ArticlePDF Available

Health Behavior Theories in Diet Apps

Authors:

Abstract and Figures

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.
No caption available
… 
Content may be subject to copyright.
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.
Running Head: DIET APPS 3
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
Running Head: DIET APPS 5
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.
Running Head: DIET APPS 6
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
Running Head: DIET APPS 7
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:
Running Head: DIET APPS 8
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
Running Head: DIET APPS 9
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
Running Head: DIET APPS 10
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
Running Head: DIET APPS 11
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
Running Head: DIET APPS 12
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.
Running Head: DIET APPS 13
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.
Running Head: DIET APPS 14
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
Running Head: DIET APPS 15
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.
Running Head: DIET APPS 16
Running Head: DIET APPS 17
References:
1. Flegal KM, Carroll MD, Ogden CL, Curtin LR. Prevalence and trends in obesity among
US adults, 19992008. JAMA. 2010; 303: 235241.
2. Mokdad AH, Serdula MK, Dietz WH, Bowman BA, Marks JS, Koplan JP. The spread of
the obesity epidemic in the United States, 1991 - 1998. JAMA. 1998; 282(16): 1519-
1522.
3. Centers for Disease Control and Prevention. U.S. obesity trends. Available at
http://www.cdc.gov/obesity/data/trends.html. Accessibility verified December 14, 2011.
4. Flegal KM, Carroll MD, Kit BK, Ogden CL. Prevalence of obesity and trends in the
distribution of body mass index among US adults, 1999-2010. JAMA. 2012; 307(9): 883-
985.
5. Ogden CL, Carroll MD. Prevalence of overweight, obesity, and extreme obesity among
adults: United States, trends 1960 1962 Through 2007 2008. National Center for
Health Statistics. Available at:
http://www.cdc.gov/NCHS/data/hestat/obesity_adult_07_08/obesity_adult_07_08.pdf.
Accessibility verified December 14, 2011.
6. Ebbeling CB, Pawlak DB, Ludwig DS. Childhood obesity: public-health crisis, common
sense cure. Lance. 2002; 360: 473-482.
7. Freedman SS, Dietz WH, Srinivasan SR, Berenson GS. The relation of overweight to
cardiovascular risk factors among children and adolescents: the Bogalusa heart study.
Pediatrics. 1999; 103: 1175-1182.
Running Head: DIET APPS 18
8. Hill JO. Can a small-changes approach help address the obesity epidemic? A report of the
Joint Task Force of the American Society for Nutrition, Institute of Food Technologists,
and International Food Information Council. Am J Clin Nutr. 2009; 89(2): 477-484.
9. Mirmiran P, Mirbolooki M, Azizi AF. Familial clustering of obesity and the role of
nutrition: Tehran Lipid and Glucose Study. Int J Obes. 2002; 26(12): 1617-1622.
10. Nigg, CR. Technology’s influence on physical activity and exercise science: the present
and the future. Psychol Sport Sci. 2003; 4: 57-65.
11. Fox S. Pew Internet: health. Pew Research Center. Available at:
http://pewinternet.org/Commentary/2011/November/Pew-Internet-Health.aspx.
Accessibility verified December 14, 2011.
12. Muller T, Bowcock J. Apple’s app store downloads top 10 billion. Available at:
http://www.apple.com/pr/library/2011/01/22Apples-App-Store-Downloads-Top-10-
Billion.html. Accessibility verified December 27, 2011.
13. Nielsenwire. The state of mobile apps. Available at:
http://blog.nielsen.com/nielsenwire/online_mobile/the-state-of-mobile-apps/.
Availability verified December 27, 2011.
14. Kellogg D. Among mobile phone users, Hispanics, Asians are most-likely smartphone
owners in the U.S. Available at: http://blog.nielsen.com/nielsenwire/?p=25901.
Accessibility verified December 19, 2011.
15. Fox S. Mobile health 2010. Pew Research Center. Available at
http://pewinternet.org/~/media//Files/Reports/2010/PIP_Mobile_Health_2010.pdf.
Accessibility verified December 14, 2011.
Running Head: DIET APPS 19
16. Breton ER, Fuemmeler BF, Abroms LC. Weight lossthere is an app for that! But does
it adhere to evidence-informed practices? Transl Behav Med. 2011; 1-7.
17. Yarow J, Angelova K. How will the iPad sell compared to other mobile gadgets?
Business Insider. Available at: http://articles.businessinsider.com/2010-04-
15/tech/29964254_1_ipad-sales-gadgets-mobile-devices. Accessibility verified December
14, 2011.
18. Doshi A, Patrick K, Sallis JF, Calfas K. Evaluation of physical activity web sites for use
of behavior change theories. Ann Behav Med. 2003; 25: 105-110.
19. Landis JR, Koch GG. The measurement of observer agreement for categorical data.
Biometrics. 1977; 33: 159-174.
20. Glanz K, Rimer BK, Viswanath K. Health Behavior and Health Education. (4th ed.)
Hoboken, NJ: Wiley, John & Sons, 2008.
21. Wantland D, Portillo C, Holzemer W, Slaughter R, McGhee E. The effectiveness of web-
based vs. non-web-based interventions: a meta-analysis of behavior change outcomes. J
Med Internet Research. 2004; 6(4).
22. Portnoy D, Scott-Sheldon L, Johnson B, Carey M. Computer-delivered interventions for
health promotion and behavioral risk reduction: a meta-analysis of 75 randomized
controlled trial, 1988-2007. Prev Med. 2008; 47(1): 3-16.
23. Kroeze W, Werkman A, Brug J. A systematic review of randomized trials on the
effectiveness of computer-tailored education on physical activity and dietary behaviors.
Ann Behav Med. 2006; 31(3): 205-223.
24. Crushing C, Steele R. A meta-analytic review of eHealth interventions for pediatric
health promoting and maintaining behaviors. J Pediatr Psychol. 2010; 35(9) :937-949.
Running Head: DIET APPS 20
25. Winett R, Tate D, Anderson E, Wojcik J, Winett S. Long-term weight gain prevention: a
theoretically based Internet approach. Prev Med. 2005; 41(2): 629-641.
26. Strecher VJ, Kreuter M, Den Boer DJ, Kobrin S, Hospers HJ, Skinner CS. The effects of
computer-tailored smoking cessation messages in family practice settings. J Fam Pract.
1994; 39(3): 262-271.
27. Molaison EF. Stages of change in clinical nutrition practice. Nut Clin Care. 2002; 5(5):
251 -257.
28. Campbell MK, DeVellis BM, Strecher VJ, Ammerman AS, DeVellis RF, Sandler RS.
Improving dietary behavior: the effectiveness of tailored messages in primary care
settings. Am J Public Health. 1994; 84(5): 783-787.
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
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
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
Best Diet Foods
12%
4%
10%
4%
3
YourCalc
0%
0%
0%
0%
0
Average
12%
8%
10%
6%
6.19
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
... Empirical evidence indicates that implementing BCTs successfully leads to health behavior changes (Bert, Giacometti, Gualano, & Siliquini, 2014;Hasman, 2011;West et al., 2017;West et al., 2013), and specific strategies are beginning to gain attention in the context of app-based mobile interventions. Setting personal goals, receiving feedback, and reviewing relevant goals have proven to be successful strategies for promoting eating behavior changes (Direito et al., 2014;Samdal, Eide, Barth, Williams, & Meland, 2017). ...
... So far, it remains unclear which BCTs or combination of BCTs should be implemented to promote a specific target behavior or induce changes in specific target audiences. Moreover, the lack of a theoretical foundation of both BCTs and app-based mobile interventions remains an important issue (Prestwich et al., 2014;West et al., 2013). It is acknowledged that health behavior interventions are more effective if they are based in theory (Atkins & Michie, 2013;Coughlin et al., 2015;Dunn et al., 2018;Mummah, 2016;Webb et al., 2010;West et al., 2017;Zhao et al., 2016), which was already established by Lewin (1943), who stated that 'there is nothing as practical as a good theory' (see e.g. ...
... In [12], iPhone apps to promote healthier diets are analyzed from the health behavior theory adoption point of view. The study consisted of 58 diet apps from iTunes' Health & Fitness category, showing that most apps were theory deficient and provided just general information/assistance. ...
Conference Paper
This paper describes the Diet module of the PERCIVAL project, that aims at improving the quality of life (QoL) of patients affected by chronic diseases through the promotion, sharing, deliberation and monitoring of decisions about their different aspects among all the actors involved. The module is composed of two Android apps developed according to the principles of behavior change theories, in order to maximize the acceptability level by the user and the overall benefits for him/her. The paper focuses on the architectural, design and implementation aspects, presenting how the two apps are correlated to provide an adequate support to the user.
... Over 2 billion people use inexpensive mobile smartphone apps to measure, track, display and share their personal health data, with these digital tracking tools monitoring medications, food and water intake, glucose, oxygen saturation, body temperature, weight, pain levels, sleep patterns and many other activities (Miller and Polson 2019). Too often, however, apps are designed by developers, with limited engagement with research, theory, or end-users-which results in poorly designed apps and a "chaotic mix of apps of varying degrees of usefulness, quality, effectiveness and danger" (Hilty et al. 2017, p. 14;Joshi et al. 2019;West et al. 2013). Education, income, health, and age also influences engagement with digital health technologies, which are rarely designed with and for caregivers or with the specific needs of our ageing population in mind (Göransson et al. 2020;Wilson et al. 2021). ...
Article
Full-text available
Being an informal caregiver to a loved one with an illness, disease, or chronic disability is a rewarding but frequently stressful experience. In this design research project, caregivers participated in a half-day workshop to (1) share their caregiving experience, (2) reflect on the potential of a mobile smartphone ‘app’ for carers and (3) co-design this app, as well as participate in in-depth interviews. Our design research process used multiple arts-based methods, including visual experience mapping tools, storytelling, photo-elicitation, documentary photography, cartoons, drawing, and research poetry, to provide rich and empathic insight into daily life as a caregiver and illuminate the potential of technology. Workshop activities included creating a visual collage of lived experience, annotated visual maps illustrating the reality and misconceptions of caregiving, pathways of care, and mapping a day in their life using the visual metaphor of a clock. Carers then trialled and provided feedback on a prototype app, creating a collective map of desired features. This co-design feedback informed the final app design, which was formally launched at a public exhibition showcasing stories collected from our arts and design-led processes. This paper outlines the value of arts and design methods in the design of future health technologies, which provide a critical space for an informed, reflexive, and empathic dialogue about illness and caregiving, resulting in designs that truly met consumer’s needs.
... The current study reported an even lower number of BCTs in the MedDiet apps analysed; this ranged from 0 to 6, with a mean of 2.3 BCTs per app. An earlier study assessing whether the content of 58 dietary apps, identified from the Apple App Store Health and Fitness category, was guided by theories of behaviour change also found that most apps were not informed by theory [47]. It should be noted that comparisons of the presence of BCTs with previous studies is hindered by the use of different app selection criteria, different targeting behaviours and different platforms used to identify apps. ...
Article
Full-text available
Smartphone apps might represent an opportunity to promote adherence to the Mediterranean diet (MedDiet). This study aimed to evaluate the quality of commercially available apps for the MedDiet and the presence of behavioural change techniques (BCTs) used by these apps. A systematic search was conducted on the Apple App and Google Play stores in November 2021. Apps were included if they provided information on the MedDiet or if their objective was to promote a healthy lifestyle through adherence to the MedDiet. Eligible apps were independently evaluated by two reviewers with regard to their quality (engagement, functionality, aesthetics and information quality) using the 5-point Mobile App Rating Scale (MARS; with higher scores indicating higher quality), and the presence of BCTs using an established 26-item BCT taxonomy. Of the 55 analysed apps, 52 (94.5%) were free, 50 (90.9%) provided recipe ideas, 29 (52.7%) provided meal plans, and 22 (40%) provided information on the health benefits of the MedDiet. The overall quality mean MARS score was 2.84 (standard deviation (SD) = 0.42), with functionality being the highest scored MARS domain (mean = 3.58, SD = 0.44) and engagement the lowest (mean = 2.29, SD = 0.61). The average number of BCTs in the analysed apps was 2.3 (SD = 1.4; range: 0–6 per app). The number of BCTs was positively correlated with app information quality (rrho = 0.269, p = 0.047), overall MARS score (rrho = 0.267, p = 0.049), app subjective quality (rrho = 0.326, p = 0.015) and app-specific quality (rrho = 0.351, p = 0.009). These findings suggest that currently available apps might provide information on the MedDiet, but the incorporation of more BCTs is warranted to maximise the potential for behaviour change towards the MedDiet.
... We used Cohen's kappa ( ) [22] to measure interrater reliability [23][24]. ...
Article
Aims: Increased usage of health apps has led to need for their quality assessment for safeguarding interest of various stakeholders. This study attempts to undertake qualitative assessment of health apps in an emerging market, India. Study Design: Health apps were evaluated by the experts and secondary data was used for rating of the health apps. Indian food data base was used for evaluating content accuracy. Place and Duration of Study: Department of Food and Nutrition, College of Community Science, Punjab Agricultural University, Ludhiana, between January 2019 and August 2019. Methodology: Top 10 health apps, identified from response of 400 users, are assessed qualitatively by expert raters using App Quality Evaluation Questionnaire. Content accuracy in terms of macronutrient measurements is assessed using Mean Absolute Percent Error (MAPE). Relationship between average user ratings and various aspects of qualitative assessment is explored using linear regression. Results: Majority of the apps performed well in terms of functionality, interactivity, security and aesthetics. Relatively poor performance is observed in terms of accountability, behavior change techniques and scientific coverage. Regression analysis indicate that Functionality (p=0.035) and engagement (p=0.024) features significantly influence user ratings and overshadow scientific coverage and accuracy (p=0.798). MAPE values indicate considerable variations from Indian food data base across the apps especially in terms of protein and energy. Conclusion: Quality assessment of top 10 health apps, in Indian scenario, indicates that the apps are proficient on functionality. At the same time, the apps fair poorly in terms of scientific content and accountability. There is a pertinent need for statutory regulations as well as voluntary efforts for improving the scientific content. Collaborative efforts of app developers with scientific institutions should be promoted.
... This recommendation is particularly relevant for those segments of the population that are more vulnerable to chronic conditions or suffer from allergies and intolerances, like the elderly, people with an impaired immune system, individuals characterized by frailty or neuromuscular conditions, etc. Promoting healthy and balanced diet plans can foster the prevention of the above-mentioned diseases and conditions, but the task of developing and implementing these diet plans is not easy. In the past ten years many mobile phone apps have been developed with the aim of supporting people in engaging themselves in a healthier nutrition, but the majority of them did not reach the goal of fostering the adoption of a balanced nutrition and failed in proving their effectiveness in both medium and long term [4]. ...
Chapter
Nutrition-related diseases can considerably contribute to many different health-related problems and can impact on several segments of the population. Promoting balanced diet plans is therefore pivotal; however, this is not a trivial task as it requires different stakeholders (nutrition experts, agro-industrial businesses and consumers) to cooperate. This work introduces a prototypical ontology-based decision support system to enable such a cooperation, allowing nutrition experts to rely on a support tool when developing diet plans, consumers to received tailored suggestions and to be informed regarding new food products that could have an effect on specific their health condition, and agro-industrial companies to divulge characteristics of novel food products and their expected effects. These stakeholders can also exchange comments, suggestions and observations. The decision support system relies on widely-adopted ontologies and its use is introduced by two scenarios.
... Apps developed utilizing behaviour change theories and strategies such as goal setting, barrier identification, self-monitoring, and action planning have been reported to be particularly effective in initiating behaviour change among their users Direito et al., 2014;Lyzwinski, 2014). Of note, several reviews of commercial health apps have revealed a significant lack of evidence-based guidelines Breton et al., 2011;Nundy et al., 2014) and behaviour change theories (Conroy et al., 2014;Direito et al., 2014;McKay et al., 2019b;West et al., 2013) embedded in their features. Most notably, a systematic review rating the behaviour change potential of physical activity apps (n = 275) found that most included a limited number of behaviour change techniques (BCTs; an average of 7 to 9; McKay et al., 2019aMcKay et al., , 2019b. ...
Article
Full-text available
The purpose of this study was to provide a detailed and systematic outline of how a theoretical behaviour change framework was applied in the development of ParticipACTION’s app to support a more active Canada. The app development process was guided by the Behaviour Change Wheel (BCW) framework, a theoretically-based approach for intervention development, in collaboration with the commercial app industry. Specifically, a behavioural diagnosis was used to understand what needs to change for the targeted behaviour to occur. Current literature, along with a series of surveys, and market research informed app development. Additionally, a validated app behaviour change scale, was consulted throughout development to help ensure app features maximized behaviour change potential. The behavioural diagnosis revealed that the app needed to target individuals’ physical and psychological capabilities, physical and social opportunities, and reflective and automatic motivations in order to increase physical activity levels. To accomplish this, 6 of a possible 9 intervention functions and 2 of 7 policy categories were selected from the BCW to be included in the app. Goals and planning, feedback and monitoring, behaviour identification, action planning and knowledge shaping were selected as the main behaviour change techniques for the app. Collaboration with a mobile app development firm helped to embed the selected behaviour change techniques, policy categories, intervention functions, and sources of behaviour within the app. Using a systematic approach, this study used the BCW to ensure the health promotion app was theoretically informed. Future research will evaluate its effectiveness in increasing the physical activity of Canadians.
Article
Background: Nuts are nutrient-dense foods that contribute to healthier eating. Food image datasets enable artificial intelligence (AI) powered diet-tracking apps to help people monitor daily eating patterns. Aim: This study aimed to create an image dataset of commonly consumed nut types and use it to build an AI computer vision model to automate nut type classification tasks. Methods: iPhone 11 was used to take photos of 11 nut types-almond, brazil nut, cashew, chestnut, hazelnut, macadamia, peanut, pecan, pine nut, pistachio, and walnut. The dataset contains 2200 images, 200 per nut type. The dataset was randomly split into the training (60% or 1320 images), validation (20% or 440 images), and test sets (20% or 440 images). A neural network model was constructed and trained using transfer learning and other computer vision techniques-data augmentation, mixup, normalization, label smoothing, and learning rate optimization. Results: The trained neural network model correctly predicted 338 out of 440 images (40 per nut type) in the validation set, achieving 99.55% accuracy. Moreover, the model classified the 440 images in the test set with 100% accuracy. Conclusion: This study built a nut image dataset and used it to train a neural network model to classify images by nut type. The model achieved near-perfect accuracy on the validation and test sets, demonstrating the feasibility of automating nut type classification using smartphone photos. Being made open-source, the dataset and model can assist the development of diet-tracking apps that facilitate users' adoption and adherence to a healthy diet.
Article
Full-text available
Objective Characterize capabilities of nutrition applications (apps) for weight management and associations between features, ratings, and app installations. Design Calorie tracking apps with weight management as a primary outcome were selected from the Apple App Store and Google Play Store using keywords “diet” and “weight loss.” Methods Reviewers assessed free and upgraded versions of nutrition apps (n = 15) for features within 4 categories: (1) dietary intake, (2) anthropometrics, (3) physical activity, and (4) behavior change strategies. Outcome Measures Presence of specific app features, app ratings, and app installations. Analysis Descriptive statistics of free and paid app versions. Spearman rank-order correlations were used to determine associations between feature inclusion, app ratings, and installations. Results The apps had the greatest number of features in the dietary intake category. Additional dietary intake features were those most likely obtained through a subscription purchase. Behavior change content was absent from most apps. The macronutrient adjustment feature was strongly associated with average app ratings (rs = 0.74; P < 0.002) and with subscription costs (rs = 0.60; P < 0.019). Conclusions and Implications This study found most nutrition apps possess an abundance of features dedicated to dietary intake, anthropometric, and physical activity tracking while also being notably devoid of behavior change content features.
Article
Full-text available
Little is known about how much smartphone apps for weight control adhere to evidence-informed practices. The aim of this study was to review and summarize the content of available weight control apps. Information on content, user rating, and price was extracted from iTunes on September 25, 2009. Apps (n = 204) were coded for adherence to 13 evidence-informed practices for weight control. Latent class analysis was used to identify subgroups of apps based on endorsement practices. Only a small percentage of apps had five or more of the 13 practices (15%). Latent class analysis revealed three main types of apps: diet, physical activity, and weight journals (19%); dietary advice and journals (34%); and weight trackers (46%). User ratings were not associated with apps from these three classes. Many apps have insufficient evidence-informed content. Research is needed that seeks to develop, improve, and evaluate these apps.
Article
Background: People are increasingly using the Internet and social networking sites for behavior support. Almost no literature exists exploring the utility of these sites for supporting breastfeeding behavior. Purpose: The purpose of this study was to determine the extent to which blogs are currently being used to support breastfeeding behavior. Methods: Data for this study came from a sample of 32 active blogs, resulting in 354 posts and 881 comments for analysis. Evidence of intent to support behavior was determined by the presence of theoretical behavior support constructs from the Integrated Behavioral Model. Results: In posts, attitudes (28.5%), behavioral cues (23.8%), and consciousness-raising (25.3%) appeared frequently. Praise (43.3%), behavioral cues (37.4%), and attitudes (30.4%) were the most prominent constructs in comments. More behavior support appeared on industry-affiliated blogs than on private blogs. Posts that presented mostly information only were least effective at eliciting behavior support. Discussion: Blogs are being used to support breastfeeding behavior, and blogs with industry affiliation appear to offer more support. Translation to Health Education Practice: Health educators wishing to support breastfeeding may use blogs and may want to partner with industry. Such efforts might also focus on functional content knowledge aimed at supporting breastfeeding behavior.
Article
Interventions to change health-related behaviors have had some success, but behavior change has proved to be a formidable challenge. Substantial advances in efforts to improve the behavioral determinants of health will require renewed commitment to the science of behavior. In particular, we believe there are three areas that would benefit from greater attention. Refining theory Theories provide an explanatory framework for understanding the relation between constructs – whether they be features of the environment, psychological states, or biological markers – and in doing so inform the work conducted by all health psychologists. To this end, there is no shortage of articles that extol the virtue and value of theories. There are repeated calls for research and practice to be theoretically informed (e.g., Marteau, Dieppe, Foy, Kinmonth, & Schneiderman, 2006) and the creation of the new journal, Health Psychology Review, is motivated, in part, to enrich the role of theory in health psychology. But how do we treat this object of our affection? Despite a shared commitment to theory, there is a growing concern that we are not tending to our theories as well as we ought (Noar & Zimmerman, 2005; Rothman, 2004; Weinstein & Rothman, 2005). As Kurt Lewin once said, ''There is nothing so practical as a good theory'' (1951, p. 169). Although we share Lewin's faith in the value of theory, it is essential that we recognize that it is predicated on the availability of good theories. Theories need to be nurtured by the community of researchers and practitioners. Over time, theoretical models should evolve, based on a series of activities in which formal predictions are derived from the theory and tested, with the results feeding back into our understanding of the theory. Through this process, our theories should be able to specify more precisely when findings will be obtained as well as the underlying processes that regulate those effects. For example, empirical work should allow investigators to transform the initial thesis that satisfaction is a critical determinant of sustained behavior change (Rothman, ISSN 0887-0446 print/ISSN 1476-8321 online ß 2007 Taylor & Francis DOI: 10.1080/14768320701233582 Baldwin, & Hertel, 2004) to a more refined thesis, if supported, regarding the factors that determine feelings of satisfaction and the conditions under which satisfaction is not a relevant determinant of behavior. To date, the extent to which the dominant theories in health psychology have evolved would appear to be slow and inefficient at best. We believe this is due, in part, to poor specification of the processes through which theories are refined. Among the many issues that would benefit from careful consideration are: How much evidence is needed to refine a theory? At what point should a modification of theory require a new name (e.g., consider the change from the ''theory of reasoned action'' to the ''theory of planned behavior'')? When should an elaboration of a theory (e.g., the specification of the factors that determine an important construct) be considered a new theory and when should it be merely integrated into the initial theory? If we can clarify the processes by which theories are refined, they should prove not only more productive, but also easier to use by researchers and practitioners throughout the health sciences. Methods, measurement, and mediation
Article
Context: The increasing prevalence of obesity is a major public health concern, since obesity is associated with several chronic diseases. Objective: To monitor trends in state-specific data and to examine changes in the prevalence of obesity among adults. Design: Cross-sectional random-digit telephone survey (Behavioral Risk Factor Surveillance System) of noninstitutionalized adults aged 18 years or older conducted by the Centers for Disease Control and Prevention and state health departments from 1991 to 1998. Setting: States that participated in the Behavioral Risk Factor Surveillance System. Main outcome measures: Body mass index calculated from self-reported weight and height. Results: The prevalence of obesity (defined as a body mass index > or =30 kg/m2) increased from 12.0% in 1991 to 17.9% in 1998. A steady increase was observed in all states; in both sexes; across age groups, races, educational levels; and occurred regardless of smoking status. The greatest magnitude of increase was found in the following groups: 18- to 29-year-olds (7.1% to 12.1%), those with some college education (10.6% to 17.8%), and those of Hispanic ethnicity (11.6% to 20.8%). The magnitude of the increased prevalence varied by region (ranging from 31.9% for mid Atlantic to 67.2% for South Atlantic, the area with the greatest increases) and by state (ranging from 11.3% for Delaware to 101.8% for Georgia, the state with the greatest increases). Conclusions: Obesity continues to increase rapidly in the United States. To alter this trend, strategies and programs for weight maintenance as well as weight reduction must become a higher public health priority.
Article
This research brief describes our recent efforts collecting daily experience data from college undergraduates at a large midwestern U.S. university through mobile phone text messaging. By daily experience data, we mean data that are collected at multiple points from individuals within their natural context, over a period of time. This approach to data collection provides a way to study phenomena under the conditions in which they naturally occur and to examine how those phenomena progress over time or across contexts (Bolger, Davis, & Rafaeli, 2003). Gathering data from individuals at multiple points over the course of time as a way to better understand their experiences has been used as early as the 1920s. Since then, the methods of collecting that data have evolved alongside technological advances, with early paper and alarm watches eventually giving way to beepers and personal digital assistant (PDA) devices (Scollon, Kim-Prieto, & Diener, 2003). A variety of approaches are possible, including (a) time-based designs, in which participants are asked to respond at fixed intervals (e.g., at 10:00 a.m. and 4:00 p.m. each day), (b) event-based designs, in which participants are asked to respond when a certain event occurs (e.g., before each meal), and (c) interval-based designs, in which participants are asked to respond whenever prompted (e.g., by an electronic beeper). In the current study, we examined the feasibility of an interval-based approach that might be considered a natural extension of these methods: collecting data from college students via text messaging. The experience sampling method (ESM) is a term associated with interval-based designs in which participants provide daily experience data when they are signaled at various (usually random) times during the day and across an extended period of time (Hektner, Schmidt, & Csikszentmihaly, 2007; Scollon et al., 2003). Other compatible terms include ecological momentary assessment (Stone, Shiffman, & DeVries, 1999) and time-based diary research (Bolger et al., 2003). Collecting data through this method has several strengths compared with traditional survey or laboratory-based methodologies. First, experience-sampling allows a useful way to explore the link between context and behavior or feelings, because data can be collected while the participant is within a particular context. Second, time-based methods allow the ability to assess changes that occur within individuals over time or across situations. Third, the accuracy of data need not rely on participants’ retrospective memory, as is often required in traditional survey methods. A large number of ESM studies have focused on adolescence. Topics of ESM research conducted with that population have included studies of time use (Larson, 1989), the context of mood (Larson, Moneta, Richards, & Wilson, 2002), student engagement during instructional activities (Shernoff, Csikszentmihalyi, Schneider, & Shernoff, 2003), and the relationship of cortisol levels to emotions (Adam, 2006). ESM methods have also been utilized to better understand the experiences of college students. For example, in order to study the experiences of Black students on predominately White campuses, Cole and Yip (2008) provided Black college freshmen with electronic pagers and paper data diaries. Over a 10-day period, participants logged data regarding their location and mood whenever they were beeped. The multiple data points allowed the researchers to explore the relationship between participants’ emotional states in school versus nonschool settings. Other research topics studied in college settings through daily experience data methods have included motivators of alcohol use (Hussong, 2003), and events evoking social anxiety (M. R. Lee, Okazaki, & Yoo, 2006). In their study of risk perceptions among college students, Hogarth, Portell, and Cuxart (2007) used an event-based ESM design, but utilized students’ own mobile phones rather than providing pagers. In that study, participants were supplied with questionnaires that they were asked to complete whenever they received a text message from the researcher. Researchers are not limited to having participants complete responses on paper, however. In a number of EMS studies, participants have been provided with PDA devices so that they could enter responses directly into those devices whenever prompted (for a review of methods including PDA use, see Hektner et al., 2007). Each approach appears to come with advantages and disadvantages. In reviewing electronic and paper-based methods, Broderick (2008) pointed...