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Fitness mobile apps positively affect attitudes, perceived behavioral control and physical activities

April 2018
The Journal of sports medicine and physical fitness 59(3)

DOI:10.23736/S0022-4707.18.08260-9

Authors:

Alessandro Gabbiadini

Università degli Studi di Milano-Bicocca

Tobias Greitemeyer

University of Innsbruck

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.

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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

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

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

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

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

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

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

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

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

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

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

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

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

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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RUNNING HEAD: Fitness apps, attitudes, PBC and healthy behavior

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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RUNNING HEAD: Fitness apps, attitudes, PBC and healthy behavior

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

Table 1. Means, standard deviations, and bivariate correlations.



1 Attitude T1

6.18

0.56

.65

2 Attitude T2

6.32

0.52

.73

.677**

3 Subjective norm T1

4.30

1.35

.60

-.119

-.025

4 Subjective norm T12

4.31

1.14

.42

.019

.178

.533**

5 PBC T1

4.39

1.14

.70

.174

.154

.331**

.314**

6 PBC T2

4.91

0.99

.61

-.009

.110

.305**

.337**

.508**

7 Intentions T1

5.42

1.15

.95

.066

.095

.513**

.304**

.264*

.296**

8 Intentions T2

5.67

1.21

.92

.136

.225*

.362**

.407**

.276*

.426**

.382**

9 Behavior T1

3.65

0.99

-.003

.007

.299**

.291**

.334**

.319**

.473**

.085

10 Behavior T2

3.83

1.11

.030

.149

.191

.134

.374**

.387**

.297**

.352**

Note. * p < .05 ** p < .01.