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Journal of Occupational Health
Psychology
Improving Employees’ Work-Related Well-Being and
Physical Health Through a Technology-Based Physical
Activity Intervention: A Randomized Intervention-
Control Group Study
Thomas Lennefer, Elisa Lopper, Amelie U. Wiedemann, Ursula Hess, and Annekatrin Hoppe
Online First Publication, September 2, 2019. http://dx.doi.org/10.1037/ocp0000169
CITATION
Lennefer, T., Lopper, E., Wiedemann, A. U., Hess, U., & Hoppe, A. (2019, September 2). Improving
Employees’ Work-Related Well-Being and Physical Health Through a Technology-Based Physical
Activity Intervention: A Randomized Intervention-Control Group Study. Journal of Occupational
Health Psychology. Advance online publication. http://dx.doi.org/10.1037/ocp0000169
Improving Employees’ Work-Related Well-Being and Physical Health
Through a Technology-Based Physical Activity Intervention:
A Randomized Intervention-Control Group Study
Thomas Lennefer and Elisa Lopper
Humboldt University of Berlin
Amelie U. Wiedemann
DearEmployee LLC, Berlin, Germany
Ursula Hess and Annekatrin Hoppe
Humboldt University of Berlin
Although activity trackers are becoming more popular, little is known whether this new technology
qualifies to improve employees’ health. This study aimed to evaluate the effect of a workplace
intervention applying activity trackers (behavioral approach) along with an online coach (cognitive
approach) on work-related well-being (e.g., burnout) and physical health (e.g., body mass index). To test
for intervention effects, 116 employees at risk were recruited at 1 large mobility enterprise in Germany
and randomly assigned to an intervention group (n⫽59) and a control group (n⫽57). Intervention
effects were assessed 1 month, 3 months, and 1 year after the intervention. Analyses of variance for
repeated measures revealed no intervention or long-term effects on work-related well-being. In the
intervention group, we found a significant increase in health perception and a significant decrease in body
mass index. These effects were stable over time 3 months after the intervention for health perception and
1 year after the intervention for body mass index. Our study shows that a cognitive– behavioral
intervention with activity trackers improved physical health over time but was not effective in enhancing
work-related well-being.
Keywords: randomized control group design, workplace health promotion, burnout, employees’ health,
activity trackers
Physical activity is defined as any bodily movement produced
by skeletal muscles that results in energy expenditure (Caspersen,
Powell, & Christenson, 1985). Previous research has shown that
physical activity has a major impact on various physical and
mental health outcomes (Reiner, Niermann, Jekauc, & Woll, 2013;
Warburton, Nicol, & Bredin, 2006). For example, physically active
employees show a lower level of burnout and feel more vigorous
during working hours. (Jonsdottir, Rödjer, Hadzibajramovic, Börjes-
son, & Ahlborg, 2010; ten Brummelhuis & Bakker, 2012). As regard
physical health, it has been shown that physical activity is associated
with a more positive perception of employees’ health (Bogaert, De
Martelaer, Deforche, Clarys, & Zinzen, 2014) and leads to a decrease
in employees’ body mass index (BMI; Anderson et al., 2009). Given
these consistent positive effects, different worksite interventions have
been implemented to increase employees’ physical activity. In their
meta-analysis, Taylor, Conner, and Lawton (2012) showed that work-
site interventions could increase employees’ physical activity with a
small effect size (d⫽.21). Most studies focused either on a cognitive
(e.g., Spittaels, De Bourdeaudhuij, Brug, & Vandelanotte, 2007) or a
behavioral approach (Schuna et al., 2014). However, Hutchinson and
Wilson (2012) pointed out that there is encouraging evidence from
one intervention study combining cognitive and behavioral ap-
proaches for increasing physical activity with a large effect (d⫽.90).
Even though intervention studies are available, there is still a great
need for sound randomized controlled intervention studies with em-
ployees (Rongen, Robroek, van Lenthe, & Burdorf, 2013) and for
studies focusing on long-term effects exceeding several weeks
(Hutchinson & Wilson, 2012).
In this study, we tested whether a combined cognitive–behavioral
intervention would improve employees’ work-related well-being
(i.e., burnout and vigor) and physical health (i.e., health perception
and BMI). The effectiveness of the intervention was examined
using a randomized control group design (RCT design) with em-
ployees in one company. We moreover tested for long-term effects
within the intervention group 1 month, 3 months, and 1 year after
the intervention. During the intervention employees received an
activity tracker as a behavioral approach to monitor their physical
activity (e.g., number of steps taken). Activity trackers are a new
technology which might be interesting for occupational health
promotion, as they are cost-effective and easy accessible (can be
used anywhere, at any time; Borrelli & Ritterband, 2015). Due to
Thomas Lennefer and Elisa Lopper, Department of Psychology, Hum-
boldt University of Berlin; Amelie U. Wiedemann, DearEmployee LLC,
Berlin, Germany; Ursula Hess and Annekatrin Hoppe, Department of
Psychology, Humboldt University of Berlin.
Correspondence concerning this article should be addressed to Thomas
Lennefer, Department of Psychology, Humboldt-Universität zu Berlin, Ru-
dower Chaussee 18, 12489 Berlin, Germany. E-mail: thomas.lennefer@hu-
berlin.de
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
Journal of Occupational Health Psychology
© 2019 American Psychological Association 2019, Vol. 1, No. 999, 000
ISSN: 1076-8998 http://dx.doi.org/10.1037/ocp0000169
1
these advantages, activity trackers can be easily integrated into
daily work routines, as employees are able to use them whenever
or wherever in their spare time at work. To further raise awareness
and motivation for health behavior change, we implemented an
online coach as a cognitive approach offering advice on health
behavior change (e.g., how to set health behavior goals).
Overall, our study advances the literature in several ways. First,
we evaluate the effectiveness of an activity tracker based interven-
tion, which broadens the theoretical understanding of a new tech-
nology for health behavior change. By investigating the effect on
work-related well-being (i.e., burnout and vigor) and physical
health (i.e., body mass index and health perception), we consider
new outcome measures, as so far research including activity track-
ers has concentrated solely on non–work-related outcomes. Sec-
ond, we focused specifically on employees at risk recruited at one
large enterprise in Germany. This allowed us access to a popula-
tion of physically inactive employees at risk who are usually
difficult to approach. Finally, the effectiveness of the intervention
was assessed using an RCT design, which enables us to draw
causal conclusions on the effects of the intervention. Due to the
limited evidence of the long-term effects of activity trackers
(Cadmus-Bertram, Marcus, Patterson, Parker, & Morey, 2015),
multiple points of measurement were considered. This enabled us
to evaluate the sustainability of a possible intervention effect 1
month, 3 months, and 1 year after the intervention.
Theoretical Background, Previous Empirical
Investigations, and Hypotheses
Work-Related Well-Being
Burnout. Burnout is considered as an affective reaction oc-
curring due to exposure to prolonged stress at work (Shirom,
Melamed, Toker, Berliner, & Shapira, 2005). According to Shirom
(1989), burnout is a multidimensional construct characterized by a
combination of physical fatigue, emotional exhaustion, and cog-
nitive weariness (Shirom, Nirel, & Vinokur, 2006). Employees
experiencing physical fatigue are tired and lack energy to manage
daily tasks at work. Emotional exhaustion is defined as the feeling
of being too weak to invest in relationships with colleagues or
clients (e.g., displaying empathy). Cognitive weariness refers to
one’s feelings of thinking slowly and a reduction of mental agility
(Melamed, Shirom, Toker, Berliner, & Shapira, 2006; Shirom &
Melamed, 2006).
Besides these negative consequences for the employee, burnout
also entails disadvantages for the employer due to its association
with absenteeism (Duijts, Kant, Swaen, van den Brandt, &
Zeegers, 2007; Petitta & Vecchione, 2011) and a decline in per-
formance (Dewa, Loong, Bonato, Thanh, & Jacobs, 2014; Swider
& Zimmerman, 2010; Taris, 2006).
Promoting physical activity can function as an effective inter-
vention to reduce burnout. For instance Tsai et al. (2013) imple-
mented a 12-weeks exercise program at a bank and insurance
company. One hundred and nine employees were randomly as-
signed to a low-intensity exercise group, a high-intensity exercise
group, or a control group. The low-intensity group participated in
exercise sessions led by a professional trainer once a week,
whereas the high-intensity group took these sessions twice a week.
The control group did not engage in any of the exercise sessions;
they had to plan and carry out exercise regimes on their own. The
results revealed that after the intervention participants’ burnout
was significantly lower than before the intervention for both in-
tensity groups. Further studies supported the effectiveness of work-
place interventions promoting physical activity to alleviate burnout
(Bretland & Thorsteinsson, 2015; Gerber et al., 2013). Neverthe-
less, these studies had some limitations. Besides not considering
possible long-term effects, two of these studies included relatively
small sample sizes (Gerber et al., 2013: n⫽12; Bretland &
Thorsteinsson, 2015: n⫽49). Regarding long-term effects, van
Rhenen, Blonk, van der Klink, van Dijk, and Schaufeli (2005)
tested the effectiveness of an intervention involving relaxation and
physical exercise directly after the intervention period and at
6-week follow-up. The results showed that employees had a lower
burnout level after the intervention. Moreover, a persistent reduc-
tion of burnout at 6-month follow-up could be found as well.
However, this study did not include a control group, which is why
it is not possible to draw causal conclusions. Therefore, Naczenski,
Vries, van Hooff, and Kompier (2017) pointed out that there is still
a lack of high-quality long-term intervention studies investigating
the influence of physical activity on burnout.
Vigor. Vigor is one core component of work engagement ac-
cording to Schaufeli, Bakker, and Salanova (2006). It can be defined
as high levels of energy and mental resilience at work. Vigorous
employees invest effort in their work and persist even in the face of
difficulties (Schaufeli et al., 2006). As this definition implies, vigor is
a conceptual opposite to emotional exhaustion, which is a core di-
mension of burnout (González-Romá, Schaufeli, Bakker, & Lloret,
2006).
So far only a few studies have tested whether employees’ vigor
can be enhanced through physical activity interventions. While
being physically active, various physiological reactions are caused
within the body (e.g., endorphin release; Mikkelsen, Stojanovska,
Polenakovic, Bosevski, & Apostolopoulos, 2017). One of these
physiological mechanisms could particularly be related to vigor.
Physiologically, physical activity is perceived by the body as a
stressor (Anderson & Wideman, 2017), thus being physically
active releases a high level of cortisol into the body resulting in an
increased cortisol concentration (Gomes de Souza Vale, Rosa,
José, Júnior, & Dantas, 2012). As one function of cortisol is to
obtain energy by promoting gluconeogenesis (Gomes de Souza
Vale et al., 2012), it is likely that increased cortisol level enhances
vigor. Cortisol was indeed found to reduce fatigue and increase
vigor directly, as shown by Tops, van Peer, Wijers, and Korf
(2006). Participants who received cortisol capsule showed less
fatigue and more vigor than did participants receiving a placebo.
Moreover, cortisol is a hormone that helps the organism to adapt
to stress or exertion (Anderson & Wideman, 2017). Accordingly,
regular physical activity accelerates recovery from stress reaction
(Jackson & Dishman, 2006; Teisala et al., 2014). In sum, it can be
assumed that physical activity has a positive effect on vigor via
increased cortisol concentration in the short term and reduced
stress reaction in the longer term, which together results in higher
vigor.
This assumption has been supported by Hansen, Blangsted,
Hansen, Søgaard, and Sjøgaard (2010), who showed that physi-
cally active employees generally feel more energetic. Additionally,
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2LENNEFER, LOPPER, WIEDEMANN, HESS, AND HOPPE
they found that employees who were physically active during their
leisure time showed higher cortisol levels in the evening, which
was associated with higher perceived energy. These results can
also be supported by ten Brummelhuis and Bakker (2012), who
showed that being physically active after work increased employ-
ees’ vigor in the following morning. Pronk, Katz, Lowry, and
Payfer (2012) conducted an intervention to enhance activity at
work by reducing sitting time. The results showed that employees
in the intervention group felt more vigorous after the intervention
than did employees in the control group.
Nevertheless some studies have found no association between
physical activity and vigor (Isoard-Gautheur, Scotto-di-Luzio, Gi-
noux, & Sarrazin, 2018; Strijk, Proper, van Mechelen, & van der
Beek, 2013; van Berkel et al., 2013). Van Berkel et al. (2013)
assumed that they found no effect as their participants were not
sufficiently physically active. This is supported by the fact that a
rise in cortisol only occurs if the physical activity is strenuous for
the individual (Anderson & Wideman, 2017; Gomes de Souza
Vale et al., 2012). As Strijk et al. (2013) found that only partici-
pants who were extremely complied in activities to increase phys-
ical activity showed an increase in work-related vitality, whereas
less compliant participants showed no intervention effect. Based
on this evidence, we conducted an intervention focusing on mod-
erate to vigorous physical activity.
In light of the foregoing, we assume that a cognitive– behavioral
intervention with activity trackers would improve work-related
well-being. We therefore hypothesize as follows:
Hypothesis 1: Employees in the intervention group show
lower levels of burnout after the intervention than employees
in the control group.
Hypothesis 2: Employees in the intervention group show
higher levels of vigor after the intervention than employees in
the control group.
Physical Health
Health perception. Health perception is defined as individu-
als’ subjective perceptions of their own health status (Benyamini,
2012). Even though these perceptions are not always medically
accurate (Benyamini, 2012), they serve as an important measure,
as they integrate several relevant health factors such as physiolog-
ical factors, symptom status, and functioning (Havranek et al.,
2001; Wilson & Cleary, 1995). As earlier studies have shown,
health perception is a strong predictor for mortality (DeSalvo,
Bloser, Reynolds, He, & Muntner, 2006), which makes it a useful
health outcome measure (Burström & Fredlund, 2001). Moreover,
employees’ health perception is relevant, as it is associated with
job absenteeism (Roelen, Koopmans, de Graaf, van Zandbergen, &
Groothoff, 2007), work ability (Rongen, Robroek, Schaufeli, &
Burdorf, 2014), and employee’s performance (van Scheppingen et
al., 2013).
Given that health perception is related to bodily sensations and
symptoms that may reflect diseases in their clinical and preclinical
stages (Benyamini, 2011), it is likely that physical activity that
lowers the risk for several diseases (Jeon, Lokken, Hu, & van
Dam, 2007; Li & Siegrist, 2012; Mammen & Faulkner, 2013), also
has a positive effect on health perception. This assumption has
been supported by several studies. For instance, Bogaert et al.
(2014) showed in a population of secondary school teachers that
teachers who were physically active during their leisure time
reported more positive perceived health. Okano, Miyake, and Mori
(2003) surveyed various lifestyle factors (e.g., nutritional status
and physical activity) and their contribution to health perception in
a population of middle-aged male employees. The results revealed
that physical activity was the only lifestyle factor which predicted
good health perception. Further studies have corroborated the
positive association between physical activity and health percep-
tion (Eurenius & Stenström, 2005; Kaleta, Makowiec-Da˛browska,
Dziankowska-Zaborszczyk, & Jegier, 2006; Pohjonen & Ranta,
2001).
Body mass index. BMI is a traditional index for body weight
relative to height (kg/m
2
or lbs/inch
2
). Based on the BMI, an
individual’s body weight can be categorized into underweight
(BMI ⬍18.5), normal weight (BMI 18.5–25), overweight (BMI
25–30), and obese (BMI ⱖ30; World Health Organization, 2000).
Research has shown that high BMI is a serious risk factor for
several diseases, such as coronary heart disease (Bogers et al.,
2007), different types of cancer (Renehan, Tyson, Egger, Heller, &
Zwahlen, 2008), and depression (Luppino et al., 2010). Seen from
an economic perspective, high BMI entails increased costs for
employers (Finkelstein, DiBonaventura, Burgess, & Hale, 2010).
As Van Nuys et al. (2014) pointed out, employers’ costs rise when
BMI exceeds 25. Although a normal weight employee causes costs
about $3,830 per year, an obese employee costs the employer more
than twice that amount, $8,067. This expenditure is mainly due to
absenteeism, health care costs, and loss of productivity (Finkel-
stein et al., 2010; van Nuys et al., 2014).
Because past research has shown that inactivity is a major risk
factor for high BMI or associated diseases (e.g., obesity; Mum-
mery, Schofield, Steele, Eakin, & Brown, 2005; Vandelanotte,
Sugiyama, Gardiner, & Owen, 2009), it follows that physical
activity is conducive to BMI reduction. Empirically this associa-
tion has been confirmed by several studies (Goodpaster et al.,
2010; Koepp et al., 2013; Morgan et al., 2011). With regard to
workplace intervention research, Anderson et al. (2009) found by
reviewing 15 studies that a workplace intervention involving phys-
ical activity and nutrition could decrease employees’ weight and
BMI. At 6 and 12 months after the intervention employees showed
a weight reduction of 1.3 kg on average. Consequently, the inter-
vention reduced employee BMI by 0.5 points. More recent studies
corroborate the beneficial effect of physical activity interventions
on employees’ BMI (Burn, Norton, Drummond, & Ian Norton,
2017; Viester, Verhagen, Bongers, & van der Beek, 2018). For
instance, Reed et al. (2017) conducted a meta-analysis concentrat-
ing on working-aged women in high-income countries. They showed
that workplace physical activity interventions reduced employees’
BMI by 0.35 points. Nevertheless, Tam and Yeung (2018) stated that
there is still a high need for high-quality intervention studies. More-
over, they suggested that physical activity interventions should in-
clude a motivational component, as these studies were most effective.
These results led to the assumption that an intervention promoting
physical activity in a work setting, while rising employees motivation
and awareness for behavior change through a cognitive approach, is
beneficial for employees’ health perception and BMI. Based on the
results of previous studies, we therefore assume the following hypoth-
eses:
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3
TECHNOLOGY-BASED PHYSICAL ACTIVITY INTERVENTION
Hypothesis 3: Employees in the intervention group show
higher levels of health perception after the intervention than
employees in the control group.
Hypothesis 4: Employees in the intervention group show a
lower BMI after the intervention than employees in the control
group.
Activity Trackers in Intervention Research
To date only a few studies have used activity trackers for health
interventions. Nevertheless, there is sound evidence of the effec-
tiveness of pedometers, which can be seen as a predecessor of
activity trackers (i.e., simply tracks steps, no further health-related
data). For instance, Bravata et al. (2007) in a review of 26 studies
showed that participants using pedometers increased their physical
activity by 26.9% in comparison to their baseline physical activity.
More recent studies have elaborated on these findings by including
modern activity trackers in interventions (Choi, Lee, Vittinghoff,
& Fukuoka, 2016; Wang et al., 2015). Cadmus-Bertram et al.
(2015) assigned 51 overweight women to two different interven-
tion groups. The activity tracker group each received a Fitbit
activity tracker, whereas the participants in the other group each
received a pedometer. The results showed that the activity tracker
group increased their physical activity, whereas the pedometer
group showed no significant increase in physical activity. Despite
this initial evidence on physical activity, there are only a few
studies showing that using an activity tracker improves health
(Abrantes et al., 2017; Lunney, Cunningham, & Eastin, 2016;
O’Brien, Troutman-Jordan, Hathaway, Armstrong, & Moore,
2015; Wilson, Ramsay, & Young, 2017). So far only one study has
investigated the effect of activity trackers on health in a work
setting (Finkelstein et al., 2016). Employees from 13 companies
were assigned to one control group and three intervention groups,
all of whom received and activity tracker. The results showed that
all intervention conditions increased employees’ physical activity.
Nevertheless, no changes in weight or other health-related out-
comes were found, possibly because the participants had better
health conditions than the average worker. Moreover, it may be
that in addition to an activity tracker, a cognitive approach is
necessary to raise employees’ awareness to bring about change in
health behavior.
Our Physical Activity Intervention and Study Design
Intervention. To enhance employees’ physical activity, we
integrated a behavioral and a cognitive intervention approach. At
the beginning of the intervention, participants were provided with
the Garmin Vivofit 3 activity tracker, which constitutes the behav-
ioral approach of the intervention. The Vivofit 3 is a wristband that
registers daily steps or energy consumption. A summary of this
information can be monitored on the activity tracker itself or in
more detail on the Garmin Connect App. According to Shuger et
al. (2011), this type of self-monitoring is a key aspect of how
activity trackers affect health. Besides the opportunity to gain
information about one’s own activity, the Vivofit 3 provides a
reminder function, to encourage participants to become physically
active after an hour of inactivity. We would like to note at this
point that the activity tracker was only used as an intervention
approach and did not serve to measure physical activity for a data
collection purpose.
To raise employees’ awareness and motivate them for a behav-
ior change, an online coach was implemented, which constitutes
the cognitive approach of the intervention. The online coach was
a website from which participants could retrieve advice on how to
increase their physical activity. In total four pieces of advice were
offered over the course of 3 weeks, and these were based on recent
studies or approved methods of behavior change (Bauman et al.,
2012; Biagini et al., 2012; Heath et al., 2012; Sniehotta, Schwar-
zer, Scholz, & Schüz, 2005; Ziegelmann, Lippke, & Schwarzer,
2006). As the first piece of advice, participants were offered a tool
for goal setting. Several studies have shown that generating action
plans benefits behavior change (Luszczynska, 2006; Wiedemann,
Lippke, Reuter, Ziegelmann, & Schüz, 2011; Williams & French,
2011). We therefore asked participants to set an individual health
behavior goal. As a second step they were required to generate a
plan on how they could achieve their individual goals. Further
advice on physical activity was given twice a week (Monday and
Friday) and aimed to support the participants in achieving their
health behavior goals. For instance, the online coach informed
participants about the benefits of coping plans in physical activity
(Wiedemann et al., 2011; Ziegelmann et al., 2006). Coping plan-
ning is an approved method of health behavior change, where
individuals are required to indicate internal and external barriers
that inhibit them from achieving the desired health behavior
(Sniehotta et al., 2005; Wiedemann et al., 2011). An example for
a coping plan could be: “If it rains and I want to go out for a run,
I will go to the gym instead.” By linking anticipated risk situations
to suitable coping responses, coping plans facilitate participants to
act on their intentions (Sniehotta et al., 2005). Thus, coping plans
are important for the maintenance of a desired health behavior
such as physical activity (Ziegelmann et al., 2006).
In the second week of the intervention, we conducted a step
challenge as a gamification element to increase participants’ mo-
tivation and pleasure at being physically active (Cugelman, 2013;
Hamari, Koivisto, & Sarsa, 2014; Lin, Mamykina, Lindtner, De-
lajoux, & Strub, 2006). The term gamification refers to the process
of including game design elements (e.g., challenges) into nongame
context to invoke a gameful experience (e.g., enjoyment) while
performing nongame-related activities (Groh, 2012; Huotari &
Hamari, 2012). The step challenge took 4 days in total. During this
period participants were required to walk more than 40,000 steps,
which, according to current research, is a reasonable target for
healthy adults (Schneider, Bassett, Thompson, Pronk, & Bielak,
2006; Tudor-Locke et al., 2011). If this goal was achieved by at
least 50% of the participants a reward was given for winning the
challenge. Subsequently to the step challenge, the online coach
supported the participants with two more pieces of advice to
enhance their physical activity in the last week of the intervention.
On average the online coach was visited 11.5 times per participant
over the course of the intervention. This illustrates that on average
every time new information (e.g., advice) was uploaded to the
online coach participants visited the website.
Study design. The effectiveness of the intervention was ex-
amined using an RCT design over a 3-week period. Due to
requirements of the enterprise from which participants were re-
cruited, the control group (CG) had to engage in intervention
activities immediately after completion of the intervention group
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4LENNEFER, LOPPER, WIEDEMANN, HESS, AND HOPPE
(IG). Therefore, we only had access to the IG to assess long-term
effects. Thus, measurements at five points were collected for the
IG to evaluate the sustainability of intervention effects. Before the
intervention group engaged in the intervention activities, IG and
CG completed a prequestionnaire (Time 1 [T1]; n⫽116; 95.9%).
After finishing the intervention, IG and CG answered a postques-
tionnaire (Time 2 [T2]; n⫽105; 86.8%). Three more follow-up
questionnaires were distributed to the IG 1 month after the inter-
vention (Time 3 [T3]; IG: n⫽47; 78.3%), 3 months after the
intervention (Time 4 [T4]; IG: n⫽35; 58.3%), and 1 year after the
intervention (Time 5 [T5]; IG: n⫽35; 58.3%). Before starting
data collection, the study design was approved by the works
council and the data protection officer of the enterprise from which
participants were recruited. The works council is a committee that
is responsible for representing the interest of the employees. Thus,
we guaranteed the works council and the data protection officer
that confidentiality was given at any time of the study and that the
data do not permit conclusions on the individual level.
Materials and Method
Sample
To evaluate the effectiveness of the intervention, we recruited
employees in one large enterprise in Germany between December
2016 and January 2017. The enterprise belonged to the mobility
industry focusing on the transportation of freight and passengers.
During recruitment the focus was explicitly on physically inactive
employees at risk, who wanted to improve their health behavior.
By means of posters, flyers, and an e-mail sent by the executive,
employees’ attention was drawn to a website offering information
about the study (e.g., information about data security) and the
opportunity to register for participation. Participation was volun-
tary and free of charge (including the use of the activity tracker),
but employees had to be aged 18 or older and were not medically
required to be on a diet or activity plan. In total 121 employees
were enlisted as participants and were randomly assigned to the IG
(n⫽60) or the waitlist CG (n⫽61). At the beginning of the study
both groups received an invitation to complete the prequestion-
naire (T1). Participants who did not complete the first question-
naire were excluded from further analysis. Therefore, the final
sample consisted of 116 employees (IG: n⫽59; CG: n⫽57). In
the final sample the age ranged from 19 to 62 years (M⫽43.01;
SD ⫽12.72), and 45.7% were female. The majority had acquired
vocational training and worked full-time (86.1%). Because we
focused on physically inactive employees at risk during the re-
cruiting procedure, employees worked in rather sedentary jobs.
Moreover, 68.1% of the employees in the sample were classified
as overweight or obese (BMI: M⫽27.21, SD ⫽4.74).
Because only a very small number of employees dropped out
due to not answering any further questionnaire after T1 (n⫽4;
3.45%), we could not conduct a dropout analysis. However, we
conducted a randomization check, testing whether the IG and CG
differed in relation to sociodemographic variables and study vari-
ables at baseline (T1). No significant difference in sociodemo-
graphic variables and study variables were found.
Measures
The five questionnaires (T1–T5) were assessed online and in-
cluded all study variables at each point of measurement. To match
the questionnaires, participants were requested to create a personal
code at the beginning of each questionnaire.
Physical activity. Physical activity was measured with a mod-
ified version of the Godin Leisure-Time Exercise Questionnaires
(GLTEQ; Godin & Shephard, 1985). Participants were asked how
many times per week they had engaged in moderate and strenuous
physical activity during the last month. Moderate physical activity
was defined as not exhausting activities with a light perspiration
(e.g., fast walking, gentle bicycling, badminton). Strenuous phys-
ical activity included activities such as running, vigorous long-
distance cycling, or football, where the heartbeats rapidly and the
perspiration is intense. To calculate a total score of weekly mod-
erate to vigorous physical activity (MVPA), answers were con-
verted to their metabolic equivalent. Metabolic equivalent (MET)
expresses the energy expenditure as a result of being physical
active (Byrne, Hills, Hunter, Weinsier, & Schutz, 2005). Accord-
ing to the different categories of physical activity, we multiplied
the frequency of moderate physical activity by five MET and the
frequency of strenuous physical activity by nine MET (Godin,
2011). The products of the various categories were then summed
up into a total weekly MVPA score. For instance, for an employee
who cycled to work four times a week (moderate physical activity)
and played football twice a week (vigorous physical activity), the
total MVPA would be calculated in the following way:
(4 times cycling to work ⫻5 MET)
moderate physical activity
⫹(2 times football ⫻9 MET)
vigorous physical activity
⫽38 MVPA
Outliers were truncated to seven sessions of activity per week
based on the assumption that most people engage in both moderate
and strenuous activity only once per day. Accordingly the total
MVPA score ranged from 0 to 98 MVPA (7 sessions ⫻5 MET ⫹
7 sessions ⫻9 MET) per week. The GLTEQ yields a retest
reliability at 1 month of .62 for the total MVPA score (Sallis &
Saelens, 2000). Moreover, the GLTEQ has been validated with
physiological measures such as body fat and maximum oxygen
intake (Amireault & Godin, 2015; Godin & Shephard, 1985).
Burnout. We assessed participants’ burnout with the German
version of the Shirom–Melamed Burnout Measure (Shirom &
Melamed, 2006). The Shirom–Melamed Burnout Measure consists
of 14 items divided into three subscales: Physical Fatigue (e.g., “I
feel physically drained”), Cognitive Weariness (e.g., “I have dif-
ficulty concentrating”), and Emotional Exhaustion (e.g., “I feel I
am unable to be sensitive to the needs of coworkers and custom-
ers”). Participants were asked how often they had experienced
these feelings at work during the last 3 weeks. Response alterna-
tives were given on a scale from 1 (never or almost never)to7
(always or almost always). Based on the participants’ ratings, a
global burnout score was calculated by aggregating the three
different subscales.
Vigor. To assess vigor we used the three-item-subscale taken
from the German Version of the Utrecht Work Engagement Scale
(Sautier et al., 2015; Schaufeli et al., 2006). A sample item reads:
“When I get up in the morning, I feel like going to work.”
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5
TECHNOLOGY-BASED PHYSICAL ACTIVITY INTERVENTION
Participants could rate their individual vigor on a scale ranging
from 1 (never)to7(always/everyday).
The results of the reliability analysis for burnout and vigor are
presented in Table 1 and show good internal consistencies at all
measurement points. We conducted a confirmatory factor analysis
(CFA) including burnout and vigor measured at T1 using MPLUS 8.
Concerning the two-factor model with correlated but independent
factors, the CFA revealed an acceptable fit for burnout (respecting the
subscale structure) and vigor:
2
(115, N⫽116) ⫽227.64, p⫽.000;
comparative fit index (CFI) ⫽.92; root mean square error of approx-
imation (RMSEA) ⫽.09; standardized root mean square residual
(SRMR) ⫽.09.
General health perception. General health perception was
measured with a single-item of the Short-Form-36 Health Survey
(Bullinger, 1995; Ware & Sherbourne, 1992). Participants were
requested to indicate on a scale from 1 (excellent)to5(poor) how
they perceive their health in general. The reliability and validity of
this single-item measure has been confirmed by DeSalvo et al.
(2006).
Body mass index. To assess participants’ BMI we asked
participants for information about their weight in kilograms (“How
much do you weight?”) and their height in centimeters (“What is
your height?”). To calculate BMI, we divided the weight of each
participant by their height in meters squared (kg/m
2
).
Time pressure. Besides the dependent variables, we as-
sessed time pressure as a control variable at T1. Time pressure
was measured by using the subscale of the German Instrument
for Stress-Oriented Task Analysis (Semmer, Zapf, & Dunckel,
1995, 1999). In total the subscale consisted of four items, which
can be rated on a scale reaching from 1 (very seldom/never)to
5(very often). An examples item said: “How often are you
under time pressure?.” Cronbach’s ␣for time pressure was .73
within our sample.
Statistical Analysis
Analyses were conducted using IBM SPSS Statistics 24. First,
the data were tested for normal distribution as a requirement for
applying repeated analyses of variance. The Sharipov-Wilk test did
not show normal distribution for all study variables; therefore,
nonparametric tests were performed additionally. However, the
results remained qualitatively unchanged for all analyses.
Intervention effects were tested with repeated analyses of vari-
ance (ANOVA) comparing the intervention with control condition
between T1–T2 with ␣⫽.05 as a criterion for significance. For
the investigation of long-term effects, ANOVAs for repeated mea-
sures were conducted with the IG only. Additionally, a manipula-
tion check was conducted by using a repeated analysis of covari-
ance (ANCOVA), investigating the effect of the intervention on
physical activity by controlling for time pressure.
Partial eta-squared (p
2) was used to interpret the relevance of
the effects. A small effect is taken to be p
2ⱖ.01, a medium effect,
p
2ⱖ.06, and a large effect p
2ⱖ.14 (Cohen, 1988).
Results
Before testing for intervention effects, we conducted a manip-
ulation check to test whether the IG showed a higher level of
physical activity after completing the intervention. An ANCOVA
for repeated measures revealed a significant interaction effect of
Group ⫻Time after controlling for time pressure at T1, F(1,
102) ⫽4.20, p⫽.043. p
2⫽.04. Thus, the ANCOVA confirmed
that the IG showed increased physical activity after the interven-
tion compared with the CG (see Table 2), which indicates that the
intervention was effective in increasing employees’ physical ac-
tivity. The covariate, time pressure, was not significantly related to
employees’ physical activity, F(1, 102) ⫽0.61, p⫽.801, p
2⫽.00.
Additionally to the manipulation check, we conducted a post hoc
analysis testing Baseline ⫻Treatment effects, namely, whether em-
Table 1
Zero-Order Correlations and Reliability of Study Variables
Variables 1234567891011121314151617181920
1. Burnout T1 (.93)
2. Burnout T2 .77
ⴱⴱ
(.96)
3. Burnout T3 .79
ⴱⴱ
.85
ⴱⴱ
(.95)
4. Burnout T4 .71
ⴱⴱ
.84
ⴱⴱ
.83
ⴱⴱ
(.97)
5. Burnout T5 .72
ⴱⴱ
.60
ⴱⴱ
.65
ⴱⴱ
.50
ⴱⴱ
(.98)
6. Vigor T1 ⫺.61
ⴱⴱ
⫺.66
ⴱⴱ
⫺.58
ⴱⴱ
⫺.44
ⴱⴱ
⫺.44
ⴱⴱ
(.90)
7. Vigor T2 ⫺.53
ⴱⴱ
⫺.69
ⴱⴱ
⫺.63
ⴱⴱ
⫺.48
ⴱⴱ
⫺.44
ⴱⴱ
.84
ⴱⴱ
(.91)
8. Vigor T3 ⫺.61
ⴱⴱ
⫺.72
ⴱⴱ
⫺.66
ⴱⴱ
⫺.64
ⴱⴱ
⫺.62
ⴱⴱ
.84
ⴱⴱ
.91
ⴱⴱ
(.95)
9. Vigor T4 ⫺.60
ⴱⴱ
⫺.71
ⴱⴱ
⫺.77
ⴱⴱ
⫺.62
ⴱⴱ
⫺.46
ⴱⴱ
.78
ⴱⴱ
.80
ⴱⴱ
.86
ⴱⴱ
(.94)
10. Vigor T5 ⫺.62
ⴱⴱ
⫺.54
ⴱⴱ
⫺.64
ⴱⴱ
⫺.43
ⴱⴱ
⫺.70
ⴱⴱ
.70
ⴱⴱ
.63
ⴱⴱ
.76
ⴱⴱ
.56
ⴱⴱ
(.95)
11. Health perception T1 ⫺.33
ⴱⴱ
⫺.31
ⴱⴱ
⫺.32
ⴱⴱ
⫺.27
ⴱ
⫺.25
ⴱ
.28
ⴱⴱ
.28
ⴱⴱ
.18 .16 .25
ⴱ
—
12. Health perception T2 ⫺.35
ⴱⴱ
⫺.34
ⴱⴱ
⫺.34
ⴱⴱ
⫺.23
ⴱ
⫺.33
ⴱⴱ
.28
ⴱⴱ
.32
ⴱⴱ
.24
ⴱ
.14 .49
ⴱⴱ
.69
ⴱⴱ
—
13. Health perception T3 ⫺.20 ⫺.23 ⫺.15 ⫺.29 ⫺.34 .13 .28 .08 ⫺.05 .35
ⴱ
.71
ⴱⴱ
.73
ⴱⴱ
—
14. Health perception T4 ⫺.38
ⴱ
⫺.40
ⴱ
⫺.46
ⴱⴱ
⫺.48
ⴱⴱ
⫺.49
ⴱⴱ
.42
ⴱ
.40
ⴱ
.33 .50
ⴱⴱ
.42
ⴱⴱ
.31 .35
ⴱ
.57
ⴱⴱ
—
15. Health perception T5 ⫺.00 ⫺.03 ⫺.05 ⫺.11 ⫺.22 .20 .18 .09 ⫺.14 .33 .39
ⴱ
.32 .62
ⴱⴱ
.24 —
16. BMI T1 ⫺.06 .05 ⫺.02 .06 .04 ⫺.06 ⫺.07 ⫺.01 .05 .00 ⫺.24
ⴱⴱ
⫺.30
ⴱⴱ
⫺.24
ⴱ
⫺.06 ⫺.24
ⴱ
—
17. BMI T2 .03 .07 .03 .09 .05 ⫺.12 ⫺.08 ⫺.05 ⫺.02 ⫺.11 ⫺.20 ⫺.27
ⴱⴱ
⫺.20 ⫺.08 ⫺.19 .99
ⴱⴱ
—
18. BMI T3 ⫺.11 .06 ⫺.02 .09 ⫺.06 ⫺.08 ⫺.17 .01 .00 .00 ⫺.28 ⫺.17 ⫺.28 ⫺.14 ⫺.34
ⴱ
.99
ⴱⴱ
.99
ⴱⴱ
—
19. BMI T4 ⫺.00 .07 ⫺.00 .12 .01 ⫺.08 ⫺.03 .04 .07 .00 ⫺.26 .16 ⫺.17 ⫺.06 ⫺.13 .98
ⴱⴱ
.98
ⴱⴱ
.98
ⴱⴱ
—
20. BMI T5 ⫺.07 .02 ⫺.05 .12 ⫺.03 ⫺.08 ⫺.13 .01 .18 ⫺.00 ⫺.35
ⴱ
⫺.30 ⫺.31 .05 ⫺.35
ⴱ
.97
ⴱⴱ
.98
ⴱⴱ
.97
ⴱⴱ
.98
ⴱⴱ
—
Note.T1⫽Time 1; T2 ⫽Time 2; T3 ⫽Time 3; T4 ⫽Time 4; T5 ⫽Time 5; BMI ⫽body mass index. Correlations involving T3, T4, and T5 only
include participants of the intervention group (n: follow-up I ⫽47, follow-up II ⫽35, follow-up III ⫽35).
ⴱ
p⬍.05.
ⴱⴱ
p⬍.01.
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6LENNEFER, LOPPER, WIEDEMANN, HESS, AND HOPPE
ployees in the IG who were inactive at T1 benefited more from the
intervention than active employees. Therefore, a median split (Mdn ⫽
14) was used with the physical activity data at T1 creating a group of
inactive employees (n⫽29) and a group of active employees (n⫽
28). The ANOVA for repeated measures could find a significant
interaction of Group ⫻Time, F(1, 45) ⫽4.47, p⫽.040, p
2⫽.09.
Inactive employees benefited significantly more from the intervention
than employees who were already active at T1. Nevertheless, the
analysis still revealed a significant main effect showing that both
groups increase their physical activity form T1 to T2, F(1, 45) ⫽5.53,
p⫽.023, p
2⫽.11.
As a second manipulation check, participants were asked if they
were still using the activity tracker 3 months and 1 year after the
intervention and 84.2% of them reported that they were still using
the activity tracker 3 months after the intervention. One year after
the intervention, 74.3% of the participants were still using their
activity trackers. Zero-order correlations between all study vari-
ables for all times of measurements are shown in Table 1.
Intervention Effects on Work-Related Well-Being
Intervention effects on burnout. Our first hypothesis pro-
posed that the employees engaging in the intervention activities show
a lower level of burnout than employees not performing the interven-
tion. To test this assumption, we conducted an ANOVA for repeated
measures between T1–T2. The results showed no significant interac-
tion effect for Group ⫻Time, F(1, 103) ⫽.57, p⫽.452, p
2⫽.01
(see Figure 1a). Thus, Hypothesis 1 was not supported.
Table 2
Means and Standard Deviation for Study Variables at All Points of Measurement for Intervention and Control Group
M(SD)
Variable Group Pre (T1) Post (T2) Follow-up I (T3) Follow-up II (T4) Follow-up III (T5)
Physical activity (MVPA) IG 18.98 (16.36) 24.86 (21.91)
CG 20.05 (16.91) 19.89 (16.45)
Burnout IG 2.75 (0.94) 2.57 (1.11) 2.47 (1.03) 2.60 (1.26) 2.45 (1.18)
CG 3.01 (0.97) 3.05 (1.12) — — —
Vigor IG 4.77 (1.19) 4.72 (1.33) 4.67 (1.34) 4.77 (1.23) 4.89 (1.22)
CG 4.43 (1.19) 4.20 (1.16) — — —
Health perception IG 3.56 (0.75) 3.87 (0.82) 3.85 (0.71) 3.94 (0.73) 3.83 (0.75)
CG 3.51 (0.83) 3.53 (0.83) — — —
Body mass index IG 26.95 (4.55) 26.77 (4.63) 26.77 (4.68) 26.50 (4.71) 26.87 (4.89)
CG 27.48 (4.94) 27.53 (4.91) — — —
Note.T1⫽Time 1; T2 ⫽Time 2; T3 ⫽Time 3; T4 ⫽Time 4; T5 ⫽Time 5; MVPA ⫽moderate to vigorous physical activity; IG ⫽intervention group;
CG ⫽control group. n(IG): pre ⫽59, post ⫽48, follow-up I ⫽47, follow-up II ⫽35, follow-up III ⫽35; n(CG): pre ⫽57, post ⫽57.
Figure 1. (a). Development of means for burnout for the IG and CG between T1 and T2. (b) Development of
means for burnout within the IG across T1, T2, T3, T4, and T5. IG ⫽intervention group; CG ⫽control group;
T1 ⫽Time 1; T2 ⫽Time 2; T3 ⫽Time 3; T4 ⫽Time 4; T5 ⫽Time 5.
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7
TECHNOLOGY-BASED PHYSICAL ACTIVITY INTERVENTION
We tested for long-term effects within the IG 1 month, 3
months, and 1 year after the intervention (see Figure 1b). One
month after the intervention participants showed a significant
reduction in burnout compared with burnout at baseline (see
Table 2). The ANOVA for repeated measures between T1–T3
revealed a significant effect on burnout with a medium effect
size, F(1, 46) ⫽5.10, p⫽.029, p
2⫽.10. No significant effect
could be found 3 months (T1–T4) after the intervention, F(1,
34) ⫽.05, p⫽.82, p
2⫽.00, or 1 year after the intervention,
F(1, 34) ⫽1.47, p⫽.234, p
2⫽.04. In summary, we found a
significant reduction in burnout level 1 month after the inter-
vention, but this effect did not persist 3 months and 1 year after
the intervention.
Intervention effects on vigor. To test the second hypothesis,
stating that the intervention has a positive effect on vigor, we
performed an ANOVA for repeated measures between T1–T2. The
results revealed no significant interaction effect for Group ⫻Time,
F(1, 103) ⫽.74, p⫽.391, p
2⫽.01 (see Figure 2a). Hence,
Hypothesis 2 was not supported.
Long-term effects were tested by conducting an ANOVA for
repeated measures within the IG between T1–T3, T1–T4, and
T1–T5 (see Figure 2b). No significant intervention effect could be
found 1 month after the intervention (T1–T3), F(1, 46) ⫽1.43,
p⫽.237, p
2⫽.03, 3 months after the intervention, F(1, 34) ⫽
2.02, p⫽.165, p
2⫽.06, and 1 year after the intervention, F(1,
34) ⫽1.86, p⫽.181, p
2⫽.05.
Intervention Effects on Physical Health
Intervention effects on health perception. As proposed in
Hypothesis 3, we expected that employees engaging in interven-
tion activities show higher levels of health perception. An
ANOVA for repeated measures revealed a significant interaction
effect for Group ⫻Time with a small effect size, F(1, 103) ⫽4.93,
p⫽.029, p
2⫽.05 (see Figure 3a). The IG showed significantly
higher means in health perception after the intervention period (see
Table 2). Thus, Hypothesis 3 was supported.
To test whether the intervention effect was persistent over time,
an ANOVA for repeated measures between T1–T3 was conducted
with the IG. It showed that a significant intervention effect on
health perception still persisted 1 month after the intervention
revealing a large effect size, F(1, 47) ⫽9.40, p⫽.004, p
2⫽.17.
Further long-term effects were tested 3 months (T1–T4) and 1
year (T1–T5) after the intervention. Three months after the
intervention the IG showed significant higher means in health
perception than before engaging in the intervention activities,
F(1, 34) ⫽4.99, p⫽.032, p
2⫽.13. However, 1 year after the
intervention the ANOVA for repeated measures revealed no
significant intervention effect, F(1, 34) ⫽1.09, p⫽.304, p
2⫽
.03. The significant increase in health perception within the IG
is shown in Figure 3b.
Intervention effects on BMI. Our fourth hypothesis proposed
that the intervention affects employees’ BMI in the sense that
employees in the IG have a lower BMI than employees in the CG.
The ANOVA for repeated measures revealed a significant inter-
action effect for Group ⫻Time between T1–T2 with a medium
effect size, F(1, 103) ⫽9.07, p⫽.003, p
2⫽.08 (see Figure 4a).
Hence, Hypothesis 4 was supported.
We moreover tested if the intervention effect on BMI within the
IG was long-lasting and therefore conducted an ANOVA for
repeated measures between T1–T3. It showed that there was still a
reduction of BMI with a large effect 1 month after the intervention,
F(1, 47) ⫽13.77, p⫽.001, p
2⫽.23. Additionally, we tested for
Figure 2. (a) Development of means for vigor for the IG and CG between T1 and T2. (b) Development of
means for vigor within the IG across T1, T2, T3, T4, and T5. IG ⫽intervention group; CG ⫽control group;
T1 ⫽Time 1; T2 ⫽Time 2; T3 ⫽Time 3; T4 ⫽Time 4; T5 ⫽Time 5.
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8LENNEFER, LOPPER, WIEDEMANN, HESS, AND HOPPE
long-term effects 3 months after the intervention (T1–T4) and 1
year after the intervention (T1–T5). The ANOVA for repeated
measures revealed a significant intervention effect with a large
effect size 3 months after the intervention, F(1, 36) ⫽18.25, p⫽
.000, p
2⫽.34. One year after the intervention the ANOVA for
repeated measures between T1–T5 showed a significant interven-
tion effect on BMI with a large effect size, F(1, 34) ⫽6.85, p⫽
.013, p
2⫽.17. Consequently, 1 year after the intervention,
employees in the IG had a lower BMI than before engaging in
the intervention activities (see Table 2). The significant reduc-
tion of mean in BMI overtime within the IG is illustrated in
Figure 4b.
Figure 3. (a) Development of means for health perception for the IG and CG between T1 and T2. (b)
Development of means for health perception within the IG across T1, T2, T3, T4, and T5. IG ⫽intervention
group; CG ⫽control group; T1 ⫽Time 1; T2 ⫽Time 2; T3 ⫽Time 3; T4 ⫽Time 4; T5 ⫽Time 5.
Figure 4. (a) Development of means for BMI for the IG and CG between T1 and T2. (b) Development of
means for BMI within the IG across T1, T2, T3, T4, and T5. IG ⫽intervention group; CG ⫽control group;
BMI ⫽body mass index; T1 ⫽Time 1; T2 ⫽Time 2; T3 ⫽Time 3; T4 ⫽Time 4; T5 ⫽Time 5.
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9
TECHNOLOGY-BASED PHYSICAL ACTIVITY INTERVENTION
Discussion
The aim of this study was to examine the effect of a cost-
effective new technology on physical activity as well as work-
related well-being and physical health in a sample of employees at
risk within one company. By combining a behavioral intervention
approach using activity trackers and a cognitive intervention ap-
proach providing an online coach, we offered an attractive inter-
vention tool that was still being used by most of the participants
even 1 year after the intervention. With an RCT design and
long-term follow-up analyses, we were able to show that high-risk
employees benefited substantially from the intervention through an
increase in health perception and a reduction of BMI.
Long-term analyses were conducted with the IG only and revealed
that the intervention effect on health perception and BMI were per-
sistent over time (3 months for health perception and 1 year for BMI).
Contrary to our expectations we found no effect on work-related
well-being. Interestingly, long-term analyses showed a significant
reduction in burnout in the IG with a medium effect size 1 month after
completion of the intervention. However, no further long-term effects
on work-related well-being were found. Overall, the results demon-
strate that the intervention did indeed improve employees’ physical
health, but had no impact on work-related well-being.
To the best of our knowledge, this is the first study to investigate
the effectiveness of a combined cognitive– behavioral intervention
using activity trackers. Due to their accessibility and availability
(can be used anywhere, at any time; Borrelli & Ritterband, 2015),
activity trackers combined with an online tool are an effective and
economic way to improve employees’ physical health. We discuss
the findings in relation to work-related well-being (i.e., burnout
and vigor) and physical health (i.e., health perception and BMI) in
the following text.
Enhancing Work-Related Well-Being
Contrary to our hypotheses, we found no direct intervention
effect on burnout and vigor. We found a delayed effect for burnout
1 month after the intervention in the IG. However, this effect was
not maintained 3 weeks or 1 year after the intervention. Contrary
to our findings, various studies have indeed reported significant
associations between physical activity and burnout (Bretland &
Thorsteinsson, 2015; Gerber et al., 2013; Naczenski et al., 2017;
Tsai et al., 2013; Van Rhenen et al., 2005). Given that our
manipulation check showed an increase in physical activity, we
would also have expected to see effects on burnout. The following
explanations are possible.
In contrast to these studies, we aimed to reduce burnout by
introducing activity trackers as a cost-effective intervention sup-
ported by an online coach. Most of the interventions capable of
significantly reducing burnout have included face-to-face exercise
sessions with a professional trainer (Bretland & Thorsteinsson,
2015; Gerber et al., 2013; Tsai et al., 2013; van Rhenen et al.,
2005). One typical symptom of burnout is withdrawal from social
contacts and consequently the danger of isolation (Maslach &
Pines, 1977). Face-to-face exercise sessions may possibly exten-
uate this symptom by linking the positive experience of being
physically active (e.g., enjoyment) to social interaction. Moreover,
meeting with a professional trainer may be experienced as social
support, so that factors other than physical activity alone may have
contributed to reduction in burnout. This assumption is supported
by findings from Tsai et al. (2013), who showed that participants
who attended exercise sessions led by a professional trainer expe-
rienced significantly reduced burnout whereas participants who
had to plan and carry out exercises on their own showed no
alleviation of burnout. Another study that reported a significant
decrease in burnout included relaxation techniques in addition to
physical activity exercises that possibly boost the positive effect of
physical activity on burnout (Van Rhenen et al., 2005). Because
our intervention did not address these factors, this might also be
the reason why we only found an intervention effect on burnout 1
month after the intervention. As Schaufeli, Maassen, Bakker, and
Sixma (2011) postulated 72–77% of the variance in burnout is
accounted to a change component influenced by several factors
such as long working hours (Lim, Kim, Kim, Yang, & Lee, 2010)
or social support (Adriaenssens, De Gucht, & Maes, 2015; Hal-
besleben, 2006). It is possible that these factors fluctuate over the
course of the study, which might have caused the effect on burnout
1 month after the study. Apart from the intervention design in-
volving no face-to-face interaction, the assessment of burnout
which we used in our study may also have yielded different results.
Contrary to many studies, we assessed all dimensions of burnout,
whereas other studies have focused on subscales, primarily the
Emotional Exhaustion subscale (Naczenski et al., 2017). Reducing
burnout overall instead of its components is likely to be harder to
achieve or may require different intervention approaches.
In relation to vigor, we found no intervention effects. Here, the
correlational studies are also more inconsistent: Some studies have
reported a positive association between physical activity and vigor
(ten Brummelhuis & Bakker, 2012), whereas other studies report
no such association (van Berkel et al., 2013). The only RCT-
intervention study to report a positive effect of physical activity on
employees’ vigor consisted of a very small sample size (IG: n⫽
24; CG: n⫽10; Pronk et al., 2012). The second RCT study on
physical activity and employees’ vigor found no overall effect of
the intervention (Strijk et al., 2013). In this study only employees
engaging intensively in physical activity (i.e., yoga workouts), felt
significantly more vigorous after the intervention. First, the study
by Strijk et al. (2013) may indicate that a large amount of physical
activity is necessary to significantly increase vigor. Other compo-
nents such as mental relaxation and mindfulness inherent in the
practice of yoga accentuated the effect on vigor. We argued above
that being physically active produces higher cortisol concentration
(Anderson & Wideman, 2017), which in turn is associated with
feeling more vigorous (Tops et al., 2006). However, González-
Romá et al. (2006) pointed out that high intensity of physical
activity is needed to raise cortisol concentration. It may be that our
intervention did not enhance the intensity of employees’ physical
activity sufficiently so as to increase cortisol concentration and in
turn vigor. Charmas et al. (2009) corroborated this when showing
that a 1-hr aerobic session occasioned no increase in cortisol
concentration. Van Berkel et al. (2013) moreover assumed that
they failed to find an association between vigor and physical
activity because the employees did not perform enough physical
activity. Because the rise of cortisol as a bodily response to
physical activity occurs short term, it is also plausible that we
found no intervention effect on vigor because no measurements
were taken immediately after employees were physically active. It
may be that the increase in physical activity caused by our inter-
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10 LENNEFER, LOPPER, WIEDEMANN, HESS, AND HOPPE
vention did indeed increase vigor, but that effect declined before
the employees reported their vigor.
It may well be overall that physical activity affects work-related
well-being less directly than other health outcomes. One option for
more effective physical activity interventions could be to include
components such as face-to-face sessions and mental relaxation to
support the positive effect of physical activity on burnout and
vigor. By including such components in our intervention the slight
effect of the intervention on burnout could likely be reinforced and
vigor significantly enhanced.
Improving Physical Health
In line with our hypotheses, the intervention did effectively
improve employees’ health perceptions and reduce BMI. Addi-
tionally, we were able to show that the IG benefited from the
intervention as evidenced a continuous improvement in health
perception with a medium effect size up to 3 months after the
intervention and a constant decrease in BMI with a large effect size
up to 1 year after the intervention. Large effect sizes in workplace
intervention research, especially with interventions involving no
face-to-face interactions or multiple workshops over several months
are rare and serve to underline the efficacy of this intervention.
Our results support the findings of various other intervention
studies on physical activity that increased employees’ health per-
ceptions and lowered their BMI (Anderson et al., 2009; Bogaert et
al., 2014; Okano et al., 2003; Reed et al., 2017). Interestingly, the
only study to investigate the effect of an intervention with activity
trackers on employees’ health found no effects in relation to health
outcomes (Finkelstein et al., 2016). These inconclusive results may
be attributable to differences in intervention design. Finkelstein et
al. (2016) offered employees a financial incentive in addition to the
activity tracker when they walked a certain number of steps per
week. The external reward offered by Finkelstein et al. (2016)
rather enhances extrinsic motivation, whereas our intervention was
intended to increase extrinsic and intrinsic motivation by enhanc-
ing enjoyment (e.g., the step challenge), competence for behavior
change (advice from the online coach), or setting personal objec-
tives (tool for goal setting; Ryan, Patrick, Deci, & Williams, 2008).
It is well established that intrinsic motivation is more effective
than extrinsic motivation with regard to promoting health behavior
change and health, especially when aiming at long-term effects
(Ng et al., 2012; Silva et al., 2010; Teixeira, Carraça, Markland,
Silva, & Ryan, 2012). We therefore believe that the combination of
a cognitive and behavioral approach is effective and necessary to
ensure strong effects over time.
Limitations and Future Research
In the following we will discuss the limitations of our study and
provide suggestions for future research. First, 43.3% of the par-
ticipants in the IG dropped out between the first and the last
questionnaire 1 year later. Although other eHealth studies inves-
tigating long-term effects have reported higher dropout rates (Etter
[2005]: 64.3%; Eysenbach, 2005), in our study relatively small
sample sizes resulted from the dropout for the long-term analyses
(T4: n⫽35; T5: n⫽35) given that we started the study with a
relatively small sample of 60 employees.
One major strength of our study was the use of an RCT design
that enabled causal conclusions on the effectiveness of the inter-
vention. We can firmly state that our results are not a cause of
survey effects (Sitzmann & Wang, 2015) or a result of participat-
ing in a study (Hawthorne effect; Wickström & Bendix, 2000).
Nevertheless, we only had access to the CG between T1–T2.
Therefore, long-term effects could only be tested within the IG.
However, as effects on behavior change as a result of participating
in a study mainly occurs short term, it is less probable that our
long-term effects are skewed due to Hawthorne effects (McCam-
bridge, Witton, & Elbourne, 2014). However, further studies
should include a CG when testing for long-term effects to totally
exclude possible survey effects. Also, future studies should include
an alternative intervention to contrast different types of physical
activity interventions.
Despite recruiting at a single mobility enterprise in Germany
enabled us to collect data from a relatively homogenous population
of employees at risk, the generalizability to other occupational
sectors might be reduced. Thus, it could be possible that the
intervention show different efficacy under different working con-
ditions. For instance, employees who are required to work in a
fixed body positions for a longer period of time (e.g., airplane
pilots), might be restricted to fulfill the intervention due to their
low job control on how to carry out their job. Because it has been
shown that low job control is associated with physical inactivity
(Fransson et al., 2012) and a higher BMI (Berset, Semmer, Elfer-
ing, Jacobshagen, & Meier, 2011; Kottwitz, Grebner, Semmer,
Tschan, & Elfering, 2014), future studies might consider the in-
fluence of job control when recruiting at specific occupational
sector.
Another potential limitation of our study is that some measures
may not have been sensitive enough to detect possible intervention
effects. With regard to work-related well-being it is possible that
the intervention would have shown an effect on a less extreme
stress-syndrome than burnout. Burnout has been described as a
syndrome that occurs after prolonged exposure to stress at work
(Shirom & Melamed, 2006). Therefore, it would be interesting to
investigate the effect of a cognitive– behavioral intervention with
activity trackers on employees’ stress levels using a more sensitive
stress measure. Moreover, it might be possible that other work-
related stressors could influence the effectiveness of the interven-
tion. In our study we included time pressure as a stressor, which,
however, had no impact on employees’ physical activity. Never-
theless, previous studies showed that other work-related stressors
such as long working hours could diminish physical activity
among employees (Kirk & Rhodes, 2011; Schneider & Becker,
2005; Wemme & Rosvall, 2005). Thus, future intervention studies
including activity trackers should consider further potential work-
related stressors when aiming to improve work-related well-being.
Furthermore, our intervention may not have been effective enough
to increase physical activity to a level that in turn boosts cortisol
levels beyond a putative threshold that is needed to improve vigor.
Practical Implications and Conclusion
The results of our study show that activity trackers are a prom-
ising technology for workplace health promotion. In combination
with an online coach as a cognitive approach, activity trackers are
effective in improving physical health for employees at risk over
time. Given that obese employees show a 22.6% higher work
absence (Duijts et al., 2007) and that a decrease of one BMI point
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11
TECHNOLOGY-BASED PHYSICAL ACTIVITY INTERVENTION
will lower the costs for an obese employee by 3.7% (Van Nuys et
al., 2014), it is most likely that a cognitive– behavioral intervention
with activity tracker could reduce employers’ costs for absentee-
ism in the long term. Moreover, activity trackers are not costly and
therefore offer an interesting approach for companies to foster
physical activity among employees. Also, the online coach can be
developed and programmed to include considerable resources.
Both approaches are extremely attractive, as they offer brief inter-
ventions that can be conducted during and after working hours.
Moreover, it is noteworthy that the majority of employees in the IG
continued using the activity tracker after the intervention period.
Three months after the intervention, 84.2% of the employees were
still using their activity trackers and 74.3% of the employees were
still using the activity tracker 1 year after the intervention. This
suggests that employees enjoy using activity trackers and that
these serve to motivate employees to engage perseveringly in
health behavior. Along with the fact that our intervention yielded
large effect sizes, this seems to be a promising approach for
employees at risk. We wish to note at this point that this interven-
tion was strongly promoted by the human resources department of
the enterprise. Employees received detailed information through
e-mail, flyers, and the project website. Also, the works council and
the data protection officer were involved at all times and supported
our study. Even though the intervention itself is quite feasible it
involved a considerable amount of resources from the organizers
and was well-integrated into the wider health promotion strategy
of the company.
We need to acknowledge that apart from positive effects for
physical health, this intervention approach was not effective in
reducing burnout and promoting vigor. Other approaches such as
mental relaxation, positive work reflection, or mindfulness seem
more promising for reducing burnout, emotional exhaustion, and
increasing vigor (Clauss et al., 2018; Luken & Sammons, 2016;
Steidle, Gonzalez-Morales, Hoppe, Michel, & O’Shea, 2017).
In conclusion, our study showed that a cognitive– behavioral
intervention with activity trackers effectively improved employ-
ees’ health perception and reduced BMI with a medium and a large
effect size. These effects were sustainable over time. Three months
after the intervention employees still perceived their health status
to be superior to what it had been before the intervention. Regard-
ing BMI, employees showed a significant decrease until 1 year
after the intervention. We therefore conclude that our cognitive–
behavioral intervention with activity trackers was effective in
sustainably improving physical health for employees at risk.
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Received October 25, 2018
Revision received May 26, 2019
Accepted July 29, 2019 䡲
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