A Randomized,Controlled Trial of Lottery-Based Financial Incentives to Increase Physical Activity Among Overweight and Obese Adults

Abstract

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.

Keywords

behavioral economics financial incentives wellness incentives lottery incentives physical activity workplace wellness program

Purpose

Regular physical activity is associated with lower rates of cardiovascular disease, diabetes, hypertension, obesity, and all-cause mortality.^1
–3 Despite this, more than half of adults in the United States do not achieve enough physical activity to obtain these benefits.⁴ The Centers for Disease Control and Prevention has recommended the workplace as a potential setting to implement interventions to address this issue and has published guidelines for employers.⁵ Workplace wellness programs are growing in popularity, and more than 85% of large employers use financial incentives to promote healthy behaviors.^6,7

Wellness programs increasingly use lottery incentives to engage individuals in health promotion activities.⁶ Their design relies on several important behavioral economic principles. Individuals are more motivated by immediate rather than delayed gratification,⁸ place undue weight on smaller probabilities,^9,10 and try to avoid the feeling of regret.^11

–14 However, most wellness programs use single-tiered lotteries in which employees have a fixed probability of winning a reward. If the reward magnitude is larger, then the probability of winning is smaller, and individuals can be demotivated by infrequent reinforcement. If the probability of winning is larger, then smaller magnitude of the reward can be demotivating—a phenomenon known as the peanuts effect.^15,16

Combined or 2-tiered lotteries offer both a higher probability of a smaller reward and a lower probability of a larger reward and might be more effective in increasing ongoing engagement by providing a better balance of frequent reinforcement of small winnings with the overestimation of the probability of larger winnings.¹¹ Our group has previously demonstrated that combined lottery incentives can promote weight loss,¹⁷ medication adherence,^18,19 and engagement with remote monitoring devices.²⁰ However, single-tiered and combined lottery incentive designs have not been directly compared to evaluate their comparative effectiveness in increasing the rate of engagement in health-promoting activities.

To test the effects of single-tiered versus combined lotteries, we conducted a randomized, controlled trial among overweight and obese employees using smartphones to track physical activity. We compared a control group to 2 forms of the typical approach to single-tiered lotteries (“higher frequency, smaller reward” and a “lower frequency, higher reward” or “jackpot”) and to a “combined” lottery.

Methods

Study Design

We conducted a 26-week, randomized, controlled trial between March and October 2014, consisting of a 13-week intervention and 13-week follow-up period (Clinicaltrials.gov number, NCT02005276). Two hundred and nine participants provided informed consent and were randomly assigned to control or 1 of the 3 interventions. Similar to prior work,^21

–24 all participants were given a goal of achieving at least 7000 steps per day for the entire study. Step counts were tracked using the Moves smartphone application (ProtoGeo Oy), which uses accelerometers within the phone and has been demonstrated in our prior work to be accurate.²¹ Each participant was given a unique personal identification number to enter into the application to verify permission for the study team to access their data. The University of Pennsylvania Institutional Review Board approved this study (see Supplement files).

Study Sample

University of Pennsylvania employees were recruited between February to March 2014 by e-mail invitation. Eligible participants were aged 18 years or older, had a body mass index (BMI) of 27 or greater, and had an iPhone or Android smartphone compatible with the step-tracking application. Exclusion criteria included conditions that made participation infeasible (eg, inability to provide consent) or unsafe, already participating in a physical activity study, and pregnant or intending to become pregnant within 6 months.

Study Enrollment

Participants enrolled online using Way to Health, a research information technology platform based at the University of Pennsylvania that has been used in prior physical activity intervention studies.^22

–25 All eligible participants provided electronic informed consent, completed a sociodemographic questionnaire, self-reported measures of height and weight, and reported recent physical activity using the long form of the International Physical Activity Questionnaire.²⁶ Participants selected whether they preferred to receive study communications by e-mail, text message, or both.

Randomization and Interventions

A computer-generated random number sequence was used to assign each participant to 1 of the 4 study groups using simple randomization. For 26 weeks, all participants including those in the control group received daily feedback on whether or not they had achieved the 7000-step goal on the prior day. The control group received no other intervention. The 3 financial intervention arms were offered financial incentives for the first 13 weeks. In the “higher frequency, smaller reward” incentive arm, each participant had a 1 in 4 chance of winning $5 each day. In the “jackpot” incentive arm, each participant had a 1 in 400 chance of winning $500 each day. In the “combined” incentive arm, each participant had both an 18% chance (approximately 1 in 5 chance) of winning $5 and a 1% chance of winning $50, designed similar to prior work.²³ In all 3 incentive arms, participants were informed of these amounts and probabilities at the beginning of the trial. Each day a participant won, they were eligible to collect their winnings only if they had achieved at least 7000 steps on the prior day. Participants who won the lottery but did not achieve their goal were informed what they would have won had they been adherent, drawing on research showing that the desire to avoid regret can be motivating.^11

–14

The combined lottery design was based on prior work.^17,23 For the other 2 incentive arms, we chose to keep the reward amounts ($5 and $500) and odds of winning (1 in 4 and 1 in 400, respectively) well rounded to avoid the chance that less well-rounded amounts or odds of winning might negatively influence the participants’ perceptions of these incentives. However, due to this design, the combined lottery had a slightly higher daily expected value than the other 2 incentive arms ($1.40 per day vs $1.25 per day).

All participants received $25 for enrolling and $75 for completing the 13-week intervention period and a survey on their experience. There was no participation incentive for the 13-week follow-up period. Participants were mailed a bank check at the end of each month with accumulated earnings.

Neither the participants nor the study coordinator could be blinded to the group assignment. All investigators, statisticians, and data analysts were blinded to group assignments until the study and its analyses were completed.

Measures

The primary outcome was the mean proportion of participant days that the 7000-step goal was achieved during the 13-week intervention period. Secondary outcomes included the number of steps per day during the intervention and follow-up and the mean proportion of participant days achieving the goal during the follow-up period.

Analysis

A priori, we estimated that a sample of at least 260 participants (65 per arm) would ensure 80% power to detect a 0.20 difference between each of the intervention groups and the control group. We used a conservative Bonferroni adjustment of the type I error rate using a 2-sided α of .017. This calculation assumed that the mean proportion of participant days achieving goal in the control group would be .40. However, study was closed early due to timing constraints required by the funding source, and therefore based on these assumptions, we estimate that there was 80% power to detect a 0.25 difference between each intervention group and control.

For each participant on each day of the study (participant-day level), we obtained the number of steps achieved as a continuous variable. Data could be missing if for any days a participant turned off the smartphone or moves application, disabled the study team’s permission before data were accessed, or did not carry the smartphone at all. For the unadjusted analyses, we used only collected data (a step count value was received). This approach is based on the assumption that missing data occur at random within an arm and does not bias outcomes for arms with differing levels of missing data. We estimated the mean daily steps among participants in each arm during the intervention and follow-up periods. We dichotomized the data at the participant-day level to create a binary variable indicating whether the 7000-step goal was achieved (value = 1) or not (value = 0). Using this binary variable, we estimated the mean proportion of participant days achieving goal for the group of participants in each arm during the intervention period, the follow-up period, and for each week during the study.

Similar to prior work,²³ we used PROC GLIMMIX in SAS (version 9.4, SAS Institute, Cary, North Carolina) to fit a generalized linear mixed models for adjusted analyses with participant random effect, a random intercept, time (weekly) fixed effects, and treatment fixed effects (by study arm).^27
–29 We assumed a normal distribution for models using the continuous outcome and obtained difference in steps between arms using the least squared means command. We assumed a binomial distribution with logit link for models using the binary outcome to estimate the odd ratios of achieving goal for each intervention arm compared to the control.

Our modeling approach was to sequentially add adjustments to observe any changes in estimates of intervention effects in 3 separate models. In model 1, we adjusted for participant’s repeated measures, time fixed effects at the week level, and treatment effects. In model 2, we further adjusted for device (iPhone or Android). In the fully adjusted model 3, we also classified missing outcome data as not meeting goal. Since this model includes all observations, it provides the most power for detecting differences between arms while conservatively assuming the worst outcome (eg, not achieving the step goal) for missing data. For analysis of the secondary outcome of “daily steps” in model 3, we excluded values less than 1000 steps because evidence suggests that these values are unlikely to represent accurate data capture of actual activity.^30
–32

To examine trends in performance of each arm over the 13-week intervention period, we fit a linear regression to the weekly mean proportion of participants achieving goal using collected data. The model included covariates for study arm, week, and interaction terms for week and study arm, using the control arm as the referent group.

Results

Figure 1 reports the Consolidated Standards of Reporting Trials flow diagram. Among the 209 participants who were randomized, 198 (94.7%) completed the 13-week intervention and 196 (93.8%) completed the entire 26-week study. Table 1 displays participant characteristics that were well balanced across arms. Participants had a mean BMI of 33.2 kg/m2, and 77% were female. The percentage of participant days on which step data were missing during the intervention was 20% in the control arm, 17% in the higher frequency, smaller reward lottery arm, 13% for the jackpot lottery arm, and 13% for the combined lottery arm.

Figure 1.

CONSORT flow diagram

Table 1.

Characteristics of the Study Participants.

Characteristic	Control, n = 65	Higher Frequency, Smaller Reward Lottery, n = 41	Jackpot Lottery, n = 59	Combined Lottery, n = 44
Female gender, n (%)	51 (78.5)	32 (78.0)	44 (76.4)	33 (75.0)
Age, mean (SD)	40.0 (11.0)	41.2 (11.1)	40.6 (10.5)	42.9 (10.3)
Self-reported baseline measures
Body mass index, mean (SD)	33.8 (6.0)	32.0 (4.8)	33.5 (4.9)	32.9 (4.3)
Physical activity in the last 7 days (MET-min), Median (IQR)	2110.5 (1437.0, 4377.0)	2154.5 (1266.0, 3965.5)	2065.8 (1170.0, 4416.0)	3012.0 (1242.0, 4677.0)
Race/ethnicity, n (%)
White non-Hispanic	45 (69.2)	30 (73.2)	36 (61.0)	34 (77.3)
African American non-Hispanic	12 (18.50	7 (17.1)	12 (20.3)	8 (182)
Other non-Hispanic	5 (7.7)	2 (49)	5 (85)	2 (4.5)
Hispanic	3 (4.6)	2 (49)	6 (10.2)	0 (0.0)
Education, n (%)
Less than college	1 (15)	1 (2.4)	4 (6.8)	4 (9.1)
Some college	13 (20.0)	9 (22.0)	11 (18.6)	11 (25.0)
College graduate	51 (78.5)	31 (75.6)	44 (74.6)	29 (65.9)
Marital status, n (%)
Single	23 (35.4)	14 (34.1)	14 (23.7)	17 (38.6)
Married	32 (49.2)	16 (39.0)	37 (62.7)	22 (50.0)
Other	10 (15A)	11 (26.8)	8 (13.6)	5 (11.4)
Annual household income, n (%)
Less than 550 000	17 (26.2)	12 (29.3)	12 (20.3)	8 (18.2)
550 000 to 5100 000	22 (33.8)	18 (43.9)	22 (37.3)	25 (56.8)
Greater than 5100 000	21 (32.3)	9 (22.0)	19 (32.2)	8 (18.2)
iPhone smartphone, n (%)	43 (66.2)	30 (73.2)	38 (64.4)	27 (61.4)

Abbreviations: IQR, interquartile range; MET, metabolic equivalent; SD, standard deviation.

The mean proportion of participant days that the 7000-step goal was achieved at the weekly level for the control arm remained slightly above 0.25 for most of the intervention period (Figure 2). In comparison, all 3 incentive arms began in week 1 between 0.35 and 0.40. The combined lottery incentive arm steadily increased to 0.47 by week 8 and then remained between 0.35 and 0.40 for the remainder of the intervention period. The higher frequency, smaller reward lottery arm ranged between 0.25 and 0.37, while the jackpot lottery arm peaked near 0.39 before declining to 0.21 by the end of the intervention period.

Figure 2.

Unadjusted mean proportion of participant days the 7000-step goal was achieved, displayed by study arm and week.

The unadjusted mean proportion of participant days that goal was achieved during the intervention period was 0.26 in the control arm, 0.32 in the higher frequency, smaller reward lottery arm, 0.29 in the jackpot lottery arm, and 0.38 in the combined lottery arm. These levels declined during the follow-up period to 0.19, 0.21, 0.16, and 0.22, respectively.

In the weekly trend analysis, the jackpot arm had a significant weekly decline in the proportion of participants achieving goal (−0.011 per week, 95% confidence interval [CI]: −0.017 to −0.005, P < .001), accounting for a 0.13 decline compared to the control group over the course of the 13-week intervention period. Trends in the higher frequency lottery arm (−0.002, 95% CI: −0.008 to 0.003, P = .41) and combined lottery arm (−0.0002, 95% CI: −0.006 to 0.006, P = .93) were not significant compared to control.

Adjusted differences between each intervention arm and control are displayed in Table 2. In the fully adjusted model 3, participants in the combined lottery arm had significantly greater odds of achieving goal than participants in the control (odds ratio: 3.00, 95% CI: 1.28-7.02, P = .012). The other models qualitatively supported this finding. There were no significant differences between control and either the higher frequency, smaller reward lottery arm, or the jackpot lottery arm. There were no significant differences between any of the intervention arms and control during the follow-up period, indicating that these interventions were not successful in creating habits around increased physical activity.

Table 2.

Adjusted Odds of Achieving the 7000-Step Goal During the Intervention and Follow-up Periods.

Model^a	Arm Comparison	Intervention (Weeks 1-13)		Follow-Up (Weeks 14-26)
Model^a	Arm Comparison	Odds Ratio (95% CI)	P Value	Odds Ratio (95% CI)	P Value
Model 1	Higher frequency, smaller reward vs control	1.48 (0.66-3.31)	.34	1.05 (0.48-2.34)	.90
	Jackpot lottery vs control	1.18 (0.58-2.42)	.65	0.57 (0.28-1.19)	.13
	Combined lottery vs control	2.35 (1.08-5.09)	.03	0.94 (0.42-2.09)	.88
Model 2	Higher frequency, smaller reward vs control	1.43 (0.64-3.19)	.38	1.03 (0.47-2.27)	.94
	Jackpot lottery vs control	1.20 (0.59-2.44)	.62	0.58 (0.28-1.18)	.13
	Combined lottery vs control	2.38 (1.10-5.14)	.03	0.95 (0.43-2.10)	.90
Model 3	Higher frequency, smaller reward vs control	1.42 (0.59-3.40)	.44	1.39 (0.57-3.37)	.47
	Jackpot lottery vs control	1.46 (0.67-3.22)	.34	0.68 (0.30-1.54)	.36
	Combined lottery vs control	3.00 (1.28-7.02)	.012	0.96 (0.40-2.32)	.93

Abbreviation: CI, confidence interval

^aModel 1 adjusts for repeated measures of daily participant step counts and time fixed effects by week and for temporal trends by week using all collected data. Model 2 also adjusted by device (iPhone or Android); model 3 also adjusts by device and classifies missing data as not achieving goal.

The unadjusted mean daily steps during the intervention period was 4745 in the control arm, 5023 in the higher frequency, smaller reward lottery arm, 4838 in the jackpot lottery arm, and 5440 in the combined lottery arm. These levels declined during the follow-up period to 4069, 4408, 3800, and 4450, respectively. Adjusted differences between each intervention arm and control are displayed in Table 3. While the combined lottery arm performed the best in both the intervention and the follow-up periods, mean daily steps were not significantly different than control. No adverse events were reported during the entire study period.

Table 3.

Adjusted Daily Step Differences Between Study Arms During the Intervention and Follow-Up Periods.

Model^a	Arm Comparison	Intervention (Weeks 1-13)		Follow-Up (Weeks 14-26)
Model^a	Arm Comparison	Difference in Daily Steps (95% CI)	P Value	Difference in Daily Steps (95% CI)	P Value
Model 1	Higher frequency, smaller reward vs control	294 (−705 to 1294)	.56	288 (−664 to 1240)	.55
	Jackpot lottery vs control	92 (−797 to 980)	.84	−319 (−1173 to 535)	.46
	Combined lottery vs control	742 (−223 to 1707)	.13	288 (−655 to 1231)	.55
Model 2	Higher frequency, smaller reward vs control	242 (−747 to 1232)	.63	241 (−692 to 1174)	.61
	Jackpot lottery vs control	111 (−767 to 990)	.80	−315 (−1152 to 522)	.46
	Combined lottery vs control	757 (−198 to 1711)	.12	287 (−637 to 1211)	.54
Model 3	Higher frequency, smaller reward vs control	258 (−673 to 1189)	.59	130 (−743 to 1004)	.77
	Jackpot lottery vs control	61 (−763 to 885)	.89	−397 (−1184 to 389)	.32
	Combined lottery vs control	784 (−673 to 1681)	.09	237 (−629 to 1104)	.59

Abbreviation: CI, confidence interval.

Discussion

Workplace wellness programs are increasingly using incentives to promote healthy behaviors and target health outcomes. While lottery-based incentives are a common approach used within these programs, there is limited evidence on the effectiveness of different lottery designs. In this randomized trial, we found that a combined lottery, which included both a higher frequency, smaller reward and a lower frequency, higher reward, was the most effective in increasing physical activity. The single-tiered lotteries both performed no differently than control and participants in the jackpot incentive achieved step goals at lower rates over time.

These findings provide new insights in how behavioral economics can be used to design lottery-based financial incentives and have important implications for employers and workplace wellness programs. First, while high frequency, smaller reward lotteries are commonly used in health promotion interventions, in this study, we find little evidence of improvement in physical activity from this approach relative to the control arm. Because the higher probability of winning is balanced by (and in many cases due to cost considerations requires) a smaller reward magnitude, this may have led to a phenomenon known as the “peanuts effect” in which rewards are of such small magnitude that they are too small to be motivating.^15,16

Second, while the jackpot lottery arm began with the highest rate of goal achievement in week 1, this group demonstrated a significant decline in goal achievement throughout the intervention period when compared to the control group. In fact, the goal attainment level was lower than the control group by week 12 and remained that way through 26 weeks. Based on described probabilities, participants may have initially overestimated their chances of winning the high reward of $500.³³ In addition, in contrast to described probabilities, in settings in which people rarely win and the experienced probability is low, people may underestimate probabilities over time.^9,10 Since the probability of winning the jackpot was only 1 in 400, most participants were likely to never win, and the consistent nonwinning of the lottery appeared to significantly reduce the likelihood that participants would achieve their step goals over time. This thinking suggests that jackpot lotteries are likely to be less effective in improving continuous, repeated behaviors (eg, physical activity) as opposed to a 1-time behavior (eg, completion of a health risk assessment or getting a flu shot).

Third, the combined lottery performed the best, as participants in this intervention had significantly higher rates of goal attainment on average (0.38 vs 0.26). Combined lotteries are often used to balance the anticipated regret induced by frequent opportunities to win and the probability inflation associated with the low probability reward. For example, many state and federal lotteries use multiple combinations in which matching a series of numbers leads to prizes of different magnitudes so that even if people don’t win the jackpot they often win something.^11,34 Future studies might further examine different combinations or weighting of high frequency, smaller rewards and lower frequency, larger rewards to further optimize participant performance.

Fourth, the effects from the combined lottery rapidly dissipated during the follow-up period after the incentives ceased. In order to achieve longer term increases in physical activity, workplace wellness programs may need offer the incentives for longer periods, increase their magnitude, design them to better align with the participants’ own goals, or combine them with other intervention strategies. For example, in another randomized trial, we found that a gamification intervention designed to enhance social incentives increased physical activity and differences from control were sustained in the 3 months after the intervention ceased.³⁵ We evaluated a wellness program offered across the United States that combined both financial incentives and gamification and found high rates of physical activity and sustained engagement.³⁶ Further study is needed to evaluate how different designs and combinations of financial and social incentives influence intrinsic and extrinsic motivation for longer term behavior change.

Our study is subject to several limitations. First, participants were from a single employer in Philadelphia and were required to have a smartphone which may limit generalizability. Second, we did not obtain baseline step counts from participants. However, randomization resulted in well-balanced study arms in measured characteristics. Third, by keeping the lottery reward amounts well rounded, the expected daily value of the combined lottery was about 12% higher than the expected value of the other lotteries ($1.40 vs $1.25). Since the most effective incentive arm also had the highest expected economic value, this design limits our ability to draw definitive conclusions and direct comparisons on the effectiveness of combined versus single-tiered lotteries.

In conclusion, we found that a lottery incentive design valued at about $1.40 per day that combined a higher frequency, smaller reward with a lower frequency, larger reward was most effective in increasing physical activity. Workplace programs focused on promoting healthy behaviors can leverage these findings in the design of wellness incentives.

So What?

What is already known on this topic?

Many workplace wellness programs use financial incentives to promote healthy behaviors including physical activity. While lottery-based incentives are commonly used, the comparative effectiveness of different lottery designs is unknown.

What does this article add?

This is one of the first randomized trials to compare different designs of lottery-based financial incentives to increase physical activity among overweight and obese adults. We found that a combined or 2-tiered lottery design was the most effective in increasing physical activity and that single-tiered lotteries were no different than control.

What are the implications for health promotion practice or research?

Few comparative effectiveness studies have been done on different financial incentive programs, despite the prevalence of their use in benefit designs. This study highlights that combined lotteries are likely more effective than either a lower probability jackpot or a higher frequency smaller rewards in shifting behavior. Further study is needed to evaluate how to better sustain effects over longer term periods.

Supplemental Material

Supplemental Material, Protocol_2013_11_07 - A Randomized, Controlled Trial of Lottery-Based Financial Incentives to Increase Physical Activity Among Overweight and Obese Adults

Supplemental Material, Protocol_2013_11_07 for A Randomized, Controlled Trial of Lottery-Based Financial Incentives to Increase Physical Activity Among Overweight and Obese Adults by Mitesh S. Patel, Kevin G. Volpp, Roy Rosin, Scarlett L. Bellamy, Dylan S. Small, Jack Heuer, Susan Sproat, Chris Hyson, Nancy Haff, Samantha M. Lee, Lisa Wesby, Karen Hoffer, David Shuttleworth, Devon H. Taylor, Victoria Hilbert, Jingsan Zhu, Lin Yang, Xingmei Wang, and David A. Asch in American Journal of Health Promotion

Footnotes

Authors’ Note

Patel had full access to all data in the study, takes responsibility for the integrity of the data and the accuracy of the data analysis, and had final responsibility for the decision to submit for publication. The funder of the study had no role in the study design, data collection, data analysis, data interpretation, or writing of the report.

Declaration of Conflicting Interests

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Dr. Patel is supported by career development awards from the Department of Veterans Affairs HSR&D and the Doris Duke Charitable Foundation. Dr. Patel is also founder of Catalyst Health, a technology and behavior change consulting firm. Dr. Patel also has received research funding from Deloitte, which is not related to the work described in this manuscript. Dr. Volpp and Dr. Asch are principals at VAL Health, a behavioral economics consulting firm. Dr. Volpp also has received consulting income from CVS Caremark and research funding from Humana, CVS Caremark, Discovery (South Africa), Hawaii Medical Services Association, Oscar, and Merck, none of which are related to the work described in this manuscript.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was funded by the National Institute on Aging (RC4 AG039114) to Drs. Asch and Volpp.

Supplemental Material

Supplementary material for this article is available online.

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