Health Behavior Theories in Diet Apps

Abstract

iPhone apps are being used to promote healthier diets. The purpose of this study was to evaluate the extent to which diet apps' content was guided by health behavior theory in their design and user interface. This study consisted of 58 diet apps from iTunes' Health & Fitness category. Coders downloaded the apps and rated their inclusion of theory. Scores ranged from 0 to 26 on a 100-point scale. Most apps were theory deficient and provided just general information/assistance. An opportunity exists for health behavior change experts to partner with app developers to incorporate health behavior theories into the development of individually tailored apps.

Full text

Running Head: DIET APPS 1
Diet Apps and the Inclusion of Health Behavior Theory

Running Head: DIET APPS 2
Abstract
Background: Obesity rates are increasing and have become a major public health priority. The
development of applications (apps) for iPhones can promote healthier diets. Health behavior
theory underlying apps remains unstudied.
Purpose: The study evaluates the extent diet apps utilize behavior change theory in their design
and user interface.
Methods: The study conducted an analysis of 58 diet apps from iTunes’ Health & Fitness
category. Coders downloaded the apps and used a theory-based instrument to rate their inclusion
of theoretical constructs.
Results: Theory scores ranged from 0 to 26 on a 100-point scale. The health belief model was
the most prevalent theory, accounting for an average of 12% of all constructs. Most apps
provided general information or general assistance, with few providing, assessments or tailored
recommendations.
Conclusions: An opportunity exists for health behavior change experts to partner with app
developers to incorporate behavior change theories into the development of individually-tailored
apps.
Keywords
Diet, App, Behavior Change Theory, iPhone, Smartphone, Health Technology, User Interaction,
Weight Loss, Obesity
Implications
Practice: Key elements of theory and user interaction can be developed into app intervention
strategies for weight loss using technical abilities of app developers and theory knowledge of
public health professionals.

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Policy: App technological developments coupled with sound theory and user interaction
principles will result in more effective weight loss and other behavior changes.
Research: There continues to be a significant gap in devising apps that integrates both theory
and user interaction to achieve weight loss and change.

Running Head: DIET APPS 4
Obesity in the United States represents an ongoing public health concern [1,2,3]. While
the dramatic increases in obesity observed over the past 30 years appear to be leveling off [4],
the percentage of adult Americans who are obese still exceeds 30% for most demographics [4,5].
Obesity is linked to general poor health status and chronic diseases such as hypertension, high
cholesterol, stroke, heart disease, certain cancers, and diabetes [1,6,7]. Reversing obesity trends
has become a major public health priority with considerable resources devoted to research and
development of interventions to address this problem [8]. While obesity is a complex problem
that will not be solved with one single solution, promoting healthy eating and improving dietary
habits is widely recognized as a critical component in the fight against obesity [8,9].
Advances in technology dramatically increase the ease of disseminating knowledge,
increasing awareness, and emphasizing the importance of healthy food choices [10]. The Internet
and mobile devices are an example of powerful potential purveyors of information related to
healthy eating [11]. In particular, the emergence of smartphones has provided a platform for
freelance developers to design third party applications (apps), which greatly expand the
functionality and utility of these mobile devices. Apps are pieces of software that can run on
mobile devices. Smartphones are recognized by their diverse platforms, such as Windows Phone,
Blackberry, Android, and iPhone. In the Health & Fitness category on Apple’s App Store,
developers have created thousands of downloadable apps for Apple’s mobile devices, which
include the iPhone, iPad, and iPod. Since the launch of the Apple App Store in July of 2008,
approximately 500,000 apps have become available with more than 25 billion downloads [12].
By 2016, it is anticipated that more than 44 billion apps will have been downloaded – which is
equivalent to six app downloads for every man, woman and child in the world. Currently, the
average American’s smartphone has 22 apps [13]. As smartphones--including iPhones--become

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more prevalent [14], developing health and fitness apps offers an innovative, wide-reaching, and
cost-effective way of altering the dietary habits of Americans. Research conducted through the
Pew Research Center’s Internet & American Life Project indicates that the online health-
information environment is “going mobile,” especially among younger adults [15]. Research
evaluating the design and effectiveness of mobile online health information is limited. In a recent
analysis of health and weight loss app descriptions, Breton, Fuemmeler, and Abroms [16],
concluded that the majority of such apps contain insufficient evidence-informed content. A
similar analysis still under review of over 3,000 health and fitness apps conducted by West et al.
similarly concluded that the majority of study apps lacked theoretical components known to
facilitate health behavior change. These recent investigations examining evidence-informed
content and the inclusion of theoretical constructs considered important for behavior change and
important first steps in analyzing the efficacy of apps in the promotion of health behavior. The
work of Breton et al. [16] and West et al. are limited, however, as each analysis was based upon
app descriptions rather than actual app content. To date, no study has examined the actual
content of diet-related apps. The purpose of this study was to examine actual content of diet-
related apps available in Apple’s App Store. Specifically, this study aimed to explore how
available diet-related apps utilize behavior change theory constructs in their design and
understand more about each app’s theory-based user interface.
METHODS
Study Design
This study design involved a content analysis of health behavior theory contained in diet-
related apps selected from apps available in the Health & Fitness category on Apple’s App Store.

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Graduate research assistants trained in health behavior theory purchased, downloaded and then
coded the apps.
Sample
The study sample came from apps available at www.apple.com/itunes on October 17,
2011, in the Health & Fitness category. Graduate research assistants identified potential apps by
querying the iTunes database with the keywords ‘dieting’ and ‘diet AND weight loss,’ which
returned 287 iPhone apps. These keywords were chosen based upon plausible search terms that
could be entered by a user attempting to find apps related to losing weight through dieting. Only
apps designed for the iPhone were considered. iPhone apps are more numerous and more widely
used than those designed for iPads and may be downloaded to and used on iPads as well as
iPhones [17]. Due to funding limitations, the study sample excluded apps costing more than
$5.00. Apps designed to address behaviors in addition to dieting (e.g.,both exercise and diet)
were also excluded because of the difficulty in discerning which of the multiple behaviors the
theoretical construct was intended for. Screening for additional behaviors occurred initially from
the summary page on the App Store and then through the coding process. Any recommendations
for physical activity or other behaviors outside of dieting excluded the app from further analysis.
Furthermore, apps were included if they pertained to human dieting, nutritional intake, or caloric
restriction and control, as indicated by the summary provide on the App Store. Apps were
excluded if they contained only a list of recipes; however, if additional language indicated the
purpose of the app was for dieting and weight loss, these recipe apps were included. These
inclusion/exclusion criteria were designed to find and assess apps the lay public would likely
download and use if attempting to lose weight through dieting alone. The inclusion/exclusion

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criteria returned 101 relevant apps. After downloading the apps, coders determined that three
apps were not fully functional, six were outside the aims of the current study and 34 apps
included additional behaviors outside of dieting. The final study sample consisted of 58 apps.
Procedure
Four graduate research assistants worked together to downloaded each iPhone app to an
iPad and thoroughly explored each one to become familiar with the user interface. All iPhone
apps in the sample were found to work on an iPad. Next, the research assistants utilized all the
apps’ functions such as diagrams, videos, record keeping, and reminders. Lastly, the research
assistants used a theory-based instrument adapted from Doshi, Patrick, Sallis, and Calfas [18] to
conduct the content analysis of each app. Coding data was entered into an electronic database as
it was collected.
Measurement
The instrument and methodology used for coding was adapted from a study conducted by
Doshi et al. [18] to evaluate the theoretical content of physical activity websites. The coding
instrument included constructs from four major theories of behavior change, including the Health
Belief Model (HBM), the Theory of Reasoned Action/Planned Behavior (TPB), the
Transtheoretical Model (TTM), and Social Cognitive Theory/Social Learning Theory (SCT). The
instrument included 20 theory-based constructs, which can be found in Table 1. The assessment
for each strategy included five levels of user interaction for a total of 100 (20 constructs x 5
levels of user interaction) theory-based items. The five levels of user interaction included:

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1. General information or guidelines: The app provided primarily general information or
data that were not individualized.
2. Assessment: The app asked the user for current behavioral practices or use of strategies.
3. Feedback: The app commented on the user’s current behavioral practices or strategies as
supplied in Item 2.
4. General assistance: The app offered nonindividualized suggestions about how to change
or apply a strategy that are not responses to any assessment (Item 2) and do not require
feedback (Item 3).
5. Individually tailored assistance: The app had suggestions about how to change or apply a
strategy specifically tailored to the user.
Other variables measured included app affiliation and price. App affiliation options
included business, non-profit/NGO, government, and individual. Apps identified as business
endeavored to sell more products on the apps or were tied with a business entity such as Weight
Watchers or Jenny Craig. Non-profit/NGO apps were those who were also identified with a
stated group or organization that were non-government and non-profit groups, such as obesity
watch groups. Government affiliated apps could include the Centers for Disease Control and
Prevention or National Institutes of Health. All other apps were considered to be individually
developed apps. Each app was assigned an app affiliation according to best fit one of the four
options. Prices ranged from free to $4.99 and each app was coded for price according to how
much the app cost at the time of purchase for this research study.
To verify the level of inter-rater reliability, each coder independently coded 10 randomly
selected, common apps. The researchers calculated the Cohen's Kappa coefficient to measure
inter-rater agreement (k = .74). This coefficient is categorized as substantial agreement, which

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includes a division ranging from 0.61-.80 and is an acceptable standard for inter-rater agreement
[19]. Following the coding of the sample of apps, the coders coded an additional 10 randomly
selected, common apps to test for rater drift. The Cohen’s Kappa was .84, indicating no drift.
Analysis
Each app was coded with a total of 100 theoretical items, each accounting for 1 point
(Yes = 1, No = 0). A total theory score was calculated by summing each app’s theoretical items,
for a possible score ranging from 0 – 100.
RESULTS
Apps were categorized into four groups: business, non-profit/NGO, government, or
individual. Businesses created 38 (66%) apps, while private individuals accounted for 20 (34%).
Non-profit/NGO or government apps were not observed in this study sample. The price of apps
ranged from free to $3.99. The majority of apps were either free (21%) or $0.99 (57%). The
remaining apps were $1.99 (9%), 2.99 (4%), and 3.99 (7%).
The twenty constructs that were included in the evaluated theories are presented in Table
1. Five questions for each construct were scored using yes (1) and no (0) for a total possible
value range of 0-5. These five questions asked if the app provided general information regarding
the construct, if the app assessed the user’s knowledge regarding the construct, if the app
provided feedback about the user’s knowledge regarding the construct, if the app provided
general assistance to modify knowledge regarding the construct, and if the app provided
individually-tailored assistance to modify or correct the user’s knowledge regarding the

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construct. The mean for the total of the five questions for each of the twenty constructs are
presented in Table 1. Knowledge, perceived benefits, self-monitoring, and perceived barriers
appeared most frequently while self-talk, negative affect management, motivations, modeling,
and stress management appeared least frequently. Relative to other constructs, knowledge
maintained the highest mean score (0.98).
Table 2 shows the distribution of health behavior theories and the total theory score for
each of the apps evaluated in this study. The average theory score was 6.19 (standard deviation =
6.52) out of a possible 100. Nutrition gurus had the highest total theory score at 26. Nine-percent
(N = 5) of the study apps had a theory score of 0. Among the apps that included theoretical
content, components of HBM were most represented, accounting for 12% of the constructs,
followed by TPB (10%), TTM (8%), and SCT (6%).
Figure 1 displays the results of the analysis of the user interaction levels. The five
measured user interaction variables included general information or guidelines, general
assistance, individually tailored assistance, user assessment, and feedback. All five of the user
interaction levels were coded for each of the 20 theory constructs; therefore, apps could have a
maximum score of 20 for each user interaction level. Higher scores for theory constructs would
indicate that the app applied the particular user interaction level (e.g. general information or
guidelines) to a wider range of behavior change theories. The pie charts show the percentage of
apps which applied 0, 1-5, 6-10, 11-15, or 16-20 theoretical constructs into their design for each
user interaction level. General information or guidelines and general assistance were the two user
interaction levels in which behavior change theory was most widely applied. Six-percent of apps
incorporated general information or guidelines and 7% of apps incorporated general assistance
into their design across 11-15 theory constructs. Assessment, feedback, and individually tailored

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assistance were the three user interaction levels in which behavior change was least applied. No
study app applied 11-15 theory constructs. In addition, 91% of the apps scored a zero across all
twenty constructs in feedback and assessment, and 96% of apps scored a zero across all twenty
constructs in individually tailored assistance. Although some of the apps did incorporate a wide
range of theory constructs (up to 14), the constructs were mostly applied through general
information or guidelines and general assistance.
Figure 2 displays the results of the number of theory constructs in each app. Numbers on
the X-axis (0, 1, 2, 3,...19, 20, etc.) correspond with the 20 possible theory constructs to which
each user interaction could have been applied in the app design. Higher scores on the X-axis
indicate that more theory constructs were incorporated into the app for that particular user
interaction method (e.g. provided general information). The Y-axis indicates the number of apps
out of 58 total. General information or guidelines and general assistance were the two user
interaction levels in which behavior change theory was most widely applied. Twenty-percent of
apps provided general information or guidelines and 15% of apps provided general assistance in
at least seven of the 20 constructs. Assessment, feedback, and individually tailored assistance
were the three user interaction levels in which behavior change was least applied. No app in this
study applied theory to seven or more of the 20 constructs for these three user interaction levels.
DISCUSSION
The current study employed a content analysis of 58 apps from iTunes’ Health & Fitness
category to determine the extent to which these apps included health behavior theoretical
constructs. This study is significant as theory-based interventions been shown effective in

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changing behavior [20] and web-based interventions have proven effective in behavior change
[21,22,23]. Hopefully the promising results of behavior modification web-based interventions
can be achieved with apps created for smartphones [16,21]. However, findings from the current
study highlight the need for increased inclusion of theoretical behavior change constructs in
apps. The general lack of theoretical constructs included in apps analyzed in this study is not
entirely unexpected given that app developers’ expertise relates to software development and not
health behavior theory. Findings from the current study underscore the need for collaboration
between health behavior change experts (i.e., public health professionals and certified health
education specialists) and app developers.
Despite a large number of apps with low total theory scores, certain apps did contain a
variety of theoretical constructs. One of the reasons web-based or smartphone app interventions
may be chosen over other delivery methods is the ability to customize the application and
monitor desired outcomes [16,24,25]. Unfortunately, the vast majority of apps coded in the
current study merely provided general information and general assistance regarding dieting
and/or healthy eating. Few study apps included more interactive levels of user interaction
considered significant in modifying behavior. Indeed, apps providing materials based on
knowledge and general assistance are comparable to books and pamphlets that health care
professionals and public health officials already produce. The provision of health information
alone is less likely to stimulate meaningful and long-term behavior change [26,27]. App
developers wishing to influence behavior change should consider integrating a two-way
interaction, such as providing feedback or tailoring specific recommendations for individual
users.

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Incorporating behavior change theory in app design provides an opportunity for
maximum utilization of app technologies. In this sample, the HBM and the TPB scored highest
among the four theories. While these theories promote behavior change, a more effective method
for changing dietary habits incorporates more individually-tailored intervention methods, which
first assess the user’s current behaviors, and then customize an intervention accordingly [26,28].
Moving forward, public health practitioners could work with app developers to incorporate more
of these types of approaches, which may have a greater impact on behavior change. If such an
approach were adopted, developers may consider advising users to consult a physician while
using the app. Although unlikely, it is possible that a tailored intervention could recommend
engaging in dietary practices unhealthy for individuals with certain chronic health conditions.
Low scores in user interaction and theory constructs suggest that app designers and
public health professionals may benefit from mutual collaboration in developing diet-related
apps. Advanced technologies allow for the design of apps that are effective in guiding an
individual through weight loss. Strecher et al. [26] found that successful behavior change
initiatives include components that interact with users, are easy to use, and are free of irrelevant
information. Through applying these principles to the development of advanced technologies
aimed at reducing obesity rates, consumers will have access to apps capable of initiating
sustained weight loss. Public health practitioners likely lack the technical ability to
independently develop apps, but they specialize in behavior change theory and its attending
constructs. Capitalizing on each discipline’s strengths has the potential to produce diet-related
apps capable of providing behavior change guidance and assessment on an individually tailored
level. Such apps may prove to be a valuable tool for public health practitioners and health care
professionals in their efforts to address the current obesity epidemic.

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Limitations
This study’s findings should be interpreted in the context of its limitations. One limitation
of this study was that many of the apps that aim to help the user achieve weight loss do so
through multiple behavior change focuses outside of diet alone. More comprehensive apps
which address both diet and physical activity may incorporate more theoretical constructs than
those included in the current study. Even after reading through the descriptions of many of the
apps, it was later discovered that some of the apps incorporated other behavior changes such as
smoking, colon cleanses, and physical exercise. To preserve the focus on diet and healthy eating,
apps addressing additional behaviors were excluded. In this way, the researchers were able to
preserve the inclusion/exclusion criteria and code apps purely related to healthy eating and
dieting alone. Despite strict inclusion/exclusion criteria, some apps may have been included that
incorporated additional behaviors and some apps may have been excluded that did not violate the
standard for inclusion.
Another limitation is that attention was not given to user ratings of study apps. User
rating estimates popularity of an app and could provide information regarding number of raters
or number of downloads with the possibility of demonstrating higher rated apps being
downloaded more often and containing more theoretical constructs in design. While not having
coded for user ratings, the researchers did code for price with the assumption that most diet-
related apps downloaded by users are free or very affordable ($0.99). With the majority of apps
in the sample falling in the price range of free to $0.99 (78%), the study is justified in focusing
on apps within these price ranges for their analysis. However, apps costing more than 44.99 were

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excluded from the current study, which may have impacted results related to theoretical
constructs correlated with price and quality.
With new apps being created each day, existing apps are quickly outdated [16]. These
advancements make studying current technology trends difficult, resulting in little understanding
and research done in this area to date. This study’s analysis of theoretical content in diet-related
apps sets the groundwork for future research.

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Table 1 Descriptive information about the theoretical constructs measured in the study sample
Theory
Construct
Mean
Std.
Dev.
Theory
Construct
Mean
Std.
Dev.
Theory
Construct
Mean
Std.
Dev.
HBM, TTM,
TPB, SCT
Knowledge
0.983
0.827
HBM, TPB
Perceived Risks
0.362
0.742
SCT
Time Management
0.121
0.422
HBM, TTM,
TPB, SCT
Perceived Benefits
0.621
0.813
TTM, TPB,
SCT
Self-efficacy
0.345
0.664
TTM,
SCT
Self-talk
0.103
0.447
TTM, SCT
Self-monitoring
0.586
0.937
TTM, SCT
Relapse
Prevention
0.328
0.659
SCT
Negative Affect
0.103
0.447
HBM, TTM, SCT
Perceived Barriers
0.500
0.822
SCT
Goal Setting
0.276
0.670
TTM
Motivational
Readiness
0.103
0.406
HBM, TTM, TPB
Increase
knowledge
0.448
0.626
TTM, SCT
Stimulus
Control
0.207
0.614
SCT
Modeling
0.086
0.339
TTM, SCT
Skill Building
0.414
0.650
TTM, SCT
Self-reward
0.190
0.576
SCT
Stress Management
0.052
0.292
TTM, TPB
Perceived Social
Norms
0.379
0.697
TTM, SCT
Social Support
0.138
0.476

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Note: HBM = Health Belief Model, TTM = Transtheoretical Model, TPB = Theory of Planned Behavior, SCT = Social Cognitive
Theory.

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Table 2 Theory score distribution by app, N = 58
Name
HBM
TTM
TPB
SCT
Total
Name
HBM
TTM
TPB
SCT
Total
Nutrition Guru's
20%
27%
23%
28%
26
Chain Restaurant
8%
4%
7%
4%
3
Diet Cakes Weight
24%
30%
23%
25%
23
CountEat. Calori
4%
4%
3%
4%
3
Diet Donuts
24%
30%
23%
26%
23
Fast Food Calorie
4%
4%
3%
4%
3
Glycemic 101
36%
24%
37%
18%
22
Fat Nav - Weight
0%
3%
0%
4%
3
30 Day Low Carb D
28%
20%
17%
19%
18
Food Scanner- Cal
4%
4%
3%
4%
3
A Great Weight Lo
40%
19%
30%
15%
17
Food Scanner: goo
4%
4%
3%
3%
3
Healthy Recipes:
24%
17%
23%
15%
15
Low Carbs-Food St
4%
3%
3%
3%
3
Lose it - best w
24%
17%
23%
15%
15
Nutrition Facts
8%
4%
7%
4%
3
A Taste of Slimmi
20%
13%
23%
10%
11
A Fast Food Nutri
4%
3%
0%
3%
2
Colon cleanse 101
28%
11%
27%
9%
11
Eatright - Daily
0%
3%
0%
3%
2
Inner Slim in 7 d
16%
11%
13%
14%
11
Heart healthy foo
4%
1%
3%
3%
2
Insider Nutrition
28%
14%
20%
10%
11
My Food-nutrition
8%
3%
7%
1%
2

Running Head: DIET APPS 24
Super Fruits and
36%
13%
23%
10%
11
Pts Tracker plus
0%
3%
0%
3%
2
Friday night diet
20%
10%
17%
8%
9
Sugar Levels in D
8%
3%
7%
3%
2
South Beach Diet
20%
13%
17%
10%
9
Uk Points
4%
3%
3%
3%
2
Weight Loss Revol
12%
13%
13%
9%
9
35 potent foods
4%
1%
3%
1%
1
101 tips for colo
20%
9%
20%
3%
8
Beer Nutrition Fa
4%
1%
3%
1%
1
I eat healthy
12%
10%
10%
10%
8
Colon cleanse gui
24%
11%
27%
6%
1
FoodMeter: Good f
16%
10%
17%
9%
7
Diet Recipes
4%
1%
3%
1%
1
Intelli-diet weig
0%
6%
0%
9%
7
Fast Food Chain R
4%
1%
3%
1%
1
Amazing weight lo
16%
6%
20%
3%
6
Good carb diet re
4%
1%
3%
1%
1
Dining Out Guide
16%
9%
17%
8%
6
iCalorie Counter
0%
1%
0%
1%
1
A 7-day detox
16%
7%
13%
4%
5
icountCalories
0%
1%
0%
1%
1
Bon' app
12%
4%
10%
3%
5
This for That
4%
1%
3%
1%
1
Weight Wise Recip
12%
7%
10%
5%
5
500+ diet recipie
0%
0%
0%
0%
0
35 Superfoods
12%
6%
13%
4%
4
A 5- Day Slim Dow
0%
0%
0%
0%
0
Diet Shakes~for f
12%
6%
10%
5%
4
Diabetic Diet Rec
0%
0%
0%
0%
0
A Fast food diet
8%
4%
7%
4%
3
Low Fat recipes f
0%
0%
0%
0%
0

Running Head: DIET APPS 25
Best Diet Foods
12%
4%
10%
4%
3
YourCalc
0%
0%
0%
0%
0
Average
12%
8%
10%
6%
6.19
Note: HBM = Health Belief Model, TTM = Transtheoretical Model, TPB = Theory of Planned Behavior, SCT = Social Cognitive
Theory.

Running Head: DIET APPS 26
Figure 1 Theory inclusion by user interaction level

Running Head: DIET APPS 27
Figure 2 Inclusion of theoretical constructs in apps, N = 58