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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.
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.
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.
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.
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.
In 2012, noncommunicable diseases (NCD) such as cardiovascular diseases, cancers, respiratory diseases, and diabetes were responsible for 68% of deaths worldwide [
The emerging trend of self-tracking [
With health care costs being on the rise in Switzerland and other countries [
Two different streams of research suggest favorable effects of incentives (eg, financial rewards) when addressing the problem outlined above. First, financial incentives have proven to be beneficial in the context of health behavior interventions. Financial incentive schemes have been effectively used to tackle obesity [
Recent research [
Second, rooting in the view of privacy as a commodity [
In conclusion, we assume the benefits of financial incentives to be 2-fold within a physical activity intervention offered by a health insurance company: first, a financial incentive may act as a benefit in the privacy calculus of potential intervention participants, compensating for possible privacy concerns. This effect should be reflected in higher participation rates for experimental groups (EG) in which a financial incentive is provided. Second, in line with previous research, financial incentives may have motivational effects and affect the treatment adherence of participants. Therefore, this study protocol describes the design and methodology in order to examine the effects of the two different incentives on the acceptance of and adherence to a pedometer-based health intervention (PHI). Demographics and baseline characteristics of study participants are presented in the results section. Subsequently, strengths and limitations of the study design are discussed.
Over the course of the PHI, participants had to achieve a fixed level of physical activity each month that was tracked using a commercial pedometer device or app that automatically counts the number of steps when walking. In order for the PHI to be effective, 150 minutes of moderate physical activity are recommended [
Financial incentive (EG1)
• In this condition, participants were entitled to a $10 reward each month they reached an average of 10,000 steps per day or more. Participants achieving more than 7500 steps per day were granted $5 in order to prevent frustration [
Charitable incentive (EG2)
• Here, participants received the same rewards as in the financial incentive condition. However, participants had to decide whether a certain proportion of the money should be donated to a charitable organization chosen from a predefined list (proportions varied from 0% to 100% in steps of 5% with 50% being the default).
Control group (CG/EG1)
• Participants of the control group received no incentives over the first 3 months of the PHI. Due to the practical setting of our study, ethical consent and fair treatment of all participants is of highest relevance. Participants in the control group were therefore entitled to a $20 reward each month they averaged over 10,000 steps per day and a $10 reward each month they averaged over 7500 steps per day over the fourth to sixth month of the intervention. To avoid anticipatory effects on the participation rate, participants in the control group were not informed of the opportunity to receive financial rewards during the second half of the PHI.
Thus, all participants had the chance to earn a maximum of $60 that is paid at the end of the PHI.
Customers of a large Swiss health insurance company that met the following requirements were eligible for participation: they had to be at least 18 years old, be registered in a complementary insurance program, accept the participation conditions and privacy terms, and declared to be free of any medical condition that prohibits physical activity. Absence of medical conditions was required in order to avoid potential negative effects on subject’s health due to increased daily activity. In case of uncertainty regarding the health-related eligibility for participation the consultation of a physician was required. Privacy terms essentially stated that only the number of steps will be forwarded to the insurance company for bonus calculation and that data will be analyzed by researchers of the University St. Gallen and ETH Zurich for scientific purposes.
To avoid spill-over effects between the different incentive strategies [
After providing consent, all participants were instructed on how to use the pedometer or the app, respectively, and how to share the number of tracked steps via the Web-based platform of the health insurance company. The Web-based platform supported devices of the brands Garmin, Jawbone, and Fitbit, all commonly known manufacturers of wearables and fitness technology. Alternatively, participants could use the Fitbit app that is available for selected mobile phones. A systematic review has confirmed the validity of commercial pedometers [
During the course of the intervention, participants received short informational texts in order to maintain motivation for daily physical activity (eg, “If you are going by bus consider getting off two stops prior to your destination to reach your goal of 10,000 steps per day”). Those texts were based on information material and recommendations for health effective daily activity provided by the Federal Bureau of Sports as well as on recommendations for increasing step count in everyday life [
At any time, participants were able to opt out of the PHI and request the deletion of all submitted data without giving reasons. In order to prevent high dropout rates that have been observed in past pedometer-based interventions [
Data for analysis is partly collected by submission of information by the participants via the Web-based platform of the insurance company and partly by administering a Web-based questionnaire at 2 different points in time (T1 and T2) over the course of the intervention. After participants registered their pedometer or smartphone at the Web-based platform of the insurance company, the number of steps were synchronized automatically with the Web-based platform each day at midnight. However, participants could choose to deactivate automatic synchronization and enter their step count manually on the Web-based platform. Days where no step data is available (eg, because the pedometer was not worn or not charged) will be treated as missing data. The first measurement (T1) is set at the beginning of the PHI for all groups, whereas the second measurement (T2) is set at the end of the intervention for the experimental groups and after the first half of the intervention for the control group before they received financial incentives. Additional data, such as age, gender, or participants’ health service billings, were provided by the insurance company. To guarantee appropriate response rates, participants received additional $5 for each time they completed the questionnaire resulting in an additional bonus of $10. See
The following variables were measured for analysis: the participation rate represents the primary outcome and is measured by calculating the participation rate in total and for the different groups, respectively. Participation rate is defined as the proportion of active participants that is participants that shared their data with the Web-based platform of the insurance company at least once. Secondary outcomes are continued use of the pedometer, performance of the participants, and health condition. The number of days at which participants share their step count with their health insurance company is used as an indicator of the continued use of the pedometer. The number of steps and the amount of money saved or donated indicate the performance of the participants. Apart from the number of steps, physical activity was also assessed by questionnaire measures namely hours of moderate to vigorous physical activity and hours of walking per week at T1 and T2 (based on the International Physical Activity Questionnaire [
To exclude possible confounding influences, we will measure the following control variables: sociodemographic variables (age, gender, education, income, and nationality [
Additional variables were measured to better understand the participants behaviour. These variables are participants’ perception of the Web-based platform, perception of the insurance company (eg, perceived social responsibility), customer loyalty, participants’ willingness to share data with their insurance company, willingness to donate (in the charitable incentive group), reasons for participating and not participating, reasons for opting out, and improvement suggestions to the program.
Due to the nested structure of the data, mixed-effect models will be used for data analysis. As measurements are nested within participants, the step count measurements represent the level 1 unit of analysis, whereas the participants represent the level 2 unit of analysis. A recent article [
Study design.
In total, 1319 persons participated in the survey at T1. Of those, 47.46% (626/1319) belonged to the financial incentives group, 42.61% (562/1319) to the charitable incentives group, and 9.93% (131/1319) to the control group.
Participants were mostly Swiss (1195/1319, 90.60%), living in a village or on the countryside (836/1319, 63.38%), holding a university degree (597/1319, 45.26%), and were 43-years old on average (M=42.95, SD = 13.11). Slightly more men than women participated in the T1 survey (638/1319, 48.14% vs 585/1319, 44.35%). A Fitbit pedometer or the Fitbit app was most often used for tracking physical activity (1116/1319, 84.61%) and more than half of the participants (709/1319, 53.75%) bought a pedometer in order to participate in the PHI.
While baseline characteristics show no meaningful group differences regarding age, gender, education, income, nationality, self-reported physical activity at work and during spare time, walking on the way to work, pedometer brand, prior possession of a pedometer, and participation of a family member or friend, group differences could be observed regarding residence of participants, self-reported physical activity and walking, and subjective health status. Differences regarding residence of participants indicate that matching groups according to population density may not be sufficient to account for residence differences.
Because these baseline characteristics are related to physical activity they are primarily relevant for the analysis of the secondary outcomes of the study. Consequently, residence of participants and subjective health status will be used as covariates in the statistical analyses of the secondary outcomes. Because mixed-effects models will be used for data analysis, group differences regarding baseline physical activity will be directly modelled by allowing different intercepts for the experimental groups.
Demographics and baseline characteristics.
Charachteristica | Total |
Financial incentives/ EG1 |
Charitable incentives/ EG2 |
Control group/ CG |
Effect sizeb | |||
Number of cantons | 26 | 8 | 11 | 7 | ||||
Number of customers contacted | 18,638 | 7487 | 8216 | 2935 | ||||
Population densityc (residents/km2, median) | 233.56 | 255.15 | 173.45 | 221.08 | ||||
Age | 42.95 (13.11) | 43.06 (13.25) | 42.50 (12.88) | 44.37 (13.40) | .36 | .002 | ||
Gender (%) | .89 | .01 | ||||||
Female | 585 (44.35) | 285 (45.53) | 244 (43.42) | 56 (42.75) | ||||
Male | 635 (48.14) | 301 (48.08) | 270 (48.04) | 64 (48.85) | ||||
Not declared | 99 (7.51) | 40 (6.39) | 48 (8.54) | 11 (8.40) | ||||
Educationd (%) | .17 | .10 | ||||||
University | 597 (45.26) | 301 (48.08) | 244 (43.42) | 51 (39.69) | ||||
Professional School | 421 (31.92) | 194 (30.99) | 188 (33.45) | 39 (29.77) | ||||
High School | 219 (16.60) | 95 (15.18) | 95 (16.90) | 29 (22.14) | ||||
Secondary School | 25 (1.90) | 13 (2.08) | 10 (1.78) | 2 (1.53) | ||||
Primary School | 6 (0.45) | 4 (0.64) | 1 (0.18) | 1 (0.76) | ||||
Not declared | 51 (3.87) | 19 (3.04) | 24 (4.27) | 8 (6.11) | ||||
Place of Residence (%) | < .001 | .27 | ||||||
Town | 156 (11.83) | 92 (14.70) | 49 (8.72) | 15 (11.45) | ||||
Outskirts of town | 327 (24.79) | 185 (29.55) | 116 (20.64) | 26 (19.85) | ||||
Village | 644 (48.82) | 270 (43.13) | 303 (53.91) | 71 (54.20) | ||||
Countryside | 192 (14.56) | 79 (12.62) | 94 (16.73) | 19 (14.50) | ||||
Income in CHF (%) | .25 | .11 | ||||||
< 2500 | 68 (5.16) | 29 (4.63) | 35 (6.23) | 4 (3.05) | ||||
2501–5000 | 203 (15.39) | 90 (14.38) | 91 (16.19) | 22 (16.79) | ||||
5001–7500 | 418 (31.69) | 204 (32.59) | 176 (31.32) | 38 (29.01) | ||||
7501–10,000 | 220 (16.68) | 107 (17.09) | 87 (15.48) | 26 (19.85) | ||||
>10,000 | 137 (10.39) | 78 (12.46) | 50 (8.90) | 9 (6.87) | ||||
Not declared | 273 (20.70) | 118 (18.85) | 123 (21.89) | 32 (24.43) | ||||
Nationality (%) | .03 | .13 | ||||||
Swiss | 1195 (90.60) | 554 (88.50) | 520 (92.53) | 121 (92.37) | ||||
German | 56 (4.25) | 36 (5.75) | 17 (3.02) | 3 (2.29) | ||||
Other | 54 (4.09) | 32 (5.11) | 16 (2.85) | 6 (4.58) | ||||
Not declared | 14 (1.06) | 4 (0.64) | 9 (1.60) | 1 (0.76) | ||||
Self-reported moderate to vigorous physical activitye (hours/week) | < .001 | .03 | ||||||
Mean (SD) | 8.90 (11.10) | 8.96 (11.38) | 8.75 (10.59) | 9.26 (11.25) | ||||
Median | 6.00 | 6.00 | 6.00 | 5.25 | ||||
Self-reported walkinge (hours/week) | <.001 | .03 | ||||||
Mean (SD) | 10.01 (13.70) | 10.31 (13.44) | 9.99 (15.55) | 8.61 (10.87) | ||||
Median | 6.00 | 6.54 | 6.00 | 4.50 | ||||
Physical activity at work | 3.45 (1.88) | 3.37 (1.84) | 3.48 (1.91) | 3.67 (1.90) | < .001 | .009 | ||
Physical activity during spare time | 5.26 (1.17) | 5.36 (1.19) | 5.19 (1.13) | 5.09 (1.22) | .06 | .003 | ||
Walking on way to work (%) | ||||||||
Yes | 234 (17.74) | 126 (20.13) | 87 (15.48) | 21 (16.03) | .10 | .06 | ||
No | 1085 (82.26) | 500 (79.87) | 475 (84.52) | 110 (84.97) | ||||
Subjective health status | 3.60 (0.73) | 3.66 (0.73) | 3.55 (0.71) | 3.53 (0.80) | <.001 | .02 | ||
Pedometer brand (%) | .73 | .09 | ||||||
Fitbit | 832 (62.08) | 387 (61.82) | 359 (63.88) | 86 (65.65) | ||||
Fitbit App | 284 (21.53) | 141 (22.52) | 121 (21.53) | 22 (16.79) | ||||
Garmin | 138 (10.46) | 69 (11.02) | 55 (9.79) | 14 (10.69) | ||||
Jawbone | 65 (4.93) | 29 (4.63) | 27 (4.80) | 9 (6.87) | ||||
Pedometer bought for participation (%) | .04 | .07 | ||||||
Yes | 709 (53.75) | 316 (50.48) | 325 (57.83) | 68 (51.91) | ||||
No | 571 (43.29) | 289 (46.17) | 221 (39.32) | 61 (46.56) | ||||
Not declared | 39 (2.96) | 21 (3.35) | 16 (2.85) | 2 (1.53) | ||||
Participation of family member or friend | .65 | .03 | ||||||
Yes | 251 (19.03) | 122 (19.49) | 108 (19.22) | 21 (16.03) | ||||
No | 1068 (80.97) | 504 (80.51) | 454 (80.78) | 110 (83.97) |
a Unless otherwise indicated, mean (SD) are displayed for continuous variables and absolute frequencies (relative frequencies) are displayed for categorical variables.
b η2 is used as a measurement of effect size for one-way ANOVAs and Cramer’s
c Based on information of the Swiss Federal Office for Statistics for the year 2013 [
dCategories with expected frequencies <5 were not considered for between-group comparison.
e Due to violation of normality a logarithmic transformation was applied for between-group comparison and the median is reported in addition to the mean.
This study protocol describes the design and baseline characteristics of a longitudinal cluster-randomized controlled trial testing the effects of monetary and charitable incentives on the acceptance of and adherence to a pedometer-based health prevention program. To the best of our knowledge, this is the first study to systematically test the effects of different incentive strategies within a pedometer-based health intervention offered by a large health insurance company in western cultures. External validity has to be pointed out as a key strength of the described trial. Both study design and incentive strategies are tested in a real-world setting, thus ensuring the applicability of the results and conclusions.
When interpreting the results of this study, some limitations have to be considered: selection effects might affect the participation in the PHI. For example, by especially attracting highly motivated or physically active participants, those effects could potentially undermine the power of our analyses. However, we will be able to control our analyses for prior level of physical activity. Further, comparisons of T2 measures between the groups have to be interpreted with caution, because T2 reflects different time points for experimental and control groups. T2 was set at 6 months after start of the intervention for the EGs and at 3 months for the CG. However, the main focus of this study is on the acceptance of the promotion program, which is operationalized using the participation rate, and is thus not dependent on any T2 measurement. Lastly, the goal of reaching 10,000 steps per day on average might have detrimental motivational effects for some participants. It might be perceived as too challenging for very inactive participants or when participants were not able to achieve sufficiently high step counts for several days in a month.
Considering the importance of physical activity for the course of various NCDs, this study yields important insights for insurance companies, public health institutions, and health practitioners alike. If the effectiveness of the examined incentive strategies is demonstrated, this study provides the basis for simple yet powerful health interventions that can easily be implemented by various health care institutions.
control group
experimental group
mean
noncommunicable disease
pedometer-based health intervention
repeated-measures analysis of variance
standard deviation
We would like to thank the institutional review board of the University of St. Gallen for their valuable feedback and support.
The study protocol was approved by the Ethics Committee of the University of St. Gallen, Switzerland (reference number: HSG-EC-2015-04-22-A; date of approval June, 4th, 2015). Informed consent to participate was obtained from all participants of the study.
All authors have read and understood the editorial policies on competing interests. We declare the following possible competing interest: the study is partly funded by the CSS Insurance.