Obesity is associated with amplified risk of health conditions such as cardiovascular disease and metabolic conditions [1
]. Body weight loss of 5–10% can reduce the risk of these conditions [4
]. It is estimated that many individuals use commercial programs, but there is little knowledge on how dietary quality or nutrition knowledge is impacted via these programs [5
]. Most studies on nutritional factors in weight loss have taken place in non-commercial settings, such as clinical trials or free-living settings [7
]. In digital commercial programs, however, individuals self-initiate and self-manage their participation with the setting and timeline of their choice. It is largely unknown how nutritional factors relate to weight loss on a self-managed commercial program. On one hand, previous studies found that individuals with varying degrees of self-management who maintained greater weight loss had healthier diets and more nutritional knowledge than individuals with less weight loss [15
]. This suggests that nutritional factors such as better dietary quality or nutrition knowledge may be associated with greater weight loss on a self-managed commercial program. On the other hand, when self-managing their own weight loss, some individuals follow diets that are unsustainable and may restrict nutritional value long-term [15
]. In addition, greater weight loss could be indicative of lower nutritional value [16
]. It is possible that greater weight loss is associated with less optimal nutritional factors, or that associations between weight change and nutritional factors are not sustained long-term. These questions highlight the need to examine long-term nutritional factors in a self-managed commercial weight loss program to inform improving nutritional intake and knowledge for the large number of individuals who use these programs.
A number of studies suggest that energy density is associated with weight loss and healthy nutritional factors [21
]. Energy density is the amount of energy in a food per weight (kcal/g) [24
]. Individuals who eat a greater proportion of low-energy-dense foods consume fewer calories overall but report feeling just as full [25
]. Along these lines, randomized controlled trials have found that individuals encouraged to eat low-energy-dense foods lose more weight and consume more foods with high micronutrient content like fruits and vegetables [21
]. Low-energy-dense food intake is also associated with higher consumption of Vitamins A, C, and B-6 [10
]. To our knowledge, no study has assessed the direct association between energy dense food choices and nutrition knowledge during weight loss. Since energy density and nutrition knowledge are each associated with better diet quality in general populations [10
], there is a possibility that energy density could in turn be associated with nutrition knowledge during weight loss. At the same time, severely restricting diet to low energy density foods may not be sustainable long-term and lead to frustration [31
]. Therefore, a flexible food system such as color coding that guides individuals towards low-energy-dense foods but allows moderate consumption of medium and high-density foods could be beneficial, particularly long-term [32
In this study, we explored how nutritional factors are associated with weight loss in individuals on Noom, a self-managed commercial weight loss program with a food color categorization system based on energy density. One aim of the study was to understand how energy density relates to short-term and long-term weight loss. We hypothesized that users with more weight loss at 4 months and 18 months would eat lower energy dense diets than individuals with less weight loss or stable weight. The study’s primary aim was to examine how nutritional factors at 18 months are associated with weight loss. We hypothesized that fruit and vegetable intake, dietary quality, nutrition knowledge, and food choice would all be associated with weight loss at 18 months. Exploring the relation between weight loss and nutritional factors retrospectively can provide understanding of what is currently occurring for individuals on self-managed commercial programs and inform how to improve them to aid in short-term and long-term nutrition.
To our knowledge, this is the first study examining nutritional factors, such as energy density, fruit and vegetable intake, diet quality, and nutrition knowledge, on a self-managed commercial weight loss program. Using a program with a food color categorization system based on energy density, we examined how daily energy density food proportions related to weight lost at 4 months and 18 months in a retrospective analysis, and how nutritional factors related to weight loss at 18 months in a cross-sectional survey. Compared to participants who lost less weight, participants with greater weight loss at 18 months (>10%, 5–10%) and 4 months (≥5%) ate greater proportions of low-energy-dense foods and smaller proportions of high-energy-dense foods. At 18 months, the differences across groups were most pronounced for dinner foods. These results corroborate previous studies showing that individuals in RCT and self-managed contexts without a program had greater weight loss when eating low-energy-dense foods [20
]. In addition, participants at 18 months with greater weight loss (5–10%, 10%) had significantly higher self-reported fruit and vegetable intake, dietary quality, nutrition knowledge, and healthier food choice. This aligns with qualitative evidence that individuals with greater long-term weight loss have better dietary quality and nutrition knowledge than individuals who have not maintained weight loss [14
]. Despite its limitations, this study is an important first step in ascertaining whether similar relationships are found in more self-managed environments, particularly long-term.
We also found that of factors studied, only nutrition knowledge and food choice were associated with weight change in both crude and adjusted models. This is consistent with other studies showing that food choices at ad libitum buffets predict weight loss and that weight loss is most associated with increased intake (i.e., choice) of healthy foods and reduced consumption of foods such as dessert, red meat, and cheese in free-living conditions [58
]. In the Multiple Food Test used in this study, among choices such as meat, cheese, dessert, fruit, vegetables, and more, individuals who chose the healthiest foods had greater weight loss. For the first time, our results highlight the potential importance in weight loss of initial inclinations towards food based on depictions as in the Multiple Food Test but not actual consumption of test foods. Because of the small sample size involved in this study, future results should clarify whether is associated with long-term weight loss in other samples. Our results on nutrition knowledge also build on previous studies showing that for individuals in free living conditions and clinical trials, nutrition knowledge is associated with greater weight loss and weight control behaviors [9
]. Our results also contribute to a small body of work showing that energy density predicted weight change the most out of factors such as baseline BMI or caloric intake [22
]. In this study, dietary quality was not a significant predictor while nutrition knowledge and food choice were, which highlights the need for research that investigates the effects of more nutritional factors when exploring effects of energy density.
Unexpectedly, dietary quality and fruit and vegetable intake were not significantly associated with weight loss, though they differed by weight loss in expected ways. Dietary quality, assessed as adherence to the DASH diet, was not significantly associated with weight loss in either model. Previous studies indicate associations between dietary quality, whether measured as DASH diet adherence or not, and weight loss [48
], though a meta-analysis suggests fruit and vegetable intake is not associated with weight loss [66
]. Our results demonstrate for the first time to our knowledge that fruit and vegetable intake is associated with weight loss in an individual model but not in an adjusted model when accounting for baseline characteristics, energy density, and average adherence to calorie budgets. Future research should investigate whether these other characteristics influence weight loss more than fruit and vegetable intake on its own or adhering to a diet such as DASH. This is particularly the case since some fruit and vegetables included in the DASH diet (i.e., avocados, figs) may add calories but not enough volume to be helpful for weight loss. However, our results could be due to lack of variance from the short survey measures used, even though they have been validated against longer dietary assessments, or from the fact that all weight groups had moderate DASH-Q scores compared to previous studies [67
In the linear mixed model at 4 months and regression models at 18 months, we accounted for age and gender, since each can be associated with weight loss [5
]. However, there could be other variables that we did not capture, such as socioeconomic factors, that may be associated with 18-month weight loss. In a previous study, socioeconomic factors such as income and education did not emerge in a stepwise regression as important factors associated with weight loss at 4 months on Noom [6
]. However, one study found that socioeconomic factors are associated with maintenance of long-term weight loss [70
]. In another study, individuals with obesity who used a commercial program and maintained weight loss had higher income and were more likely to be employed and college educated than individuals with obesity who were weight stable and did not use a commercial program [71
]. Future research on weight loss in a commercial program should account for as many factors as possible, including but not limited to socioeconomic factors.
This was an observational retrospective investigation seeking to provide knowledge based on a context as close as possible to how individuals use a digital commercial program in the real world (i.e., not within the context of a clinical trial or prospective study). This is a first step towards informing improved nutritional intake and knowledge on these programs. However, this approach has limitations. For example, there was no control group, which would illuminate how the program improves nutritional factors compared to usual care. This limits generalizability to those who pay for a commercial program. Moreover, intent-to-treat analyses were not used, which means that results are limited to those who responded to the survey and met inclusion criteria, and may not generalize well to those who would not have met this criteria. In addition, we could not measure how self-reported nutritional factors changed from baseline to 4 months and 18 months. In using a cross-sectional observational cohort design, we could not assess how any factors predict subsequent weight loss. Nutritional factors were assessed via short self-reported assessments, which could mean that fruit and vegetable intake and dietary quality measures lack precision and fail to pick up on variation that would be related to weight loss. Though the direction of results across weight loss groups indicates that they were sensitive enough to pick up differences, future research should use longer assessments to measure dietary intake and quality. Additionally, weight was self-reported rather than objectively measured. Energy density proportions were also based on self-reported food intake during the program. Self-reported food intake is often subject to underreporting [72
]. Future studies should examine the extent of underreporting across weight loss groups in detail and particularly take into account the source of inaccuracy (e.g., deliberate and unintentional underreporting, as opposed to changed and motivated eating behavior after learning from a program; [72
For 4-month analyses, only participants in one of two weight loss groups (5% or more, 0 ± 1%) were included. For 18-month analyses, only participants who were from this original sample and who were still on the program at 18 months (25%), who responded to the survey (30% response rate), and who self-reported weight between weeks 68–74 (60% of the remaining participants) were included. This likely represents a minority of individuals on commercial programs, limiting generalizability. This also means the sample could be biased in that these participants were likely more motivated than users who do not stay on the program for 18 months. Retention rates in weight loss programs tend to vary considerably, and can range from 20% to over 90% [73
]. Retention through the study was low but comparable to the low range for other weight loss interventions [73
]. This could be due to a few reasons. First, unlike interventions that have a set time period from the start, participants could choose how long they wanted to use the program. In addition, retention can drop drastically over time, particularly when considering long-term weight loss; one study found that retention in a commercial weight loss program was more than 70% at 4 weeks but 6.6% at one year [76
]. The survey response rate (30%) is also comparable to those found in the survey methodology literature for online surveys (e.g., 35%; [77
]). Of the remaining participants, 60% were still actively self-reporting weight, which is comparable to rates of self-monitoring in other studies (e.g., 65%, [78
]). Future studies should compare these usage and retention factors between those who continue in this type of commercial program and those who do not.