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Introduction: There has been limited research investigating whether small financial incentives can promote participation, behavior change, and engagement in physical activity promotion programs. This study evaluates the effects of two types of small financial incentives within a physical activity promotion program of a Swiss health insurance company. Study design: Three-arm cluster-randomized trial comparing small personal financial incentives and charity financial incentives (10 Swiss Francs, equal to US$10.40) for each month with an average step count of >10,000 steps per day to control. Insureds' federal state of residence was the unit of randomization. Data were collected in 2015 and analyses were completed in 2018. Setting/participants: German-speaking insureds of a large health insurer in Switzerland were invited. Invited insureds were aged ≥18 years, enrolled in complementary insurance plans and registered on the insurer's online platform. Main outcome measures: Primary outcome was the participation rate. Secondary outcomes were steps per day, the proportion of participant days in which >10,000 steps were achieved and non-usage attrition over the first 3 months of the program. Results: Participation rate was 5.94% in the personal financial incentive group (OR=1.96, 95% CI=1.55, 2.49) and 4.98% in the charity financial incentive group (OR=1.59, 95% CI=1.25, 2.01) compared with 3.23% in the control group. At the start of the program, the charity financial group had a 12% higher chance of walking 10,000 steps per day than the control group (OR=1.68, 95% CI=1.23, 2.30), but this effect dissipated after 3 months. Steps per day and non-usage attrition did not differ significantly between the groups. Conclusions: Small personal and charity financial incentives can increase participation in physical activity promotion programs. Incentives may need to be modified in order to prevent attrition and promote behavior change over a longer period of time. Trial registration: This study is registered at ISRCTN24436134.
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A Cluster-Randomized Trial on Small Incentives
to Promote Physical Activity
D1X XJan-Niklas Kramer, D2X XMSc,
D3X XPeter Tinschert, D4X XMSc,
D5X XUrte Scholz, D6X XPhD,
D7X XElgar Fleisch, D8X XPhD,
D9X XTobias Kowatsch, D10X XPhD
Introduction: There has been limited research investigating whether small nancial incentives can
promote participation, behavior change, and engagement in physical activity promotion programs.
This study evaluates the effects of two types of small nancial incentives within a physical activity
promotion program of a Swiss health insurance company.
Study design: Three-arm cluster-randomized trial comparing small personal nancial incentives
and charity nancial incentives (10 Swiss Francs, equal to US$10.40) for each month with an aver-
age step count of >10,000 steps per day to control. Insuredsfederal state of residence was the unit
of randomization. Data were collected in 2015 and analyses were completed in 2018.
Setting/participants: German-speaking insureds of a large health insurer in Switzerland were
invited. Invited insureds were aged 18 years, enrolled in complementary insurance plans and reg-
istered on the insurers online platform.
Main outcome measures: Primary outcome was the participation rate. Secondary outcomes
were steps per day, the proportion of participant days in which >10,000 steps were achieved and
non-usage attrition over the rst 3 months of the program.
Results: Participation rate was 5.94% in the personal nancial incentive group (OR=1.96, 95%
CI=1.55, 2.49) and 4.98% in the charity nancial incentive group (OR=1.59, 95% CI=1.25, 2.01)
compared with 3.23% in the control group. At the start of the program, the charity nancial group
had a 12% higher chance of walking 10,000 steps per day than the control group (OR=1.68, 95%
CI=1.23, 2.30), but this effect dissipated after 3 months. Steps per day and non-usage attrition did
not differ signicantly between the groups.
Conclusions: Small personal and charity nancial incentives can increase participation in physical
activity promotion programs. Incentives may need to be modied in order to prevent attrition and
promote behavior change over a longer period of time.
Trial registration: This study is registered at ISRCTN24436134.
Am J Prev Med 2019;56(2):e45
e54. © 2018 American Journal of Preventive Medicine. Published by Elsevier Inc.
This is an open access article under the CC BY-NC-ND license
Strong empirical evidence indicates that physical
inactivity increases the risk of mortality
as well
as of noncommunicable diseases, such as coro-
nary heart disease, diabetes, or cancer.
More than 20%
of the worlds population is inactive (i.e., accumulating
less than 150 minutes of moderate-intensity or 75
minutes of vigorous-intensity aerobic physical activity
From the
Center for Digital Health Interventions, Institute of Technology
Management, University of St. Gallen, St. Gallen, Switzerland;
ment of Psychology, Applied Social and Health Psychology, University of
Zurich, Zurich, Switzerland; and
Center for Digital Health Interventions,
Department of Management, Technology, and Economics, ETH Zurich,
Zurich, Switzerland
Address correspondence to: Tobias Kowatsch, PhD, Center for Dig-
ital Health Interventions, Institute of Technology Management, Uni-
versity of St. Gallen, Dufourstrasse 40a, 9000 St. Gallen, Switzerland.
© 2018 American Journal of Preventive Medicine. Published by Elsevier Inc.
This is an open access article under the CC BY-NC-ND license (
Am J Prev Med 2019;56(2):e45e54 e45
per week),
illustrating the need for effective promotion
of physical activity at a large scale. Technology may pro-
vide a viable means to address this problem: Driven by
the promise and increasing popularity of wearable devi-
many key stakeholders in health care, including
employers and health insurers, have designed large-scale
physical activity promotion programs that utilize com-
mercial-grade activity trackers.
For example, partici-
pants of the U.S. health insurer Humanas Go365
program can earn points for every 1,000 steps they log
using a smartphone or activity tracker.
To provide an effective solution to the high prevalence
of physical inactivity, those programs have to attract the
individuals in need, effectively change behavior, and
engage participants in the long term. Therefore, health
insurers and employers often promote their physical
activity programs using nancial incentives like cash,
bonus points, or vouchers.
This strategy is supported
by empirical evidence: Previous research demonstrated
that nancial incentives can increase participation in
health promotion programs
and change physical
activityrelated behaviors, such as exercise session
exercise behavior,
and objectively
measured physical activity.
However, these studies
mostly use large incentive amounts. A review of RCTs,
for example, calculated a mean maximum incentive
value of $20.75 per week.
Because changes in physical
activity are typically not sustained after incentives are
larger incentives are unsuitable for
application in large-scale physical activity programs.
A potential solution is the use of nancial incentives
that are small enough to be sustained indenitely, but
little is known about whether small incentives can pro-
duce the desired effects. In previous studies, small
incentives have primarily been shown to change simple
behaviors, such as completing child vaccinations,
attending preventive health examinations,
or com-
pleting tuberculosis skin test readings
primarily in
deprived populations. In studies investigating the effect
of incentives on physical activity, the incentive value
seems to correlate with the effect size,
but very few
studies have specically investigated the effect of small
nancial incentives. Thus, it is unclear whether small
nancial incentives can promote physical activity and
how they affect behavioral change.
Incentives in the form of donations to charity (charity
nancial incentives) are a promising alternative incen-
tive design. Donating to charity activates an additional
neural reward system compared with mere nancial
and has been related to happiness in obser-
vational and experimental studies.
This warm glow of
has been used in marketing strategies to
motivate purchase behavior,
and recent
and ongo-
studies investigate its potential to promote physical
activity. These previous studies have found mixed
results: In a large RCT in Singapore, weekly personal
nancial incentives but not charity nancial incentives
($30 for walking 70,000 steps per week) increased daily
steps of full-time workers at 6 months.
In a smaller
RCT with older adults in Philadelphia, both weekly per-
sonal nancial and charity nancial incentives ($20 for
meeting a personalized step goal on at least 5 of 7 days)
were successful in promoting step goal achievement over
the 16-week study period but not during the 4-week fol-
low period.
The present study adds to this evidence base by com-
paring the effects of personal nancial and charity nan-
cial incentives within a physical activity promotion
program offered by a large Swiss health insurance com-
pany. In contrast to previous research, this study deliber-
ately investigates small incentive values that have the
potential to be sustained indenitely. In addition to
changes in physical activity, this study also analyzes
effects on participation and retention, because they rep-
resent central outcomes for the success of health promo-
tion programs.
The design of this study is described in greater detail elsewhere.
Briey, this study was conducted in cooperation with a large Swiss
health insurance company that implemented a 6-month pilot ini-
tiative to promote physical activity among its insureds from July
to December 2015. In June 2015, eligible insureds were randomly
assigned to a type of incentive (personal nancial, charity nan-
cial, or no incentive) and invited via e-mail to participate in the
pilot program. Invitation e-mails contained information about the
promotion program, the incentive condition of the e-mail recipi-
ent, and a link to the insurances online platform where insureds
could log in and register for the program (Appendix, available
online). Additionally, the invitation e-mail informed insureds
about the opportunity to buy a compatible activity tracker at a
reduced price if they did not own one already. Insureds who were
not interested in the program were asked to indicate the underly-
ing reasons of their decision by completing a brief survey via a
separate link at the bottom of the invitation e-mail.
On the insurance providers online platform, insureds
received detailed information about the pilot program, data
protection policies, and eligibility criteria and could provide
consent to participate. To provide consent, participants had to
conrm that they did not have a medical condition that pro-
hibits increased levels of physical activity by ticking a check-
box. Insureds were advised to consult a physician if they were
in doubt. Insureds did not receive any information about the
existence of different incentive groups. To complete registra-
tion, insureds linked their wearable manufacturerscustomer
account to the insurance providersonlineplatformsothat
their steps would be synchronized daily via an application
e46 Kramer et al / Am J Prev Med 2019;56(2):e45
programming interface. To facilitate automatic synchroniza-
tion of step counts, compatible activity trackers were limited
to devices developed by the major wearable manufacturers on
the Swiss market Garmin, Jawbone, and Fitbit. Alternatively,
insureds could use the Fitbit smartphone application to track
daily steps. Once registered, participants were able to add
family members who also met the eligibility criteria to the
pilot program.
At the beginning of the program, participants were asked to
complete a web-based survey to collect data on demographic vari-
ables and covariates of physical activity. Participants received 10
Swiss Francs (CHF) for the completion of the survey. The insur-
ance company provided data on age, sex, nationality, and federal
state (canton) of all invited insureds as well as step data of partici-
pants. The IRB of the University of St. Gallen approved the study.
Analyses of primary and secondary outcomes were completed in
Study Sample
Because of legal regulations in Switzerland, the physical activity
promotion program is not part of the statutory health insurance
but offered to insureds with complementary insurance plans only.
Note, however, that in Switzerland 75% of people are enrolled in
complementary insurance plans.
To facilitate enrolment, only
insureds who met the predened eligibility criteria were invited to
participate: insureds had to be aged >18 years, German speaking,
enrolled in a complementary insurance plan, and registered on
the insurers online platform. There was no racial or gender bias
in the selection of invited insureds. Naturally, invited insureds
resided primarily in the German-speaking parts of Switzerland.
No eligibility criteria were dened on the canton level.
In the personal nancial incentive condition, participants received
CHF 10 for each month they walked >10,000 steps per day on
average and CHF 5 for each month they walked <10,000 but
>7,500 steps per day, which matches the approximate minimum
recommendation for daily physical activity.
The insurance com-
pany considered a value of CHF 10 as appropriate to be sustained
indenitely. Charity nancial incentives coincided with personal
nancial incentives, with the exception that participants could
donate a chosen proportion of their earned reward to one of three
charity organizations (a foundation supporting the rights and
needs of Swiss children and adolescents, a foundation for health
promotion of Swiss adolescents, and an organization committed
to preserving the Swiss hiking track network). Participants in the
control group were informed that participation can enhance health
and well-being but did not receive any incentives during the rst 3
months of the program. However, from the fourth month onwards
until the end of the program, the control group was entitled to per-
sonal nancial incentives of CHF 20 for walking 10,000 steps on
average and CHF 10 for walking 7,500 steps on average per month.
Thus, participants of all groups could earn or donate a maximum
of CHF 60 during the 6-month pilot program. Participants could
log in to their online account on the insurance companys website
to view a summary of their daily step counts and the amount of
their earned or donated rewards if applicable. At the beginning of
each month, all participants received an e-mail containing infor-
mation on whether they earned a reward in the past month as well
as a tip on how to increase physical activity.
An important study design consideration was minimizing the
risk of spillover effects between study groups, especially between
the incentivized groups and the control group, to prevent frustra-
tion and dropout among insureds. Insureds were therefore ran-
domized using blocked cluster-randomization based on their
canton of residence (n=20) with a block size of ve and an alloca-
tion ratio of 2:2:1 with fewer insureds allocated to the control con-
dition. Each block consisted of two pairs of neighboring cantons
and one single canton. An additional consideration in the studys
randomization scheme was to account for differences in activity
preferences between urban and rural areas in Switzerland.
blocks were therefore matched for population density. Next, can-
ton pairs within each block were randomized to the incentive con-
ditions using the toss of a coin, and the remaining canton was
allocated to the control group.
Because the incentive structure changed for the control group
in the fourth month, all outcomes and analyses refer to the rst 3
months of the pilot program. Primary outcome was the participa-
tion rate in the three different groups. Insureds were considered
as participants if they provided consent to participate and shared
their step counts at least once during the rst 3 months of the
study. Participantsnon-usage attrition,
daily step counts, and
the proportion of participant days with >10,000 steps during the
rst 3 months of the program were analyzed as secondary out-
comes. A participant was coded as non-usage attrition observed,
when she or he stopped synchronizing step counts with the insur-
ance company.
Statistical Analysis
The approach by Gao and colleagues
for non-aggregate clus-
ter RCTs with binary outcomes was used to determine the
minimum number of insureds to invite to the pilot program.
Accordingly, a sample size of N=15,822 invited insureds is
necessary to detect a 5% difference in participation rates
between control and incentive groups assuming an a-level of
0.05, a power of 0.80, an intra-cluster correlation of r=0.01
and a mean cluster size of 879 (SD=1,326; based on data from
the health insurance company).
Linear mixed models and generalized linear mixed models
were tted to the data to analyze group differences of primary
and secondary outcomes. The model of participation rate
included a xed effect for incentive condition and a random
intercept for canton. Models of step counts and participant
days with >10,000 steps included xed effects for time, self-
reported physical activity measures at baseline, covariates of
physical activity, incentive condition, the incentive condition
by time interaction, and a random intercept for participants.
The time variable was mean-centered before entering the
model. The non-usage attrition model included xed effects
for incentive condition, age, sex, and nationality and a ran-
dom intercept for canton. In addition, all models of secondary
outcomes were adjusted for group differences at baseline. To
be able to adjust models for covariates and group differences
at baseline, only participants who completed the baseline sur-
vey were included in the analyses of secondary outcomes. Sev-
eral sensitivity analyses were performed to assess the
robustness of the results. Differences in participation rates
betweengroupswerefurtheradjustedusingxed effects for
age, sex, and nationality of participants and cantonal
Kramer et al / Am J Prev Med 2019;56(2):e45
e54 e47
February 2019
population density. For the secondary outcomes of steps and
participant days with >10,000 steps, a nested random effect
for canton was added to the model to account for potential
clustering on the canton level. Additionally, sensitivity analy-
ses with regard to missing data were performed for all out-
comes (Appendix Tables 14, available online).
The reported results are pooled over separate analyses of 20
imputed data sets created using 50 imputation iterations for
each data set. Data were imputed in wide format using all varia-
bles in the statistical models plus additional auxiliary variables
to set up the imputation models (Appendix Table 6, available
online). Subsequently, convergence of the imputation algorithm
was examined by visual inspection of plots of mean and SD of
imputations across all iterations. Based on data from a Swiss
representative sample,
daily step counts >25,000 or <1,000
steps were set to missing before starting the imputation. Data
were analyzed in R, version 3.4.2, using the lme4 package
tting (generalized) linear mixed models and the mice pack-
for multiple imputation.
Because of an error in the randomization process, six of all
26 Swiss cantons were non-randomly allocated to the three
study groups. These cantons contained 831 (3.09%) of invited
insureds and were excluded from all analyses. The study pro-
tocol further specied self-reported health status assessed after
3 months for the control group and after 6 months for both
incentive groups as a further secondary outcome. This com-
parison is not reported because the time difference in meas-
urements does not allow valid group comparisons. Lastly,
participants service billings with the insurance company could
not be included in the analyses, because these data were not
available at the time of writing.
In total, N=26,091 insureds (mean age 45.48 years,
SD=14.97, 38.52% female) met the predened
eligibility criteria and were randomized. Of those,
1,338 (5.13%) participated in the pilot program. Addi-
tionally, 209 family members of invited insureds par-
ticipated, bringing the total number of participants to
n=1,547 (Figure 1). The proportion of women was
higher among participants than among all invited
insureds (47.83% vs 38.52%), indicating that women
were more likely to participate. Participants were on
average aged 42.65 (SD=13.03) years and mostly Swiss
(90.20%). Based on baseline survey data (n=1,223),
43.58% had a university degree and only 3.84%
reported poor or fair health conditions. There were sta-
tistically signicant and meaningful group differences
with regard to participantsresidential environment,
self-reported health status, and sitting minutes per
week (Table 1). In the charity nancial incentive
condition, the mean proportion of donated rewards
was 20.29% (SD=26.81%). However, 310 of 623 partici-
pants (49.76%) chose not to donate any proportion of
their reward to charity.
Among invited insureds, 5.94% participated in the
personal nancial incentive group and 4.98% in the
charity nancial incentive group compared with 3.23%
in the control group. Differences between incentive
groups and control group were statistically signicant
(personal nancial incentives: OR=1.96, 95% CI=1.55,
2.49, p<0.001; charity nancial incentives: OR=1.59,
95% CI=1.25, 2.01, p<0.001). Contrast analysis revealed
that participation rates also differed signicantly
between insureds in the personal nancial incentive and
the charity nancial incentive group (OR=1.24, 95%
CI=1.06, 1.44, p=0.006).
Figure 1. Flow diagram.
e48 Kramer et al / Am J Prev Med 2019;56(2):e45
Table 1. Baseline Characteristics of Invited Insurees and Participants
Characteristics p-value
versus CG
versus CG
PFI versus
Invited insurees (N=26,091), n11,094 11,133 3,864
Canton level
Number of cantons 8 8 4
Cluster size 1,353.38 (2,080.96) 1,362.75 (1,439.82) 934.00 (1,263.82) 0.907 0.25 0.32 0.01
Population density
619.32 (244.93) 505.92 (1,721.03) 270.15 (826.41) 0.732 0.65 0.19 0.11
Individual level
Age, years 44.81 (14.73) 45.87 (15.12) 46.30 (15.15) <0.001 0.10 0.03 0.07
Female 4,441 (40.03) 4,261 (38.27) 1,348 (34.89) <0.001 0.11 0.07 0.04
Nationality <0.001 0.16 0.07 0.13
Swiss 9,491 (85.55) 9,973 (89.58) 3,486 (90.22)
German 582 (5.25) 384 (3.45) 111 (2.87)
Other 654 (5.90) 476 (4.28) 191 (4.94)
NA 367 (3.31) 300 (2.69) 76 (1.97)
Participants (N=1,547),
n784 623 140
Age, years 42.71 (12.99) 42.14 (12.95) 44.63 (12.53) 0.126 0.15 0.18 0.04
Female 382 (48.72) 299 (47.99) 59 (42.14) 0.424 0.13 0.12 0.01
Educational attainment 0.473 0.25 0.19 0.09
Secondary school 12 (1.99) 8 (1.59) 2 (1.72)
Vocational school 181 (30.02) 163 (32.34) 35 (30.17)
High school 92 (15.26) 85 (16.87) 27 (23.28)
University 278 (46.10) 213 (42.26) 42 (36.21)
NA 40 (6.63) 35 (6.94) 10 (8.62)
Residential environment <0.001 0.29 0.15 0.32
Town 85 (14.10) 42 (8.33) 11 (9.48)
Outskirts of town 173 (28.69) 102 (20.24) 24 (20.69)
Village 253 (41.96) 260 (51.59) 64 (55.17)
Countryside 73 (12.11) 88 (17.46) 14 (12.07)
NA 19 (3.15) 12 (2.38) 3 (2.59)
Monthly net income 0.343 0.26 0.24 0.17
CHF 2,500 27 (4.48) 30 (5.95) 3 (2.59)
CHF 2,5015,000 83 (13.76) 80 (15.87) 18 (15.52)
CHF 5,0017,500 190 (31.51) 147 (29.17) 33 (28.45)
CHF 7,50110,000 101 (16.75) 78 (15.48) 26 (22.41)
>CHF 10,000 73 (12.11) 46 (9.13) 8 (6.90)
No answer 110 (18.24) 111 (22.02) 25 (21.55)
NA 19 (3.15) 12 (2.38) 3 (2.59)
Health status 0.032 0.23 0.21 0.18
Poor 2 (0.33) 1 (0.20) 0 (0.00)
Fair 15 (2.49) 20 (3.97) 9 (7.76)
Good 230 (38.14) 225 (44.64) 43 (37.07)
Very good 266 (44.11) 203 (40.28) 48 (41.38)
Excellent 68 (11.28) 40 (7.94) 12 (10.34)
NA 22 (3.65) 15 (2.98) 4 (3.45)
Activity tracker brand 0.520 0.13 0.17 0.04
Fitbit 511 (84.74) 432 (85.71) 94 (81.03)
Garmin 67 (11.11) 55 (10.91) 14 (12.07)
Jawbone 25 (4.15) 17 (3.37) 8 (6.90)
(continued on next page)
Kramer et al / Am J Prev Med 2019;56(2):e45
e54 e49
February 2019
On average, participants walked 10,709 (SD=4,555)
steps per day and reached the 10,000 steps goal on
54.24% of all days during the rst 3 months of the pilot
program. To investigate group differences, Table 2 and
Figure 2 compare steps per day and the probability of
walking >10,000 steps per day between the groups at the
beginning, in the middle, and at the end of the rst 3
months of the pilot program. Although participants in
the charity nancial incentive group consistently accu-
mulated a higher number of steps than participants in
the personal nancial incentive and in the control group,
these step count differences were not statistically signi-
cant at any time point. The difference between the incen-
tive groups and the control group diminished over time
(Figure 2), but this trend was also not statistically
signicant (change of the charity nancial incentive
effect over time: 3 steps/day, 95% CI= 6, 1, p=0.16;
change of the personal nancial incentive effect over
time: 3 steps/day, 95% CI= 6, 1, p=0.15).
At the beginning of the program, participants
receiving charity nancial incentives had a 12% higher
chance (OR=1.68, 95% CI=1.23, 2.30, p=0.004) of
walking >10,000 steps per day compared with the
control group. This difference diminished signicantly
over time (change of the charity nancial incentive
effect over time: 0.003, 95% CI= 0.01, 0.001,
p=0.003) and was no longer signicant 3 months after
the start of the program. After adjusting p-values for
multiple comparisons across time, the probability of
walking >10,000 steps per day did not differ
Table 1. Baseline Characteristics of Invited Insurees and Participants (continued)
Characteristics p-value
versus CG
versus CG
PFI versus
Bought an activity
tracker to participate
320 (53.07) 303 (60.12) 62 (53.45) 0.048 0.01 0 .15 0.14
Sitting (minutes/week)
2,435.80 (1,378.83) 2,263.40 (1,303.22) 2,165.52 (1,247.31) 0.039 0.21 0.08 0.13
Moderate activities and
walking (MET-minutes/
week), median (IQR)
0.243 0.09 0.04 0.05
Note: Boldface indicates statistical signicance (p<0.05). Table displays M (SD) for continuous and n(percentage) for categorical variables unless
stated otherwise.
Based on one-way ANOVA for normal, Kruskal-Wallis test for non-normal, and x
test of independence for categorical variables.
Values greater 0.20 dened as small effect size.
Non-normal variables were log-transformed before calculating SMD.
Reported values are weighted by cluster size.
Data on age and sex of par ticipants were provided by the insurance company (n=1,547), data on other variables are based on baseline survey data
Assessed using the International Physical Activity Questionnaire.
CFI, charity nancial incentives; CHF, Swiss Francs; CG, control group; IQR, interquartile range; NA, not available; PFI, personal nancial incentives;
SMD, absolute standardized difference.
Figure 2. Steps per day and probability of walking more than 10,000 daily steps over time by incentive condition.
e50 Kramer et al / Am J Prev Med 2019;56(2):e45
signicantly between the personal nancial incentive
group and the control group at any time point. Like-
wise, the probability of walking >10,000 steps per day
did not differ signicantly between the personal and
the charity nancial incentive group.
Lastly, non-usage attrition during the rst 3 months
was highest in the charity nancial incentive group
(10.91%), followed by the personal nancial incentive
group (9.62%) and the control group (9.48%). In the
adjusted model, differences between the incentive
groups and the control group were not statistically sig-
nicant (OR=1.05, 95% CI=0.41, 2.69, p=0.92, for the
personal nancial incentive group; OR=1.21, 95%
CI=0.48, 3.08, p=0.69, for the charity nancial incen-
tive group).
This three-arm cluster RCT investigated whether small
personal and charity nancial incentives can promote
participation in a physical activity promotion program
utilizing activity trackers. Secondary analyses examined
group differences with regard to physical activity and
attrition of participants. This study is among the rst to
investigate the potential of small personal nancial and
charity nancial incentives for the promotion of physical
activity. A strength of this study is that its pragmatic set-
ting makes it possible to demonstrate real-world effec-
tiveness and to inform the design of large-scale physical
activity promotion programs.
Small monthly personal nancial and charity nancial
incentives signicantly increased participation, with per-
sonal nancial incentives being more effective than char-
ity nancial incentives. However, the effects of these
incentives on behavior may be limited. The data of this
study suggest that charity nancial incentives increase
the likelihood of walking 10,000 steps per day only in
the short term, with effects dissipating after 3 months.
Personal nancial incentives did not lead to signicant
changes in physical activity. In addition, there was no
evidence that personal nancial and charity nancial
incentives prevent attrition of participants. Conse-
quently, this study supports utilizing small personal
nancial or charity nancial incentives to promote par-
ticipation but not behavior change in large-scale physical
activity promotion programs. These ndings add to the
understanding of the effects of nancial and charity
incentives for the promotion of physical activity.
Regarding physical activity, these results deviate from
previous studies that typically nd a stable effect of
incentives as long as they are in place.
This may indi-
cate that an incentive value of CHF 10 per month could
have been too low to sustain motivation after an initial
Table 2. Group Comparisons Regarding Steps per Day and Probability of Walking More Than 10,000 Steps
Outcome Day 1 p-value
Day 46 p-value
Day 92 p-value
Difference in steps (95% CI)
PFI versus CG 206.49 (322.13, 735.12) 1.00 91.99 (415.57, 599.55) 1.00 22.51 (557.45, 512.43) 0.934
CFI versus CG 507.24 (29.48, 1043.96) 0.192 383.81 (128.35, 895.98) 0.284 260.39 (280.95, 801.72) 0.346
PFI versus CFI 300.75 (620.39, 18.90) 0.130 291.82 (597.34, 13.70) 0.184 282.90 (603.73, 37.94) 0.084
Walking >10,000 steps per
day, OR (95% CI)
PFI versus CG 1.41 (1.03, 1.92) 0.093 1.20 (0.89, 1.61) 0.477 1.02 (0.74, 1.39) 0.919
CFI versus CG 1.68 (1.23, 2.30) 0.004 1.45 (1.07, 1.96) 0.031 1.25 (0.91, 1.71) 0.168
PFI versus CFI 0.84 (0.69, 1.01) 0.063 0.83 (0.69, 0.99) 0.069 0.81 (0.68, 0.98) 0.092
Note: Boldface indicates statistical signicance (*p<0.05).
p-values are adjusted for multiple testing across time points using the Holm-Bonferroni method. 95% CIs are not adjusted, because Holm-Bonferroni-adjusted CIs are non-informative.
CFI, charity nancial incentives; CG, control group; PFI, personal nancial incentives.
February 2019
Kramer et al / Am J Prev Med 2019;56(2):e45
e54 e51
phase of excitement at the beginning of the program had
passed. To the best of the authorsknowledge, the lowest
incentive value that has demonstrated effects on physical
activity in randomized trials is around $1 per day,
which is three times the incentive value of the present
study. In addition, previous studies often used daily
or weekly
incentive schedules instead of the monthly
incentives in the present study. Research in the eld
of behavioral economics reveals that people tend to
place more weight on immediate and discount future
a phenomenon known as present bias. The
monthly reward schedule in the present study may have
caused participants to further attenuate the subjective
value of the already small incentives. Both factors, the
small incentive value and the monthly incentive sched-
ule, may explain the studys limited results on the ability
of incentives to change behavior. Future research could
investigate whether small incentives with immediate or
dynamic incentive schedules lead to sustained behavior
changes or what minimum incentive amount is neces-
sary to produce sustained effects.
Designing physical activity promotion programs that
rely on activity trackers may entail the risk of a favorable
selection effect because individuals who already own a
tracker face less barriers for participation and may be
more likely to meet the recommended activity levels.
Indeed, in the present study, the requirement to pur-
chase an activity tracker was named the main barrier for
participation (selected by 41.05 % of insureds who gave
reasons for refusing to participate; Appendix Table 7,
available online) and 44% of participants already owned
an activity tracker before being invited to the program.
However, although owners of activity trackers were sig-
nicantly younger, better educated, and reported better
health than non-owners (Appendix Table 6, available
online), they accumulated on average 1,004 fewer steps
(95% CI=708, 1,301) than those who purchased an activ-
ity tracker for participation (Appendix Figure 1, avail-
able online). Thus, although the investment in an
activity tracker might have increased commitment to the
program, it remains a big barrier to participation. Rely-
ing on smartphones for tracking physical activity or fur-
ther reimbursing the nancial investment (e.g.,
conditional on meeting activity goals after 1 year) could
reduce this barrier and help to better reach people in
need of the program.
The present study has a few limitations to consider. First,
because data were collected from a single insurer, results
may not generalize to other programs with other insur-
ers or populations. Second, results regarding secondary
outcomes have to be interpreted with caution because
voluntary registration of participants may have resulted
in selection bias. Indeed, program participants were bet-
ter educated and earned higher wages compared with
the general Swiss population.
Incentive effects may
differ in populations with lower education or income.
Further, nonsignicant results may not be taken as evi-
dence for the absence of meaningful effects because the
study was not powered for secondary outcomes. In addi-
tion, because participants could respond to the baseline
survey after the program had started, it is possible that
some self-reported baseline information was affected by
the incentive strategies. This could potentially lead to
overly conservative estimates of group differences in
adjusted models. Third, participants in the charity incen-
tive group had the opportunity to select the proportion
of their reward that they wish to donate to charity. Thus,
results of this study are not directly comparable to those
of previous studies, which did not offer this option.
Lastly, reported absolute step counts and proportions of
days with more than 10,000 steps are likely to be conser-
vative because consumer-grade activity trackers tend to
underestimate true step counts.
In this large cluster-RCT from Switzerland, small monthly
personal nancial and charity nancial incentives
increased participation in a physical activity promotion
program utilizing activity trackers. Organizations offering
such programs can thus encourage participation even
when using relatively small incentives. However, the
short-term effects of these incentives on physical activity
and attrition limits their utility in the context of health pro-
motion programs. Incentives may need to be modied in
order to prevent attrition and promote behavior change
over a longer period of time.
We thank the involved employees of CSS insurance, Switzer-
land, for their contribution to the implementation of the study.
We thank Grace Xiao for proofreading the manuscript.
Research reported in this publication was par tly funded by
CSS insurance, Switzerland. We developed the incentive
schemes and the randomization strategy in collaboration with
CSS insurance. CSS insurance supported recruitment and data
collection but had no role in other aspects of the study design,
data analysis and interpretation, or in reviewing and approving
the manuscript for publication.
The IRB of the University of St. Gallen, Switzerland, approved
the study (reference number: HSG-EC-2015-04-22-A).
Tobias Kowatsch and Elgar Fleisch developed the conception
and design of the study. Jan-Niklas Kramer developed the
methodology, performed the data analyses, and wrote the man-
uscript. Peter Tinschert, Tobias Kowatsch, and Urte Scholz
e52 Kramer et al / Am J Prev Med 2019;56(2):e45
provided critical reviews on earlier drafts of the manuscript and
aided in data analysis and interpretation. All authors approved
the nal version of the manuscript before submission.
Elgar Fleisch co-chairs the Center For Digital Health Interven-
tions (CDHI), a joint initiative of the Department of Manage-
ment, Technology, and Economics at ETH Zurich and the
Institute of Technology Management at the University of St.
Gallen, which is funded in part by the Swiss health insurer CSS.
Tobias Kowatsch is the scientic director of the CDHI and Jan-
Niklas Kramer and Peter Tinschert are doctoral researchers at
the CDHI. Elgar Fleisch and Tobias Kowatsch are also co-found-
ers of Pathmate Technologies, a university spin-off company
that creates and delivers digital clinical pathways. No other
nancial disclosures were reported by the authors of this paper.
Supplemental materials associated with this article can be
found in the online version at
1. Lear SA, Hu W, Rangarajan S, et al. The effect of physical activity on
mortality and cardiovascular disease in 130 000 people from 17 high-
income, middle-income, and low-income countries: the PURE study.
Lancet. 2017;390(10113):26432654.
2. Lee I-M, Shiroma EJ, Lobelo F, et al. Effect of physical inactivity on
major non-communicable diseases worldwide: an analysis of burden
of disease and life expectancy. Lancet. 2012;380(9838):219229.
3. Kyu HH, Bachman VF, Alexander LT, et al. Physical activity and risk
of breast cancer, colon cancer, diabetes, ischemic heart disease, and
ischemic stroke events: systematic review and dose-response meta-
analysis for the global burden of disease study 2013. BMJ. 2016;354:
4. Sallis JF, Bull F, Guthold R, et al. Progress in physical activity over the
Olympic quadrennium. Lancet. 2016;388(10051):13251336. https://
5. Steinhubl SR, Muse ED, Topol EJ. The emerging eld of mobile health.
Sci Transl Med. 2015;7(283):283rv3.
6. HERO. Wearables in wellnessemployer use of wearable tracking
devices in wellness programs.
Published June 2015. Accessed March 22, 2017.
7. Patel MS, Foschini L, Kurtzman GW, et al. Using wearable devices and
smartphones to track physical activity: initial activation, sustained use,
and step counts across sociodemographic characteristics in a national
sample. Ann Intern Med. 2017;167(10):755757.
8. Nyce S. Boosting wellness participation without breaking the bank.
Published July 2010. Accessed March 2, 2018.
9. Batorsky B, Taylor E, Huang C, Liu H, Mattke S. Understanding the
relationship between incentive design and participation in U.S. work-
place wellness programs. Am J Health Promot. 2016;30(3):198203.
10. Volpp KG, Troxel AB, Pauly MV, et al. A randomized, controlled trial
of nancial incentives for smoking cessation. N Engl J Med. 2009;360
11. Strohacker K, Galarraga O, Williams DM. The impact of incentives on
exercise behavior: a systematic review of randomized controlled trials.
Ann Behav Med. 2014;48(1):9299.
12. Mitchell MS, Goodman JM, Alter DA, et al. Financial incentives
for exercise adherence in adults: systematic review and meta-anal-
ysis. Am J Prev Med. 2013;45(5):658667.
13. Barte JC, Wendel-Vos GW. A systematic review of nancial incentives
for physical activity: the effects on physical activity and related out-
comes. Behav Med. 2017;43(2):7990.
14. Patel MS, Asch DA, Rosin R, et al. Individual versus team-based nan-
cial incentives to increase physical activity: a randomized, controlled
trial. J Gen Intern Med. 2016;31(7):746754.
15. Patel MS, Asch DA, Roy Rosin M, et al. Framing nancial incentives
to increase physical activity among overweight and obese adults: a ran-
domized, controlled trial. Ann Intern Med. 2016;164(6):385394.
16. Patel MS, Volpp KG, Rosin R, et al. A randomized, controlled trial of
lottery-based nancial incentives to increase physical activity among
overweight and obese adults. Am J Health Promot. 2018;32(7):
17. Harkins KA, Kullgren JT, Bellamy SL, Karlawish J, Glanz K. A trial of
nancial and social incentives to increase older adultswalking. Am J
Prev Med. 2017;52(5):e123e130.
18. Finkelstein EA, Haaland BA, Bilger M, et al. Effectiveness of activity
trackers with and without incentives to increase physical activity
(TRIPPA): a randomised controlled trial. Lancet Diabetes Endocrinol.
19. Banerjee AV, Duo E, Glennerster R, Kothari D. Improving immuni-
sation coverage in rural India: clustered randomised controlled evalua-
tion of immunisation campaigns with and without incentives. BMJ.
20. Lagarde M, Haines A, Palmer N. Conditional cash transfers for
improving uptake of health interventions in low- and middle-income
countries: a systematic review. JAMA. 2007;298(16):19001910.
21. Malotte CK, Rhodes F, Mais KE. Tuberculosis screening and com-
pliance with return for skin test reading among active drug users.
Am J Public Health. 1998;88(5):792796.
22. Moll J, Krueger F, Zahn R, Pardini M, de Oliveira-Souza R, Grafman J.
Human fronto-mesolimbic networks guide decisions about charitable
donation. Proc Natl Acad Sci U S A. 2006;103(42):1562315628.
23. Dunn EW, Aknin LB, Norton MI. Spending money on others pro-
motes happiness. Science. 2008;319(5870):16871688.
24. Andreoni J. Impure altruism and donations to public goods: a theory
of warm-glow giving. Econ J (London). 1990;100(401):464477.
25. Varadarajan PR, Menon A. Cause-related marketing: a coalignment of
marketing strategy and corporate philanthropy. J Mark. 1988;52
26. Williams DM, Lee HH, Connell L, et al. Small sustainable mone-
tary incentives versus charitable donations to promote exercise:
rationale, design, and baseline data from a randomized pilot
study. Contemp Clin Trials. 2018;66:8085.
27. Kowatsch T, Kramer J-N, Kehr F, Wahle F, Elser N, Fleisch E. Effects
of charitable versus monetary incentives on the acceptance of and
February 2019
Kramer et al / Am J Prev Med 2019;56(2):e45
e54 e53
adherence to a pedometer-based health intervention: study protocol
and baseline characteristics of a cluster-randomized controlled trial.
JMIR Res Protoc. 2016;5(3):e181.
28. Eisler R, L
uber A, Wie wichtig ist den schweizern eine spitalzusatz-
versicherung? 2016;130.»/media/les/medien-
die.pdf. Accessed September 27, 2018.
29. Tudor-Locke C, Craig CL, Brown WJ, et al. How many steps/day are
enough? For adults. Int J Behav Nutr Phys Act. 2011;8:117. https://
30. Lamprecht M, Fischer A, Stamm H. Sport schweiz 2014: Sportaktivit
und sportinteresse der schweizer bev
olkerung. Observatorium Sport
und Bewegung Schweiz c/o Lamprecht & Stamm Sozialforschung und
Beratung AG; 2014.
31. Eysenbach G. The law of attrition. J Med Internet Res. 2005;7(1):e11.
32. Gao F, Earnest A, Matchar DB, Campbell MJ, Machin D. Sample size
calculations for the design of cluster randomized trials: a summary of
methodology. Contemp Clin Trials. 2015;42:4150.
33. Donner A, Klar N. Design and Analysis of Cluster Randomization Tri-
als in Health Research. London, UK: Arnold, 2000.
34. Raudenbush SW, Bryk AS. Hierarchical Linear Models: Applications
and Data Analysis Methods. Thousand Oaks, CA: Sage Publications,
35. Sequeira MM, Rickenbach M, Wietlisbach V, Tullen B, Schutz Y. Physi-
cal activity assessment using a pedometer and its comparison with a
questionnaire in a large population survey. Am J Epidemiol. 1995;142
36. Bates D, M
achler M, Bolker B, Walker S. Fitting linear mixed-effects
models using lme4. arXiv preprint arXiv:14065823. 2014.
37. van Buuren S, Groothuis-Oudshoorn K. Mice: multivariate imputation
by chained equations in R. J Stat Softw. 2011;45(3).
38. Yang D, Dalton JE. A unied approach to measuring the effect size
between two groups using SASÒ.SAS Global Forum. 2012;2012:16.
39. Craig CL, Marshall AL, Sjo
activity questionnaire: 12-country reliability and validity. Med
Sci Sports Exerc. 2003;35:13811395.
40. Strohacker K, Gal
arraga O, Emerson J, Fricchione SR, Lohse M, Wil-
liams DM. Impact of small monetary incentives on exercise in univer-
sity students. Am J Health Behav. 2015;39(6):779786.
41. Shin DW, Yun JM, Shin JH, et al. Enhancing physical activity and
reducing obesity through smartcare and nancial incentives: a pilot
randomized trial. Obesity (Silver Spring). 2017;25(2):302310. https://
42. Volpp KG, Asch DA, Galvin R, Loewenstein G. Redesigning employee
health incentiveslessons from behavioral economics. N Engl J Med.
43. Bundesamt f
ur Statistik. St
andige wohnbev
olkerung ab 15 jahren nach
ochster abgeschlossener ausbildung, 2016.
tail.4242913.html. Published January 2018. Accessed March 13, 2018.
44. Bundesamt f
ur Statistik. H
augkeitsverteilung (monatlicher nettolohn),
voll- und teilzeitbesch
aftigte nach lohnh
ohenklassen - privater und
offentlicher sektor zusammen - schweiz.
Published April 2016. Accessed March 13, 2018.
45. Mantzari E, Vogt F, Shemilt I, Wei Y, Higgins JP, Marteau TM. Per-
sonal nancial incentives for changing habitual health-related behav-
iors: a systematic review and meta-analysis. Prev Med. 2015;75:7585.
46. Toth LP, Park S, Springer CM, Feyerabend MD, Steeves JA, Bassett
DR. Video-recorded validation of wearable step counters under free-
living conditions. Med Sci Sports Exerc. 2018;50(6):13151322. https://
e54 Kramer et al / Am J Prev Med 2019;56(2):e45
... [25] Moreover, the female gender was dominant in three studies; [25][26][27] and the male gender was the dominant gender in the other three studies. [28][29][30] However, the female gender was more prominent in the three studies with the highest percentage (see Supplementary). Five studies stated the participants' level of education. ...
... Five studies stated the participants' level of education. [25][26][27][28]30] Three studies stated their employment status. [25,29,30] Four study groups used short-term interventions, [24,27,29,31] while the other four used various study periods, as described in Supplementary. ...
... Participants reported their activity through emails in two studies, [24,31] while activity data were transmitted automatically to the study server in the rest of the research. [25][26][27][28][29][30] PA trackers were used for different purposes including social support, [24,26,27] self-monitoring, [24-27, 29, 30] or sharing tips. [24,25,29] ...
Objective: The objective of this study is to analyse the researchers’ studies on the effectiveness of mobile Apps to encourage people to undertake physical activity (PA), to determine what strategy makes utilising the mobile Apps an effective experience in increasing PA in healthy people, and to identify the gaps in their research studies.Study design: The researcher utilised a scoping review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Scoping Review extension protocol.Methods: This scoping review was conducted to identify under what conditions the mobile Apps could lead to the increased PA of the participants through analysing the research studies on mobile App features and participants’ characteristics. Studies included those with high internal validity (randomised controlled trials) that dealt mainly with PA. The articles were selected according to specific criteria including 1) quantitative studies in English language, 2) participants from 18-70 years of age, 3) healthy participants who were recruited from community/primary healthcare centres and at high risk of cardiovascular diseases, and 4) the studies’ outcomes on the apps’ effectiveness and efficiency in increasing PA. The articles were critiqued using the Specialist Unit for Review Evidence.Results: Eight articles were finally selected and analysed. Four intervention strategies were identified from the studies – social aspect (3/8 studies), texting (3/8 studies), health sessions (3/8 studies), and feedback (5/8 studies). Results showed that some of the motivational strategies had a significant influence in improving PA.Conclusions: The long-term effect was not tested on all studies. Therefore, long-term studies need to be conducted to test the consistency of the PA. Additionally, subgroup analysis should be performed to gauge the influence of individual characteristics on increasing PA.
... CLIs are lifestyle interventions that tackle multiple health behaviours with the aim of improving participants' health and lifestyle. From 2019, three CLIs were covered by the basic healthcare insurance scheme (CooL, BeweegKuur and SLIMMER ), and in 2020 Samen Sportief in Beweging (SSiB) was added as well [42][43][44][45]. These CLIs fulfil the Dutch criteria for successfully and effectively promoting healthy lifestyles [40]; however, only the health care-related portion of these CLIs (i.e., care provided by lifestyle coaches, dietitians or physiotherapists) is paid for by the healthcare insurance companies. ...
... First, care-PA initiatives are deemed effective for the promotion of PA and healthy lifestyles for the general population, and therefore improve the health and QoL of their participants [43,44]; however, they are typically developed for the general population and not specifically for citizens with a low SES. Consequently, little is known about their effectiveness and impact on health, QoL, societal participation and healthcare utilisation for this specific population. ...
... The individual case descriptions of the municipalities and neighbourhoods will enable a cross-case analysis to create more robust evidence than can be provided by a single case study [42]. The combination of information from multiple sources (e.g., policies, neighbourhoods, initiatives, different stakeholders' perspectives) and multiple methods (e.g., body measurements, questionnaires, interviews, focus groups) increases the validity of the study by providing different options for triangulation of information [43]. ...
... Nineteen RCTs [46][47][48][49][50][51][52][53][54][55][56][57][58][59][60][61][62][63][64] were included in the NMA of physical activity (Fig. 2b). Standard FI, deposit, and lottery significantly increased the chances of goal achievement compared to no-FI with pooled RRs (95%CI) of 1.38 (1.13, 1.68), 1.63 (1.24, 2.14) and 1.43 (1.14, 1.80), respectively (Table 2 and Supplementary Figure 12). ...
Full-text available
Background Healthy diet, weight control and physical activity to reduce obesity can be motivated by financial incentives (FI). Behavioral-economic approaches may improve the incentivization effectiveness. This study compares and ranks the effectiveness of standard and behavioral incentivization for healthy diet, weight control, and physical activity promotion. Purpose To investigate whether behavioral-economic insights improve incentivization effectiveness. Methods A systematic search of Medline and Scopus was performed from database inception to December 2020. Study characteristics, program designs, and risk ratio (RR) were extracted. A two-stage network meta-analysis pooled and ranked intervention effects. Results There were 35 eligible RCTs. For diet-weight control, standard FI, deposit contract (deposit), lottery-based incentive (lottery), and standard-FI + lottery increased goal achievement compared to no-FI but only deposit was statistically significant with pooled RRs and 95% confidence intervals (CI) of 1.21 (0.94, 1.56), 1.79 (1.04, 3.05), 1.45 (0.99, 2.13), and 1.73 (0.83, 3.63). For physical activity, standard-FI, deposit, and lottery significantly increased goal achievement compared to no-FI, with pooled RRs of 1.38 (1.13, 1.68), 1.63 (1.24, 2.14) and 1.43 (1.14, 1.80), respectively. In a follow-up period for physical activity, only deposit significantly increased goal achievement compared to no-FI, with pooled RRs of 1.39 (1.11, 1.73). Conclusion Deposit, followed by lottery, were best for motivating healthy diet, weight control and physical activity at program end. Post-intervention, deposit then standard-FI were best for motivating physical activity. Behavioral insights can improve incentivization effectiveness, although lottery-based approaches may offer only short-term benefit regarding physical activity. However, the imprecise intervention effects were major concerns.
... Thus, mHealth interventions coupled with financial incentives may be a potential method to overcome these challenges. Previous systematic reviews have shown that financial incentives can be an effective strategy to improve lifestyle-related behaviours, as well as promote program engagement [24,25]. However, there is a lack of PA and financial incentive-based hypertension prevention mHealth intervention tailored for the Canadian population. ...
Full-text available
BACKGROUND Regular physical activity (PA) is a key lifestyle component to hypertension prevention. Previous studies have already shown that mobile health apps can be an effective tool to improve PA behaviours. However, adherence and poor engagement in with these apps is a challenge. A potential solution to overcome this challenge may be to combine financial incentive with innovative behaviour theory, such as the Multi-Process Action Control (M-PAC) framework. Currently, there is a lack of PA financial incentive-driven M-PAC mHealth program aimed for hypertension prevention. OBJECTIVE To describe the process of developing an eight-week mHealth PA and financial incentive hypertension education program (Healthy Hearts) and to evaluate the acceptability and usability of the Healthy Hearts program. METHODS The Integrate, Design, Assess, Share (IDEAS) framework was used to guide the development of the Healthy Hearts program. The development process consisted of two phases. In phase 1, we used the M-PAC framework to adopt an existing web-based hypertension prevention program to a mobile app. The app was developed using a no-code app development platform (Pathverse) to help decrease overall development time. In phase 2, we conducted acceptability and usability testing to evaluate Lesson 1 of the Healthy Hearts program to further enhance the user experience. Focus group interviews and the mHealth App Usability Questionnaire were applied to evaluate program acceptability and usability. RESULTS Intervention development successfully created an eight-week financial incentive hypertension education program for adults aged 40-65 not currently meeting the Canadian PA Guidelines (<150 minutes of moderate-to-vigorous PA per week). This program was 8 weeks in length and was composed of 25 lessons guided by the M-PAC framework. Usability testing of the first lesson was successful, with 6 participants recruited for 2 rounds of testing, and feedback was gathered to enhance the content, layout, and design of the Healthy Hearts program to prepare the mHealth program for Study 2. Results of round 1 usability testing suggested that the content delivered in the lessons was too long. Content was therefore divided into multiple lessons prior to round 2 of usability testing, where feedback was only on design preferences. CONCLUSIONS This study has reinforced the importance of an interactive usability testing process between the users and the research team to design an mHealth PA intervention. Through this process, the users were able to provide valuable feedback on the content, design, and layout of the program before advancing to feasibility testing. A study is now needed to evaluate the feasibility of the Healthy Hearts mHealth program.
... Existing research argues that immediate monetary incentives can help overcome present bias in health decision-making and improve adaptation of preventative mHealth interventions [34][35][36]. However, extrinsic monetary incentives might lead to motivational crowding-out and diminish intrinsic motivation for behavior change over time [37,38]. ...
... Amongst the first things we can notice is that goal-setting sustains its maximum "PAST Score" both for large-scale and long-term interventions, whereas self-monitoring, and rewards' effectiveness whines with time (+4 from +5). This is in accordance with prior literature [30], which reports diminished long-term effectiveness of incentives for inciting and maintaining HBC. On the contrary, "Social Support" features, such as social comparison and social learning, show improved effectiveness for large-scale interventions (+4 from +3). ...
Full-text available
Despite the indisputable personal and societal benefits of regular physical activity, a large portion of the population does not follow the recommended guidelines, harming their health and wellness. The World Health Organization has called upon governments, practitioners, and researchers to accelerate action to address the global prevalence of physical inactivity. To this end, an emerging wave of research in ubiquitous computing has been exploring the potential of interactive self-tracking technology in encouraging positive health behavior change. Numerous findings indicate the benefits of personalization and inclusive design regarding increasing the motivational appeal and overall effectiveness of behavior change systems, with the ultimate goal of empowering and facilitating people to achieve their goals. However, most interventions still adopt a "one-size-fits-all" approach to their design, assuming equal effectiveness for all system features in spite of individual and collective user differences. To this end, we analyze a corpus of 12 years of research in self-tracking technology for health behavior change, focusing on physical activity, to identify those design elements that have proven most effective in inciting desirable behavior across diverse population segments. We then provide actionable recommendations for designing and evaluating behavior change self-tracking technology based on age, gender, occupation, fitness, and health condition. Finally, we engage in a critical commentary on the diversity of the domain and discuss ethical concerns surrounding tailored interventions and directions for moving forward.
... The psychological and pedagogical effect of spoken opinions vs. written recommendations is evident, establishing effective relationships. In this sense, computer software programs simulating a human conversation via text or voice have been used to either manage chronic conditions or promote healthy behaviors including physical activity behavior [4,10,11]. ...
Full-text available
Digital interventions for increasing physical activity behavior have shown great potential, especially those with social media. Chatbots, also known as conversational agents, have emerged in healthcare in relation to digital interventions and have proven effective in promoting physical activity among adults. The study’s objective is to explore users’ experiences with a social media chatbot. The concept and the prototype development of the social media chatbot MYA were realized in three steps: requirement analysis, concept development, and implementation. MYA’s design includes behavior change techniques effective in increasing physical activity through digital interventions. Participants in a usability study answered a survey with the Chatbot Usability Questionnaire (CUQ), which is comparable to the Systems Usability Scale. The mean CUQ score was below 68, the benchmark for average usability. The highest mean CUQ score was 64.5 for participants who thought MYA could help increase their physical activity behavior. The lowest mean CUQ score was 40.6 for participants aged between 50 and 69 years. Generally, MYA was considered to be welcoming, very easy to use, realistic, engaging, and informative. However, some technical issues were identified. A good and diversified user experience promotes prolonged chatbot use. Addressing identified issues will enhance users’ interaction with MYA.
... Existing research argues that immediate monetary incentives can help overcome present bias in health decision-making and improve adaptation of preventative mHealth interventions [34][35][36]. However, extrinsic monetary incentives might lead to motivational crowding-out and diminish intrinsic motivation for behavior change over time [37,38]. ...
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Background: Insufficient physical activity and unhealthy diets are contributing to the rise in noncommunicable diseases. Preventative mobile health (mHealth) interventions may help reverse this trend, but present bias might reduce their effectiveness. Future-self avatar interventions have resulted in behavior change in related fields, yet evidence of whether such interventions can change health behavior is lacking. Objective: We aimed to investigate the impact of a future-self avatar mHealth intervention on physical activity and food purchasing behavior and examine the feasibility of a novel automated nutrition tracking system. We also aimed to understand how this intervention impacts related attitudinal and motivational constructs. Methods: We conducted a 12-week parallel randomized controlled trial (RCT), followed by semistructured interviews. German-speaking smartphone users aged ≥18 years living in Switzerland and using at least one of the two leading Swiss grocery loyalty cards, were recruited for the trial. Data were collected from November 2020 to April 2021. The intervention group received the FutureMe intervention, a physical activity and food purchase tracking mobile phone app that uses a future-self avatar as the primary interface and provides participants with personalized food basket analysis and shopping tips. The control group received a conventional text- and graphic-based primary interface intervention. We pioneered a novel system to track nutrition by leveraging digital receipts from loyalty card data and analyzing food purchases in a fully automated way. Data were consolidated in 4-week intervals, and nonparametric tests were conducted to test for within- and between-group differences. Results: We recruited 167 participants, and 95 eligible participants were randomized into either the intervention (n=42) or control group (n=53). The median age was 44 years (IQR 19), and the gender ratio was balanced (female 52/95, 55%). Attrition was unexpectedly high with only 30 participants completing the intervention, negatively impacting the statistical power. The FutureMe intervention led to small statistically insignificant increases in physical activity (median +242 steps/day) and small insignificant improvements in the nutritional quality of food purchases (median -1.28 British Food Standards Agency Nutrient Profiling System Dietary Index points) at the end of the intervention. Intrinsic motivation significantly increased (P=.03) in the FutureMe group, but decreased in the control group. Outcome expectancy directionally increased in the FutureMe group, but decreased in the control group. Leveraging loyalty card data to track the nutritional quality of food purchases was found to be a feasible and accepted fully automated nutrition tracking system. Conclusions: Preventative future-self avatar mHealth interventions promise to encourage improvements in physical activity and food purchasing behavior in healthy population groups. A full-powered RCT is needed to confirm this preliminary evidence and to investigate how future-self avatars might be modified to reduce attrition, overcome present bias, and promote sustainable behavior change. Trial registration: NCT04505124;
... Although the use of chatbots for health-related purposes is still an emerging field [8,9], its benefits related to increase in physical activity and user satisfaction have already been shown [10][11][12]. Thus, a chatbot on a social media platform (e.g., Telegram) could provide an inexpensive and widely available health intervention that appeals to a large population. ...
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Fully automated self-help interventions integrated with social media chatbots could serve as highly cost-effective physical activity promotion tools for a large population. We have developed MYA, a Telegram-based chatbot for increasing physical activity. The objective of this study was to assess the usability of MYA. To identify usability issues, we recruited volunteers and asked them to interact with MYA and to answer the Chatbot Usability Questionnaire. Thirty volunteers participated in the study, 83.3% agreed MYA was welcoming during initial setup and 63.3% agreed MYA was very easy to use. MYA was perceived as realistic and engaging, easy to navigate, and its responses were useful, appropriate, and informative (all 53.3%). However, 63.3% of respondents agreed MYA failed to recognize most of their inputs, and 43.3% claimed it would be easy to get confused when using MYA. Although the results are encouraging, it remains unclear if a social media chatbot can motivate people to increase their physical activity. MYA has the potential to do that, with improvements in functionalities like challenge personalization. The efficacy of these approaches should be studied in a clinical trial.
Background: Hypertension is the leading modifiable risk factor for cardiovascular disease and mortality. Adopting lifestyle modifications, like increasing physical activity (PA), can be an effective strategy in blood pressure (BP) control, but many adults do not meet the PA guidelines. Financial incentive interventions have the power to increase PA levels but are often limited due to cost. Further, mobile health technologies can make these programs more scalable. There is a gap in the literature about the most feasible and effective financial incentive PA framework; thus, pay-per-minute (PPM) and self-funded investment incentive (SFII) frameworks were explored. Objective: The aims were to (1) determine the feasibility (recruitment, engagement, and acceptability) of an 8-week mobile-based PPM and SFII hypertension prevention PA program and (2) explore the effects of PPM and SFII interventions relative to a control on the PA levels, BP, and PA motivation. Methods: In total, 55 adults aged 40-65 years not meeting the Canadian PA guidelines were recruited from Facebook and randomized into the following groups: financial incentive groups, PPM or SFII, receiving up to CAD $20 each (at the time of writing: CAD $1=US $0.74), or a control group without financial incentive. PPM participants received CAD $0.02 for each minute of moderate-to-vigorous PA (MVPA) per week up to the PA guidelines and the SFII received CAD $2.50 for each week they met the PA guidelines. Feasibility outcome measures (recruitment, engagement, and acceptability) were assessed. Secondary outcomes included changes in PA outcomes (MVPA and daily steps) relative to baseline were compared among PPM, SFII, and control groups at 4 and 8 weeks using linear regressions. Changes in BP and relative autonomy index relative to baseline were compared among the groups at follow-up. Results: Participants were randomized to the PPM (n=19), SFII (n=18), or control (n=18) groups. The recruitment, retention rate, and engagement were 77%, 75%, and 65%, respectively. The intervention received overall positive feedback, with 90% of comments praising the intervention structure, financial incentive, and educational materials. Relative to the control at 4 weeks, the PPM and SFII arms increased their MVPA with medium effect (PPM vs control: η2p=0.06, mean 117.8, SD 514 minutes; SFII vs control: η2p=0.08, mean 145.3, SD 616 minutes). At 8 weeks, PPM maintained a small effect in MVPA relative to the control (η2p=0.01, mean 22.8, SD 249 minutes) and SFII displayed a medium effect size (η2p=0.07, mean 113.8, SD 256 minutes). Small effects were observed for PPM and SFII relative to the control for systolic blood pressure (SBP) and diastolic blood pressure (DBP) (PPM: η2p=0.12, Δmean SBP 7.1, SD 23.61 mm Hg; η2p=0.04, Δmean DBP 3.5, SD 6.2 mm Hg; SFII: η2p=0.01, Δmean SBP -0.4, SD 1.4 mm Hg; η2p=0.02, Δmean DBP -2.3, SD 7.7 mm Hg) and relative autonomy index (PPM: η2p=0.01; SFII: η2p=0.03). Conclusions: The feasibility metrics and preliminary findings suggest that a future full-scale randomized controlled trial examining the efficacy of PPM and SFII relative to a control is feasible, and studies with longer duration are warranted.
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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.
Purpose: To evaluate the effect of lottery-based financial incentives in increasing physical activity. Design: Randomized, controlled trial. Setting: University of Pennsylvania Employees. Participants: A total of 209 adults with body mass index ≥27. Interventions: All participants used smartphones to track activity, were given a goal of 7000 steps per day, and received daily feedback on performance for 26 weeks. Participants randomly assigned to 1 of the 3 intervention arms received a financial incentive for 13 weeks and then were followed for 13 weeks without incentives. Daily lottery incentives were designed as a "higher frequency, smaller reward" (1 in 4 chance of winning $5), "jackpot" (1 in 400 chance of winning $500), or "combined lottery" (18% chance of $5 and 1% chance of $50). Measures: Mean proportion of participant days step goals were achieved. Analysis: Multivariate regression. Results: During the intervention, the unadjusted mean proportion of participant days that goal was achieved was 0.26 in the control arm, 0.32 in the higher frequency, smaller reward lottery arm, 0.29 in the jackpot arm, and 0.38 in the combined lottery arm. In adjusted models, only the combined lottery arm was significantly greater than control ( P = .01). The jackpot arm had a significant decline of 0.13 ( P < .001) compared to control. There were no significant differences during follow-up. Conclusions: Combined lottery incentives were most effective in increasing physical activity.
Purpose: The purpose of this study was to determine the accuracy of 14 step counting methods under free-living conditions. Methods Twelve adults (mean±SD, age: 35±13 yr) wore a chest harness that held a GoPro camera pointed down at the feet during all waking hours for one day. The GoPro continuously recorded video of all steps taken throughout the day. Simultaneously, participants wore two StepWatch [SW] devices on each ankle (all programmed with different settings), one activPAL [AP] on each thigh, four devices at the waist (Fitbit Zip [FZ], Yamax Digi-Walker SW-200 [DW], New Lifestyles NL-2000 [NL], and ActiGraph GT9X [AG]), and two devices on the dominant and non-dominant wrists (Fitbit Charge [FC] and AG). The GoPro videos were downloaded to a computer and researchers counted steps using a hand tally device, which served as the criterion method. Results: The SW devices recorded between 95.3% to 102.8% of actual steps taken throughout the day (P>0.05). Eleven step counting methods estimated less than 100% of actual steps; FZ, DW, and AG with the Moving Average Vector Magnitude algorithm (MAVM) on both wrists recorded 71% to 91% of steps (P>0.05), while the AP, NL, AG (without Low Frequency Extension [no-LFE], MAVM) worn on the hip, and FC recorded 69% to 84% of steps (P<0.05). Five methods estimated more than 100% of actual steps; AG (no-LFE) on both wrists recorded 109% to 122% of steps (P>0.05), while the AG (LFE) on both wrists and the hip recorded 128% to 220% of steps (P<0.05). Conclusion: Across all waking hours of one day, step counts differ between devices. The SW, regardless of settings, was the most accurate method of counting steps.
Regular physical activity (PA) enhances weight-loss and reduces risk of chronic disease. However, as few as 10% of U.S. adults engage in regular PA. Incentive programs to promote PA have shown some promise, but have typically used incentives that are too large to sustain over time and have not demonstrated habit formation or been tested in community settings. This report presents the rationale and design of a randomized pilot study testing the feasibility and preliminary efficacy of small monetary incentives for PA (n=25) versus charitable donations in the same amount (n=25) versus control (n=25) over 12months among 75 low-active but otherwise healthy adults at a local YMCA. Incentives are based on YMCA attendance, which is verified by electronic swipe card data and is the primary study outcome, with self-reported minutes/week of PA assessed as a secondary outcome. Incentives are intentionally small enough-$1/session, maximum of $5/week-such that they could be indefinitely sustained by community organizations, privately-owned health clubs, healthcare organizations, or employers (e.g., employer fitness facilities). Costs of the incentive program for the sponsoring organization may be partially offset by increases in membership resulting from the appeal of the program. Moreover, if efficacious, the charitable donation incentive program may have the added benefit of building social capital for the sponsoring organization and potentially serving as a tax write-off, thus further offsetting the cost of the incentives. Findings will also have implications for the use of financially sustainable community-based incentive programs for other health-related behaviors (e.g., weight loss, smoking).
Background: Physical activity has a protective effect against cardiovascular disease (CVD) in high-income countries, where physical activity is mainly recreational, but it is not known if this is also observed in lower-income countries, where physical activity is mainly non-recreational. We examined whether different amounts and types of physical activity are associated with lower mortality and CVD in countries at different economic levels. Methods: In this prospective cohort study, we recruited participants from 17 countries (Canada, Sweden, United Arab Emirates, Argentina, Brazil, Chile, Poland, Turkey, Malaysia, South Africa, China, Colombia, Iran, Bangladesh, India, Pakistan, and Zimbabwe). Within each country, urban and rural areas in and around selected cities and towns were identified to reflect the geographical diversity. Within these communities, we invited individuals aged between 35 and 70 years who intended to live at their current address for at least another 4 years. Total physical activity was assessed using the International Physical Activity Questionnaire (IPQA). Participants with pre-existing CVD were excluded from the analyses. Mortality and CVD were recorded during a mean of 6·9 years of follow-up. Primary clinical outcomes during follow-up were mortality plus major CVD (CVD mortality, incident myocardial infarction, stroke, or heart failure), either as a composite or separately. The effects of physical activity on mortality and CVD were adjusted for sociodemographic factors and other risk factors taking into account household, community, and country clustering. Findings: Between Jan 1, 2003, and Dec 31, 2010, 168 916 participants were enrolled, of whom 141 945 completed the IPAQ. Analyses were limited to the 130 843 participants without pre-existing CVD. Compared with low physical activity (<600 metabolic equivalents [MET] × minutes per week or <150 minutes per week of moderate intensity physical activity), moderate (600-3000 MET × minutes or 150-750 minutes per week) and high physical activity (>3000 MET × minutes or >750 minutes per week) were associated with graded reduction in mortality (hazard ratio 0·80, 95% CI 0·74-0·87 and 0·65, 0·60-0·71; p<0·0001 for trend), and major CVD (0·86, 0·78-0·93; p<0·001 for trend). Higher physical activity was associated with lower risk of CVD and mortality in high-income, middle-income, and low-income countries. The adjusted population attributable fraction for not meeting the physical activity guidelines was 8·0% for mortality and 4·6% for major CVD, and for not meeting high physical activity was 13·0% for mortality and 9·5% for major CVD. Both recreational and non-recreational physical activity were associated with benefits. Interpretation: Higher recreational and non-recreational physical activity was associated with a lower risk of mortality and CVD events in individuals from low-income, middle-income, and high-income countries. Increasing physical activity is a simple, widely applicable, low cost global strategy that could reduce deaths and CVD in middle age. Funding: Population Health Research Institute, the Canadian Institutes of Health Research, Heart and Stroke Foundation of Ontario, Ontario SPOR Support Unit, Ontario Ministry of Health and Long-Term Care, AstraZeneca, Sanofi-Aventis, Boehringer Ingelheim, Servier, GSK, Novartis, King Pharma, and national and local organisations in participating countries that are listed at the end of the Article.
Objective: A pilot randomized trial assessed the feasibility and effectiveness of an intervention combining Smartcare (activity tracker with a smartphone application) and financial incentives. Methods: A three-arm, open-label randomized controlled trial design involving traditional education, Smartcare, and Smartcare with financial incentives was involved in this study. The latter group received financial incentives depending on the achievement of daily physical activity goals (process incentive) and weight loss targets (outcome incentive). Male university students (N = 105) with body mass index of ≥27 were enrolled. Results: The average weight loss in the traditional education, Smartcare, and Smartcare with financial incentives groups was -0.4, -1.1, and -3.1 kg, respectively, with significantly greater weight loss in the third group (both Ps < 0.01). The final weight loss goal was achieved by 0, 2, and 10 participants in the traditional education, Smartcare, and Smartcare with financial incentives groups (odds ratio for the Smartcare with financial incentive vs. Smartcare = 7.27, 95% confidence interval: 1.45-36.47). Levels of physical activity were significantly higher in this group. Conclusions: The addition of financial incentives to Smartcare was effective in increasing physical activity and reducing obesity.
Introduction: Despite evidence that regular physical activity confers health benefits, physical activity rates among older adults remain low. Both personal and social goals may enhance older adults' motivation to become active. This study tested the effects of financial incentives, donations to charity, and the combined effects of both interventions on older adults' uptake and retention of increased levels of walking. Study design: RCT comparing three interventions to control. Data collection occurred from 2012 to 2013. Analyses were conducted in 2013-2016. Participants: Ninety-four adults aged ≥65 years from Philadelphia-area retirement communities. Intervention: All participants received digital pedometers, walking goals of a 50% increase in daily steps, and weekly feedback on goal attainment. Participants were randomized to one of four groups: (1) Control: received weekly feedback only; (2) Financial Incentives: received payment of $20 each week walking goals were met; (3) Social Goals: received donation of $20 to a charity of choice each week walking goals were met; and (4) Combined: received $20 each week walking goals were met that could be received by participant, donated to a charity of choice, or divided between the participant and charity. Main outcome measures: Mean proportion of days walking goals were met during the 16-week intervention and 4-week follow-up period. Results: After adjusting for baseline walking, the proportion of days step goals were met during the 16-week intervention period was higher in all intervention groups versus controls (relative risk, 3.71; 95% CI=1.37, 10.01). During the 4-week follow up period, the proportion of days step goals were met did not differ in intervention groups compared to control (relative risk, 2.91; 95% CI=0.62, 13.64). Conclusions: Incentive schemes that use donations to a charity of choice, personal financial incentives, or a combination of the two can each increase older adults' initial uptake of increased levels of walking. Trial registration: This study is registered at NCT01643538.
Background Despite the increasing popularity of activity trackers, little evidence exists that they can improve health outcomes. We aimed to investigate whether use of activity trackers, alone or in combination with cash incentives or charitable donations, lead to increases in physical activity and improvements in health outcomes. Methods In this randomised controlled trial, employees from 13 organisations in Singapore were randomly assigned (1:1:1:1) with a computer generated assignment schedule to control (no tracker or incentives), Fitbit Zip activity tracker, tracker plus charity incentives, or tracker plus cash incentives. Participants had to be English speaking, full-time employees, aged 21–65 years, able to walk at least ten steps continuously, and non-pregnant. Incentives were tied to weekly steps, and the primary outcome, moderate-to-vigorous physical activity (MVPA) bout min per week, was measured via a sealed accelerometer and assessed on an intention-to-treat basis at 6 months (end of intervention) and 12 months (after a 6 month post-intervention follow-up period). Other outcome measures included steps, participants meeting 70 000 steps per week target, and health-related outcomes including weight, blood pressure, and quality-of-life measures. This trial is registered at, number NCT01855776. Findings Between June 13, 2013, and Aug 15, 2014, 800 participants were recruited and randomly assigned to the control (n=201), Fitbit (n=203), charity (n=199), and cash (n=197) groups. At 6 months, compared with control, the cash group logged an additional 29 MVPA bout min per week (95% CI 10–47; p=0·0024) and the charity group an additional 21 MVPA bout min per week (2–39; p=0·0310); the difference between Fitbit only and control was not significant (16 MVPA bout min per week [–2 to 35; p=0·0854]). Increases in MVPA bout min per week in the cash and charity groups were not significantly greater than that of the Fitbit group. At 12 months, the Fitbit group logged an additional 37 MVPA bout min per week (19–56; p=0·0001) and the charity group an additional 32 MVPA bout min per week (12–51; p=0·0013) compared with control; the difference between cash and control was not significant (15 MVPA bout min per week [–5 to 34; p=0·1363]). A decrease in physical activity of −23 MVPA bout min per week (95% CI −42 to −4; p=0·0184) was seen when comparing the cash group with the Fitbit group. There were no improvements in any health outcomes (weight, blood pressure, etc) at either assessment. Interpretation The cash incentive was most effective at increasing MVPA bout min per week at 6 months, but this effect was not sustained 6 months after the incentives were discontinued. At 12 months, the activity tracker with or without charity incentives were effective at stemming the reduction in MVPA bout min per week seen in the control group, but we identified no evidence of improvements in health outcomes, either with or without incentives, calling into question the value of these devices for health promotion. Although other incentive strategies might generate greater increases in step activity and improvements in health outcomes, incentives would probably need to be in place long term to avoid any potential decrease in physical activity resulting from discontinuation. Funding Ministry of Health, Singapore.