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Quality Evaluation and Descriptive Analysis/Reviews of Multiple Existing Mobile Apps (362) Immunology, Allergy, Rheumatology (76) mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1269) mHealth for Wellness, Behavior Change and Prevention (2681)

Published on in Vol 8, No 9 (2020): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/18339, first published .
Mobile Phone Apps for Food Allergies or Intolerances in App Stores: Systematic Search and Quality Assessment Using the Mobile App Rating Scale (MARS)

Mobile Phone Apps for Food Allergies or Intolerances in App Stores: Systematic Search and Quality Assessment Using the Mobile App Rating Scale (MARS)

Mobile Phone Apps for Food Allergies or Intolerances in App Stores: Systematic Search and Quality Assessment Using the Mobile App Rating Scale (MARS)

Authors of this article:

Floriana Mandracchia1 Author Orcid Image ;   Elisabet Llauradó1 Author Orcid Image ;   Lucia Tarro1, 2 Author Orcid Image ;   Rosa Maria Valls1 Author Orcid Image ;   Rosa Solà1, 2, 3 Author Orcid Image
Article Authors Cited by (32) Tweetations (12) Metrics

    Original Paper

    1Functional Nutrition, Oxidation, and Cardiovascular Diseases Group (NFOC-Salut), Healthy Environment Chair, Facultat de Medicina i Ciències de la Salut, Universitat Rovira i Virgili, Reus, Spain

    2Unit of Nutrition and Health, EURECAT-Technology Centre of Catalonia, Reus, Spain

    3Hospital Universitari Sant Joan de Reus, Reus, Spain

    *these authors contributed equally

    Corresponding Author:

    Lucia Tarro, PhD

    Functional Nutrition, Oxidation, and Cardiovascular Diseases Group (NFOC-Salut), Healthy Environment Chair

    Facultat de Medicina i Ciències de la Salut

    Universitat Rovira i Virgili

    C/Sant Llorenç, 21

    Reus

    Spain

    Phone: 34 977758920

    Email: lucia.tarro@urv.cat


    Background: Food allergies and intolerances are increasing worldwide, and mobile phone apps could be a promising tool for self-management of these issues.

    Objective: This study aimed to systemically search and assess food allergy or intolerance apps in app stores using the multidimensional Mobile App Rating Scale (MARS) to rate the objective and subjective quality and to identify critical points for future improvements.

    Methods: This systematic search identified apps through the keywords “food allergy,” “food intolerance,” and “allergens” in English, Spanish, and Italian in the Apple App Store (iOS) and Google Play Store (Android). The inclusion criteria were a user star rating of ≥3 (of 5 stars) to limit the selection to the most highly rated apps; ≥1000 reviews as an indicator of reliability; and the most recent update performed up to 2017. Then, the apps were divided according to their purpose (searching for allergen-free “food products,” “restaurants,” or recipes in “meal planners”) and evaluated on a scale of 1 to 5 points using the MARS in terms of (1) app classification category with a descriptive aim; (2) app subjective and objective quality categories comprised of engagement, functionality, esthetics, and information sections (Medline was searched for eligible apps to check whether they had been tested in trials); and (3) an optional app-specific section. Furthermore, the output and input features were evaluated. Differences between MARS sections and between app purposes and correlations among MARS sections, star ratings, and numbers of reviews were evaluated.

    Results: Of the 1376 apps identified, 14 were included: 12 related to food allergies and intolerances that detect 2-16 food allergens and 2 related only to gluten intolerance. The mean (SD) MARS scores (maximum 5 points) were 3.8 (SD 0.4) for objective quality, highlighting whether any app had been tested in trials; 3.5 (SD 0.6) for subjective quality; and 3.6 (SD 0.7) for the app-specific section. Therefore, a rating ≥3 points indicated overall acceptable quality. From the between-section comparison, engagement (mean 3.5, SD 0.6) obtained significantly lower scores than functionality (mean 4.1, SD 0.6), esthetics (mean 4, SD 0.5), and information (mean 3.8, SD 0.4). However, when the apps were compared by purpose, critical points were identified: meal planner apps showed significantly higher engagement (mean 4.1, SD 0.4) than food product (mean 3.0, SD 0.6; P=.05) and restaurant (mean 3.2, SD 0.3; P=.02) apps.

    Conclusions: In this systematic search of food allergy or intolerance apps, acceptable MARS quality was identified, although the engagement section for food product and restaurant purpose apps should be improved and the included apps should be tested in trials. The critical points identified in this systematic search can help improve the innovativeness and applicability of future food allergy and intolerance apps.

    JMIR Mhealth Uhealth 2020;8(9):e18339

    doi:10.2196/18339

    Keywords

    food allergy; food hypersensitivity; food intolerance; allergens; mobile applications; mobile health; mHealth; eHealth. 


    Food allergies and intolerances are adverse reactions to the ingestion of, contact with, or inhalation of a specific food, derivative, or additive [1]. The prevalence of such adverse food allergy and intolerance reactions is increasing worldwide, especially in developed countries [2].

    On the one hand, food allergies involve an immune-mediated reaction that occurs between a few minutes and 1 hour after exposure to the allergen, with symptoms ranging from moderate to severe [3]. The prevalence of food allergies is higher in children (<10%) than in adults (approximately 1%-2%) [3]. On the other hand, food intolerances are nonimmunological hypersensitivity responses due to a nontolerated dose of a food or a component of a food, with symptoms or signs occurring several hours after food consumption and lasting from hours until several days afterward [4]. Food intolerances are more common worldwide than food allergies, affecting up to 15%-20% of the general population [5].

    Although new approaches to food allergies have recently been under clinical investigation [6], one strategy is to correctly identify food allergens to avoid the consumption of even small amounts of an allergen that causes a reaction [7]. To help consumers easily identify food allergens in food products, prepackaged or not, European legislation from 2014 (EU Food Information for Consumer Regulation No. 1169/2011) requires food businesses to clearly provide consumers, through labels or other verbal or written communications, with information about nutritional values and the presence of any of 14 specified food allergens (cereals containing gluten, crustaceans, eggs, fish, peanuts, soya, milk, nuts, celery, mustard, sesame, sulfur dioxide, lupin, and mollusks) [8]. Despite the European legislation, a 2019 study showed gaps in compliance with the regulation, finding that only 83 of the 295 evaluated restaurants (28.1%) labeled food allergens on the menus and that the restaurant staff had deficiencies in their food allergen knowledge and management [9]. In addition to relying on the information provided by food businesses and their employees, consumers must fundamentally self-monitor and self-manage their health [10].

    In this context, there is increasing interest in mobile technology, such as apps, that focuses on helping consumers supervise what they are eating [11] by detecting allergens [12] not derived from cross-contamination and delivering specific health information [13,14] in relation to preparing daily meals, purchasing suitable food products, or searching for restaurants with allergen-free menus.

    In recent years, mobile health (mHealth) technologies, including software, sensors, and mobile phones [15], have improved the management of health care services [16] and interventions such as the achievement of weight loss and smoking cessation as well as the management of several chronic and mental diseases [17]. Currently, the potential of apps for food-related conditions [18] such as food allergies and intolerances, whose incidence is growing worldwide [19], is also being studied. The convenience of apps in health management is favored by approximately 59% of the world population, corresponding to 4.57 billion people, mostly in northern Europe and the United States, who were active internet users in 2020 [20]. Apps enabling consumers to identify food allergens in foods and products, find allergen-free restaurants, and report and evaluate symptoms related to food allergies are already available, but most of them offer irrelevant and poor content [21].

    Since plenty of apps currently exist, their reliability must be verified [22], as the traditional systems used to test app quality, such as users’ star ratings (evaluating apps on a scale of 1 to 5 stars) and reviews, could allow fake or subjective reviews, giving wrong indications to users [23]. Furthermore, app descriptions in app stores are often incomplete or incorrect and are not a valid tool for assessing the quality of an app [24], especially when dealing with sensitive topics such as food allergies.

    The necessity of regulating the quality and safety of mHealth technologies, defined by the World Health Organization as medical and public health practices supported by mobile phones, patient monitoring devices, personal digital assistants, and other wireless devices [25], is particularly important for apps intended to be used for the diagnosis, cure, mitigation, treatment, and prevention of a disease or other conditions by aiding clinical decision-making [26]. These kinds of apps are classified and regulated as medical devices by the US Food and Drug Administration to ensure the safety of apps that are recommended by health professionals to their patients [26]. For instance, in 2015, the government of Catalonia (Spain) introduced a public platform for apps with quality accreditation from health professionals (mConnecta platform), thus establishing a safe and reliable environment for people to use these mHealth apps to self-monitor their health practices [27]. However, nonmedical apps intended to provide information and education to users, such as apps for food allergies and intolerances, also need to be regulated since incomplete information is often provided [28]. In this way, apps will provide better information to help users make health-related choices [29], mHealth will have more value, and fewer ineffective and unsafe apps will be available [30].

    Owing to the necessity of ensuring better app quality for users, a Mobile App Rating Scale (MARS) was developed by a multidisciplinary team of experts as a simple, objective, and reliable tool for researchers, developers, and health professionals to assess app quality and provide suggestions for future designs [31]. The MARS tool provides a multidimensional evaluation of app quality, whereas other existing tools mostly use one-dimensional measures. For example, the Intercontinental Medical Statistics Institute for Healthcare Informatics tool [32] assesses only app functionality, and the criteria of the Health Care Information and Management Systems Society tool [33] evaluate only app usability. The MARS tool has already been used for the quality assessment of different apps related to nutrition [34-36], sleep management [37], food provision [38], calorie counting [39], smoking cessation [40], physical activity [41], and weight management [42] but has not previously been used for food allergy or intolerance apps.

    The aim of this paper was to systemically search app stores for apps about food allergies or intolerances, to assess the apps using the multidimensional MARS ratings of objective and subjective quality, and to identify the critical points for future improvements of these apps.


    Search Strategy

    The present study featured a systematic search and content analysis of apps about food allergies or intolerances available in the Apple App Store (iOS) and Google Play Store (Android). The apps were searched by the two authors between May 2019 and June 2019. The searches were conducted anonymously by logging out of the user accounts for the stores. Specific keywords such as “food allergy,” “food intolerance,” and “allergens” in English, Spanish, and Italian were used to search for the available apps in any of these 3 languages.

    App Selection

    The app selection process is described in Figure 1. Specific inclusion and exclusion criteria were applied to limit the search to the most relevant and reliable apps, in line with previous studies [35,38,41,42]. In particular, only apps that offered a free version were included in the search, as they are most commonly used by the general population. Apps in English, Spanish, and Italian were considered if they had (1) a minimum user star rating ≥3 (of 5 stars) to limit the search to the apps most highly rated by users, (2) ≥1000 reviews to identify the apps that were most commonly used and experienced, and (3) a last update up to 2017 to evaluate the most recently produced and revised apps. Finally, apps were included if their aim was to help allergic or intolerant users select suitable food products to buy or consume, personalize their daily nutrition on the basis of their needs and food restrictions, detect allergens in recipes and food product labels, search for specific restaurants or supermarkets according to their needs, and obtain information and advice about allergen self-management. Duplicates and apps that did not fulfill the aforementioned inclusion criteria or did not work were excluded from the study.

    ‎
    Figure 1. Flow diagram for the selection process of the apps included in the study.
    View this figure

    Data Extraction

    All the identified apps were registered in an initial list to count the total number of apps and the number of duplicates. The general characteristics of the included apps were extracted from the information in the app stores, while the main app features were verified by the authors by using the app. Furthermore, the features were categorized as input and output features on the basis of whether the app content was created by the users or automatically generated.

    After data extraction, the authors divided the apps according to 3 purpose types (considering that the apps included presented different purposes): (1) searching for allergen-free food products, (2) searching for restaurants offering menus adapted to different food allergies and intolerances, and (3) functioning as meal planners for suitable daily meals according to users’ food allergies or intolerances. This division of the included apps allowed us to compare the MARS quality ratings among apps with a similar purpose and to provide suggestions for future app designs in line with this purpose.

    Moreover, web-based searches on the Medline database were conducted by app name (Eat This Much, Fitberry, Mealime, Recetas Vegetarianas y Veganas, SideChef, Tasty, Mercadona, Mi Intolerancia Alimentaria, Open Food Facts, ¿Qué Puedo Comer?, Club VIPS, Find Me Gluten Free, Foster’s Hollywood, and Happy Cow) and by “apps for food allergies and/or intolerances” to determine whether they had already been evaluated in scientific trials.

    MARS App Quality Assessment

    App quality was assessed using the MARS rating scale, a reliable tool with a high internal consistency (α=0.90) and an interrater reliability interclass correlation coefficient of 0.79 [31]. The following steps were taken. First, before assessing the app quality, the authors followed specific web-based training organized by the MARS developers [43], such as an exercise to better understand how to classify the apps. Then, to experience and test the functionality of the included apps, the two authors independently used each of the 14 apps for 1 month. Finally, the quality assessment was conducted in agreement between the two authors, and disagreements were resolved through discussion with a third author.

    The MARS rating scale consists of 2 categories. The first is the app classification category, with 6 items of descriptive and technical information for each app: (1) descriptive information (name, number, and type of ratings for all versions; developer; version; cost; platform; description; update), (2) focus, (3) theoretical background and strategies, (4) affiliations, (5) age group, and (6) technical aspects (login, password protection, web access, app community, social sharing, and reminder functions). The second category is the app quality category, which is divided into objective and subjective quality. Objective quality has 4 sections (engagement, functionality, esthetics, and information) with 19 items, while subjective quality is comprised of 4 items, for a total of 23 items.

    In addition to these 2 categories, there is an optional app-specific section with 6 items to collect further information about the perceived impact of the app on the user (awareness, knowledge, attitudes, intention to change, help-seeking, behavior change).

    The app classification category was not rated since its purpose was only descriptive. Instead, to evaluate the app quality category, each item was scored on a 5-point rating scale from 1 to 5 (1: inadequate; 2: poor; 3: acceptable; 4: good; 5: excellent). For each app, the total mean score was the sum of the score of each item divided by the number of total items. The mean score of the 4 objective quality sections (engagement + functionality + esthetics + information) was calculated separately from that of the subjective and app-specific sections to strengthen the impartiality of the measure.

    For each objective quality section, the maximum score was 25 points for engagement, 20 points for functionality, 15 points for esthetics, and 35 points for information, for a total of 95 points for objective quality. Subjective quality could reach a maximum of 20 points, and the app-specific section could reach a maximum of 30 points.

    In addition to the objective and subjective quality ratings, the app-specific section was evaluated on the 5-point rating scale.

    Statistical Analysis

    Continuous variables of the scores obtained for each section of the MARS quality assessment, with the exception of the app classification category, are presented as the mean and SD. Categorical variables for the included apps and their input and output features are presented as percentages. Multiple comparisons between the 3 purposes of the included apps (food products, restaurants, meal planners), MARS scores, and user star ratings were performed and adjusted using the generalized linear model of the Bonferroni test. Correlations among the MARS scores, user star ratings, and number of reviews were analyzed using Pearson correlation coefficients (for normally distributed variables) and Spearman correlation coefficients (for not normally distributed variables), which were interpreted as strong or moderate according to previously published cutoff points [44]. The analysis was performed with SPSS Statistics version 25. Statistical significance was considered at P≤.05.


    App Selection

    Figure 1 shows the flowchart of the app selection process. After the removal of duplicates found in both stores, 1376 apps about food allergies or intolerances were screened by title and description by the two authors, resulting in 1350 apps being excluded on the basis of the inclusion criteria. To further evaluate the eligibility of their content, 26 apps were downloaded, and 12 of these were excluded by common agreement because of irrelevant content (apps from the same developer with equivalent features and findings) and technical problems. As a result, 14 apps about food allergies or intolerances were finally included in the study for quality assessment using the MARS tool; 5 of the 14 (36%) were found in the Google Play Store, and 9 of the 14 (64%) were found in the Apple App Store. Moreover, 4 of the 14 apps (29%) operated only on the Android system, and 10 of the 14 apps (71%) operated on both the Android and iOS systems. None of the included apps had previously been evaluated in scientific trials.

    Data Extraction: App General Characteristics

    The general characteristics of the 14 included apps about food allergies or intolerances, shown in Multimedia Appendix 1, are described in the following sections.

    App Purpose

    First, the 14 included apps were divided according to their purpose.

    Of the 14 apps, 6 (43%) were meal planners, helping users search for and plan meals adapted to allergies or intolerances. In particular, 4 apps (Tasty, Recetas Vegetarianas y Veganas, SideChef, and Fitberry) propose food recipes that can be filtered by the users’ allergies or intolerances and on the basis of personal preferences, such as cooking difficulty and type of meal, diet, and cuisine. The other 2 apps (Mealime and Eat This Much) are meal planners that allow weekly meals to be organized on the basis of personal preferences, dietary goals, and food restrictions, such as food allergies and intolerances. In this way, users can create a personal profile indicating allergens to eliminate from their diet and organize their daily or weekly diet plan, choosing among the dishes proposed automatically by the apps and filtering them by the selected allergen.

    Of the 14 apps, 4 (29%) function as food product search tools, helping users search for suitable food products according to their food allergies and/or intolerances. In particular, 3 apps (Open Food facts, ¿Qué Puedo Comer?, and Mercadona) help users search, through barcode scanning or database searches, for the most suitable food by showing the allergens declared on the food product label and indicating the nearest place to buy them, and 1 app (Mi Intolerancia Alimentaria) is a calculator of food compatibility. According to the presence of an allergen, the user’s individual tolerance of the food or meal is calculated and shown using a 3-color code alert system (red, orange, and green) according to whether the compatibility of the food is low, medium, or high.

    Restaurant searches represented the main purpose of 4 of the 14 apps (29%), helping users search for restaurants that offer menus adapted for allergic or intolerant consumers. In particular, 1 app (Happy Cow) searches for gluten-free, vegetarian, and vegan restaurants, hotels, supermarkets, and caterers; 1 app (Foster’s Hollywood) belongs to a popular restaurant chain and offers the possibility of looking at the restaurant’s allergen-free menu by checking the available meals in advance; 1 app (Club VIPS) searches for the nearest locations of different restaurant chains with allergen-free options; and 1 app (Find Me Gluten Free) searches for restaurants with gluten-free options.

    Operating System

    Of the 14 apps, 10 (72%) operate on both the Android and iOS systems, and 4 (28%) operate only on the Android system.

    Number of Reviews

    The number of reviews of the included apps varied from 1013 to 48,597 reviews.

    Languages Available

    Of the 14 apps, 4 (28%) are available only in Spanish, 5 (36%) are available only in English, and 5 (36%) are offered in 3-130 different languages.

    Actions

    The included apps enable users to benefit from different actions for the daily management of food allergies or intolerances.

    Focus

    Of the 14 apps, 12 (86%) are related to food allergies or intolerances, while 2 (14%) deal with gluten intolerance only.

    Allergens Detected

    The included apps differed in the number of allergens detected. Specifically, 10 of the 14 apps for food allergies identified milk and eggs; 9 identified crustaceans, peanuts, and nuts; 8 identified fish and soya; 6 identified sesame, mustard, and sulfur dioxide; 5 identified celery and lupin; and 1 identified wheat and grain. In addition, all 14 of the apps for food intolerances identified gluten, 4 identified lactose, and 2 identified fructose, sorbitol, histamine, and salicylic acid.

    Thus, 5 of the 14 apps (36%) detected all 14 allergens that must be declared in the European Union (cereals containing gluten, crustaceans, eggs, fish, peanuts, soya, milk, nuts, celery, mustard, sesame, sulfur dioxide, lupin, and mollusks) above other food allergens present on the food product label, 2 of the 14 apps (14%) detected only gluten, and 7 of the 14 apps (50%) detected 2-10 food allergens.

    Input and Output Features

    The app features were distinguished as output features (Multimedia Appendix 2), where content is automatically generated by the app, and input features (Multimedia Appendix 3), where content is inserted and created by the user.

    The lowest-rated app in the objective quality category, Mi Intolerancia Alimentaria (mean MARS score 3.2, SD 0.5), has fewer output features (4 of the 20 features) than the apps scoring >4 points, which offer 12-15 of the 20 output features and also had the highest scores in the engagement section. The same situation occurred for the input features, with apps scoring >4 points offering 8-9 of the 9 input features.

    According to the app purposes, the most used features for the meal planner apps were allergen detection, search filters, sending of reminders and notifications, shopping list creation, suggestions and tips, rating and reviewing possibilities, personal profile, creation of a favorites list, and social sharing. For the food product apps, the most used features were allergen detection, listing of ingredients and additives, personal profile, and rating and reviewing possibilities. For the restaurant apps, the most used features were allergen detection, search filters, prompts and discounts, geolocation, rating and reviewing possibilities, personal profile, and social sharing.

    MARS App Quality Assessment

    The MARS app classification category is the part that collects descriptive and technical information about the included apps. Descriptive data include general information (app name, rating of all versions, developer, number of ratings of all versions, version, cost of basic and upgraded versions, platform, description and last update, focus, theoretical background and strategies, affiliations, age group) and technical aspects present in the app description in the app store. Of these data, only the relevant aspects were extracted (focus, theoretical strategies, affiliation, age group, and technical aspects); they are described in Multimedia Appendix 4.

    According to the MARS evaluation, the quality of the 14 included apps assessed in terms of the 4 objectives (engagement, functionality, esthetics, and information) and the one subjective section are shown in Multimedia Appendix 4. Additionally, the results of the optional app-specific section are included.

    The overall mean (SD) MARS objective quality score, which allows the evaluation of the general app quality (maximum of 5 points), was 3.8 points (SD 0.4 points); thus, the quality of the 14 included apps was considered acceptable. The score of the subjective quality section was 3.5 points (SD 0.6 points), and that of the app-specific section was 3.6 points (SD 0.7 points).

    In particular, the mean scores of the 4 single objective quality sections, from the highest to the lowest score, were as follows: functionality section, 4.1 points (SD 0.6 points); esthetics section, 4 points (SD 0.5 points); information section, 3.8 points (SD 0.4 points); and engagement section, 3.5 points (SD 0.6 points).

    When the scores of the 6 MARS sections (4 objective, 1 subjective, and 1 app-specific) were compared, the score of the esthetics section (mean 4, SD 0.5) was significantly higher than that of the engagement section (mean 3.5, SD 0.6; P=.007), and the score of the functionality section (mean 4.1, SD 0.6) was significantly higher than that of the subjective quality section (mean 3.5, SD 0.6; P<.001). Moreover, the score of the information section (mean 3.8, SD 0.4) was significantly higher than that of the subjective quality (mean 3.5, SD 0.6; P=.002) and app-specific (mean 3.6, SD 0.7; P=.001) sections. No further significance was found in the other between-section comparisons.

    Among the 3 app purposes (food products, restaurants, and meal planners), comparisons between the MARS sections, as shown in Table 1, were evaluated. The score of the engagement section was significantly higher for meal planner apps (mean 4.1, SD 0.4) than for the food product (mean 3.0, SD 0.6; P=.05) and restaurant (mean 3.2, SD 0.3; P=.02) apps. Furthermore, it emerged that for meal planner apps, the scores of the engagement (mean 4.1, SD 0.4; P=.04) and functionality (mean 4.3, SD 0.7; P=.02) sections were significantly higher than those of the subjective quality section (mean 3.9, SD 0.5), and the score of the functionality section was significantly higher than that of the esthetics section (mean 4.3, SD 0.3; P=.04). No further significance was found in the other between-section comparisons among the 3 app purposes.

    Table 1. Differences in the mean MARS scores between app purposes.
    Mean MARSa scoresMeal plannersFood productsRestaurantsP valuebP valuecP valued
    Engagement4.103.003.20.05.021.0
    Functionality4.294.123.691.0.43.96
    Esthetics4.283.673.83.17.461.0
    Information3.973.793.621.0.791.0
    Subjective quality3.873.313.19.46.261.0
    App-specific3.973.463.25.75.351.0

    aMARS: Mobile App Rating Scale.

    bComparison between meal planners and food products.

    cComparison between meal planners and restaurants.

    dComparison between food products and restaurants.

    Additional Analysis

    The relationships between MARS score quality and user star rating and number of reviews were determined using correlations (described in Table 2) and showed that the star ratings were significantly and strongly positively correlated with the MARS engagement section (r=0.69; P=.007) and app-specific section (ρ=0.79; P=.001). A moderate correlation was also found between MARS subjective (r=0.63; P=.01) and total objective quality (r=0.60; P=.02). However, no significant correlations were found between MARS sections and number of reviews.

    Table 2. Correlation coefficients between MARS scores, user star ratings, and number of reviews.
    Mobile App Rating Scale (MARS)Number of reviewsStar ratingsP valueaP valueb
    Functionalityc0.130.33.65.25
    Estheticsc0.110.43.71.12
    App-specificc0.050.79.87.001
    Number of reviewsc1.000.30N/Ad.30
    Engagemente0.200.69.50.007
    Informatione–0.140.42.62.14
    Total objective qualitye0.030.60.92.02
    Subjective qualitye–0.030.63.93.01
    Star ratingse0.301.00.29N/A

    aCorrelation between MARS scores and number of reviews.

    bCorrelation between MARS scores and star ratings.

    cSpearman (ρ).

    dN/A: not applicable.

    ePearson (r).

    In addition, to verify whether the star ratings assessed by users were similar to the MARS scores obtained in our study, the comparisons were analyzed. The star ratings were significantly higher (mean 4.2, SD 0.4) than the MARS subjective quality score (mean 3.5, SD 0.6; P=.04).


    The present systematic search and quality assessment study provides information about the objective (engagement, functionality, esthetics, and information) and subjective quality of the available apps for food allergies or intolerances in app stores. The quality assessment using the MARS tool indicated that the overall app quality of the 14 included apps was acceptable, according to MARS mean ratings of ≥3 from a maximum of 5 points.

    By comparing the 6 MARS sections (4 objective quality, 1 subjective quality, and 1 app-specific), the most significant results were related to the apps’ functionality, esthetics, and information, as they appeared visually pleasant, sufficiently descriptive, well arranged, and easy to use, whereas the engagement section of most of these apps needs to be improved. As observed in other studies, apps with simple functionality can motivate people who have no familiarity with technology to adopt mobile apps [45]. Moreover, esthetics, such as visual attractiveness, is another key element for increasing users’ motivation to use the app [46].

    Regarding the information section, the included 14 apps clearly presented their content through the support of images, graphics, and videos. Nevertheless, none of the apps has been tested in scientific trials, which is an important aspect of this section of the MARS tool. In addition, it is important to evaluate the apps’ efficacy in helping consumers self-manage food allergies or intolerances, since previous studies have demonstrated that commercial apps do not always provide the expected results when they are evaluated in trials [47-49]. For meal planner apps, future trials could evaluate the improvement in user knowledge and awareness of food allergens, which are considered important targets for the management of food allergies [50].

    Moreover, the efficiency of food product apps should be tested in clinical trials to increase users’ confidence when food shopping and reading product labels. For the allergic and intolerant population, it is fundamental for the food labeling system to be available and comprehensive [51], and this kind of app could help consumers more quickly detect allergens in food products. Finally, for restaurant apps, customer satisfaction when eating away from home could be evaluated as a measure of food businesses’ compliance with the European regulation and with the allergen-free menus published on the app. Positive experiences when eating away from home are correlated with the availability of food allergen information provided by the restaurants [52].

    Moreover, none of the 14 included apps claims any validation of the content by health professionals or allows remote support. Actually, a critical assessment published in 2015 found that most apps about food allergies lack important health information and are not developed with the support of health professionals [21]. It is important for such apps to be evaluated by health professionals to provide better information to help users make health-related choices [53-55]. Furthermore, apps providing professionally oriented support and communication are more engaging and favored by users, especially adolescents [54].

    The results obtained in the present study indicate that app engagement is the section with the lowest score with respect to functionality, esthetics, and information, in line with other MARS assessments of apps for food provision [38], checking for drug interactions [56], and drunk driving prevention [57], and the lack of interactive features influences the engagement quality of these apps. However, in a comparison of the 3 purposes of the included apps, the engagement section of the meal planner apps received higher quality scores than that of the food product and restaurant apps. In fact, food product and restaurant apps do not use interactive features that motivate users to use them repeatedly [58], but for these apps, which are designed for short and specific use such as finding restaurants or products, user engagement and daily use are not really as essential as in meal planner apps. However, including features such as tips and suggestions to support consumers’ decisions or sending notifications [59] to notify users of new products or restaurants could improve the user app experience, growth of the app community, and app competitiveness. To increase user enjoyment and participation, meal planner, food product, and restaurant apps should perhaps include features such as rewards, goal-setting options, challenges, and leader boards, which have been recognized as effective tools in past studies [60-62], especially in adolescent populations, where game competition can motivate users to participate [63]. Finally, features such as feedback and self-monitoring, which have been demonstrated in previous studies to be effective in increasing users’ motivation [32,58] and health behavior [64,65], should be available in apps focused on self-managing food allergies or intolerances; however, only 2 of the 14 apps included in the present systematic search offer these features.

    The subjective quality and app-specific sections need to be improved in relation to the 3 purposes of the included apps (meal planners, food products, and restaurants). These sections refer to general users’ impressions of the app, which, if positive, would lead them to recommend and use it. In this context, the lack of enough engagement could influence users’ perceptions. Thus, it is important to increase users’ subjective quality perception and impact of the apps (app-specific) by reinforcing, for example, the engagement profile, as discussed earlier, which mainly influences users’ view of the app.

    Based on the number of input and output features offered, among the meal planner apps, Eat This Much, Mealime, and SideChef were found to be the most practical for users, obtaining higher scores in the MARS assessment than other apps with the same purpose. Previous studies have shown that food allergies and intolerances impact people’s quality of life and emotional status, increasing anxiety and depression [66,67]. The avoidance of food allergens requires constant attention because their presence in food is not always evident or is unknown [68]. This problem becomes even more complicated when consumers have to adapt food recipes or make appropriate ingredient substitutions according to their allergy or intolerance [69] without accurate recommendations or support. In this sense, these 3 apps could better help users while providing suggestions for self-managing food allergies or intolerances in terms of cooking and daily menus. Among the food product apps, the ¿Qué Puedo Comer? app was the most practical for users, offering more features and gaining higher scores in the MARS assessment. This app helps consumers understand food product labels, detect food allergens, and search for food products according to allergies, intolerances, or dietary requirements. Since food product ingredients change regularly and consumers may need to read packaging labels several times [69], these apps can provide instant information and support [70]. Among the restaurant apps, the Find Me Gluten Free and Happy Cow apps were the most practical for users compared to others with the same purpose. The provision of food allergen information on restaurant menus is very important for consumers, and these kinds of apps encourage the dissemination of such information by making it easier to search for restaurants with allergen-free menus [52].

    The correlations of star ratings with the app-specific, engagement, and subjective sections suggest that when evaluating an app, users refer more to the subjective impression of the app given by the engaging features offered than to the quality and quantity of the information provided [71], as shown by the results obtained in the present study. As observed in previous studies, there is an evident difference between the quality evaluation obtained by a researcher using a more objective tool such as the MARS and a real-world user who tends to evaluate app quality through star ratings in a much more subjective way [72]. Nevertheless, app store user star ratings cannot be totally trusted since they are sometimes derived from piloted reviews or paid bots deployed by the developer [73].

    Thus, according to the results obtained, we consider that MARS quality assessment is a valid tool for providing more accurate app quality information and suggestions for future apps.

    Suggestions for Future App Development

    Based on the present app assessment, several suggestions emerged for the future design of high-quality apps focused on improving the wellbeing of subjects with food allergies or intolerances:

    1. Further features should be included, especially in meal planner apps, to improve the user app experience and increase participation.
    2. Content should be validated by health professionals and scientists to provide users with more reliable information about food allergies or intolerances [36].
    3. Remote support by health professionals would help users manage their food allergies or intolerances [54].
    4. Testing in scientific trials would demonstrate the apps’ reliability and effectiveness [74] in detecting food allergens and improving user knowledge.
    5. Regulation of nonmedical apps should be considered in the future since it would avoid the development of unrealistic and ineffective apps, provide more correct information to users [29], and provide more value to mHealth technology [30].
    6. App quality should be evaluated through innovative methods, including multiple dimension perspectives, as in the MARS tool. The MARS tool, compared to other scales [32,33], represents a multidimensional evaluation of app subjective quality as well as engagement, functionality, esthetics, and information as indicators of objective quality. However, although the MARS tool has been widely tested, it should be validated in the near future [75] to increase its value, and, depending on the area of interest of the app (eg, health care, nutrition, sports, psychology), the items in each section should be more specific and theme-based. Apps for food allergies or intolerances, for example, should include items asking whether food allergen information is effectively and appropriately provided to users.

    Limitations

    The present study also has several limitations. First, the majority of the apps about food allergies or intolerances found in the app stores had fewer than 1000 reviews and a user star rating <3, indicating low interest by users. Consequently, it was not possible to include most of the apps because we considered a rating of 3 stars as the minimum threshold for app quality. However, it was important for the inclusion criteria to limit the findings to the most reliable and popular apps, as the market includes plenty of dubious apps. Second, apps with only a paid version were excluded from the search. Third, several apps were excluded because of technical problems, such as being unable to open or use the app. Fourth, because this study is not a systematic review of the literature but is a systematic search of app stores, it was not possible to register it in PROSPERO [76]. Finally, despite the increasing attention to apps, the literature about the assessment of app quality is very scarce [77] and not oriented to food allergies and intolerances.

    Conclusions

    In this systematic search of food allergy or intolerance apps, acceptable MARS quality was identified, although the engagement of food product and restaurant apps should be improved and the included apps should be tested in trials. The critical points identified in this systematic search can help improve the innovativeness and applicability of future food allergy and intolerance apps.

    Acknowledgments

    This publication was cofunded by the European Regional Development Fund (ERDF) of the European Union within the framework of the ERDF operative programme of Catalonia 2014-2020 aimed at an objective of investment in growth and employment. This publication is framed within the initiative of coordinated PECT TurisTIC en familia, Operation 12: “Healthy Meals”. The NFOC-Salut group is a consolidated research group of Generalitat de Catalunya, Spain (2017 SGR522).

    This publication received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 713679. This publication has been possible with the support of the Universitat Rovira i Virgili (URV) and Banco Santander.

    Conflicts of Interest

    None declared.

    ‎
    Multimedia Appendix 1

    General characteristics of the mobile phone apps included in the review.

    PDF File (Adobe PDF File), 313 KB
    ‎
    Multimedia Appendix 2

    Output features of the included apps.

    PDF File (Adobe PDF File), 146 KB
    ‎
    Multimedia Appendix 3

    Input features of the included apps.

    PDF File (Adobe PDF File), 125 KB
    ‎
    Multimedia Appendix 4

    Mobile App Rating Scale (MARS) and user star ratings of the included apps in mean (SD).

    PDF File (Adobe PDF File), 217 KB
    1. Montalto M, Santoro L, D'Onofrio F, Curigliano V, Gallo A, Visca D, et al. Adverse reactions to food: allergies and intolerances. Dig Dis 2008;26(2):96-103. [CrossRef] [Medline]
    2. Tang R, Wang Z, Ji C, Leung PSC, Woo E, Chang C, et al. Regional Differences in Food Allergies. Clin Rev Allergy Immunol 2019 Aug 5;57(1):98-110. [CrossRef] [Medline]
    3. Turnbull JL, Adams HN, Gorard DA. Review article: the diagnosis and management of food allergy and food intolerances. Aliment Pharmacol Ther 2015 Jan 14;41(1):3-25 [FREE Full text] [CrossRef] [Medline]
    4. Tuck CJ, Biesiekierski JR, Schmid-Grendelmeier P, Pohl D. Food Intolerances. Nutrients 2019 Jul 22;11(7):1684 [FREE Full text] [CrossRef] [Medline]
    5. Lomer MCE. Review article: the aetiology, diagnosis, mechanisms and clinical evidence for food intolerance. Aliment Pharmacol Ther 2015 Feb 03;41(3):262-275 [FREE Full text] [CrossRef] [Medline]
    6. Jones SM, Burks AW, Dupont C. State of the art on food allergen immunotherapy: oral, sublingual, and epicutaneous. J Allergy Clin Immunol 2014 Feb;133(2):318-323. [CrossRef] [Medline]
    7. Licari A, Manti S, Marseglia A, Brambilla I, Votto M, Castagnoli R, et al. Food Allergies: Current and Future Treatments. Medicina (Kaunas) 2019 May 01;55(5):120 [FREE Full text] [CrossRef] [Medline]
    8. European Parliament and of the Council of the European Union. Regulation (EU) No 1169/2011 of the European Parliament and of the Council. Official Journal of the European Union. 2011.   URL: https://eur-lex.europa.eu/legal-content/EN/ALL/?uri=CELEX%3A32011R1169 [accessed 2020-08-31]
    9. Loerbroks A, Tolksdorf SJ, Wagenmann M, Smith H. Food allergy knowledge, attitudes and their determinants among restaurant staff: A cross-sectional study. PLoS One 2019 Apr 24;14(4):e0214625 [FREE Full text] [CrossRef] [Medline]
    10. Muraro A, Agache I, Clark A, Sheikh A, Roberts G, Akdis CA, European Academy of Allergy and Clinical Immunology. EAACI food allergy and anaphylaxis guidelines: managing patients with food allergy in the community. Allergy 2014 Aug 18;69(8):1046-1057. [CrossRef] [Medline]
    11. Çelik D, Elçi A, Akçiçek R, Gökçe B, Hürcan P. A safety food consumption mobile system through semantic web technology. 2014 Presented at: 2014 IEEE 38th International Computer Software and Applications Conference Workshops; July 21-25, 2014; Vasteras, Sweden. [CrossRef]
    12. Ross GMS, Bremer MGEG, Nielen MWF. Consumer-friendly food allergen detection: moving towards smartphone-based immunoassays. Anal Bioanal Chem 2018 Sep 26;410(22):5353-5371 [FREE Full text] [CrossRef] [Medline]
    13. Lu C, Hu Y, Xie J, Fu Q, Leigh I, Governor S, et al. The Use of Mobile Health Applications to Improve Patient Experience: Cross-Sectional Study in Chinese Public Hospitals. JMIR Mhealth Uhealth 2018 May 23;6(5):e126 [FREE Full text] [CrossRef] [Medline]
    14. Mosa ASM, Yoo I, Sheets L. A systematic review of healthcare applications for smartphones. BMC Med Inform Decis Mak 2012 Jul 10;12:67 [FREE Full text] [CrossRef] [Medline]
    15. Oreskovic NM, Huang TT, Moon J. Integrating mHealth and Systems Science: A Combination Approach to Prevent and Treat Chronic Health Conditions. JMIR Mhealth Uhealth 2015 Jun 02;3(2):e62 [FREE Full text] [CrossRef] [Medline]
    16. Silva BMC, Rodrigues JJPC, de la Torre Díez I, López-Coronado M, Saleem K. Mobile-health: A review of current state in 2015. J Biomed Inform 2015 Aug;56:265-272 [FREE Full text] [CrossRef] [Medline]
    17. Yang Q, Van Stee SK. The Comparative Effectiveness of Mobile Phone Interventions in Improving Health Outcomes: Meta-Analytic Review. JMIR Mhealth Uhealth 2019 Apr 03;7(4):e11244 [FREE Full text] [CrossRef] [Medline]
    18. Matricardi PM, Dramburg S, Alvarez-Perea AD, Antolín-Amérigo DS, Apfelbacher C, Atanaskovic-Markovic M, et al. The role of mobile health technologies in allergy care: An EAACI position paper. Allergy 2020 Feb 16;75(2):259-272. [CrossRef] [Medline]
    19. Arens-Volland A, Spassova L, Rösch N. Review of Mobile Health (mHealth) Solutions for Food-related Conditions and Nutritional Risk Factors. 2013 Presented at: eTELEMED 2013: The Fifth International Conference on eHealth, Telemedicine, and Social Medicine; February 24 - March 1, 2013; Nice, France p. 284-289. [CrossRef]
    20. Clement J. Global digital population as of April 2020. Statista. 2020.   URL: https://www.statista.com/statistics/617136/digital-population-worldwide/#:~:text=How [accessed 2020-06-15]
    21. Cuervo-Pardo L, Barcena-Blanch MA, Gonzalez-Estrada A, Schroer B. Apps for food allergy: A critical assessment. J Allergy Clin Immunol Pract 2015 Nov;3(6):980-1.e1. [CrossRef] [Medline]
    22. Pereira AM, Jácome C, Almeida R, Fonseca JA. How the Smartphone Is Changing Allergy Diagnostics. Curr Allergy Asthma Rep 2018 Oct 25;18(12):69. [CrossRef] [Medline]
    23. Kuehnhausen M, Frost VS. Trusting smartphone Apps? To install or not to install, that is the question. 2013 Presented at: 2013 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA); February 25-28, 2013; San Diego, CA p. 30-37. [CrossRef]
    24. Salazar A, de Sola H, Failde I, Moral-Munoz JA. Measuring the Quality of Mobile Apps for the Management of Pain: Systematic Search and Evaluation Using the Mobile App Rating Scale. JMIR Mhealth Uhealth 2018 Oct 25;6(10):e10718 [FREE Full text] [CrossRef] [Medline]
    25. WHO Global Observatory for eHealth. mHealth: New horizons for health through mobile technologies: second global survey on eHealth. World Health Organization. 2011.   URL: https://apps.who.int/iris/handle/10665/44607 [accessed 2020-08-29]
    26. US Food and Drug Administration. Policy for Device Software Functions and Mobile Medical Applications: Guidance for Industry and Food and Drug Administration Staff. 2019 Sep 27.   URL: https://www.fda.gov/media/80958/download [accessed 2020-08-29]
    27. Generalitat de Catalunya. TIC Salut Social: Pla Mestre de Mobilitat "mHealth.Cat". 2015.   URL: https://ticsalutsocial.cat/area/mhealth/ [accessed 2020-06-15]
    28. Akbar S, Coiera E, Magrabi F. Safety concerns with consumer-facing mobile health applications and their consequences: a scoping review. J Am Med Inform Assoc 2020 Feb 01;27(2):330-340 [FREE Full text] [CrossRef] [Medline]
    29. Dayton SJ. Rethinking Health App Regulation: The Case for Centralized FDA Voluntary Certification of Unregulated Non-Device Mobile Health Apps. IHLR 2013 Dec 31;11(2). [CrossRef]
    30. Carpenter D, Grimmer J, Lomazoff E. Approval regulation and endogenous consumer confidence: Theory and analogies to licensing, safety, and financial regulation. Regulation & Governance 2010 Nov 22;4(4):383-407. [CrossRef]
    31. Stoyanov SR, Hides L, Kavanagh DJ, Zelenko O, Tjondronegoro D, Mani M. Mobile app rating scale: a new tool for assessing the quality of health mobile apps. JMIR Mhealth Uhealth 2015 Mar 11;3(1):e27 [FREE Full text] [CrossRef] [Medline]
    32. Aitken M, Gauntlett C. Patient apps for improved healthcare from novelty to mainstream. IMS Institute for Healthcare Informatics. 2013 Oct.   URL: http:/​/moodle.​univ-lille2.fr/​pluginfile.php/​215345/​mod_resource/​content/​0/​IIHI_Patient_Apps_Report.​pdf [accessed 2020-08-29]
    33. mHIMSS App Usability Work Group. mHIMSS transforming healthcare. Selecting a Mobile App: Evaluating the Usability of Medical Applications. 2012.   URL: http://s3.amazonaws.com/rdcms-himss/files/production/public/HIMSSguidetoappusabilityv1mHIMSS.pdf [accessed 2020-08-29]
    34. Franco RZ, Fallaize R, Lovegrove JA, Hwang F. Popular Nutrition-Related Mobile Apps: A Feature Assessment. JMIR Mhealth Uhealth 2016 Aug 01;4(3):e85 [FREE Full text] [CrossRef] [Medline]
    35. Ferrara G, Kim J, Lin S, Hua J, Seto E. 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 Uhealth 2019 May 17;7(5):e9232 [FREE Full text] [CrossRef] [Medline]
    36. Lyzwinski LN, Edirippulige S, Caffery L, Bambling M. Mindful Eating Mobile Health Apps: Review and Appraisal. JMIR Ment Health 2019 Aug 22;6(8):e12820 [FREE Full text] [CrossRef] [Medline]
    37. Choi YK, Demiris G, Lin S, Iribarren SJ, Landis CA, Thompson HJ, et al. Smartphone Applications to Support Sleep Self-Management: Review and Evaluation. J Clin Sleep Med 2018 Oct 15;14(10):1783-1790 [FREE Full text] [CrossRef] [Medline]
    38. Mauch CE, Wycherley TP, Laws RA, Johnson BJ, Bell LK, Golley RK. Mobile Apps to Support Healthy Family Food Provision: Systematic Assessment of Popular, Commercially Available Apps. JMIR Mhealth Uhealth 2018 Dec 21;6(12):e11867 [FREE Full text] [CrossRef] [Medline]
    39. Davis SF, Ellsworth MA, Payne HE, Hall SM, West JH, Nordhagen AL. Health Behavior Theory in Popular Calorie Counting Apps: A Content Analysis. JMIR Mhealth Uhealth 2016 Mar 02;4(1):e19 [FREE Full text] [CrossRef] [Medline]
    40. Haskins BL, Lesperance D, Gibbons P, Boudreaux ED. A systematic review of smartphone applications for smoking cessation. Transl Behav Med 2017 Jun;7(2):292-299 [FREE Full text] [CrossRef] [Medline]
    41. Simões P, Silva AG, Amaral J, Queirós A, Rocha NP, Rodrigues M. Features, Behavioral Change Techniques, and Quality of the Most Popular Mobile Apps to Measure Physical Activity: Systematic Search in App Stores. JMIR Mhealth Uhealth 2018 Oct 26;6(10):e11281 [FREE Full text] [CrossRef] [Medline]
    42. Bardus M, van Beurden SB, Smith JR, Abraham C. A review and content analysis of engagement, functionality, aesthetics, information quality, and change techniques in the most popular commercial apps for weight management. Int J Behav Nutr Phys Act 2016 Mar 10;13:35 [FREE Full text] [CrossRef] [Medline]
    43. Stoyanov S, Leanne H, Kavanagh D, Tjondronegoro D, Zelenko O, Mani M. MARS training video. Youtube. 2016 Jun 14.   URL: https://www.youtube.com/watch?v=25vBwJQIOcE [accessed 2019-08-10]
    44. Schober P, Boer C, Schwarte LA. Correlation Coefficients: Appropriate Use and Interpretation. Anesth Analg 2018 May;126(5):1763-1768. [CrossRef] [Medline]
    45. Boulos MNK, Wheeler S, Tavares C, Jones R. How smartphones are changing the face of mobile and participatory healthcare: an overview, with example from eCAALYX. Biomed Eng Online 2011 May 05;10:24 [FREE Full text] [CrossRef] [Medline]
    46. Silvennoinen J, Vogel M, Kujala S. Experiencing Visual Usability and Aesthetics in Two Mobile Application Contexts. Journal of Usability Studies 2014 Nov;10(1):46-62 [FREE Full text]
    47. Agarwal P, Mukerji G, Desveaux L, Ivers NM, Bhattacharyya O, Hensel JM, et al. Mobile App for Improved Self-Management of Type 2 Diabetes: Multicenter Pragmatic Randomized Controlled Trial. JMIR Mhealth Uhealth 2019 Jan 10;7(1):e10321 [FREE Full text] [CrossRef] [Medline]
    48. Direito A, Jiang Y, Whittaker R, Maddison R. Apps for IMproving FITness and Increasing Physical Activity Among Young People: The AIMFIT Pragmatic Randomized Controlled Trial. J Med Internet Res 2015 Aug 27;17(8):e210 [FREE Full text] [CrossRef] [Medline]
    49. Laing BY, Mangione CM, Tseng C, Leng M, Vaisberg E, Mahida M, et al. Effectiveness of a smartphone application for weight loss compared with usual care in overweight primary care patients: a randomized, controlled trial. Ann Intern Med 2014 Dec 18;161(10 Suppl):S5-12 [FREE Full text] [CrossRef] [Medline]
    50. Soon JM. Structural modelling of food allergen knowledge, attitude and practices among consumers in Malaysia. Food Res Int 2018 Sep;111:674-681. [CrossRef] [Medline]
    51. Ju S, Park J, Kwak T, Kim K. Attitudes and preferences of consumers toward food allergy labeling practices by diagnosis of food allergies. Nutr Res Pract 2015 Oct;9(5):517-522 [FREE Full text] [CrossRef] [Medline]
    52. Begen FM, Barnett J, Payne R, Gowland MH, DunnGalvin A, Lucas JS. Eating out with a food allergy in the UK: Change in the eating out practices of consumers with food allergy following introduction of allergen information legislation. Clin Exp Allergy 2018 Mar;48(3):317-324. [CrossRef] [Medline]
    53. Chen J, Lieffers J, Bauman A, Hanning R, Allman-Farinelli M. Designing Health Apps to Support Dietetic Professional Practice and Their Patients: Qualitative Results From an International Survey. JMIR Mhealth Uhealth 2017 Mar 31;5(3):e40 [FREE Full text] [CrossRef] [Medline]
    54. Hilliard ME, Hahn A, Ridge AK, Eakin MN, Riekert KA. User Preferences and Design Recommendations for an mHealth App to Promote Cystic Fibrosis Self-Management. JMIR Mhealth Uhealth 2014 Oct 24;2(4):e44 [FREE Full text] [CrossRef] [Medline]
    55. Hartzler AL, BlueSpruce J, Catz SL, McClure JB. Prioritizing the mHealth Design Space: A Mixed-Methods Analysis of Smokers' Perspectives. JMIR Mhealth Uhealth 2016 Aug 05;4(3):e95 [FREE Full text] [CrossRef] [Medline]
    56. Kim BY, Sharafoddini A, Tran N, Wen EY, Lee J. Consumer Mobile Apps for Potential Drug-Drug Interaction Check: Systematic Review and Content Analysis Using the Mobile App Rating Scale (MARS). JMIR Mhealth Uhealth 2018 Mar 28;6(3):e74 [FREE Full text] [CrossRef] [Medline]
    57. Wilson H, Stoyanov SR, Gandabhai S, Baldwin A. The Quality and Accuracy of Mobile Apps to Prevent Driving After Drinking Alcohol. JMIR Mhealth Uhealth 2016 Aug 08;4(3):e98 [FREE Full text] [CrossRef] [Medline]
    58. Woldaregay A, Issom DZ, Henriksen AL, Marttila HR, Mikalsen M, Pfuhl G, et al. Motivational Factors for User Engagement with mHealth Apps. Stud Health Technol Inform 2018;249:151-157. [Medline]
    59. Bidargaddi N, Pituch T, Maaieh H, Short C, Strecher V. Predicting which type of push notification content motivates users to engage in a self-monitoring app. Prev Med Rep 2018 Oct;11:267-273 [FREE Full text] [CrossRef] [Medline]
    60. Marston HR, Hall AK. Gamification: Applications for health promotion and health information technology engagement. In: Novák D, Brendryen H, Tulu B, editors. Handbook of Research on Holistic Perspectives in Gamification for Clinical Practice. Hershey, PA: IGI Global; 2015:78-104.
    61. Ignacia SN, Wiastuti RD, Lemy DM. Restaurant Mobile Application towards Purchase Intention. IJAST 2018 Aug 31;117:113-128. [CrossRef]
    62. Yu Y, Luo M, Zhu D. The Effect of Quality Attributes on Visiting Consumers’ Patronage Intentions of Green Restaurants. Sustainability 2018 Apr 15;10(4):1187. [CrossRef]
    63. Jeminiwa RN, Hohmann NS, Fox BI. Developing a Theoretical Framework for Evaluating the Quality of mHealth Apps for Adolescent Users: A Systematic Review. J Pediatr Pharmacol Ther 2019;24(4):254-269 [FREE Full text] [CrossRef] [Medline]
    64. Klasnja P, Pratt W. Healthcare in the pocket: mapping the space of mobile-phone health interventions. J Biomed Inform 2012 Mar;45(1):184-198 [FREE Full text] [CrossRef] [Medline]
    65. Kayyali R, Peletidi A, Ismail M, Hashim Z, Bandeira P, Bonnah J. Awareness and Use of mHealth Apps: A Study from England. Pharmacy (Basel) 2017 Jul 14;5(2) [FREE Full text] [CrossRef] [Medline]
    66. King R, Knibb RC, Hourihane JO. Impact of peanut allergy on quality of life, stress and anxiety in the family. Allergy 2009 Mar;64(3):461-468. [CrossRef] [Medline]
    67. Zarkadas M, Dubois S, MacIsaac K, Cantin I, Rashid M, Roberts KC, et al. Living with coeliac disease and a gluten-free diet: a Canadian perspective. J Hum Nutr Diet 2013 Mar;26(1):10-23. [CrossRef] [Medline]
    68. Cummings A, Knibb RC, King RM, Lucas JS. The psychosocial impact of food allergy and food hypersensitivity in children, adolescents and their families: a review. Allergy 2010 Aug;65(8):933-945. [CrossRef] [Medline]
    69. Muñoz-Furlong A. Daily coping strategies for patients and their families. Pediatrics 2003 Jul;111(6 Pt 3):1654-1661. [Medline]
    70. Wilhide Iii CC, Peeples MM, Anthony Kouyaté RC. Evidence-Based mHealth Chronic Disease Mobile App Intervention Design: Development of a Framework. JMIR Res Protoc 2016 Mar 16;5(1):e25 [FREE Full text] [CrossRef] [Medline]
    71. Davis A, Ellis R. A quasi-experimental investigation of college students' ratings of two physical activity mobile apps with varied behavior change technique quantity. Digit Health 2019;5:2055207619891347 [FREE Full text] [CrossRef] [Medline]
    72. Domnich A, Arata L, Amicizia D, Signori A, Patrick B, Stoyanov S, et al. Development and validation of the Italian version of the Mobile Application Rating Scale and its generalisability to apps targeting primary prevention. BMC Med Inform Decis Mak 2016 Jul 07;16:83 [FREE Full text] [CrossRef] [Medline]
    73. Zhu H, Xiong H, Ge Y, Chen E. Discovery of Ranking Fraud for Mobile Apps. IEEE Trans. Knowl. Data Eng 2015 Jan;27(1):74-87. [CrossRef]
    74. Byambasuren O, Sanders S, Beller E, Glasziou P. Prescribable mHealth apps identified from an overview of systematic reviews. NPJ Digit Med 2018;1:12 [FREE Full text] [CrossRef] [Medline]
    75. Grainger R, Townsley H, White B, Langlotz T, Taylor WJ. Apps for People With Rheumatoid Arthritis to Monitor Their Disease Activity: A Review of Apps for Best Practice and Quality. JMIR Mhealth Uhealth 2017 Mar 21;5(2):e7 [FREE Full text] [CrossRef] [Medline]
    76. PROSPERO International Prospective register of systematic reviews. National Institute for Health Research.   URL: https://www.crd.york.ac.uk/PROSPERO/ [accessed 2020-08-29]
    77. Boudreaux ED, Waring ME, Hayes RB, Sadasivam RS, Mullen S, Pagoto S. Evaluating and selecting mobile health apps: strategies for healthcare providers and healthcare organizations. Transl Behav Med 2014 Dec;4(4):363-371 [FREE Full text] [CrossRef] [Medline]

    ‎
    IMS: Intercontinental Medical Statistics
    MARS: Mobile App Rating Scale
    mHealth: mobile health

    Edited by G Eysenbach; submitted 21.02.20; peer-reviewed by M Khalemsky, T Muto, C Reis, E Chioma, M K.; comments to author 02.04.20; revised version received 27.05.20; accepted 25.06.20; published 16.09.20

    Copyright

    ©Floriana Mandracchia, Elisabet Llauradó, Lucia Tarro, Rosa Maria Valls, Rosa Solà. Originally published in JMIR mHealth and uHealth (http://mhealth.jmir.org), 16.09.2020.

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