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A key challenge in human nutrition is the assessment of usual food intake. This is of particular interest given recent proposals of eHealth personalized interventions. The adoption of mobile phones has created an opportunity for assessing and improving nutrient intake as they can be used for digitalizing dietary assessments and providing feedback. In the last few years, hundreds of nutrition-related mobile apps have been launched and installed by millions of users.
This study aims to analyze the main features of the most popular nutrition apps and to compare their strategies and technologies for dietary assessment and user feedback.
Apps were selected from the two largest online stores of the most popular mobile operating systems—the Google Play Store for Android and the iTunes App Store for iOS—based on popularity as measured by the number of installs and reviews. The keywords used in the search were as follows: calorie(s), diet, diet tracker, dietician, dietitian, eating, fit, fitness, food, food diary, food tracker, health, lose weight, nutrition, nutritionist, weight, weight loss, weight management, weight watcher, and ww calculator. The inclusion criteria were as follows: English language, minimum number of installs (1 million for Google Play Store) or reviews (7500 for iTunes App Store), relation to nutrition (ie, diet monitoring or recommendation), and independence from any device (eg, wearable) or subscription.
A total of 13 apps were classified as popular for inclusion in the analysis. Nine apps offered prospective recording of food intake using a food diary feature. Food selection was available via text search or barcode scanner technologies. Portion size selection was only textual (ie, without images or icons). All nine of these apps were also capable of collecting physical activity (PA) information using self-report, the global positioning system (GPS), or wearable integrations. Their outputs focused predominantly on energy balance between dietary intake and PA. None of these nine apps offered features directly related to diet plans and motivational coaching. In contrast, the remaining four of the 13 apps focused on these opportunities, but without food diaries. One app—FatSecret—also had an innovative feature for connecting users with health professionals, and another—S Health—provided a nutrient balance score.
The high number of installs indicates that there is a clear interest and opportunity for diet monitoring and recommendation using mobile apps. All the apps collecting dietary intake used the same nutrition assessment method (ie, food diary record) and technologies for data input (ie, text search and barcode scanner). Emerging technologies, such as image recognition, natural language processing, and artificial intelligence, were not identified. None of the apps had a decision engine capable of providing personalized diet advice.
Noncommunicable diseases such as diabetes and cardiovascular diseases account for almost two-thirds of deaths globally. The general recommendations for addressing this epidemic are related to lifestyle changes, mainly encouraging healthy diets, physical activity (PA), and the reduction of tobacco use and alcohol consumption [
Valid dietary intake recording is key for nutritional intervention. The methods used for collecting food intake data can be classified in a number of ways. Based on the time of the collection, the retrospective methods, such as the 24-hour food recall and the food frequency questionnaire (FFQ), require memory for recollection of foods eaten. In contrast, the prospective methods require diet reporting as the consumption occurs, acting as food diaries. In clinical nutrition, prospective methods are usually applied between 4 and 7 days. It is also possible to classify the methods as quantitative daily consumption or food frequencies. The first group focuses on recording the detailed food consumption as accurately as possible, typically for a couple of days. The latter assesses typical consumption patterns over longer periods [
With the proliferation of mobile phones and tablets, there has been a rise in the number of software apps aimed at improving nutrition and physical fitness. The simple digitalization of input data is important and useful, but these devices have built-in capabilities that can increase the accuracy of data collection and decrease the time burden of the process and possible biases [
Due to the large number of nutrition-related apps, it is difficult to understand what these apps are offering and how the apps compare with each other. This study aims to review the main features and technologies used by popular nutrition-related apps available in the online market and to analyze their use of emerging technologies in the field of online nutrition assessment and intervention. This review will be beneficial for industry, academia, and health professionals who are interested in taking advantage of the benefits of technology in nutrition assessment and intervention.
During the publication of a mobile app, a developer specifies in which stores—usually divided by countries—the app will be available. They also specify what device requirements (eg, versions of the operating system and mobile phone or tablet) are necessary in order to install the app. Searching for apps from a specific device in a particular country can alter the apps that appear available to the user. In order to mitigate this, the initial search was conducted on a desktop personal computer (PC) not logged into any particular user account, but located in the United Kingdom. Searches were conducted in November 2015.
For the Google Play Store, the initial search was executed using the Google Chrome browser in an incognito window (ie, private mode), logged off from the Google account, using the following keywords: calorie(s), diet, diet tracker, dietician, dietitian, eating, fit, fitness, food, food diary, food tracker, health, lose weight, nutrition, nutritionist, weight, weight loss, weight management, weight watcher, and ww calculator. An initial list of popular apps, ordered by number of installs and reviews, was created. For the iTunes App Store, the initial search was performed via iTunes—software provided by Apple—logged off from any user account. The apps were ordered by number of reviews because the App Store does not list the number of installs. The user rating was used as an exclusion criterion. The rating range is between 0 and 5 and represents the user satisfaction with the app, with 5 being the most satisfied. Apps were excluded if ratings were below 3, in order to avoid considering apps that were downloaded by many users but may not be in use (eg, because they were not working properly or did not deliver what was advertised in the store). Apps which only monitored weight or PA, such as Google Fit, or that only provided recipes were also not considered. After the creation of an initial list of apps, user accounts linked with a UK address and credit card were used to install the apps and verify the apps against the inclusion criteria.
Once the apps were installed, their features were reviewed from both nutritional and technological perspectives. From the nutritional perspective, features in the following categories were considered: dietary intake, phenotype, physical activity, and others. The technological perspective analyzed what technologies were being used in order to compare with emerging technologies in the field of human nutrition assessment and intervention. The functionalities were analyzed in two main groups: input and output features. Features that required data from the user (eg, weight and height) were considered as input features, while the results shown to the user were termed output features.
In the Google Play Store, it is not possible to sort the results by number of installs. It has an internal algorithm that classifies the relevance of the apps and presents them in a list. For this reason, it was necessary to open the first 20 results by keyword to get the number of installs in order to mitigate the risk of missing an app with a high number of installs. The app list created in this process was ordered by number of installs and a total of 21 apps with greater than 500,000 installs were identified (see
Popular (>500,000 installs) nutrition-related apps available in the UK Google Play Store.
App namea | Abbreviation | Installs (range), n | Reviews, n | Rating (0-5) |
S Health-Fitness Diet Tracker | SH | 100m-500m | 33,619 | 3.7 |
Calorie Counter-MyFitnessPal | MFP | 10m-50m | 1,140,897 | 4.6 |
Calorie Counter by FatSecret | FS | 10m-50m | 178,438 | 4.3 |
Noom Coach: Weight Loss Plan | NC | 10m-50m | 161,237 | 4.3 |
My Diet Coach-Weight Lossb | MDC | 5m-10m | 102,318 | 4.3 |
Lose it! by FitNow Inc | LI | 5m-10m | 45,391 | 4.4 |
Weight Watchers Mobilec | WW | 1m-5m | 66,897 | 3.9 |
Lose Weight Without Dieting | LW | 1m-5m | 56,617 | 4.6 |
Lifesum-The Health Movement | LS | 1m-5m | 46,856 | 4.2 |
Diet Point-Weight Loss by Diet Pointd | DP | 1m-5m | 28,906 | 4.2 |
My Diet Diary Calorie Counter | MDD | 1m-5m | 17,711 | 4.1 |
Effective Weight Loss Guided | EWL | 1m-5m | 16,156 | 4.1 |
Diet Assistant-Weight Lossd | DA | 1m-5m | 10,722 | 3.9 |
Calorie Counter by Calorie Count | CC | 1m-5m | 7529 | 4.0 |
MyNetDiary Calorie Counter PROe | N/Af | 500,000-1m | 10,405 | 4.4 |
Weight Watchers Mobile UKe | N/A | 500,000-1m | 9896 | 3.7 |
Calorie Counter & Diet Trackere | N/A | 500,000-1m | 9306 | 4.3 |
WWDiary by Canofsleepe | N/A | 500,000-1m | 8564 | 4.6 |
Calorie, Carb & Fat Countere | N/A | 500,000-1m | 7923 | 4.3 |
Diet Plan-Weight loss 7 dayse | N/A | 500,000-1m | 5013 | 3.8 |
Calculator & Tracker for WWPPe | N/A | 500,000-1m | 1898 | 3.8 |
aResults from November 2015.
bMy Diet Coach provides some diet recommendations in the free version. The food diary is available only in the
cThis app was later excluded due to subscription.
dDiet Point, Effective Weight Loss, and Diet Assistant are not food diaries, but they provide diet recommendations via diet plans.
eThese apps were later excluded due to minimum threshold.
fN/A: not applicable.
All of the apps were in the “health & fitness” category of the store. No app was excluded by the rating criterion (ie, rating <3). However, although the Weight Watchers (WW) app is free to download, a subscription—£12.95 monthly for the online plan—was required to join the online program [
The same search keywords were used in the iTunes App Store (see
Nutrition-related apps available in the UK iTunes App Store, ordered by number of reviews.
App namea | Abbreviation | Reviews, n | Rating (0-5) |
Calorie Counter and Diet Tracker by MyFitnessPal | MFP | 108,072 | 4+ |
Calorie/KJ Counter and Food Diary by MyNetDiary | N/Ab | 6484 | 3.5 |
Calorie/KJ Counter PRO by MyNetDiary | N/A | 3818 | 4+ |
Lifesum-Healthier living, better eating | N/A | 2952 | 3.5 |
Tap and Track-Calorie Counter | N/A | 2317 | 3.5 |
Easy Weight Loss Tips, by Michael Quachc | N/A | 2286 | 2.5 |
Calorie Counter and Diet Tracker by Calorie Count | N/A | 1716 | 4 |
Calorie Counter+ by Nutratech | N/A | 1501 | 4+ |
Argus-Calorie Counter and Activity Tracker | N/A | 1291 | 4 |
Calorie Counter by FatSecret | N/A | 1048 | 3.5 |
aResults from November 2015.
bN/A: not applicable.
cThis app was not included in the analysis due to a rating of less than 3.
One app did not meet the rating criterion—Easy Weight Loss Tips, by Michael Quach, rating 2.5—and was, therefore, excluded. The most reviewed app— Calorie Counter and Diet Tracker by MyFitnessPal (MFP), with 108,072 reviews—had around 17 times more reviews than the second-most reviewed app, which had 6484 reviews. As the latter had fewer reviews than the least popular of the apps included from the Google Play Store—Calorie Counter by Calorie Count (CC), with 7529 reviews—only MFP was considered suitable for inclusion in the study. However, since MFP had already been included from the Google Play Store list and because an initial assessment of both the Google Play Store and iTunes App Store versions of the app did not reveal any notable differences, only the Google Play Store version was used in subsequent analysis.
Input features were analyzed for four categories of recording: dietary intake, phenotype, PA, and others (eg, personal reminders) (see
Nutrition-related app input features for dietary intake and phenotype.
Feature/app | SHa | MFPb | FSc | NCd | LIe | LWf | LSg | MDDh | CCi | MDCj | DPk | EWLl,m | DAn,o | |
Text search | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | N/Ap | N/A | N/A | N/A | |
Barcode scanner | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | N/A | N/A | N/A | N/A | |||
Serving size | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | N/A | N/A | N/A | N/A | |
Food by meal | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | N/A | N/A | N/A | N/A | |
Favorite foods | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | N/A | N/A | N/A | N/A | |
Create meal or recipe | ✓ | ✓ | ✓ | N/A | N/A | N/A | N/A | |||||||
Add kcal/kJ | ✓ | ✓ | ✓ | N/A | N/A | N/A | N/A | |||||||
Water consumption | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | N/A | N/A | ||||
Water settings | ✓ | ✓ | ||||||||||||
Macronutrients |
✓ | N/A | N/A | N/A | N/A | |||||||||
Save photo | ✓ | ✓ | ||||||||||||
Current weight | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |
Height | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ||
Gender | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |
Age/date of birth | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |||
Waist circumference | ✓ | ✓ | ||||||||||||
Hips circumference | ✓ | ✓ | ||||||||||||
Neck circumference | ✓ | ✓ | ||||||||||||
Target weight | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |||||
Target dateq | ✓ | ✓ | ✓ | |||||||||||
Body type | ✓ |
aSH: S Health.
bMFP: MyFitnessPal.
cFS: FatSecret.
dNC: Noom Coach.
eLI: Lose it!.
fLW: Lose Weight Without Dieting.
gLS: Lifesum.
hMDD: My Diet Diary.
iCC: Calorie Count.
jMDC: My Diet Coach.
kDP: Diet Point.
lEWL: Effective Weight Loss.
mWeight and height for body mass index (BMI) calculation. Age and gender for calorie calculation.
nDA: Diet Assistant.
oWeight and height for BMI calculation. Age and gender for profile.
pN/A: not applicable. These features were assessed only in apps providing food diaries.
qTarget date in
Nutrition-related app features for physical activity and other input features.
Feature/app | SHa | MFPb | FSc | NCd | LIe | LWf | LSg | MDDh | CCi | MDCj | DPk | EWLl | DAm | |
Type of PAn | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |||||
Native GPSo | ✓ | ✓ | ||||||||||||
Third-party GPS |
✓ | ✓ | ✓ | ✓ | ✓ | |||||||||
Integration with wearablesq | ✓ | ✓ | ✓ | ✓ | ✓ | |||||||||
Pedometer | ✓ | ✓ | ||||||||||||
Average activity level | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |||||||
Exercise goal | ✓ | ✓ | ✓ | ✓ | ||||||||||
Community forums | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||
Personal reminders | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||
Challenges | ✓ | ✓ | ✓ | |||||||||||
Health conditions | ✓ | |||||||||||||
Daily notes | ✓ | ✓ |
aSH: S Health.
bMFP: MyFitnessPal.
cFS: FatSecret.
dNC: Noom Coach.
eLI: Lose it!.
fLW: Lose Weight Without Dieting.
gLS: Lifesum.
hMDD: My Diet Diary.
iCC: Calorie Count.
jMDC: My Diet Coach.
kDP: Diet Point.
lEWL: Effective Weight Loss.
mDA: Diet Assistant.
nMDD does not calculate the energy by type of activity, but asks the user to enter the amount of calories spent in the physical activity (PA).
oGPS: global positioning system.
pMFP integrates with other apps provided by the same company. FS integrates with Google Fit.
qLS provides wearable integration only after upgrade to paid version.
Nutrition-related app output features.
Feature/app | SHa | MFPb | FSc | NCd | LIe | LWf | LSg | MDDh | CCi | MDCj | DPk | EWLl | DAm | |
Calculated energy (kcal) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | N/An | ✓ | ✓ | N/A | |
Macronutrients |
✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | N/A | N/A | N/A | N/A | ||
Micronutrients |
✓ | ✓ | ✓ | ✓ | ✓ | N/A | N/A | N/A | N/A | |||||
Nutrition facts | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | N/A | N/A | N/A | N/A | ||
Calories by meal | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | N/A | N/A | N/A | N/A | |
Recommended |
✓ | ✓ | ✓ | ✓ | ✓ | |||||||||
Maximum calories to reach a target weight | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |||||
Calories of the new recipe | ✓ | ✓ | ✓ | ✓ | N/A | N/A | N/A | N/A | ||||||
Diet plan | ✓ | ✓ | ✓ | ✓ | ||||||||||
Shopping list | ✓ | |||||||||||||
Energy by type of PAp | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||
Weight progress | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |||
Circumferences monitoring | ✓ | ✓ | ||||||||||||
Body mass index | ✓ | ✓ | ✓ | ✓ | ✓ | |||||||||
Forums or blogs | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |||||||
Social media sharing | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||||
Private social media | ✓ | ✓ | ✓ | ✓ | ||||||||||
Sharing with |
✓ | |||||||||||||
Healthy habits/ |
✓ | ✓ |
aSH: S Health.
bMFP: MyFitnessPal.
cFS: FatSecret.
dNC: Noom Coach.
eLI: Lose it!.
fLW: Lose Weight Without Dieting.
gLS: Lifesum.
hMDD: My Diet Diary.
iCC: Calorie Count.
jMDC: My Diet Coach.
kDP: Diet Point.
lEWL: Effective Weight Loss.
mDA: Diet Assistant.
nN/A: not applicable. Features assessed only in apps providing food diaries.
oPA: physical activity.
pMDD does not calculate PA since it asks for the amount of calories instead of type of PA and duration.
My Diet Coach (MDC), Diet Point (DP), Effective Weight Loss (EWL), and Diet Assistant (DA) were not evaluated for some criteria because they are not food diaries; rather, they propose diet recommendations using different approaches. Food items could be selected by
The most common phenotype inputs were current weight, height, gender, and age (see
For reporting PA (see
Eight of the apps had internal forums, similar to blogs, where users post questions and recipes and can share information (see
My Diet Diary (MDD) was the only software that required information about
Output features refer to the data and results presented by the app to the users. In terms of nutrition assessment and diet recommendation, food diaries had similar features in terms of feedback on calories and macronutrients (ie, protein, fat, and carbohydrates) (see
Five apps provided information on micronutrient intake. MFP and SH provided tables with the daily micronutrient intake (eg, sodium, potassium, vitamin C, and iron) and the consumption
The apps that monitored dietary intake did not provide diet plans. In contrast, diet plans were the focus of DP, EWL, and DA. These apps suggested diet plans, divided by meals during the day. DP also suggested a related shopping list to the users. MDC followed a distinct approach providing generic diet recommendations via challenges and tips. Some examples of these general tips are “drink a flavored coffee (up to two cups a day),” “reduce your carbs consumption,” “restrain yourself, eat an apple instead,” and “eat a low fat yogurt.”
In terms of nutritional assessment, SH had an interesting feature named nutrient balance score. During the day, it showed this score (0-100) based on the nutritional value of the recorded daily food intake. It was not clear if this was calculated from the macronutrient distribution only or micronutrients and other possible variables. Similarly, CC had a grade (eg, A-, D+, and F) for the nutritional analysis and highlighted with colors (ie, green, yellow, and red) if the nutrients were within the recommended threshold.
The apps also had output features related to PA and phenotype (see
In addition, this review identified the existence of
As mentioned, MDC is not a food diary. It has a clear motivational focus using virtual rewards via the Healthy Habits (HH) points, which can be obtained by drinking more water, eating vegetables, or parking the car far away from one’s destination.
The most popular dietary intake apps available in November 2015 used prospective nutrition assessments. The focus of the food diaries was on the balance between the food intake and energy expenditure, with personalized recommendation of diet plans not featuring in these apps. The four generic diet plans were based on a number of inputs required from the user—weight, height, gender, and age—without subsequent dietary intake assessment. The feature for saving favorite foods and meals is an effective time-saving feature, mainly for those who consume the same food items frequently. Three apps allowed the user to set a date for reaching a target weight, but only NC limited the weight loss rate.
There is a general focus on weight loss and calorie counting, with the majority of apps containing either
A quantitative approach is the usual strategy used by apps to balance the energy content of diets with energy expenditure. Data from the diet diary is used as the estimated energy intake and the basal metabolic rate, and the energy expended through physical activities as the energy expenditure. However, this method does not take into account the quality of foods consumed. For instance, the distribution of food groups, as recommended by some public health organizations, is not considered [
All the apps collecting dietary intake used the same nutrition assessment method (ie, food diary record). However, there are alternative methods that are less time-consuming, such as the 24-hour recall method [
Within the apps offering food diaries, aspects of PA monitoring were available via the use of GPS or wearables. These features allow users to monitor their outdoor activities (eg, walking and running) and the use of application programming interfaces (APIs) plays an important role in these integrations because they are created to facilitate the communication with other external apps. In general, the wearable devices collect data and save them in their own systems and allow third-party apps, such as the nutrition-related apps, to import that data via APIs. In addition, indoor activities can be logged by selecting the type of activity and duration. Using the same strategy, LS and MFP provided the possibility to import weight measurements from Withings body scales (Withings Inc, Cambridge, MA), which can measure weight, BMI, and heart rate and send this information via Wi-Fi to the Internet [
Emerging technologies, such as image recognition and natural language processing, are not present in the most popular nutrition apps. The combination of these technologies could simplify the food and portion selection processes. Image recognition seems to be promising for recognizing food items and estimating their portion sizes [
There is room for improvement in terms of connecting users and health professionals, in that the process of making diet recommendations could include more input from trained professionals. An automated system that offers personalized nutrition advice was proposed and developed by the Food4Me study, based on a decision tree created by nutritionists and dietitians [
We acknowledge that the Google Play Store and the iTunes App Store have different app-ranking systems and market share. Hence, using the lowest number of reviews for the included Google Play Store apps as a threshold for including apps from the iTunes App Store may not reflect the number of downloads from the iTunes App Store. It is difficult to directly compare app popularity between the two stores, as the number of downloads from the iTunes App Store is not publically available. As the Google Play Store does not provide the exact number of installs, it is possible that some apps in the range
Chen et al have recently published research assessing the most popular mobile phone apps for weight loss used in Australia [
A total of 13 apps that had at least 1 million installs were identified. Nine of the apps collected dietary intake, all using the same assessment method (ie, food diary record). Food selection was accomplished via text search and barcode scanning. Portion size selection was conducted by selecting text, and not by images or icons. Image recognition, natural language processing, and artificial intelligence did not feature in the apps. There is significant opportunity for improvement in terms of personalized nutrition, which could include individualized feedback, diet plans, or nutrition education.
application programming interface
behavior change technique
body mass index
Calorie Count
National Council of Technological and Scientific Development
comma separated value
Diet Assistant
Diet Point
Effective Weight Loss
food frequency questionnaire
FatSecret
global positioning system
Healthy Habits
Lose it!
Lifesum
Lose Weight Without Dieting
My Diet Coach
My Diet Diary
MyFitnessPal
not applicable
Noom Coach
physical activity
personal computer
S Health
Weight Watchers
RZF was responsible for the data collection and analysis and drafting the manuscript. All authors contributed to the research design, reviewed and edited the manuscript, and approved the final version for publication. RZF is sponsored by the National Council of Technological and Scientific Development (CNPq) from the Brazilian government, via the
None declared.