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Original Paper
Using Feedback to Promote Physical Activity: The Role of the
Feedback Sign
Jan-Niklas Kramer, MSc; Tobias Kowatsch, PhD
Center for Digital Health Interventions, Institute of Technology Management, University of St. Gallen, St.Gallen, Switzerland
Corresponding Author:
Tobias Kowatsch, PhD
Center for Digital Health Interventions
Institute of Technology Management
University of St. Gallen
Dufourstrasse 40a
St.Gallen,
Switzerland
Phone: 41 0 71 224 ext 7244
Fax: 41 0 71 224 7301
Email: tobias.kowatsch@unisg.ch
Abstract
Background: Providing feedback is a technique to promote health behavior that is emphasized by behavior change theories.
However, these theories make contradicting predictions regarding the effect of the feedback sign—that is, whether the feedback
signals success or failure. Thus, it is unclear whether positive or negative feedback leads to more favorable behavior change in
a health behavior intervention.
Objective: The aim of this study was to examine the effect of the feedback sign in a health behavior change intervention.
Methods: Data from participants (N=1623) of a 6-month physical activity intervention was used. Participants received a feedback
email at the beginning of each month. Feedback was either positive or negative depending on the participants’ physical activity
in the previous month. In an exploratory analysis, change in monthly step count averages was used to evaluate the feedback
effect.
Results: The feedback sign did not predict the change in monthly step count averages over the course of the intervention
(b=−84.28, P=.28). Descriptive differences between positive and negative feedback can be explained by regression to the mean.
Conclusions: The feedback sign might not influence the effect of monthly feedback emails sent out to participants of a large-scale
physical activity intervention. However, randomized studies are needed to further support this conclusion. Limitations as well as
opportunities for future research are discussed.
(J Med Internet Res 2017;19(6):e192) doi:10.2196/jmir.7012
KEYWORDS
feedback; internet; physical activity; health behavior; activity trackers
Introduction
Background
In 2012, noncommunicable diseases (NCDs) such as diabetes,
cardiovascular diseases, chronic respiratory diseases, or cancer
were responsible for 68% of deaths worldwide [1].
Physical activity plays a crucial role in the prevention and
management of NCDs, as it has been found to affect the
incidence and course of NCDs such as diabetes [2], asthma [3],
and cancer [4], as well as associated risk factors such as
hypertension [5], overweight [6], or high blood sugar [7]. To
reduce the burden of NCDs on the worlds’ health and health
care systems, researchers have focused on the development of
effective physical activity interventions among others.
Physical activity interventions often use feedback as a method
to change behavior [8-11]. For example, in a review of
technology-enabled health interventions [11], feedback was
employed in 55 out of approximately 110 (50.0%) reviewed
interventions targeting physical activity and was identified as
the second-most used behavior change technique. Abraham and
Michie [12] defined feedback as “providing data about recorded
behavior or evaluating performance in relation to a set standard
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or others’ performance” (p.382). Despite its widespread use in
physical activity interventions however, detailed analysis of
feedback and its characteristics has so far been limited to
behavioral domains other than health behavior, such as learning
[13], professional care practice [14], or employee performance
[15]. Research in these areas has indicated positive, yet highly
variable effects of feedback on behavior [13-18]. For example,
in their meta-analysis, Kluger and DeNisi [18] found a general
positive effect of feedback on performance of mostly cognitive
and motor tasks (d=.41), but in over one-third of all considered
studies, feedback decreased performance. In a Cochrane review
examining the effect of feedback on the compliance of health
care professionals with desired practice, Jamtvedt et al [17]
found a median increase in compliance of 5%. However, results
varied from −16% to 70%. These results suggest the existence
of further variables that may mediate or moderate the effect of
feedback on behavior.
Behavioral theories provide a detailed specification of causal
processes that lead to behavior change and can thus help to
understand how feedback affects behavior [19]. Two different
theories, control theory (CT) [20] and social-cognitive theory
(SCT) [21], advocate the use of feedback, but both define
different underlying processes. A better understanding of these
processes can facilitate the design of feedback in physical
activity (and other behavioral) interventions and can help to
explain the variability of results of past research.
Feedback According to Control Theory
CT provides a model of self-regulation for intentional (or
goal-directed) behavior (eg, walking 10,000 steps a day).
Self-regulation is vital for physical activity promotion as it
constitutes the basis for self-directed change [21], and physical
activity interventions based on CT strategies have been found
to be more effective than other physical activity interventions
[22]. According to CT, people regulate their behavior by
periodically comparing the perceived qualities of their own
behavior with a salient reference value (eg, a goal) [23].
Whenever a discrepancy between one’s performance and a goal
is recognized, a behavior is triggered in order to reduce the
discrepancy (negative feedback loop). Thus, feedback affects
behavior via the comparison with a set goal or standard, which
has important implications for feedback design: performance
is likely to be increased after negative feedback, as negative
feedback informs the recipient that a goal or standard has not
been met. In turn, performance is likely to be maintained or
decreased after positive feedback, as positive feedback signals
that the recipient exceeded the goal, which can thus be reached
with less effort as well. Figure 1 illustrates the underlying causal
processes of the effect of feedback according to CT.
Feedback According to Social Cognitive Theory
In contrast to CT, SCT assumes that the mere perception of
behavior and standards is insufficient to regulate behavior. It
rather posits that cognitions such as self-efficacy beliefs are
central factors that impact goal pursuit and self-regulation [24].
Self-efficacy beliefs are personal beliefs about the capability to
exercise control over one’s actions and constitute the foundation
of human motivation and action [24]. More precisely, unless
one believes that a desired health outcome (eg, walking 10,000
steps a day) can be produced by one’s own actions, he or she
has no incentive to act in the first place. Furthermore,
self-efficacy beliefs influence the regulation of behaviors in a
variety of ways. According to SCT, self-efficacy directly
influences how goals are set and how strong one feels committed
to achieving them, what outcomes one expects to realize, and
how impediments are viewed [24]. It further affects how failures
are attributed and how one reacts to setbacks [25]. People
holding strong self-efficacy beliefs tend to be more motivated
to reach set goals as they are convinced of their own capabilities,
expect to realize favorable outcomes with their actions, and
view impediments as surmountable.
Understandably, a major source of self-efficacy includes
personal experiences of success and failure [26]. Since feedback
usually contains an evaluation of one’s performance with regard
to a set goal, feedback conveys success if it is positive, that is,
the recipient has achieved or exceeded the goal. In turn, feedback
conveys failure if it is negative, that is, the recipient has not met
the goal. Following the reasoning of SCT, performance is likely
to increase after positive feedback because self-efficacy (and
in turn performance) is increased. Consequently performance
is decreased after negative feedback, which can undermine one’s
confidence in the ability to reach a set standard. Figure 2
illustrates the underlying causal processes of the effect of
feedback according to SCT.
Figure 1. Effect of feedback according to control theory.
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Figure 2. Effect of feedback according to social cognitive theory.
Research Question
Both CT and SCT contradict each other in their implications
for feedback design. Whereas CT predicts that negative
evaluation of performance leads to favorable behavior change,
SCT predicts the same for positive evaluation of performance.
Whether the feedback contains a positive or a negative
evaluation of performance is often referred to as the sign of the
feedback message [18]. Past research has so far produced
inconclusive results regarding the effect of the feedback sign.
In their meta-analysis, Kluger and DeNisi found no significant
effect of the feedback sign, thus favoring neither of the two
theoretical positions [18]. Both authors suggested later that the
effect might be moderated by the regulatory focus of participants
[27,28]. A more recent meta-analysis looking at the effect of
feedback [29] could not evaluate the effect of the feedback sign,
since too few studies directly compared positive and negative
feedback. Consequently, it is unclear whether positive or
negative feedback is more likely to change behavior when
incorporated in physical activity interventions. Summarizing
the line of reasoning above, we therefore pose the following
research question: Does positive or negative feedback lead to
more favorable behavior change in a physical activity
intervention?
Methods
Procedure
In order examine the effects of positive and negative feedback,
we exploratory analyzed data from a cluster-randomized trial
that primarily focused on the effects of different incentives on
the acceptance of a digital physical activity intervention [30].
This paper specifically examines the effect of monthly feedback
emails that were sent out over the course of this intervention.
The study was approved by the ethics committee of the authors’
university.
A total of 26,773 customers of a large Swiss health insurance
company were invited through email, along with eligible family
members, to participate in a physical activity intervention that
was conducted from July 2015 to December 2015. In order to
participate, customers had to be at least 18 years old, be
registered in a complementary insurance program, accept
participation conditions and privacy terms, and declare to be
free of any medical condition that does not permit increased
physical activity.
Before invitation, potential participants were clustered based
on their state of residence and clusters were then randomly
allocated to one out of three incentive conditions: In the financial
incentive condition, participants received CHF10 (US $10) for
each month they walked >10,000 steps a day on average. To
prevent frustration, participants received CHF5 when their
monthly step count average was below 10,000 but over 7500
steps, which matches the approximate minimum
recommendation for daily physical activity [31]. The charitable
incentive condition was equal to the financial incentive
condition, except that participants could choose to donate an
amount of choice of their earned money to a charitable
organization. The control group received no incentives. To
ensure fair treatment of all participants, the control group was
entitled to financial incentives of CHF20 (for monthly averages
above 10,000 steps) and CHF10 (for monthly averages below
10,000 and above 7500 steps) for the second half of the
intervention. Participants completed a Web-based questionnaire
at the beginning (T1) and at the end of the intervention (T2) to
measure demographic and control variables relevant for the
primary purpose of the study. A detailed description of the
intervention as well as of all variables measured is available in
Kowatsch et al [30].
In line with recommendations for health promotion, participants
were advised to perform at least 150 min of moderate-intensity
activity a week, which on average translates to a goal of 10,000
steps a day [31]. Via the Web-based customer account,
participants could at any time gain insight into their physical
activity data as well as their degree of achievement with regard
to their goal of 10,000 steps a day.
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Figure 3. Exemplary feedback email (authors’ translation). A: Feedback message with positive or negative feedback depending on the performance of
the participant. B: Season-based tip on how to increase physical activity (here: recommendation to participate in a geocaching activity).
Feedback Emails
Starting after the first month, every participant received a
feedback message by email at the beginning of the month that
contained information on goal achievement of the last month.
Consequently, every participant received 5 feedbacks over the
course of the intervention. If participants failed to reach an
average step count of at least 7500 steps a day, a negative
feedback was provided (eg, “Unfortunately you did not reach
the goal of 7500 steps a day on average last month”). In all other
cases the feedback was positive (eg, “Well done, you have
achieved at least 7500 steps a day on average over the last month
and did a lot for your health”). In the financial and charitable
incentive condition, feedback emails also contained information
about the amount of money earned in the past month. Moreover,
and in line with theory [18], all feedback emails contained a
season-based tip on how to increase physical activity over the
next month (eg, winter specific activities like snow shoe hiking
or visiting Christmas markets were recommended in winter
months, and outdoor activities like hiking or geocaching were
recommended in summer and autumn months). Feedback emails
were sent out by the insurance company. Figure 3 provides an
example of a feedback email that was sent out in September.
Analyses
Descriptive Statistics
Data from the baseline questionnaire was used to describe the
sample of this study. We calculated means and standard
deviations (SDs) for continuous variables and absolute and
relative frequencies for categorical variables. Data on age and
gender of the participants was provided by the insurance
company. Statistics on monthly average step counts were
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obtained by calculating the mean of all participants’ mean step
counts for each month.
Effect of the Feedback Sign
Because feedback on participants’ physical activity referred to
monthly average step counts, we used the change in monthly
step averages as the outcome variable to compare the effect of
positive and negative feedback. Specifically, we calculated the
difference between monthly step count averages before and
after dispatch of the feedback email. Since each participant
received 5 feedback emails, we consequently obtained 5
difference measures per participant. This difference indicates
whether a participant increased or decreased his or her average
monthly step count in the month after receiving feedback.
Differences with an absolute value of more than 10,000 steps
are likely to be the result of irregular recorded step counts (eg,
very few and very low recorded step counts in 1 month) and
were regarded as outliers and excluded from analysis. Exclusion
of outliers resulted in the removal of 26/8115 (0.03%)
observations and did not affect the results of the analyses.
To determine what analyses should best be used to examine the
effect of the feedback sign, a 2-level hierarchical linear model
with measurements as level-1 unit of analysis and participants
as level-2 unit of analysis was fit to the data. However, the
comparison of an intercept-only and a random-intercept model
revealed that modeling the nested data structure did not
significantly improve the model fit (χ2
1=0.0, P=.99). Hence,
measurements were treated as independent, and a linear
regression model was fit to the data in order to examine the
effect of the feedback sign. Because the feedback emails
informed participants in the incentive groups also about the
reception of a financial incentive, the main effect of the
experimental group was included in the model. To further
account for confounding effects of seasonality and regression
to the mean, the model was adjusted for the effects of time and
participants’ baseline physical activity [32]. In line with Barnett
et al [32], baseline physical activity was defined as the average
step count of the month preceding the dispatch of the feedback
mail.
Results
Descriptive Statistics
In total, 1410 directly invited customers and 213 family
members participated in the program resulting in a sample of
N=1623 participants. On average, participants were 42.66
(SD=13.06) years old and slightly more men (848/1623, 52.25%)
than women (770/1623, 47.44%) participated in the prevention
program. Five participants (0.31%, 5/1623) did not disclose
their gender. Compared with the Swiss population [33], people
aged 20-39 years were overrepresented in the sample (46.40%
[753/1623] vs 33.45% [2,225,129/6,651,623]), whereas people
aged 64 years and above were underrepresented (7.64%
[124/1623] vs 22.48% [1,495,052/6,651,623]). Moreover,
program participants who filled out the Web-based questionnaire
at T1(n=1220) tend to have higher net income (between CHF
5000 and 10,000: 61.4% [593/966] vs 33.5%, more than CHF
10,000: 13.2% [127/966] vs 3.5%; no absolute values were
available for the different income categories) and a lower chance
of not obtaining a high school degree (2.30% [27/1172] vs
12.09% [564,889/4,671,164]; education data from the Federal
Bureau of Statistics was available only for persons aged between
25 and 64 years and participants’ characteristics were adjusted
accordingly). Additional participant characteristics obtained via
the baseline questionnaire are outlined in Table 1.
Over the course of the intervention, participants walked on
average 10,410 steps a day (Table 2), which can be considered
high [31] but comparable with the results of other studies
conducted with Swiss samples [34]. Monthly average step counts
showed a slight negative trend over time. Naturally, step counts
were higher for participants receiving a positive feedback mail
because a positive feedback was sent out only if the average
step count of the previous month exceeded 7500 steps. The
attrition rate (23.35%, 379/1623) was comparable with previous
pedometer-based interventions [35].
Effect of the Feedback Sign
Descriptively, a clear pattern emerged from the data as it is
apparent from Table 3. For every feedback mail, step count
averages decreased after a positive feedback and increased after
a negative feedback. In absolute terms, the average increase
after negative feedback (450 steps, SD=2032) was comparable
with the average decrease after positive feedback (−425 steps,
SD=1858). For emails 2 and 3, the negative change after a
positive feedback was less pronounced whereas for those emails
the positive change after a negative feedback was almost twice
the amount of steps when compared with emails 1, 4, and 5.
However, mean changes in step counts were accompanied by
large SDs, calling into question whether positive and negative
feedback indeed led to significantly different changes in step
counts. Moreover, the observed pattern of results can possibly
be explained by a regression to the mean due to the dependency
of the feedback sign on the participants’ physical activity.
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Table 1. Participant characteristics.
T1questionnaire, n (%)
(n=1220)
Characteristics
Education
548 (44.92)University
208 (17.05)Professional school
389 (31.89)High school
23 (1.89)Secondary school
4 (0.33)Primary school
48 (3.93)Not declared
Place of residence
142 (11.64)Town
300 (24.59)Outskirts of town
598 (49.02)Village
180 (14.75)Countryside
Income in CHF
62 (5.08)<2500
184 (15.08)2501-5000
383 (31.39)5001-7500
210 (17.21)7501-10,000
127 (10.41)>10,000
254 (20.82)Not declared
Nationality
1098 (90.00)Swiss
55 (4.51)German
53 (4.34)Other
14 (1.15)Not declared
Pedometer brand
782 (64.10)Fitbit
249 (20.41)Fitbit app
130 (10.66)Garmin
59 (4.84)Jawbone
Pedometer bought for participation
673 (55.16)Yes
511 (41.89)No
36 (2.95)Not declared
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Table 2. Descriptive statistics of monthly average step counts by feedback sign.
Dropout, n (%)Monthly step count average (SD)
Totala
Negative feedbacka
Positive feedbacka
53 (03.27)10967.02 (3744.64)--Month 1
68 (04.19)10710.19 (3732.68)6293.99 (2668.99)11581.79 (3273.48)Month 2
65 (04.00)10714.99 (3597.49)6470.87 (2298.96)11639.40 (3145.38)Month 3
53 (03.27)10657.20 (3717.10)6450.72 (2269.40)11533.57 (3326.82)Month 4
91 (05.61)10366.47 (3742.52)6046.97 (1945.79)11216.21 (3395.22)Month 5
49 (03.02)10299.51 (4479.57)5968.97 (2144.36)11308.43 (4283.81)Month 6
379 (23.35)10409.96 (3427.29)6252.79 (2291.78)11462.60 (3489.37)Total
aValues represent monthly average step counts depending on the feedback at the beginning of the month.
Table 3. Descriptive statistics of change in monthly step count averages by feedback sign.
Mean difference in step counts (SD)
Totala
Negative feedbacka
Positive feedbacka
−349.37 (2070.88)363.30 (2114.51)−482.71 (2035.95)Mail 1
−148.82 (1952.29)717.76 (2153.88)−328.77 (1858.73)Mail 2
−234.52 (1929.12)587.71 (2012.82)−399.11 (1869.87)Mail 3
−382.55 (1842.09)215.26 (1654.28)−497.77 (1854.33)Mail 4
−285.12 (1735.20)329.19 (2112.57)−422.96 (1607.90)Mail 5
−279.51 (1916.24)450.29 (2032.06)−425.34 (1858.38)Total
aValues represent the mean change in monthly average step counts after dispatch of the feedback mail.
Table 4. Summary of multiple regression results predicting change in average step counts.
P
Beta
Standard error (b)b
Model parameter
.16-98.39−139.97Intercept
.12−0.01815.84−24.98Time
<.001−0.2390.01−0.13
Baseline physical activitya
.96.00280.303.71
Group: financial incentivesb
.95.00181.855.42
Group: charitable incentivesb
.28−0.06278.74−84.28
Feedback signc
aBaseline physical activity was centered before entering the model.
bGroup membership was represented as 2 dummy variables with the control group serving as the reference group.
cFeedback sign was represented as 1 dummy variable with negative feedback serving as the reference group.
Results of the multiple regression analysis of change in monthly
step counts on time, baseline physical activity, group, and
feedback sign are presented in Table 4. Overall, the linear model
significantly predicted the changes in monthly average step
counts (F5,6836=90.84, P<.001) with R2=.062. Baseline physical
activity emerged as the only significant predictor of change in
step counts. The feedback sign did not significantly predict the
change in monthly average step counts when controlling for the
effects of time, group, and baseline physical activity (Figure 4,
right plot), indicating that the descriptive patterns in Table 3
were caused by regression to the mean.
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Figure 4. Left: Scatterplot of changes in monthly step counts against baseline physical activity for the first feedback email; the dark solid line represents
perfect agreement (no change) and the colored lines are regression lines for positive and negative feedback. Right: Overall difference between negative
and positive feedback emails adjusted for baseline physical activity and other covariates.
Figure 4 helps to further disentangle the effects of the feedback
sign and baseline physical activity. The scatterplot in Figure 4
plots the dependent variable against participants’ baseline
physical activity to illustrate the influence of regression to the
mean using data from the first feedback email [32]. The colored
lines represent separate regression lines for participants that
received positive and negative feedback. The slopes of the
regression lines indicate that the change in physical activity
following the feedback mail is dependent on physical activity
in the previous month for both positive and negative feedback.
More specifically, changes in physical activity occurred
predominantly for individuals with extreme baseline values. In
line with the assumption of regression to the mean being the
cause of the observed change, participants with very low
baseline activity levels increased their activity in the following
month, whereas participants with very high baseline levels
showed a decrease in the following month. The descriptively
observed difference between positive and negative feedback
vanishes when controlling for baseline physical activity (Figure
4, right plot).
Discussion
Principal Findings
In this paper we analyzed the effect of positive and negative
feedback emails on physical activity of participants of a
large-scale physical activity intervention. Using a
quasi-experimental approach, we found no difference between
the effect of positive and negative feedback emails. Substantial
differences found on a descriptive level could be explained by
regression to the mean. Contrary to the theory outlined in this
paper, our results might suggest that the feedback sign does not
influence the effect of feedback. Similar results have been found
by previous research in other behavioral domains [18]. However,
due to the quasi-experimental setting and possible alternative
explanations for our results, we refrain from drawing firm
conclusions regarding our research question.
Both frequency and relevance of the feedback could have limited
the general effect of feedback on participants’ physical activity
levels in our study, thereby, explaining the missing effect of the
feedback sign. Feedback in our study was only provided once
at the beginning of each month, which might have not been
frequent enough to affect monthly physical activity levels.
Indeed, meta-analyses of feedback interventions in the area of
health care demonstrate that feedback is more effective if it is
delivered more frequently, for example, weekly [14]. Moreover,
although the feedback messages contained novel and potentially
interesting information (such as the final confirmation of earning
a reward or tips on how to increase physical activity levels), the
relevance of the feedback messages may have been
compromised due to the permanent opportunity for participants
to receive information on their step counts via their Web-based
customer account or their activity tracker. Because irrelevant
feedback is less likely to be processed by the recipient [36], this
as well could have limited the effectiveness of the feedback
emails. Including further personal information that is not yet
provided by the activity tracker (eg, social comparisons) could
enhance relevance of the feedback messages and might reveal
differences between positive and negative feedback.
Limitations
Some methodological issues arise as part of the practical setting
of our study. Participants were not randomly allocated to a
negative feedback and a positive feedback condition. Rather,
positive and negative feedback was dependent on participants’
physical activity. As a consequence, we must not infer causality
as inherent group differences beyond the included control
variables may have affected our results. Furthermore, the internal
validity of our results is limited as we were not able to check
whether participants actually read the feedback emails. If a
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substantial proportion of participants ignored the feedback, we
might be able to observe an effect of the feedback sign only
within a subgroup of our sample that read the feedback emails.
Finally, we were not able to include a true control group that
did not receive any feedback emails. However, providing
evidence for the general effectiveness of feedback was not the
primary focus of this paper as this has been investigated and
confirmed in other studies [8,14,18]. Feedback represents a
widely used technique of physical activity interventions and is
likely to be continuously used in the future due to the central
role of self-regulation for both theoretical models of behavior
change [20,21,37] and the practical effectiveness of physical
activity interventions [22,38]. We therefore chose to focus on
the comparison between positive and negative feedback to
explore the way to best incorporate feedback into physical
activity and health behavior change interventions.
Future Research
Research regarding the effect of feedback in health behavior
change interventions is in its infancy. Thus, we urgently call
for the conduction of randomized controlled trials examining
the effects of feedback on health behavior as well as related
mediators and moderators. Results of those studies can help
researchers and practitioners to decide how to best incorporate
feedback in their health behavior interventions and thereby
ensure a positive effect of feedback. In this context, digital
technology might be a promising resource to maximize the
effect of feedback [29]. Digital technology not only allows the
delivery of automated and personalized feedback [39], sensors
incorporated in mobile phones and wearables also facilitate so
called just-in-time adaptive interventions (JITAIs) [40]. JITAIs
are digital interventions that provide support to the user
considering his or her state of vulnerability, opportunity, and
receptivity. In other words, JITAIs provide support only to those
persons who need it at the exact moment when they need it and
can most benefit from it. Using the mobile phone, for example,
feedback messages could be sent out only when the recipient
is likely to actively process the feedback message (eg, when
she is at home and no activities are scheduled in her calendar).
Identifying the right conditions and context for feedback to be
most effective holds great potential for health behavior change
interventions.
Conclusions
There is no difference between the effect of positive and
negative feedback emails that were sent out on a monthly basis
in a large-scale physical activity intervention. Framing of the
feedback in terms of success and failure may not be crucial
when the feedback is given infrequently and in situations when
individuals are likely to be aware of their levels of behavior.
However, feedback characteristics, including the feedback sign,
should be carefully considered when designing feedback to
change health behaviors.
Acknowledgments
We would like to thank the institutional review board of the University of St. Gallen for their valuable feedback and support. The
study protocol was approved by the Ethics Committee of the University of St. Gallen, Switzerland (reference number:
HSG-EC-2015-04-22-A; date of approval February, 17th, 2016). Informed consent to participate was obtained from all participants
of the study.
The study was funded in part by the CSS insurance, Switzerland. The funder had no role in reviewing or approving the manuscript
for publication.
Conflicts of Interest
None declared.
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Abbreviations
CT: control theory
JITAI: Just-in-Time adaptive Intervention
NCD: Non-communicable Disease
SCT: social-cognitive theory
SD: standard deviation
Edited by G Eysenbach; submitted 21.11.16; peer-reviewed by S Hermsen, RJ Renes, J Whiteley, K Ng; comments to author 05.02.17;
revised version received 09.03.17; accepted 08.04.17; published 02.06.17
Please cite as:
Kramer JN, Kowatsch T
Using Feedback to Promote Physical Activity: The Role of the Feedback Sign
J Med Internet Res 2017;19(6):e192
URL: http://www.jmir.org/2017/6/e192/
doi:10.2196/jmir.7012
PMID:28576757
©Jan-Niklas Kramer, Tobias Kowatsch. Originally published in the Journal of Medical Internet Research (http://www.jmir.org),
02.06.2017. This is an open-access article distributed under the terms of the Creative Commons Attribution License
(https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium,
provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic
information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be
included.
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