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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.
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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
Phone: 41 0 71 224 ext 7244
Fax: 41 0 71 224 7301
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
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
feedback; internet; physical activity; health behavior; activity trackers
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
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’
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.
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
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 (%)
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
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)
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)
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.
Standard error (b)b
Model parameter
Baseline physical activitya
Group: financial incentivesb
Group: charitable incentivesb
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).
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.
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
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.
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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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
©Jan-Niklas Kramer, Tobias Kowatsch. Originally published in the Journal of Medical Internet Research (,
02.06.2017. This is an open-access article distributed under the terms of the Creative Commons Attribution License
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... Tailoring takes the form of personalized information based on psychological profiles [1,25,63,85,100,106], gender [63,139,143], age [5,14,50], professional occupation [100,143,153], interests [65], health status [55,126], or season [79], as well as tailored exercise plans based on user experience [59,121] Tunneling is realized through information provision and PA recommendation pairing [45,95], reminders and goal-setting pairing [2,89,125,137], reminders and PA recommendation pairing [162], context or goal identification and PA recommendation pairing [83,132], gradual goal adjustments [15,45], graded rewards availability [26,170], or step-by-step guided PA routines [18,23,119]. The concept behind tunneling is transforming PA, or any related target behavior, into a step-by-step process that the user can follow. ...
... Praise is achieved through motivational messages [1,5,15,31,37,45,69,70,91,97,106,106], daily feedback depending on goal achievement [79,85,102,117], or happy icons and emojis [119,157]. Similarly to reminders, praise should not be too intrusive or repetitive, as it can become an annoyance to the user. ...
... Goal-setting is implemented as static or dynamic PA goals usually in terms of steps, active minutes or MVPA duration [2, 8, 9, 14, 15, 18-20, 22, 24-32, 37, 40, 41, 49, 50, 55-57, 59 Punishment takes the form of negative visual or textual feedback for under-performance [70,79,84,119,157], and virtual or monetary reward loss for goal accomplishment failure [29,117]. While most of the investigated papers utilize positive feedback to promote HBC, it is unclear whether positive or negative feedback leads to more favorable BC in an HBC intervention. ...
Full-text available
In today's connected society, many people rely on mHealth and self-tracking (ST) technology to help them break their sedentary lifestyle and stay fit. However, there is scarce evidence of such technological interventions' effectiveness, and there are no standardized methods to evaluate the short- and long-term impact of such technologies on people's physical activity and health. This work aims to help ST and HCI practitioners and researchers by empowering them with systematic guidelines and an extensible framework for constructing such technological interventions. This survey and the proposed design and evaluation framework aim to contribute to health behavior change and user engagement sustainability. To this end, we conduct a literature review of 117 papers between 2008 and 2020, which identifies the core ST HCI design methods and their efficacy, as well as and the most comprehensive list to date of user engagement evaluation metrics for ST. Based on the review's findings, we propose the PAST SELF end-to-end framework to facilitate the classification, design, and evaluation of ST technology. PAST SELF systematically organizes common methods and guidelines from existing works in ubiquitous ST research. Hence, it has potential applications in industrial and scientific settings and can be utilized by practitioners and researchers alike.
... Future research could draw samples from other populations for examining feedback effects in exercise contexts. This may provide an understanding on how the differential effects of feedback valence translate, for example, to athlete performance [63,64] as well as regular exercise engagement for the general population [65]. Second, the mean split for trait competitiveness meant that average levels of competitiveness within both groups were included, potentially impacting the ability to find the true influence of valenced feedback on low and high competitiveness for the performance and psychological experiences of exercise outcomes. ...
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Exercise is beneficial for physical and psychological health, yet the majority of Australian adults are not sufficiently active to gain health benefits. Novel methods are needed to enhance the experience of exercise and ultimately exercise participation. The present study examined performance and psychological experiences during a (non-immersive) virtual reality cycling task that incorporated affective feedback. Female participants (N = 137, university students) received either positive, negative, or neutral virtual feedback while cycling on a stationary bicycle in a virtual reality laboratory environment under the instruction to maintain at least 70% of their maximal heart rate for as long as possible (or up to 30 minutes). Participants also responded to measures of affect, motivation, enjoyment, and competitiveness. Data were analysed with ANOVA's performed with feedback groups and trait competitiveness for the psychological and performance dependent measures. Results showed that positive feedback elicited greater interest and enjoyment during the task than neutral and negative feedback. In addition, perceived competence was greater with positive feedback than for neutral and negative feedback in low competitive participants. The type of feedback did not affect performance (cycling persistence, perceived exertion, and effort). The findings indicate the potential importance of providing positive virtual feedback and considering the interaction of individual difference factors, specifically competitiveness, to enhance virtual exercise experiences.
... At one site, an MDT of clinicians, physiotherapists and physiologists have met monthly and used retrieved smartwatch data to set goals and track recovery (Fig 1), using closed loop feedback to promote physical recovery. 7 Initial exercise programmes were set based on the discharge measures collected, which included individual illness severity, age and performance on physical discharge assessments (incremental shuttle walk [ISWT] 8 , Chelsea critical care physical assessment [CPAx] 9 and 1-minute sit-to-stand). Exercise plans were reviewed and tailored by the remote MDT, supported by video or phone discussions with the patients. ...
Full-text available
During the first wave of intensive care unit admissions with COVID-19, in response to the constraints of social distancing we introduced a new digitally enabled critical care rehabilitation pathway. Using smartwatch technology, this pathway rapidly enabled our multidisciplinary team to observe the recovery of a COVID-19 cohort across eight NHS acute hospitals across the south of England. This represents one of the geographically largest smartwatch studies of its kind.
... For example, accelerometers provide an objective measure of physical activity over a few days compared to standard physical performance measures [15]. Moreover, physical activity feedback with wearable sensors may also be incentive to increase daily activity [16][17][18][19]. ...
Background Recent World Health Organization reports propose wearable devices to collect information on activity and walking speed as innovative health indicators. However, mainstream consumer-grade tracking devices and smartphone apps are often inaccurate and require long-term acceptability assessment. Objective Our aim is to assess the user acceptability of an instrumented shoe insole in frail older adults. This device monitors participants’ walking speed and differentiates active walking from shuffling after step length calibration. Methods A multiphase evaluation has been designed: 9 older adults were evaluated in a living lab for a day, 3 older adults were evaluated at home for a month, and a prospective randomized trial included 35 older adults at home for 3 months. A qualitative research design using face-to-face and phone semistructured interviews was performed. Our hypothesis was that this shoe insole was acceptable in monitoring long-term outdoor and indoor walking. The primary outcome was participants' acceptability, measured by a qualitative questionnaire and average time of insole wearing per day. The secondary outcome described physical frailty evolution in both groups. Results Living lab results confirmed the importance of a multiphase design study with participant involvement. Participants proposed insole modifications. Overall acceptability had mixed results: low scores for reliability (2.1 out of 6) and high scores for usability (4.3 out of 6) outcomes. The calibration phase raised no particular concern. During the field test, a majority of participants (mean age 79 years) were very (10/16) or quite satisfied (3/16) with the insole's comfort at the end of the follow-up. Participant insole acceptability evolved as follows: 63% (12/19) at 1 month, 50% (9/18) at 2 months, and 75% (12/16) at 3 months. A total of 9 participants in the intervention group discontinued the intervention because of technical issues. All participants equipped for more than a week reported wearing the insole every day at 1 month, 83% (15/18) at 2 months, and 94% (15/16) at 3 months for 5.8, 6.3, and 5.1 hours per day, respectively. Insole data confirmed that participants effectively wore the insole without significant decline during follow-up for an average of 13.5 days per 4 months and 5.6 hours per day. For secondary end points, the change in frailty parameters or quality of life did not differ for those randomly assigned to the intervention group compared to usual care. Conclusions Our study reports acceptability data on an instrumented insole in indoor and outdoor walking with remote monitoring in frail older adults under real-life conditions. To date, there is limited data in this population set. This thin instrumentation, including a flexible battery, was a technical challenge and seems to provide an acceptable solution over time that is valued by participants. However, users still raised certain acceptability issues. Given the growing interest in wearable health care devices, these results will be useful for future developments. Trial Registration NCT02316600;
... For this purpose, the participants received positive and negative feedback based on their weekly step-counts. A study by Kramer and Kowatsch (42) indicated that providing feedback on performance enhanced PA. ...
Full-text available
Background : Increasing physical activity plays an important role in the promotion of the quality of life of women with polycystic ovarian syndrome (PCOS). This study examined the impact of a theory- informed behavior change techniques intervention in conjunction with a pedometer-based intervention on physical activity and weight of women with PCOS. Method : This research is an unmasked controlled trial study. 88 women with PCOS referred to Mahdieh hospital Tehran, Iran and met the inclusion criteria were assigned by random assignment method to the intervention (12-week pedometer-based intervention plus a theory- informed behavior change intervention) and the comparison (12-week pedometer-based intervention) group. The primary outcome was physical activity that was measured in two ways. Step-counts were measured using pedometer, and self-reported physical activity was measured via the international physical activity questionnaire. The secondary outcomes were predisposing, enabling, and reinforcing factors that were measured through a psychometric self-administered questionnaire. Weight was also measured as a secondary outcome. All outcomes were measured at baseline and after 12 weeks. Data were analyzed using STATA software version 13. Data analysis was performed on an intention to treat basis using Independent T-Test, ANOVA ANCOVA test, Repeated Measure ANOVA, and post-hoc analysis. Results : After adjusted mean, significant increases with a strong and moderate effect size were found in the intervention group relative to the comparison group for step-counts (P= 0.000, d = 2.01), walking (P=0.000, d=0.92), predisposing factors (P=0.000, d=1.05), enabling and reinforcing factors (P <0.05, d= 0.54 - 0.60). Significant decrease was found in the intervention group compared to the comparison group for weight (P=0.005) with a moderate effect size (d = 0.66). Conclusion : This pedometer-based intervention that applied behavior change techniques on the basis of predisposing, enabling, and reinforcing factors of the PRECEDE framework was affective to enhance physical activity and diminish the weight of women with PCOS. Therefore, it is suggested to incorporate theory- informed behavior change techniques into pedometer-based interventions for promoting physical activity. Trial registration : Iranian Registry of Clinical Trials IRCT20161116030923N3. Keywords: Physical activity, Polycystic Ovarian Syndrome, Theoretical framework, Behavior change techniques, Pedometer
... Most feedback provided in physical activity interventions is performance based, which gives data about recorded activity levels or evaluates physical activity performance in relation to a set goal (11). The effects of feedback-based interventions on behavior change are highly variable (12,13). One reason that sedentary individuals are not physically active is that they are not sufficiently motivated to change their current behavior. ...
Background: Regular physical activity (PA) is associated with a lower risk of several types of cancers. However, two-thirds of overweight/obese adults are not sufficiently active; this, in combination with the unfavorable effect of excess body weight, puts them at greater risk for cancer. One reason that these individuals do not engage in enough PA may be their lack of motivation to change their current behavior due to the perception of putting in effort for possible future gain without obvious short-term benefits. There is a need for innovative ways to help individuals recognize the immediate health benefits of PA and thus increase their motivation. Methods: This pilot intervention tested a PA education module that included a one-on-one counseling session highlighting the acute effects of PA on glucose patterns, followed by a 10-day self-monitoring period with a continuous glucose monitor (CGM) and a Fitbit. Participants rated the acceptability of the education module on a 5-point Likert scale and completed surveys assessing stages of change for motivational readiness. Results: Nineteen overweight/obese adults (84% female) completed the study. Participants gave high ratings to the counseling session for improving their PA-related knowledge (mean=4.22), increasing motivation (mean=4.29), and providing personally-relevant information (mean=4.35). The summary acceptability scores for self-monitoring period were 4.46 for CGM and 4.51 for Fitbit. Participants reported a significant decrease in the pre-contemplation stage and an increase in the action stage (p<.05). Conclusions: CGM is a feasible tool for PA interventions. Impact: Information from CGM could be used as biological-based feedback to motivate PA.
... This encompasses both behavioral, cognitive, and emotional processes (Mann et al. 2013). Self-regulation ability has been emphasized as a crucial factor in order to achieve health promotion (Bandura 2004(Bandura , 1998Kramer and Kowatsch 2017;Mann et al. 2013). According to Gavora et al., selfregulation can be divided into four different dimensions; goal orientation (the degree to which an individual attempts to fulfill personal goals, e.g., by plan making), self-direction (the degree to which one can formulate learning goals and learns from previous experiences), decision making (the ability to make decisions and find multiple ways to achieve goals), and impulse control (the ability for an individual to manage short-term interferences with goals). ...
Full-text available
Self-tracking of health may have positive effects on lifestyle behavior and weight loss; however, not much is known about the role of psychological processes in this effect. The purpose of this study was to assess to what extent a change in self-regulation capabilities can explain weight loss after 4 and 12 months of self-tracking physical activity and weight. An explorative cohort study was conducted with measurements at baseline (T0), 4 months (T1), and 12 months (T2). Healthy adult volunteers (N = 80) were included and provided with a digital weight scale and an activity tracker. Personal characteristics as well as the intention to change weight and physical activity were measured at T0. Self-regulation capabilities (goal orientation, self-direction, decision making, and impulse control) were measured with the Self-Regulation Questionnaire at T0, T1, and T2, together with body weight. At T0, all four dimensions of self-regulation were negatively related to BMI (p < .01). At T1, weight significantly declined compared to T0 (− 2.0 kg/− 0.64 kg/m2, p < .001). At T2, this weight loss was maintained (− 1.8 kg/− 0.57 kg/m2, p < .01). At T1, intention to lose weight, self-weighing frequency, and an increase in goal orientation explained weight loss. At T2, an increase in decision making explained weight loss. Incremental self-regulation capabilities may explain weight loss after engaging in self-tracking of physical activity and weight. Future research should focus on exploring effective ways to further enhance self-regulation when using self-tracking technology and to assess the impact of different types of self-regulation stimuli on weight loss.
... Avatars have long been applied in the HCI domain [34,56] and related nutritional behavioral change mHealth [2,34,47], but not yet in sodium intake monitoring and interventions, despite their ability to precisely promote consumer awareness and stimulate intention to behavior change. Studies confirm the efficacy of persuasive avatars to change human behavior, especially when using a human-like appearance [35,40]. ...
Conference Paper
Full-text available
Excessive salt intake is increasingly seen as global health threat. As contemporary education campaigns and current mHealth solutions only reach health literate users, an often unaffected minority, there exists demand for more inclusive solutions. Avatar-based health interventions have been shown effective in such a context, but have not been tested for promoting low-sodium dieting specifically. Therefore, we designed, implemented and tested a novel smartphone-mediated and future-self avatar-based sodium reduction intervention (N = 28). Because most consumers remain unaware of the relationship between sodium intake and high blood pressure, the system was also tailored to support users in gaining risk awareness and intention for low-sodium dieting. Results indicate that participants significantly increase outcome expectancy, risk awareness and intention towards balanced, low-sodium dieting. The majority of users identify themselves with the future-self avatar and confirm the system's usefulness, ease of use, enjoyment.
Full-text available
Regular physical activity reduces the progression of several cancers and offers physical and mental health benefits for cancer survivors. However, many cancer survivors are not sufficiently active to achieve these health benefits. Possible biological mechanisms through which physical activity could affect cancer progression include reduced systemic inflammation and positive changes in metabolic markers. Chronic and acute hyperglycemia could have downstream effects on cell proliferation and tumorigenesis. One novel strategy to motivate cancer survivors to be more active is to provide personalized biological-based feedback that demonstrates the immediate positive impact of physical activity. Continuous glucose monitors (CGMs) have been used to demonstrate the acute beneficial effects of physical activity on insulin sensitivity and glucose metabolisms in controlled lab settings. Using personal data from CGMs to illustrate the immediate impact of physical activity on glucose patterns could be particularly relevant for cancer survivors because they are at a higher risk for developing type 2 diabetes (T2D). As a pilot project, this study aims to (1) test the preliminary effect of a remotely delivered physical activity intervention that incorporates personalized biological-based feedback on daily physical activity levels, and (2) explore the association between daily glucose patterns and cancer-related insulin pathway and inflammatory biomarkers in cancer survivors who are at high risk for T2D. We will recruit 50 insufficiently active, post-treatment cancer survivors who are at elevated risk for T2D. Participants will be randomly assigned into (1) a group that receives personalized biological feedback related to physical activity behaviors; and (2) a control group that receives standard educational material. The feasibility and preliminary efficacy of this wearable sensor-based, biofeedback-enhanced 12-week physical activity intervention will be evaluated. Data from this study will support the further refinement and enhancement of a more comprehensive remotely delivered physical activity intervention that targets cancer survivors. Trial registration: Identifier: NCT05490641.
Full-text available
The habituation of physical activity is considered as primary chronic disease prevention in public health. The university students' physical activity levels are insufficient. Therefore, their physical inactivity is receiving more attention around the world. People could be more aware of their unhealthy behaviour if they get feedback, and they are likely to change it. This study investigates the effects of self-activity monitoring with real-time feedback on university students' awareness of physical activity and overall health status and explores the physical activity trends. For this purpose, forty Japanese university students participated in the trial wearing activity trackers for 12 weeks in a real-life setting. Two measures were used to assess participants' awareness of physical activity and overall health status: (1) a general questionnaire to detect overall changes in the participants' self-reported health status and physical activity levels before and after the trial, and (2) a weekly questionnaire to explore the changes in the participants' awareness of their physical activity during the trial term. Overall, some items of the participants' self-reported status improved after the trial. However, during the trial, the positive effects of self-monitoring physical activity were not clearly identified on the participants' awareness of the contribution and enhancement of physical activity or the condition which inhibiting them from doing it. There were more negative changes than positive ones in participants' awareness related to willingness to monitor physical activity and sleeping time. Accordingly, the participants' steps and activity calories did not have positive trends. The university student's awareness of physical activity could not be increased by real-time feedback. Therefore, they should be educated about physical activity and its health impact to increase their awareness.
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Background: Research has so far benefited from the use of pedometers in physical activity interventions. However, when public health institutions (eg, insurance companies) implement pedometer-based interventions in practice, people may refrain from participating due to privacy concerns. This might greatly limit the applicability of such interventions. Financial incentives have been successfully used to influence both health behavior and privacy concerns, and may thus have a beneficial effect on the acceptance of pedometer-based interventions. Objective: This paper presents the design and baseline characteristics of a cluster-randomized controlled trial that seeks to examine the effect of financial incentives on the acceptance of and adherence to a pedometer-based physical activity intervention offered by a health insurance company. Methods: More than 18,000 customers of a large Swiss health insurance company were allocated to a financial incentive, a charitable incentive, or a control group and invited to participate in a health prevention program. Participants used a pedometer to track their daily physical activity over the course of 6 months. A Web-based questionnaire was administered at the beginning and at the end of the intervention and additional data was provided by the insurance company. The primary outcome of the study will be the participation rate, secondary outcomes will be adherence to the prevention program, physical activity, and health status of the participants among others. Results: Baseline characteristics indicate that residence of participants, baseline physical activity, and subjective health should be used as covariates in the statistical analysis of the secondary outcomes of the study. Conclusions: This is the first study in western cultures testing the effectiveness of financial incentives with regard to a pedometer-based health intervention offered by a large health insurer to their customers. Given that the incentives prove to be effective, this study provides the basis for powerful health prevention programs of public health institutions that are easy to implement and can reach large numbers of people in need.
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Sedentary behaviour - i.e., low energy-expending waking behaviour while seated or lying down - is a health risk factor, even when controlling for physical activity. This review sought to describe the behaviour change strategies used within interventions that have sought to reduce sedentary behaviour in adults. Studies were identified through existing literature reviews, a systematic database search, and hand-searches of eligible papers. Interventions were categorized as 'very promising', 'quite promising', or 'non-promising' according to observed behaviour changes. Intervention functions and behaviour change techniques were compared across promising and non-promising interventions. Twenty-six eligible studies reported 38 interventions, of which 20 (53%) were worksite-based. Fifteen interventions (39%) were very promising, 8 quite promising (21%), and 15 non-promising (39%). Very or quite promising interventions tended to have targeted sedentary behaviour instead of physical activity. Interventions based on environmental restructuring, persuasion, or education, were most promising. Self-monitoring, problem solving, and restructuring the social or physical environment were particularly promising behaviour change techniques. Future sedentary reduction interventions might most fruitfully incorporate environmental modification and self-regulatory skills training. The evidence base is however weakened by low-quality evaluation methods; more RCTs, employing no-treatment control groups, and collecting objective data, are needed. Supplemental_PRISMA_checklist.pdf Supplemental References.pdf Supplemental Table_1.pdf Supplemental Table_2.pdf Supplemental Table_3.pdf Supplemental Table_4.pdf Supplemental Table_5.pdf.
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Content, delivery and effects of physical activity (PA) interventions are heterogeneous. There is a need to identify intervention features (content and delivery) related to long-term effectiveness. Behaviour change techniques (BCTs) and modes of intervention delivery were coded in 19 randomised controlled trials included in a systematic review of PA interventions for adults aged 55-70 years, published between 2000 and 2010, with PA outcomes ≥12 months after randomisation; protocol registration: PROSPERO CRD42011001459. Meta-analysis, moderator analyses and meta-regression were conducted. Meta-analysis revealed that interventions were effective in promoting PA compared with no/minimal intervention comparators (d=0.29, 95% CI=0.19 to 0.40, I(2)=79.8%, Q-value=89.16 (df=18, p<0.01)). Intervention features often concurred and goal setting was the most commonly used BCT. Subgroup analyses suggested that interventions using the BCT feedback may be more effective, whilst interventions using printed materials or the BCTs information on where and when to perform the behaviour and information on consequences of behaviour to the individual may be less effective. Meta-regression revealed that neither the number of BCTs nor self-regulatory BCTs significantly related to effect size. Feedback appears to be a potentially effective candidate BCT for future interventions promoting long-term PA. Considering concurrence of intervention features alongside moderator analyses is important.
Background: All major guidelines on antihypertensive therapy recommend weight loss; anti-obesity drugs may be able to help in this respect. Objectives: Primary objectives: To assess the long-term effects of pharmacologically induced reduction in body weight in adults with essential hypertension on all-cause mortality, cardiovascular morbidity, and adverse events (including total serious adverse events, withdrawal due to adverse events, and total non-serious adverse events). Secondary objectives: To assess the long-term effects of pharmacologically induced reduction in body weight in adults with essential hypertension on change from baseline in systolic blood pressure, change from baseline in diastolic blood pressure, and body weight reduction. Search methods: We obtained studies using computerised searches of the Cochrane Hypertension Group Specialised Register, the Cochrane Central Register of Controlled Trials (CENTRAL), Ovid MEDLINE, Ovid EMBASE, the clinical trials registry, and from handsearches in reference lists and systematic reviews (status as of 13 April 2015). Selection criteria: Randomised controlled trials in hypertensive adults of at least 24 weeks' duration that compared long-term pharmacologic interventions for weight loss with placebo. Data collection and analysis: Two review authors independently selected studies, assessed risk of bias, and extracted data. Where appropriate and in the absence of significant heterogeneity between studies (P > 0.1), we pooled studies using fixed-effect meta-analysis. When heterogeneity was present, we used the random-effects method and investigated the cause of heterogeneity. Main results: After updating the literature search, which was extended to include four new weight-reducing drugs, we identified one additional study of phentermine/topiramate, bringing the total number of studies to nine that compare orlistat, sibutramine, or phentermine/topiramate to placebo and thus fulfil our inclusion criteria. We identified no relevant studies investigating rimonabant, liraglutide, lorcaserin, or naltrexone/bupropion. No study included mortality and cardiovascular morbidity as predefined outcomes. Incidence of gastrointestinal side effects was consistently higher in those participants treated with orlistat versus those treated with placebo. The most frequent side effects were dry mouth, constipation, and headache with sibutramine, and dry mouth and paresthaesia with phentermine/topiramate. In participants assigned to orlistat, sibutramine, or phentermine/topiramate body weight was reduced more effectively than in participants in the usual-care/placebo groups. Orlistat reduced systolic blood pressure as compared to placebo by -2.5 mm Hg (mean difference (MD); 95% confidence interval (CI): -4.0 to -0.9 mm Hg) and diastolic blood pressure by -1.9 mm Hg (MD; 95% CI: -3.0 to -0.9 mm Hg). Sibutramine increased diastolic blood pressure compared to placebo by +3.2 mm Hg (MD; 95% CI: +1.4 to +4.9 mm Hg). The one trial that investigated phentermine/topiramate suggested it lowered blood pressure. Authors' conclusions: In people with elevated blood pressure, orlistat and sibutramine reduced body weight to a similar degree, while phentermine/topiramate reduced body weight to a greater extent. In the same trials, orlistat and phentermine/topiramate reduced blood pressure, while sibutramine increased it. We could include no trials investigating rimonabant, liraglutide, lorcaserin, or naltrexone/bupropion in people with elevated blood pressure. Long-term trials assessing the effect of orlistat, liraglutide, lorcaserin, phentermine/topiramate, or naltrexone/bupropion on mortality and morbidity are unavailable and needed. Rimonabant and sibutramine have been withdrawn from the market, after long-term trials on mortality and morbidity have confirmed concerns about the potential severe side effects of these two drugs. The European Medicines Agency refused marketing authorisation for phentermine/topiramate due to safety concerns, while the application for European marketing authorisation for lorcaserin was withdrawn by the manufacturer after the Committee for Medicinal Products for Human Use judged the overall benefit/risk balance to be negative.
Advances in wireless devices and mobile technology offer many opportunities for delivering just-in-time adaptive interventions (JITAIs)-suites of interventions that adapt over time to an individual's changing status and circumstances with the goal to address the individual's need for support, whenever this need arises. A major challenge confronting behavioral scientists aiming to develop a JITAI concerns the selection and integration of existing empirical, theoretical and practical evidence into a scientific model that can inform the construction of a JITAI and help identify scientific gaps. The purpose of this paper is to establish a pragmatic framework that can be used to organize existing evidence into a useful model for JITAI construction. This framework involves clarifying the conceptual purpose of a JITAI, namely, the provision of just-in-time support via adaptation, as well as describing the components of a JITAI and articulating a list of concrete questions to guide the establishment of a useful model for JITAI construction. The proposed framework includes an organizing scheme for translating the relatively static scientific models underlying many health behavior interventions into a more dynamic model that better incorporates the element of time. This framework will help to guide the next generation of empirical work to support the creation of effective JITAIs. (PsycINFO Database Record
The question of how affect arises and what affect indicates is examined from a feedback-based viewpoint on self-regulation. Using the analogy of action control as the attempt to diminish distance to a goal, a second feedback system is postulated that senses and regulates the rate at which the action-guiding system is functioning. This second system is seen as responsible for affect. Implications of these assertions and issues that arise from them are addressed in the remainder of the article. Several issues relate to the emotion model itself; others concern the relation between negative emotion and disengagement from goals. Relations to 3 other emotion theories are also addressed. The authors conclude that this view on affect is a useful supplement to other theories and that the concept of emotion is easily assimilated to feedback models of self-regulation.