This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Cardio, is properly cited. The complete bibliographic information, a link to the original publication on https://cardio.jmir.org, as well as this copyright and license information must be included.
A strong association exists between consuming a healthy diet and lowering cholesterol levels among individuals with high cholesterol. However, implementing and sustaining a healthy diet in the real world is a major challenge. Digital technologies are at the forefront of changing dietary behavior on a massive scale, as they can reach broad populations. There is a lack of evidence that has examined the benefit of a digital nutrition intervention, especially one that incorporates nutrition education, meal planning, and food ordering, on cholesterol levels among individuals with dyslipidemia.
The aim of this observational longitudinal study was to examine the characteristics of people with dyslipidemia, determine how their status changed over time, and evaluate the changes in total cholesterol, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), non-HDL-C, and triglycerides among individuals with elevated lipids who used Foodsmart, a digital nutrition platform that integrates education, meal planning, and food ordering.
We included 653 adults who used Foodsmart between January 2015 and February 2021, and reported a lipid marker twice. Participants self-reported age, gender, weight, and usual dietary intake in a 53-item food frequency questionnaire, and lipid values could be provided at any time. Dyslipidemia was defined as total cholesterol ≥200 mg/dL, HDL-C ≤40 mg/dL, LDL-C ≥130 mg/dL, or triglycerides ≥150 mg/dL. We retrospectively analyzed distributions of user characteristics and their associations with the likelihood of returning to normal lipid levels. We calculated the mean changes and percent changes in lipid markers among users with elevated lipids.
In our total sample, 54.1% (353/653) of participants had dyslipidemia at baseline. Participants with dyslipidemia at baseline were more likely to be older, be male, and have a higher weight and BMI compared with participants who had normal lipid levels. We found that 36.3% (128/353) of participants who had dyslipidemia at baseline improved their lipid levels to normal by the end of follow-up. Using multivariate logistic regression, we found that baseline obesity (odds ratio [OR] 2.57, 95% CI 1.25-5.29;
This study characterized users of the Foodsmart platform who had dyslipidemia and found that users with elevated lipid levels showed improvements in the levels over time.
Cardiovascular disease (CVD) is the leading cause of morbidity and mortality in the United States and globally [
Atherogenic lipoproteins play an important role in the initiation and progression of atherosclerosis; therefore, maintaining optimal lipid levels is crucial for achieving ideal cardiovascular health [
For years, guidelines have suggested dietary modification to be a crucial component in strategies to reduce CVD risk [
Many barriers to adopting and sustaining a healthy dietary pattern exist, such as time, cost, accessibility, and knowledge. Foodsmart is a digital nutrition and meal planning platform that is designed to make healthier eating achievable and sustainable among the general population, and it addresses the most common barriers to eating well. Foodsmart uses a multipronged approach including educating individuals on how to eat healthy, leveraging the food frequency questionnaire, recommending personalized healthy recipes based on food preferences, and automating grocery list creation and online grocery purchasing, all while tracking the individual’s improvements in biometrics. The platform has been found to be associated with at least 5% weight loss and has been shown to sustain weight loss over 3 years [
While many digital applications seek to improve eating behaviors and health outcomes, few studies have evaluated their effectiveness for changing lipid levels among users with dyslipidemia. The aim of this study was to examine the characteristics of users with dyslipidemia and evaluate the changes in lipid markers over time.
As of February 2021, 13,754 users of Foodsmart had entered a plausible value (defined later) for at least one lipid marker (total cholesterol, HDL-C, LDL-C, or triglycerides). Of those, 1445 users of Foodsmart had entered at least one lipid marker at two different time points. We excluded participants who reported their second lipid marker less than 1 month after their first report and those with implausible changes. Our final sample size was 653 participants who had at least two reports of at least one lipid marker.
Foodsmart is a digital nutrition platform that uses precision nutrition to create lasting behavior change through nutrition education and personalized recipe recommendations, and facilitates healthy eating through online grocery and food ordering integration. Rooted in behavior change theory, Foodsmart has two components, FoodSmart and FoodsMart, to help users access and engage with affordable, tasty, and healthy food.
The FoodSmart component emphasizes learning by helping the user understand how their typical eating behaviors compare to national targets and how to plan their meals for the week. Once users create their account, they are directed to the in-app Nutriquiz, a dietary assessment (based on the National Cancer Institute Diet History Questionnaire). Users report their usual dietary habits, and the quiz provides immediate and specific feedback on aspects of their diet to improve on. Over time, users can retake the Nutriquiz to track their progress on diet and biometrics. Based on the Nutriquiz results, personalized recipe recommendations are given to the user. The second component is FoodsMart, which focuses on altering the food purchasing environment to make healthier options the easier default path. This is achieved through personalized meal plan conversion to a grocery list and integrated online ordering and delivery of groceries, meal kits, and prepared foods, where food advertising paid for by food manufacturers is removed and replaced with nudges to healthier substitutions that align with user preferences and their personalized meal plan. Customized grocery discounts on healthier options help users save money and further nudge users to make healthier choices.
Foodsmart is available through health plans and employers and can be accessed via the web or the iOS or Android operating system.
Users were given the option upon enrollment to input self-reported total cholesterol, HDL-C, LDL-C, triglycerides, weight, and height data. They could update their biometrics at any time during usage of the platform. All lipid markers were reported in mg/dL. Given the self-reported nature of lipids, we considered the following values as missing data: total cholesterol ≤65 or ≥750 mg/dL, HDL-C ≤10 or ≥120 mg/dL, LDL-C ≤30 or ≥200 mg/dL, and triglycerides ≤10 or ≥2000 mg/dL [
Baseline BMI was calculated as first weight entry in kilograms divided by height in meters squared (kg/m2). We categorized participants by baseline BMI category as follows: normal BMI was defined as BMI <25 kg/m2, overweight was defined as BMI between 25 and 29.9 kg/m2, and obese was defined as BMI ≥30 kg/m2.
Participants self-reported their usual dietary intake in Foodsmart. Upon registration, users were prompted to fill out a dietary questionnaire called Nutriquiz, a 53-item food frequency questionnaire adapted from the National Cancer Institute Diet History Questionnaire [
We used descriptive analyses to examine the baseline demographic characteristics, lipid markers, and diet quality of the total study population and according to whether they had dyslipidemia at baseline. We reported categorical variables as number (percentage) and continuous variables as mean (SD). We used the chi-square test and analysis of variance to test for differences in categorical and continuous variables, respectively.
In order to better understand how the dyslipidemia status changed over time, we calculated the percent of participants by category of change in the dyslipidemia status from the beginning to the end of the program as follows: dyslipidemia to normal, normal to dyslipidemia, dyslipidemia to dyslipidemia, and normal to normal. Multivariate logistic regression was used to estimate the odds ratios (ORs) and 95% CIs of achieving normal lipid levels among participants with baseline dyslipidemia and was mutually adjusted for gender, age category, baseline BMI category, baseline Nutriscore, and change in Nutriscore (per 5 points).
Among participants who had elevated lipid levels, we calculated the mean start value, mean end value, and mean changes in total cholesterol, cholesterol ratio, HDL-C, LDL-C, non-HDL-C, and triglycerides. We used paired
We considered a
The study was declared exempt from institutional review board oversight by the Pearl Institutional Review Board given the retrospective design of the study and the less than minimal risk to participants.
Baseline characteristics of the total study sample and those stratified by baseline dyslipidemia status are shown in
Baseline characteristics of the total study sample and those stratified by baseline dyslipidemia status.
Characteristic | Total (N=653) | Normal (N=300) | Dyslipidemia (N=353) |
|
|||||||||||
|
Number of participants | Percentage or mean (SD) | Number of participants | Percentage or mean (SD) | Number of participants | Percentage or mean (SD) |
|
|
|||||||
|
|
|
|
|
|
|
.11 |
|
|||||||
|
<40 | 196 | 33% | 100 | 36% | 96 | 30% |
|
|||||||
|
40-59 | 306 | 51% | 138 | 50% | 168 | 52% |
|
|||||||
|
≥60 | 99 | 16% | 38 | 14% | 61 | 19% |
|
|||||||
Female gender | 346 | 53% | 177 | 59% | 169 | 48% | <.001 |
|
|||||||
Weight (kg) | 649 | 79.4 (20.0) | 299 | 75.3 (18.6) | 350 | 83.0 (20.4) | <.001 |
|
|||||||
BMI (kg/m2) | 649 | 27.3 (5.7) | 299 | 26.1 (5.3) | 350 | 28.3 (5.9) | <.001 |
|
|||||||
Total cholesterol (mg/dL) | 632 | 180.1 (37.2) | 289 | 163.1 (25.5) | 343 | 194.4 (39.5) | <.001 |
|
|||||||
Cholesterol ratiob | 608 | 3.6 (1.4) | 276 | 2.8 (0.6) | 332 | 4.3 (1.4) | <.001 |
|
|||||||
HDL-Cc (mg/dL) | 623 | 54.6 (17.5) | 284 | 61.2 (13.9) | 339 | 49.0 (18.3) | <.001 |
|
|||||||
LDL-Cd (mg/dL) | 606 | 102.0 (31.8) | 275 | 88.0 (101.8) | 331 | 113.7 (34.3) | <.001 |
|
|||||||
non-HDL-C (mg/dL) | 608 | 125.1 (37.2) | 276 | 101.8 (25.2) | 332 | 144.5 (34.3) | <.001 |
|
|||||||
Triglycerides (mg/dL) | 639 | 117.6 (37.2) | 288 | 90.8 (30.8) | 351 | 139.6 (77.2) | <.001 |
|
|||||||
Baseline Nutriscore (range 0-70) | 351 | 34.3 (8.3) | 168 | 34.6 (8.1) | 183 | 34.1 (8.4) | .58 |
|
|||||||
Change in Nutriscore | 389 | 2.4 (7.3) | 181 | 2.4 (7.3) | 208 | 2.4 (7.3) | .99 |
|
|||||||
Follow-up duration (months) | 653 | 16.9 (11.3) | 300 | 17.4 (11.5) | 353 | 16.5 (11.2) | .33 |
|
aChi-square tests and analysis of variance were used to test differences for categorical and continuous variables, respectively.
bCholesterol ratio was defined as total cholesterol/high-density lipoprotein cholesterol.
cHDL-C: high-density lipoprotein cholesterol.
dLDL-C: low-density lipoprotein cholesterol.
There were 653 participants included in the analysis, of which 306 were between 40 and 59 years old and 346 were female (
We calculated the percent of participants based on what category of dyslipidemia status change they were in. We categorized participants into four groups based on their dyslipidemia status at the beginning and end of their follow-up as follows: dyslipidemia to normal, normal to dyslipidemia, dyslipidemia to dyslipidemia, and normal to normal. We found that 19.6% (128/653) of participants had dyslipidemia in the beginning and achieved normal lipid levels by the end, 12.4% (81
In order to better understand what type of user was successful in achieving normal lipid levels, we examined the association between baseline characteristics and odds of achieving normal lipid levels in a multivariate logistic regression model (
Association between predictors and the likelihood of changing the dyslipidemia status to normal in multivariate logistic regression models.
Variable | Odds ratio (95% CI) | |||
Female | 0.81 (0.45-1.46) | .49 | ||
|
|
|
||
|
<40 | 1 (reference) | N/Aa | |
|
40-59 | 0.69 (0.33-1.43) | .32 | |
|
≥60 | 2.13 (0.96-4.76) | .07 | |
|
|
|
||
|
Normal | 1 (reference) | N/A | |
|
Overweight | 1.26 (0.63-2.55) | .51 | |
|
Obese | 2.57 (1.25-5.29) | .01 | |
Baseline Nutriscore | 1.04 (1.00-1.09) | .04 | ||
Change in Nutriscore (per 5 points) | 1.07 (0.86-1.33) | .56 |
aN/A: not applicable.
Changes in lipid levels among users with elevated lipid levels.
Variable | Number of participants | Start value, mean (SD) | End value, mean (SD) | Change, mean (SD) | |
Total cholesterol (mg/dL) | 171 | 223.5 (21.1) | 207.1 (31.8) | −16.4 (34.4) | <.001 |
Cholesterol ratiob | 64 | 6.1 (1.3) | 4.7 (0.9) | −1.5 (1.7) | <.001 |
HDL-Cc (mg/dL) | 115 | 33.3 (5.9) | 44.5 (13.2) | 11.2 (14.0) | <.001 |
LDL-Cd (mg/dL) | 90 | 150.7 (16.9) | 130.1 (27.1) | −20.6 (30.1) | <.001 |
Non-HDL-C (mg/dL) | 193 | 158.7 (23.0) | 145.2 (30.7) | −13.6 (31.3) | <.001 |
Triglycerides (mg/dL) | 107 | 213.3 (77.0) | 179.1 (74.3) | −34.2 (95.1) | <.001 |
a
bCholesterol ratio was defined as total cholesterol/high-density lipoprotein cholesterol.
cHDL-C: high-density lipoprotein cholesterol.
dLDL-C: low-density lipoprotein cholesterol.
We determined the mean percent changes in total cholesterol, cholesterol ratio, HDL-C, LDL-C, non-HDL-C, and triglycerides among users with elevated lipid levels. The greatest percent change was in HDL-C (+38.5%), followed by cholesterol ratio (−20.9%), LDL-C (−12.9%), triglycerides (−10.8%), non-HDL-C (−7.8%), and total cholesterol (−6.8%).
To better understand how LDL-C changed according to baseline LDL-C, we examined the mean changes in LDL-C stratified by the category of baseline LDL-C (normal, slightly elevated, and moderate or highly elevated) (
Changes in low-density lipoprotein cholesterol (LDL-C) levels according to the category of baseline LDL-C.
Category of baseline LDL-Ca | Number of participants | Start LDL-C (mg/dL), mean (SD) | End LDL-C (mg/dL), mean (SD) | Change in LDL-C (mg/dL), mean (SD) |
Normal (<100 mg/dL) | 202 | 76.1 (16.9) | 92.8 (30.8) | 16.6 (31.3) |
Slightly elevated (≥100 and <130 mg/dL) | 148 | 113.5 (8.7) | 111.5 (24.6) | −2.0 (23.1) |
Moderate or highly elevated (≥130 mg/dL) | 90 | 150.7 (16.9) | 130.1 (27.1) | −20.6 (30.1) |
aLDL-C: low-density lipoprotein cholesterol.
In our study, of 653 users who reported at least two lipid markers, we found that 54.1% (353/653) of participants had dyslipidemia at baseline, and of those, 36.3% (128/353) showed improvements in their lipid levels to normal by the end of follow-up. Participants with dyslipidemia at baseline were more likely to be older, be male, and have a higher weight and BMI. Baseline obesity and Nutriscore were associated with a higher likelihood of achieving normal lipid levels. Between the start and end of using the Foodsmart platform, total cholesterol, cholesterol ratio, LDL-C, and triglycerides all significantly decreased and HDL-C significantly increased. These findings suggest that usage of the Foodsmart platform may be associated with improvements in lipid markers, most likely through improved diet quality.
The results of this study support the findings of previous studies that found beneficial effects of dietary interventions on lipid levels among people with dyslipidemia. A meta-analysis of over 200 studies that examined the impact of dietary interventions on cholesterol levels found that a reduction in saturated fats and an increase in polyunsaturated fats were primary factors in lowering total cholesterol levels [
Though the association between diet and cholesterol is strong and has been established for decades, implementing and sustaining behavior change in real life, especially with diet, is complex and challenging. It has been noted that physicians face many challenges in encouraging behavior change to improve lipid profiles and other CVD risk factors in patients [
The annual per person expenditure related to dyslipidemia among people without CVD has been estimated to be about US $856 [
The present study has several limitations worth addressing. The first is that all lipid measurements were self-reported and were not validated. However, in a validation study among about 40,000 female health professionals in the Women’s Health Study, investigators found that the Spearman correlation coefficients between self-reported and blood samples for triglycerides and HDL-C were 0.57 and 0.63, respectively [
There are also many strengths of this study. Very few studies have demonstrated the real-life application of a digital intervention that changes a user’s meal planning and food ordering behaviors, and its effect on cholesterol levels. By leveraging our large database of users of the Foodsmart platform, we could evaluate real-world data to draw patterns and associations that provide insights into the utility of commercial digital applications. Additionally, many participants were enrolled for at least a year, allowing us to examine changes in lipids over a long time span. Few studies, especially randomized clinical trials, on digital applications have follow-up data for lipids after more than 2 years.
In conclusion, this is one of the first studies of this scale and duration to examine changes in lipids among individuals with dyslipidemia who were users of a digital nutrition platform with personalized dietary recommendations, as well as online meal planning, food ordering, and grocery discounts and incentives. Future studies are warranted to examine specific food components that are associated with lowering cholesterol levels, perform cost comparisons between pharmaceutical and digital interventions, and identify causal associations by comparing interventions to a control.
cardiovascular disease
high-density lipoprotein cholesterol
low-density lipoprotein cholesterol
odds ratio
EAH, JS, VN, and JL are employees of Zipongo, Inc, DBA Foodsmart.