Noom Weight users. In terms of HRU, Noom Weight users had fewer inpatient visits (MD -0.03, 95% CI -0.04 to -0.03), fewer outpatient visits (MD -0.78, 95% CI -0.93 to -0.62), fewer surgeries (MD -0.01, 95% CI -0.01 to 0.00), and fewer prescriptions (MD -1.39, 95% CI -1.76 to -1.03) per user than non-Noom Weight users. Among a subset of individuals with 24-month follow-up data, Noom Weight users incurred lower overall prescription costs (MD -US $1139.52, 95% CI -US $1972.21 to -US $306.83) and lower overall health care costs (MD -US $1219.06, 95% CI -US $2061.56 to -US $376.55) per user than non-Noom Weight users. The key differences were associated with reduced prescription use.

CONCLUSIONS

Noom Weight use is associated with lower HRU and costs than non-Noom Weight use, with potential cost savings of up to US $1219.06 per user at 24 months after the index date. These findings suggest that Noom Weight could be a cost-effective weight management program for individuals with overweight and obesity. This study provides valuable evidence for health care providers and payers in evaluating the potential benefits of digital weight loss interventions such as Noom Weight.

Predicting weight loss success on a new Nordic diet: an untargeted multi-platform metabolomics and machine learning approach

Background and aim

Results from randomized controlled trials indicate that no single diet performs better than other for all people living with obesity. Regardless of the diet plan, there is always large inter-individual variability in weight changes, with some individuals losing weight and some not losing or even gaining weight. This raises the possibility that, for different individuals, the optimal diet for successful weight loss may differ. The current study utilized machine learning to build a predictive model for successful weight loss in subjects with overweight or obesity on a New Nordic Diet (NND).

Methods

Ninety-one subjects consumed an NND ad libitum for 26 weeks. Based on their weight loss, individuals were classified as responders (weight loss ≥5%, n = 46) or non-responders (weight loss <2%, n = 24). We used clinical baseline data combined with baseline urine and plasma untargeted metabolomics data from two different analytical platforms, resulting in a data set including 2,766 features, and employed symbolic regression (QLattice) to develop a predictive model for weight loss success.

Results

There were no differences in clinical parameters at baseline between responders and non-responders, except age (47 ± 13 vs. 39 ± 11 years, respectively, p = 0.009). The final predictive model for weight loss contained adipic acid and argininic acid from urine (both metabolites were found at lower levels in responders) and generalized from the training (AUC 0.88) to the test set (AUC 0.81). Responders were also able to maintain a weight loss of 4.3% in a 12 month follow-up period.

Conclusion

We identified a model containing two metabolites that were able to predict the likelihood of achieving a clinically significant weight loss on an ad libitum NND. This work demonstrates that models based on an untargeted multi-platform metabolomics approach can be used to optimize precision dietary treatment for obesity.

Factors influencing adults to drop out of intensive lifestyle interventions for weight loss

Reducing ≥5% of body weight can decrease the risk of developing chronic diseases in adults with excess weight. Although Intensive Lifestyle Interventions (ILIs) that include cognitive-behavioral techniques to improve physical activity and eating habits are the best approach for losing weight, the failure to retain participants is a barrier to their successful implementation. We aimed to investigate the factors influencing adults to drop out of ILIs for weight loss at six months. We conducted retrospective multiple logistic regression analysis of 268 participants with excess weight (body mass index ≥ 25 kg/m2) from a multicenter study (n = 237, in-person ILI in five clinics, delivered by nutrition interns), and a randomized controlled trial (n = 31, one online ILI, delivered by a master's degree student). The same research team conducted both studies in Northern Mexico, using the same intervention components, and identical instruments and techniques to collect the data. We found that older participants (≥50 years) were less likely to drop out of the ILI for weight loss compared to participants <35 years old (OR = 0.34, 95% CI = 0.16-0.70). For each unit increase in the bodily pain scale of the SF-36 (less perceived pain), the risk of dropping out decreased by 2% (OR = 0.98, 95% CI = 0.97, 0.996), while a change in the interventionist during the 6-month intervention more than doubled the risk of dropping out (OR 2.25, 95% CI = 1.23-4.14). Retention in ILIs may be improved by ensuring that the same interventionist remains during the six-month intervention. In addition, ILIs may need further tailoring for younger ages and for participants with higher perceived pain.

The path to healthy ageing in China: a Peking University-Lancet Commission

Around the world, populations are ageing at a faster pace than in the past and this demographic transition will have impacts on all aspects of societies. In May 2020, the UN General Assembly declared 2021–2030 the Decade of Healthy Ageing, highlighting the importance for policymakers across the world to focus policy on improving the lives of older people, both today and in the future. While rapid population ageing poses challenges, China’s rapid economic growth over the last forty years has created space for policy to assist older persons and families in their efforts to improve health and well-being at older ages. As China is home to 1/5 of the world’s older people, China is often held up as an example for other middle-income countries. This Commission Report aims to help readers to understand the process of healthy ageing in China as a means of drawing lessons from the China experience. In addition, with the purpose of informing the ongoing policy dialogue within China, the Commission Report highlights the policy challenges on the horizon and draws lessons from international experience.

Goal language is associated with attrition and weight loss on a digital program: Observational study

Behavioral weight loss reduces risk of weight-related health complications. Outcomes of behavioral weight loss programs include attrition and weight loss. There is reason to believe that individuals' written language on a weight management program may be associated with outcomes. Exploring associations between written language and these outcomes could potentially inform future efforts towards real-time automated identification of moments or individuals at high risk of suboptimal outcomes. Thus, in the first study of its kind, we explored whether individuals' written language in actual use of a program (i.e., outside of a controlled trial) is associated with attrition and weight loss. We examined two types of language: goal setting (i.e., language used in setting a goal at the start of the program) and goal striving (i.e., language used in conversations with a coach about the process of striving for goals) and whether they are associated with attrition and weight loss on a mobile weight management program. We used the most established automated text analysis program, Linguistic Inquiry Word Count (LIWC), to retrospectively analyze transcripts extracted from the program database. The strongest effects emerged for goal striving language. In striving for goals, psychologically distanced language was associated with more weight loss and less attrition, while psychologically immediate language was associated with less weight loss and higher attrition. Our results highlight the potential importance of distanced and immediate language in understanding outcomes like attrition and weight loss. These results, generated from real-world language, attrition, and weight loss (i.e., from individuals' natural usage of the program), have important implications for how future work can better understand outcomes, especially in real-world settings.

Engagement in Digital Health App-Based Prevention Programs Is Associated With Weight Loss Among Adults Age 65+

Background

Digital health programs have been shown to be feasible and effective for the prevention of chronic diseases such as diabetes. Contrary to expectations, findings also suggest that older adults have higher levels of engagement with digital health programs than younger adults. However, there is a paucity of research examining outcomes among older adults in digital health programs and whether higher engagement is related to better outcomes.

Methods

We examined weight loss outcomes for 538 users aged 65 and older participating in one of two app-based prevention programs called the Diabetes Prevention Program and the Prevention Program, respectively. Both programs were available on a single artificial intelligence (AI)-powered digital health platform and shared a common goal of weight loss. We also examined the relationship between key engagement metrics (i.e., conversing with the AI-powered coach, weigh-ins, and initiating educational lessons early in the program) and weight loss outcomes.

Results

The average weight loss of all enrollees having a weight measurement after after the 9th week was 4.51%, and the average weight loss of the Diabetes Prevention Program enrollees meeting a minimum engagement level was 8.56%. Greater weight loss was associated with a greater number of days with AI-powered coaching conversations (p = 0.03), more weigh-ins (p = 0.00), and early educational lesson initiation (p = 0.02).

Conclusions

Digital health programs powered by AI offer a promising solution for health management among older adults. The results show positive health outcomes using app-based prevention programs, and all three engagement metrics were independently associated with weight loss.

Development of a computer-aided text message platform for user engagement with a digital Diabetes Prevention Program: a case study

Digital Diabetes Prevention Programs (dDPP) are novel mHealth applications that leverage digital features such as tracking and messaging to support behavior change for diabetes prevention. Despite their clinical effectiveness, long-term engagement to these programs remains a challenge, creating barriers to adherence and meaningful health outcomes. We partnered with a dDPP vendor to develop a personalized automatic message system (PAMS) to promote user engagement to the dDPP platform by sending messages on behalf of their primary care provider. PAMS innovates by integrating into clinical workflows. User-centered design (UCD) methodologies in the form of iterative cycles of focus groups, user interviews, design workshops, and other core UCD activities were utilized to defined PAMS requirements. PAMS uses computational tools to deliver theory-based, automated, tailored messages, and content to support patient use of dDPP. In this article, we discuss the design and development of our system, including key requirements and features, the technical architecture and build, and preliminary user testing.

Retention, Fasting Patterns, and Weight Loss With an Intermittent Fasting App: A Large-scale, 52-week Observational Study (Preprint)

Background

Intermittent fasting (IF) is an increasingly popular approach to dietary control that focuses on the timing of eating rather than the quantity and content of caloric intake. IF practitioners typically seek to improve their weight and other health factors. Millions of practitioners have turned to purpose-built mobile apps to help them track and adhere to their fasts and monitor changes in their weight and other biometrics.

Objective

This study aimed to quantify user retention, fasting patterns, and weight loss by users of 2 IF mobile apps. We also sought to describe and model starting BMI, amount of fasting, frequency of weight tracking, and other demographics as correlates of retention and weight change.

Methods

We assembled height, weight, fasting, and demographic data of adult users (ages 18-100 years) of the LIFE Fasting Tracker and LIFE Extend apps from 2018 to 2020. Retention for up to 52 weeks was quantified based on recorded fasts and correlated with user demographics. Users who provided height and at least 2 readings of weight and whose first fast and weight records were contemporaneous were included in the weight loss analysis. Fasting was quantified as extended fasting hours (EFH; hours beyond 12 in a fast) averaged per day (EFH per day). Retention was modeled using a Cox proportional hazards regression. Weight loss was analyzed using linear regression.

Results

A total of 792,692 users were followed for retention based on 26 million recorded fasts. Of these, 132,775 (16.7%) users were retained at 13 weeks, 54,881 (6.9%) at 26 weeks, and 16,478 (2.1%) at 52 weeks, allowing 4 consecutive weeks of inactivity. The survival analysis using Cox regression indicated that retention was positively associated with age and exercise and negatively associated with stress and smoking. Weight loss in the qualifying cohort (n=161,346) was strongly correlated with starting BMI and EFH per day, which displayed a positive interaction. Users with a BMI ≥40 kg/m² lost 13.9% of their starting weight by 52 weeks versus a slight weight gain on average for users with starting BMI <23 kg/m². EFH per day was an approximately linear predictor of weight loss. By week 26, users lost over 1% of their starting weight per EFH per day on average. The regression analysis using all variables was highly predictive of weight change at 26 weeks (R²=0.334) with starting BMI and EFH per day as the most significant predictors.

Conclusions

IF with LIFE mobile apps appears to be a sustainable approach to weight reduction in the overweight and obese population. Healthy weight and underweight individuals do not lose much weight on average, even with extensive fasting. Users who are obese lose substantial weight over time, with more weight loss in those who fast more.

Body Positivity and Self-Compassion on a Publicly Available Behavior Change Weight Management Program

According to recent research, body positivity and self-compassion are key outcomes that are tied to better psychological and physical health. To date, it is unclear whether body positivity and self-compassion improve, stay constant, or deteriorate over the course of a weight management program, particularly one that addresses the psychological roots of behavior change. Additionally, beyond controlled settings, there are no studies on body positivity and self-compassion in individuals who choose to join a commercial weight management program. Therefore, this single-arm prospective study examined changes in body positivity and self-compassion from baseline to the 16 week milestone of Noom Weight, a commercial behavior change weight management program informed by acceptance and commitment therapy (ACT), dialectical behavior therapy (DBT), and cognitive behavioral therapy (CBT). We also examined how baseline and over-time changes in body positivity and self-compassion predicted engagement in program-measured relevant behaviors (e.g., exercises logged). Participants were a random subset of individuals who had recently self-enrolled in the program (n = 133). Body positivity and self-compassion were measured via survey at baseline and end of the core program (16 weeks). Self-reported weight and program-recorded engagement were extracted from the program database. Compared to baseline, body appreciation, body image flexibility, self-compassion, and body-focused rumination significantly improved at 16 weeks (all ps < 0.007). Participants lost a statistically significant amount of weight (3.9 kg; t(128)) = 10.64, p < 0.001) by 16 weeks, which was 4.4% body weight. Greater engagement, especially messaging a coach, reading articles, and logging meals, was associated with improvements over time in body appreciation (r = 0.17, p = 0.04), body image flexibility (r = −0.23, p = 0.007), and the brooding component of rumination (r = −0.23, p = 0.007). Greater engagement was also associated with baseline total self-compassion (r = 0.19, p = 0.03) and self-judgment (r = 0.24, p = 0.006). The results suggest that individuals experience improvements in body positivity and self-compassion while learning about ACT, DBT, and CBT through curriculum and coaching in this setting. The results also have important clinical implications, such as the possibility that psychologically-oriented (i.e., ACT, DBT, and CBT-based) weight management could be important to improve body positivity or that baseline self-compassion could be used to target individuals at risk for lower engagement. Future work should investigate these possibilities as well as delineate the causal relationships between body positivity, self-compassion, engagement, and 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.

The Comparison of the Effects between Continuous and Intermittent Energy Restriction in Short-Term Bodyweight Loss for Sedentary Population: A Randomized, Double-Blind, Controlled Trial

Objective: To compare the effects of continuous energy restriction (CER) and intermittent energy restriction (IER) in bodyweight loss plan in sedentary individuals with normal bodyweight and explore the influence factors of effect and individual retention. Methods: 26 participants were recruited in this randomized controlled and double-blinded trial and allocated to CER and IER groups. Bodyweight (BW), body mass index (BMI), and resting metabolic rate (RMR) would be collected before and after a 4-week (28 days) plan which included energy restriction (CER or IER) and moderate-intensity exercise. Daily intake of three major nutrients (protein, carbohydrate, fat) and calories were recorded. Results: A significant decrease in BW and BMI were reported within each group. No statistically significant difference in the change of RMR in CERG. No statistically significant difference was reported in the effect between groups, neither as well the intake of total calories, three major nutrients, and individual plan retention. The influence factors of IER and CER are different. Conclusion: Both CER and IER are effective and safe energy restriction strategies in the short term. Daily energy intake and physical exercise are important to both IER and CER.

Comparing Outcomes of a Digital Commercial Weight Loss Program in Adult Cancer Survivors and Matched Controls with Overweight or Obesity: Retrospective Analysis

Maintaining a healthy weight is beneficial for cancer survivors. However, weight loss program effectiveness studies have primarily been in highly controlled settings. This is a retrospective study exploring real-world outcomes (weight loss and program engagement) after use of a digital commercial weight loss program (Noom) in cancer survivors and matched controls. All participants had voluntarily self-enrolled in Noom. Weight and engagement data were extracted from the program. Cancer-related quality of life was secondarily assessed in a one-time cross-sectional survey for survivors. Controls were a sample of Noom users with overweight/obesity who had no history of cancer but 0–1 chronic conditions. Primary outcomes were weight change at 16 weeks and program engagement over 16 weeks. Engagement included frequency of weight, food, and physical activity logging, as well as number of coach messages. Multiple regression controlling for baseline age, gender, engagement, and BMI showed that survivors lost less weight than controls (B = −2.40, s.e. = 0.97, p = 0.01). Survivors also weighed in less (survivors: 5.4 [2.3]; controls: 5.7 [2.1], p = 0.01) and exercised less (survivors: 1.8 [3.2]; controls: 3.2 [4.1], p < 0.001) than controls. However, survivors sent more coach messages (survivors: 2.1 [2.4]; controls: 1.7 [2.0], p < 0.001). Despite controls losing more weight than cancer survivors (−7.0 kg vs. −5.3 kg), survivors lost significant weight in 4 months (M = −6.2%). Cancer survivors can have success on digital commercial programs available outside of a clinical trial. However, they may require additional support to engage in weight management behaviors.

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.

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.

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.

Role of Digital Engagement in Diabetes Care Beyond Measurement: Retrospective Cohort Study

BACKGROUND

The use of remote data capture for monitoring blood glucose and supporting digital apps is becoming the norm in diabetes care. One common goal of such apps is to increase user awareness and engagement with their day-to-day health-related behaviors (digital engagement) in order to improve diabetes outcomes. However, we lack a deep understanding of the complicated association between digital engagement and diabetes outcomes.

OBJECTIVE

This study investigated the association between digital engagement (operationalized as tagging of behaviors alongside glucose measurements) and the monthly average blood glucose level in persons with type 2 diabetes during the first year of managing their diabetes with a digital chronic disease management platform. We hypothesize that during the first 6 months, blood glucose levels will drop faster and further in patients with increased digital engagement and that difference in outcomes will persist for the remainder of the year. Finally, we hypothesize that disaggregated between- and within-person variabilities in digital engagement will predict individual-level changes in blood glucose levels.

METHODS

This retrospective real-world analysis followed 998 people with type 2 diabetes who regularly tracked their blood glucose levels with the Dario digital therapeutics platform for chronic diseases. Subjects included "nontaggers" (users who rarely or never used app features to notice and track mealtime, food, exercise, mood, and location, n=585) and "taggers" (users who used these features, n=413) representing increased digital engagement. Within- and between-person variabilities in tagging behavior were disaggregated to reveal the association between tagging behavior and blood glucose levels. The associations between an individual's tagging behavior in a given month and the monthly average blood glucose level in the following month were analyzed for quasicausal effects. A generalized mixed piecewise statistical framework was applied throughout.

RESULTS

Analysis revealed significant improvement in the monthly average blood glucose level during the first 6 months (t=-10.01, P<.001), which was maintained during the following 6 months (t=-1.54, P=.12). Moreover, taggers demonstrated a significantly steeper improvement in the initial period relative to nontaggers (t=2.15, P=.03). Additional findings included a within-user quasicausal nonlinear link between tagging behavior and glucose control improvement with a 1-month lag. More specifically, increased tagging behavior in any given month resulted in a 43% improvement in glucose levels in the next month up to a person-specific average in tagging intensity (t=-11.02, P<.001). Above that within-person mean level of digital engagement, glucose levels remained stable but did not show additional improvement with increased tagging (t=0.82, P=.41). When assessed alongside within-person effects, between-person changes in tagging behavior were not associated with changes in monthly average glucose levels (t=1.30, P=.20).

CONCLUSIONS

This study sheds light on the source of the association between user engagement with a diabetes tracking app and the clinical condition, highlighting the importance of within-person changes versus between-person differences. Our findings underscore the need for and provide a basis for a personalized approach to digital health.

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.