Introduction

Obesity is the fifth leading cause of mortality worldwide, with at least 2.8 million deaths each year from direct and indirect complications [1, 2]. Globally there are 650 million obese adults, a prevalence of 13%, which has nearly tripled between 1975 and 2016 [3]. In Indonesia, obesity prevalence has steadily increased from 2007–2018, from 10.5% to 21.8% [4].

The treatments for obesity include dietary modifications, physical exercise, pharmacological interventions, and surgery [5,6,7]. A restricted balanced diet is a standard recommendation for people with obesity [8]. A sustainable diet would be both healthy and protective of biodiversity and planetary health, and would meet nutritional needs and environmental impact requirements [9,10,11,12,13,14]. The working group of the UK's Green Food Project, set up by the Department for the Environment, Food and Rural Affairs, has produced principles and guidelines for a sustainable diet: consumption of more plant-based foods, the inclusion of at least five portions of fruits and vegetables per day, valuing food by asking where it comes from and how it is produced, moderation in meat consumption, enjoying more peas, beans, nuts, and other sources of plant-based protein, choosing fish from sustainable stocks, the inclusion of milk and dairy products while seeking plant-based alternatives including those fortified with additional vitamins and minerals, drinking tap water, and eating fewer foods high in fat, sugar, and salt [9], as was also outlined by the EAT-Lancet Commission on a healthy diets from sustainable food systems [15].

The main challenge in obesity management is maintaining compliance with weight loss programs. Mobile applications (apps) are tools that may potentially enhance short- and long-term compliance with weight reduction programs [16, 17]. Self-monitoring with apps may enable individuals to become aware and capable of identifying success or failure in meeting set goals. Indeed, self-monitoring of behaviors during weight management programs has been shown to facilitate adherence and lead to increased effectiveness [18]. Increased monitoring has been associated with greater efficacy for weight loss. In fact, weight measurement every one to three days has been shown to improve weight loss [19].

In Indonesia, 67.2% of the population (more than 183 million people) currently use smartphones with internet connections [20]. While there are many smartphone apps for dietary tracking and meal management, few available apps provide menus with Indonesian foods, and those that exist are still very limited. Therefore, the study aimed to design, deploy, validate and assess a mobile app that included the appropriate content, graphic design, and logic flow to enhance compliance with weight control regimens and selecting healthy and sustainable foods. We report herein the acceptance of a mobile app's contents, features, and design for a balanced and sustainable diet.

Methods

Design and setting

The study had four stages: formative research, app development, assessment of acceptance, and a validity test. In the formative research stage, qualitative methods were employed to obtain information from users to conceptualize the app's development. The development stage included selecting a developer and programming language, and building the app design. The acceptance stage assessed the initial development and aimed to improve the app's performance. The validity stage compared compliance tracking with the app to tracking with conventional paper records. The study was approved by the Health Research Ethics Committee of the Faculty of Medicine Universitas Indonesia – Dr. Cipto Mangunkusumo General Hospital (FMUI–RSCM) with ethical number 829/UN2.F1/ETIK/2017.

Procedures

Formative research was critical for developing the app and consisted of two phases: a literature review and focus group discussions. The literature review identified the relevant evidence-based theories on innovative obesity management programs, and potential gaps in current interventions (Table 1). A crucial step in developing the app was to systematically examine previous research about weight loss guidelines for people with obesity, the existence of balanced and sustainable diet programs, and other available dietary mobile apps. This identified methods by which weight loss guidelines had been applied and how effective they were as short- to medium- or long-term behavioral change strategies. Subsequently, previous studies were translated into technical requirements, and used by the information technology specialists to guide app development. This meant the app's technical features (e.g., push notifications and the user interface) and the tailored interactive content were all rooted in an evidence- and theory-based framework of a sustainable diet and weight loss guidelines for people with obesity.

Table 1 Key points from the literature review on the management of obesity and targeted weight loss
Full size table

Papers published between 2013 and 2017 from several databases, including PubMed, NCBI, ScienceDirect, Elsevier, Plos One, and Biomed Central, were reviewed. The keywords used were a combinations of "balanced and sustainable diet", "obesity", "weight loss", and "mobile apps". In the next step of the formative research, the app's feasibility was tested by organizing the focus group discussions (FGDs). Subjects were recruited in September 2017 and consisted of 39 healthy government employees comprising 30 males and nine females aged 19–64 years who lived in Jakarta, Indonesia, and owned Android smartphones. Participants were divided into six groups (3–5 people in each group) based on their education levels (senior secondary school and graduate), dietary habits (balanced diet or not), and physical activity as very active or not (i.e. sedentary, low activity and active) [29,30,31]. Each group was asked about their sociodemographic characteristics such as age, sex, and occupation and their prior knowledge of balanced and sustainable diets, history of using food records, history of using dietary mobile apps, and the kind of app that they expected would be useful.

The second stage of this study included developing the mobile app for the Android platform by collaborating with information technology application developers, graphic designers, communication experts, physicians, and dietitians. The app was developed using Android Studio IDE and the Java programming language. Backend support and data processing used web-based server systems, and MySQL was used as the database management system. The app was compatible with Android version 4.4 (KitKat) and above. Physicians identified the total calories, carbohydrates, proteins, fats, and other micronutrients needed by each user, while the dietitians helped to make menu recommendations. The total caloric needs were calculated based on the Harris-Benedict Equation [32]. Hence, the macronutrient requirements were divided into 55% carbohydrate, 20% protein, and 25% fat. Using these features, the app provided users with guidance on attaining a balanced and sustainable diet and increased awareness of staying healthy throughout their dietary programs. The app could guide people to achieve weight loss by suggesting adjusted menu recommendations, monitoring habitual and actual intakes (calories, macronutrients, and micronutrients), physical activity, and greenhouse gas emission (GHGE). The app enabled people to track their targets for losing weight, body mass index (BMI), waist circumference, and body compositions (i.e., fat mass and fat-free mass). Also, the app helped users to monitor their insulin resistance indicators (fasting glucose, fasting insulin, HOMA IR, HbA1C), lipid profiles, uric acid levels, and inflammation markers, such as TNFα and hsCRP. Additionally, the app facilitated communication with physicians to ask questions about intake profiles, health conditions, or other nutrition and health concerns.

The third stage was the assessment of acceptance of the app, which was named EatsUp®. Subjects were recruited in January 2018 and consisted of 19 healthy males and 34 healthy females aged 19–64 years who lived in Jakarta, Indonesia, and had the willingness to join the study, and owned Android smartphones. Subjects with interest and consenting to this study were mostly females. This is in line with a study conducted by Mohajer, et al. (2019) that reported study cooperation of female participants tended to exceed that of males [33].

A minimum sample size of 53 subjects was calculated based on previous studies that were analogous to the formative research (stage one) of this study. The results of the acceptability study are described in Table 4 [34]. Each subject installed EatsUp® on their Android smartphone and was shown how to use it. Sociodemographic information, including age, sex, and occupation, was recorded. EatsUp® allowed users to input their weight, height, and date of birth, which were then used automatically to calculate BMI, nutritional status classification, total energy expenditure, and dietary macronutrient composition. The app permitted users to enter specific types of foods or beverages and the respective dietary data, and this information was later used to build the dietary record. After one to two days, the subjects were interviewed regarding their acceptance of EatsUp® in terms of contents, design, and logic flow. They were also asked to comment on the limitations and advantages and suggest future improvements.

A validity test for EatsUp®was performed in the fourth stage. Subjects comprised 30 overweight or obese women aged 19–59 years with BMI ≥ 25.0 kg/m2 who had Android smartphones [35]. This sample size was determined based on a rule of thumb [36], and in our study yielded a power of 48% to discern a difference of 26.4% for mean difference of energy intake between a paper-based method and the app. Subjects underwent anthropometry assessments for body weight and height, and dietary assessments using a paper-based 2 × 24-h food recall method. Subjects were asked to use EatsUp® for a week, starting with the personal data registration process, input of individual body weight and height for the nutritional status and individual total energy requirement calculation, and inputting all food and drinks consumed.

Data analysis

Qualitative analysis was performed to describe the formative research using matrix analysis. A descriptive analysis was performed to illustrate the characteristics of the subjects and the acceptance of the app. The subjects' characteristics and the 17 key assessment responses were described in frequency and percentage as indicated in Table 2. Three rankings were used to describe EatsUp® based on subjects' preferences: good, moderate, and poor. The statistical significance of the differences between statements with different rankings was measured using the Kolmogorov–Smirnov Z-test. The validation test was measured using a one-sample T-test, and the Bland–Altman plot defined the agreement for both methods.

Table 2 Characteristics of subjects in the formative research, acceptance, and validity testing stages of the application
Full size table

Results

From the matrix analysis of FGD results on Table 3, some participants lacked knowledge about a balanced and sustainable diet, 80% did not realize they were practicing it, and 85% had never known about food records, but 45% knew about existing dietary mobile apps and 70% had not used them. Participants stated they needed an easy, simple, attractive, informative, and user-friendly app that could monitor calorie intake and dietary composition and provide nutrition facts, menu recommendations, and energy output from physical activity.

Table 3 Topics and statements based on a matrix analysis of the focus group discussions among Indonesian adults
Full size table

In the development stage, pictures of food, detailed food items (name of food, portion size, and nutrient content), and menu recommendations were uploaded into the EatsUp®database. Detailed food items were uploaded from the NutriSurvey database that was based on Indonesian food composition tables [37].

Menu recommendations and pictures of foods were based on balanced and sustainable diets and uploaded to the server database. Calculations of the BMI and total energy expenditure were based on the Harris-Benedict equation, and programmed to provide nutritional status and total energy expenditure for each individual. EatsUp® had a push notification reminder to alert the user of deviations from the recommendations (calories, macronutrient composition, water, and GHGE).

Table 4 indicates that the app was well-accepted regarding content, graphics, and logic flow. However, some parameters scored below 70%, such as understanding of terminology (57%), ability to monitor the goal (68%), and easy-to-follow menu recommendations (62%). Based on the Kolmogorov–Smirnov Z test, there was no significant difference between the proportions for acceptance of content, graphics, and app flow (p > 0.05), as indicated in Fig. 1.

Table 4 Acceptance of the mobile application by adults in Jakarta (n = 53)
Full size table
Fig. 1
figure 1

The proportion of rankings of statements from the subjects (n = 53)

Full size image

The mean difference for energy intake between the EatsUp® app and the paper-based method was not significant (9.96 ± 39.2 kcal/day without excluding outliers; and 2.63 ± 28.4 kcal/day after excluding outliers, p > 0.05). The lower and upper limits of agreement (LOA) between energy intake methods ranged from -8.4 to 24.9 kcal/day. According to Figs. 2, A, and B, the Bland–Altman plots for energy intakes demonstrated that data for 30 subjects were within the 95% LOA, albeit with a few outliers.

Fig. 2
figure 2

Agreement between methods (apps versus paper-based calculation) for energy intake using the Bland–Altman plot A. Without excluding outliers (n = 30) and B. After excluding outliers (n = 28); LoA, limit of agreement

Full size image

Discussion

Our formative research and preliminary results herein show that Indonesian adults could define a dietary mobile app's desired qualities such as being easy, informative, and exciting to use. In the development process, EatsUp® was engineered to record dietary data, give users menu recommendations based on their caloric targets, and include an evaluation of all biomarkers linked to complications of obesity for obese individuals. Finally, the acceptance test of EatsUp® showed that although it could be used as part of an obesity management regimen, it still needed improvement.

According to Duff et al., the app's technical aspects, such as push notifications and the user interface, and the interactive content, should be based on evidence and a theoretical framework of health behavior change. Developing an app-based health behavior change should consist of four key stages, 1) systematic review, 2) app development, 3) feasibility and acceptability testing of the prototype, and 4) evaluation and implementation [38]. A good app for managing obesity should have a feature for self-monitoring, a series of programs for weight loss, and strategies for changing lifestyle behaviors such as improvement of motivation, stress management, and problem-solving assistance [22]. Our app aimed to meet these requirements, and additional specific ones from users we assessed. The qualitative findings and randomized clinical trial (RCT) conducted by Mummah et al. showed that people need a mobile app that is accountable, efficient, and easy to use for dietary self-monitoring. Also, the apps should have reminder notifications, simple features, weekly reports, and features for planning meals in advance [39]. These findings were similar to those from our formative research study. It can thus be concluded that the app should be easy to use for diverse populations, provide health news, push notifications, a food database, estimated portion sizes, and food images to facilitate compliance with long-term dietary interventions.

Schoffman et al. reported that essential health content and concrete recommendations in an app required collaboration between diverse experts [40]. Ananda et al. stated that a mobile app might be an innovative tool to facilitate individual health behavior change interventions [41]. One limitation of this study was the lack of a complete Indonesian food composition table to estimate the nutrient intake. All food databases in the application were from the 2007 NutriSurvey food database, and hence, were not up-to-date.

The app was found to have a good overall acceptance among users regarding its graphics and app flow. Nevertheless, the content still needed improvement, especially in making sure that users understood the terminologies and that menu recommendations were easy to follow. Smith et al. indicated that web and mobile phone apps have advantages over standard face-to-face programs, such as 24/7 availability, less burden, high acceptability in target populations, greater program adherence, lower costs, the possibility of self-monitoring, and the ability to reach a large target population [42]. In addition, Ansa et al. stated that health promotion interventions should be culturally appropriate and tailored to meet the needs of the target population [43].

The Bland–Altman plots showed a good level of agreement between EatsUp® and the paper-based dietary tracking approach; and for a range of intakes most of the data points were located within the LOA. This suggested the mobile app was able to estimate individual intakes accurately. This is consistent with the results of Ahmed et al. [44], who reported that a tablet app was comparable to the traditional dietary assessment method. Similar to the findings of Timon et al. [45], the feasibility of computer-based dietary assessment was comparable to that of a 4-day estimated food diary.

The EatsUp® app had some limitations. It required a live internet connection and was designed for only the Android platform. However, respondents found the app useful, easy to carry as it is part of a smartphone, and easy to use because of its paperless food records, attractive design, use of images of foods, and attractive color schemes. The EatsUp®, therefore, had useful features and good potential for usability but needed to be improved for optimal use. The other limitations of this study were the relatively small sample size, and that we did not assess app users below the level of senior secondary school. While the ease-to-use features in the app may be suitable for lower educated individuals [39], further research is needed to verify this.

Conclusion

In conclusion, this preliminary study of the EatsUp® app suggests it may increase adherence to an obesity management program. Further studies would be warranted for EatsUp® and similar apps. The app had good acceptance regarding its graphics and app flow. The balanced and sustainable dietary mobile app with added nutritional data was comparable to conventional dietary assessment methods, and performed well in assessing energy, macronutrient, and selected micronutrient intakes. The future improvement of EatsUp® might include resolving technical problems such as minor bugs, addition of offline capability and expansion to other platforms such as iOS and migration to the Fast Healthcare Interoperability Resources (FHIR) HL7 standard to enhance its interoperability. EatsUp® could be used for further research concerning intervention tools for obese persons.