Background: Diabetes-related costs are the highest across all chronic conditions in the United States, with type 2 diabetes accounting for up to 95% of all cases of diabetes. A healthy diet is strongly associated with lowering glycated hemoglobin A1c (HbA1c) levels among individuals with diabetes, which can help curtail other health complications. Digital health platforms can offer critical support for improving diet and glycemic control among individuals with diabetes. Less is known about the characteristics of people with diabetes who use digital health platforms (specifically, a platform that integrates personalized healthy meal plans and food ordering) and changes in their HbA1c levels.
Objective: The aim of this study is to characterize Foodsmart users with diabetes and evaluate the longitudinal impact of Foodsmart—a personalized digital nutrition platform with meal planning, food ordering, and nutrition education features—on changes in HbA1c levels.
Methods: We retrospectively analyzed data collected from 643 adults with at least two self-reported HbA1c entries in the Foodsmart platform between January 2016 and June 2021. Participants self-reported their HbA1c levels, height, weight, health conditions, and diet in a 53-item food frequency questionnaire. Diabetes was defined as HbA1c ≥6.5%. We analyzed distributions of characteristics by baseline diabetes status and examined the association of characteristics with the likelihood of having diabetes at baseline. To evaluate the change in HbA1c levels among Foodsmart users, we calculated mean changes (absolute and percent) in HbA1c among participants with diabetes and by length of follow-up. We also compared changes in HbA1c and weight between participants with diabetes at baseline who achieved a normal HbA1c level and those who did not.
Results: We found that 43.5% (280/643) of the participants with at least two HbA1c level entries had diabetes at baseline. Participants with diabetes at baseline were more likely to be male, have a higher weight and BMI, report high blood pressure, and have a poorer diet in comparison to participants without diabetes. Using a multivariable logistic regression model, we found that being male and obese were statistically significantly associated with baseline diabetes. Among participants with diabetes at baseline, HbA1c was reduced, on average, by 0.46%. In addition, 21.4% (60/280) of participants with diabetes achieved a normal HbA1c level (<6.5%) in their last HbA1c level entry; this percentage increased with longer follow-up time (39% [7/18] at >24 months). In a sensitivity analysis, users with an HbA1c ≥7.0% at baseline had an average absolute change of –0.62% and 31.2% (62/199) of these participants achieved HbA1c levels of less than 7.0%.
Conclusions: This study assessed characteristics of individuals enrolled on the Foodsmart platform with HbA1c levels and found that users with diabetes had lower HbA1c levels over time and a sizable percentage of participants were successful in achieving normal levels.
Over 34 million individuals in the United States have diabetes, comprising 13% of US adults . In 2018 alone, 1.8 million new cases were diagnosed [ ]. Diabetes-related costs are the highest of any condition in the US health care system, with the cost of care increasing each year [ ]. Recent estimates state that direct medical costs of diabetes and related complications amount to approximately $237 billion each year, accounting for one of every seven health care dollars spent [ ]. Notably, type 2 diabetes accounts for 90%-95% of all cases in the United States [ ].
For adults with diabetes, the body either does not produce enough insulin or its cells are insulin resistant . Because insulin facilitates the uptake of sugar into cells from the bloodstream, diabetes results in elevated levels of blood glucose. Diabetes is defined by high blood sugar, or hyperglycemia, and glycemic control is fundamental to diabetes management [ ]. Type 2 diabetes can also occur due to drug-induced hyperglycemia, which is often caused by beta blockers, thiazide diuretics, corticosteroids, and others [ ]. In the long-term, high levels of blood glucose can lead to macrovascular (heart disease, stroke, poor blood circulation) and microvascular (loss of sight, nerve damage, and kidney disease) damage [ ]; these health issues lead to greater health care costs. By controlling their blood sugar, however, patients can limit the effect of these negative health consequences [ ]. A healthy diet is a critical component in this treatment plan; this includes a meal plan of healthy carbohydrates and fats as well as fiber-rich foods, while limiting foods that are high in trans fats, sodium, and added sugars [ ]. Generally, healthy diets for those with diabetes or hyperglycemia are nutrient rich and low in fat and calories. The incorporation of this type of diet for those with type 2 diabetes has been shown to decrease individuals’ glycated hemoglobin A1c (HbA1c) levels, a measure of one’s mean blood glucose levels over the prior three months [ , ]. Despite evidence of the benefits of a healthy diet, there are many barriers to adopting and sustaining these changes in one’s diet, such as lack of time, financial resources, accessibility, and information [ ]. Previous studies have shown that lower income neighborhoods have an increased exposure to advertisements for tobacco and alcohol, and are more likely to be food deserts, with less access to healthy foods [ , ]. A study conducted in the United Kingdom found that while many participants understood what a healthy diet was, they found it difficult to achieve due to lack of time, advertising, community norms, and conflicting advice from professionals [ ]. Furthermore, dietary habits are also shaped by education and nutritional and cooking knowledge, as well as motivation and convenience [ ].
Foodsmart is a meal planning platform that addresses access to affordable and healthy foods to enable its users to develop healthy eating habits. The Foodsmart platform improves its participants’ health by providing users with a basic understanding of their current diet and potential areas for improvement. It also supplies participants with personalized recipe recommendations and facilitates the purchasing of healthy options through ad-free online ordering of groceries, meal kits, and prepared foods at discounted prices. Previous research has shown that Foodsmart members with obesity have achieved weight loss that has been sustained over the time during which they used the platform .
Previous studies have shown that digital nutritional and dietary interventions can improve glycemic control among individuals with type 2 diabetes [- ]. One digital low-carbohydrate intervention (with comprehensive diabetes and nutritional education and a social support component) was associated with a mean absolute decrease of 1.17% in HbA1c levels after one year [ ]. In another study, a dietary intervention that included both a low-carbohydrate Mediterranean diet and a low-fat diet that was conducted over the course of four years showed that changes in one’s diet can lead to sustained differences in HbA1c levels [ ]. Another study’s intervention aimed to encourage participants to adopt a plant-based diet and engage in regular exercise through a digital intervention paired with specialized human support, resulting in a mean change in HbA1c of –0.8% within 12 weeks [ ]. In a systematic review of internet interventions, the majority of web-based interventions also focused on the glucose monitoring process and on insulin titration, while very few focused on lifestyle modification, behavior theory, and education with tailored feedback [ , ]. Of the 9 studies that fit the review’s criteria and did promote behavior change, 6 of them targeted healthy eating, which further validates the effect of diet on diabetes [ ]. Foodsmart differs from these interventions in its complete digital interface, personalization of meal planning, and online food ordering system. The platform alters the food purchasing environment by integrating recipe recommendations into a grocery list, removing online advertisements for unhealthy options, and providing discounts and price comparisons, all of which ease the process of behavior change. By assisting participants through the process of making healthy, sustainable behavior changes, Foodsmart may, in turn, be able to assist users living with diabetes in reducing their HbA1c levels. Given the complexities of healthy eating, especially among people with diabetes, we wanted to characterize users with diabetes who used Foodsmart.
The main objectives of this study were to better understand and characterize participants with diabetes compared to participants without diabetes and evaluate changes in HbA1c levels, weight, and nutrition quality over time among Foodsmart participants with diabetes through its features including nutritional assessment, personalized meal planning, and altered food environment for grocery purchasing.
As of June 2021, 10,197 participants (aged >18 years and living in the United States) of Foodsmart who enrolled since January 2016 had entered a plausible value for HbA1c (HbA1c >3% or HbA1c <15%). Of those, 643 Foodsmart participants had entered at least two HbA1c entries, with the first and last entry at least 30 days apart. The final sample size was 643 participants who had at least two reports of HbA1c.
Foodsmart is a digital nutrition platform that encourages sustained behavior change through nutrition education and personalized meal planning, and promotes healthy eating and nutrition through online grocery and food ordering integration. Foodsmart has two components, FoodSmart and FoodsMart, to help users learn how to eat healthy to meet their nutrition targets and order affordable, tasty, and healthy food online, respectively.
The FoodSmart component provides participants with digital dietetics information on how to better plan meals to meet their nutrition targets. Once participants enroll, they are prompted to fill out the Nutriquiz, an online dietary assessment. Participants report their usual dietary intake and meal planning habits and based on the responses, the assessment provides specific dietary recommendations and a tailored meal plan. Participants can retake the Nutriquiz assessment at any time to track their progress toward their health goals.
The second component is FoodsMart, an online food purchasing environment that promotes buying healthy groceries and meals. Personalized meal plans are converted into a grocery list and integrated into online ordering and delivery of meal kits, prepared foods, and groceries. Participants are encouraged to purchase healthy options that align with their preferences and personalized meal plan. Customized grocery discounts for healthier food options and budget-based purchasing that compares prices across integrated grocery partners help participants save money and further encourage participants to choose healthy food options.
Foodsmart is available through health plans and employers and can be accessed via the web or iOS or Android operating systems.
Measurements of HbA1c and Weight
On the Foodsmart platform, participants were able to enter biometrics such as height, weight, HbA1c, blood pressure, and lipids, and were able to update their biometrics at any time. Given the potential for error when entering self-reported metrics, the following values were considered as incorrect entries and were replaced with a missing value: HbA1c ≤3% or ≥15%, BMI ≤15 kg/m2 or ≥50 kg/m2, and weight ≤27.2 kilograms or ≥181.1 kilograms. We only included participants who reported an HbA1c measurement at least twice, and we used the first (baseline) and last (end) values entered. Length of follow-up was calculated as the number of months between the date of the first value and the date of the last value. We defined HbA1c ≥6.5% as the cutoff for diabetes as defined by the American Diabetes Association . The same method was applied to the end HbA1c value to assess diabetes status at the end of follow-up. Since a glycemic target of HbA1c <7% is recommended for nonpregnant adults, as defined by the American Diabetes Association, we used the cutoff of 7% for sensitivity analyses [ ]. Changes in HbA1c were calculated by subtracting the first reported value from the end value. Percent change was calculated by dividing the change in HbA1c by the first HbA1c entry.
Baseline BMI was calculated as the first weight entry in kilograms divided by height in meters squared (kg/m2). Participants’ baseline BMI was categorized as normal BMI (BMI <25 kg/m2), overweight (25-29.9 kg/m2), or obese (BMI ≥30 kg/m2). Participants were also able to report any conditions they currently had (eg, high blood pressure, high cholesterol) in the Nutriquiz.
Participants self-reported their usual dietary intake and habits in Foodsmart. Upon enrollment, participants were prompted to fill out a 53-item food frequency questionnaire called Nutriquiz (adapted from the National Cancer Institute Diet History Questionnaire I ). Demographic information (age, sex, height), weight, and daily dietary intake (added sugars, fiber, fruits, vegetables, whole grains, fats, proteins, water, and sodium) were also obtained using the Nutriquiz.
Based on responses from the Nutriquiz, a score (Nutriscore) was calculated to assess overall diet quality, which is based on the Alternative Healthy Eating Index-2010 and the Commonwealth Scientific and Industrial Research Organization Healthy Diet Score [, ]. Participants were assigned a total Nutriscore from 0 to 70 based on the sum of scores for 7 components: fruits, vegetables, protein ratio (white meat/vegetarian protein to red/processed meat), carbohydrate ratio (total fiber to total carbohydrate), fat ratio (polyunsaturated to saturated/trans fats), sodium, and hydration (percent of daily fluid goal). Each of the components was scored from 0 to 10, with 10 being optimal. Change in the Nutriscore was calculated as a participant’s last Nutriscore minus the participant’s first Nutriscore. A positive change in Nutriscore indicates the participant improved their dietary quality.
We used descriptive analyses to examine the baseline demographic characteristics, HbA1c levels, and diet quality of the study population as a whole and according to whether participants had diabetes at baseline or not. We reported categorical variables as number of participants (percentage of study population) and continuous variables as mean (SD). We used chi-square tests to assess whether categorical variables are independent of baseline diabetes status, and two-sample t tests to evaluate differences in continuous variables.
Univariate and multivariable logistic regression were used to estimate the odds ratios (ORs) and 95% CIs of having diabetes at baseline. The multivariable logistic regression model was mutually adjusted for gender, age category, baseline BMI category, baseline Nutriscore, high blood pressure, and high cholesterol.
Among participants who had diabetes, we calculated the mean changes in HbA1c overall and by time of follow-up (>6 months, >12 months, >24 months). We used paired t tests to test whether the changes were statistically significant. Additionally, we calculated the mean percent change for HbA1c. In a sensitivity analysis, we used a threshold of HbA1c ≥7% to calculate mean changes in HbA1c.
We also calculated the percentage of participants with diabetes at baseline who returned to normal HbA1c levels by the end of follow-up, and stratified by follow-up length. We conducted a sensitivity analysis using a threshold of HbA1c ≥7%.
To further explore the performance of HbA1c, we examined changes in weight and HbA1c stratified by whether participants with diabetes at baseline achieved normal HbA1c levels (HbA1c ≥6.5%) by the end of follow-up.
We considered P values less than .05 to be significant for all tests. R Studio (version 1.4.1106) and R (version 4.0.5; R Foundation for Statistical Computing) were used for all analyses.
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 diabetes status are shown in. We found that 43.5% (280/643) of participants had diabetes at baseline. There were 643 participants included in the analysis, of which 64% (411/643) were female and 61% (391/643) were between 40 and 59 years old ( ). The mean weight was 93.9 (SD 23.8) kilograms, the mean baseline Nutriscore was 31.4 (SD 8.5) points, and the mean change in the Nutriscore was 3.2 (SD 7.1) points. The mean follow-up length was 10.4 (SD 7.1) months and ranged from 1 to 38 months. Compared to participants who did not have diabetes, participants who did have diabetes were significantly more likely to be male, to have a higher weight and BMI, to have a lower baseline Nutriscore, and to self-report having high blood pressure. Participants with diabetes at baseline were also more likely to have a higher increase in Nutriscore, a longer follow-up duration, and self-reported high cholesterol compared with participants without diabetes at baseline, although the differences were not statistically significant.
To better understand what type of participant was likely to have diabetes at baseline, we examined the association between baseline characteristics and odds of having diabetes in univariate and multivariable logistic regression models (). In the univariate regression models, participants who were female were 40% less likely to have diabetes at baseline than participants who were male (OR 0.60, 95% CI 0.43-0.82, P=.002). Participants classified in the overweight BMI category were 86% more likely to have diabetes at baseline than participants classified in the normal BMI category (OR 1.86, 95% CI 1.10-3.19, P=.02). Participants classified in the obese BMI category were 151% more likely to have diabetes at baseline than participants classified in the normal BMI category (OR 2.51, 95% CI 1.58-4.09, P<.001). Participants who self-reported having high blood pressure were also 46% more likely to have diabetes at baseline than participants who did not self-report having high blood pressure (OR 1.46, 95% CI 1.06-1.99, P=.02). Participants with a higher baseline Nutriscore were less likely to have diabetes at baseline (OR 0.98, 95% CI 0.96-1.00, P=.03).
After adjusting for all other variables in the multivariable logistic regression model, we found that being female was associated with 44% lower odds of having diabetes at baseline (OR 0.56, 95% CI 0.39-0.79, P=.001). Additionally, participants who were obese were 134% more likely to have diabetes at baseline than those in the normal BMI category (OR 2.34, 95% CI 1.40-3.97, P=.001).
|Characteristic||Total participants||Participants without diabetes||Participants with diabetes||P valuea|
|Sample size, n||Values||Sample size, n||Values||Sample size, n||Values|
|Female, n (%)||643||411 (64)||363||251 (69)||280||160 (57)||.002|
|Age (years), n (%)||.65|
|<40||643||35 (5)||363||19 (5)||280||16 (6)|
|40-59||643||391 (61)||363||216 (60)||280||175 (63)|
|≥60||643||217 (34)||363||128 (35)||280||89 (32)|
|Weight (kg), mean (SD)||637||93.9 (23.8)||362||90.3 (23.5)||275||98.4 (23.4)||<.001|
|Change in weight (kg), mean (SD)||466||–1.7 (8.0)||265||–0.8 (8.3)||201||–2.9 (7.6)||.005|
|BMI category, n (%)||<.001|
|Normal||643||106 (17)||363||77 (21)||280||29 (10)|
|Overweight||643||158 (25)||363||93 (26)||280||65 (23)|
|Obese||643||360 (56)||363||185 (51)||280||175 (62)|
|Missing||643||19 (3)||363||8 (2)||280||11 (4)|
|Baseline HbA1c (%), mean (SD)||643||6.6 (1.4)||363||5.8 (0.5)||280||7.8 (1.5)||<.001|
|Follow-up duration (months), mean (SD)||643||10.4 (7.1)||363||10.1 (7.0)||280||10.7 (7.3)||.34|
|High blood pressure, n (%)||147||47||70||44||78||53||.02|
|High cholesterol, n (%)||209||57||92||54||120||61||.10|
|Baseline Nutriscore (0-70), mean (SD)||643||31.4 (8.5)||363||32 (8.5)||280||30.5 (8.5)||.03|
|Change in Nutriscore, mean (SD)||601||3.2 (7.1)||337||3.0 (7.1)||264||3.4 (7.0)||.46|
aChi-square tests and two-sample t tests were used to test differences for categorical and continuous variables, respectively.
|Parameter||Univariate odds ratio (95% CI)||P value||Multivariable odds ratio (95% CI)||P value|
|Gender (female)||0.60 (0.43-0.82)||.002||0.56 (0.39-0.79)||.001|
|<40||1 (reference)||1 (reference)|
|40-59||0.96 (0.48-1.95)||.91||0.84 (0.41-1.78)||.65|
|≥60||0.83 (0.40-1.71)||.60||0.72 (0.34-1.57)||.40|
|Baseline BMI category|
|Normal||1 (reference)||1 (reference)|
|Overweight||1.86 (1.10-3.19)||.02||1.64 (0.96-2.85)||.08|
|Obese||2.51 (1.58-4.09)||<.001||2.34 (1.40-3.97)||.001|
|High blood pressure||1.46 (1.06-1.99)||.02||1.18 (0.83-1.69)||.36|
|High cholesterol||1.32 (0.96-1.81)||.09||1.13 (0.80-1.60)||.48|
|Baseline Nutriscore (0-70)||0.98 (0.96-1.00)||.03||0.99 (0.97-1.01)||.47|
Changes in HbA1c Levels
presents the mean and percent changes in HbA1c levels among participants who were classified as having diabetes for the overall group and by length of follow-up, at >6, >12, and >24 months. The mean changes in HbA1c overall and at >6, >12, and >24 months were –0.46, –0.37, –0.45, and –0.70 points, respectively. Percent changes in HbA1c overall and at >6, >12, and >24 months were –6%, –5%, –6%, and –9%, respectively. All changes were statistically significant (P<.05) using paired t tests. For users with an HbA1c ≥7.0%, mean change in HbA1c was –0.62 points (P<.001), and percent change was –7.6%.
We calculated the percentage of participants with diabetes at baseline who achieved a normal (<6.5%) HbA1c level overall and by cumulative length of follow-up time. Among all participants with diabetes, 21.4% (60/280) achieved normal HbA1c levels, using a threshold of 6.5%. Among participants whose follow-up time was longer than 6, 12, and 24 months, the percentage of participants who achieved normal HbA1c levels was 21.0% (43/205), 22% (21/97), and 39% (7/18), respectively. In a sensitivity analysis, for participants with an HbA1c ≥7%, 31.2% (62/199) of them achieved an HbA1c level less than 7%.
To better understand how weight and HbA1c changed according to end diabetes status, we examined changes in weight and HbA1c stratified by whether participants with diabetes at baseline achieved normal HbA1c levels (). Reductions in weight and HbA1c were greater for those who achieved normal HbA1c levels at the end of follow-up versus those who did not.
|Biometrics||Diabetes to normal||Diabetes to diabetes|
|Weight change (kg)||–4.1||–2.5|
|HbA1c change (%)||–1.7||–0.1|
In this study of 643 participants who used the Foodsmart platform, we found that 43.5% (280/643) had diabetes at baseline, as defined by their baseline HbA1c level. Foodsmart participants with diabetes at baseline were more likely to be male and have a higher weight and BMI. On average, HbA1c decreased by 0.46% among participants with diabetes over a mean duration of follow-up of 10.7 (SD 7.3) months. Among participants with diabetes at baseline, 21.4% (60/280) of those participants achieved a normal HbA1c level by the end of follow-up. These findings suggest that use of the Foodsmart platform may be associated with improved glycemic control among users with diabetes.
In line with our findings, prior studies evaluating the association between diet interventions and clinical biomarkers showed that various nutrition therapies significantly improved glucose regulation and reduced HbA1c levels in patients with diabetes mellitus. For instance, Esposito et al  conducted a randomized trial to evaluate the effects of a low-carbohydrate Mediterranean diet versus a low-fat diet on HbA1c levels among individuals with type 2 diabetes. The trial was conducted in Italy and included 215 participants with type 2 diabetes who were classified as obese, had never previously taken antihyperglycemic medication, and had HbA1c less than 11%. After two years, those on the low-carbohydrate Mediterranean diet had a decrease in HbA1c of 1.1%, while those on the low-fat diet had a decrease of 0.5%. In our study, participants who had a follow-up time period greater than 2 years were observed to have a 0.7% decrease in HbA1c. Esposito et al [ ] also found that, at the end of their study, 37% and 24% of participants returned to normal HbA1c levels (using a threshold of 7%) after following the Mediterranean and low-fat diet, respectively. In our study, 31% of participants returned to normal HbA1c levels (using a threshold of 7.0%). The effects of the low-carbohydrate Mediterranean diet were likely greater than what was observed with Foodsmart because the trial had more specific and stringent guidelines for the participants’ diets than there were with Foodsmart, as participants can make changes that best fit their lifestyles. Therefore, with assistance from Foodsmart in making flexible dietary changes, participants can see changes in their HbA1c levels that are similar to that of a strict low-fat diet.
In our study, we found that men were more likely to have diabetes, which is in line with a previous study conducted by Nordström et al . The authors analyzed data from The Healthy Aging Initiative, a population-based prospective study of men and women 70 years of age or older in northern Sweden, and found a significantly greater prevalence of diabetes in men than women. They hypothesized that this was due to differences in visceral fat mass among men and women and found that when visceral fat mass was adjusted for, male sex was no longer associated with diabetes. Therefore, their findings suggest that differences in the prevalence of diabetes between males and females may be due to differences in visceral fat mass, which is known to be a strong predictor of diabetes [ , ]. Additionally, we found that people with diabetes were more likely to be obese, which has been established by several studies that have found obesity to be a risk factor for diabetes [ - ]. Finally, we found that there was greater weight change among those who achieved normal HbA1c levels. This is consistent with findings from Gummesson et al [ ], who conducted a systematic review aimed at understanding the association between weight loss and HbA1c for overweight and obese patients with type 2 diabetes. They found a dose-response relationship between weight loss and reduction in HbA1c in their participants, which may explain why we see a larger weight change for those who have a greater reduction in HbA1c and return to normal levels in our study.
Patients with diagnosed diabetes incur mean medical expenditures of $16,750 per year, of which about $9600 is attributed to diabetes . Glucose-lowering drugs such as sodium–glucose cotransporter 2 (SGLT2) inhibitors and glucagon-like peptide 1 (GLP-1) receptor agonists account for a large proportion of these expenditures, with an estimated mean annual cost of $2727 per patient [ ]. Significant savings can be achieved if patients meet the target American Diabetes Association HbA1c level of <7% [ , ]. A 1%, 1.25%, or 1.5% reduction in HbA1c for a commercially insured patient could result in savings of $801, $1033, and $1266 per patient per year, respectively [ ]. In this study, participants with diabetes at baseline who achieved an end normal HbA1c level reduced their HbA1c by 1.7% on average, achieving a clinically significant change of 0.5% [ ]. Given the high cost of medications, prevention and management of diabetes through eating healthier could be an attractive, low-cost alternative. Unfortunately, we do not know whether participants were on glucose-lowering medications before or during enrollment on the Foodsmart platform. Despite this, we can estimate the difference in costs between prescription medications and Foodsmart. Improved glucose management would cost $327 more per person with diabetes annually relative to current care, largely due to use of antihyperglycemic medications [ ]. In comparison, as of 2021, the Foodsmart platform on average costs $12.30 per eligible member annually. Using the results above, a 1% reduction in HbA1c would cost $26.98 on average. On the other hand, using metformin or liraglutide (a GLP-1 receptor agonist) to reduce HbA1c levels by 1% would cost on average $120 and $8640, respectively. Given that the cost of metformin is 4 times higher than the cost of using Foodsmart, and assuming participants on the Foodsmart platform were not on glucose-lowering medication, the cost of a digital platform like Foodsmart would be significantly more affordable than standard treatment with diabetes medications [ - ].
There are some important limitations to note for this study. The first is that HbA1c levels were self-reported and were not clinically validated. However, these values should still be fairly accurate, particularly for participants with diabetes who used the app to track their HbA1c levels. Since participants were not required to enter HbA1c levels, we have reason to believe people who did—in particular, participants with diabetes—had purposefully entered their HbA1c levels rather than entering an arbitrary HbA1c level, which would lead to greater inaccuracy. Additionally, follow-up time was based on when the biometrics were entered, but did not necessarily line up with when the labs were conducted. Another issue is potential selection bias for participants with diabetes who choose to use the platform and are included in the study. For example, those with diabetes who use the app, particularly as a tracker, might be more inclined to want to make changes to their lifestyle. They may have made changes outside of what they did in the app that resulted in changes in HbA1c. Therefore, we cannot definitively conclude that Foodsmart's platform caused these changes in HbA1c, but there could be an association between using the platform and HbA1c changes. A randomized controlled trial must be conducted to determine if there is a causal link. In addition, there are other potential factors influencing diabetes status at baseline and changes in HbA1c that we might not be able to evaluate because certain types of data are not collected in the Foodsmart app. For example, we do not have participants’ personal or family medical histories to understand their influence on diabetes status . We are also unable to assess how the use of diabetic medications may influence HbA1c, as well as other medication-induced fluctuations in HbA1c. However, either prevalence of use of these medications or the incident hyperglycemia as a result of these medications in the US population is fairly rare [ ]. Some other influencing factors for HbA1c that we did not collect include sleep and amount of exercise [ , ]. We also did not account for socioeconomic factors, such as educational level, which might confound the associations seen and the accuracy of the self-reported biometrics, as stated earlier [ ]. Additional studies are required to obtain more information about these covariates. We also did not account for the frequency of use of the Foodsmart platform, which could affect the associations found. Finally, due to missing data for several biometrics (such as BMI) and only single values input for HbA1c, our study had a small sample size relative to the total number of participants who use the Foodsmart platform.
This study also has many strengths. To our knowledge, this is the first study that evaluated the real-life impact of behavior change with online food ordering, diet, and meal planning through a digital intervention and its impact on diabetes and HbA1c levels. Using Foodsmart’s large user base, this study was able to draw real-world associations between changes in dietary habits and HbA1c levels and the use of a commercial digital health platform. Furthermore, participants on the Foodsmart platform had a broad range of durations of enrollment; this allowed us to measure changes in HbA1c over different lengths of time, including time spans of greater than 2 years.
This study evaluated changes in self-reported HbA1c levels among participants with diabetes who were using a digital nutrition intervention with personalized recipe recommendations, meal planning, food ordering, and grocery discounts and price comparisons. Future research through a randomized controlled trial will be needed to assess the causal effect of the Foodsmart platform on dietary changes and improvements in HbA1c levels, the difference in cost between pharmaceutical and digital interventions, and which specific components of the dietary score are associated with a reduction in HbA1c levels.
The authors would like to thank Dr David Ashley for helping provide clinical expertise in reviewing the manuscript. The study was funded by Foodsmart.
BS acquired data, analyzed the data, interpreted the results, and drafted the manuscript. EAH designed the study, interpreted results, and drafted the manuscript. SB interpreted results and drafted the manuscript. KC acquired data. JL and JS interpreted results. All the authors reviewed and approved the final version of the manuscript and take responsibility for the manuscript.
Conflicts of Interest
BS, EAH, SB, JL, KC, and JS are employees of Foodsmart. EAH, JL, KC, and JS own Foodsmart stock.
- National Diabetes Statistics Report, 2020. Centers for Disease Control and Prevention. Atlanta, GA: Centers for Disease Control and Prevention, U.S. Dept of Health and Human Services; 2020. URL: https://www.cdc.gov/diabetes/pdfs/data/statistics/national-diabetes-statistics-report.pdf [accessed 2021-01-06]
- Dieleman JL, Baral R, Birger M, Bui AL, Bulchis A, Chapin A, et al. US Spending on Personal Health Care and Public Health, 1996-2013. JAMA 2016 Dec 27;316(24):2627-2646 [FREE Full text] [CrossRef] [Medline]
- American Diabetes Association. Economic Costs of Diabetes in the U.S. in 2017. Diabetes Care 2018 Dec;41(5):917-928. [CrossRef] [Medline]
- Chausmer AB. Zinc, insulin and diabetes. J Am Coll Nutr 1998 Apr;17(2):109-115. [CrossRef] [Medline]
- American Diabetes Association. 6. Glycemic Targets: Standards of Medical Care in Diabetes-2021. Diabetes Care 2021 Jan;44(Suppl 1):S73-S84. [CrossRef] [Medline]
- Luna B, Feinglos MN. Drug-induced hyperglycemia. JAMA 2001;286(16):1945-1948. [CrossRef] [Medline]
- Department of Health, State Government of Victoria, Australia. Diabetes - Long-Term Effects. BetterHealth Channel. URL: https://www.betterhealth.vic.gov.au/health/conditionsandtreatments/diabetes-long-term-effects#about-diabetes [accessed 2021-06-01]
- Mayo Clinic. Diabetes diet: Create your healthy-eating plan. URL: https://www.mayoclinic.org/diseases-conditions/diabetes/in-depth/diabetes-diet/art-20044295 [accessed 2021-06-01]
- Pastors JG, Warshaw H, Daly A, Franz M, Kulkarni K. The evidence for the effectiveness of medical nutrition therapy in diabetes management. Diabetes Care 2002 Mar;25(3):608-613. [CrossRef] [Medline]
- Pastors JG, Franz MJ, Warshaw H, Daly A, Arnold MS. How effective is medical nutrition therapy in diabetes care? J Am Diet Assoc 2003 Jul;103(7):827-831. [CrossRef] [Medline]
- Walker RE, Keane CR, Burke JG. Disparities and access to healthy food in the United States: A review of food deserts literature. Health Place 2010 Sep;16(5):876-884. [CrossRef] [Medline]
- Morello-Frosch R, Pastor M, Porras C, Sadd J. Environmental justice and regional inequality in southern California: implications for future research. Environ Health Perspect 2002 Apr;110 Suppl 2:149-154 [FREE Full text] [CrossRef] [Medline]
- Morland K, Wing S, Diez Roux A, Poole C. Neighborhood characteristics associated with the location of food stores and food service places. Am J Prev Med 2002 Jan;22(1):23-29. [CrossRef] [Medline]
- O'Neill M, Rebane D, Lester C. Barriers to healthier eating in a disadvantaged community. Health Education Journal 2016 Jul 27;63(3):220-228. [CrossRef]
- Mozaffarian D. Dietary and Policy Priorities for Cardiovascular Disease, Diabetes, and Obesity: A Comprehensive Review. Circulation 2016 Jan 12;133(2):187-225 [FREE Full text] [CrossRef] [Medline]
- Hu EA, Pasupuleti M, Nguyen V, Langheier J, Shurney D. Sustaining weight loss among adults with obesity using a digital meal planning and food purchasing platform for 12, 24, and 36 months: a longitudinal study. Nutr J 2021 Jan 21;20(1):8 [FREE Full text] [CrossRef] [Medline]
- Saslow LR, Summers C, Aikens JE, Unwin DJ. Outcomes of a Digitally Delivered Low-Carbohydrate Type 2 Diabetes Self-Management Program: 1-Year Results of a Single-Arm Longitudinal Study. JMIR Diabetes 2018 Aug 03;3(3):e12 [FREE Full text] [CrossRef] [Medline]
- Esposito K, Maiorino MI, Ciotola M, Di Palo C, Scognamiglio P, Gicchino M, et al. Effects of a Mediterranean-style diet on the need for antihyperglycemic drug therapy in patients with newly diagnosed type 2 diabetes: a randomized trial. Ann Intern Med 2009 Sep 01;151(5):306-314. [CrossRef] [Medline]
- Berman MA, Guthrie NL, Edwards KL, Appelbaum KJ, Njike VY, Eisenberg DM, et al. Change in Glycemic Control With Use of a Digital Therapeutic in Adults With Type 2 Diabetes: Cohort Study. JMIR Diabetes 2018 Feb 14;3(1):e4 [FREE Full text] [CrossRef] [Medline]
- Chomutare T, Fernandez-Luque L, Arsand E, Hartvigsen G. Features of mobile diabetes applications: review of the literature and analysis of current applications compared against evidence-based guidelines. J Med Internet Res 2011;13(3):e65 [FREE Full text] [CrossRef] [Medline]
- Cotter AP, Durant N, Agne AA, Cherrington AL. Internet interventions to support lifestyle modification for diabetes management: a systematic review of the evidence. J Diabetes Complications 2014;28(2):243-251 [FREE Full text] [CrossRef] [Medline]
- American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care 2013 Jan;36 Suppl 1:S67-S74 [FREE Full text] [CrossRef] [Medline]
- Subar AF, Thompson FE, Kipnis V, Midthune D, Hurwitz P, McNutt S, et al. Comparative validation of the Block, Willett, and National Cancer Institute food frequency questionnaires : the Eating at America's Table Study. Am J Epidemiol 2001 Dec 15;154(12):1089-1099. [CrossRef] [Medline]
- Chiuve SE, Fung TT, Rimm EB, Hu FB, McCullough ML, Wang M, et al. Alternative dietary indices both strongly predict risk of chronic disease. J Nutr 2012 Jun;142(6):1009-1018 [FREE Full text] [CrossRef] [Medline]
- Hendrie GA, Baird D, Golley RK, Noakes M. The CSIRO Healthy Diet Score: An Online Survey to Estimate Compliance with the Australian Dietary Guidelines. Nutrients 2017 Jan 09;9(1):47 [FREE Full text] [CrossRef] [Medline]
- Nordström A, Hadrévi J, Olsson T, Franks PW, Nordström P. Higher Prevalence of Type 2 Diabetes in Men Than in Women Is Associated With Differences in Visceral Fat Mass. J Clin Endocrinol Metab 2016 Oct;101(10):3740-3746. [CrossRef] [Medline]
- Ohlson LO, Larsson B, Svärdsudd K, Welin L, Eriksson H, Wilhelmsen L, et al. The influence of body fat distribution on the incidence of diabetes mellitus. 13.5 years of follow-up of the participants in the study of men born in 1913. Diabetes 1985 Oct;34(10):1055-1058. [CrossRef] [Medline]
- Wei M, Gaskill SP, Haffner SM, Stern MP. Waist circumference as the best predictor of noninsulin dependent diabetes mellitus (NIDDM) compared to body mass index, waist/hip ratio and other anthropometric measurements in Mexican Americans--a 7-year prospective study. Obes Res 1997 Jan;5(1):16-23 [FREE Full text] [CrossRef] [Medline]
- Hu FB, Manson JE, Stampfer MJ, Colditz G, Liu S, Solomon CG, et al. Diet, lifestyle, and the risk of type 2 diabetes mellitus in women. N Engl J Med 2001 Sep 13;345(11):790-797. [CrossRef] [Medline]
- Edelstein SL, Knowler WC, Bain RP, Andres R, Barrett-Connor EL, Dowse GK, et al. Predictors of progression from impaired glucose tolerance to NIDDM: an analysis of six prospective studies. Diabetes 1997 Apr;46(4):701-710 [FREE Full text] [CrossRef] [Medline]
- Folsom AR, Kushi LH, Anderson KE, Mink PJ, Olson JE, Hong CP, et al. Associations of general and abdominal obesity with multiple health outcomes in older women: the Iowa Women's Health Study. Arch Intern Med 2000 Jul 24;160(14):2117-2128. [CrossRef] [Medline]
- Dagenais GR, Auger P, Bogaty P, Gerstein H, Lonn E, Yi Q, HOPE Study Investigators. Increased occurrence of diabetes in people with ischemic cardiovascular disease and general and abdominal obesity. Can J Cardiol 2003 Nov;19(12):1387-1391. [Medline]
- Gummesson A, Nyman E, Knutsson M, Karpefors M. Effect of weight reduction on glycated haemoglobin in weight loss trials in patients with type 2 diabetes. Diabetes Obes Metab 2017 Sep;19(9):1295-1305. [CrossRef] [Medline]
- Taylor SI. The High Cost of Diabetes Drugs: Disparate Impact on the Most Vulnerable Patients. Diabetes Care 2020 Oct;43(10):2330-2332. [CrossRef] [Medline]
- Fitch K, Pyenson BS, Iwasaki K. Medical claim cost impact of improved diabetes control for medicare and commercially insured patients with type 2 diabetes. J Manag Care Pharm 2013 Oct;19(8):609-20, 620a. [CrossRef] [Medline]
- Little RR, Rohlfing CL. The long and winding road to optimal HbA1c measurement. Clin Chim Acta 2013 Mar 15;418:63-71 [FREE Full text] [CrossRef] [Medline]
- Nuckols TK, McGlynn EA, Adams J, Lai J, Go M, Keesey J, et al. Cost implications to health care payers of improving glucose management among adults with type 2 diabetes. Health Serv Res 2011 Aug;46(4):1158-1179 [FREE Full text] [CrossRef] [Medline]
- Sherifali D, Nerenberg K, Pullenayegum E, Cheng JE, Gerstein HC. The effect of oral antidiabetic agents on A1c levels: a systematic review and meta-analysis. Diabetes Care 2010 Aug;33(8):1859-1864 [FREE Full text] [CrossRef] [Medline]
- Trujillo JM, Nuffer W, Ellis SL. GLP-1 receptor agonists: a review of head-to-head clinical studies. Ther Adv Endocrinol Metab 2015 Mar;6(1):19-28 [FREE Full text] [CrossRef] [Medline]
- Cavaiola T, Pettus J. Management of Type 2 Diabetes: Selecting Amongst Available Pharmacological Agents. South Dartmouth (MA): Endotext; 2017.
- Klein BE, Klein R, Moss SE, Cruickshanks KJ. Parental history of diabetes in a population-based study. Diabetes Care 1996 Aug;19(8):827-830. [CrossRef] [Medline]
- Fathallah N, Slim R, Larif S, Hmouda H, Ben Salem C. Drug-Induced Hyperglycaemia and Diabetes. Drug Saf 2015 Dec;38(12):1153-1168. [CrossRef] [Medline]
- Nakajima H, Kaneita Y, Yokoyama E, Harano S, Tamaki T, Ibuka E, et al. Association between sleep duration and hemoglobin A1c level. Sleep Med 2008 Oct;9(7):745-752. [CrossRef] [Medline]
- de Lade CG, Marins JCB, Lima LM, de Carvalho CJ, Teixeira RB, Albuquerque MR, et al. Effects of different exercise programs and minimal detectable changes in hemoglobin A1c in patients with type 2 diabetes. Diabetol Metab Syndr 2016;8:13 [FREE Full text] [CrossRef] [Medline]
- Short ME, Goetzel RZ, Pei X, Tabrizi MJ, Ozminkowski RJ, Gibson TB, et al. How accurate are self-reports? Analysis of self-reported health care utilization and absence when compared with administrative data. J Occup Environ Med 2009 Jul;51(7):786-796 [FREE Full text] [CrossRef] [Medline]
|GLP-1: glucagon-like peptide 1|
|OR: odds ratio|
|SGLT2: sodium–glucose cotransporter 2|
Edited by G Eysenbach; submitted 30.07.21; peer-reviewed by A Dominguez-Rodriguez; comments to author 30.08.21; revised version received 22.09.21; accepted 26.09.21; published 18.10.21Copyright
©Benjamin Shea, Shivani Bakre, Keaton Carano, Jared Scharen, Jason Langheier, Emily A Hu. Originally published in JMIR Diabetes (https://diabetes.jmir.org), 18.10.2021.
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