Factors associated with early dropout in an employer-based commercial weight-loss program

Adherence and retention to the self-managed community-based Step Into Health program in Qatar (2012-2019)

Purpose

Investigate adherence and retention to the "Step Into Health (SIH)" initiative (www.stepintohealth.qa [website access only available from within the State of Qatar]), a Qatari self-managed community-based health program, from 2012 to 2019.

Methods

Participants (16,711; 16-80 years; 37% females, 34% Qatari) used a pedometer or smartphone application (app) to measure step count. Absolute adherence (ADH) and retention (RET) were calculated, with ADH (%) the ratio between number of days data and SIH enrollment length (RET). Linear Mixed Models identified differences in ADH between RET groups, main effects (i.e., sex, device, age, BMI, nationality) and interaction effects for ADH (RET entered as a covariate).

Results

Average ADH and RET to SIH (irrespective of sex, age, device and BMI) was 50% (±31%), and 16% (±20%), respectively, with ADH differing significantly between RET groups (F = 460.2, p < 0.001). RET (as a covariate) revealed a significant main effect for device (F = 12.00, p < 0.001) and age (F = 4.31, p = 0.001) on ADH observed. There was a significant association between RET and sex (p < 0.001), device (p < 0.001), and age groups 16-25 y (p < 0.001), and 26-35 y (p < 0.001). There were no significant main effects for sex or BMI on ADH, and no interaction effects (p ≥ 0.21) observed.

Conclusions

Follow-up data (e.g., interviews, focus groups, etc.) determining why differences in ADH and RET are observed appears prudent. To convert those that lapsed and/or abandoned SIH/PA into committed long-term PA adherers. This would be a first step to develop targeted public health promotions and initiatives to enhance health outcomes at a population level.

Obesity treatment initiation, retention, and outcomes in the Veterans Affairs MOVE! Program among rural and urban veterans

Objective

Rural veterans have high obesity rates. Yet, little is known about this population's engagement with the Veterans Affairs (VA) weight management program (MOVE!). The study objective is to determine whether MOVE! enrollment, anti-obesity medication use, bariatric surgery use, retention, and outcomes differ by rurality for veterans with severe obesity.

Methods

This is a retrospective cohort study using Veterans Health Administration patient databases, including VA patients with severe obesity during 2015-2017. Patients were categorized using Rural-Urban Commuting Area codes. Primary outcomes included proportion of patients and risk-adjusted likelihood of initiating VA MOVE!, anti-obesity medication, or bariatric surgery and risk-adjusted highly rural|Hazard Ratio (HR) of any obesity treatment. Secondary outcomes included treatment retention (≥12 weeks) and successful weight loss (5%) among patients initiating MOVE!, and risk-adjusted odds of retention and successful weight loss.

Results

Among 640,555 eligible veterans, risk-adjusted relative likelihood of MOVE! treatment was significantly lower for rural and HR veterans (HR = 0.83, HR = 0.67, respectively). Initiation rates of anti-obesity medication use were significantly lower as well, whereas bariatric surgery rates, retention, and successful weight loss did not differ.

Conclusions

Overall treatment rates with MOVE!, bariatric surgery, and anti-obesity medications remain low. Rural veterans are less likely to enroll in MOVE! and less likely to receive anti-obesity medications than urban veterans.

Weight Loss Trajectories and Related Factors in a 16-Week Mobile Obesity Intervention Program: Retrospective Observational Study

Background

In obesity management, whether patients lose ≥5% of their initial weight is a critical factor in clinical outcomes. However, evaluations that take only this approach are unable to identify and distinguish between individuals whose weight changes vary and those who steadily lose weight. Evaluation of weight loss considering the volatility of weight changes through a mobile-based intervention for obesity can facilitate understanding of an individual’s behavior and weight changes from a longitudinal perspective.

Objective

The aim of this study is to use a machine learning approach to examine weight loss trajectories and explore factors related to behavioral and app use characteristics that induce weight loss.

Methods

We used the lifelog data of 13,140 individuals enrolled in a 16-week obesity management program on the health care app Noom in the United States from August 8, 2013, to August 8, 2019. We performed k-means clustering with dynamic time warping to cluster the weight loss time series and inspected the quality of clusters with the total sum of distance within the clusters. To identify use factors determining clustering assignment, we longitudinally compared weekly use statistics with effect size on a weekly basis.

Results

The initial average BMI value for the participants was 33.6 (SD 5.9) kg/m², and it ultimately reached 31.6 (SD 5.7) kg/m². Using the weight log data, we identified five clusters: cluster 1 (sharp decrease) showed the highest proportion of participants who reduced their weight by >5% (7296/11,295, 64.59%), followed by cluster 2 (moderate decrease). In each comparison between clusters 1 and 3 (yo-yo) and clusters 2 and 3, although the effect size of the difference in average meal record adherence and average weight record adherence was not significant in the first week, it peaked within the initial 8 weeks (Cohen d>0.35) and decreased after that.

Conclusions

Using a machine learning approach and clustering shape-based time series similarities, we identified 5 weight loss trajectories in a mobile weight management app. Overall adherence and early adherence related to self-monitoring emerged as potential predictors of these trajectories.

Team members influence retention in a First Peoples’ community-based weight-loss program

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Program completion is strongly influenced by teammate completion.

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Other factors influencing program completion vary with participation occasion.

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Previous participation and health behaviors do not influence subsequent completion.

The aim of this study was to evaluate program retention factors in a repeated team-based weight-loss and healthy lifestyle program for Aboriginal and Torres Strait Islander Peoples. Data comprised 3107 participants in 10 Aboriginal Knockout Health Challenge contests. Multiple variable and bivariate analyses compared age, gender, self-reported behaviors (physical activity and fruit and vegetable consumption) and objectively measured weight between completers and non-completers. First-time participants (n = 3107) who completed were more likely to be female, be older, weigh less and have more completing members in their team; only the number of team members completing was significant among participants (n = 1245) who took part in a second contest participation. Multivariate results were similar, with a participant’s odds of completing on their first and second participation occasion increasing by 1.16 and 1.18, respectively, with every teammate completed. Given that the strongest effect centered on a social factor, this highlights the importance of having community-driven design and the benefits of a group-based approach to engage and maintain First Peoples’ engagement in preventive health programs. Further, by identifying a change in factors associated with retention in successive weight-loss attempts, this study improves understanding of retention in weight-loss programs more generally.

How do overweight people dropout of a weight loss diet? A qualitative study

Background

The growing trend of overweight and obesity in many developed and developing countries in recent years has made obesity one of the most significant health problems in the world. The treatment of overweight and obese people is challenging, as patients have difficulty adhering to a weight-loss diet. Thus, the present study aimed to identify the reasons for the dropout of weight-loss diets.

Methods

This qualitative study using content analysis was conducted in a comprehensive health center in Shiraz, southern Iran, between April and October 2020. The study was performed on 27 participants with a history of obesity and diet dropout selected via purposive and theoretical sampling. The data were gathered through semi-structured interviews and were thematically analyzed.

Results

The participants included 25 females (92.6%) and two males (7.4%) with a mean age of 33.4 ± 8.4 years. Data analysis resulted in the emergence of three themes and 14 sub-themes. The first theme was personal reasons for diet dropout, which included six sub-themes; i.e., misunderstanding of diet, not having enough motivation, stress and hormonal disorder, having the feel of “being harmful to health”, lack of mental and psychological preparation, and personal taste. The second theme was familial and social reasons for diet dropout, including two sub-themes, i.e., social and familial problems. Finally, the third theme was the reasons related to diet characteristics, including six sub-themes: ineffectiveness of diet, expensiveness of diet food and dietary supplements, family problems, unavailability of food, unscientific and unconventional diets feeling bad about the diet, and unpalatable diet food. All the concepts were related to each other and resulted in a pattern revealing the experiences of overweight people and who had dropped out of weight-loss diets.

Conclusion

The reasons for diet dropout were divided into three levels: personal reasons, familial and social reasons, and diet characteristics. Overall, clinicians should pay attention to the complexity of diets to increase the success rate of weight management. Based on the current study findings, a guideline is recommended to guide patients who dropout of weight-loss diets.

Complementary and Alternative Therapies for Weight Loss: A Narrative Review

Despite various strategies, overweight and obesity problems are still increasingly prevalent worldwide with serious health outcomes. Consequently, the continuous demand for more effective, safe and acceptable therapies for reducing body weight is also escalating—including complementary and alternative therapies (CATs). The aim of this review is to provide a summary of the most commonly- and recently-used CATs, with evaluation of their safety and efficacy for weight loss. Electronic scientific databases such as Scopus, PubMed and EBSCO Host were explored for articles that reported CATs for overweight and obesity treatment from 2015 to December 2019. Only systematic reviews, meta-analysis and randomized controlled trials (RCTs) published in English were included. Studies whereby CATs were not utilized for reducing body weight were excluded. Eight systematic reviews and meta-analyses and 11 additional RCTs with 765 participants (50.2% overweight and 49.8% obese) related to hypnotherapy, acupuncture and dietary supplements met the inclusion criteria. Their results suggested that spirulina, chitosan, probiotic, EPA + DHA, vitamin D, fiber, and herbal extract supplementation may all provide small reductions in body weight (ranging from 1-10 kg). Interestingly, hypnotherapy and acupuncture reported significantly greater reduction in body weight compared with placebo (p < 0.001 and p < 0.0001, respectively). Nonetheless, the evidence is still relatively limited and not encouraging to provide a definitive conclusion due to the methodological shortcomings and the presence of adverse events in chitosan and fiber supplementation. Hence, studies of this nature need to be further replicated and improved to corroborate the efficacy and safety of the CATs to combat weight issues.

Interpretable Conditional Recurrent Neural Network for Weight Change Prediction: Algorithm Development and Validation Study

Background

In recent years, mobile-based interventions have received more attention as an alternative to on-site obesity management. Despite increased mobile interventions for obesity, there are lost opportunities to achieve better outcomes due to the lack of a predictive model using current existing longitudinal and cross-sectional health data. Noom (Noom Inc) is a mobile app that provides various lifestyle-related logs including food logging, exercise logging, and weight logging.

Objective

The aim of this study was to develop a weight change predictive model using an interpretable artificial intelligence algorithm for mobile-based interventions and to explore contributing factors to weight loss.

Methods

Lifelog mobile app (Noom) user data of individuals who used the weight loss program for 16 weeks in the United States were used to develop an interpretable recurrent neural network algorithm for weight prediction that considers both time-variant and time-fixed variables. From a total of 93,696 users in the coaching program, we excluded users who did not take part in the 16-week weight loss program or who were not overweight or obese or had not entered weight or meal records for the entire 16-week program. This interpretable model was trained and validated with 5-fold cross-validation (training set: 70%; testing: 30%) using the lifelog data. Mean absolute percentage error between actual weight loss and predicted weight was used to measure model performance. To better understand the behavior factors contributing to weight loss or gain, we calculated contribution coefficients in test sets.

Results

A total of 17,867 users’ data were included in the analysis. The overall mean absolute percentage error of the model was 3.50%, and the error of the model declined from 3.78% to 3.45% by the end of the program. The time-level attention weighting was shown to be equally distributed at 0.0625 each week, but this gradually decreased (from 0.0626 to 0.0624) as it approached 16 weeks. Factors such as usage pattern, weight input frequency, meal input adherence, exercise, and sharp decreases in weight trajectories had negative contribution coefficients of –0.021, –0.032, –0.015, and –0.066, respectively. For time-fixed variables, being male had a contribution coefficient of –0.091.

Conclusions

An interpretable algorithm, with both time-variant and time-fixed data, was used to precisely predict weight loss while preserving model transparency. This week-to-week prediction model is expected to improve weight loss and provide a global explanation of contributing factors, leading to better outcomes.

Caring for US Children: Barriers to Effective Treatment in Children with the Disease of Obesity

In 2020, impediments to pediatric obesity (PO) treatment remain pervasive, even though these barriers are clearly documented in medical literature. Providers must invest considerable resources to overcome these barriers to care. Notable barriers include gaps in medical education, misperceptions of the disease, weight bias and stigma, exclusion of coverage in health plans, and thus an unsustainable financial framework. Hence, this review offers an updated social-ecological framework of accessibility to care, wherein each barrier to care or variable is interdependent on the other and each is critical to creating forward momentum. The sum of all these variables is instrumental to overall smooth function, configured as a wheel. To treat PO effectively, all variables must be adequately addressed by stakeholders throughout the health care system in order to holistically comprehend and appreciate undertakings to advance the burgeoning field of PO medicine.

A Diet for Healthy Weight: Why Reaching a Consensus Seems Difficult

Overweight and obesity are global health problems that contribute to the rising prevalence of non-communicable diseases, such as type 2 diabetes, heart disease, and certain cancers. The World Health Organization recognizes obesity as a primarily diet-induced, preventable condition, yet losing weight or keeping weight loss permanent is a universal challenge. In the U.S., formal dietary guidelines have existed since 1980. Over the same time-period, the incidence of obesity has skyrocketed. Here, we present our perspective on why current dietary guidelines are not always supported by a robust body of scientific data and emphasize the critical need for accelerated nutrition research funding. A clear understanding of the interaction of dietary patterns with system-level biological changes in a precise, response-specific manner can help inform evidence-based nutrition education, policy, and practice.

Interest, Resources, and Preferences for Weight Loss Programs among Primary Care Patients with Obesity

OBJECTIVE

To examine interest, resources, and preferences for weight loss programs among primary care patients with obesity.

METHODS

Primary care patients having a BMI ≥30 kg/m² were recruited in the summer and fall of 2018. Eligible patients were invited to complete an anonymous survey assessing sociodemographic factors, interest in weight loss, resources, and preferences for weight loss programs. Descriptive statistics were used for analysis.

RESULTS

A total of 77 patients completed the anonymous survey. Nearly 90% of patients were interested in participating in a weight loss program and reported having a smartphone. Approximately 80% had high-speed internet and a device with videoconferencing capabilities, whereas only 40% had a tablet or laptop computer. On average, patients preferred weight loss programs delivered in-person and led by a nutritionist or personal trainer. Patients' top three preferences for weight loss content included goal setting, staying motivated, and finding ways to be more active.

CONCLUSIONS

Although primary care patients with obesity were interested in weight loss programs, availability of resources and preferred program characteristics varied.

PRACTICE IMPLICATIONS

This study provides insight on patient interest, resources, and preferences for weight loss programs that may help guide the development of future programs.

Association of the Gut Microbiota with Weight-Loss Response within a Retail Weight-Management Program

Retail programs offer popular weight-loss options amid the ongoing obesity crisis. However, research on weight-loss outcomes within such programs is limited. This prospective-cohort observational study enrolled 58 men and women between ages 20 and 72 years from a retail program to assess the influence of client features on energy-restriction induced weight-loss response. DESeq2 in R-studio, a linear regression model adjusting for significantly correlating covariates, and Wilcoxon signed-rank and Kruskal–Wallis for within- and between-group differences, respectively, were used for data analyses. An average 10% (~10 kg) reduction in baseline-weight along with lower total-, android-, gynoid-, and android:gynoid-fat were observed at Week 12 (all, p < 0.05). Fifty percent of participants experienced a higher response, losing an average of 14.5 kg compared to 5.9 kg in the remaining low-response group (p < 0.0001). Hemoglobin-A1C (p = 0.005) and heart rate (p = 0.079) reduced in the high-response group only. Fat mass and A1C correlated when individuals had high android:gynoid fat (r = 0.55, p = 0.008). Gut-microbial β-diversity was associated with BMI, body fat%, and android-fat (all, p < 0.05). Microbiota of the high-response group had a higher baseline OTU-richness (p = 0.02) as well as differential abundance and/or associations with B. eggerthi, A. muciniphila, Turicibacter, Prevotella, and Christensenella (all, p/p_adj < 0.005). These results show that intestinal microbiota as well as sex and body composition differences may contribute to variable weight-loss response. This highlights the importance of various client features in the context of real-world weight control efforts.

Role of Commercial Weight-Loss Programs in Medical Management of Obesity

Rates of obesity counseling are low among physicians because of the lack of time and training in this area. In recognition of this challenge, recent national guidelines encourage physicians to refer patients with obesity to intensive, comprehensive lifestyle programs to lose weight. Some commercial weight-loss programs meet these criteria, and this article reviews the evidence from randomized controlled trials regarding such programs' weight-loss efficacy and safety as well as glycemic outcomes among patients with and without diabetes mellitus. A discussion of how physicians might approach the referral process and continued management of patients participating in these programs is included.

Influence of subsidies and promotional strategies on outcomes in a beneficiary-based commercial weight-loss programme

To determine whether initial engagement, continued participation, and weight loss vary by subsidy and promotional strategies in a beneficiary-based, commercial weight-loss programme. We conducted a retrospective analysis of data from 2013 to 2016. Our dependent variables included initial engagement (≥1 calls; ≥2 weights), coach calls and weight change. Our independent variables were subsidy strategy (total subsidy (n = 9) vs cost sharing (n = 3)) and combination of promotional-subsidy strategies (mixed campaign + total subsidy (n = 6) vs mass media + total subsidy (n = 3)). We used logistic and linear regression analyses adjusted for beneficiary factors and clustering by organization. From 12 participating organizations, 26 068 beneficiaries registered of which 6215 initially engaged. Cost sharing was associated with significantly greater initial engagement as compared to total subsidy (OR 3.73, P < 0.001); however, no significant between-group differences existed in calls or weight change. Mass media + total subsidy group had significantly greater calls and weight loss at 12 months compared to mixed campaign + total subsidy (-2.6% vs -1.8%, P = 0.04). Cost sharing may promote greater initial engagement, although does not contribute to better participation or weight loss relative to total subsidy. If organizations elect total subsidy, then pairing this strategy with a mass media campaign may promote greater participation and weight loss among beneficiaries.