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JNFS 2025, 10(3): 364-372 |
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Dehghani Mahmoodabadi A, Nadjarzadeh A, Yousefianzadeh O, Pourrokni Salehan S, Maayeshi N, Sajjadi S S, et al . The Function Analysis and Quality Assessment of Persian-Language Nutrition-Related Mobile Apps on Google Play and Iranian App Stores. JNFS 2025; 10 (3) :364-372
URL: http://jnfs.ssu.ac.ir/article-1-1261-en.html
URL: http://jnfs.ssu.ac.ir/article-1-1261-en.html
Arezoo Dehghani Mahmoodabadi 
, Azadeh Nadjarzadeh , Omid Yousefianzadeh , Sara Pourrokni Salehan , Najmeh Maayeshi , Sana Sadat Sajjadi , Hossein Khosravi-Boroujeni , Saeed Sherafatmanesh *
, Azadeh Nadjarzadeh , Omid Yousefianzadeh , Sara Pourrokni Salehan , Najmeh Maayeshi , Sana Sadat Sajjadi , Hossein Khosravi-Boroujeni , Saeed Sherafatmanesh *
Department of Clinical Nutrition, School of Nutrition and Food Sciences, Shiraz University of Medical Sciences, Shiraz, Iran.
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The Function Analysis and Quality Assessment of Persian-Language Nutrition-Related Mobile Apps on Google Play and Iranian App Stores
Arezoo Dehghani Mahmoodabadi; PhD1,2, Azadeh Nadjarzadeh; PhD3,4, Omid Yousefianzadeh; PhD1,2,Sara Pourrokni Salehan; BSc1, Najmeh Maayeshi; MSc3,4, Sana Sadat Sajjadi; MSc5,Hossein Khosravi-Boroujeni; PhD6, Saeed Sherafatmanesh; PhD *3,7,8
Arezoo Dehghani Mahmoodabadi; PhD1,2, Azadeh Nadjarzadeh; PhD3,4, Omid Yousefianzadeh; PhD1,2,Sara Pourrokni Salehan; BSc1, Najmeh Maayeshi; MSc3,4, Sana Sadat Sajjadi; MSc5,Hossein Khosravi-Boroujeni; PhD6, Saeed Sherafatmanesh; PhD *3,7,8
1 Department of Health Information Technology and Management, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran; 2 Research Center for Health Technology Assessment and Medical Informatics, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran; 3 Research Center for Food Hygiene and Safety, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran;
4 Department of Nutrition, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran;
5 Food Security Research Center and Department of Community Nutrition, School of Nutrition and Food Science, Isfahan University of Medical Sciences, Isfahan, Iran; 6 School of Medicine and Griffith Health Institute, Griffith University, Queensland, Australia; 7 Nutrition Research Center, Shiraz University of Medical Sciences, Shiraz, Iran; 8 Department of Clinical Nutrition, School of Nutrition and Food Sciences, Shiraz University of Medical Sciences, Shiraz, Iran.
4 Department of Nutrition, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran;
5 Food Security Research Center and Department of Community Nutrition, School of Nutrition and Food Science, Isfahan University of Medical Sciences, Isfahan, Iran; 6 School of Medicine and Griffith Health Institute, Griffith University, Queensland, Australia; 7 Nutrition Research Center, Shiraz University of Medical Sciences, Shiraz, Iran; 8 Department of Clinical Nutrition, School of Nutrition and Food Sciences, Shiraz University of Medical Sciences, Shiraz, Iran.
| ARTICLE INFO | ABSTRACT | |
| ORIGINAL ARTICLE | Background: A growing variety of smartphone applications (apps) have been developed to provide nutritional information for promoting illness prevention and a healthy lifestyle. However, the effectiveness of these apps have not been extensively investigated. Thus, the current study aimed to assess the functionality and quality of Persian-language nutrition-related apps in Iran. Methods: Twenty-two eligible apps were included in the study according to the systematic search on Android Google Play and Iranian app stores. Then, each app underwent an overall quality assessment by six specialists in the fields of nutrition sciences and health information technology using the Mobile App Rating Scale (MARS). Results: The MARS mean scores for the investigated apps was 3.04. The functional effectiveness scope obtained the highest mean score of 3.43, followed by aesthetics scope (mean score of 2.95), engagement scope (mean score of 2.83), and content quality scope (mean score of 2.36), respectively. In addition, the subjective quality outcomes revealed that just less than 5% of apps were desirable for users to use enthusiastically more than 50 times over the next 12 months or recommend them to other individuals. However, 27.3% of apps were appraised appropriate depending on the user's intention to pay for the app. Conclusion: The present study indicated an acceptable overall MARS quality score among the Persian-language nutrition-related mobile apps in android Google Play and Iranian popular app stores. | |
| Article history: Received: 18 Feb 2025 Revised: 17 Mar 2025 Accepted: 28 Mar 2025 |
||
| *Corresponding author saeedsherafatmanesh@gmail.com Research Center for Food Hygiene and Safety, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran. Postal code: 7153675541 Tel: +98 7137251001 |
||
| Keywords Mobile applications; mHealth; Smartphone; Nutrition; Iran. |
Introduction
Non-communicable diseases (NCDs) are known as primary culprits of mortality, which are responsible for nearly 80% of deaths in countries with low or middle incomes (Azadnajafabad et al., 2024). These global chronic health problems are recognized as cluster of endocrine or metabolic impairments mainly including overweight and obesity, diabetes, cancer, cardiovascular diseases, kidney disease, and chronic respiratory disease (Hunter and Reddy, 2013). In this regard, the important role of modifiable preventative strategies such as changing lifestyle behaviors and adherence to a nutritious eating pattern by having healthier food choices have been an important focus of interest for healthcare researchers for the management of NCDs (Budreviciute et al., 2020, Sherafatmanesh et al., 2017).
Considering the universalization of mobile technologies in the last decade, using mobile health (mHealth) applications (apps) have delivered a privileged opportunity to encourage patients for effective lifestyle modifications and reduce NCDs risk factors (Laure et al., 2015, Sherafatmanesh et al., 2021). The novel mHealth approach is particularly suitable for nutritional interventions due to easy access of users to comprehensive nutritional information requirements regarding appropriate food choices, maintaining a healthy weight, counting calories, and preparing nutritious meals (DiFilippo et al., 2015). Currently, various software programs have been designed to allow users to control their daily calorie and water intake, as well as improving nutritional knowledge and disease symptomatology (Hingle and Patrick, 2016). Other main features of a useful mHealth app comprise a user-friendly interface, a free trial, support for social networks, and the ability to sync with other health and wellness apps such as smartwatches or computers for facilitating the review and sharing of personal health statistics (Franco et al., 2016). However, as the development of mHealth apps follows an upward trend all over the world, their effectiveness examination is considered among the most important issues (Schnall et al., 2016). Although health benefits of technologies have been established, it has been documented that the effectiveness of over 50% of mHealth apps is confined by functional errors, lack of evidence-based information, unfavorable features, and little usability (Fernandez-Lazaro et al., 2024, Martinon et al., 2022). In different countries, several studies have been conducted regarding the evaluation of nutrition-related mobile apps (Fernandez-Lazaro et al., 2024, Li et al., 2019, Martinon et al., 2022). Although there are a number of Iranian mobile apps that provide nutritional guidance, their features and quality have not been yet extensively investigated. Therefore, the present study aimed to evaluate the quality and functional effectiveness of Persian-language nutrition-related mobile apps available in android Google Play and Iranian app stores.
Materials and Methods
Search strategy
From February 17 to April 14, 2023, a systematic search was conducted by academic investigators of department of nutrition, Shahid Sadoughi University of Medical Sciences, Yazd, Iran, for Persian-language nutrition-related apps on the popular Iranian online app stores (Bazar, MyKet, Sibche) and also Google Play store. The keywords entered in the search window of the app stores were as follows: "رژیم غذایی" (diet), "تغذیه" (nutrition), "برنامه غذایی" (dietary program), "رژیم غذایی سالم" (healthy diet), "غذا" (food), "مصرف غذا" (food consumption), "کالری" (calorie), "وزن" (weight), "لاغری" (slimming), and "تناسب اندام" (fitness). Given that search results in app stores are displayed at the top of the list based on the greater relevance to each search term (Chen et al., 2015), the first 50 apps listed in Bazar, MyKet, Sibche, and Google Play were identified for the screening phase.
Data screening
The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) description was used to reflect the screening procedure (Figure 1) (Page et al., 2021).
Considering the universalization of mobile technologies in the last decade, using mobile health (mHealth) applications (apps) have delivered a privileged opportunity to encourage patients for effective lifestyle modifications and reduce NCDs risk factors (Laure et al., 2015, Sherafatmanesh et al., 2021). The novel mHealth approach is particularly suitable for nutritional interventions due to easy access of users to comprehensive nutritional information requirements regarding appropriate food choices, maintaining a healthy weight, counting calories, and preparing nutritious meals (DiFilippo et al., 2015). Currently, various software programs have been designed to allow users to control their daily calorie and water intake, as well as improving nutritional knowledge and disease symptomatology (Hingle and Patrick, 2016). Other main features of a useful mHealth app comprise a user-friendly interface, a free trial, support for social networks, and the ability to sync with other health and wellness apps such as smartwatches or computers for facilitating the review and sharing of personal health statistics (Franco et al., 2016). However, as the development of mHealth apps follows an upward trend all over the world, their effectiveness examination is considered among the most important issues (Schnall et al., 2016). Although health benefits of technologies have been established, it has been documented that the effectiveness of over 50% of mHealth apps is confined by functional errors, lack of evidence-based information, unfavorable features, and little usability (Fernandez-Lazaro et al., 2024, Martinon et al., 2022). In different countries, several studies have been conducted regarding the evaluation of nutrition-related mobile apps (Fernandez-Lazaro et al., 2024, Li et al., 2019, Martinon et al., 2022). Although there are a number of Iranian mobile apps that provide nutritional guidance, their features and quality have not been yet extensively investigated. Therefore, the present study aimed to evaluate the quality and functional effectiveness of Persian-language nutrition-related mobile apps available in android Google Play and Iranian app stores.
Materials and Methods
Search strategy
From February 17 to April 14, 2023, a systematic search was conducted by academic investigators of department of nutrition, Shahid Sadoughi University of Medical Sciences, Yazd, Iran, for Persian-language nutrition-related apps on the popular Iranian online app stores (Bazar, MyKet, Sibche) and also Google Play store. The keywords entered in the search window of the app stores were as follows: "رژیم غذایی" (diet), "تغذیه" (nutrition), "برنامه غذایی" (dietary program), "رژیم غذایی سالم" (healthy diet), "غذا" (food), "مصرف غذا" (food consumption), "کالری" (calorie), "وزن" (weight), "لاغری" (slimming), and "تناسب اندام" (fitness). Given that search results in app stores are displayed at the top of the list based on the greater relevance to each search term (Chen et al., 2015), the first 50 apps listed in Bazar, MyKet, Sibche, and Google Play were identified for the screening phase.
Data screening
The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) description was used to reflect the screening procedure (Figure 1) (Page et al., 2021).
|
|
After eliminating duplicates within and between the searched app stores (considering the name and developer of the app), an independent cross-check screening method was performed by the study researchers to explore eligible nutrition-related apps which were able to provide diet. Primary analysis was limited to Persian-language apps, and according to a consensus reached between the study investigators, all apps that had no downloading possibility, no registration allowance, and no specific relation to the nutrition sciences (e.g., restaurant booking, take-away orders, meal delivery, food sales, recipes, traditional medicine, sports, food alert notifications, or general nutritional knowledge without any scientific evidence) were excluded from the investigation.
Data extraction
Based on the study inclusion and exclusion criteria, a total of 22 apps were finally considered eligible and then downloaded for further analysis. Each app underwent an in-depth review regarding general features such as name, number of installations, user ratings, number of raters, last update, developer, software platform, version, and in-app payment status.
Quality assessment of mobile apps
Six specialists in fields of nutrition sciences and health information technology, who had the experience of using mobile nutrition-related apps, were randomly divided into two equal groups. At first, all the evaluators were given time to be acquainted with each app menus and functionality. Then, a detailed guidance document was provided to assist them on how to accurately report each app quality measurement criteria.
In the present cross-sectional study, the validated Mobile Application Rating Scale (MARS) tool was applied to assess the quality of the selected nutrition-related apps (Stoyanov et al., 2015). The MARS scale encompasses 19 sections (with a 5-point Likert scale) which classified into four separate scopes of information including a) engagement: five queries regarding entertainment, interest, customization, and interactivity (e.g., sending alerts, messages, reminders, feedback, or allows sharing allowance, b) functional effectiveness: four queries regarding performance, the app ease of use, navigation features, and gestural design, c) aesthetics: three queries regarding graphic design, layout, overall visual appeal, color scheme, and style consistency, and d) content quality: seven queries regarding authenticity of app outline, objectives, quality, and quantity of data, validity, and evidence-based information. Finally, the total MARS score (ranged from 0-5) for each reviewed app was measured by summing the mean scores for each specific scope of information, and dividing by four, which a greater overall score reflects higher app quality.
In addition, a subjective quality assessment was conducted regarding information such as, a) user's interest to recommend the app to other people, b) estimated number of times the app will be used over the next 12 months, c) user's interest to pay for the app, and d) app rating (ranging from one to five stars).
Ethical considerations
The procedure of the present investigation was approved by the Ethics Committee of Shahid Sadoughi University of Medical Sciences, Yazd, Iran (IR.SSU.SPH.REC.1401.193).
Data analysis
Descriptive statistics were performed to assess the mean and minimum/maximum of MARS scores. Radar graph chart was also employed to compare differences between MARS mean scores for each section. Moreover, the number and percentage of apps were calculated for the subjective quality evaluation. All data analysis was done using the Statistical Package for Social Sciences (SPSS, version 24.0).
Results
App characteristics
General characteristics of the study-eligible mobile apps are presented in Table 1. As the table depicts, 6 apps were last updated in 2023, 8 apps were in 2022, 2 apps were in 2021, and the rest of them were before 2021. Of the 22 apps, the highest number of active installations was for " کرفس" application with one million, and then for the " زیره" and " مانکن" with more than 500 thousand. Also, the number of raters for " کرفس" application were significantly higher than other apps.
Overall quality of apps
All comparisons between MARS mean scores for each specific-section are shown in Figure 2. The MARS mean score for the evaluated apps was 3.04. In total, 6 apps received scores lower than 2.5, which indicated that these apps were not of acceptable quality. Among the four information scopes of the MARS tool, the functional effectiveness scope reached the highest mean score of 3.43, followed by aesthetics scope (mean score of 2.95), engagement scope (mean score of 2.83), and content quality scope (mean score of 2.36), respectively. Generally, apps received higher scores on gestural design, while the evidence-based, validity, and customization aspects were accompanied by lower scores.
Subjective quality of apps
Findings regarding the subjective quality assessment of the study apps are reported in Table 2. The evaluators concluded that less than 5% of apps were reasonably appropriate for users to use them willingly more than 50 times over the next 12 months or recommend them to other people. Nevertheless, 27.3% of apps were evaluated as suitable, based on the user's intention to pay for the app. It is also worth noting that 40.9% of apps received an average of three stars in app rating scale.
Data extraction
Based on the study inclusion and exclusion criteria, a total of 22 apps were finally considered eligible and then downloaded for further analysis. Each app underwent an in-depth review regarding general features such as name, number of installations, user ratings, number of raters, last update, developer, software platform, version, and in-app payment status.
Quality assessment of mobile apps
Six specialists in fields of nutrition sciences and health information technology, who had the experience of using mobile nutrition-related apps, were randomly divided into two equal groups. At first, all the evaluators were given time to be acquainted with each app menus and functionality. Then, a detailed guidance document was provided to assist them on how to accurately report each app quality measurement criteria.
In the present cross-sectional study, the validated Mobile Application Rating Scale (MARS) tool was applied to assess the quality of the selected nutrition-related apps (Stoyanov et al., 2015). The MARS scale encompasses 19 sections (with a 5-point Likert scale) which classified into four separate scopes of information including a) engagement: five queries regarding entertainment, interest, customization, and interactivity (e.g., sending alerts, messages, reminders, feedback, or allows sharing allowance, b) functional effectiveness: four queries regarding performance, the app ease of use, navigation features, and gestural design, c) aesthetics: three queries regarding graphic design, layout, overall visual appeal, color scheme, and style consistency, and d) content quality: seven queries regarding authenticity of app outline, objectives, quality, and quantity of data, validity, and evidence-based information. Finally, the total MARS score (ranged from 0-5) for each reviewed app was measured by summing the mean scores for each specific scope of information, and dividing by four, which a greater overall score reflects higher app quality.
In addition, a subjective quality assessment was conducted regarding information such as, a) user's interest to recommend the app to other people, b) estimated number of times the app will be used over the next 12 months, c) user's interest to pay for the app, and d) app rating (ranging from one to five stars).
Ethical considerations
The procedure of the present investigation was approved by the Ethics Committee of Shahid Sadoughi University of Medical Sciences, Yazd, Iran (IR.SSU.SPH.REC.1401.193).
Data analysis
Descriptive statistics were performed to assess the mean and minimum/maximum of MARS scores. Radar graph chart was also employed to compare differences between MARS mean scores for each section. Moreover, the number and percentage of apps were calculated for the subjective quality evaluation. All data analysis was done using the Statistical Package for Social Sciences (SPSS, version 24.0).
Results
App characteristics
General characteristics of the study-eligible mobile apps are presented in Table 1. As the table depicts, 6 apps were last updated in 2023, 8 apps were in 2022, 2 apps were in 2021, and the rest of them were before 2021. Of the 22 apps, the highest number of active installations was for " کرفس" application with one million, and then for the " زیره" and " مانکن" with more than 500 thousand. Also, the number of raters for " کرفس" application were significantly higher than other apps.
Overall quality of apps
All comparisons between MARS mean scores for each specific-section are shown in Figure 2. The MARS mean score for the evaluated apps was 3.04. In total, 6 apps received scores lower than 2.5, which indicated that these apps were not of acceptable quality. Among the four information scopes of the MARS tool, the functional effectiveness scope reached the highest mean score of 3.43, followed by aesthetics scope (mean score of 2.95), engagement scope (mean score of 2.83), and content quality scope (mean score of 2.36), respectively. Generally, apps received higher scores on gestural design, while the evidence-based, validity, and customization aspects were accompanied by lower scores.
Subjective quality of apps
Findings regarding the subjective quality assessment of the study apps are reported in Table 2. The evaluators concluded that less than 5% of apps were reasonably appropriate for users to use them willingly more than 50 times over the next 12 months or recommend them to other people. Nevertheless, 27.3% of apps were evaluated as suitable, based on the user's intention to pay for the app. It is also worth noting that 40.9% of apps received an average of three stars in app rating scale.
| Table 1. General characteristics of the study-eligible mobile apps. | |||||||||
| No. | Name | Number of installations |
User ratings |
Number of raters |
Last update |
Developer | Software platform |
Version | In-app payment status |
| 1 | اکسیژن فیت | +100 K | 4.1 | 722 | 6 Apr. 2023 | گروه اکسیژن | Android | 4.3.7 | Yes |
| 2 | استپ فیت | +10 K | 4.5 | 744 | 9 Apr. 2023 | تاپ گل | Android | 5.2.5 | Yes |
| 3 | باریکا | +20 K | 4.5 | 533 | 5 July 2022 | باریکا | Android | 3.0.3 | Yes |
| 4 | بدن ساز من | +50 K | 4.2 | 157 | 31 July 2020 | امید رها | Android | _ | No |
| 5 | بشقاب | +200 K | 4.6 | 26704 | 27 July 2021 | شرکت پیشرو سامانه آزاد | Android | 2.8.16 | Yes |
| 6 | بنتو | +100 K | 4.3 | 2677 | 15 Mar. 2022 | گروه کرفس | Android | 2.5.3 | Yes |
| 7 | پا به پا | +50 K | 4.2 | 297 | 4 Nov. 2019 | پا به پا | Android | 1.2.1 | Yes |
| 8 | پایه | +10 K | 4 | 96 | 7 Oct. 2019 | هایلی | Android | 2.0.10 | No |
| 9 | جزیره | +10 K | 4.6 | 444 | 23 Oct. 2022 | سروش نا | Android | 1.4.3 | Yes |
| 10 | جیم شو | +50 K | 4.1 | 563 | 21 Jan. 2023 | اریتکو | Android | 3.1.0 | Yes |
| 11 | رجیمی | +20 K | 4 | 8 | 29 Oct. 2022 | الف | Android | 2.5.1 | Yes |
| 12 | به اندام | +100 K | 4.2 | 140 | 17 Dec. 2022 | به افزار | Android | 4.0.6 | Yes |
| 13 | رژیم پلاس | +50 K | 5 | 978 | 28 Apr. 2022 | آذر مهر | Android | 3.8.6 | No |
| 14 | رژیم چاقی سریع | +10 K | 4.2 | 43 | 30 May 2022 | Ananya Reddy | Android | 1 | No |
| 15 | رژیم دکتر غیاثوند | +10 K | 5 | 515 | 14 Oct. 2021 | برنامه رژیم غذایی دکتر رضا غیاثوند | Android | 1.0.1 | Yes |
| 16 | رژیم سبز | +20 K | 4.7 | 2038 | 3 May 2016 | پرفا | Android | V4.7 | No |
| 17 | زیره | +500 K | 4.3 | 7874 | 15 Jan. 2023 | کارسو | Android | 7.3.0-GP | Yes |
| 18 | سیگما | +20 K | 4.2 | 142 | 26 Jan. 2023 | Sigma Lifestyle Team | Android | 2.5.9 | Yes |
| 19 | کرفس | +1 M | 4.3 | 62465 | 21 Sept. 2022 | گروه کرفس | Android | 5.6.5 | Yes |
| 20 | لاغری پلاس | +10 K | 4.2 | 25 | 6 Sept. 2016 | حمیدرضا السون | Android | 0.1.9 | Yes |
| 21 | مانکن | +500 K | 4.5 | 442 | 22 Jan. 2023 | مانکن | Android | 5.9.7 | Yes |
| 22 | یکا | +10 K | 3.5 | 81 | 31 Oct. 2020 | یکا | Android | 3.0.8 | No |
Discussion
To the best of our knowledge, the current investigation provides the first analytical evidence regarding the function analysis and quality assessment of Persian-language nutrition-related mobile apps.
mHealth apps for improving nutritional knowledge and dietary intakes have become increasingly popular (Martinon et al., 2022). In recent years, millions of people have downloaded various nutrition-related programs which implies people's desire to monitor and manage their diet (Franco et al., 2016, Vasiloglou et al., 2021). Since mHealth apps have shown to impose an impact on human general health, and also healthcare specialists may make important decisions based on the user's information provided in these apps, proper investigations regarding their functionality, quality, accuracy, and reliability seem to be a high priority (Choi et al., 2021, Park et al., 2014).
In the present study, the overall quality of the study-eligible apps revealed an acceptable level (mean overall MARS score 3.04). Moreover, in agreement with the results of earlier studies utilizing the MARS, the functional effectiveness and aesthetics categories in this study received the highest and subsequent highest scores, respectively (Bardus et al., 2016, Schoeppe et al., 2017, Simões et al., 2018). A proper functional effectiveness consists of higher app performance, navigation, and gestural design (Stoyanov et al., 2015), which all have been reported to be associated with better usability and enhanced individuals' motivation with mHealth technologies (Boulos et al., 2011). It has also been demonstrated that app suitable visual aesthetic features including images, tables, and graphs may become important indicators for attainment of higher overall MARS scores (Choi et al., 2021, Li et al., 2019).
The acceptable overall MARS quality score in this study was approximately in accordance with the estimated quality scores reported for nutrition-related apps in previous investigations (Li et al., 2019, McAleese et al., 2022). However, the study specialists estimated low results regarding the subjective quality of apps (less than 5% of apps seemed to be appropriate for users to often use them over next 12 months or to recommend them to other subjects), which confirming prior health care specialists’ estimations on nutrition and diet apps (Vasiloglou et al., 2020). It is noteworthy that the current study inclusion criteria were not accompanied by just high rated apps, which can describe the slightly lower overall quality score of the present study compared to earlier studies (Choi et al., 2021, Schumer et al., 2018). In addition, nutritionists and health information technology experts evaluated the quality of apps in the current study, whereas numerous conventional users can do assessments in app stores. Although star ratings or number of raters are fundamental indicators for the popularity of apps, it may not constantly reveal the proper quality or facing reliable scientific content in mHealth apps (Choi et al., 2021, Martinon et al., 2022).
To the best of our knowledge, the current investigation provides the first analytical evidence regarding the function analysis and quality assessment of Persian-language nutrition-related mobile apps.
mHealth apps for improving nutritional knowledge and dietary intakes have become increasingly popular (Martinon et al., 2022). In recent years, millions of people have downloaded various nutrition-related programs which implies people's desire to monitor and manage their diet (Franco et al., 2016, Vasiloglou et al., 2021). Since mHealth apps have shown to impose an impact on human general health, and also healthcare specialists may make important decisions based on the user's information provided in these apps, proper investigations regarding their functionality, quality, accuracy, and reliability seem to be a high priority (Choi et al., 2021, Park et al., 2014).
In the present study, the overall quality of the study-eligible apps revealed an acceptable level (mean overall MARS score 3.04). Moreover, in agreement with the results of earlier studies utilizing the MARS, the functional effectiveness and aesthetics categories in this study received the highest and subsequent highest scores, respectively (Bardus et al., 2016, Schoeppe et al., 2017, Simões et al., 2018). A proper functional effectiveness consists of higher app performance, navigation, and gestural design (Stoyanov et al., 2015), which all have been reported to be associated with better usability and enhanced individuals' motivation with mHealth technologies (Boulos et al., 2011). It has also been demonstrated that app suitable visual aesthetic features including images, tables, and graphs may become important indicators for attainment of higher overall MARS scores (Choi et al., 2021, Li et al., 2019).
The acceptable overall MARS quality score in this study was approximately in accordance with the estimated quality scores reported for nutrition-related apps in previous investigations (Li et al., 2019, McAleese et al., 2022). However, the study specialists estimated low results regarding the subjective quality of apps (less than 5% of apps seemed to be appropriate for users to often use them over next 12 months or to recommend them to other subjects), which confirming prior health care specialists’ estimations on nutrition and diet apps (Vasiloglou et al., 2020). It is noteworthy that the current study inclusion criteria were not accompanied by just high rated apps, which can describe the slightly lower overall quality score of the present study compared to earlier studies (Choi et al., 2021, Schumer et al., 2018). In addition, nutritionists and health information technology experts evaluated the quality of apps in the current study, whereas numerous conventional users can do assessments in app stores. Although star ratings or number of raters are fundamental indicators for the popularity of apps, it may not constantly reveal the proper quality or facing reliable scientific content in mHealth apps (Choi et al., 2021, Martinon et al., 2022).
| Table 2. Subjective quality assessment of the study eligible mobile apps. | |
| App (%) | Queries of the subjective quality assessment |
| User's interest to recommend the app to people who might benefit from it | |
| 4 (18.2) | Not at all |
| 4 (18.2) | Few people |
| 7 (31.8) | Maybe |
| 6 (27.3) | Many people |
| 1 (4.5) | Definitely |
| Estimated number of times the app will be used over the next 12 months | |
| 4 (18.2) | None |
| 5 (22.7) | 1-2 |
| 6 (27.3) | 3-10 |
| 6 (27.3) | 10-50 |
| 1 (4.5) | >50 |
| User's interest to pay for the app | |
| 6 (27.3) | Yes |
| 7 (31.8) | Maybe |
| 9 (40.9) | No |
| App rating (ranging from one to five stars) | |
| 1 (4.5) | ★ |
| 3 (13.7) | ★★ |
| 9 (40.9) | ★★★ |
| 8 (36.4) | ★★★★ |
| 1 (4.5) | ★★★★★ |
Meanwhile, the lowest quality scores in this investigation were detected in relation to the evidence-based, validity, and customization aspects of the MARS measurement. Similarly, in 2022, a study by Martinon et al., demonstrated major concerns regarding the lack of accurate scientific-based data in large number of mHealth apps (Martinon et al., 2022). This issue has also been found in most of well-known weight management apps, worldwide (Bardus et al., 2016). Besides, total energy consumption has been overestimated or underestimated in earlier literatures on nutrition tracking apps (Bzikowska-Jura et al., 2021, Griffiths et al., 2018). The implementation of various nutritional reference guidelines, and the absence of alternative serving sizes provided by apps may account for the discrepancy in calorie intake and nutrient assessments across nutrition-related apps (Chen et al., 2015, Fallaize et al., 2019, Ferrara et al., 2019). Since spreading invalidated information in mHealth apps might have a detrimental impact on users' well-being and overall health, the content presented by apps should be supported by reliable scientific-based data and rigorously tested for correctness (Fernandez-Lazaro et al., 2024). Therefore, it is crucial that app developers engage in providing appropriate evidence-based scientific content to improve the app overall quality (McAleese et al., 2022).
The present study findings should be interpreted while considering the main limitations. Although we identified the first 50 apps listed in studied app stores as the most relevant apps to each search term, it still may not be representative of the entire Persian-language nutrition-related apps. Other less popular Iranian app stores, such as sibapp, iapps, sibou, sibirani, and sibaneh could have been searched for app identification. Moreover, other quality assessment scales, such as ENLIGHT could have been employed to compare the final results between different scales. Hence, future well-designed studies are required to confirm the results.
Conclusion
Findings of the current study indicated an acceptable overall MARS quality score among the Persian-language nutrition-related mobile apps in android Google Play and Iranian popular app stores. However, the quality of apps regarding the evidence-based, validity, and customization aspects still need to be improved.
Acknowledgments
The authors would like to thank Shahid Sadoughi University of Medical Sciences, Yazd, Iran.
Authors’ contribution
Dehghani Mahmoodabadi A: Original draft, data curation, formal analysis, investigation, software, visualization, review and editing; Nadjarzadeh A: Methodology, formal analysis, validation, visualization, review and editing; Yousefianzadeh O: Supervision, data curation, formal analysis, visualization, review and editing; Pourrokni Salehan S, Sajjadi SS, Khosravi-Boroujeni H and Maayeshi N: Methodology, data curation, validation, visualization, review and editing; Sherafatmanesh S: Conceptualization, supervision, data curation, formal analysis, methodology, project administration, validation, visualization, original draft, review and editing.
Conflict of interest
The authors report no conflict of interest.
Funding
The current study was funded by Shahid Sadoughi University of Medical Sciences.
References
Azadnajafabad S, et al. 2024. Non-communicable diseases’ risk factors in Iran; a review of the present status and action plans. Journal of diabetes & metabolic disorders. 23 (2): 1515-1523.
Bardus M, van Beurden SB, Smith JR & Abraham C 2016. A review and content analysis of engagement, functionality, aesthetics, information quality, and change techniques in the most popular commercial apps for weight management. International journal of behavioral nutrition and physical activity. 13: 1-9.
Boulos MNK, Wheeler S, Tavares C & Jones R 2011. How smartphones are changing the face of mobile and participatory healthcare: an overview, with example from eCAALYX. Biomedical engineering online. 10: 1-14.
Budreviciute A, et al. 2020. Management and prevention strategies for non-communicable diseases (NCDs) and their risk factors. Frontiers in public health. 8: 574111.
Bzikowska-Jura A, Sobieraj P & Raciborski F 2021. Low comparability of nutrition-related mobile apps against the polish reference method—A validity study. Nutrients. 13 (8): 2868.
Chen J, Cade JE & Allman-Farinelli M 2015. The most popular smartphone apps for weight loss: a quality assessment. JMIR mHealth and uHealth. 3 (4): e4334.
Choi J, Chung C & Woo H 2021. Diet-related mobile apps to promote healthy eating and proper nutrition: a content analysis and quality assessment. International journal of environmental research and public health. 18 (7): 3496.
DiFilippo KN, Huang W-H, Andrade JE & Chapman-Novakofski KM 2015. The use of mobile apps to improve nutrition outcomes: a systematic literature review. Journal of telemedicine and telecare. 21 (5): 243-253.
Fallaize R, Franco RZ, Pasang J, Hwang F & Lovegrove JA 2019. Popular nutrition-related mobile apps: an agreement assessment against a UK reference method. JMIR mHealth and uHealth. 7 (2): e9838.
Fernandez-Lazaro CI, Santamaría G, Milano AF, Martin-Vergel MI & Fernandez-Lazaro D 2024. Nutrition-related mobile Apps in the Spanish app stores: quality and content analysis. JMIR mHealth and uHealth. 12 (1): e52424.
Ferrara G, Kim J, Lin S, Hua J & Seto E 2019. A focused review of smartphone diet-tracking apps: usability, functionality, coherence with behavior change theory, and comparative validity of nutrient intake and energy estimates. JMIR mHealth and uHealth. 7 (5): e9232.
Franco RZ, Fallaize R, Lovegrove JA & Hwang F 2016. Popular nutrition-related mobile apps: a feature assessment. JMIR mHealth and uHealth. 4 (3): e5846.
Griffiths C, Harnack L & Pereira MA 2018. Assessment of the accuracy of nutrient calculations of five popular nutrition tracking applications. Public health nutrition. 21 (8): 1495-1502.
Hingle M & Patrick H 2016. There are thousands of apps for that: navigating mobile technology for nutrition education and behavior. Journal of nutrition education and behavior. 48 (3): 213-218. e211.
Hunter DJ & Reddy KS 2013. Noncommunicable diseases. New England journal of medicine. 369 (14): 1336-1343.
Laure D, Medvedev O, Balandin S & Lagutina K 2015. Mobile apps for stimulating healthy life: Walky doggy reference example. In 2015 17th Conference of Open Innovations Association (FRUCT), pp. 110-115. IEEE.
Li Y, Ding J, Wang Y, Tang C & Zhang P 2019. Nutrition-related mobile apps in the China app store: assessment of functionality and quality. JMIR mHealth and uHealth. 7 (7): e13261.
Martinon P, et al. 2022. Nutrition-related mobile apps in the French app stores: Assessment of functionality and quality. JMIR mHealth and uHealth. 10 (3): e35879.
McAleese D, Linardakis M & Papadaki A 2022. Quality and presence of behaviour change techniques in mobile apps for the Mediterranean diet: a content analysis of Android Google Play and Apple App store apps. Nutrients. 14 (6): 1290.
Page MJ, et al. 2021. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. British medical journal. 372.
Park LG, Howie‐Esquivel J & Dracup K 2014. A quantitative systematic review of the efficacy of mobile phone interventions to improve medication adherence. Journal of advanced nursing. 70 (9): 1932-1953.
Schnall R, et al. 2016. A user-centered model for designing consumer mobile health (mHealth) applications (apps). Journal of biomedical informatics. 60: 243-251.
Schoeppe S, et al. 2017. Apps to improve diet, physical activity and sedentary behaviour in children and adolescents: a review of quality, features and behaviour change techniques. International journal of behavioral nutrition and physical activity. 14: 1-10.
Schumer H, Amadi C & Joshi A 2018. Evaluating the dietary and nutritional apps in the google play store. Healthcare informatics research. 24 (1): 38-45.
Sherafatmanesh S, et al. 2021. CeliacPlus: A Nutrition-Based Mobile Phone Application for the Management of Celiac Disease. Journal of biomedical physics & engineering. 11: 40.
Sherafatmanesh S, Ekramzadeh M & Akbarzadeh M 2017. The carcinogenicity of alcoholic beverages: A review. International journal of nutrition sciences. 2 (1): 2-9.
Simões P, et al. 2018. Features, behavioral change techniques, and quality of the most popular mobile apps to measure physical activity: systematic search in app stores. JMIR mHealth and uHealth. 6 (10): e11281.
Stoyanov SR, et al. 2015. Mobile app rating scale: a new tool for assessing the quality of health mobile apps. JMIR mHealth and uHealth. 3 (1): e3422.
Vasiloglou MF, et al. 2020. What healthcare professionals think of “nutrition & diet” apps: an international survey. Nutrients. 12 (8): 2214.
Vasiloglou MF, et al. 2021. The human factor in automated image-based nutrition apps: Analysis of common mistakes using the goFOOD lite app. JMIR mHealth and uHealth. 9 (1): e24467.
The present study findings should be interpreted while considering the main limitations. Although we identified the first 50 apps listed in studied app stores as the most relevant apps to each search term, it still may not be representative of the entire Persian-language nutrition-related apps. Other less popular Iranian app stores, such as sibapp, iapps, sibou, sibirani, and sibaneh could have been searched for app identification. Moreover, other quality assessment scales, such as ENLIGHT could have been employed to compare the final results between different scales. Hence, future well-designed studies are required to confirm the results.
Conclusion
Findings of the current study indicated an acceptable overall MARS quality score among the Persian-language nutrition-related mobile apps in android Google Play and Iranian popular app stores. However, the quality of apps regarding the evidence-based, validity, and customization aspects still need to be improved.
Acknowledgments
The authors would like to thank Shahid Sadoughi University of Medical Sciences, Yazd, Iran.
Authors’ contribution
Dehghani Mahmoodabadi A: Original draft, data curation, formal analysis, investigation, software, visualization, review and editing; Nadjarzadeh A: Methodology, formal analysis, validation, visualization, review and editing; Yousefianzadeh O: Supervision, data curation, formal analysis, visualization, review and editing; Pourrokni Salehan S, Sajjadi SS, Khosravi-Boroujeni H and Maayeshi N: Methodology, data curation, validation, visualization, review and editing; Sherafatmanesh S: Conceptualization, supervision, data curation, formal analysis, methodology, project administration, validation, visualization, original draft, review and editing.
Conflict of interest
The authors report no conflict of interest.
Funding
The current study was funded by Shahid Sadoughi University of Medical Sciences.
References
Azadnajafabad S, et al. 2024. Non-communicable diseases’ risk factors in Iran; a review of the present status and action plans. Journal of diabetes & metabolic disorders. 23 (2): 1515-1523.
Bardus M, van Beurden SB, Smith JR & Abraham C 2016. A review and content analysis of engagement, functionality, aesthetics, information quality, and change techniques in the most popular commercial apps for weight management. International journal of behavioral nutrition and physical activity. 13: 1-9.
Boulos MNK, Wheeler S, Tavares C & Jones R 2011. How smartphones are changing the face of mobile and participatory healthcare: an overview, with example from eCAALYX. Biomedical engineering online. 10: 1-14.
Budreviciute A, et al. 2020. Management and prevention strategies for non-communicable diseases (NCDs) and their risk factors. Frontiers in public health. 8: 574111.
Bzikowska-Jura A, Sobieraj P & Raciborski F 2021. Low comparability of nutrition-related mobile apps against the polish reference method—A validity study. Nutrients. 13 (8): 2868.
Chen J, Cade JE & Allman-Farinelli M 2015. The most popular smartphone apps for weight loss: a quality assessment. JMIR mHealth and uHealth. 3 (4): e4334.
Choi J, Chung C & Woo H 2021. Diet-related mobile apps to promote healthy eating and proper nutrition: a content analysis and quality assessment. International journal of environmental research and public health. 18 (7): 3496.
DiFilippo KN, Huang W-H, Andrade JE & Chapman-Novakofski KM 2015. The use of mobile apps to improve nutrition outcomes: a systematic literature review. Journal of telemedicine and telecare. 21 (5): 243-253.
Fallaize R, Franco RZ, Pasang J, Hwang F & Lovegrove JA 2019. Popular nutrition-related mobile apps: an agreement assessment against a UK reference method. JMIR mHealth and uHealth. 7 (2): e9838.
Fernandez-Lazaro CI, Santamaría G, Milano AF, Martin-Vergel MI & Fernandez-Lazaro D 2024. Nutrition-related mobile Apps in the Spanish app stores: quality and content analysis. JMIR mHealth and uHealth. 12 (1): e52424.
Ferrara G, Kim J, Lin S, Hua J & Seto E 2019. A focused review of smartphone diet-tracking apps: usability, functionality, coherence with behavior change theory, and comparative validity of nutrient intake and energy estimates. JMIR mHealth and uHealth. 7 (5): e9232.
Franco RZ, Fallaize R, Lovegrove JA & Hwang F 2016. Popular nutrition-related mobile apps: a feature assessment. JMIR mHealth and uHealth. 4 (3): e5846.
Griffiths C, Harnack L & Pereira MA 2018. Assessment of the accuracy of nutrient calculations of five popular nutrition tracking applications. Public health nutrition. 21 (8): 1495-1502.
Hingle M & Patrick H 2016. There are thousands of apps for that: navigating mobile technology for nutrition education and behavior. Journal of nutrition education and behavior. 48 (3): 213-218. e211.
Hunter DJ & Reddy KS 2013. Noncommunicable diseases. New England journal of medicine. 369 (14): 1336-1343.
Laure D, Medvedev O, Balandin S & Lagutina K 2015. Mobile apps for stimulating healthy life: Walky doggy reference example. In 2015 17th Conference of Open Innovations Association (FRUCT), pp. 110-115. IEEE.
Li Y, Ding J, Wang Y, Tang C & Zhang P 2019. Nutrition-related mobile apps in the China app store: assessment of functionality and quality. JMIR mHealth and uHealth. 7 (7): e13261.
Martinon P, et al. 2022. Nutrition-related mobile apps in the French app stores: Assessment of functionality and quality. JMIR mHealth and uHealth. 10 (3): e35879.
McAleese D, Linardakis M & Papadaki A 2022. Quality and presence of behaviour change techniques in mobile apps for the Mediterranean diet: a content analysis of Android Google Play and Apple App store apps. Nutrients. 14 (6): 1290.
Page MJ, et al. 2021. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. British medical journal. 372.
Park LG, Howie‐Esquivel J & Dracup K 2014. A quantitative systematic review of the efficacy of mobile phone interventions to improve medication adherence. Journal of advanced nursing. 70 (9): 1932-1953.
Schnall R, et al. 2016. A user-centered model for designing consumer mobile health (mHealth) applications (apps). Journal of biomedical informatics. 60: 243-251.
Schoeppe S, et al. 2017. Apps to improve diet, physical activity and sedentary behaviour in children and adolescents: a review of quality, features and behaviour change techniques. International journal of behavioral nutrition and physical activity. 14: 1-10.
Schumer H, Amadi C & Joshi A 2018. Evaluating the dietary and nutritional apps in the google play store. Healthcare informatics research. 24 (1): 38-45.
Sherafatmanesh S, et al. 2021. CeliacPlus: A Nutrition-Based Mobile Phone Application for the Management of Celiac Disease. Journal of biomedical physics & engineering. 11: 40.
Sherafatmanesh S, Ekramzadeh M & Akbarzadeh M 2017. The carcinogenicity of alcoholic beverages: A review. International journal of nutrition sciences. 2 (1): 2-9.
Simões P, et al. 2018. Features, behavioral change techniques, and quality of the most popular mobile apps to measure physical activity: systematic search in app stores. JMIR mHealth and uHealth. 6 (10): e11281.
Stoyanov SR, et al. 2015. Mobile app rating scale: a new tool for assessing the quality of health mobile apps. JMIR mHealth and uHealth. 3 (1): e3422.
Vasiloglou MF, et al. 2020. What healthcare professionals think of “nutrition & diet” apps: an international survey. Nutrients. 12 (8): 2214.
Vasiloglou MF, et al. 2021. The human factor in automated image-based nutrition apps: Analysis of common mistakes using the goFOOD lite app. JMIR mHealth and uHealth. 9 (1): e24467.
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Received: 2025/02/18 | Published: 2025/08/28 | ePublished: 2025/08/28
Received: 2025/02/18 | Published: 2025/08/28 | ePublished: 2025/08/28
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