RESEARCH ARTICLE
Open Access
App use, physical activity and healthy
lifestyle: a cross sectional study
Joan Martine Dallinga1,2*, Matthijs Mennes1, Laurence Alpay2†, Harmen Bijwaard2†
and Marije Baart de la Faille-Deutekom1,2
Abstract
Background: Physical inactivity is a growing public health concern. Use of mobile applications (apps) may be a
powerful tool to encourage physical activity and a healthy lifestyle. For instance, apps may be used in the
preparation of a running event. However, there is little evidence for the relationship between app use and change
in physical activity and health in recreational runners. The aim of this study was to determine the relationship
between the use of apps and changes in physical activity, health and lifestyle behaviour, and self-image of short
and long distance runners.
Methods: A cross sectional study was designed. A random selection of 15,000 runners (of 54,000 participants) of a
16 and 6.4 km recreational run (Dam tot Damloop) in the Netherlands was invited to participate in an online survey
two days after the run. Anthropometrics, app use, activity level, preparation for running event, running physical
activity (RPA), health and lifestyle, and self-image were addressed. A chi-squared test was conducted to analyse
differences between app users and non-app users in baseline characteristics as well as in RPA, healthy lifestyle and
perceived health. In addition, a multivariate logistic regression analysis was performed to determine if app use
could predict RPA, perceived health and lifestyle, and self-image.
Results: Of the 15,000 invited runners, 28 % responded. For both distances, app use was positively related to RPA
and feeling healthier (p < 0.05). Also, app use was positively related to feeling better about themselves, feeling like
an athlete, motivating others to participate in running, and losing weight (p < 0.01). Furthermore, for 16 km runners
app use was positively related to eating healthier, feeling more energetic and reporting a higher chance to
maintain sport behaviour (p < 0.05).
Conclusions: These results suggest that use of mobile apps has a beneficial role in the preparation of a running
event, as it promotes health and physical activity. Further research is now needed to determine a causal
relationship between app use and physical and health related behaviour.
Background
Benefits of physical activity have often been studied and
include improved health and reduced mortality rates [1–3].
However, actually becoming physically active is a challenge
for many. In the Netherlands research shows that 41 %
percent of all adults do not comply with the Dutch Public
Health Physical Activity Guideline (at least 30 min of mod-
erate to vigorous physical activity during at least 5 days of
the week) [4]. Moreover, only 20 % of Dutch adults meet the
Strenuous Intensity Physical Activity Guideline of at least
three times a week 20 min of vigorous exercise [4]. Physical in-
activity is a growing public health concern in the Netherlands
as well as in other Western countries. Significant health prob-
lems such as increased morbidity and mortality attributable to
cardiovascular disease, diabetes, cancers and increased risk of
depression may arise if the amount of physical activity in the
general population does not increase [5–9].
There is need for innovative ways to promote physical
activity and a healthy lifestyle. One promising develop-
ment is the use of smartphones during exercise. Use of
mobile applications (apps) may be a powerful tool to
encourage physical activity and health [10, 11]. Apps are
* Correspondence: j.m.dallinga@hva.nl
†Equal contributors
1School of Sports and Nutrition, Amsterdam University of Applied Sciences,
Dr. Meurerlaan 8, 1067 SM Amsterdam, The Netherlands
2Faculty of Health, Sports and Social Work, Inholland University of Applied
Sciences, Blijdorplaan 15, 2015 CE Haarlem, The Netherlands
© 2015 Dallinga et al. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Dallinga et al. BMC Public Health  (2015) 15:833 
DOI 10.1186/s12889-015-2165-8
accessible, have a large reach, and have multiple function-
alities, such as interactive possibilities and feedback op-
portunities [12, 13]. Although more than 17,000 health
and fitness apps have been developed and are available for
the public [12], the literature considering the relationship
of app use and health and physical activity is scarce.
However, preliminary evidence is promising [11, 14, 15].
Two reviews and one meta-analysis demonstrated positive
effects of mobile phone interventions, interventions with
mobile technology, and interventions with remote and
web interventions in healthy, inactive and overweight indi-
viduals [11, 14, 15]. The mobile phone interventions were
often combined with additional education, self-reporting
of frequency and type of use of the program or telephone
calls. The positive effects of these interventions included
increased physical activity (expressed by total time,
number of occasions of physical activity and energy ex-
penditure), cardiovascular fitness and reduced overweight
[11, 14, 15]. Small to moderate effect sizes were reported
[14, 15]. Nevertheless, in these three reviews few interven-
tions were included that used apps. Moreover, in some
studies additional interventions were provided next to the
mobile phone and app interventions, therefore based on
those studies no conclusions can be drawn regarding to
the isolated effects of apps on physical activity. Another
recent review demonstrated modest effects of app based
interventions on physical activity expressed by step count
[16]. It should be noted that the apps were often com-
bined with external pedometers, small sample sizes were
included, small increases in step counts and a short
duration of interventions was presented [16]. However, a
recent study has shown promising results of the isolated
effect of app use [17]. This study demonstrated that use of
a Web-based app on lifestyle indicators decreased weight
and increased physical activity of people [17]. Moreover,
app users presented a higher chance to maintain a healthy
lifestyle [17]. In summary, few studies have examined the
effect of app use on changes in physical activity and
health.
In recreational running the use of apps is high and
emerging and several apps have been developed to assist
individuals in their running exercise. Previous research
has shown that recreational running or participation in a
running mass event could also be a potential health and
physical activity promoting activity [18–20]; Chatton and
Kayser showed that participants in a 16 km run were more
active than the general population and better in shape
[18]. Additionally, in the preparation for a 5 and a 10 km
run participants increased physical activity [19, 20]. A
majority of participants train in preparation for running
events; some of them exercise individually and some of
them in a running group [21, 22]. Potentially, app use
could assist runners to increase motivation, to increase
activity level and set goals during the preparation for a
running event. Perhaps the use of apps could assist
runners to increase running physical activity and to live
and feel healthier. Therefore, the aim of this study was to
determine the relationship between the use of apps and
changes in physical activity and health and lifestyle behav-
iour of short and long distance runners. More specific, we
were interested in training volume, alcohol intake, smok-
ing behaviour, and lifestyle (e.g. weight loss and eating
behaviour).
Methods
Study design and participants
A cross sectional study was designed to analyse the rela-
tionship between app use and physical activity, health and
lifestyle of recreational runners. On September 21st 2014
the 30th Dam tot Damloop, a running event, was orga-
nized in Amsterdam, the Netherlands. The organization
of the running event randomly selected and invited 15,000
runners out of 54,410 participants (16 and 6.4 km) to
participate in an online survey. Runners of all levels were
invited to participate. Participation in the run was either
on an individual basis, with a company or for a charity.
Inclusion criteria were (a) ≥18 years and (b) signed in-
formed consent. Exclusion criteria were (a) participating
in both distances or (b) leaving all questions unanswered
after informed consent.
Two days after participation to the event, an email invi-
tation including a link to the online survey was sent to the
random selection of participants. After one week, a
reminder was sent to the participants who had not
responded yet. This online survey was based on a previ-
ously developed survey [23], with additional items for this
specific running event. An additional file presents the sur-
vey questions (see Additional file 1). In the introduction of
the survey the purpose of the study was explained and
confidentiality was guaranteed. Furthermore, it was ascer-
tained that participation was voluntary and that the
participant was allowed to quit at any time. Responding to
the questionnaire took approximately 15 min. The ethical
approval was not required in the Netherlands, however
the research was conducted in line with the Helsinki
Declaration.
Key measures
Dependent variables
Running physical activity (RPA) was collected. Participants
were invited to report on two occasions (before their
training phase (baseline) and during training phase) how
many kilometres per week they ran (<5 km a week,
between 5 and 10, between 10 and 20 a week, between 20
and 30 a week and more than 30 km per week). In
addition, the survey included questions regarding health
and lifestyle. Alcohol consumption (glasses per week) and
frequency of smoking (per day) was asked before their
Dallinga et al. BMC Public Health  (2015) 15:833 
Page 2 of 9
training phase and during training phase. Additionally,
participants were requested to indicate whether participa-
tion in the run affected their health (no effect, feel much
healthier, feel healthier, feel less healthy, feel much less
healthy). Moreover, participants indicated if the run influ-
enced their body weight, diet, and energy level (totally
agree, agree, neutral, disagree, totally disagree). To gain
insight in self-image, participants were asked whether the
run influenced their perception concerning a healthy
lifestyle (totally agree, agree, neutral, disagree, totally dis-
agree). Items included were: performing sports is good for
me, chance of maintaining physical activity, feeling better
about oneself, no change in lifestyle, and feeling tired
more often.
Potential prediction variables
Participants indicated if they used an app or other train-
ing tool. Additionally, we collected information about
several variables that needed to be controlled for (gen-
der, age, body mass index (BMI)). To calculate age, date
of birth was asked. Subsequently, age was calculated by
subtracting the year of birth from 2014. BMI (kg/m2)
was used as a proxy of body composition and calculated
as self-reported body weight (kg) divided by the square
of height (m). We used the WHO categories for the
classification: BMI < 18.5 means underweight, [18.5, 25)
equals normal weight, [25, 30) means overweight and ≥
30 corresponds to obese [24]. In addition, information
was collected to determine the participant’s preparation
for the event, fitness state, and experience with running/
sports. As an indication, exercise frequency, in number
of training sessions per year, was requested [25]. Partici-
pants were asked whether they had participated before
in this running event (and if so the number of previous
participations) to estimate
experience with running
events. The training period that participants scheduled
to prepare for this running event was asked as well. Par-
ticipants could choose between categories: no training/
barely, 1–5 weeks, 6–11 weeks, 12 weeks or more, no
specific training/train all year and don’t know/no an-
swer. Participants indicated self-reported finishing time
in hours and minutes as well.
Data reduction
The difference in RPA between baseline and training
phase was calculated. For all participants it was assessed
whether the RPA was increased or not. Furthermore, the
difference in consumption of alcohol and smoking be-
tween baseline and training phase was calculated. Calcula-
tions were performed to examine if these two factors were
decreased or not. For the outcome of perceived health it
was determined whether participants felt healthier or not.
Answers on theses concerning healthy lifestyle and self-
image were reduced from five to two categories; we
calculated if the participants agreed or answered neutral/
disagreed with the theses about these topics.
Statistical analysis
SPSS version 20.0 was used for all calculations. For both
distances, means and standard deviations (SD’s) were
calculated for age, BMI and exercise frequency. The data
was checked for outliers. For the categorical variables,
frequency and percentage were calculated.
We used a chi-squared test to determine differences
between app users and non-app users in baseline charac-
teristics as well as in physical activity, healthy lifestyle and
perceived health during training phase. In addition, a
multivariate logistic regression analysis was performed to
determine if app use could predict changes in RPA, health
and lifestyle, and self-image. Outcome variables were ef-
fects on RPA (increased, not increased), health (healthier/
not healthier), alcohol consumption (more/not more),
smoking (more/not more), eat healthier (agree/disagree),
energy level (agree/disagree), performing sports is good
for me (agree/disagree), chance of maintaining physical
activity (agree/disagree), feeling better about oneself
(agree/disagree),
no
change
in
lifestyle
(agree/
disagree), lose weight (agree/disagree), and feel tired
more often (agree/disagree). In these logistic regres-
sion analyses, we controlled for age, gender, BMI, kilo-
metres per week before preparation and exercise
frequency in last year. Separate analyses were per-
formed for the 16 and the 6.4 km. The alpha level was
set at α ≤ 0.05 a priori.
Results
Of all invited runners 4307 (28 %) agreed to participate in
the survey, of which 2838 runners participated in the
16 km and 1341 in the 6.4 km. Table 1 presents the sub-
ject characteristics of male and female 16 and 6.4 km run-
ners. Hundred-twelve participants participated in both
distances and 507 participants reported too much missing
values and were therefore excluded. The type of apps used
by participants is shown in Fig. 1. Most participants used
Runkeeper (44.4 %) in their preparation. The category
‘other apps” was the second largest app type chosen by
participants (16.9 %), these were the apps that were not
mentioned in the answer options.
Differences app and non-app users
Baseline characteristics A significant association was
found between app use and gender for both distances.
More app users were female (16 km: Chi-squared = 4.90,
p = 0.027; 6.4 km:
Chi-squared = 9.61, p = 0.002). In
addition, app users were significantly younger compared
to non-app users (16 km: t = -12.09, df = 2456.56, p <
0.001; 6.4 km: t = −4.24, df = 879.92, p < 0.001) and
trained less often in a year (16 km: t = −5.58, df =
Dallinga et al. BMC Public Health  (2015) 15:833 
Page 3 of 9
2542.24, p < 0.001; 6.4 km: t = −2.44, df = 969.84, p =
0.015). In the 6.4 km runners, app use was associated
with BMI category (Chi-squared = 7.45, p = 0.024); app
users were more often overweight. We found a signifi-
cant association between app use and kilometres per
week that participants ran before the preparation phase
(16 km: Chi-squared = 87.48, p < 0.001; 6.4 km: Chi-
squared = 16.10, p = 0.003). In general, it seemed that app
users trained fewer kilometres before they had started the
preparation for the running event, compared to non-app
users. A significant association between app use and
duration of training period was found as well (16 km: Chi-
squared = 69.36, p < 0.001; 6.4 km: Chi-squared = 30.16,
p < 0.001). For the 16 km, there were more app users who
trained 12 weeks or more and who did not schedule a spe-
cific training period for this event compared to the non-
app users. For the 6.4 km, app users trained more often 6
to 11 weeks and 12 weeks or more compared to non-app
users, whereas non-app users more often did not train or
trained barely compared to app users.
Table 1 Subject characteristics of 16 and 6.4 km runners
16 km
6.4 km
Males
Females
Males
Females
Variable
M ± SD
M ± SD
M ± SD
M ± SD
Age (years)
42.19 ± 10.73
37.11 ± 10.26
42.01 ± 11.39
36.33 ± 10.31
Training sessions per year (n/year)
120.91 ± 56.81
121.31 ± 55.39
101.17 ± 57.90
99.97 ± 56.08
N (%)a
N (%)a
N (%)a
N (%)a
BMI category
Underweight
20 (1.0)
46 (2.2)
5 (0.5)
40 (4.1)
Normal weight
756 (36.1)
646 (30.9)
97 (10.0)
443 (45.5)
Overweight
481 (23.0)
143 (6.8)
140 (14.4)
249 (25.6)
Use of app during training
Yes
736 (28.5)
543 (21.0)
160 (13.5)
537 (45.3)
No
830 (32.1)
477 (18.4)
140 (11.8)
349 (29.4)
Duration training period
No training/ barely
114 (4.4)
38 (1.5)
37 (3.1)
97 (8.2)
1–5 weeks
129 (5.0)
88 (3.4)
46 (3.9)
100 (8.4)
6–11 weeks
183 (7.1)
125 (4.8)
33 (2.8)
117 (9.9)
12 weeks or more
225 (8.7)
218 (8.4)
38 (3.9)
129 (10.9)
No separate training period
909 (35.2)
546 (21.1)
142 (12.0)
433 (36.5)
Don’t know/no answer
6 (0.2)
3 (0.1)
5 (0.4)
9 (0.8)
Kilometres before
< 5 km/week
229 (9.1)
134 (5.3)
86 (7.6)
328 (28.9)
5–10 km/week
318 (12.6)
332 (13.2)
96 (8.5)
314 (27.7)
10–20 km/week
473 (18.8)
307 (12.2)
68 (6.0)
165 (14.5)
20–30 km/week
301 (11.9)
162 (6.4)
25 (2.2)
34 (3.0)
> 30 km/week
202 (8.0)
64 (2.5)
12 (1.1)
7 (0.6)
aTotal N varies due to missing values
.0
10.0
20.0
30.0
40.0
50.0
Get Running-app
DtD 2014 app
Adidas miCoach
App + Renate Wennemars
Myasics
Strava
Endomundo
Runtastic
Nike + iPod / I Phone app
Other
RunKeeper
Percentage of participants (%)
Fig. 1 Apps used in preparation for the 16 and 6.4 km recreational run
Dallinga et al. BMC Public Health  (2015) 15:833 
Page 4 of 9
Outcome variables Table 2 shows the differences be-
tween app users and non-app users in RPA, perceived
health and lifestyle, and self-image. App users increased
more often their RPA, felt healthier, ate healthier (6.4 km
no significant difference), felt more energetic, felt that they
had a higher chance of maintaining sport behaviour, felt
better about themselves, felt more like an athlete, changed
their lifestyle, stimulated others to perform sport and lost
weight.
Predictive ability of app use
Table 3 presents results of the logistic regression analyses
for each distance, corrected for age, gender, BMI, kilo-
metres per week before preparation and frequency of
participation in this running event. Logistic regression
analyses showed that for both 16 and 6.4 km runners, app
use was positively related to RPA and feeling healthier. In
addition, the app use was related to feeling better about
themselves, feeling more like an athlete, motivating others
to participate in running, and losing weight. Also, for the
16 km runners using apps was related to eating healthier,
feeling more energetic and reporting a higher chance to
maintain sport behaviour.
Discussion
Our main finding was that app use was positively related
to RPA, feeling healthier, changing lifestyle and self-image.
Also, use of apps was positively related to stimulating
Table 2 Differences between app users and non-app users in RPA, perceived health and lifestyle, and self-image
16 km
6.4 km
App use
No app use Chi2
P
App use
No app use Chi2
P
N (%)
N (%)
N (%)
N (%)
RPA
Decreased/same
624 (23.7)
821 (31.1)
55.49 < 0.001
467 (39.1)
369 (30.9)
17.22 < 0.001
Increased
689 (26.1)
504 (19.1)
246 (20.6)
112 (9.4)
Perceived health
Not healthier
497 (18.2)
722 (27.5)
72.71 < 0.001
294 (23.5)
268 (21.4)
18.36 < 0.001
Healthier
863 (31.6)
646 (23.7)
443 (35.4)
246 (19.7)
Smoking behavioura
More/equal
164 (43.3)
111 (29.3)
0.11
0.814
91 (52.3)
52 (29.9)
2.16
0.208
Less
64 (16.9)
40 (10.6)
24 (13.8)
7 (4.0)
Alcohol consumptionb
More/equal
901 (41.5)
897 (41.3)
1.63
0.211
441 (54.4)
296 (36.5)
0.28
0.619
Less
201 (9.3)
173 (8.0)
46 (5.7)
27 (3.3)
Eat healthier
Agree
496 (18.4)
420 (15.6)
10.71
0.001
221 (18.0)
129 (10.5)
3.76
0.052
Disagree
843 (31.3)
932 (34.6)
502 (40.8)
377 (30.7)
Feel more energetic
Agree
923 (34.3)
731 (27.2)
65.17 < 0.001
467 (38.1)
281 (22.9)
9.95
0.002
Disagree
412 (15.3)
623 (23.2)
255 (20.8)
223 (18.2)
Chance of maintaining sport behaviour
Agree
949 (35.3)
868 (32.3)
13.30 < 0.001
538 (44.0)
339 (27.7)
7.33
0.007
Disagree
389 (14.5)
481 (17.9)
183 (15.0)
163 (13.3)
I know that performing sport is not my thing Agree
21 (0.8)
28 (1.0)
0.97
0.387
12 (1.0)
14 (1.1)
1.82
0.226
Disagree
1313 (49.1)
1316 (49.1)
711 (58.0)
488 (39.8)
Feel better about myself
Agree
859 (32.0)
646 (24.1)
74.19 < 0.0001 492 (40.1)
257 (21.0)
37.60 < 0.0001
Disagree
475 (17.7)
703 (26.2)
229 (18.7)
248 (20.2)
Feel more like an athlete
Agree
605 (22.5)
422 (15.7)
55.40 < 0.0001 343 (28.0)
168 (13.7)
24.68 < 0.0001
Disagree
731 (27.2)
926 (34.5)
377 (30.8)
335 (27.4)
Changed lifestyle
Agree
913 (34.1)
796 (29.7)
25.01 < 0.0001 502 (40.9)
302 (24.6)
12.76 < 0.001
Disagree
421 (15.7)
550 (20.5)
220 (17.9)
204 (16.6)
Stimulating others to perform sport
Agree
657 (24.5)
566 (21.1)
14.65 < 0.001
384 (31.3)
217 (17.7)
12.02
0.001
Disagree
676 (25.2)
784 (29.2)
339 (27.6)
287 (23.4)
Losing weight
Agree
543 (20.2)
399 (14.8)
36.72 < 0.0001 270 (22.0)
125 (10.2)
21.61 < 0.0001
Disagree
794 (29.5)
955 (35.5)
453 (36.9)
380 (30.9)
Feel tired more often
Agree
97 (3.6)
84 (3.1)
1.17
0.282
52 (4.3)
38 (3.1)
0.08
0.824
Disagree
1237 (46.1)
1266 (47.2)
668 (54.7)
463 (37.9)
aThe participants who did not smoke were excluded
bThe participants who did not drink alcohol were excluded
Dallinga et al. BMC Public Health  (2015) 15:833 
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others to become active. Moreover, app use in 16 km
runners was positively related to feeling more energetic,
eating healthier and maintaining the sport behaviour. The
odds ratios ranged from 1.24 to 1.89. Additionally, for
RPA the explained variance was 41 % and 38 % for 16 km
and 6.4 km respectively. These findings are of high
importance considering that for app users the weekly
training volume prior to the preparation phase was lower
than non-app users.
These results corroborate with the findings of other
studies, in which app use seemed to have increased
physical activity and a healthy lifestyle [11, 14–17]. In
contrast to those studies, the focus in this study was on
mobile app use only. It should be noted that we did not
analyse the effect of app use, but we examined the use of
mobile apps in relation to physical activity, perceived
health and self-image. This relationship between app use
and perceived health and self-image in the preparation
of a running event has not been considered in previous
studies. Analysing this relationship is relevant, since it
provides insight in innovative and accessible ways to
encourage physical activity and a healthier life.
Although most results were comparable for 16 and
6.4 km runners, a few differences were found. In 16 km
runners, app use was related to eating healthier, feeling
more energetic and a higher chance to maintain sport
behaviour. The relationships between app use and these
variables did not reach significance level in the 6.4 km
runners. The “fun run” character of the 6.4 km may be a
first explanation for the differences found. Compared to
Table 3 Results of multivariate logistic regression with outcome measure RPA, perceived health and lifestyle
App use
Distance
OR (95 % CI)a
P
R2b
RPA
16 km
1.43 (1.16–1.75)
0.001
0.41
6.4 km
1.89 (1.34–2.65)
<0.001
0.38
Health
16 km
1.59 (1.33–1.90)
<0.0001
0.10
6.4 km
1.33 (1.02–1.73)
0.038
0.10
Alcohol consumption
16 km
1.06 (0.83–1.35)
0.651
0.04
6.4 km
1.57 (0.86–2.85)
0.143
0.03
Smoking behaviour
16 km
1.09 (0.71–1.69)
0.691
0.06
6.4 km
2.06 (0.80–5.30)
0.134
0.05
Eat healthier
16 km
1.24 (1.03–1.48)
0.022
0.02
6.4 km
1.24 (0.93–1.66)
0.150
0.04
Feel more energetic
16 km
1.68 (1.40–2.01)
<0.0001
0.08
6.4 km
1.13 (0.99–1.70)
0.055
0.05
I know that performing sport is not my thing
16 km
0.92 (0.44–1.75)
0.701
0.02
6.4 km
0.47 (0.19–1.03)
0.058
0.12
Chance of maintaining sport behaviour
16 km
1.24 (1.03–1.50)
0.021
0.02
6.4 km
1.31 (0.98–1.74)
0.067
0.02
Feel better about myself
16 km
1.75 (1.47–2.09)
<0.0001
0.07
6.4 km
1.84 (1.41–2.40)
<0.0001
0.07
Feel more like an athlete
16 km
1.69 (1.41–2.01)
<0.0001
0.05
6.4 km
1.67 (1.28–2.18)
<0.001
0.06
Did not change lifestyle
16 km
0.70 (0.58–0.83)
<0.0001
0.02
6.4 km
0.70 (0.53–0.92)
0.010
0.06
Motivated others to participate
16 km
1.43 (1.20–1.69)
<0.0001
0.02
6.4 km
1.45 (1.12–1.87)
0.005
0.03
Lost weight
16 km
1.57 (1.31–1.89)
<0.0001
0.06
6.4 km
1.72 (1.29–2.30)
<0.0001
0.09
Feel tired more often
16 km
1.03 (0.73–1.46)
0.877
0.04
6.4 km
0.70 (0.44–1.12)
0.140
0.03
aControlled for gender, age, BMI, training sessions per year and weekly training distance before training phase
bNagelkerke R2 [39]
Dallinga et al. BMC Public Health  (2015) 15:833 
Page 6 of 9
the 16 km run, participation in a 6.4 km run may not
require a long preparation phase and lifestyle changes.
In addition, we found that in the training phase most
16 km runners trained 10–20 km per week (37.3 %) and
20–30 km per week (27.3 %), whereas the largest part of
6.4 km runners trained 5–10 km per week (42.0 %) and
10–20 km per week (26.3 %). Thus another possible
explanation might be that the differences in weekly
training distance of 16 and 6.4 km runners combined
with a shorter preparation resulted in the inconsistent
findings. Previous literature has shown that running
improves aerobic fitness and cardiovascular function at
rest [26]. In a review, a fairly strong dose–response rela-
tionship between weekly training volume and cardiore-
spiratory fitness was shown for inactive and healthy
middle aged and elderly people [27]. This may explain
why the physical fitness of the 16 km runners increased
more compared to 6.4 km runners, resulting in a higher
perceived energy level. Potentially, there is a link be-
tween weekly training volume and eating behaviour as
well. To support this suggestion, Williams et al. showed
that a larger weekly running distance promoted a health-
ier eating pattern [28]. In addition, in that study a rela-
tionship was found between weekly running distance
and years spend in running, which might provide an
explanation for our finding that app use was related to a
higher chance to maintain the physical activity of the
longer distance runners compared to the shorter dis-
tance runners.
Previous studies have shown that participating in run-
ning events can encourage physical activity [26, 29]. How-
ever, maintaining an active lifestyle is difficult for many
[30]. Moreover, the gap between intention for being phys-
ically active and actually being active is large [31]. In many
of behaviour change models, such as the Fogg behaviour
model and the attitude, social influence and efficacy (ASE)
model, the behavioural intention is assumed to be most
important in changing behaviour [32, 33]. It would be
interesting to determine the impact of an app on behav-
iour determinants such as self-efficacy, attitude and social
influence. In addition, given that behaviour change theor-
ies (BCTs) are often relatively absent in apps, it would be
valuable to find out which of these theories are taken into
account in the app [34].
This study showed that the intention to maintain the
running behaviour was higher for the app users, therefore
app use may assist in decreasing drop-out of running and
encouraging physical activity. This is a very interesting
finding, since apps were more often used by overweight
participants and the participants in the 6.4 km run (who
trained less often). For these two groups physical activity
may need to be encouraged. Furthermore, a very interest-
ing finding was that app users more often encouraged
others to engage in running compared to non-app users.
This could be explained by the fact that some apps
contain features to interact with others, such as following
and supporting their activities [13]. This interaction com-
bined with the use of social media might motivate others
to be more active [35]. These findings suggest that the use
of mobile apps can contribute to the promotion of run-
ning and prevention of drop-out. Our findings may be
related to the new phenomenon of quantified self, which
means that people are measuring their health conditions
via wearables [36]. This new trend may actually be an
underlying element in the findings of this study.
Furthermore, when we look at practical implications,
we suggest that app use could be an additional stimulus
to the training program, because it provides an easy and
accessible tool to promote physical activity and a healthy
lifestyle. Given that the use of smart phones increases
[37, 38], a large amount of individuals can be reached
with health and fitness apps. Sport organizations and
employers may therefore recommend the use of apps in
the preparation of a running event. For instance, large
recreational running events often include a business run,
in which business teams can compete. The use of apps
may encourage employers to train more and live health-
ier. This data shows that app use is related to increased
physical activity and improved health. Moreover, fre-
quency of app use is higher in inexperienced and over-
weight participants. We could hypothesize that these
group of runners have some comparable characteristics
as inactive individuals. Therefore, our results could po-
tentially be transferred to inactive individuals.
Some limitations of this study need to be addressed.
At first, a self-reported, non-validated survey was used.
Second, a causality between app use and the outcome
variables cannot be determined. It remains unclear what
would be the cause and what would be the result; did
app use increase physical activity or did physical activity
encourage app use. The involvement of other underlying
causes should be considered as well. Randomized con-
trolled studies need to be performed to determine a
causal relationship. The third limitation was that several
types of apps were included. The most used app was
Runkeeper, but also a number of other apps were used.
It would be interesting to find out why people choose
certain apps and which features make an app popular.
Apps differ in their features and may differ in their
effectiveness as well. Therefore, the possibility that the
relationships found might be different for each app has
to be kept in mind, because the way apps present infor-
mation and provide feedback differs. As a fourth limita-
tion low explained variances for app use in relation to
most of the health and lifestyle outcomes were found.
Therefore we have to keep in mind that other factors,
such as psychological factors, contributed to the runner’s
lifestyle and self-image as well. At last, this study included
Dallinga et al. BMC Public Health  (2015) 15:833 
Page 7 of 9
individuals that were already active and motivated to
participate to a running event. However, considering the
problem of increased inactivity, it would be even more
interesting to conduct research on potential of app use in
promoting a healthy lifestyle in inactive individuals includ-
ing long-term consequences. Further research is needed
to determine which features would need to be included in
such an app.
Conclusion
In conclusion, our results showed that recreational run-
ners who used an app are more likely to be more physic-
ally active and feel and live healthier. These results suggest
that use of mobile apps has a beneficial role in the prepar-
ation of a running event, as it promotes health. Further
research is now needed to determine a causal relationship
between app use and physical and health related out-
comes. More specific, a randomized controlled trial (RCT)
needs to be developed and conducted. For instance, the
effect of one app such as Runkeeper could be examined
on weekly training distance and lifestyle. Another example
would be to develop and evaluate a physical activity and
health promotion app in a group of inactive individuals.
To gain insight in long-term effects, a follow-up survey
should be included as well.
Additional files
Additional file 1: Online Survey Dam tot Damloop. This file contains
the questions that were stated in the online survey used in this study.
The file starts with an introduction in which the purpose is explained and
confidentiality is guaranteed. (PDF 239 kb)
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
JD participated in the design of the study, carried out the statistical analyses,
interpreted the data and drafted the manuscript. MM was involved in the
analysis and interpretation of the data and helped to draft the manuscript.
LA was involved in the analysis and interpretation of the data and critically
revised the manuscript. HB was involved in the analysis and interpretation of
the data and critically revised the manuscript. MB participated in the design
of the study, acquisition of data, and analysis and interpretation of these
data. In addition, MB critically revised the manuscript. All authors read and
approved the manuscript. Also, all authors agree to be accountable for all
aspects of the work in ensuring that questions related to the accuracy or
integrity of any part of the work are appropriately investigated and resolved.
Acknowledgements
The authors would like to thank Le Champion for the cooperation. Our
gratitude also goes to Cees Vervoorn for his contributions to the study
development. This publication was supported by the Dutch national
program COMMIT.
Received: 29 April 2015 Accepted: 18 August 2015
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Dallinga et al. BMC Public Health  (2015) 15:833 
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App use, physical activity and healthy lifestyle: a cross sectional study.
Dallinga, Joan Martine,Mennes, Matthijs,Alpay, Laurence,Bijwaard, Harmen,Baart de la Faille-Deutekom, Marije