# A Review of Critical Features and General Issues of Freely Available mHealth
Apps For Dietary Assessment

Ghalib Ahmed Tahir11affiliationmark:  Chu Kiong Loo11affiliationmark:  Nadine
Kong 22affiliationmark:  and Foong Ming Moy 22affiliationmark:
11affiliationmark:  Department of Artificial Intelligence, Faculty of Computer
Science and Information Technology, University of Malaya, Kuala Lumpur,
Malaysia
22affiliationmark:  Julius Centre University of Malaya, Department of Social
and Preventive Medicine, Faculty of Medicine, University of Malaya, Kuala
Lumpur, Malaysia

###### Abstract

Obesity is known to lower the quality of life substantially. It is often
associated with increased chances of non-communicable diseases such as
diabetes, cardiovascular problems, various cancers, etc. Evidence suggests
that diet-related mobile applications play a vital role in assisting
individuals in making healthier choices and keeping track of food intake.
However, due to an abundance of similar applications, it becomes pertinent to
evaluate each of them in terms of functionality, usability, and possible
design issues to truly determine state-of-the-art solutions for the future.
Since these applications involve implementing multiple user requirements and
recommendations from different dietitians, the evaluation becomes quite
complex. Therefore, this study aims to review existing dietary applications at
length to highlight key features and problems that enhance or undermine an
application’s usability. For this purpose, we have examined the published
literature from various scientific databases of the PUBMED, CINAHL (January
2010-December 2019) and Science Direct (2010-2019). We followed PRISMA
guidelines, and out of our findings, fifty-six primary studies met our
inclusion criteria after identification, screening, eligibility and full-text
evaluation. We analyzed 35 apps from the selected studies and extracted the
data of each of the identified apps. Most of the apps are engaging, according
to user feedback (68%). 62% of the apps provide timely alerts to the user, and
53% of survey apps include goal-settings features. We indicated existing apps
are lagging in several aspects. Only 37% of the survey application have
included validated databases, 22% of the surveyed applications have addressed
data privacy issues, and three applications out of 35 provide offline access
to the user. Following our detailed analysis on the comprehensiveness of
freely available mHealth applications, we specified potential future research
challenges and stated recommendations to help grow clinically accurate diet-
related applications.

Despite considerable advancements in medicine today, the number of people
getting affected by chronic diseases is significantly greater due to unhealthy
lifestyles. Obesity is one of the most common contributing factors to chronic
diseases, affecting almost every part of the world from middle to lower-income
countries. According to a survey in 2016, 1.9 billion adults aged 18 years and
older were overweight 1 . The prevalence of the aforementioned diseases poses
serious concerns. However, determining the right remedial measures is
dependent on different factors ranging from a person’s genetics to lifestyle,
which need to be adjusted according to the cause and severity of the
condition. Treatment may include medication, lifestyle changes 92  such as
choosing healthier food alternatives, exercise, and requiring patients to
follow a customized diet plan 93 .

On the other hand, with rapid technological advancements and increased usage
of handheld devices such as smartphones, tablets, and smartwatches, people’s
reliance on these devices has undoubtedly grown beyond their utility as a
means to communicate. The number of mobile users in 2012 for Android and iOS
devices 2  increased from 640 million to 2,562 million in 2016 3 . These days,
smartphone applications are being extended to support electronic healthcare
practices 4 , 5 , and evidence across several fields show promising results
which support the feasibility, acceptability and efficacy of digital health
interventions in different medical conditions. These conditions include but
not limited to managing adolescent health and wellness 97 , interventions in
sickle cell disease 99 , pediatric cancer 98  102 , chronic health conditions
100  and improving adherence to preventive behaviour 101 . Similarly, e-Health
and related diet-related applications are being increasingly used for
professional and personal purposes. People are using these applications to
make healthier lifestyle choices. Generally, these apps provide instant
nutritional values of food items with barcode scanners, which is extremely
helpful for people suffering from non-communicable diseases, and others who
intend to choose healthier products 1 . These applications not only assist
users with the selection of more nutritious alternatives but also allow them
to self-monitor their physical activity and diet intake by using behavioural
strategies of goal settings 6 . Moreover, these applications are designed to
cater to various age groups, including children battling obesity from a very
young age.

In this regard, recent developments in artificial intelligence-based
functionalities and hardware capacity enhancement of handheld devices have led
to the development of automatic food recognition and calories estimation
methods, making them an essential subset of e-health applications.

Regardless of numerous diet-related applications freely available today,
scientifically proven guidelines (both in terms of usability and
functionality) have not yet emerged from the users’ and the dietitian’s
perspective. Also, the author’s first-hand app development experience 7
suggested a dire need to have in-depth knowledge about the state-of-the-art
diet-related applications. To develop this understanding, the first step
involves identifying key components relevant to existing diet-related
applications, which are categorized in terms of general issues faced by
dieticians and users, including user experience of both parties and
functionalities required by each of them, respectively.

While the development of diet-related applications requires a significant
amount of time and effort, general issues like their credibility remain a
question. The term "Credibility" here refers to the authenticity or scientific
validation of an application to achieve goal during trials. Another challenge
present applications face is the maintenance of an updated food composition
database, as new food products are being continuously introduced in the
market. Mobile app developers also find it challenging to determine target
users, their needs, and potential feedback to improve functionality and
usability of apps 7  8 . Thus, an application with a good user experience may
increase its preference over others despite offering lesser functionality.
Therefore, the development of such applications should strongly consider
essential factors like usability and ’ease of use’ 9  10 , as poor usability
can result in users switching over to alternative options 11  7  12 .

The following paper aims to provide a review of existing diet-related
applications and seeks to equip researchers and dietitians with comprehensive
knowledge about general issues encountered by their users in terms of
usability and functionality. Thereby laying the foundation for developing
state-of-the-art generalized solutions that can cater to vastly varying user
needs. Moreover, other fields in which evidence supports the effectiveness of
digital interventions can learn a valuable lesson from the findings of this
study.

##  1 Methods

We have developed the review protocol by defining our research questions and
considering multiple inclusion/exclusion criteria. Then we formally defined
our search strategy by identifying the search terms and carried out the search
using the electronic database of PUBMED, CINAHIL, and Science Direct.
Following this, we selected relevant studies based on our study selection
criteria. Then we extracted the data and presented our results.

###  1.1 Research Questions

The primary aim of this review is to answer the research questions shown in
Table 1.

Table 1: Research Questions  No |  Research question |  Motivation
---|---|---
RQ1 |  What are the general problems that are resolved by the freely available diet-related applications? |  To provide information about the general problems faced by dietary assessment apps such as frequent app crashes 87 , cumbersome process of entering meal information, demotivating information displays 54 , periodic notifications, difficulty in portion size estimation 85 , credibility 83 ; 89 , etc.
RQ2 |  What are back-end application issues resolved by the freely available dietary applications? |  To provide information about the application’s stability, usage reports 109 , data confidentiality 90 , and offline accessibility-related issues faced by diet-related applications.
RQ3 |  To what extent do the freely available dietary applications fulfill user interface requirements? |  To provide information regarding the critical user interface components 28 ; 81 ; 82 ; 79 ; 80  catered by diet-related applications.
RQ4 |  What are the dietary components and critical features implemented by the freely available dietary applications? |  To determine and provide information regarding the dietary components 83 ; 84 ; 89  and critical features implemented by existing diet-related applications.
RQ5 |  What are the benefits and challenges stemming from the included case studies? |  To summarize the benefits of dietary-related apps and the challenges they face based on the included studies.

###  1.2 Inclusion/Exclusion Criteria

The studies that met all of the following criteria are selected for this
review.
IC1. Papers related to dietary applications for smartphones (iPhones, Android
phones, and Blackberries) and modern commercially available portable devices
such as iPads and Personal Digital assistants (PDAs).
IC2. Content is written in the English language only.
IC3. The study must be a full peer-reviewed paper (not an abstract).
IC4. Dates of Publication: PubMed and CINAHL: 1/1/10-31/12/19, Science Direct:
2010-2019

This review excludes the following studies that are conformed to at least one
of the following criteria.
EC1. Studies without a clear description of dietary application mentioned.
EC2. Dietary applications that are not freely available.

###  1.3 Database Identification

We choose PubMed, CINAHL and Science Direct due to the following reasoning.
PubMed database gives a publicly available search interface for MEDLINE and
National Library of Medicine, which makes it one of the most widely accessible
biomedical resources globally 94 . Similarly, the CINAHL database provides
allied health care literature, thus making it a good resource for literature
related to mHealth applications 95 . We selected Science Direct as it provides
broad access to a database of scientific and medical research 96 .

###  1.4 Search Strategy

We have carefully defined the search terms based on initial screening through
a consensus among authors to investigate the diet-related mobile applications.
Terms such as cellular phone, mobile phone, smartphone, mHealth, iPads
combined with terms like diet, food and nutrition are qualified as keywords in
our work. In PubMed, we limited the search to research articles of Clinical
Trial, Meta-Analysis and Randomized Controlled Trial published between 1st
January 2010 and 31st December 2019. For the CINAHL database, we limited the
search to full-text research articles published from 1st January 2010 to 31st
December 2019. For Science Direct, we defined the search to research articles
published between 2010 and December 2019. Search targets the following
keywords ("cellular phone" AND diet, "mobile phone" AND diet, smartphone AND
diet, mHealth AND diet, iPads AND diet, "cellular phone" AND food, "mobile
phone" AND food, smartphone AND food, mHealth AND food, iPads AND food,
"cellular phone" AND nutrition, "mobile phone" AND nutrition, smartphone AND
nutrition, mHealth AND nutrition, iPads AND nutrition ). The Boolean AND joins
the two major parts. These yield 25950 results, reduced to 13,897 after
duplicates removal. They are screened based on titles, and we accessed a total
of 775 articles for eligibility against our inclusion/exclusion criteria and
study selection process. We scanned references of eligible studies to identify
additional studies, but we have not included additional studies in this
review. Finally, we included a total of 56 studies in this review process.
Table 2 shows the search terms and their corresponding search results. The
PRISMA diagram in Figure 1 shows the search flow and inclusion/exclusion of
studies.

Figure 1: PRISMA flow chart of identification, screening, eligibility and inclusion of studies. Table 2: Search Results (The Boolean AND joins the two major parts)  Search Strings |  Search Results
---|---
|  PUBMED |  CINAHL |  Science Direct
"Cellular Phone" AND Diet |  5 |  104 |  87
"Mobile Phone" AND Diet |  51 |  622 |  1,057
"Mobile Telephone" AND Diet |  2 |  106 |  79
Smartphone AND Diet |  89 |  909 |  1,071
mHealth AND Diet |  211 |  573 |  200
iPads AND Diet |  6 |  287 |  263
"Cellular Phone" AND Food |  1 |  180 |  400
"Mobile Phone" AND Food |  30 |  1,018 |  4457
"Mobile Telephone" AND Food |  1 |  132 |  314
Smartphone AND Food |  64 |  1,396 |  3,418
mHealth AND Food |  103 |  640 |  239
iPads AND Food |  5 |  610 |  843
"Cellular Phone" AND Nutrition |  2 |  136 |  106
"Mobile Phone" AND Nutrition |  41 |  757 |  1,191
"Mobile Telephone" AND Nutrition |  2 |  121 |  90
Smartphone AND Nutrition |  90 |  1,030 |  1,139
mHealth AND Nutrition |  183 |  663 |  171
iPads AND Nutrition |  5 |  344 |  316

###  1.5 Evaluation Criteria and Data Extraction

Reviewer GAT extracted all the selected studies’ key characteristics (study
population, location, mobile app details, and aim of the survey) shown in
Table LABEL:tw-b3e1fe13b830. Similarly, the Expert Group compromised of the
authors of this manuscript identified the attributes of each research question
shown in Table 4, 5, 6 and 7 that are mentioned in the existing literature of
mHealth apps for data extraction to answer our questions. Under the heading of
general issues, we assessed the difficulty in portion size estimation 84 ,
demotivating information, dependence on expensive electrical devices such as
fit brands 54 , the credibility of the database 83 ; 89  etc. Table S1 and S2
provide the extracted data of general issues in the supplementary material. We
extracted the data regarding stability of the application 87 , usage reports
109 , data confidentiality 90  and offline accessibility 110  for back-end
application issues. Table S3 provides the extracted data in the supplementary
material. For user interface requirements 28 ; 81 ; 82 ; 79 ; 80 , we
extracted data of the attributes mentioned in Table 6. Table S4 and S5 in the
supplementary material provides the extracted data. Under the heading of
dietary components 83 ; 89 , we extracted the details of the attributes shown
in Table 7. Table S6 and S7 in the supplementary material provides the
extracted data. We carried out the whole process by completing the data
extraction forms. Two researchers verified the data’s soundness and ensured
data extracted from each study justified the study’s aim. When the
publications identified in the searches did not provide sufficient detail of
mHealth apps, additional literature, websites, contacts with authors, or
application use itself was used to fill gaps.

##  2 Results

This section presents the results of the essential characteristics of each
selected study. It shows the results obtained from the extracted information
to answer our research question. The brief detail of all the data extracted to
answer our research question is provided in the study’s supplementary
material.

###  2.1 Study Selection

The database search yielded 25,950 results. After removing duplicates, we
screened 13,897 based on titles. Out of that, we excluded 13,122 studies, and
775 article texts were assessed for eligibility by reviewers. Finally, we
included 56 studies after excluding 719 text articles. The first study
included is from 2010. From 2013 to 2017, the publication rate increased by
55.08%, with the highest number of studies published studies in 2017 (22.3%).
We have the publication year of all the included studies in our key
characteristics table.

In the subsequent sections, we have briefly described mobile applications’
status as per our research questions. We have evaluated existing dietary
applications by keeping in view critical features and general issues mentioned
by dietitians and users and supported by existing literature of mHealth apps.

###  2.2  RQ1: What are the general problems that are resolved by the freely
available diet-related applications?

To determine the general issues found in existing applications, we have
categorized important parameters from different perspectives of users and
dietitians as shown in Figure 2. For this purpose, we surveyed 35 freely
available mHealth apps from included studies.

These general issues mentioned by dietitians and users in existing mHealth
apps include credibility, 83  localization of database sources, 85 , and
difficulty in portion size estimation 84 . Moreover, applications that require
users to go through multiple steps for data entry 86  make the process
cumbersome and negatively impact the whole user experience. (Figure 3) (A)
shows the percentage breakdown of applications that looked to resolve these
issues.

Credibility of database sources is one of the major reasons for dietitians to
not recommend apps to clients or patients due to concerns regarding their
validity and questionable feedback in terms of accuracy. Nearly 34% of the
applications 21 ; 22 ; 25 ; 29 ; 35 ; 37 ; 47 ; 52 ; 57 ; 58 ; 64 ; 66 ; 68
managed to resolve this issue by providing extensive details about the
database, especially in terms of its sourcing. However, there are still some
applications that offer little or no information about their database sources
18 ; 20 ; 27 ; 31 ; 33 ; 73 ; 38 ; 49 ; 74 ; 75 ; 63 . Remaining applications
16 ; 19 ; 23 ; 28 ; 6 ; 32 ; 36 ; 43 ; 44 ; 69 ; 71  did not address the issue
of credibility of database resources. On other hand only 10 out of 35 21 ; 22
; 6 ; 37 ; 43 ; 47 ; 49 ; 52 ; 58 ; 63  applications surveyed had localized
databases which are specific to certain region or culture.

Subsequently, portion size estimation also plays a vital role when it comes to
dietary applications. This component usually requires prior contextual
knowledge to ensure better accuracy. Several apps deal with fixed food
measurements in terms of serving size, weight, or other simple household
measurements. Generally, it is hard for most people to convert what they see
on their plates to these measurements for entering into dietary apps.
Moreover, when it comes to Asian food, estimating portion size becomes even
more challenging when multiple food items are mixed or placed on top of each
other. Therefore, over or underestimating portion size is common for unskilled
individuals, even more so in Asian families where each meal consists of
multiple side dishes. These challenges make the estimation of portion size a
complicated task for machine learning researchers, application developers, and
dietitians. Many existing applications 21 ; 23 ; 27 ; 28 ; 29 ; 6 ; 32 ; 73 ;
35 ; 36 ; 70 ; 52 ; 71  do not accommodate features which support estimation
of portion size. Alternately, many existing applications 16 ; 18 ; 19 ; 20 ;
21 ; 23 ; 27 ; 28 ; 29 ; 32 ; 33 ; 35 ; 70 ; 73 ; 37 ; 38 ; 43 ; 49 ; 74 ; 75
; 57 ; 58 ; 63 ; 64 ; 66 ; 69 ; 71  require minimum steps for data recording
ensuring a smooth user experience.

Figure 2: General issues faced by freely available diet-related applications

General issues faced by users involve frequent app crashes 87 , cumbersome
process of entering meal details, demotivating information displays,
dependence on expensive electrical devices such as fit bands 54 , frequent
notifications and difficulty in estimating portion size 85 . As shown in
figure 3 (B), out of 35 surveyed applications, 6 applications 43 ; 44 ; 47 ;
49 ; 74 ; 75  had no information about application’s stability in terms of app
crashes. Both "happy" 18  and "lose it" 19  experienced frequent app crashes.
The remaining applications do not experience frequent app crashes.

Furthermore, large number of surveyed applications display motivating
information and require less steps to record user information 16 ; 18 ; 19 ;
20 ; 21 ; 22 ; 23 ; 27 ; 28 ; 29 ; 32 ; 33 ; 35 ; 36 ; 37 ; 73 ; 38 ; 43 ; 74
; 57 ; 58 ; 63 ; 64 ; 68 ; 66 ; 69 ; 71 . Frequent notifications often bother
the user; especially when the app provides notifications after nearly every
step. Also, 40% of surveyed applications had no information available about
notification or reminder settings, 21 ; 25 ; 27 ; 6 ; 31 ; 32 ; 73 ; 36 ; 37 ;
44 ; 47 ; 52 ; 75 ; 64  whereas remaining applications accommodated
notification system except “lose it” 19 . Furthermore, all applications except
FoodWiz2 66  and MyFitness Pal (Log2Lose) 71  do not require any sort of
electrical device. Also, almost 37% of applications 20  22  25  27  6  31  32
47  52  failed to address the cumbersome process of entering meal information.
In addition to this, a large number of existing applications do not address
the difficulty in portion size estimation. This is due to multidimensional
challenges, from users’ perspective; as it is difficult to estimate the
portion size of food items used during preparation of food at different
restaurants. Moreover, lack of guidance/reference regarding the quantity,
further complicates the procedure of estimating portion size.

Figure 3: (A) and (B) shows percentage of applications that are resolving
general problems faced by dietitians and users

###  2.3 RQ2: What are back-end application issues resolved by the freely
available dietary applications?

The backend is an essential part of any mobile application, as it involves
data storage, business logic, and security. Therefore, it plays the role of a
server for mobile applications and stores information invisible to the end-
users. Figure 4 presents backend application issues faced by end-users.
Backend issues generally involve no offline access to the key features 110 ,
absence of usage reports 109 , privacy or data confidentiality concerns 90 ,
and frequent application crashes 87 . Therefore, the issues mentioned above
should be addressed by mobile applications to enhance the usability or user-
friendliness of any diet-related application.

Reduced dependence on the Internet will not only improve usability. It will
also enhance the app’s responsiveness and data processing, as the end-user is
not restricted from recording their data offline. Thus far, only 3 out of 35
surveyed applications allow offline accessibility 23  43  71 , whereas other
applications require an internet connection for data transmission to their
respected servers. Moreover, apps like Diet Cam rely on the client-server
configuration for connectivity between mobile phones and databases 22 , which
again requires a stable internet connection.

Figure 4: Backend application issues

Similarly, technical bugs and frequent app crashes resulted in unstable
applications 18  19 20 27 6 73 57 64 . Many of the applications surveyed
suffer from technical glitches and slow processing speed due to their
dependence on a reliable internet connection. Therefore, offline accessibility
can help to address all these concerns.

Another important issue is lack of data confidentiality and privacy. Mobile
applications, especially health-related applications, should have concrete
measures to ensure user’s records’ confidentiality. Similarly, web services
included in mobile applications should extract data without any leakages and
minimal pilferage instances. Almost 22% of surveyed applications offer data
privacy, while many studies do not mention this problem. Moreover, some
existing applications such as Social POD 16 , Happy 18 , and ’mDPP’ 20  report
user engagement or adherence to the app declined over time. Figure 5 describes
percentage of applications that managed to resolve aforementioned issues.

Figure 5: Shows percentage of applications that are resolving Backend
application issues

###  2.4 RQ3: To what extent do the freely available dietary applications
fulfill user interface requirements?

Generally, user interface requirements encompass application design, user-
friendliness, tutorial page, and two-user dashboard. As application design is
one of the main requirements 28 ; 81 ; 82 , as it should be simple, have nice
and appropriate icons along with clear font size and color to improve
usability. Out of 35 surveyed applications, 29 applications met the design
criteria according to users’ requirements. However, remaining applications 16
; 20 ; 29 ; 36 ; 38 ; 52  did not endorse their design details.

Another feature which enhances usability is the presence of appropriate
tutorial pages 79 ; 80 . For many diet-related applications, tutorial pages
are preferred to show the metric measurements of serving size of food for a
better understanding of the user. Unfortunately, most of the surveyed apps
fail to provide this information, and only 20% of surveyed applications 21 ;
23 ; 49 ; 58 ; 74 ; 68 ; 71  were able to provide the details for a tutorial
page.

Two-user dashboards are another essential feature whereby a simplified or
easy-to-use version of the dashboard is available for patients or the general
public. A more detailed version is available for dietitians or researchers.
Most surveyed applications do not incorporate this feature except for the
Dietary Intake Assessment app 51 .

Apart from these, user-friendliness has been qualified as the most important
UI design component 82 . Applications are considered user-friendly when they
have a complete data set, require fewer data entry steps, provide meaningful
information, and have a user-friendly interface. Therefore, bugs, glitches,
and a cumbersome user interface of apps can negatively influence the app’s
usability. According to dietitians, almost 77% of the surveyed applications 18
; 19 ; 21 ; 22 ; 23 ; 25 ; 28 ; 6 ; 32 ; 35 ; 36 ; 37 ; 43 ; 44 ; 47 ; 49 ; 52
; 57 ; 58 ; 63 ; 64 ; 66 ; 68 ; 69 ; 71 ; 74 ; 75  were user friendly and had
higher rate of user engagement due to presence of simple user interface and
interactive design. According to the users, 91% of the total applications
surveyed were user-friendly. For application design, most of the users prefer
simple design and easy-to-use apps. 70% of applications 37 ; 18 ; 19 ; 21 ; 25
; 27 ; 28 ; 31 ; 32 ; 73 ; 43 ; 44 ; 49  were attractive according to users’
requirements.

User interface requirements also involve information to include in the user
profile, and notification alerts to the user 81 . Besides basic information,
the application should allow users to set goals in terms of desired body
weight, and diet 81 ; 83 . Only 53% of surveyed applications 16 ; 19 ; 24 ; 28
; 30 ; 33 ; 74 ; 36  included goal-setting feature, while others did not even
provide personalized profiles.

Finally, another important feature of diet-related applications is
notification alert to users 83 . Reminders assist users in punching in their
updated information and updating their body weight regularly in the app,
thereby keeping track of their progress. Similarly, alerts to consume meals at
specified times, alerts for calories, drinking water, and doing exercises
improve user engagement. 62% of total surveyed applications 17 ; 19 ; 20 ; 21
; 24 ; 29 ; 6 ; 32 ; 33 ; 36 ; 43 ; 38 ; 47 ; 49 ; 52 ; 74  were found out to
provide such alerts to their users. Figure 6 shows the percentage of existing
applications that are implementing these features.

Figure 6: Shows percentage of applications that are implementing key user
interface features

###  2.5 RQ4: What are the dietary components and critical features
implemented by the freely available dietary applications?

Dietary component functionality mainly included evaluation of diet quality,
options to add supplements to the diet, history tracking, and storage of these
records. Other essential features include validity and comprehensiveness of
database 83 ; 89 , portion size estimation 84  and diet/nutrient summary that
provides information in terms of calories for each meal as show in Figure 7.
Figure 8 below illustrates the summary of the results gathered from surveyed
applications. To provide a good evaluation of diet quality, a dietary app
should display macronutrients’ balance and include reference values for
interpretation. Based on this information, 34 out of 35 surveyed applications
were able to assess diet quality properly.

Moreover, options to track users’ weekly diet records and their storage on
websites for later use are considered important factors that can improve user
experience. As per our survey total 26 applications16 ; 18 ; 19 ; 21 ; 22 ; 23
; 28 ; 6 ; 29 ; 31 ; 32 ; 73 ; 37 ; 47 ; 49 ; 52 ; 74 ; 57 ; 58 ; 63 ; 64 ; 68
; 69 ; 71  facilitate users by giving them access to their previous records.
Items included in existing applications should also be considered an essential
feature, as some applications are particular about specific food items (like
beverages). In contrast, other diet apps provide users with options to
customize the food choices accordingly.

Another important feature which most of applications 16 ; 18 ; 19 ; 20 ; 23 ;
27 ; 28 ; 31 ; 25 ; 35 ; 43 ; 74 ; 38  (21 out of 35 applications) failed to
include, is the incorporation of a reliable and comprehensive food database.
Apart from inclusion of database, validity of database also matters and
unfortunately only 37% of the surveyed applications 21 ; 22 ; 25 ; 29 ; 6 ; 35
; 37 ; 47 ; 52 ; 57 ; 58 ; 64 ; 66  possess validated food database.

Furthermore, applications should display the breakdown of nutrient components
of the consumed food items. Data should also include the proportion of
calories of each meal (eg. Breakfast, lunch, dinner). However, some of the
current applications only include calories per meal, whereas other application
like ‘Lose it’ 19  provides information about balanced macronutrients. Another
important functionality brought forth by existing applications 19 ; 22 ; 23 ;
25 ; 28 ; 29 ; 6 ; 31 ; 25 ; 37 ; 38 ; 44 ; 47 ; 49 ; 52 ; 75  is portion size
estimation. Only few surveyed applications 22 ; 25 ; 31 ; 25 ; 37  use camera
to estimate volume of a portion size, while most other surveyed apps rely on
the standard household measurements 19 ; 22 ; 23 ; 28 ; 29 ; 6 ; 38 ; 44 ; 47
; 49 . Water is considered an essential component of the human body, ensuring
the proper functioning of multiple bodily functions. Therefore, it is equally
as important to track users’ water intake. However, 14 out of 35 applications
including Happy 18 , Lose it 19 , Metabolic Diet app 21 , MyFitnessPal 23 ,
and My Meal Mate 6  allows users to record total water intake. Whereas other
diet-related applications tend to miss out on this important feature.

As for the nutrient summary, most application databases include calorie
information while other surveyed applications provide more specific nutrient
information in databases. Apps like “Lose it” 19  provide information about
three significant macronutrients like carbohydrates, proteins, and fats, which
guide users to make better food choices.

To enhance the user-friendliness of an application, tailored messages,
feedback, and notifications according to user dietary intake 81  are essential
factors to be considered. Acting as guidelines for users, they improve the
user-friendliness and user experience of an application. For instance, apps
like My Meal Mate 6 , ‘iDAT’ 32 , and MyFitnessPal 23  display remaining
calorie allowance to guide the user to achieve dietary goals.

Furthermore, applications can make use of visual aids by providing summaries
of energy and nutrients intake in the form of a diary, pie chart, table, and
progress bar 82  for better comprehension. Applications such as ENGAGED 35 ,
provide goal thermometers to display user goals and the actual amount of
calories and fat (in grams) consumed.

Finally, we have ranked these applications based on the number of features
they have implemented and mentioned in their study. Lark application, Food
wiz2, Gocarb application had a higher score in fulfilling the number of
requirements from dietitians’ or researchers’ reference frames. On the other
hand, MyFitnessPal, Engaged, and MyFoodApp focuses more on the general
population’s requirements. Figure 10 and Figure 9 show the applications score.

Figure 7: Important dietary component by users and dietitians Figure 8: Shows
percentage of applications that are implementing important dietary components
Figure 9: Application score by keeping in view requirements from dietician
perspective.Equal weight is given to each category and applications fulfilling
more requirements have the highest score. The maximum total score is 4, and
the max score of each category is one. Figure 10: Application score by keeping
in view requirements from user perspective.Equal weight is given to each
category and applications fulfilling more requirements have the highest score.
The maximum total score is 4, and the max score of each category is one.

###  2.6 RQ5: What are the benefits and challenges stemming from the included
case studies?

Current case studies have several benefits that can help users monitor their
daily diet and help them resolve diet-related issues. Based on the shortlisted
studies, we investigated freely available diet-related applications in terms
of features, general problems, and usability challenges. Our findings aim to
provide a broader view of current solutions to dietitians, health experts, and
researchers alike. Overall, the case studies equip both general users and
health experts with information on the critical features that are not catered
by most of the existing diet-related apps. Moreover, they will help users
quickly determine the viability of existing solutions to recommend further or
use the solution that fulfills their needs in the best possible way. Thus,
these studies have paved the way for the research community to introduce
standard guidelines for future diet-related apps according to criteria. As a
result, the apps will be more substantial for patients, general users, and
dietitians.

Apart from this, the studies also highlight different challenges that can
undermine current applications’ actual purpose. Major obstacles include
integration and updating large food databases as food recipes, and their
nutrient content varies from region to region. In addition to this, new food
items are being introduced in the market every day. Therefore, making the
design and implementation of such systems a difficult task. Similarly, a
database’s comprehensiveness is one of the primary requisites of users to keep
track of their micronutrients and macronutrients. Other than this, the
incorporation of user-friendliness along with notifications and personalized
alerts are the challenges that the research community should consider. Also,
data security is of vital importance 90  due to strict policies of regulating
authorities and rising public concerns over sharing private data. Therefore,
diet-related apps should ensure data privacy and confidentiality, which,
unfortunately, many surveyed apps fail to address.

##  3 Discussion

We initiated this evaluation because of the rapid recent emergence of freely
available diet-related apps coupled with increasing concerns over general
issues, usability challenges and missingness of critical features. Following
that, we investigated the strength and weaknesses of freely available diet-
related apps. The primary emphasis of previous reviews by Rusin et al. 76 ,
Kankanhalli et al. 77 , and Prgomet et al. 78  was on functionalities and
input methods or the combined intervention of sleep and diet. Similarly,
Prgomet et al. 78  focused only on the inclusion of nutrition information in
the meal ordering system.

We focused on mHealth apps identified from existing publications between
2010-2019. After carrying out the literature search on three scientific
databases, we evaluated 35 mHealth applications based on their usability,
critical features and shows their strengths and weaknesses. The user-
friendliness and high engagement are of considerable importance 82 ,
especially since 68% of the existing mHealth apps incorporate this feature. We
recommend that user’s input in the development of mHealth interventions and
other considerations for end-users should be sought early on in the process of
app or digital health intervention design to ensure long and short term
engagement 105  106  107  108 . Similarly, the user notifications are equally
important, as it keeps them engaged and motivated 83 . According to the
survey, we found that 62% of the apps provides timely alerts to the user.
Likewise, goal setting also holds critical significance, as it gives
information about user’s personal preferences required for modification of
their behaviour accordingly 81 ; 83 . Therefore, about 53% of the surveyed
applications include the goal settings feature.

Likewise, our findings indicate that existing applications are lagging in
various aspects. Despite the importance of the credibility of database
resources 83 , only 30% of surveyed applications highlighted this issue.
Besides credibility, the comprehensive validation of the database with
detailed information on macronutrients and micronutrients is also essential
for clinical use. However, only 37% of the applications have included
validated databases. Despite the rise of artificial intelligence, the methods
for estimating portion size and logging food photos from the camera have made
significant advancements 39 ; 55 , many apps still depend on household
measurements for portion size estimation or manual entries of the food log.

Due to rising concerns of data security among users 90 , diet-related apps
must encrypt user data and use standardized protocols to ensure data privacy
and confidentiality. Yet, the results indicate that only 22% of surveyed
applications have addressed data privacy issues. Similarly, there is a lack of
economic data in existing studies to support using mHealth apps for dietary
assessment. Although the economic evaluation of mHealth apps is necessary to
provide an evidence-based assessment of sustainability and benefits of
investing in such technologies. 103  104 .

Despite considerations that existing diet-related apps should address, all of
the studies are valuable to broaden the research community’s knowledge. The
identified applications in these works serve as a guide for users to choose
between healthier alternatives and improve their dietary habits in the long
term.

Finally, we have made the following recommendations for the research community
based on our study. A localized database is essential for nutritional
assessment apps due to variations in the food recipes and diet preferences
among different cultures. Future diet-related apps should also consider the
technological advancement in artificial intelligence and explore the current
methods of logging food and automated portion size estimation from food
photos. It is noteworthy that several studies have implemented AI-based
strategies, but further investigation of these methods is required on a large
scale. Furthermore, there is a dire need to develop standard guidelines for
the development of diet-related apps, as standardized solutions will be more
reliable in the future for patients, general users, and dietitians. Finally,
when designing modern diet-related applications, the research community should
consider our findings to enhance the usability and completeness of the
solution.

###  3.1 Limitations of the data gathered for this study

This review has limitations that require further investigations. Firstly, the
analysis was limited to studies published in the searched databases and only
written in the English language. Related articles in other languages were not
included. Secondly, this research does not consider demographic information
about a particular race or culture while designing the research questions.

##  4 Conclusion

Dietary apps for nutritional assessment are developed to assist users with
their diet-related issues or keep track of their dietary intake. Such apps
tend to act as guides and enable users to choose healthier alternatives to
improve their nutritional habits in the long term. Therefore, due to the vital
importance of diet-related apps, this SLR analyzed a wide range of existing
literature on mHealth apps from scientific databases of CINAHL, Science
Direct, and PUBMED and shortlisted almost 56 studies. We have investigated the
apps’ comprehensiveness in terms of critical features, general issues, and
usability challenges from general users’ reference frames. We have further
examined the strength and weaknesses of the existing freely available diet-
related apps and summarized concerns and gaps for future work. Our findings
show that the credibility of database resources, comprehensive information
about macronutrients and micronutrients, validation of database, data privacy,
use of AI for food logs, and automated portion size estimation from the
pictures are foremost challenges. Addressing the challenges mentioned above
will improve the usability and comprehensiveness of diet-related apps.
Therefore, making them more substantial for patients, general users, and
dieticians. Moreover, implementing blockchain technology and health standards
for data security, exploring recent trends in continual learning for food
recognition, and outlining standard guidelines for regulating apps are
essential future topics that can be explored.

This research was supported by the UM Partnership Grant: Project No:
RK012-2019 from University of Malaya, IIRG Grant (IIRG002C-19HWB) from
University of Malaya, International Collaboration Fund for project
Developmental Cognitive Robot with Continual Lifelong Learning (IF0318M1006)
from MESTECC, Malaysia and ONRG grant (Project No.: ONRG-NICOP-
N62909-18-1-2086) / IF017-2018 from Office of Naval and Research Global, UK.

## References

  * (1) “Obesity and Overweight.” World Health Organization (WHO)
  * (2) P. Farago, "iOS and Android Adoption Explodes Internationally," The Flurry Blog, 2013.
  * (3) D. Amitava and P. Abhinay and R. Rahul and S. Priya, "Technology diffusion: Shift happens — The case of iOS and Android handsets," Technological Forecasting and Social Change, vol. 118, p. 28–43, 2017.
  * (4) Y. Hairong and H. Hongwei and X. Youzhi and G. Mikael, "Wireless sensor network based E-health system—implementation and experimental results," IEEE Transactions on Consumer Electronics , vol. 56, p. 2288–2295, 2010.
  * (5) H. Alfredo and M. Fernando and V. Guillermo and P. Gianfranco and C. Guy, "Real-time ECG transmission via internet for nonclinical applications.," IEEE Transactions on Information Technology in Biomedicine , vol. 5, p. 253–257, 2001.
  * (6) C. Michelle and B. Victoria and C. Janet., "Development of ’My Meal Mate’ - A smartphone intervention for weight loss.," Nutrition Bulletin, vol. 38, pp. 80-84, 2013.
  * (7) D. BL and T.Schap and R. Ettienne-Gittens and F. Zhu et al. , "Novel technologies for assessing dietary intake: evaluating the usability of a mobile telephone food record among adults and adolescents," J Med Internet Res, vol. 14, p. 58, 2012.
  * (8) S. Lim and P.Bentley and N.Kanakam and F.Ishikawa and S.Honiden., "Investigating country differences in mobile app user behavior and challenges for software engineering," IEEE Transactions on Software Engineering, vol. 41, p. 40–64, 2015.
  * (9) K.Hsu,., "Efficiently and Effectively Mining Time-Constrained Sequential Patterns of Smartphone Application Usage," Mobile Information Systems, 2017.
  * (10) E. Din, "Ergonomic requirements for office work with visual display terminals (VDTs)–Part 11: Guidance on usability, International Organization for Standardization," 1998.
  * (11) J. Nielsen, "Usability 101:Introduction to usability," 2003.
  * (12) A.Luca and S.Das and M.Ortlieb and I.Ion and B.Laurie, "Expert and Non-Expert Attitudes towards (Secure) Instant Messaging,” Symposium on Usable Privacy and Security (SOUPS)," 2016.
  * (13) L. Carvajal and A. Moreno and M.Sanchez-Segura and A. Seffah., "Usability through software design," IEEE Transactions on Software Engineering, vol. 39, no. 11, p. 1582–1596, 2013.
  * (14) D.Moher and A. Liberati and J. Tetzlaff and D.G. Altman, , "Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement," plos one medicine, 2009.
  * (15) S. Hales and GM. Turner-McGrievy and S. Wilcox , et al., "Social networks for improving healthy weight loss behaviors for overweight and obese adults: A randomized clinical trial of the social pounds off digitally (Social POD) mobile app.," Int J Med Inform., vol. 94, pp. 81-90, 2016.
  * (16) M. Mccarroll and S. Armbruster and R. Pohle-Krauza and A. Lyzen and S. Min and D. Nash and G. Roulette and S. Andrews and V. Gruenigen, " Feasibility of a lifestyle intervention for overweight/obese endometrial and breast cancer survivors using an interactive mobile application.," Gynecol Oncol. , vol. 137, p. 508–515, 2015.
  * (17) N. Ribeiro and L. Moreira and AM. Almeida and F. Santos-Silva, "Happy: Cancer Prevention Using Smartphones," Procedia Computer Science, vol. 100, p. 466 – 473 , 2016.
  * (18) L. Burke and Y. Zheng and Q. Ma and J. Mancino and I. Loar and E. Music and M. Styn and L. Ewing and B. French and D. Sieworek and A. Smailagic and S. Sereika., "The SMARTER pilot study: Testing feasibility of real-time feedback fordietary self-monitoring," Preventive Medicine Reports, vol. 6, pp. 278-285, 2017.
  * (19) Y. Fukuoka and C. Gay and K.Joiner and E. Vittinghoff, "A Novel Diabetes Prevention Intervention Using a Mobile App," American journal of preventive medicine, vol. 49, pp. 223-237, 2015.
  * (20) G. Ho and Ueda, Keiko and Houben, Roderick and Joa, Jeff and Giezen, Alette and Cheng, Barbara and Karnebeek, Clara., "Metabolic Diet App Suite for inborn errors of amino acid metabolism," Molecular Genetics and Metabolism, vol. 117, no. 3, pp. 322-327, 2016.
  * (21) Fanyu Kong, Jindong Tan, DietCam: Automatic dietary assessment with mobile camera phones, Pervasive and Mobile Computing,Volume 8, Issue 1, 2012,Pages 147-163,SSN 1574-1192, https://doi.org/10.1016/j.pmcj.2011.07.003.
  * (22) C. Levinson and L. Fewell and L. Brosof., "My Fitness Pal calorie tracker usage in the eating disorders," Eat Behav., 2017.
  * (23) C. Wharton and C. Johnston and B. Cunningham and D. Sterner, "Dietary self-monitoring, but not dietary quality, improves with use of smartphone app technology in an 8-week weight loss trial," J Nutr Educ Behav, vol. 46, 2014.
  * (24) BL. Six and TE. Schap and FM. Zhu and A. Mariappan and M. Bosch and EJ. Delp and DS. Ebert and DA. Kerr and CJ. Boushey, " Evidence-based development of a mobile telephone food record," Journal of the American Dietetic Association, vol. 110, p. 74–79, 2010.
  * (25) Six BL, Schap TE, Kerr DA, Boushey CJ. Evaluation of the Food and Nutrient Database for Dietary Studies for use with a mobile telephone food record. J Food Compost Anal. 2011 Dec 1;24(8):1160-1167. doi: 10.1016/j.jfca.2011.06.006. PMID: 22389554; PMCID: PMC3289151.
  * (26) N. Gilson and T. Pavey and S. Gomersall and C. Vandelanotte and M. Duncan and O. Wright and S.Trost and W. Brown. , "Shifting gears: Process evaluation of an activity tracker and smart phone application to promote healthy lifestyle choices in Australian truck drivers," Journal of Science and Medicine in Sport, vol. 18, p. 124, 2014.
  * (27) H. Holmen and A.Torbjørnsen and A. Wahl and A. Jenum and M. Cvancarova and E. Arsand and L. Ribu., "A Mobile Health Intervention for Self-Management and Lifestyle Change for Persons With Type 2 Diabetes, Part 2: One-Year Results From the Norwegian Randomized Controlled Trial RENEWING HEALTH.," JMIR Mhealth Uhealth, vol. 2, p. 57, 2014.
  * (28) S. Gabrielli and M. Dianti and R. Maimone and M. Betta and L. Filippi and M. Ghezzi and S. Forti., "Design of a Mobile App for Nutrition Education (TreC-LifeStyle) and Formative Evaluation With Families of Overweight Children," JMIR Cancer, vol. 5, p. 48, 2017.
  * (29) W. Lippevelde and J. Vangeel and N. De Cock and C. Lachat and L. Goossens and K. Beullens and L. Vervoort and C. Braet and L. Maes and S. Eggermont and B. Deforche and J. Camp (2016). "Using a gamified monitoring app to change adolescents’ snack intake: the development of the REWARD app and evaluation design.," BMC Public Health, 2016.
  * (30) M. Carter and V. Burley and J. Cade, "Weight Loss Associated With Different Patterns of Self-Monitoring Using the Mobile Phone App My Meal Mate," JMIR mHealth and uHealth, 2017.
  * (31) SL. Casperson and J. Sieling and J. Moon and L. Johnson and JN. Roemmich and L. Whigham, "A mobile phone food record app to digitally capture dietary intake for adolescents in a free-living environment: usability study," JMIR Mhealth Uhealth, vol. 3, p. 30, 2015.
  * (32) G. Goh and NC. Tan and R. Malhotra et al. "Short-term trajectories of use of a caloric-monitoring mobile phone app among patients with type 2 diabetes mellitus in a primary care setting," J Med Internet Res, pp. 33-33, 2015.
  * (33) DA. Kerr and CM. Pollard and P. Howat, et al. "Connecting Health and Technology (CHAT):protocol of a randomized controlled trial to improve nutrition behaviours using mobile devices and tailored text messaging in young adults," BMC Public Health, 2012.
  * (34) C. Pellegrini and J. Duncan and A. Moller and J. Buscemi and A. Sularz and A. DeMott and A. Pictor and S. Pagoto and J. Siddique and B. Spring, "A smartphone-supported weight loss program: design of the ENGAGED randomized controlled trial," BMC Public Health, vol. 12, 2012.
  * (35) J. Recio-Rodríguez et al.,"Short-Term Effectiveness of a Mobile Phone App for Increasing Physical Activity and Adherence to the Mediterranean Diet in Primary Care: A Randomized Controlled Trial (EVIDENT II Study).," J Med Internet Res, vol. 12, pp. 1-1, 2016.
  * (36) D. Rhyner and H. Loher and J. Dehais and M. Anthimopoulos and S. Shevchik and R. Botwey and D. Duke and C. Stettler and P. Diem and S. Mougiakakou., "Carbohydrate Estimation by a Mobile Phone-Based System Versus Self-Estimations of Individuals With Type 1 Diabetes Mellitus: A Comparative Study.," JMIR Mhealth Uhealth, vol. 18, p. 101, 2016.
  * (37) E. Seto and J. Hua and L.Wu and V.Shia and S. Eom et al., "Models of Individual Dietary Behavior Based on Smartphone Data: The Influence of Routine, Physical Activity, Emotion, and Food Environment," PLOS One, 2016.
  * (38) F. Zhu and M. Bosch and C. Boushey and E. Delp, "AN IMAGE ANALYSIS SYSTEM FOR DIETARY ASSESSMENT AND EVALUATION.," Proc Int Conf Image Proc:, pp. 1853-1856, 2010.
  * (39) Y. Probst and DT. Nguyen and M. Tran and W. Li., "Dietary Assessment on a Mobile Phone Using Image Processing and Pattern Recognition Techniques: Algorithm Design and System Prototyping," Nutrients, vol. 7, no. 8, pp. 6128-6138, 2015.
  * (40) K. Serrano and M. Yu and K. Coa and L. Collins and A. Atienza, . "Mining Health App Data to Find More and Less Successful Weight Loss Subgroups," J Med Internet Res, vol. 18, p. 60, 2016.
  * (41) C. Farsjø and A. Moen, "New app can give nutritional support to home-dwelling elderly," Norwegian Journal of Clinical Nursing / Sykepleien Forskning, pp. 1-10, 2017.
  * (42) I.De La Torre Díez and B. Zapirain and M. López-Coronado and J. Rodrigues and C. Vegas., "A New mHealth App for Monitoring and Awareness of Healthy Eating: Development and User Evaluation by Spanish Users.," J Med Syst, vol. 41, pp. 1-7, 2017.
  * (43) E. Delisle Nyström Forsum and H, Henriksson and Y. Lagerros and C. Larsson and R. Maddison and T. Timpka and M. Löf, "A Mobile Phone Based Method to Assess Energy and Food Intake in Young Children: A Validation Study against the Doubly Labelled Water Method and 24 h Dietary Recalls.," Nutrients, vol. 8, pp. 1-11, 2016.
  * (44) A. Rangan and L. Tieleman and JCY. Louie and LM. Tang and L.Kairey and R. Roy and J. Kay and M. Allman-Farinelli., "Electronic Dietary Intake Assessment (e-DIA): relative validity of a mobile phone application to measure intake of food groups," British Journal of Nutrition, vol. 1, pp. 2219-2226, 2016.
  * (45) S. Mummah and T. Robinson and M. Mathur and S. Farzinkhou and S. Sutton and C. Gardner., "Effect of a mobile app intervention on vegetable consumption in overweight adults: a randomized controlled trial.," Int J Behav Nutr Phys Act, vol. 14, p. 125, 2017.
  * (46) S. Mummah and A. King and C. Gardner and S. Sutton., "Iterative development of Vegethon: a theory-based mobile app intervention to increase vegetable consumption.," Int J Behav Nutr Phys Act, vol. 13, p. 90, 2016.
  * (47) P. Hull and J.Emerson and M. Quirk and J. Canedo and J. Jones and V. Vylegzhanina and D. Schmidt and S. Mulvaney and B. Beech and C. Briley and C. Harris and B. Husaini., "A Smartphone App for Families With Preschool-Aged Children in a Public Nutrition Program: Prototype Development and Beta-Testing," JMIR Mhealth Uhealth, vol. 5, p. 102, 2017.
  * (48) A. Svensson and M. Magnusson and C. Larsson and, "Overcoming Barriers: Adolescents’ Experiences Using a Mobile Phone Dietary Assessment App," JMIR Mhealth Uhealth, vol. 4, p. 92, 2016.
  * (49) C. Martin and A. Gilmore and J. Apolzan and C. Myers and D. Thomas and L. Redman. , "Smartloss: A Personalized Mobile Health Intervention for Weight Management and Health Promotion.," JMIR Mhealth Uhealth, vol. 4, p. 18, 2016.
  * (50) E. Arsand and M. Muzny and M. Bradway and J. Muzik and G. Hartvigsen., "Performance of the first combined smartwatch and smartphone diabetes diary application study," JMIR Mhealth Uhealth, vol. 9, pp. 556-563, 2015.
  * (51) F. Zhu and M. Bosch and I. Woo and S. Kim and C. Boushey and D. Ebert and E. Delp. , "The Use of Mobile Devices in Aiding Dietary Assessment and Evaluation," IEEE J Sel Top Signal Process, vol. 4, pp. 756-766, 2010.
  * (52) Ali ZC, Silvioli R, Rajai A, Aslam TM. Feasibility of Use of a Mobile Application for Nutrition Assessment Pertinent to Age-Related Macular Degeneration (MANAGER2). Transl Vis Sci Technol. 2017;6(1):4. Published 2017 Jan 20. doi:10.1167/tvst.6.1.4
  * (53) Ahmed M, Mandic I, Lou W, Goodman L, Jacobs I, L’Abbé MR. Validation of a Tablet Application for Assessing Dietary Intakes Compared with the Measured Food Intake/Food Waste Method in Military Personnel Consuming Field Rations. Nutrients. 2017 Feb 27;9(3):200. doi: 10.3390/nu9030200. PMID: 28264428; PMCID: PMC5372863.
  * (54) G. Ambrosini and M. Hurworth and R. Giglia and G. Trapp and P. Strauss., Feasibility of a commercial smartphone application for dietary assessment in epidemiological research and comparison with 24-h dietary recalls," Nutrition Journal, vol. 17, 2017.
  * (55) M. Bardus and G.Hamadeh and B. Hayek and R. Al Kherfan,. , "A Self-Directed Mobile Intervention (WaznApp) to Promote Weight Control Among Employees at a Lebanese University: Protocol for a Feasibility Pilot Randomized Controlled Trial," JMIR Research Protocols, vol. 7, 2018.
  * (56) G. Bennett, "Effectiveness of an App and Provider Counseling for Obesity Treatment in Primary Care," American Journal of Preventive Medicine, vol. 55, 2018.
  * (57) J. Chen and W. Berkman and M. Bardouh and C.Ng and M. Allman-Farinelli, "The use of a food logging app in the naturalistic setting fails to provide accurate measurements of nutrients and poses usability challenges," pp. 208-216, 2019.
  * (58) N. De Cock and J. Vangeel and C. Lachat and K. Beullens and L. Vervoort and L. Goossens and L. Maes and B. Deforche and S. Henauw and C. Braet and S. Eggermont and P. Kolsteren and J. Camp and W. Lippevelde, "Use of Fitness and Nutrition Apps: Associations With Body Mass Index, Snacking, and Drinking Habits in Adolescents," JMIR mHealth and uHealth, vol. 5, p. e58, 2017.
  * (59) E. Everett and B. Kane and A. Yoo and A. Dobs and N. Mathioudakis. "A Novel Approach for Fully Automated, Personalized Health Coaching for Adults with Prediabetes: Pilot Clinical Trial," vol. 20, no. 2, 2018.
  * (60) N. Hezar Jaribi and S. Mazrouee and H. Ghasemzadeh. "Speech2Health: A Mobile Framework for Monitoring Dietary Composition From Spoken Data," IEEE Journal of Biomedical and Health Informatics, vol. 22, pp. 252-264, 2018.
  * (61) M. Ipjian and C. Johnston. , "Smartphone technology facilitates dietary change in healthy adults," vol. 33, pp. 343-347, 2017.
  * (62) F. Jimoh and E. Lund and L. Harvey and C. Frost and W. Lay and M. Roe and R. Berry and P. Finglas. Comparing Diet and Exercise Monitoring Using Smartphone App and Paper Diary: A Two-Phase Intervention Study, JMIR mHealth and uHealth, vol. 6, p. 17, 2018.
  * (63) S. Chiu and Y. Liu and C. Chen and Y. Tsou and Y. Lin and H. Chen and J. Yeh. "Evaluating Mobile Health Application on Customized Dietary Recording for Young Adults and Elders Using Randomized Trials (Preprint)," 2018.
  * (64) Pagoto S, Tulu B, Agu E, Waring ME, Oleski JL, Jake-Schoffman DE. "Using the Habit App for Weight Loss Problem Solving: Development and Feasibility Study," vol. 6, no. 6, p. e145, 2018.
  * (65) M. Paulsen and M. Hagen and M. Fryen and RJ. Foss-Pedersen and R. Tangvik and L. Andersen. "A Dietary Assessment App for Hospitalized Patients at Nutritional Risk: Development and Evaluation of the MyFood App," JMIR Mhealth Uhealth, vol. 6, p. 175, 2018.
  * (66) J. Recio-Rodríguez and C. Rodriguez-Martin and J. Gonzalez-Sanchez and E. Rodríguez-Sánchez and C. Borràs and V. Martinez Vizcaino and M. Arietaleanizbeaskoa and O. Magdalena-Gonzalez and C. Fernández-Alonso and J. Maderuelo-Fernandez and M. Gomez-Marcos and L. Garcia-Ortiz. EVIDENT Smartphone application, a new method for the dietary record. Comparison against a food frequency questionnaire, vol. 7, no. 2, 2018.
  * (67) Log2Lose: Development and Lessons Learned from a Mobile Technology Weight Loss Intervention (Preprint)," 2018.
  * (68) Mann D, Riddell L, Lim K, Byrne LK, Nowson C, Rigo M, Szymlek-Gay EA, Booth AO Mobile Phone App Aimed at Improving Iron Intake and Bioavailability in Premenopausal Women: A Qualitative Evaluation JMIR Mhealth Uhealth 2015;3(3):e92 doi: 10.2196/mhealth.4300
  * (69) E. Brindal and G. Hendrie and J. Freyne and M. Coombe and S. Berkovsky and M. Noakes. Design and pilot results of a mobile phone weight-loss application for women starting a meal replacement programme, J Telemed Telecare, vol. 19, pp. 166-174, 2013.
  * (70) M. Rollo and S. Ash and P. Lyons-Wall and A. Russel. Trial of a mobile phone method for recording dietary intake in adults with type 2 diabetes: evaluation and implications for future applications," J Telemed Telecare, vol. 17, pp. 318-323, 2011.
  * (71) Rusin M, Arsand E, Hartvigsen G. Functionalities and input methods for recording food intake: a systematic review. Int J Med Inform. 2013 Aug;82(8):653-64. doi: 10.1016/j.ijmedinf.2013.01.007. Epub 2013 Feb 13. PMID: 23415822.
  * (72) Kankanhalli, Atreyi & Saxena, Meghna & Wadhwa, Bimlesh. (2018). Combined Interventions for Physical Activity, Sleep, and Diet using Smartphone Apps: A Scoping Literature Review. International Journal of Medical Informatics. 123. 10.1016/j.ijmedinf.2018.12.005.
  * (73) Prgomet, Mirela & Li, Julie & Li, Ling & Georgiou, Andrew & Westbrook, Johanna. (2019). The impact of electronic meal ordering systems on hospital and patient outcomes: A systematic review. International Journal of Medical Informatics. 129. 10.1016/j.ijmedinf.2019.06.023.
  * (74) A Eriksson et al. Perceived usefulness of user interface characteristics for mobile health applications
  * (75) Hsu, F. User Satisfaction and System Environment Convenience for Interface Design of Mobile Games. Ekoloji 2018, 27, 1211–1215.
  * (76) Ashish Joshi, Chioma Amadi, Harleigh Schumer, Leah Galitzdorfer, Ann Gaba A human centered approach to design a diet app for patients with metabolic syndrome
  * (77) Varadraj P. Gurupurcorresponding and Thomas T. H. Wan Challenges in implementing mHealth interventions: a technical perspective
  * (78) Wei Peng, Shaheen Kanthawala, Shupei Yuan and Syed Ali Hussain A qualitative study of user perceptions of mobile health apps
  * (79) Beenish Chaudry, Kay Connelly, Katie A. Siek, and Janet L. Welch The Design of a Mobile Portion Size Estimation Interface for a Low Literacy Population
  * (80) Wesley Tay, Bhupinder Kaur, Rina Quek, Joseph Lim and Christiani Jeyakumar Henry Current Developments in Digital Quantitative Volume Estimation for the Optimisation of Dietary Assessment
  * (81) Afua van Haasteren, Felix Gille, Marta Fadda, Effy Vayena Development of the mHealth App Trustworthiness checklist DIGITAL HEALTH
  * (82) Ruth N. Jeminiwa, Natalie S. Hohmann, and Brent I. Fox, Developing a Theoretical Framework for Evaluating the Quality of mHealth Apps for Adolescent Users: A Systematic Review
  * (83) VanAnh Vo,Lola Auroy, and Aline Sarradon-Eck Patients’ Perceptions of mHealth Apps: Meta-Ethnographic Review of Qualitative Studies JMIR Mhealth Uhealth
  * (84) Amoutzopoulos B, Steer T, Roberts C, Cade JE, Boushey CJ, Collins CE, et al. Traditional methods new technologies - dilemmas for dietary assessment in large-scale nutrition surveys and studies: a report following an international panel discussion at the 9th International Conference on Diet and Activity Methods (ICDAM9), Brisbane, 3 September 2015. J Nutr Sci 2018;7:e11 [FREE Full text] [doi: 10.1017/jns.2018.4] [Medline: 29686860]
  * (85) Michael McCarthy Experts warn on data security in health and fitness apps BMJ 2013; 347 doi: https://doi.org/10.1136/bmj.f5600
  * (86) Ruchika Malhotra, A systematic review of machine learning techniques for software fault prediction, Applied Soft Computing, Volume 27,2015, Pages 504-518,ISSN 1568-4946,https://doi.org/10.1016/j.asoc.2014.11.023.
  * (87) Ross R, Bradshaw AJ. The future of obesity reduction: beyond weight loss. Nat Rev Endocrinol. 2009 Jun;5(6):319-25. doi: 10.1038/nrendo.2009.78. PMID: 19421242.
  * (88) Colquitt JL, Pickett K, Loveman E, Frampton GK. Surgery for weight loss in adults. Cochrane Database of Systematic Reviews 2014, Issue 8. Art. No.: CD003641. DOI: 10.1002/14651858.CD003641.pub4. Accessed 01 April 2021.
  * (89) Ossom Williamson P, Minter CIJ. Exploring PubMed as a reliable resource for scholarly communications services. J Med Libr Assoc. 2019 Jan;107(1):16-29. doi: 10.5195/jmla.2019.433. Epub 2019 Jan 1. PMID: 30598645; PMCID: PMC6300231.
  * (90) https://www.ebsco.com/products/research-databases/cinahl-complete, Accessed date: 11 June 2020
  * (91) https://en.wikipedia.org/wiki/ScienceDirect, Accessed date: 12 June 2020
  * (92) Radovic A, Badawy SM. Technology Use for Adolescent Health and Wellness. Pediatrics. 2020 May;145(Suppl 2):S186-S194. doi: 10.1542/peds.2019-2056G. PMID: 32358210.
  * (93) Ramsey WA, Heidelberg RE, Gilbert AM, Heneghan MB, Badawy SM, Alberts NM. eHealth and mHealth interventions in pediatric cancer: A systematic review of interventions across the cancer continuum. Psychooncology. 2020 Jan;29(1):17-37. doi: 10.1002/pon.5280. Epub 2019 Dec 19. PMID: 31692183.
  * (94) Badawy SM, Cronin RM, Hankins J, Crosby L, DeBaun M, Thompson AA, Shah N. Patient-Centered eHealth Interventions for Children, Adolescents, and Adults With Sickle Cell Disease: Systematic Review. J Med Internet Res. 2018 Jul 19;20(7):e10940. doi: 10.2196/10940. PMID: 30026178; PMCID: PMC6072976.
  * (95) Badawy SM, Barrera L, Sinno MG, Kaviany S, O’Dwyer LC, Kuhns LM. Text Messaging and Mobile Phone Apps as Interventions to Improve Adherence in Adolescents With Chronic Health Conditions: A Systematic Review. JMIR Mhealth Uhealth. 2017 May 15;5(5):e66. doi: 10.2196/mhealth.7798. PMID: 28506955; PMCID: PMC5447825.
  * (96) Badawy SM, Kuhns LM. Texting and Mobile Phone App Interventions for Improving Adherence to Preventive Behavior in Adolescents: A Systematic Review. JMIR Mhealth Uhealth. 2017 Apr 19;5(4):e50. doi: 10.2196/mhealth.6837. PMID: 28428157; PMCID: PMC5415660.
  * (97) Shah AC, Badawy SM. Telemedicine in Pediatrics: Systematic Review of Randomized Controlled Trials. JMIR Pediatr Parent. 2021 Feb 24;4(1):e22696. doi: 10.2196/22696. PMID: 33556030; PMCID: PMC8078694.
  * (98) Iribarren SJ, Cato K, Falzon L, Stone PW. What is the economic evidence for mHealth? A systematic review of economic evaluations of mHealth solutions. PLoS One. 2017 Feb 2;12(2):e0170581. doi: 10.1371/journal.pone.0170581. PMID: 28152012; PMCID: PMC5289471.
  * (99) Badawy SM, Kuhns LM. Economic Evaluation of Text-Messaging and Smartphone-Based Interventions to Improve Medication Adherence in Adolescents with Chronic Health Conditions: A Systematic Review. JMIR Mhealth Uhealth. 2016 Oct 25;4(4):e121. doi: 10.2196/mhealth.6425. PMID: 27780795; PMCID: PMC5101413.
  * (100) Badawy SM, Thompson AA, Kuhns LM. Medication Adherence and Technology-Based Interventions for Adolescents With Chronic Health Conditions: A Few Key Considerations. JMIR Mhealth Uhealth. 2017 Dec 22;5(12):e202. doi: 10.2196/mhealth.8310. PMID: 29273573; PMCID: PMC5756319.
  * (101) Badawy SM, Thompson AA, Liem RI. Technology Access and Smartphone App Preferences for Medication Adherence in Adolescents and Young Adults With Sickle Cell Disease. Pediatr Blood Cancer. 2016 May;63(5):848-52. doi: 10.1002/pbc.25905. Epub 2016 Feb 4. PMID: 26844685.
  * (102) Perski O, Blandford A, West R, Michie S. Conceptualising engagement with digital behaviour change interventions: a systematic review using principles from critical interpretive synthesis. Transl Behav Med. 2017 Jun;7(2):254-267. doi: 10.1007/s13142-016-0453-1. PMID: 27966189; PMCID: PMC5526809.
  * (103) Perski O, Blandford A, Ubhi HK, West R, Michie S. Smokers’ and drinkers’ choice of smartphone applications and expectations of engagement: a think aloud and interview study. BMC Med Inform Decis Mak. 2017 Feb 28;17(1):25. doi: 10.1186/s12911-017-0422-8. PMID: 28241759; PMCID: PMC5329928.
  * (104) Vaghefi I, Tulu B. The Continued Use of Mobile Health Apps: Insights From a Longitudinal Study. JMIR Mhealth Uhealth. 2019;7(8):e12983. Published 2019 Aug 29. doi:10.2196/12983
  * (105) Cannon, Peter & Walkup, Katie & Rea, Joshua. (2016). mHEaL and mHealth in a Restricted Environment: Design and Usability of an Offline Mental Health Literacy App. 1-3. 10.1145/2987592.2987613.

Table 3: Main characteristics of the selected studies Author, Year |  Study Population, Country |  Mobile App |  Aim of the Study
---|---|---|---
Hales et al., 2016 16  |  N=51 Overweight adults, South Carolina USA
|  Social POD |  To scrutinize the efficacy of the mobile app in terms of weight-loss

McCarroll et al., 2015 17  |  N=50 overweight participants, USA |  LoseIt |  Investigate the usefulness of the lifestyle intervention program designed for weight loss of early-stage obese/overweight cancer survivors in the last three years.
Ribeiro et al., 2017 18  |  N=32 volunteers, Portuguese |  Happy |  To scrutinize the effectiveness of the behavior change approach to persuade users to choose healthier alternatives to minimize the risk of several types of cancer.
Burke et al., 2017 19  |  N=39 volunteers, Pittsburgh, USA |  Lose it |  A randomized clinical trial to examine the benefit of 1–4 daily FB messages customized to dietary recordings through a smartphone for weight loss purposes
Fukuoka et al., 2015 20  |  N=61 overweight adults, San Francisco, USA |  mDPP |  Explore the usefulness of a diabetes prevention intervention along with a smartphone application and pedometer among overweight individuals at risk of type 2 diabetes.
Ho et al., 2016 21  |  N= 5 families using the app, Vancouver, Canada |  Metabolic diet App |  To implement apps for medical diet for all intrinsic delusions of amino acid metabolism.
Kong and Tan, 2012 22
|  N=21 restaurants are covered to verify their vision-based method, USA |  DietCam |  To monitor the food intake with few human interventions
Levinson et al., 2017 23  |  N=105 participants diagnosed with eating disorder, USA |  My Fitness Pal |  To examine the relationship of calorie tracking app with eating disorder pathology
Wharton et al., 2014 24  |  N=19 participants, Arizona, USA |  Lose It |  To study the effectiveness of smartphone application for self-monitoring of dietary intake and weight loss compared to the traditional approaches of counseling and recording methods.
Six et al., 2010 25  |  N= 78 participants, USA |  mpFR |  To compare the calculated energy and protein content of foods provided by the app with the published estimates in the FNDDS.
Gilson et al., 2014 26  |  N= 44 drivers, Australia |  UP |  To study how drivers have accomplished the process from baseline phase to smartphone application for diet monitoring.
Holmen et al., 2014 27  |  N=151 participants, Norway |  FTA |  To determine the effectiveness of a smartphone–based self-management system to monitor the levels of glycated hemoglobin A1c (HbA1c) and health-related quality of life.
Gabrielli et al., 2017 28  |  N=6 families participating, Italy |  TreC-LifeStyle |  To demonstrate the TreC-LifeStyle nutrition education application’s design and development along with the results of a formative analysis of families.
Van Lippevelde et al., 2016 29  |  N= 1400 participants, Belgium
|  Snack Track School |  To study the benefits of a dual-system model combined with the behavioral change approach while targeting both the reflective and automatic processes.
Carter et al.,2017 30  |  N= 43 participants, United Kingdom |  My Meal Mate |  To study the association among frequency and pattern of self-monitoring diet through a smartphone app and weight loss.
Goh et al., 2015 32  |  N= 84 participants, Singapore |  iDAT |  To study short-term (8-week) trajectories resulted from iDAT application between patients with type 2 diabetes and determined the patient’s behavior associated with every trajectory.
Kerr et al., 2012 33  |  N=220 participants, Perth Australia |  CHAT |  To study the effectiveness of a smartphone app for monitoring dietary intake, giving feedback through text messages to motivate young individuals for bringing change in their diet through more intake of fruits, vegetables, and less consumption of junk foods.

Mann et al., 2015 73  |  N= 26 participants, Australia |  WIZE |  To investigate the usability, content and acceptability of a smartphone application developed to boost the absorption of dietary iron.
Pellegrini et al., 2012 35
|  N=96 obese adults,USA |  ENGAGED |  Examine the efficacy of using a smartphone application for weight loss purposes.
Recio-Rodriguez et al., 2016 36  |  N=833 individuals,Spain |  EVIDENT Mobile phone apps |  To compare the expected amount of macro and micronutrients, calorie intake, and misuse of alcohol by using the mobile app to gather data with the data assessed using the food frequency questionnaire.
Rhyner et al., 2016 37  |  N=19 Participants, Switzerland |  GoCARB |  Evaluate the accuracy of GoCARB application used by individuals with type 1 diabetes and collate it to their performance in carbohydrate counting.
Seto et al., 2016 38  |  N=12, Participants,USA |  CalFit Chi |  To investigate the relation between individual-based modeling methods and the eating behavior of a person. Also, to determine the usefulness of such approaches compared to conventional regression models.
Six et al., 2011 25-a  |  N=78 participants,Australia |  mpFR |  To compare measured energy and protein content of foods with the published estimates in the FNDDS among adolescents

Zhu et al., 2010 39  |  N= 78 participants, USA |  Food Record App |  To automate the food logging process by using image processing methods and reduce the burden on the respondents.
Probst et al., 2015 41  |  No Participants, App Prototype, Australia |  Prototype App |  To develop a prototype for automatic food recognition via a use of image processing methods
Serrano et al., 2016 42
|  No participants, Health data mining, USA |  Lose It |  To scrutinize the data from a commercial dietary app (Lose It!) about subgroups who were successful in the weight loss program by exploratory examinations and confirm the results’ stability.

Farsjø, et al., 2017 43
|  N = 4 participants, Norway |  APPETITT |  To access the effectiveness of apps for inexperienced users of technology and examine its contribution for encouragement and orientation about meals.
Torre Díez, I., et al., 2017 44
|  N= 150 participants, Spain |  DietApp |  To implement and study smartphone application to provide suggestions to achieve a healthy diet according to individual’s age, clinical history, and physical condition.
Carter, M. C., et al., 2013 6  |  N= 9 participants, United Kingdom |  My Meal Mate |  They introduced and developed a smartphone application based on focus group interviews for weight loss.
Nyström, C. D. et al., 2016 46
|  N= 39 participants, Sweden |  TECH |  To evaluate energy intake (EI) using TECH application with total energy expenditure (TEE) calculated using doubly labeled water (DLW), and to compare dietary intakes using TECH app with intakes obtained through 24 h dietary recalls.

Rangan, A. M., et al., 2016 47  |  N=80 university Students, Australia |  e-DIA |  To investigate the validity of the e-DIA app’s comparison with the 24-h recall approach to calculate dietary intake of food groups.
Casperson, S. L. et al., 2015 31  |  N= 18 participants,USA |  FRapp |  To determine the amenability of adolescents to use the FRapp to monitor their dietary intake.
Mummah, S. et al., 2016 49
|  N=17 participants,USA
|  Vegethon |  To develop a theory-driven mobile application for increasing vegetable consumption
Mummah, S. et al., 2017 50  |  N=135 participants, USA |  Vegethon |  To examine the effectiveness of a smartphone application to improve the diet’s vegetable content among overweight adults endeavoring weight loss.
Hull, P. et al., 2017 51  |  N=80 participants,USA |  CHEW |  To examine end-user’s reviews about the CHEW app regarding usage, usability, perceived limitations, and advantages of the app.
Svensson et al., 2016 52  |  N= 92 participants, Sweden
|  FR App
|  To investigate adolescents’ practices using a dietary assessment app,
considering circumstances that may impact their dietary intake reporting.
Martin, C. K. et al., 2016 53
|  USA |  Smartloss |  To demonstrate mHealth platform for weight loss.
Zhu, F., et al., 2010 55  |  No participants, USA |  Dietary Assessment app |  To accurately measure food and nutrient intake by use of image processing methods.
Ahmed, M. et al., 2017 56  |  N=18 participants, Canada |  MyFitness-Pal |  To evaluate a tablet application’s soundness that incorporates detailed nutritional composition data with a calculated food intake/waste activity.

Ali, Z. C. et al., 2017 55-a  |  N= 54 participants, United Kingdom
|  Manager App |  To examine the feasibility of a smartphone application (app.) to calculate nutrient intake as per age-related muscular degeneration
Ambrosini, G. L. et al., 2017 57  |  N= 50 participants, Australia |  Easy Diet |  To examine the application’s acceptability and relative validity as an epidemiological diet monitoring tool compared to a conventional dietary assessment method.
Bardus, M. et al., 2018 58
|  N= 5,486 participants, Lebanon
|  WaznApp |
To investigate the usefulness of a self-directed weight loss intervention and
study the outcomes of the intervention.
Bennett, Gary G. et al., 2018 59  |  N=351 participants,USA |  Track App |  To study the effect of the digital weight-loss intervention by embedding it within a community health center system.
Brindal, E. et al., 2013 74  |  N= 58 participants, Australia |  MRP Support App |  To support individual’s partial meal replacement program using a smartphone app.
Chen, J. et al., 2019 61  |  N= 43 participants, Australia |  MyFitness-Pal |  To evaluate how participants in naturalistic environments achieved when reporting their dietary intake and their usability practices.
De Cock, N. et al., 2017 62
|  N=889 participants, Belgium |  Nike+ Running and Fitness Pall etc. |  To evaluate the association of commercial nutrition or fitness apps with a lower value of BMI and healthier intake of snacks and drinking practices in youngsters
Everett, E. et al., 2018 63  |  N = 55 participants,USA |  Sweetch |  To study the acceptability, feasibility, effectiveness, and safety of the Sweetch app in alliance with an electronic scale for measuring body weight.
Hezarjaribi, N. et al., 2018 64  |  N= 30 participants, USA |  Speech2-Health |  Examine the technology adaptation rate of the voice-based mobile nutrition monitoring method
Ipjian, Michelle et al., 2017 65
|  N=30 healthy adults, USA |  MyFitness-Pal |  To decrease sodium intake and to study whether a commercial health app is beneficial for supporting dietary change.
Jimoh, F. et al., 2018 66  |  N=34 participants, United Kingdom |  FoodWiz2 |  To study the benefits of a smartphone app with regards to encouraging healthy lifestyle decisions among adolescence.
Liu, Y. C. et al., 2019 67  |  N= 105 total participants, Taiwan |  MRP Support App |  To evaluate the accuracy and time performance of two prototypes for dietary recording utilization
Pagoto, S. et al., 2018 68  |  Two iterative single arm pilots with participants N=27 and N=16, USA |  Habit App |  To automate problem-solving therapy for weight loss using smartphone apps.
Paulsen, M. M. et al., 2018 69  |  N= 32 hospitalized patients, USA |  MyFood App |  To evaluate the app’s capability to calculate intake of energy, protein, and liquid with the help of hospitalized patients’ food and beverage details.
Recio-Rodriguez, et al. J. I., 2019 70  |  N=362 individuals, Spain |  EVIDENT-II App |  To compare the calculated values of energy intake, macronutrients, micronutrients, and alcohol consumption using the dietary app with values evaluated using food frequency questionnaire.
Shaw, R.et al., 2019 71
|  N= 96 participants, USA |  My Fitness Pal (Log2Lose) |  Determine smartphone apps’ effectiveness for weight loss programs by giving financial rewards and dietary self-monitoring.

Table 4: Data extraction for answering research question RQ1  Attribute |  Data capture
---|---
Difficulty in portion size estimation |  Resolved when the method to solve the problem is stated or subject can easily key in portion size, Not resolved, Not mentioned
Involved too many steps in data entering |  Subject feedback on the app (Resolved, Not resolved, Not mentioned)
Database is not localized |  Resolved when database region or culture information is specified, Not Resolved, Not mentioned
Credibility of database sources |  Resolved when database sources are listed, Not Resolved, Not mentioned
Required to be connected to other electrical devices |  Resolved when there are no such requirements, Not Resolved, Not Mentioned
The user felt the burden to key in foods for every meal |  Subject feedback on the app (Resolved, Not resolved, Not mentioned)
Many information and steps required |  Subject feedback on the app (Resolved, Not resolved, Not mentioned)
Frequent notifications that caused a burden to the user |  Subject feedback on the app (Resolved, Not resolved, Not mentioned)
Demotivating due to the information displayed |  Subject feedback on the app (Resolved, Not resolved, Not mentioned)
App Frequently crashed |  Resolved when the subject does not face frequent crashes, Not resolved, Not mentioned
Table 5: Data extraction for answering research question RQ2  Attribute |  Data capture
---|---
Usage reports |  Feedback on user engagement with the app, Not Mentioned
Data confidentiality |  Resolved when the app specifically addressed the data security issue, Not resolved, Not mentioned
Stability of application |  Resolved, Not Resolved when subject mentioned the bugs and technical issues
Offline accessibility |  Yes when the app does not require internet connection, No)
Table 6: Data extraction for answering research question RQ3  Attribute |  Data capture
---|---
Design of the app in terms of simplicity, clear and colourful |  Keypoints of the detail, Not mentioned
Included tutorial page |  Yes, No, Not Mentioned
Include two user dashboards |  Yes if separate dashboard for patients and more detailed dashboard for dietitians or researchers, No, Not Mentioned
User friendly |  Subject feedback on the mHealth application, Not mentioned
Notification to give alert to the user |  Message details, No, Not mentioned
Table 7: Data extraction for answering research question RQ4  Attribute |  Data capture
---|---
Evaluation of diet quality |  Method to evaluate diet quality
History tracking, and storage of diet records |  Information regarding diet history, Not mentioned
Inclusion of comprehensive database |  Database name if generic, No (when the database of specific food category or item), Not mentioned
Items included in diet record |  Details of the items in diet record, Not mentioned
Methods to estimate portion size |  Method used such as grams and household measurement and integrated camera, No, Not mentioned
Methods to display nutrient components |  Key points of the method
Nutrient component to be included |  Details of the nutrient components such as calories, protein, fat etc.
Validity of database |  Name of the database if validated, No, Not mentioned
Include to record total water intake |  No, Yes
Inclusion of nutrient information in the database |  Key details of nutrient information such as calories, fat etc.
Provide diet guide that cater to individual needs |  Key details of method such as notifications, FB messages etc, No, Not mentioned
Types of nutrients required to be displayed |  Key details of the nutrients displayed in the app, Not mentioned
Ways to display summarized energy and nutrient intake |  Yes and key details, Not mentioned
Table 8: (A) Overview of mHealth applications reporting on general issues (Data extracted for RQ1)  Application Name | Year |  Credibility of database sources |  Database is not localized |  Involved too many steps in data entering |  Difficulty in portion size estimation
---|---|---|---|---|---
Social POD 16  | 2016 |  Not resolved |  Not resolved |  Resolved |  Not mentioned
Happy 18  | 2017 |  Not mentioned |  Not mentioned |  Resolved |  Not mentioned
Lose it 19  | 2017 |  Not resolved |  Not resolved |  Resolved |  Not mentioned
mDPP 20  | 2015 |  Not mentioned |  Not mentioned |  Resolved |  Not mentioned
Metabolic Diet app 21  | 2016 |  Resolved |  Resolved |  Resolved |  Not resolved
DietCam 22  | 2012 |  Resolved |  Resolved |  Not resolved |  Resolved
My Fitness Pal 23  | 2017 |  Not resolved |  Not resolved |  Resolved |  Not resolved
mpFR 25  | 2010 |  Resolved |  Not resolved |  Not resolved |  Resolved
FTA 27  | 2014 |  Not mentioned |  Not mentioned |  Resolved |  Not resolved
TreC-LifeStyle 28  | 2017 |  Not resolved |  Not resolved |  Resolved |  Not resolved
Snack Track School 29  | 2016 |  Resolved |  Not resolved |  Resolved |  Not resolved
My Meal Mate 6  | 2013 |  Not resolved |  Resolved |  Not resolved |  Not resolved
FRapp 31  | 2015 |  Not mentioned |  Not mentioned |  Resolved |  Resolved
iDAT 32  | 2015 |  Not resolved |  Not resolved |  Resolved |  Not resolved
MDFR 33  | 2012 |  Not mentioned |  Not mentioned |  Resolved |  Resolved
WIZE 73  | 2015 |  Not mentioned |  Not mentioned |  Resolved |  Not resolved
ENGAGED 35  | 2012 |  Resolved |  Not resolved |  Resolved |  Not resolved
EVIDENT Mobile app 36 70  | 2016 |  Not resolved |  Not mentioned |  Resolved |  Not resolved
GoCARB 37  | 2016 |  Resolved |  Resolved |  Resolved |  Resolved
CalFit Chi 38  | 2016 |  Not mentioned |  Not mentioned |  Resolved |  Resolved
APPETITT 43  | 2017 |  Not resolved |  Resolved |  Resolved |  Not mentioned
DietApp 44  | 2017 |  Not resolved |  Not mentioned |  Not mentioned |  Not mentioned
e-DIA 47  | 2016 |  Resolved |  Resolved |  Not resolved |  Not mentioned
Vegethon 49  | 2017 |  Not mentioned |  Resolved |  Resolved |  Resolved
Dietary Intake Assessment 52  | 2016 |  Resolved |  Resolved |  Not resolved |  Not resolved
MRP Support App 74  | 2013 |  Not mentioned |  Not mentioned |  Resolved |  Resolved
Nutricam 75  | 2011 |  Not mentioned |  Not mentioned |  Resolved |  Resolved
Easy Diet 57  | 2017 |  Resolved |  Not resolved |  Resolved |  Not mentioned
Lark 58  | 2018 |  Resolved |  Resolved |  Resolved |  Not mentioned
Sweetch 63  | 2018 |  Not mentioned |  Resolved |  Resolved |  Not mentioned
Speech2Health 64  | 2018 |  Resolved |  Not resolved |  Resolved |  Resolved
FoodWiz2 66  | 2018 |  Resolved |  Not resolved |  Resolved |  Resolved
Habit App 68  | 2018 |  Resolved |  Not resolved |  Resolved |  Not mentioned
MyFood App 69  | 2018 |  Not resolved |  Not resolved |  Resolved |  Resolved
MyFitness Pal (Log2Lose) 71  | 2019 |  Not resolved |  Not resolved |  Resolved |  Not resolved

Table 9: (B) Overview of mHealth applications reporting on general issues (Data extracted for RQ1). Application Name |  Year |  App frequently crashed |  Demotivated due to information displayed |  Frequent notifications that caused burden to user |  Many information and steps required |  The user felt the burden to key in foods for every meal |  Required to be connected to other electrical devices |  Difficulty in portion size estimation
---|---|---|---|---|---|---|---|---
Social POD 16  |  2016 |  Resolved |  Resolved |  Resolved |  Resolved |  Resolved |  Resolved |  Not mentioned
Happy 18  |  2017 |  Not resolved |  Resolved |  Resolved |  Resolved |  Resolved |  Resolved |  Not mentioned
Lose it 19  |  2017 |  Not resolved |  Not resolved |  Not resolved |  Resolved |  Resolved |  Resolved |  Not mentioned
mDPP 20  |  2015 |  Resolved |  Resolved |  Resolved |  Resolved |  Not resolved |  Resolved |  Not mentioned
Metabolic diet app 21  |  2016 |  Resolved |  Resolved |  Not mentioned |  Resolved |  Resolved |  Resolved |  Not resolved
DietCam 22  |  2012 |  Resolved |  Resolved |  Resolved |  Not resolved |  Not resolved |  Resolved |  Resolved
My Fitness Pal 23  |  2017 |  Resolved |  Resolved |  Resolved |  Resolved |  Resolved |  Resolved |  Not resolved
mpFR 25  |  2010 |  Resolved |  Resolved |  Not mentioned |  Not resolved |  Not resolved |  Resolved |  Resolved
FTA 27  |  2014 |  Resolved |  Not resolved |  Not mentioned |  Resolved |  Not resolved |  Resolved |  Not resolved
TreC-LifeStyle 28  |  2017 |  Resolved |  Resolved |  Resolved |  Resolved |  Resolved |  Resolved |  Not resolved
Snack Track School 29  |  2016 |  Resolved |  Resolved |  Resolved |  Resolved |  Resolved |  Resolved |  Not resolved
My Meal Mate 6 27  |  2013 |  Resolved |  Resolved |  Not mentioned |  Not resolved |  Not resolved |  Resolved |  Not resolved
FRapp 31  |  2015 |  Resolved |  Resolved |  Not mentioned |  Not resolved |  Not resolved |  Resolved |  Resolved
iDAT 32  |  2015 |  Resolved |  Resolved |  Not mentioned |  Resolved |  Not resolved |  Resolved |  Not resolved
MDFR 33  |  2012 |  Resolved |  Resolved |  Not resolved |  Resolved |  Resolved |  Resolved |  Resolved
WIZE 73  |  2015 |  Resolved |  Not resolved |  Not mentioned |  Resolved |  Resolved |  Resolved |  Not resolved
ENGAGED 35  |  2012 |  Resolved |  Resolved |  Resolved |  Resolved |  Resolved |  Resolved |  Not resolved
EVIDENT Mobile app 36 70  |  2016 |  Resolved |  Resolved |  Not mentioned |  Resolved |  Resolved |  Resolved |  Not resolved
GoCARB 37  |  2016 |  Resolved |  Resolved |  Not mentioned |  Resolved |  Resolved |  Resolved |  Resolved
Calfit Chi 38  |  2016 |  Resolved |  Resolved |  Resolved |  Not resolved |  Resolved |  Resolved |  Not resolved
APPETITT 43  |  2017 |  Not mentioned |  Resolved |  Resolved |  Resolved |  Resolved |  Resolved |  Not mentioned
DietApp 44  |  2017 |  Not mentioned |  Resolved |  Not mentioned |  Not mentioned |  Resolved |  Resolved |  Not mentioned
e-DIA 47  |  2016 |  Not mentioned |  Not mentioned |  Not mentioned |  Not resolved |  Not mentioned |  Resolved |  Not resolved
Vegethon 49  |  2017 |  Not mentioned |  Resolved |  Resolved |  Resolved |  Resolved |  Resolved |  Resolved
Dietary Intake Assesment 52  |  2016 |  Resolved |  Resolved |  Not mentioned |  Not resolved |  Not resolved |  Resolved |  Not resolved
MRP Support App 74  |  2013 |  Not mentioned |  Resolved |  Resolved |  Resolved |  Resolved |  Resolved |  Resolved
Nutricam 75  |  2011 |  Not mentioned |  Resolved |  Not mentioned |  Resolved |  Resolved |  Resolved |  Resolved
Easy Diet 57  |  2017 |  Resolved |  Resolved |  Resolved |  Resolved |  Resolved |  Resolved |  Not mentioned
Lark 58  |  2018 |  Resolved |  Resolved |  Resolved |  Resolved |  Not resolved |  Resolved |  Not mentioned
Sweetch 63  |  2018 |  Resolved |  Resolved |  Resolved |  Resolved |  Not resolved |  Resolved |  Not mentioned
Speech2Health 64  |  2018 |  Resolved |  Not mentioned |  Not mentioned |  Resolved |  Resolved |  Resolved |  Resolved
FoodWiz2 66  |  2018 |  Resolved |  Resolved |  Resolved |  Resolved |  Not resolved |  Not resolved |  Resolved
Habit App 68  |  2018 |  Resolved |  Resolved |  Resolved |  Resolved |  Not resolved |  Resolved |  Not resolved
MyFood App 69  |  2018 |  Resolved |  Resolved |  Resolved |  Resolved |  Resolved |  Resolved |  Resolved
My Fitness Pal (Log2Lose) 71  |  2019 |  Resolved |  Resolved |  Resolved |  Resolved |  Resolved |  Not resolved |  Not resolved

Table 10: Overview of mHealth applications reporting on the Backend issues (Data extracted for RQ2). Application Name | Year |  Offline accessibility |  Stability of application |  Data Confidentiality |  Usage Reports
---|---|---|---|---|---
Social POD 16  | 2016 |  No |  Resolved |  Not mentioned |  User engagement declined over time
Happy 18  | 2017 |  No |  Not resolved(Bugs and technical issues) |  Not mentioned |  Engagement with the app was low
Lose it 19  | 2017 |  No |  Not resolved (App crash and bugs) |  Not mentioned |  Not mentioned
mDPP 20  | 2015 |  No |  Not resolved (App glitches) |  Not mentioned |  Declined Adherence to app overtime
Metabolic Diet app 21  | 2016 |  No |  Resolved |  Not mentioned |  Not mentioned
Diet Cam 22  | 2012 |  No |  Resolved |  Not mentioned |  Not mentioned
My Fitness Pal 23  | 2017 |  Yes |  Resolved |  Not mentioned |  Not mentioned
mpFR 25  | 2010 |  No |  Resolved |  Not mentioned |  Not mentioned
FTA 27  | 2014 |  No |  Not resolved |  Resolved (Secure server) |  Not mentioned
TreC-LifeStyle 28  | 2017 |  No |  Resolved |  Resolved |  Good usability with high adherence.
Snack Track School app 29  | 2016 |  No |  Resolved |  Not mentioned |  Good usability with the weekly report to measure user engagement.
My Meal Mate 6  | 2013 |  No |  Not resolved |  Not mentioned |  Apps adherence declined over time.
FRapp 31  | 2015 |  No |  Resolved |  Not mentioned |  Not mentioned
iDAT 32  | 2015 |  No |  Resolved |  Resolved (Personal information & data were kept confidential) |  Usability is good and app usage is tracked continuously
MDFR 33  | 2012 |  No |  Resolved |  Not mentioned |  Not mentioned
WIZE 73  | 2015 |  No |  Not resolved (User interface and design issues) |  Not mentioned |  Not mentioned
ENGAGED 35  | 2012 |  No |  Resolved |  Resolved |  Good usability and usage access reports based on user reporting.
EVIDENT Mobile phone app 36 70  | 2016 |  No |  Resolved |  Not mentioned |  The adherence score is based on user records.
GoCARB 37  | 2016 |  No |  Not resolved |  Resolved (Images are transmitted to a dedicated secure server) |  Not mentioned
CalFit Chi 38  | 2016 |  No |  Resolved |  Resolved (Data are encrypted and stored locally in the memory on the phone) |  Not mentioned
APPETITT 43  | 2017 |  Yes |  Resolved |  Not mentioned |  Not mentioned
DietApp 44  | 2017 |  No |  Resolved |  Not mentioned |  Not mentioned
e-DIA 47  | 2016 |  No |  Resolved |  Not mentioned |  Not mentioned
Vegethon 49  | 2017 |  No |  Resolved |  Not mentioned |  Not mentioned
Dietary Intake Assessment 52  | 2016 |  No |  Resolved |  Not mentioned |  The researcher can view user profile data, food recordings, and user responses to questions.
MRP Support App 74  | 2013 |  No |  Resolved |  Not mentioned |  Not mentioned
Nutricam 75  | 2011 |  No |  Resolved |  Not mentioned |  Not mentioned
Easy Diet 57  | 2017 |  No |  Not resolved |  Not mentioned |  Continuous feedback and high engagement
Lark 58  | 2018 |  No |  Resolved |  Secure Server |  Continuous feedback on behavior out comes using Artificial Intelligence helps to engage users
Sweetch 63  | 2018 |  No |  Resolved |  Not mentioned |  Personalized notifications engages users of the app.
Speech2Health 64  | 2018 |  No |  Not resolved |  Not mentioned |  Not mentioned
FoodWiz2 66  | 2018 |  No |  Resolved |  Not mentioned |  Personalized feedback and help increase motivation and user engagement.
Habit App 68  | 2018 |  No |  Resolved |  Not mentioned |  Adherence to the app was high as it helps users to lose weight by resolving the problems
MyFood App 69  | 2018 |  No |  Resolved |  Resolved (Encrypted Data Transfer) |  High adherence and the majority of users can learn new information.
My Fitness Pal (Log2Lose) 71  | 2019 |  Yes |  Resolved |  Not mentioned |  Usability is high as apps offers financial incentives for logging foods and achieving goals

Table 11: (A)Overview of mHealth apps reporting on user interface requirement (Data extracted for RQ3).  Application Name | Year |  Design of the app |  Include tutorial page |  Include two user dashboards |  User friendly
---|---|---|---|---|---
Social POD 16  | 2016 |  Not mentioned |  Not mentioned |  Not mentioned |  Not mentioned
Happy 18  | 2017 |  Simple, font size is medium |  Not mentioned |  Not mentioned |  Easy to use app
Lose it 19  | 2017 |  Font size is okay, clear images and relevant icon |  Not mentioned |  Not mentioned |  Less step to key in data
mDPP 20  | 2015 |  Not mentioned |  Not mentioned |  Not mentioned |  App glitches,
Metabolic Diet app 21  | 2016 |  Simple, relevant icon, nice color, clear font |  Specify preferred IEM diet app, option for users to enter weight |  Personalized dashboard according to preferred IEM diet |  User friendly interface, the use of lay language and distinctive icons
Diet Cam 22  | 2012 |  Simple design, |  Not mentioned |  Calorie, camera, calendar and album tab |  Easy to operate with few tabs
My Fitness Pal 23  | 2017 |  Simple and Interactive Design |  Yes |  Mobile and Web Platform |  Easy to use app
mpFR 25  | 2010 |  Very simple design |  Not mentioned |  Not mentioned |  Less steps
FTA 27  | 2014 |  Simple design, large icon |  Not mentioned |  Food habit registration system |  Cumbersome user interfaces
TreC-LifeStyle 28  | 2017 |  Simple, icons and clear font |  Not mentioned |  Web and mobile platform |  Easy to use
Snack Track School app 29  | 2016 |  Not mentioned |  Not mentioned |  Not mentioned |  Not mentioned
My Meal mate 6 30  | 2013 |  Simple, less icon, large font |  Not mentioned |  Mobile and Web Platform |  Encountered bug but easy to use
FRapp 31  | 2015 |  Very simple, not attractive, large font, less icon & images |  Not mentioned |  Mobile app |  Not mentioned
iDAT 32  | 2015 |  Simple design, nice icon, font and image clear |  Not mentioned |  Mobile App |  Easy to use and interactive
MDFR 33  | 2012 |  Simple interface |  Not mentioned |  Mobile app |  Not mentioned
WIZE 73  | 2015 |  Simple and basic, large icon |  Not mentioned |  Mobile app |  Cumbersome to use
ENGAGED 35  | 2012 |  Simple interface, less attractive, clear font |  Not mentioned |  Mobile app and coaching application |  Simple interface
EVIDENT Mobile phone apps 36 ; 70  | 2016 |  Not mentioned |  Not mentioned |  Mobile app |  Easy to use interface for logging food
GoCARB 37  | 2016 |  Simple, big icons, small font |  Not mentioned |  Mobile app |  Graphical user interface, Easy to use
CalFit Chi 38  | 2016 |  Not mentioned |  Not mentioned |  Mobile app |  Not mentioned
APPETITT 43  | 2017 |  Simple user interface without menu function, distinctive color contrasts, and large letters. |  No |  Not mentioned |  Yes
DietApp 44  |  |  Simple design |  No |  Not mentioned |  Yes. 84% of users were of the opinion that the app was easy to use
e-DIA 47  | 2016 |  Simple & clean |  No |  No |  Yes
Vegethon 49  | 2018 |  Uses icon, graphic, simple and colorful design |  Yes |  Not mentioned |  High rate of engagement.
Dietary Intake Assessment 52  | 2016 |  Not mentioned |  Not mentioned |  Mobile and Web Platform |  Most Users found it was easy to record dietary intake see the results, but there were also views that the recording of dietary intake was burdensome.
MRP Support App 74  | 2013 |  Simple interface, more icon & less word. |  Yes |  Not mentioned |  Yes
Nutricam 75  | 2011 |  Simple interface |  No |  No |  Yes
Easy Diet 57  | 2017 |  Simple and interactive interface |  Not mentioned |  Mobile app |  Easy to use app
Lark 58  | 2018 |  Simple Interface |  Yes |  Mobile App |  Easy to use app
Sweetch 63  | 2018 |  Simple And Interactive Interface |  Not mentioned |  No |  83 percent are of opinion that app was easy to use
Speech2Health 64  | 2018 |  Simple And Interactive Interface |  Not mentioned |  No |  High rate of user friendliness as user adds meal using speech to text conversation
FoodWiz2 66  | 2018 |  Simple Interface |  Not mentioned |  Mobile and web platform |  Easy to use as users are able to add meal using barcode scanning
Habit App 68  | 2018 |  Simple and interactive interface |  Yes |  Mobile and Web Platform |  The high rate of engagement as it introduced the problem-solving process for weight loss
MyFood App 69  | 2018 |  Simple and interactive interface |  Not mentioned |  Mobile and Web Platform |  Ninety percent of participant reported that the app was easy to use.
My Fitness Pal (Log2Lose) 71  | 2019 |  Simple and interactive design |  Yes |  Mobile and Web Platform |  High Rate of engagement as it offers financial incentives for logging food

Table 12: (B) Overview of mHealth apps reporting on the user interface requirement (Data extracted for RQ3). Application Name | Year |  Design of app in terms of simplicity, clear and colorful |  Items to include in the user profile |  Notification to give alert to user |  Things to be considered as user friendly
---|---|---|---|---|---
Social POD 16  | 2016 |  Not mentioned |  User Goals |  Message send to user who did not enter data for previous 48H |  Saving a meal as a template to be loaded the next time when an identical meal is consumed; User could take a picture of their meal as a memory aid if the consumed foods could not be entered until later
Happy 18  | 2017 |  Simple, soft color and clear images |  Not mentioned |  Tailored messages sent to user everyday |  It is easy to use and learn to operate. Messages sent easy to understand, and meaningful information
Lose it 19  | 2017 |  Clear images, simple design, easy to use |  Dietary goals, |  FB messages generated (software) |  Easy to use and less step for data entry
mDPP 20  | 2015 |  Not mentioned |  Not mentioned |  Automated text message was generated as reminder |  App glitches reflect adherence
Metabolic Diet app 21  | 2016 |  Simple design with soft color and clear icon/words |  Body weight, preferred IEM apps |  Not mentioned |  User friendly interface, the use of lay language and distinctive icons
Diet cam 22  | 2012 |  Simple, not attractive |  Not mentioned |  No |  Easy to operate with few tabs
My Fitness Pal [23] [56] [61] [65] | 2017 |  Not mentioned |  Allow user to set weight and nutrition goals |  Feedback on the number of calories and nutrient needed to reach goal |  User friendly interface
mpFR 25  | 2010 |  Very simple design (Interaction design) |  Not mentioned |  No |  Easy to use
FTA 27  | 2014 |  Clear images, nice color, simple design |  Management of personal goals |  No |  Cumbersome user interface
TreC-Lifestyle 28  | 2017 |  Simple with soft colors clear icons and words |  Configuration of an app based on children’s age and weight |  Feedback and virtual coaching function |  High adherence of usage
Snack Track School app 29  | 2016 |  Not mentioned |  Goal Setting |  No |  Not mentioned
My Meal Mate 6 27  | 2013 |  Design and color not attractive |  Goal setting |  Feedback via weekly message |  Easy to use
FRapp 31  | 2015 |  Simple design, relevant color, clear icon |  Not mentioned |  No |  Camera function with easy interface
iDAT 32  | 2015 |  Color too soft, icon and words are clear |  Weight loss goal |  Reminder |  Easy to use, interactive
MDFR 33  | 2012 |  Very simple, less function |  Not mentioned |  Text message as reminder |  Easy to use
WIZE 73  | 2015 |  Simple and basic, colorful |  Goal setting( dietary) |  No |  Personalized dashboard to individual not exist, prompt and reminders, to add pictures and colors. User interface and design issues
ENGAGED 35  | 2012 |  Black background not suitable, simple interface |  Calorie goals |  Real time feedback and coaching |  Simple interface
EVIDENT Mobile phone apps 36 70  | 2016 |  Not mentioned |  Not mentioned |  No |  Easy to use interface
GoCARB 37  | 2016 |  Basic color and simple design |  Not mentioned |  No |  Useful and easy to use
CalFit Chi 38  | 2016 |  Not mentioned |  Not mentioned |  Reminder text messages |  -
APPETITT 43  | 2017 |  A stable user interface without menu function, distinctive color contrasts and large letters, consistent touch functions and large touch surfaces |  No feature for personalized profile |  APPETITT had a function that notified the user with a short signal at mealtime |  Minimal entering of data, everything is on the same screen. Very simple and user friendly
DietApp 44  | 2016 |  Simple and clear design. 43% users found it attractive. |  Stores name, password, email, weight, height, gender and any illnesses that the user may suffer from. |  No |  Covers the most common foods; simple home page that can access all feature just from there; ease of installation and use can be used by all type of public (experts and non-expert)
e-DIA 47  | 2016 |  Simple & clean |  Not mentioned |  a text message reminder sent to participant to record food intake on a dedicated days in a week |  Has a complete dataset for varieties of food. They also can put custom food
Vegethon 49  |  |  Uses icon, graphic, simple and colorful design |  Not mentioned |  Push notifications: notifications to prompt self-monitoring of vegetable consumption |  Provide tutorial; simple graphic and attractive design; targeted apps (focus on veggie intake only)
Dietary Intake Assessment 52  | 2016 |  Not clearly mention |  Name, date of birth, gender, weight, height, email address, and phone number. |  Push notification reminders to record meals at a chosen time interval |  Complete dataset with varieties of foods; connected to the mobile phone camera and the user could take a picture of their meal as a memory aid if the consumed foods could not be entered until later; save a meal as a template to be loaded the next time the same meal was consumed
MRP Support App 74  | 2013 |  Simple, more icon clean but less attractive |  No personalized user profile |  Push notifications to remind user to record meal in 3 mealtimes |  Clean UI, simple icon, not many step require to log meal and weight
Nutricam 75  | 2011 |  Simple and clear |  No personalized user profile |  No |  Few step to record food. Very simple app
Easy Diet 57  | 2017 |  Clear image, simple design, easy to use |  Not mentioned |  Disabled for this study |  Easy to use and less step for data entry. Automatically detects food and beverages
Lark 58  | 2018 |  Simple And Clear |  Goals Settings |  Sends notification to user to review activity throughout the day |  Interactive Conversations
Sweetch 63  | 2018 |  Simple and clear according to majority of users |  Goal settings |  Push notifications based on habits |  Well Integrated, Consistence, Easy To Learn And Use
Speech2Health 64  | 2018 |  Simple and clear |  Not mentioned |  Feedback on calories and nutrients to reach goal |  Record food without manual logging by using speech to text conversation
FoodWiz2 66  | 2018 |  Simple and clear |  Goal settings |  Personalized feedback text messages for motivation and help |  Allow users to record home prepared recipes and food products through barcode scanning
Habit App 68  | 2018 |  Simple and clear |  User profile, |  Goal settings |  Notifications on problem solving process and Automatic Problem solving process
MyFood App 69  | 2018 |  Interactive design and clear icons with soft colors |  User profile |  Not mentioned |  Allow users to add food and determine its nutrient content by taking picture
My Fitness Pal (Log2Lose) 71  | 2019 |  Not mentioned |  Allow user to set weight and nutrition goals |  User receives messages about financial incentives every week |  User friendly interface

Table 13: (A) Overview of mHealth apps reporting on the implementation of the dietary components (Data extracted for RQ4). Application Name | Year |  Evaluation of diet quality |  History tracking and storage of diet records |  Inclusion of comprehensive database |  Items included in diet record |  Methods to display nutrient components |  Methods to estimate portion size |  Nutrient component to be included |  Validity of database
---|---|---|---|---|---|---|---|---|---
Social POD 16  | 2016 |  To track diet and reach target goal(calorie consumed) |  Able to review history |  No |  Not mentioned |  Calories per meal |  Not mentioned |  Calories only |  Not validated
Happy 18  | 2017 |  The intake of fruit and vegetables |  Trends history |  No Fruit and vegetables |  Not mentioned |  Not mentioned |  Not mentioned |  No |  Not mentioned
Lose it 19  | 2017 |  Achieve dietary goals, calories, fat, sugar( calorie allowance and balances) |  Select date to view history |  Not mentioned |  Not mentioned |  Balance macronutrient |  Manual estimation with household measurement |  Calories, fat and sugar |  Not mentioned
mDPP 20  | 2015 |  The intake of calories,SF, sugar( dietary goals) |  Calorie diary |  No |  Not mentioned |  Diary pattern |  Not mentioned |  Calories, saturated fat, sugar |  Not mentioned
Metabolic Diet app 21  | 2016 |  Specific nutrient goals for daily goals |  Daily food diary and review food log |  GMDI Metabolic Pro database with 100,000 food |  Not mentioned |  A pie chart |  Not mentioned |  Nutrient information is based on Metabolic Pro (Energy, CHO, fat, protein, amino acid) |  USDA food database
Diet cam 22  | 2012 |  Calorie intake |  Calendar menu view history by date |  Global database and small personal database |  Not mentioned |  Food images with calorie |  Volume estimation from the integrated camera |  Calories |  USDA food database
My Fitness Pal 23  | 2017 |  Breakdown of daily calories and nutrient based on goal |  Select date to review history |  Not mentioned |  Not mentioned |  Breakdown of daily calories and nutrient |  Household measurement |  Calories and nutrients |  Not mentioned
mpFR 25  | 2010 |  Nutrient content of food consumed |  Not mentioned |  Food and nutrient database |  Not mentioned |  Labeled image and nutrient analysis |  Integrated camera |  Nutrients |  FNDDS
FTA 27  | 2014 |  Personal goals |  Not mentioned |  No |  Not mentioned |  Food diary |  Not mentioned |  Not mentioned |  Not mentioned
TreC-LifeStyle 28  | 2017 |  Color bars to achieve balance nutrients and meal |  Select date to view history record |  No |  Not mentioned |  Progress bar of meals and nutrient balance |  Grams |  Calories, carbs, protein, fat |  No
Snack Track School app 29  | 2016 |  Points for nutritional value of snack |  Monitor in weekly report |  A large snack database |  Snack |  Nutrition value of snack |  Common standard measure |  Calories |  Based on International Trade Name Database
My Meal Mate 6  | 2013 |  Remaining calories (Daily summary) |  Select date to view history record |  UK-specific branded food database |  Not mentioned |  Breakdown of calories per meal in table |  Common standard measure |  Calorie |  Database provided by Weight Loss resources, a commercial company
FRapp 31  | 2015 |  Food record with image |  Tab to review history record |  No |  Not mentioned |  Breakdown of daily meal with images |  Integrated camera |  No |  No
iDAT 32  | 2015 |  Balanced calorie to consumed |  Tab to review history record |  A database with locally available food |  Not mentioned |  Breakdown of calories per meal, calories consumed progress bar |  Not mentioned |  Calories |  Not mentioned
mpFR 25  | 2012 |  Food record with dietary feedback |  Not mentioned |  No |  Not mentioned |  Food images before and after eating |  Camera |  No |  No
WIZE 73  | 2015 |  Goals tracker weekly review |  Goal week review |  No |  Not mentioned |  Weekly review goals in list |  No |  Not mentioned |  No
ENGAGED 35  | 2012 |  Showing remaining calories and fat |  Not mentioned |  Calorie king database |  Not mentioned |  Goal thermometer display calorie and fat consumed |  Not mentioned |  Calories and fat |  Comprehensive nutritional source containing over 50, 000 food entries
EVIDENT Mobile phone apps 36 70  | 2016 |  Personalized recommendation |  Not mentioned |  Not mentioned |  Not mentioned |  Not mentioned |  Not mentioned |  Carb, protein, fats, fiber |  Not mentioned
GoCARB 37  | 2016 |  Images and CHO estimation |  Select by date |  USDA nutrient database |  Not mentioned |  Food type, volume and cho estimation in table |  Integrated camera, recognition, volume estimation |  CHO |  USDA
CalFit Chi 38  | 2016 |  Not mentioned |  Not mentioned |  Not mentioned |  Not mentioned |  Not mentioned |  Video camera, record contents of meal, coded the portion size by dietitian |  Not mentioned |  Not mentioned
APPETITT 43  | 2017 |  Only a graphic chart for displaying goal achievement. |  No function to see what food and drink reported earlier. |  No food database info |  Not mentioned |  Graphic chart with the capacity of 4 meals and 20 units of liquid keeps track of food and water intake. |  No portion size info. User only tell whether they eat or not. |  No macro/micronutrient included. Only water and simple meal |  No food database incorporated
DietApp 44  | 2017 |  Shows total energy balance and alert the user |  Not stated |  Not mentioned |  Only calories is shown |  Food History is not displayed, and the app alerts the user when taking certain food affects the calorie goal. |  User can choose the portion size of food |  Calories shown based on RDI |  Not mentioned
e-DIA 47  | 2016 |  Food displayed in grams, and only the researcher can view other nutrients |  24h Food record only stored in the phone and other Data is stored in the cloud |  Using Australian food composition database |  Only food item with gram |  Simple table |  User estimate portion sizes using metric cups and spoons with manual booklet before entering gram of food |  Gram only |  Database used was AUSNUT 2007.
Vegethon 49  | 2017 |  Only focus on veggie intake |  Store veggie intake record |  Not clearly mention what database used |  Only servings sizes of veggies S |  how plate graphic icon of veggies serving sizes |  User estimate using ‘1 serving’ standard of veggies |  Not mentioned |  Not mentioned
Dietary Intake Assessment 52  | 2016 |  The user can see details of TEE, EI, food in grams, dietary fiber, calcium, iron, vitamin C, vitamin D, and folic acid acc. to RDI. |  Displays energy and nutrient content in the records per food, meal, or day, and data is stored in the server. |  Swedish national food database version 2010-05-05 is used |  Estimated TEE; EI; gram of food; dietary fiber, calcium, iron, vitamin C, vitamin D, and folic acid acc. to RDI |  Not mentioned |  Estimated with well-suited units (eg, gram, deciliter, tablespoon, teaspoon, and piece). |  Estimated TEE; EI; dietary fiber, calcium, iron, vitamin C, vitamin D, and folic acid |  Validated database
MRP Support App 74  | 2016 |  Only show meal type with no evaluation of the nutrient |  User views meal and feedback from researcher by clicking the date on the calendar |  No database |  No nutrient and calories shown. Only type of meal taken |  No nutrients and calories information and Meals are displayed in 5 categories (meal replacements, balanced meals, other meals, allowed snacks, and other snacks) |  No portion size estimation |  No nutrient and calories shown. |  No database
Nutricam 75  | 2011 |  User cannot view evaluation by the dietitian |  No history. Data directly send to dietitian for analysis |  No |  No diet record |  No |  Take photograph and voice recording explaining the food and/or drink items for consumption. |  Not mentioned |  No database
Easy Diet 57  | 2017 |  Calorie and Macro and Micro Nutrients intake |  Type of meal and meals information |  AusBrands 2015) and AusFoods 2015 |  Meal type and nutrient Information |  Calories , Micro and Macro nutrient Information of each category of meal |  Common standard measure |  Calories, Macro and Micro Nutrient Components |  Validated database
Lark 58  | 2018 |  Calories intake, Energy Expenditure and messages of health consequences of foods |  Goals Achievement, Meals Calories and nutrient Information |  USDA database and traditional recipes |  Meal Type and Calories Information |  Break down of calories and nutrient information per meal |  Not mentioned |  Calories, Micro and Macro Nutrient components |  Validated
Sweetch 63  | 2018 |  Meal Calories and Nutrients Tracking Charts |  Review Summarized History, Meals, calories, Nutrient information , weight changes |  Not mentioned |  Meal Type, Calories and Nutrient Information |  Not mentioned |  Not mentioned |  Calories |  Not mentioned
Speech2Health 64  | 2018 |  Break down of daily calories and charts for nutrients based on goal |  Select date to review |  USDA database |  Not mentioned |  Break down of daily calories and nutrient components |  Text Processing for mapping Food information to dietary composition |  Calories and nutrients |  Validated database
FoodWiz2 66  | 2018 |  Track calories intake according to set targets |  Not mentioned For User side but dietician are able to view all meals added from app through web portal |  UK McCance and Widdowson’s database |  Calories and weight information |  Not mentioned |  Bluetooth scale to estimate portion size |  Calories |  Validated database
Habit App 68  | 2018 |  Tracking calories intake and steps to achieve weight loss goal |  Able To Review Previous history |  Not mentioned |  Calories |  Calories taken relative to Calories goal |  Not mentioned |  Calories |  Not mentioned
MyFood App 69  | 2018 |  Estimation of Energy, protein and liquid consumption |  Able to Review previous history of Energy, protein and liquid consumption |  Not mentioned |  Energy, protein, liquid consumption, meal type and portion size |  Energy, protein and liquid consumption of each meal type |  Integrated mobile camera for portion estimation |  Calories, protein and liquid consumption |  Not mentioned
My Fitness Pal (Log2Lose) 71  | 2019 |  Breakdown of daily calories and nutrient based on goal |  Select date to review history |  Not mentioned |  Not mentioned |  Breakdown of daily calories and nutrient |  Household measurement |  Calories and nutrients |  Not mentioned

Table 14: (B) Overview of mHealth apps reporting on the implementation of the dietary components (Data extracted for RQ4). Application Name | Year |  Include to record total water intake |  Inclusion of nutrient information in the database |  Provide diet guide that cater to individual needs |  Types of nutrients required to be displayed |  Ways to display the summarized energy and nutrient intake
---|---|---|---|---|---|---
Social POD 16  | 2016 |  No |  Calories |  Notifications |  Calories |  Calories per meal
Happy 18  | 2017 |  Yes |  Not mentioned |  Messages followed the European Code Against Cancer guidelines |  Not mentioned |  Not mentioned
Lose it 19  | 2017 |  Yes |  Calories, fat, sugar |  FB messages for dietary intake |  Calories, fat, sugar |  Calories balanced per meal
mDPP 20  | 2015 |  No |  Calories, SF, sugar |  Tailored feedback was provided |  Calories, SF, sugar |  Calorie diary
Metabolic Diet app 21  | 2016 |  Yes |  Energy, carbo, fat, protein, amino acid |  No |  Amino acid, energy, macronutrients |  Nutrient information per serving size, and pie charts showing target and actual intake of specific amino acid and total protein
Diet cam 22  | 2012 |  No |  Calories |  Not mentioned |  Calories |  The Album menu organizes all the food items as frequency list, and shows them in a table.
My Fitness Pal 23  | 2017 |  Yes |  Calories and nutrients |  Balance calories |  Daily calories and nutrient intake |  Breakdown of daily calories and nutrient in pie chart and table
mpFR 25  | 2010 |  No |  Energy and nutrient |  Not mentioned |  Energy and nutrient |  Image analysis with volume estimation and nutrient information
FTA 27  | 2014 |  No |  No |  Food habit registration |  Not mentioned |  Food information diary
TreC-LifeStyle 28  | 2017 |  Yes |  Calories, carbs, protein, fat |  Food pyramid and Mediterranean diet guidelines |  Calories, carbs, protein, fat |  Colors of food categories on dashboard, reports of meals and nutrient balance
Snack Track School app 29  | 2016 |  No |  Calories |  Flemish guidelines |  Nutritional value |  Size and nutritional content of each snack
My Meal Mate 6 ; 27  | 2013 |  Yes |  Calories |  Remaining calories allowance |  Calories |  Remaining calories table and breakdown of calories per meal
FRapp 31  | 2015 |  Yes |  Not mentioned |  Real time communication between user & clinician |  Not mentioned |  Breakdown food images according to meal
iDAT [32] | 2015 |  No |  Calories |  Calories left to consume |  Calories |  Calories consumed progress bar, breakdown calories per meal
Metabolic Diet App 20  | 2012 |  Yes |  Not mentioned |  Text messages & dietary feedback |  Not mentioned |  Before and after eating food images table
WIZE 73  | 2015 |  No |  Facts, games,& goals |  List of weekly goals |  |
ENGAGED 35  | 2012 |  No |  Calories & fat |  Calories and fat |  Goal thermometers display user goal & actual amount of calories & fat gram consumed |
Mobile phone apps [36] | 2016 |  No |  Calories |  Personalized recommendation, balance calories |  Calories |  Summary of food intake, a balance of ingested and spent calories
GoCARB 37  | 2016 |  Yes |  CHO |  CHO estimation |  Volume and CHO |  CHO and volume estimation of each food type
CalFit Chi 38  | 2016 |  No |  Not mentioned |  Not mentioned |  Standard portion size |  Not mentioned
APPETITT 43  | 2017 |  Yes. But actual volume no |  No applicable |  Not mentioned |  No nutrient displayed |  Very simple and to little info shown
DietApp 44  | 2017 |  No |  Only calories info |  Shows food whether harmful or beneficial; user receive personalized dietary suggestions acc. to illnesses they provide in profile. |  Calories, calories burn, balance calories left |  Calories, calories burn from activity, balance calories left all shown in table in one page
e-DIA 47  | 2016 |  No |  No |  Not mentioned |  Very minimal. Only gram from user app side |  Simple table only with food item, mealtime and gram
Vegethon 49  | 2017 |  No |  Only shows veggies serving |  Not mentioned |  Not mentioned |  Not mentioned
Dietary Intake Assessment 52  |  |  No |  Swedish food database calculated the intake of 49 nutrients |  Energy percentages of each meal in relation to recommended intakes was given. |  Estimated TEE; EI; gram of food; dietary fiber, calcium, iron, vitamin C, vitamin D, and folic acid |  The user could see details regarding his/her body mass index (BMI); estimated TEE; EI; and intake of macronutrients, fruits and vegetables, dietary fiber, calcium, iron, vitamin C, vitamin D, and folic acid concerning recommended daily intakes. Energy percentages of each meal with recommended intakes were also given.
MRP Support App 74  |  |  No |  No nutrient and calories shown. |  At the end of each week users received tailored feedback messages, which reflected their progress to date based on their reported compliance to the MRP and their weight loss. |  No nutrient and calories shown. |  No nutrient and calories shown. Meals shown in 5 simple categories (meal replacements,balanced meals, other meals, allowed snacks and other snacks) for each mealtime in a table
Nutricam 75  | 2011 |  No |  No |  No |  No |  No
Easy Diet 57  | 2017 |  yes |  Calories, Micro and Macro Nutrients |  No |  Calories |  Breakdown of Meal according to meal time
Lark 58  | 2018 |  No |  Calories, Micro and Macro Nutrient Information |  Notifications, messages on behavior and outcome of behavior |  Calories |  Goals, Break down of Meal according to Meal Type, Outcomes of various Foods
Sweetch 63  | 2018 |  No |  Calories, Micro And Macro Nutrient Information |  Notifications |  Calories |  Calories Consumed and Total Calories
Speech2Health 64  | 2018 |  Yes |  Calories and Nutrients Information |  Balance Calories |  Daily calories and nutrient intake |  Breakdown of daily calories and nutrient in pie chart and table
FoodWiz2 66  | 2018 |  No |  Calories |  Personalized Feedback text messages for motivation and help |  Calories |  Breakdown of daily Calories
Habit App 68  | 2018 |  No |  Calories |  Problem Solving process for weight loss and breaking bad habits |  Calories |  Calories taken by user relative to calories goal
My Food App 69  | 2018 |  Yes |  Energy and protein |  Not mentioned |  Calories and protein |  Breakdown of calories and protein intake according to meal type
My Fitness Pal (Log2Lose) 71  | 2019 |  Yes |  Calories and nutrients |  User Receives weekly Feedback on goals and their financial incentives. |  Daily calories and nutrients intake |  Breakdown of daily calories and nutrient