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Abstract

Background: Recent studies suggest that about 6 out of 10 users have installed a fitness tracking application on their smartphone. Nevertheless, more than 59% of adults do not engage in sufficient daily physical activity and much remains unknown with regard to the effectiveness of mobile applications. By adopting the Theory of Planned Behavior, we tested whether the use of fitness apps for daily steps tracking could positively influence people's health behavior. Methods: Participants (N = 78) were randomly assigned to one of two experimental conditions; in one condition, they were asked to adopt a fitness app for two weeks. No information regarding mobile apps was given for participants in the control condition. In order to test the effects of using a fitness app, a series of two-way mixed ANOVAs were conducted. Results: Participants in the experimental condition reported more favorable attitudes in the post- test compared to the pre-test, t(43)=4.09, p < .001, d = 0.50. By contrast, in the control condition, the difference on attitudes between pre-test and post-test was not significant (p = 1.00). They also reported higher perceived behavioral control (PBC) scores, t(43) = 4.97, p < .001, d = 0.76, whereas the difference on PBC for the control condition was not significant (p = .27). Participants who used a fitness app reported to have walked more in the post-test compared to the pre-test, t(43) = 2.41, p = .02, d = 0.87, whereas self-reported behavior did not change for participants in the control condition (p = .46). Conclusions: The present study provides encouraging evidence for the positive effects of using a fitness-tracking app in promoting health behavior.
The Journal of Sports Medicine and Physical Fitness
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EDIZIONI MINERVA MEDICA
Fitness mobile apps positively affect attitudes, perceived
behavioral control and physical activities
Alessandro GABBIADINI, Tobias GREITEMEYER
The Journal of Sports Medicine and Physical Fitness 2018 Apr 04
DOI: 10.23736/S0022-4707.18.08260-9
Article type: Original Article
© 2018 EDIZIONI MINERVA MEDICA
Article first published online: April 04, 2018
Manuscript accepted: March 9, 2018
Manuscript revised: February 13, 2018
Manuscript received: October 10, 2017
RUNNING HEAD: Fitness apps, attitudes, PBC and healthy behavior
1
Fitness mobile apps positively affect attitudes, perceived behavioral control and physical
activities
Alessandro Gabbiadini1*, Tobias Greitemeyer2
1Department of Psychology, University of Milano Bicocca, Milano, Italy; 2Department of
Psychology, University of Innsbruck, Innsbruck, Austria.
Corresponding Author
Alessandro Gabbiadini,
Department of Psychology,
University of Milano-Bicocca,
Piazza Ateneo Nuovo, 1, 20126 Milano, Italy.
E-mail: ale.gabbiadini@gmail.com
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RUNNING HEAD: Fitness apps, attitudes, PBC and healthy behavior
2
ABSTRACT
BACKGROUND. Recent studies suggest that about 6 out of 10 users have installed a fitness
tracking application on their smartphone. Nevertheless, more than 59% of adults do not engage in
sufficient daily physical activity and much remains unknown with regard to the effectiveness of
mobile applications. By adopting the Theory of Planned Behavior, we tested whether the use of
fitness apps for daily steps tracking could positively influence people’s health behavior.
METHODS. Participants (N = 78) were randomly assigned to one of two experimental conditions;
in one condition, they were asked to adopt a fitness app for two weeks. No information regarding
mobile apps was given for participants in the control condition. In order to test the effects of using a
fitness app, a series of two-way mixed ANOVAs were conducted.
RESULTS. Participants in the experimental condition reported more favorable attitudes in the post-
test compared to the pre-test, t(43)=4.09, p < .001, d = 0.50. By contrast, in the control condition,
the difference on attitudes between pre-test and post-test was not significant (p = 1.00). They also
reported higher perceived behavioral control (PBC) scores, t(43) = 4.97, p < .001, d = 0.76,
whereas the difference on PBC for the control condition was not significant (p = .27). Participants
who used a fitness app reported to have walked more in the post-test compared to the pre-test, t(43)
= 2.41, p = .02, d = 0.87, whereas self-reported behavior did not change for participants in the
control condition (p = .46).
CONCLUSIONS. The present study provides encouraging evidence for the positive effects of
using a fitness-tracking app in promoting health behavior.
Key words: Fitness mobile apps, attitudes toward physical activities, Theory of Planned behavior
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RUNNING HEAD: Fitness apps, attitudes, PBC and healthy behavior
3
Introduction
Mobile devices have revolutionized the way people communicate. In recent years, we have seen a
steady growth in mobile device development and wearable connected devices. During the last three
years, the number of mobile apps for health monitoring has reached more than 100.000 units
available in the virtual stores 1. One recent study 2 reported that 58% of all smartphone users have
downloaded a health-related app and Google’s data indicate that the global healthcare-fitness
mobile app market will be worth about $26 billion by 2017.
These applications represent an expression of the so-called “Quantified Self”, that is, the
ability to self-monitor aspects of a person's daily life, also known as lifelogging 3. However, despite
their high popularity, there is little research about their effectiveness 2 4 5. A study on the effect of
mobile technology for promoting healthy behaviors was conducted by Fjeldsoe and colleagues 6
who developed an SMS-based intervention to increase physical activity in post-natal mothers. In
another recent study 7 Kirwan and colleagues measured the effectiveness of a smartphone
application (i.e., iStepLog) to improve health behaviors in existing members of an online physical
activity program (i.e., 10.000 Steps online program). The authors addressed the effect of a mobile
logging app on self-monitoring and examined the relationship between the perceived usefulness and
usability of the application and its actual use. However, both studies investigated mobile
technologies that are far distant from the features offered by modern fitness apps. Indeed, modern
smartphones make use of sensors to detect body movements and this peculiarity has allowed the
development of mobile apps targeting health improvement whose goal is to promote physical well-
being.
In the present research, we address the effectiveness of modern mobile fitness apps in
actually changing people’s attitudes toward physical activities to pursue health improvement.
Attitudes are woven into the fabric of daily life and they are defined as a psychological tendency
that is expressed by evaluating a particular entity with some degree of favor or disfavor 8. People
register an immediate and automatic reaction towards everything they encounter but attitudes can be
changed by imparting new information 8. In this regard, media messages are built on the premise
that behavior follows attitude, and attitude can be influenced with the right message delivered in the
right way 9.
Many theoretical models attempt to explain the relationship between attitudes and specific
behaviors. Perhaps the most important is the Theory of Planned Behavior (TPB) 10, which has been
shown to predict health-related behavioral intention 11 in various health-related fields 12. According
to this theoretical model, intentions are the proximal determinant of behavior, with intentions being
influenced by three factors: attitudes, subjective norm, and perceived behavioral control. The
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RUNNING HEAD: Fitness apps, attitudes, PBC and healthy behavior
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attitude component refers to the individual’s attitude toward the behavior—whether the person
thinks that performing the behavior is good or bad. Subjective norm refers to people’s beliefs about
how other people who are important to them view the relevant behavior. People’s intention is also
influenced by whether they feel they can perform the behavior. This is captured by the concept of
perceived behavioral control (PBC). Finally, PBC is proposed to also have a direct impact on
behavior. Following the TPB theoretical framework, individuals will be successful in performing a
specific behavior if they have sufficient control over internal and external factors that influence the
behavioral goal success 13. Recently, Zhao and colleagues 14 conducted a comprehensive meta-
analysis of 23 studies on health behavior change using mobile phone apps published between 2010
and 2015 and the TPB has been found to be the most adopted theory.
Adopting a fitness app for fitness (i.e., a mobile app for monitoring the number of daily
steps) could provide individuals with more information about their goals, fostering a more positive
attitude toward physical activity. Previous research has shown that making people self-aware
promotes consistency between attitudes and behavior 15 16. Indeed, having a positive attitude toward
exercise represents one relevant predictor of the adoption of an active lifestyle 17 18.
Attitudes have been already found to predict the use of mobile apps in several domains (e.g.,
e-business research) 19. However, most of these studies focus on attitudes as a predictor of the
adoption of a mobile technology, whereas the effects of using mobile apps on attitude change are
still unexplored.
Our study represents an attempt to investigate the effects of mobile apps in the specific
domain of healthy behaviors. We assumed that making people self-conscious by adopting a
smartphone app for tracking physical activities could enhance their positive attitude toward physical
activities and their life-style. Under the assumptions of the TPB, in a single-blind experiment, we
tested whether the daily use of fitness mobile applications for daily steps tracking could positively
affect attitudes toward physical activities as well as subjective norm, PBC, intention, and behavior.
In our study, participants were randomly assigned to be asked to use a fitness app (experimental
condition) or were not asked to do so (control condition). Because of this random assignment, the
impact of confounding variables should be eliminated because one can be relatively certain that
participants in the experimental condition were no different from participants in the control
condition. Nevertheless, to ensure that the two experimental conditions were indeed comparable
before the experimental manipulation, we employed a pre-test-post-test design. Whereas no
differences across experimental conditions were expected at the pre-test, participants in the
experimental condition should differ from the participants in the control condition at the post-test.
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RUNNING HEAD: Fitness apps, attitudes, PBC and healthy behavior
5
Materials and methods
Procedure
Students from the University of Milan were contacted during class and took part as volunteers in
our study. Data collection was carried out during one semester, with the aim of running as many
participants as possible. In the first wave (T1), data collection was carried out through a written
questionnaire. At the beginning of the questionnaire, after providing written informed consent,
participants were asked to report the number of steps that they considered as a goal to achieve for
their physical well-being. We then measured demographics as well as the antecedents of the actual
behavioral goal postulated in the TPB: attitudes, subjective norm, PBC, and intentions toward
healthy activities. Participants were then randomly assigned to two experimental conditions; in one
condition, they were asked to install a fitness app on their own smartphone and to use it for the
following two weeks. As a cover story, they were told that we were interested in the evaluation of
the user interface usability of some mobile apps. We used four different apps (Pedometer or Google
Fit for Android users and Stepz or Pacer for iOS users) to increase the generalizability of findings
20. We carefully selected apps with similar functionalities. All the adopted apps count daily steps,
burned calories, and active time, alerting users with notifications about daily achievements directly
on the smartphone. Moreover, at the time of data collection, the selected apps were free without any
restrictions on monitoring features.
No information regarding mobile apps was given for participants in the control condition. Two
weeks after the first wave, partcipants who had voluntarily left a valid e-mail address at the end of
the T1 questionnaire were provided with a link to a web survey. In the follow-up phase (T2), we
reassessed measures for attitudes, subjective norm, PBC, and intentions toward healthy activities.
Finally, at the end of the survey all participants were fully debriefed by disclosing the purpose of
the study.
Sample characteristics.
The study has been carried out in accordance with the code of ethics of the world medical
association (Declaration of Helsinki) for experiments involving humans. A total of 78 participants
completed both the first and the second part of the survey; 9 were male (11.5%) and 69 were female
(88.5%). Their mean age was 19.94 years (SD = 1.36).
Measures.
All measures were adapted to the behavioral domain of healthy activities (e.g., adopting
healthy behaviors, such as having a walk). Descriptive statistics, intercorrelations, and scale
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RUNNING HEAD: Fitness apps, attitudes, PBC and healthy behavior
6
reliabilities of all measures are shown in Table 1. It is worth noticing that some scale reliabilities
were relatively poor, which is a typical psychometric cost of using short measures (Gosling,
Rentfrow, & Swann, 2003).
Attitudes toward healthy and fitness activities were evaluated using an adapted version of
the Attitude Regarding Physical Activities for Health and Fitness scale21 (6 items). Sample items
were “To promote better health conditions, people may take part in healthy activities, such as
having a walk every day” and “Physical activities, healthy activities, such as having a walk every
day, are one of the source for fitness (on a scale from 1 = totally disagree to 7 = totally agree).
Subjective norm, PBC, and intentions were measured by adapting the original items proposed by
Perugini and Bagozzi 22. Subjective norm was assessed employing the following two items: “Most
people who are important in my life think that I should not (1) / I should (7) adopt healthy
behaviors, such as having a walk of more than the number of steps I have declared as my personal
goal” and “Most people who are important to me would disapprove (1) / approve (7) of me
adopting healthy behaviors, such as having a walk of more of more than the number of steps I have
declared as my personal goal”. The two items for PBC were “How much control do you think you
have over adopting healthy behaviors, such as having a walk of more than the number of steps you
have declared as your personal goal?” (1= no control, to 7 = total control, midpoint 4 = moderate
control) and “How easy or difficult do you think it is for you to adopt healthy behaviors, such as
having a walk of more than the number of steps you have declared as your personal goal?” (1 = very
difficult, 7 = very easy). Intentions were measured with these two items: “I intend to adopt healthy
behaviors, such as having a walk of more than the number of steps I have declared as my personal
goal in the near future” and “My intention is to adopt healthy behaviors, such as having a walk of
more than the number of steps I have declared as my personal goal in the near future” (on a scale
from 1 = totally disagree to 7 = totally agree).
One single item was employed to estimate participants’ behavior. The item was: “How
many times you think you had a walk of more than the number of steps you have declared as your
personal goal, during the last two weeks?” (anchored with 1 = less than once in two weeks, 2 = once
in two weeks, 3 = twice in two weeks, 4 = twice a week , 5 = every day).
We also assessed participant’s previous sport habits by asking them to name three preferred
physical activities and to rate how frequently they practice them (“Now please write the name of
your favorite physical activity and how often do you practice it” anchored with 1 = never, 2 = once
per week, 3 = twice per week, 4 = more than twice per week, 5 = daily). The three items were then
combined ( = .56). Participants were also asked to report the frequency of their sport behavior
during the two weeks of the study (“How frequently in the past two weeks have you engaged in
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RUNNING HEAD: Fitness apps, attitudes, PBC and healthy behavior
7
physical and sport activities?” anchored with 1 = never, 2 = once per week, 3 = twice per week, 4 =
more than twice per week, 5 = daily).
Furthermore, participants’ previous use of apps for tracking physical activities was assessed
by asking how frequently they use several famous app for tracking physical activity (i.e., “Please
now indicate how often do you use the following applications: Runtastic, Seven, MapMyWalk,
MapMyRun, Google Fit, Pedometer, Apple Health, Runkeeper, Runtrainer, Nike tracking app. If
you do not use this mobile app, please choose never” on a scale from 1 = never to 7 = very often”).
Participants were also asked to name their favorite fitness app and to rate how frequently they use it
(“Now please write the name of your favorite application for training / fitness monitoring and
indicate how often you use it. If you do not use any mobile app for training / fitness tracking, please
choose never” on a scale from 1 = never to 7 = very often). The scores reported for each application
were then combined to obtain a measure of previous use of mobile applications for tracking
physical activities ( = .90).
Finally, to check if participants in the experimental condition actually used the suggested
apps, we asked them to report how frequently they accessed Pacer / Pedometer / Google Fit / Stepz
during the two weeks of data collection. The scale was anchored with 1 = never, 2 = rarely, 3 =
occasionally, 4 = sometimes, 5 = frequently, 6= often, 7 = very often. Furthermore, in the final part
of the questionnaire we asked them to report the number of steps recorded by the adopted app on
their smartphone for each day of the two-weeks study period note 1.
Results
All statistical analyses were performed using SPSS v.24.0. We first checked whether
participants in the experimental condition actually used one of the suggested app. All the
participants reported the number of steps walked on each day of the two weeks of data collection.
The most frequently used app was Google Fit (M = 3.61, SD = 2.78), followed by Pacer (M = 2.50,
SD = 2.40), Stepz (M = 2.16, SD = 2.340, and Pedometer (M = 1.55, SD = 1.74).
In order to test the effects of using a fitness app on the dependent variables, a series of two-
way mixed ANOVAs were conducted. App usage (using vs. non-using a fitness app) was
considered as a between-groups variable and time of test (pre-test vs. post-test) as a within-
participants variable. Participant’s previous sport habits and previous use of fitness apps were
entered as covariates in all analyses. Analyses revealed no significant main effect, neither for
experimental condition, F(1, 73) = 1.81, p = .18,
2= .02, nor for time of test, F(1, 73) = 0.09, p =
.77,
2=.00, on attitudes towards fitness activities. However, the hypothesized interaction was
significant, F(1, 73) = 5.54, p = .02,
2 = .07 (Figure 1). No significant differences in attitudes
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RUNNING HEAD: Fitness apps, attitudes, PBC and healthy behavior
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towards physical activity were found between the app condition (M = 6.19, SD = 0.54) and the
control condition (M = 6.17, SD = 0.59) in the pre-test, t(76) = 0.13, p = .89, d = 0.03. In contrast,
in the post-test, participants who used a fitness app reported more favorable attitudes in the post-test
(M = 6.45, SD = 0.37) than did participants in the control condition (M = 6.17, SD = 0.64), t(76) =
2.23, p = .028, d = 0.51. None of the considered covariates was significant (all ps > .17). To put it
differently, participants who used a fitness app reported more favorable attitudes in the post-test
compared to the pre-test, t(43) = 4.09, p < .001, d = 0.50 even when controlling for participant’s
previous sport habits, previous use of fitness apps and the frequency of sport behavior during the
two weeks of the study. By contrast, in the control condition, the difference on attitudes towards
fitness activities between pre-test and post-test was not significant, t(33) = 0.00, p = 1.00, d = 0.00.
Taken together, these results suggest that mobile fitness apps promote a more positive attitude
toward healthy activities, such as having a daily walk.
-- Insert figure 1 here
A similar pattern of results was found for PBC. No significant main effects, neither for
experimental condition, F(1, 73) = 3.84, p = .06,
2 = .05, nor for time of test, F(1, 73) = 0.09, p =
.75,
2= .00, were found. Most important, the interaction between app usage and time of test was
significant, F(1, 73) = 4.56, p = .036,
2= .06 (Figure 2). No significant differences were found
between the app condition (M = 4.48, SD = 0.96) and the control condition (M = 4.27, SD = 1.36)
in the pre-test, t(76) = 0.79, p = .42, d = 0.18. In contrast, in the post-test, participants who used a
fitness app reported higher PBC scores (M = 5.25, SD = 1.07) than did participants in the control
condition (M = 4.48, SD = 0.71), t(76) = 3.62, p = .001, d = 0.83. To put it differently, participants
who used a fitness app reported higher PBC scores in the post-test compared to the pre-test, t(43) =
4.97, p < .001, d = 0.76. By contrast, in the control condition, the difference on PBC between the
pre-test and the post-test, was not significant, t(33) = 1.11, p = .27, d = 0.16.
-- Insert figure 2 here --
Significant differences for self-reported behavior were also found. There was a significant
main effect of experimental condition, F(1, 73) = 5.14, p = .02,
2 = .06, whereas the effect for time
of test was not significant F(1, 73) = 0.19, p = .66,
2= .00. However, the significant main effect
was qualified by a significant interaction between app usage and time of test, F(1, 73) = 6.91,
p=.002,
2= .12 (Figure 3). No significant differences were found between the app condition (M =
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RUNNING HEAD: Fitness apps, attitudes, PBC and healthy behavior
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3.73, SD = 0.94) and the control condition (M = 3.56, SD = 1.05) in the pre-test, t(76) = 0.74, p =
.46, d = 0.17. In contrast, in the post-test, participants who used a fitness app reported to have
walked more (M = 4.18, SD = 1.02) than did participants in the control condition (M = 3.38, SD =
1.07), t(76)=3.36, p =.001, d = 0.76. In other words, participants who used a fitness app reported to
have walked more in the post-test compared to the pre-test, t(43) = 2.41, p = .02, d = 0.87, whereas
self-reported behavior did not change for participants in the control condition, t(33) = 0.74, p = .46,
d = 0.17.
-- Insert figure 3 here --
In contrast, no significant interactions were found for subjective norm and time of test (p =
.22) and for intentions and time of test (p = .27).
Please recall that the TPB proposes that attitudes, norms, and PBC determine intentions,
whereas intentions and PBC determine behavior. Hence, we examined whether intentions are
influenced by attitudes, norms, and PBC and whether self-reported behavior is influenced by
intentions and PBC. To this end, multiple regressions were performed (separately for pre-test and
post-test). In the first regression, attitudes, norms, and PBC were used as predictors for intentions.
For both time-points, the overall regressions were significant, F(3, 73) = 9.62, R2 = .28, p < .001;
F(3, 74) = 9.53, R2 = .28, p < .001, respectively. At both pre-test and post-test, norms were
significantly associated with intentions, β=.49, p < .001, β = .28, p = .012, respectively. At the post-
test, PBC was also significantly associated with intentions, β = .32, p = .003. The other predictors
were not significant.
In the second regression, intentions and PBC were used as predictors for self-reported
behavior. For both time-points, the overall regressions were significant, F(2, 74) = 14.02, R2 = .28,
p < .001; F(2, 75) = 8.95, R2 = .19, p < .001, respectively. At the pre-test, both intentions, β = .41, p
< .001, and PBC, β = .24, p = .025, were significantly associated with self-reported behavior. At the
post-test, PBC was significantly associated with self-reported behavior, β = .29, p = .014, whereas
the impact of intentions was marginally significant, β = .23, p = .050.
Discussion
In 2014, almost 46 millions of people actively used apps from the fitness and health
category 1 and this number is even increasing, thanks to the diffusion of fitness band and smart
watches. Given the large popularity of fitness apps, we deemed it important to investigate whether
the use of mobile apps for monitoring physical activities could positively influence people’s health
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RUNNING HEAD: Fitness apps, attitudes, PBC and healthy behavior
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behaviors as well as its antecedents. Most importantly, fitness app use did have an impact on
participant’s self-reported health behavior. Participants who used a fitness app reported to be more
likely to have a walk than did participants in the control condition and the use of the fitness app
increased the reported likelihood that they had a walk (whereas participants’ reported behavior in
the control condition did not change).
With regard to possible antecedents, and based on the TPB theoretical framework, we
examined whether the use of a pedometer mobile app could have positively affected attitudes,
subjective norm, PBC, and intentions in two weeks of use. According to TPB, attitudes, subjective
norm, PBC, and intentions explain the way in which goals and plans guide behavior, and the factors
that induce people to change their behavior when it is conceived as a behavior-goal unit 10. Our
study corroborated the two main postulates of TPB, namely that intentions are influenced by
attitudes, norms, and PBC, whereas intentions and PBC are the most direct predictors of behavior.
At both pre-test and post-test did these predictors explain considerable variance of the criteria, with
norms being the most robust predictor of intentions and intentions and PBC explaining self-reported
behavior.
It is worth noting that the adoption of a mobile app for a period of two weeks positively
affected some, but not all, antecedents. It did change participant’s attitudes. Self-perception theory
suggests that behavior may change a person's perception of the self and may alter attitudes
specifically related to a given behavior 24 25. Indeed, people come to know their own attitudes,
beliefs, and other internal states by inferring them from their own behavior. We assume that the
daily use of a mobile app for tracking physical activities could have made participants more aware
of their performances by monitoring their actual behavior, which in turn has led to more positive
attitudes toward healthy activities.
The use of the app also positively affected PBC. This is not surprising, since the main
feature of this kind of mobile apps is the setting of a daily goal, allowing people to control for their
own progresses toward that goal. We speculate that the adoption of a mobile app may have offered
users a strong instrument for controlling their behavior, their progresses, achievements or failures
enhancing the perceptions of their ability to perform better every day.
In contrast, no significant effects of app usage on intentions were found. When considering
health behaviors, before they change their intentions people need to be motivated to do so 26. It is
important to note that our sample consisted of university students who did not spontaneously decide
to use a fitness app (i.e., because they were motivated to walk more); rather, they were asked to do
it. The mere use of a mobile app without any previous motivation to adopt a healthy life-style, may
not be sufficient to influence volitional intentions. For this reason, we speculate that individual
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RUNNING HEAD: Fitness apps, attitudes, PBC and healthy behavior
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motivations could play a moderating role on the relationship between the use of a mobile app and
intentions and future studies should explore this possibility.
Some limitations have to be acknowledged. First, we employed a self-reported measure for
participants behavior. Second, even if correlational analyses revealed a significant association
between attitudes, subjective norm, PBC and intentions at T2, our sample was too small to test a
structural model; thus, we tested the effect of the apps on these constructs separately. By
considering such limitations, future research should consider a larger sample size for a longer
period of time with multiple observations for determining the cause-effect relationships among
constructs and the long-term effects of using a fitness app on attitude and behavior. Third, our
results may be due in part to demand characteristics of the situation. Although we employed a cover
story and there was no indication that participants questioned it, future studies should consider this
aspect. Fourth, it is noteworthy that we controlled for participants’ previous use of apps, but we
only focused on pedometer-like apps. Future studies should measure the actual behavior and should
also consider apps designed for different fitness activities. Moreover, we did not check whether
participants had turned off the notifications pushed by the apps. Indeed, as proposed by previous
research, notifications can be used for sustaining motivation over time 27. Although we controlled
for previous use of fitness apps, we did not ask participants whether they used smartwatches or
other wearable devices to keep track of daily physical activity. Indeed, we speculate that the
adoption of wearable devices, which are easier to use, could be more effective than accessing an
app on the smartphone in fostering motivation toward a more active life-style. In this regard, more
research is needed to explore whether the combined use of apps and wearable devices have stronger
effects on attitude-behavior change. Indeed, the constant presence of a bracelet may further
influence the PBC.
Conclusions
Despite its well-known benefits, more than 59% of adults do not engage in sufficient
physical activity 28. Therefore, the present study provides encouraging evidence of the positive
effects of using a mobile app on people’s life-style. Psychological research on the impact of fitness
app it still is in its infancy and constantly chases technological developments. It is important that
social psychology, health psychology, and computer science work together for developing more
effective technological instruments to promote people’s physical well-being. Our findings suggest
that fitness apps could be such an instrument.
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Notes
1. Although we asked participants in the experimental condition to report the actual number of
daily steps recorded by the apps for each day of the two-week study, we did not have a
comparable measure for the participants in the control condition. For this reason, analyses
were conducted considering the self-reported item.
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the electronic copy of the article through online internet and/or intranet file sharing syste ms, electronic mailing or any other means which may allow acce ss to th e Article. The use of a ll or any
part of t he Article for any Commercial Use is not p ermitted. The cre ation of derivative works from th e Article is no t permitted. The production of reprints for personal or commercial use is not
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Authors’ contributions. Alessandro Gabbiadini and Tobias Greitemeyer conceived of the
presented idea. Alessandro Gabbiadini carried out the experiment and wrote the manuscript with
support from Tobias Greitemeyer. Both authors developed the theory, performed the computations
and verified the analytical methods. All authors discussed the results and contributed to the final
manuscript.
Funding. This research did not receive any specific grant from funding agencies in the public,
commercial, or not-for-profit sectors.
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TITLES OF TABLES
Table I. Means, standard deviations, and bivariate correlations.
TITLES OF FIGURES
Figure 1. Attitudes change between Time 1 and Time 2 across the two experimental conditions. The
capped vertical bars denote 1 standard error (SE).
Figure 2. PBC change between Time 1 and Time 2 across the two experimental conditions. The
capped vertical bars denote 1 standard error (SE).
Figure 3. Self-reported behavior between Time 1 and Time 2 across the two experimental
conditions. The capped vertical bars denote 1 standard error (SE).
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RUNNING HEAD: Fitness apps, attitudes, PBC and healthy behavior
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Table 1. Means, standard deviations, and bivariate correlations.
M
SD
2
4
5
7
8
1 Attitude T1
6.18
0.56
.65
2 Attitude T2
6.32
0.52
.73
3 Subjective norm T1
4.30
1.35
.60
-.025
4 Subjective norm T12
4.31
1.14
.42
.178
.533**
5 PBC T1
4.39
1.14
.70
.154
.331**
.314**
6 PBC T2
4.91
0.99
.61
.110
.305**
.337**
7 Intentions T1
5.42
1.15
.95
.095
.513**
.304**
.296**
8 Intentions T2
5.67
1.21
.92
.225*
.362**
.407**
.426**
.382**
9 Behavior T1
3.65
0.99
-
.007
.299**
.291**
.319**
.473**
10 Behavior T2
3.83
1.11
-
.149
.191
.134
.387**
.297**
Note. * p < .05 ** p < .01.
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... The advent of smartphone and wearable technologies has provided a platform for innovative approaches that motivate activity [19,40,[43][44][45]. A specific type of smartphone application aims to increase physical activity through extrinsic incentives and rewards [22]. ...
... Such findings are consistent with research showing that financial incentives may encourage physical activity [2,17,30]. However, while apps and wearables can improve levels of physical activity [19,40,45], existing evidence shows that changes are usually not sustained in the long run (i.e. no longer than 3 months) [43]. ...
... Our results suggest that the use of the 'rewards-for-exercise' application Sweatcoin is associated with a shortterm increase in self-reported physical activity, life satisfaction, positive affect, and sleep quality while it was not associated with changes in device-measured physical activity. The findings are in line with previous studies showing that self-monitoring and rewarding applications contribute to increased exercise levels [19,40,45], SWB [3,6,8,18,31,39,42], and sleep quality [26]. Several mechanistic pathways have been suggested by which physical activity may improve sleep quality which involve changes in circadian regulation, increased build-up of sleep pressure, and decrease in anxiety symptoms related to improved ability to relax [26,47]. ...
Article
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Background This study examined the impact of a ‘rewards-for-exercise’ mobile application on physical activity, subjective well-being and sleep quality among 148 employees in a UK university with low to moderate physical activity levels. Methods A three-month open-label single-arm trial with a one-year follow-up after the end of the trial. Participants used the Sweatcoin application which converted their outdoor steps into a virtual currency used for the purchase of products available at the university campus’ outlets, using an in-app marketplace. The primary outcome measure was self-reported physical activity. Secondary measures included device-measured physical activity, subjective well-being (i.e., life satisfaction, positive affect, negative affect), and self-reported sleep quality. Results The findings show an increase in self-reported physical activity (d = 0.34), life satisfaction (d = 0.31), positive affect (d = 0.29), and sleep quality (d = 0.22) during the three-month trial period. Conclusion The study suggests that mobile incentives-for-exercise applications might increase physical activity levels, positive affect, and sleep quality, at least in the short term. The observed changes were not sustained 12 months after the end of the trial.
... With many individuals experiencing a drop in physical activity levels due to the ongoing COVID-19 pandemic [12], fitness apps have gained more popularity than ever before. A growing body of evidence suggests that these interventions might be more effective in helping users reach their weight loss goals when compared with traditional forms of counseling [13]. Self-monitoring, although a relatively recent development, has been increasingly integrated into behavioral weight loss apps [14]. ...
... This not only leads to a financial burden of the state but also increases the stress on the health system. Since self-monitoring health applicationslike Waya are becoming increasingly more popular than traditional forms of counseling [13], it is important to understand whether these types of interventions can Weight loss and BMI reduction among Waya participants. (a) The bar graph above shows differences in median weight during module 1(baseline) and module 10 (n = 123; ****P < 0.0001). ...
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The increasing prevalence of lifestyle-driven metabolic disorders poses a heavy burden on the healthcare system. Several low-cost, easily accessible, and effective weight loss interventions are being developed to improve this situation. Waya is one such German digital application that guides users to reach their desired weight in a healthy manner, by monitoring their eating habits and physical activity levels. In this retrospective real-world observational pilot study, we aimed to identify if the use of Waya helps in reducing weight as intended and the underlying factors associated with it. Methods: Data from healthy overweight or obese participants who provided their weight information and answered the short form of the Weight Efficacy Lifestyle Questionnaire and the International Physical Activity Questionnaire activity questionnaires once before the completion of the first module (baseline) were compared with data provided after the beginning of the last module. Age and sex-based distribution were studied and the correlation between nutrition, physical activity, and weight was analyzed. Results: Waya participants showed an improvement in nutritional behavior, physical activity levels, and weight reduction compared with baseline. These changes were independent of age and sex. Weight loss mainly correlated with improvements in nutritional behavior but not physical activity. Conclusion: The results from our pilot study showed that Waya is beneficial in bringing about short-term weight loss mainly through behavioral changes in nutrition. Although physical activity levels improved, its influence on weight loss was not apparent.
... The same study showed that exercise and exercise using eHealth are not influenced by peer influence (subjective norm). Intention to exercise using eHealth had low advocacy (behavioral intention), and those who used the eHealth were more likely to have high attitude (Herrmann and Kim, 2017;Gabbiadini and Greitemeyer, 2019) and behavioral belief advocacy about the physical activity in eHealth (Hoj et al., 2017). In terms of physical activity behaviors, positive attitudes are associated with high daily walk time (Füssl et al., 2019;Gabbiadini and Greitemeyer, 2019), and positive intentions are more likely to predict physical activity levels than unfavorable intentions (Chatzisarantis et al., 2019). ...
... Intention to exercise using eHealth had low advocacy (behavioral intention), and those who used the eHealth were more likely to have high attitude (Herrmann and Kim, 2017;Gabbiadini and Greitemeyer, 2019) and behavioral belief advocacy about the physical activity in eHealth (Hoj et al., 2017). In terms of physical activity behaviors, positive attitudes are associated with high daily walk time (Füssl et al., 2019;Gabbiadini and Greitemeyer, 2019), and positive intentions are more likely to predict physical activity levels than unfavorable intentions (Chatzisarantis et al., 2019). ...
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Objectives The aims of this research were (1) to compare the levels of physical activity of eHealth users and non-users, (2) to determine the effects of these technologies on motivations, and (3) to establish the relationship that could exist between psychological constructs and physical activity behaviors.Methods This cross-sectional study involved 569 adults who responded to an online questionnaire during confinement in France. The questions assessed demographics, usage of eHealth for exercise and physical activity, and behavioral levels. The questionnaire also measured the constructs of Social Cognitive Theory, the Theory of Planned Behavior, and automaticity facets toward eHealth for exercise and physical activity.ResultsParticipants who were users of eHealth for exercise and physical activity presented significantly higher levels of vigorous physical activity and total physical activity per week than non-users (p < 0.001). The chi-square test showed significant interactions between psychological constructs toward eHealth (i.e., self-efficacy, behavioral attitudes, intentions, and automaticity) and physical activity levels (all interactions were p < 0.05). Self-efficacy was significantly and negatively correlated with walking time per week. Concerning the automaticity facets, efficiency was positive and significantly correlated with vigorous physical activity levels per week (p < 0.05). Then, regressions analyses showed that self-efficacy and automaticity efficiency explained 5% of the variance of walking minutes per week (ß = −0.27, p < 0.01) and vigorous physical activity per week (ß = 0.20, p < 0.05), respectively.Conclusion This study has shown that people during confinement looked for ways to stay active through eHealth. However, we must put any technological solution into perspective. The eHealth offers possibilities to stay active, however its benefits and the psychological mechanisms affected by it remains to be demonstrated: eHealth could be adapted to each person and context.
... Compared to information in printed formats, information in interactive formats, for example, smartphone applications (apps), often is more effective in increasing knowledge and promoting behavior change. [5][6][7] We therefore developed a radon-education app for smartphones. 8 Here, we compared the performance of the radon app on individuals' radon knowledge, attitudes, and testing behaviors in a randomized controlled trial vs. the same information delivered via print brochures. ...
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Radon is a preventable cause of lung cancer, but the percentage of homes tested for radon is low. We previously developed a smartphone app that informs users about radon and allows them to request a free radon test. Here we conducted a randomized, controlled trial comparing the radon app versus printed brochures on radon knowledge, attitudes, and behaviors, including the proportion of participants requesting radon tests. Participants (N = 138) were undergraduates at a midwestern university. Data were analyzed by t‐tests, general linear models, and logistic regression. App users showed significantly greater increases in radon knowledge (p = 0.010) and self‐efficacy (p < 0.001) and requested tests three times more often than brochure recipients (41.4% vs. 13.2%, p < 0.001). However, the rate of test usage in each condition was low, ~3%. In conclusion, the radon app markedly outperformed brochures in increasing knowledge and requests for radon tests. Future work should focus on methods to increase test usage. We developed an app for smartphones to encourage radon testing of homes and compared it to print brochures in its ability to stimulate radon test requests.The radon app was markedly superior to brochures and increased test requests 3‐fold.
... The increase in participants' exercise overall, combined with the increased use of online workouts and fitness apps, supports research suggesting technological applications might help to stimulate exercise (Middelweerd et al., 2014) and that the use of fitness apps positively affects individual's attitudes and physical activity engagement (Gabbiadini & Greitemeyer, 2019). Apps provide opportunities to deliver personalised materials to promote physical activity (Krebs et al., 2010), potentially replacing the position of 'coach' whilst in lockdown. ...
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... While digital tools such as messenger services and process optimization were once primarily used in private life, today's use and acceptance of digital instruments can be increasingly observed in medicine and the everyday working life [10][11][12]. ...
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Background Changes in demographics and dynamics of our society are affecting the healthcare system, leading to an intensified “war for talents,” especially for surgical departments. Also with regard to the current COVID-19 pandemic, the present work analyzes the potential of digitalization for human resource management of surgical departments in hospitals. Methods PubMed and Google Scholar were searched to identify articles referring to the specific subject of human resource management and its digital support in hospitals and surgical departments in particular. Results The main topics include the digital affinity of young physicians and surgeons in terms of staff recruiting, digital support for everyday working life in surgical departments, and the potential of digital approaches for surgical training. These topics are put into the context of company strategies, and their future potential is identified accordingly. Conclusion Digital programs, digital structures, and digital tools can today be used by human resources departments to advertise the hospital and to make the recruitment of future candidates increasingly attractive. In addition, by making digital tools available, the employees’ satisfaction can be raised with the potential of a strong employer branding. In times of the COVID-19 pandemic, digital personnel strategies and training formats have to be regarded a contemporary offering.
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The fight against sexism is nowadays one of the flagship social movements in western countries. Adolescence is a crucial period, and some empirical studies have focused on the socialization of teenagers, proving that the socialization with the surrounding environment prevent sexist practices. In a previous work, we developed and tested the effectiveness of a mobile app, called Liad@s , with the goals of helping teenagers to prevent sexism and build healthy couple relationships. In this article, we carry out a study where (using a real situation) we compare the effectiveness of the Liad@s app in front of traditional interventions like a workshop about sexism for teenagers. Also, we evaluate the usability of the app and the user satisfaction with this application. In this study, our primary hypothesis is that the effectiveness of using our mobile application, in terms of knowledge acquired about sexism, would be at least as good as attending the workshop. Our secondary hypothesis is that the user satisfaction with the mobile application would be higher than the one with the workshop, causing a preference for the app. The results of this study show significant differences in learning appeared between gender and between the two different procedures when separately evaluating the data collected from both hostile sexism (HS) and benevolent sexism (BS) questionnaires. These results validate our primary hypothesis. Also, most of the population under study preferred the mobile app in front of the traditional workshop, validating also our secondary hypothesis.
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This study aimed to develop and test a model to explain factors that affect continuous behavior while using fitness apps via expectation-confirmation theory (ECT), Technology Acceptance Model (TAM), and Post-Acceptance Model of Information Systems Continuance (PAM-ISC). Participants completed an online survey. A structural equation model was used to test the proposed model composed of the main factors from ECT, TAM, and PAM-ISC. Achievement motivation was the most important motivation in fitness app use; social, exercise, economic, and interest motivations were also significant factors, while self-development and emotional motivations did not influence initial experience behavior. Five key factors regarding continuous behavior were also identified: expectation confirmation, satisfaction, perceived ease of use, perceived usefulness, and trust. As the frequency of fitness app use are expected to increase, this model provides theoretical and practical guidance for app designers and marketers
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Abstract: Wearable fitness trackers have gained a new level of popularity due to their ambient data gathering and analysis. This has signalled a trend toward self-efficacy and increased motivation among users of these devices. For consumers looking to improve their health, fitness trackers offer a way to more readily gain motivation via the personal data-based insights the devices offer. However, the user experience (UX) that accompanies wearables is critical to helping users interpret, understand, gain motivation and act on their data. Despite this, there is little evidence as to specific aspects of fitness tracker user engagement and long-term motivation. We report on a 4-week situated diary study and Healthcare Technology Self-efficacy (HTSE) questionnaire assessment of 34 users of two popular American fitness trackers: JawBone and FitBit. The study results illustrate design implications and requirements for fitness trackers and other self-efficacy mobile healthcare applications.
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Research dealing with various aspects of* the theory of planned behavior (Ajzen, 1985, 1987) is reviewed, and some unresolved issues are discussed. In broad terms, the theory is found to be well supported by empirical evidence. Intentions to perform behaviors of different kinds can be predicted with high accuracy from attitudes toward the behavior, subjective norms, and perceived behavioral control; and these intentions, together with perceptions of behavioral control, account for considerable variance in actual behavior. Attitudes, subjective norms, and perceived behavioral control are shown to be related to appropriate sets of salient behavioral, normative, and control beliefs about the behavior, but the exact nature of these relations is still uncertain. Expectancy— value formulations are found to be only partly successful in dealing with these relations. Optimal rescaling of expectancy and value measures is offered as a means of dealing with measurement limitations. Finally, inclusion of past behavior in the prediction equation is shown to provide a means of testing the theory*s sufficiency, another issue that remains unresolved. The limited available evidence concerning this question shows that the theory is predicting behavior quite well in comparison to the ceiling imposed by behavioral reliability.
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Reports 3 studies which tested the hypothesis, derived from self-awareness theory, that behavior would be more consistent with personal attitudes or standards when attention was self-focused. In the 1st study, 52 male undergraduates' attitudes toward erotica were measured, and 1 mo later the Ss were asked to rate pictures of nude women, while either self-focused (in front of a mirror) or not. There was little relationship between pretested attitudes and reactions toward the pictures for the non-self-focused group; however, the same relationship was very strong for the group that rated pictures in front of a mirror. In the 2nd and 3rd studies, female Ss (51 and 48 undergraduates, respectively) were first pretested on the Mosher Sex-Guilt Scale. Two weeks later they read and rated pornographic passages, again, while either self-focused or not. The relationship between pretested standards (sex guilt) and reactions to sexual literature was weak in the non-self-aware condition, but considerably stronger for the self-focused Ss. Results suggest that focusing attention upon the self tends to inhibit behaviors that are inconsistent with personal attitudes or standards. (35 ref) (PsycINFO Database Record (c) 2006 APA, all rights reserved).