When beginning to use an app, it may ask the user to provide personal information, such as an email, to allow the user to sign up and create an account to use the app. This allows for information on the mobile device to be saved as the user uses the app, linking the user’s information to their account. The review of papers underlined two methods for the storage of information on an app: the creation of a new account on the app’s software itself or signing in by linking to an already existing social media account. Of the 46 total apps discussed, 16 (35%) were said to have a login feature. One additional paper discussing this feature was asked not to specify the app portrayed. Doctor Diary [27], Akser Waznk [22], and Lose It! [61] were apps that were mentioned to use email addresses as a means of signing up to the platform. Akser Waznk was also described as offering the option of linking to a Facebook, Instagram, or X (formerly known as Twitter) account for users to sign up to the app [22].

Additionally, papers also described the process of entering personal and health information in order to accurately customize goals and recommendations. Akser Waznk [22], Lose It! [62], and Noom [52] were reported to ask users to enter their gender, age, height, and weight at the time of sign-up. Additionally, Akser Waznk had an optional step of adding health information, such as the user’s medications and chronic diseases [22]. Doctor Diary [27], MyFitnessPal [63], and FDDB extender [64] had mentions of a login feature requiring user information, but no further description was provided. The 18 other apps had no mention of the logging-in process, according to this study, although it is possible that this feature is present in these apps and has yet to be described.

When determining the acceptability of an app, the way that the process of calorie counting is done is crucial [65]. On that same note, an app that allowed for fast analysis of calorie content (meaning that the user needed to spend as little time as possible to log their calorie intake, as well as the ease of use of the said feature) was generally viewed as favorable [66]. Among the 68 papers, 17 (25%) included more than one app or did not mention the availability of calorie-counting functionality. The remaining 51 papers described 3 main methods of calorie counting: manual entry, picture-based entry, and barcode scanning.

Manual entry was generally described as the ability of the user to enter food products to get an estimation of total calories (ie, diary data entry). A total of 44 out of the 46 (95.6%) apps used calorie manual entry. Although the general concept of manual entry is well described, there is one significant difference when it comes to the functionality of calorie estimation: the integration of a food database. These databases, which could include up to 6 million foods within the app [67], provided a base of caloric information to be retrieved when users logged their consumption and removed the need for users to enter the calorie counting themselves. The databases could also be tailored to match certain population demographics (17/46, 37% apps had country-specific food databases). Once a food item was found, the user quantified the amount consumed. Of the 44 manual entry method apps, 26 (56.5%) had the use of a food database mentioned within their respective paper. Finally, it is worth noting that LIBIT had a voice recognition feature for logging meals [43].

Picture-based entry was described as the process of taking a picture of a food before consumption to get a calorie count estimation by the app. This was usually done through an integrated software that recognizes food items [62]. A total of 14 (30.4%) of the 46 apps mentioned this method of calorie counting (Figure 4).

The last method of calorie counting was through barcode scanning. This method relied on scanning barcodes of store-bought food items, which allowed the app to identify food items, and the user then logged the quantity consumed to get a calorie intake estimate [64]. A total of 17 (36.9%) of the 46 apps were mentioned to have this feature.

It is important to mention that certain apps include multiple methods for calorie counting. A total of 17 apps had both manual entry and barcode entry, while 5 apps had a combination of all 3 methods. These relationships are portrayed in Figure 4.

Another feature that was often described within the calorie-counting apps designed for individuals living with chronic health conditions was the ability for users to set goals within the app. Indeed, among the 46 individual apps discussed in papers, 24 (52.2%) had a form of goal setting. While the specific type of goal could vary in composition, the most commonly described is one that calculates a calorie budget or allotment via the input of a weight loss goal determined by the user, as well as the input of certain characteristics such as current weight, height, and physical activity level (usually done when going through the account setup). One method by which this caloric budget calculation was made was through the Harris-Benedict equation [29]. Goal setting in the form of a weight loss target and a caloric budget was clearly described by Wharton et al [68] and was found to be the most common type of goal setting since it is used by 17 of the 24 apps (70.8%) that have a goal setting functionality.

Of the remaining 7 apps, 6 had goal settings mentioned without specifying the way it was integrated, and 1 was mentioned to have goal setting in the form of nutritional balance, which consisted of encouraging users to preset a goal of servings per food category, such as fruits and vegetables [69].

Some mobile apps could interact with the user to encourage use of the app and its functionalities (ie, tracking). These interactions could be done while the user was actively using the app (synchronous) or when the app was not in current use (asynchronous), such as providing reminders to the user. Of the 46 total apps, 30 (65.2%) mentioned in the papers interacting with the user by using reminders or by providing intake recommendations and feedback. Notably, MyFitnessPal [67], Akser Waznk [22], Noom [70], EatsUp [29], nBuddy Diabetes [51], Lose It! [44], January AI [40], Keenoa [41], WeightWatchers (WW) [59], FatSecret, Cronometer [24], Yazio [60], MyPlate by Livestrong, Lifesum, SparkPeople, Argus, and MyMealMate [49] used reminder messages to log intake or to increase adherence to recommendations. Eisenhauer et al [71] found that text messages, such as Lose It!, would improve self-monitoring in users at 3 and 6 months. While the presence of reminder functionalities was extracted, the frequency or timing of these reminders was not consistently reported across studies and, therefore, was not included in the data synthesis.

Interactions also consisted of providing feedback and health information to the users. Doctor Diary [27], MyFitnessPal [67], DialBetics Lite [25], SAlBi educa [55,72], Nutritionix [53], January AI [40], LIBIT [43], EatsUp [29], MyMealMate [49], and Good Measures [37] provided feedback on nutrition. This consisted of proposition suggestions on energy intake and food recommendations. Gonzalez-Ramirez et al [55] stated that users found the tailored feedback messages to be one of the most useful features used in the SAlBi educa app and that 86.1% of users believed this feedback would have a positive effect on user diet, while Kay et al [53] stated that only 55% of users believed this feedback was useful in the Nutritionix app. Nutrition feedback was also conducted in different manners other than live in-app messages, such as providing health-related papers in Noom [73] or educational videos on nBuddy Diabetes [74]. Some apps also displayed the logging of calories in visual feedback for the users, which allowed a more comprehensive way of showcasing their data. Easy Diet Diary [28], MyFitnessPal [67], Lose It! [75], Fitbit [31], ENGAGED [30], SAlBi educa [55], and MyMealMate [49] were apps that mentioned presenting the user’s intake data in graphs or charts, according to the select papers.

Most of the apps studied in the selected papers had multiple functions other than calorie counting, for example, weight or blood glucose monitoring. Other key features described in the literature were related to the ways users can log health information. These key features included remote patient monitoring by health care providers, graphical or visual data, and sharing progress with other users. Some apps included information on recipes and color schemes, as well as the possibility to upgrade to a premium version (with more features and functionalities). Out of the 46 total apps, 34 (74%) were mentioned to have some of these features. Doctor Diary [27], EatsUp [29], LIBIT [43], Good Measures [37], MyFitnessPal [76], Lose It! [71], SAlBi educa [55], January AI [40], MyMacros+, Lifesum, SparkPeople, Argus, and WeightWatchers (WW) [13] had features other than calorie counting for users to track, such as weight tracking or glucose monitoring, according to the literature retrieved.

Sharing the user’s progress with health care providers could be done in real-time tracking or later, providing insight into the user’s tracking habits. A total of 15 (32.6%) out of the 46 total apps included this feature. Doctor Diary [27], Noom [70], EatsUp [29], Easy Diet Diary [28], nBuddy Diabetes [51], FDDB extender [64], LIBIT [43], Nutrinaut [42], Good Measures [37], Cronometer, MyNetDiary, WeightWatchers, Lifesum, MyPlate by Livestrong, and SparkPeople were described as apps that allowed health care providers to access the user’s tracking data. Where specified, health care providers involved were physicians and registered dieticians.

An additional key feature discussed for 15 (32.6%) of the 46 apps was the ability for users to have real-time chats with other users or to share their progress on the app for others to see. Lose It! offered discussion boards that would have benefited the users more if they were done in a synchronous manner, which would have increased the feeling of support, according to participants [71]. Akser Wankz and WeightWatchers also provided features for users to chat with others about their progress [22]. Other apps like iDAT, Yazio, Lifesum, SparkPeople, Argus, WeightWatchers, and Fooducate allowed users to share their progress directly to a social media platform, Facebook [78]. MyFitnessPal’s networking feature was assessed, and 80% of participants reported having “no friends” on the app, which highlighted the minimal use of this feature [79].

Color schemes were also discussed in select papers, providing an insight into the importance of aesthetics in applications. Out of the 46 total apps, 13 (28.3%) had discussions on the aesthetics used. Both EatsUp [29] and Akser Waznk [22] were discussed in terms of their use of color schemes. The use of colors allowed for an increase in appeal and encouraged the use of apps. According to Alshathri et al [80], MyFitnessPal, Fitbit, and Lose It! were also some of the highest-ranked apps in engagement and aesthetics.

Some apps also had the option of premium access, allowing the user to pay for more advanced features not included in the free version of the app, such as Noom [73], MyFitnessPal [81], and Lose It! [62]. In general, 13 (28.3%) of the 24 apps mentioned have a premium option, which offers exclusive functions with enhanced support and patient monitoring. Although premium features were available, most participants expressed a preference for free apps, with cost emerging as one of the primary factors influencing their app selection [64].

Overall, free, of charge, and self-explanatory apps were most preferred by users. Sharing their progress with others or through a platform were features deemed to be less important by users [66].

Calorie-counting apps that were available to the public used different methods to log calories and could provide recommendations to users for healthier options. For reliability, these apps could rely on evidence-based theories, use known food databases, or be developed by a team of health care professionals. Of the total 46 apps, 22 (47.8%) were mentioned in papers to use at least one of these methods. EatsUp [29], Nutritionix [53], Kit-Nutrition [42], and SAlBi educa [55] were apps mentioned in the retrieved literature to have been developed or maintained by health care professionals, such as dieticians or physicians.

As discussed previously, food databases were sometimes used by the apps to correctly estimate calorie counting for the user. This thereby provided reliable, accurate information to the users. Easy Diet Diary was described to use the Australian Food and Nutrient Database 2011-2012 [28] while FDDB extender used the FoodData Central of the US Department of Agriculture and the Souci-Fachmann-Kraut databases [64]. Additionally, SAlBi educa used the BEDCA database for reference during food logging [55]. January AI was also said to use known databases, but this was not further detailed in the chosen literature [40]. On the other hand, Nutritionix itself was highlighted as the largest verified database for nutrition information by [53].

In terms of evidence-based theories, these apps were mainly developed to manage chronic diseases, such as obesity or diabetes. They could thereby rely on chronic disease management or behavioral theories to develop an effective calorie-counting app, like the social cognitive theory. Akser Waznk [22], EatsUp [29], MyMealMate [82], SAlBi educa [72], and Good Measures [37] were apps that were described to have relied on evidence-based theories on nutrition or chronic disease management. Lee et al [73] rated Noom the highest score of information accuracy, but the paper did not provide additional information on the theories used to develop the app. Keenoa was evaluated to have moderate to strong relative validity compared to an Automated Self-Administered Dietary Assessment Tool, but more validity establishment was needed [41]. Table 4 summarizes the main identified features and functionalities, along with the number of papers where they were presented and a short description.

To develop a calorie-counting app that is well-suited for clinical implementation, it is important to consider some factors that keep users engaged. Motivation is one of the major factors fostering long-term app use and thus promoting adherence. Our scoping review showed that reminders, feedback, encouragement, and progress reports were well received by users (see the Acceptability section). For the best results, they should be thoughtfully spaced out so that they help users to stay on track without being too frequent to avoid fatigue or discouragement. This is especially true for the progress reports, where too frequent reports can lead to stagnant results and loss of motivation. In addition, allowing users to opt out of notifications to avoid feeling discouraged represents another associated recommendation. To increase motivation to use calorie-counting apps, other behavioral recommendations have been suggested, such as including game features in apps, delivering app-based interventions in a context of group competition, and providing financial incentives associated with app use [79].

Our scoping review suggested that greater user understanding of calorie intake and its health implications may be linked to better engagement with calorie-counting apps [85]. Accordingly, providing structured guidance on healthy food choices and strategies to address dietary lapses could support user adherence and long-term behavior change.

We formulated various recommendations related to the app function. First, ensuring regular app updates to stop the frequent app crashes would decrease frustration from app users. In addition, having an offline option would enable users to be more consistent by tracking calories when they do not have internet access. To reduce the workload and time spent tracking calories, we recommend offering users simplified calorie-counting options, such as only keeping track of high-calorie and low-nutrition food, for instance. Another recommendation to improve function while reducing workload for users would be to rely on artificial intelligence to perform automatic estimation of the calorie content of food items shown in pictures. This could significantly simplify the task of calorie logging for users and hence increase motivation and adherence.

The content of calorie-counting apps could also be improved. To reduce workload for users and increase app usefulness, we recommend including a variety of multiethnic, local, and home-cooked foods, as well as take-out meals and restaurant options for users to choose from when logging into dietary intake. Moreover, app content should reflect diverse cultural and racial backgrounds to ensure inclusivity and relevance across populations with different dietary habits and nutritional needs. In addition, to tailor the app for users with low vision, we recommend the use of simplified terminology and screen organization with clearly defined tabs that can be easily seen. Finally, including in the app content a tutorial for users to understand all the functions the apps have to offer also represents a key recommended improvement for calorie-counting apps.