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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 www.isrctn.com ISRCTN24436134.
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RESEARCH ARTICLE
A Cluster-Randomized Trial on Small Incentives
to Promote Physical Activity
D1X XJan-Niklas Kramer, D2X XMSc,
1
D3X XPeter Tinschert, D4X XMSc,
1
D5X XUrte Scholz, D6X XPhD,
2
D7X XElgar Fleisch, D8X XPhD,
1,3
D9X XTobias Kowatsch, D10X XPhD
1
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 www.isrctn.com 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
(http://creativecommons.org/licenses/by-nc-nd/4.0/)
INTRODUCTION
Strong empirical evidence indicates that physical
inactivity increases the risk of mortality
1
as well
as of noncommunicable diseases, such as coro-
nary heart disease, diabetes, or cancer.
2,3
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
1
Center for Digital Health Interventions, Institute of Technology
Management, University of St. Gallen, St. Gallen, Switzerland;
2
Depart-
ment of Psychology, Applied Social and Health Psychology, University of
Zurich, Zurich, Switzerland; and
3
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.
E-mail: tobias.kowatsch@unisg.ch.
0749-3797/$36.00
https://doi.org/10.1016/j.amepre.2018.09.018
© 2018 American Journal of Preventive Medicine. Published by Elsevier Inc.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Am J Prev Med 2019;56(2):e45e54 e45
per week),
4
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-
ces,
5
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.
6
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.
7
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.
6,8
This strategy is supported
by empirical evidence: Previous research demonstrated
that nancial incentives can increase participation in
health promotion programs
9,10
and change physical
activityrelated behaviors, such as exercise session
attendance,
1113
exercise behavior,
12
and objectively
measured physical activity.
13
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.
11
Because changes in physical
activity are typically not sustained after incentives are
withdrawn,
1418
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,
19
attending preventive health examinations,
20
or com-
pleting tuberculosis skin test readings
21
primarily in
deprived populations. In studies investigating the effect
of incentives on physical activity, the incentive value
seems to correlate with the effect size,
12
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
incentives
22
and has been related to happiness in obser-
vational and experimental studies.
23
This warm glow of
giving
24
has been used in marketing strategies to
motivate purchase behavior,
25
and recent
17,18
and ongo-
ing
26
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.
18
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.
17
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.
METHODS
The design of this study is described in greater detail elsewhere.
27
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
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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
2018.
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.
28
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.
Measures
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.
29
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.
30
The
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,
31
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
32
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
33
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
34
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,
35
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
36
for
tting (generalized) linear mixed models and the mice pack-
age
37
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.
RESULTS
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
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Table 1. Baseline Characteristics of Invited Insurees and Participants
Characteristics p-value
a
SMD
b
PFI CFI CG
PFI
versus CG
CFI
versus CG
PFI versus
CFI
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
(residents/km
2
)
c
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),
d
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
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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
a
SMD
b
PFI CFI CG
PFI
versus CG
CFI
versus CG
PFI versus
CFI
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)
e
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)
e
2,628.00
(3,306)
2,745.75
(3,327)
2,079.00
(3,720)
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.
a
Based on one-way ANOVA for normal, Kruskal-Wallis test for non-normal, and x
2
test of independence for categorical variables.
b
Values greater 0.20 dened as small effect size.
38
Non-normal variables were log-transformed before calculating SMD.
c
Reported values are weighted by cluster size.
d
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
(n=1,223).
e
Assessed using the International Physical Activity Questionnaire.
39
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
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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).
DISCUSSION
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.
17,18
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
a
Day 46 p-value
a
Day 92 p-value
a
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).
a
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.
40
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,
15,40,41
which is three times the incentive value of the present
study. In addition, previous studies often used daily
15,41
or weekly
17,18
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
rewards,
42
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.
Limitations
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.
43,44
Incentive effects may
differ in populations with lower education or income.
45
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.
46
CONCLUSIONS
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.
ACKNOWLEDGMENTS
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
www.ajpmonline.org
e52 Kramer et al / Am J Prev Med 2019;56(2):e45
e54
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 MATERIAL
Supplemental materials associated with this article can be
found in the online version at https://doi.org/10.1016/j.
amepre.2018.09.018.
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... [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] ...
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... However, in this study, we did not find such reluctance to practice physical exercise in the overweight and obese adolescent population, which could be due to the fact that they opted for a reward in the form of a 10% increase in their final grade of the physical education course, which could have favored extrinsic motivation as the main factor for following the intervention [41,42]. In this regard, previous research has shown that maintaining high levels of motivation towards physical activity is associated with higher levels of practice and retention in adolescents with obesity [43,44] and that rewards can contribute to increased extrinsic motivation and participation in physical activity programs [45][46][47]. ...
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... Moreover, I was included in discussions around the interpretation of data and the writing of articles. I was even included in one of the first publications on ALLY (Kramer et al., 2019a). To summarize, I interacted with ALLY designers as they gathered evidence and published results. ...
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Walking is both a sustainable transportation option and an easy physical activity. Despite the clear health, environmental and economic benefits, many people are reluctant to walk to their destinations because it can be tiring or time-consuming; therefore, incentives are often needed to encourage walking. This paper presents a novel application (InWalk) that can be used to promote walking as a sustainable transportation alternative, a healthy lifestyle and a way to boost economic activity in an area. The application suggests walking routes to users that are linked to financial rewards (discounts, etc.) offered by businesses along the way. Users are motivated to walk and even take a longer route to gain access to a promotional service or product upon completion of their route. A pilot implementation of the developed application in a Greek suburb shows that it has the potential to promote walking as an exercise and stimulate economic activity, but also that it needs to be tested more thoroughly to contribute to a sustainable transportation paradigm.
... The vast body of evidence on digital physical activity interventions shows that they can increase physical activity in adults [20][21][22][23][24][25]. In recent times, chatbots have emerged as digital intervention tools for promoting healthy lifestyles and encouraging behavioural change, including increasing physical activity [26][27][28][29][30][31][32][33]. A chatbot is a computer program designed to simulate human conversations. ...
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Background Regular physical activity helps to reduce weight and improve the general well-being of individuals living with obesity. Chatbots have shown the potential to increase physical activity among their users. We aimed to explore the preferences of individuals living with obesity for the features and functionalities of a modern chatbot based on social media, Artificial intelligence (AI) and other recent and relevant technologies. Methods In this study, we used qualitative methods. Focusing on individuals’ preferences for a chatbot to increase physical activity, we conducted both individual interviews and focus groups with nine adult patients staying at Evjeklinikken, a Norwegian rehabilitation clinic for individuals living with morbid obesity. The interviews were fully transcribed and then analysed inductively using thematic analysis. Results Participants preferred motivational features such as social support, goal setting, physical activity illustrations, monitoring of physical activity behaviour and outcomes, and feedback, prompts and reminders. They also preferred features for connecting and synchronising with smartwatches and training device apps. Participants wanted a chatbot that is easy to use and allows for human assistance when needed. Regarding personalising the chatbot, the participants wanted to choose the language, number of messages, and turn functionalities on and off. Conclusions Co-designing chatbots with potential users is essential to understand their specific needs and preferences. We gained valuable insight into a diverse set of features and functionalities relevant to designing physical activity chatbots for individuals living with obesity. Behaviour change techniques are equally important as personalisation features and the option for synchronising with third-party devices. In future work, we will consider the collected needs in the development of a physical activity chatbot to ensure acceptance and adherence to the digital health intervention.
... Thus, mHealth interventions coupled with financial incentives may be a potential method for overcoming these challenges. Previous systematic reviews have shown that financial incentives can be an effective strategy to improve lifestyle-related behaviors and promote program engagement [21,22]. However, there is a lack of PA-based and financial incentive-based hypertension prevention mHealth interventions tailored for the Canadian population. ...
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Background Regular physical activity (PA) is a key lifestyle component for hypertension prevention. Previous studies have shown that mobile health (mHealth) apps can be an effective tool for improving PA behaviors. However, adherence to and poor engagement with these apps is a challenge. A potential solution to overcome this challenge may be to combine financial incentives with innovative behavior theory, such as the Multiprocess Action Control (M-PAC) framework. Currently, there is a lack of PA financial incentive–driven M-PAC mHealth programs aimed at hypertension prevention. Objective We aimed to describe the process of developing an 8-week mHealth PA and financial-incentive hypertension education program (Healthy Hearts) and to evaluate usability of the Healthy Hearts program. Methods The first 2 stages of the Integrate, Design, Assess, and Share framework were used to guide the development of the Healthy Hearts program. The development process consisted of 2 phases. In phase 1, the research team met to discuss implementing 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 (Pathverse Inc), to help decrease overall development time. In phase 2, we created a prototype and conducted usability testing to evaluate lesson 1 of the Healthy Hearts program to further enhance the user experience. We used semistructured interviews and the mHealth App Usability Questionnaire to evaluate program acceptability and usability. ResultsIntervention development among the research team successfully created an 8-week financial-incentive hypertension education program for adults aged 40-65 years who did not currently meet the Canadian Physical Activity Guidelines (
... Numerous behavior change techniques [25] were used throughout the lessons to support the participant's advancement through the M-PAC constructs and develop positive exercise habits and exercise identity. Financial incentives are also an effective behavior change technique that can improve engagement in PA interventions [26,27]. ...
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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 20each(atthetimeofwriting:CAD20 each (at the time of writing: CAD 1=US 0.74),oracontrolgroupwithoutfinancialincentive.PPMparticipantsreceivedCAD0.74), 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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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(1in400chanceofwinning5), “jackpot” (1 in 400 chance of winning 500), or “combined lottery” (18% chance of 5and15 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.
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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.
Article
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.
Article
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,maximumof1/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).
Article
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.
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.
Article
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 20eachweekwalkinggoalsweremet;(3)SocialGoals:receiveddonationof20 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 www.clinicaltrials.gov NCT01643538.
Article
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 ClinicalTrials.gov, 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.