Impact of Small Monetary Incentives on Exercise in University Students Kelley Strohacker, PhD; Omar Galárraga, PhD; Jessica Emerson, ScM; Samuel R. Fricchione, ScM; Mariah Lohse, AB; David M. Williams, PhD Objectives: Research has demonstrated that health outcomes are significantly improved with the application of financial incentives. However, relatively larger incentives are not typically sustainable and removal of incentives tends to result in attrition of behavior. The feasibility of using relatively smaller incentives to improve physical activity is unclear. The aim of the present study is to determine whether small financial incentives (maximum $5.00 per week) can improve exercise-related energy expenditure of inactive individuals. Methods: Twenty-two university students (20 ±1.6 years old) were randomized into incentive or non-incentive conditions. Exercise-related caloric expenditure was tracked over 10 weeks. Results:
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romotion of regular physical activity is a priority. The American College of Sports Medicine recommends that adults accumulate a minimum of 150 minutes per week of moderate intensity aerobic activity, 75 minutes per week of vigorous intensity aerobic activity, or a combination of the 2, aiming to expend at least 1000 kcal each week.1 Substantial research has demonstrated that meeting the minimum recommendations reduces risk of all-cause mortality and major disease, such as cardiovascular disease and type 2 diabetes mellitus.2,3 Moreover, regular physical activity promotes important psychological benefits.4-6 Despite such benefits, only 20.6% of American adults report meeting the minimum aerobic guidelines.7 When measured objectively, the number of adults meet-
Kelley Strohacker, Assistant Professor, Department of Kinesiology, Recreation, and Sport Studies, The University of Tennessee, Knoxville, TN. Omar Galárraga, Assistant Professor, Jessica Emerson, Graduate Research Assistant, Samuel R. Fricchione, Graduate Research Assistant, Mariah Lohse, Undergraduate Research Assistant, and David M. Williams, Associate Professor, Brown University School of Public Health, Providence RI. Correspondence Dr Strohacker; [email protected]
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The sample size yielded 62% power. The repeated measures ANCOVA, controlling for body mass index, indicated a main effect of condition (F = 5.50, p =.03) with no significant interaction (F = 2.25, p = .06). Conclusions: This pilot study demonstrates initial feasibility in implementing small financial incentives to promote exercise behavior in previously inactive young adults. Due to the small sample size, results should be interpreted with caution and further research is warranted to improve and maintain exercise behavior in response to relatively smaller incentives. Key words: cash incentives; caloric expenditure; reinforcement Am J Health Behav. 2015;39(6):779-785 DOI: http://dx.doi.org/10.5993/AJHB.39.6.5
ing minimum recommendations is substantially worse, with less than 10% of adults meeting physical activity guidelines.7 Thus, it is important for researchers and practitioners to determine how to best promote regular physical activity. Providing financial incentives is a widely recognized method of promoting health behavior. According to operant conditioning theory, positive or negative reinforcements or punishments can be implemented to manipulate the frequency of a target behavior.8 Furthermore, “present bias,” a principle of behavioral economics, suggests that financial incentives represent a more tangible and immediate reward, compared to the health-related benefits that may not be realized for years or decades.9,10 Incentivizing behavior change has been shown to be effective in weight loss11.12 and smoking cessation programs.13,14 In addition, incentive-based programs are popular among worksite wellness programs15,16 and the use of incentivized programs for disease prevention has been recommended by the Patient Protection and Affordable Care Act (PPACA).17 Although less research has been conducted regarding incentives and exercise behavior, a review of the randomized controlled trials suggests that incentives tend to improve behavior during inter-
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Impact of Small Monetary Incentives on Exercise in University Students ventions.18 However, relatively few studies assessed behavior at follow-up. Those that did observed a regression to baseline behavior following the removal of incentives, a common limitation of incentivebased programs for health behavior change in general. This limitation could be addressed potentially by incorporating relatively smaller incentives that may be sustainable in promoting long-term behavior change. In 2013, Schumacher et al19 found that providing relatively minimal incentives (maximum of $0.20 per day) improved stair use relative to baseline levels within an employee-based health rewards program. However, no research to date has determined the impact of relatively smaller monetary incentives contingent upon measured exercise behavior relative to a non-incentivized control condition. To understand the potential impact, acceptability, and feasibility of using theoretically sustainable incentive structures to promote exercise behavior, a small-scale pilot study was conducted in a sample of inactive university students. The purpose was to test the effects of an incentive-based exercise promotion program that uses relatively small incentives ($0.01 earned per every 4 kilocalories expended through exercise, with a maximum amount of $5.00 per week [eg, 2000 kcal per week]). The maximum possible reward was determined based on 2 considerations: (1) $5.00 was estimated to be the largest amount of money that would be feasible for a funding organization to provide indefinitely (ie, maximum incentive of roughly $20/month/person or $260/year/person); and (2) 2000 kcal per week (ie, 300 minutes of moderateintensity aerobic exercise) is the upper limit of current public health recommendations for exercise. METHODS Participants A total of 22 students from Brown University (ages 18-24) were recruited in 2 cohorts to participate during spring and fall semesters of 2013. Participants were healthy, but sedentary or with low activity level (ie reporting less than 60 minutes of minutes of moderate-intensity exercise per week). Included participants reported no pre-existing conditions or symptoms indicative of increased risk of a cardiac event during exercise according to the Physical Activity Readiness Questionnaire (PARQ). Furthermore, all participants in both cohorts were required to have a current Brown University student identification card. In cohort 2, participants were required to have a smartphone capable of taking and emailing photos. Procedures Overview. The current study was designed as a small-scale, pilot randomized control trial occurring over 10 weeks during the fall (cohort 1, N = 10) and spring (cohort 2, N = 12) semesters of 2013. Eligible participants were randomized to incentive (N = 11) or non-incentive (N = 11) conditions, and then instructed to exercise at the main fitness
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center (Nelson Fitness Center) at Brown University and communicate with research staff on a weekly basis once randomized. Recruitment and randomization. Participants were recruited using flyer advertisements and word-of-mouth. Interested individuals were asked to respond through email to be assessed for eligibility and those interested were sent an email containing instructions on how to self-evaluate eligibility based on an attached PAR-Q and body mass index (BMI) chart. Those who had a BMI < 40, answered NO to all questions in the PAR-Q, and reported < 60 minutes per week of planned exercise were considered eligible and were contacted to schedule an orientation session. A research study email account was created so that participants could respond to posted advertisements, schedule an orientation session once eligible, and communicate with research staff on a weekly basis once randomized. Participants deemed eligible based on email screening procedures were invited to group-based orientation sessions to inform them of study eligibility requirements and expectations. Following informed consent procedures, eligible participants completed a questionnaire packet used to assess baseline physical activity behavior and basic demographic information. Following an objective measurement of height and weight, participants were randomized into one of 2 groups: no incentive (control) or monetary incentive. Participant contact and behavior tracking. Participants were given the goal to complete at least 30 minutes of moderate-intensity aerobic exercise on 5 days of the week (eg, moderate intensity walking or cycling). In Cohort 1, exercise had to be performed on Life Fitness® treadmills and cycle ergometers that had the capacity to download exercise data onto USB drives. Participants would then upload USB data to the LifeFitness website each week (https://www.virtualtrainer.lifefitness.com/ vt/), where data could be accessed by research staff with usernames and passwords provided by the participants. Anecdotal information collected during this cohort indicated that participants felt limited in exercise choice under this method. Therefore, participants in Cohort 2 were required to own a smartphone with capabilities of taking and emailing photos. Following the completion of each exercise bout, participants were told to take a photo of the equipment monitor to track caloric expenditure and immediately email the photo to research staff. Progress reports were sent in response to these materials each week, wherein incentivized participants were provided a running total of money earned in addition to accumulated caloric expenditure. Data verification. Several approaches were implemented to distinguish between true “zeroes” (ie participant performed no aerobic exercise at the fitness center on a given week) and missing data. First, any individual who did not report any exercise was sent an additional email asking for verification that no exercise had been performed over
Strohacker et al the previous week. Second, each participant was provided with a document outlining caloric expenditure across each week at the study completion visit. For each week, participants were asked to confirm their total caloric expenditure at the Nelson Fitness Center, including confirmation of zero calories expended. Finally, electronic swipe data from the Nelson Fitness Center were used to confirm the dates and times of exercise sessions according to participant records (USB data or smartphone photos). This process was implemented to prevent dishonest conduct, such as swiping 2 ID cards, swiping someone else’s ID card, or having friends take a picture of their workout summaries for study participants to use. Incentives structure. Participants in the incentive condition earned $0.01 for every 4 kilocalories expended through moderate-intensity treadmill or cycling exercise. As national guidelines recommend a weekly caloric expenditure of 1000-2000 kcal (eg, 5 bouts of moderate-intensity aerobic exercise per week of 30-60 minutes duration each), any caloric expenditure beyond 2000 kcal/week was not incentivized further. By meeting the maximum recommendation (2000 kcal), participants in the incentive group had the potential to earn $5.00 per week. Participants in the control condition were not given any monetary incentives based on weekly caloric expenditure, but were provided with weekly updates of exercise accumulation. All participants, regardless of condition, were compensated $10 for attending the post-intervention visit and completing their participation in the study. Measures. Participants completed a sociodemographic questionnaire at baseline asking them about their sex, age, ethnicity, current year of education, family household income, and employment status. Height and weight were measured using a calibrated digital scale with a stadiometer attachment (Detecto Pro Doc PD300) to record BMI objectively. All participants were asked to remove their shoes along with anything in their pockets (wallet, keys, cellphone). BMI was calculated by dividing weight in kilograms by height in meters squared. Weekly caloric expenditure was calculated by summing caloric expenditure reported each week via USB drive information uploaded to the LifeFitness website in Cohort 1 and via photographs of exercise equipment monitors sent to research study staff using smart phones in Cohort 2. Dates from exercise records provided by participants were verified by assessing Nelson Fitness Center attendance records. Statistical analysis. Statistical analyses were conducted using the Statistical Package for the Social Sciences (SPSS, version 21; SPSS Inc., Chicago, IL). All instances of weekly caloric expenditure included in the analyses were verified by NFC attendance data. Caloric expenditure reported that was not verified by NFC attendance was assigned a score of zero. Regarding accurate reporting, 93.6%
of reports (206 out of 220 weeks) was verified as accurate by the participants (for zero exercise) or by NFC attendance data (for reported exercise). Missing data (ie unconfirmed by participants) were replaced by carrying-over values from the most recent verified week (found to be zero in all cases). Prior to testing the primary outcomes, potential differences in baseline sociodemographic factors were assessed using separate independent t-tests (continuous variables) and chi-square analyses (categorical variables). Any continuous or categorical variable found to be different between conditions at baseline were tested further as predictors of exercise behavior using General Linear Models (GLM) Univariate procedures. Total caloric expenditure (summed across the 10-week intervention) was entered as a continuous dependent variable with other continuous variables entered as covariates and categorical variables (including condition) entered as fixed factors. Any variable found to significantly predict overall energy expenditure was entered as a covariate in a 2 (condition) x 10 (weeks) repeated measures ANCOVA to test the impact of condition on weekly caloric expenditure. Statistical significance was set at p < .05. Post hoc power analyses were conducted using GLIMMPSE software.20
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RESULTS Sociodemographic Characteristics Table 1 summarizes sociodemographic variables of participants. The majority of participants (86%) were undergraduate students. There were no significant differences between incentive and control conditions at baseline regarding age (t20 = .13, p = .89), the proportions of participants who were women (χ24, N=22 = .92, p = .34), non-Hispanic Whites (χ24, N=22 = 2.78, p = .60), or reported family income ≥$50K (χ24, N=22 = 3.07, p = .69). However, several important baseline differences between conditions were noted. Individuals in the incentive condition were more likely to be employed (χ24, N=22 = 4.55, p = .03). Although the baseline difference in BMI did not reach statistical significance (t20 = -1.80, p = .09), the average BMI of individuals in the incentive condition met the threshold for overweight status (BMI 25.0-29.9 kg/m2), whereas the average BMI of the individuals in the control group is categorized as normal weight. Given the inverse relationship between overweight and obese status with exercise behavior, BMI was subjected to further testing in order to determine whether it (and/ or employment status) should be included as a covariate in the statistical model. Predictors of Total Caloric Expenditure In order to provide a more conservative determination of potential covariates of weekly caloric expenditure (beyond baseline differences), employment status and BMI were assessed in regards to their ability to predict total caloric expenditure across the intervention. Both variables were entered in the GLM Univariate analysis to determine
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Table 1 Baseline Sociodemographic Characteristics Incentive (N = 11) Mean (SD) or %
Control (N = 11) Mean (SD) or %
Total (N = 22) Mean (SD) or %
Age (years)
20.09 (1.30)
20.18 (1.83)
20.14 (1.55)
BMI (kg/m2)*
25.43 (3.83)
22.89 (2.69)
24.16 (3.48)
Income (>50k)
63.64
72.73
68.18
Women
63.64
75
69.32
Non-Hispanic White
45.45
36.36
40.91
Employed
72.73
27.27
50.00
**
Incentive versus Control, * = p < .10, ** = p < .05
potential predictors of overall caloric expenditure. Despite the fact that the proportion of employed participants differed between conditions at baseline, employment status was not shown to predict overall energy expenditure, F(1,21) = 2.20, p = .16, or interact with condition, F (1,21) = 0.15, p = .70. However, BMI was found to be a significant predictor of overall energy expenditure, F(1,21) = 5.25, p = .04, and thus was included as a covariate in the subsequent repeated measures analysis. Caloric Expenditure over Time Repeated measures ANCOVA (controlling for BMI) was conducted comparing incentive and control conditions on exercise completed at the Nelson Fitness Center to examine treatment effects over 10 weeks (Figure 1) using weekly caloric expenditure provided by participants using their USB drives (Cohort 1) and cell phone camera and email functions (Cohort 2). There was a significant effect of condition on caloric expenditure after controlling for the effect of BMI, F(1, 19) = 5.50, p = .03. No significant effect of time was observed, F(1, 9) = 1.62, p = .17. The condition by time interaction did not reach statistical significance, F(1,171) = 2.25, p = .06, This represented a medium interaction effect (Cohen’s d = .69). Power Analysis Post hoc power analyses were conducted to determine power of the current study and to estimate the minimum sample size needed to examine interaction effects for caloric expenditure with at least 80% power and a Type I error rate of 5%. The effect of repeated observations over time was considered with a base correlation of 0.5 between adjacent measurement time points. With the sample size of 22, estimated power for the current study was 62%. Assuming that variances remain constant, a sample size of 30 is required to achieve 80% power. Conversely, a sample size of 46 is the minimum requirement if variances were to double.
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DISCUSSION The aim of this pilot study was to examine proofof-concept and initial feasibility of providing relatively small monetary incentives to improve exercise behavior in apparently healthy university students. Mean energy expenditure fell substantially below national recommendations (1000-2000 kcal per week) and appeared to diminish over time in both conditions. Further investigation is warranted regarding how to incorporate relatively small incentives for promoting consistent exercise behavior. Several methodological considerations for future research are noted. The greater exercise-related caloric expenditure observed in the incentive condition, relative to the control condition, is consistent with prior research. To date, few randomized controlled trials21-23 have incentivized exercise behavior using cash as positive reinforcement for each enrolled individual (ie, non-lottery). In the earliest of these studies21 participants earned cash in return for attending supervised walking sessions over 18 months ($1-3 per walk). In 2009, Charness et al22 paid university students $100 if they visited the campus recreation center 8 times within 4 weeks. In a more recent study by Pope et al,23 recreation center visits were rewarded with cash incentives ($5 per visit initially) and earned on an escalating scale (additional $0.25 per visit each week) during the 12-week intervention. In each study, providing incentives improved exercise behavior (attendance) relative to a control condition. Although incentives are typically effective in improving behavior, mean caloric expenditure appeared to diminish in both conditions over time in the present study. This pattern also emerged in the study conducted by Pope et al,23 wherein the percentage of university students meeting the fitness center visit goal diminished each week (83% in week 1 vs 47% in week 12) despite escalating rewards. Regression of exercise behavior is a common occurrence in intervention-based research.24 Because the current study sample was comprised
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Mean Kilocalories Expended
Figure 1 Weekly Caloric Expenditure 600 500 400 300 200 100 0
1
2
3
4
5
6
7
8
9
10
Weeks Note. Mean kilocalories expended each week and verified by fitness center attendance observed in control (white bars, N = 11) and incentive (gray bars, N = 11) conditions.
of university students, the observed declines may be attributable to an increase in academic demands as the semester progressed, with a concomitant reduction in time available for exercise. Indeed, perceived lack of time has been cited previously as the most important barrier for not participating in regular exercise in university students.25-27 Furthermore, given the size of the incentives, the decline may have been due to a loss of interest in obtaining them. Although research staff members provided a running tally of total earnings each week, providing cash-in-hand at the end of each week is likely a more salient approach compared to providing all incentives upon study completion,28 a consideration to be evaluated prior to future research. Although the current study utilized positive reinforcement to encourage exercise behavior, incentives may prove more reinforcing if deployed using a “buy-in” or “deposit-contract” approach, wherein participants provide cash to enroll in a program and stand to lose a portion each time they fail to exercise. This method relies on the concept of loss aversion29 and suggests that an individual’s fear of losing something they already have is more motivating that gaining something new and of equal value. One large-scale RCT demonstrated that when controlling for participant acceptance of treatment condition assignment, smoking cessation rates were 13% greater in deposit-based incentives relative to reward-based incentives.30 Deposit-based incentives are rarely employed in incentivized exercise interventions;18 thus, more research in this area is also warranted. It is also important to note that no structured
behavioral intervention was implemented in the current study. Like many health behaviors, maintaining newly adopted exercise behavior consistently has been shown to be difficult.31 The promotion of additional cognitive behavioral strategies and skills (eg, goal-setting, stimulus control, selfmonitoring, social support, problem-solving) purportedly enhance exercise behavior.32,33 Given that the minimal rewards in the current study yielded a significant condition effect behavior appeared to decrease in both conditions, it is likely that implementing relatively small incentives would be most appropriate within a behavioral exercise promotion program that also provides tools and strategies for initiating and sustaining behavior. Some researchers have criticized the use of external motivators on the basis of potentially undermining the development of intrinsic motivation.34-37 However, this issue remains controversial with contrary evidence suggesting that external motivation can enhance rather than undermine intrinsic motivation.38,39 Thus, more research is needed on this issue and it remains possible that smaller and indefinitely continued incentives can be effective. Additionally, the amount of time necessary for individuals to engage in exercise to make it habitual has yet to be established. If the duration is relatively long, the use of smaller incentives could be continued for a longer period of time, and thus, be more effective at establishing habitual exercise compared to the larger incentives that typically have been utilized. Whereas findings of the present pilot study are preliminary, the approach begins to address important limitations of previous incentive-based ex-
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Impact of Small Monetary Incentives on Exercise in University Students ercise interventions. Although the aforementioned randomized controlled trials21-23 found that providing cash incentives improved behavior, the metric assessed in each study was attendance (at a fitness center or supervised walking sessions). Attendance, particularly when assessed via a card swipe entry system at a fitness center, provides no information regarding exercise volume; exercise amount is, of course, a primary determinant of health improvement. Thus, whereas average energy expenditure fell below minimum health recommendations each week, the results provide meaningful information regarding estimated energy expenditure of previously inactive individuals in unsupervised settings when minimal incentives are promised with no further behavioral intervention. Although attendance at supervised group exercise session may be more indicative of a prescribed exercise volume, this approach introduces additional staffing costs and imposes time limitations on participants that may not be feasible in real-life (private-sector) settings. Designed with these limitations in mind, our study supports the feasibility of tracking exercise volume (caloric expenditure) within fitness centers using common devices (USB drives and smartphones) that can be verified using swipe card attendance data. In particular, the use of smartphone tracking to allow more choice in aerobic exercise mode may be more appropriate in future studies as variety has been found to be favorably associated with exercise behavior.40,41 The second limitation of prior research relates to relatively high cost of incentives. Adjusted for cost of inflation as of 2015 for comparison purposes, the maximum incentive costs per participant per week were $14,21 $30,22 and $26.23 Larger incentives within a program may necessitate a reduced enrollment capacity and/or shortened program duration. Our study aimed to determine the feasibility and impact of providing relatively small, potentially sustainable incentives ($5 maximum per participant per week). Given the encouraging results of this pilot study, further research into the impact of relatively smaller incentives is warranted. Limitations Finally, the current study has several important limitations to note. The sample size was relatively small, yielding 62% power. Using data from the current study, replication of these results within a fully powered trial (minimum of 30 participants) is necessary to support the use of small incentives to promote exercise behavior. Also, the use of university students as a convenience sample limits the generalizability of this study and it is possible that age as well as health, employment, and education would mediate the impact of relatively small incentives. Students attending Brown University, in particular, are likely to be above the mean for socioeconomic status, which may have biased the study to underestimate potential effects. It is also important to note that the accuracy of exercise-re-
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lated caloric expenditure displayed by commercial aerobic equipment is inherently limited. Whereas this approach was convenient in testing the feasibility of small incentives in the current study, future approaches may benefit from incentivizing more objective/accurate outcomes related to behavior. Finally, the relatively short duration of the intervention (10 weeks) limits extrapolation of these findings to longer periods of time. Thus, an important future aim would be to determine whether long-term, sustainable incentives result in long-term exercise behavior. Conclusion Our study provides preliminary evidence regarding feasibility of using relatively small monetary rewards to promote exercise-related caloric expenditure among university students. Strengths of the study include the relative ease of physical activity monitoring for participants and implementation of the intervention in an externally valid setting (ie, fitness center). This approach is important because there is currently a growing interest in using incentives to promote health behavior in research, commercial, and worksite settings.15,16,42 Given the magnitude of and time-based decline in caloric expenditure in both groups, further study into the efficacy and effectiveness of utilizing smaller, sustainable incentives is strongly warranted. To improve behavioral outcomes, it may be beneficial for future studies to provide cash incentives at more frequent intervals and implement other behavior change techniques within the intervention or program to supplement incentive-based effects. Human Subjects Statement All procedures were approved by the Institutional Review Board of Brown University (IRB#1210000722) and all participants provided written informed consent in the presence of a study staff member prior to enrollment and randomization. Conflict of Interest Statement All authors declare that they have no conflicts of interest. Acknowledgments The authors sincerely thank Mr Mathew Tsimkas and Ms Allyson Caudell for their support in the implementation of this pilot study and providing Nelson Fitness Center use records for enrolled participants. The authors also acknowledge Dr Tyler Stanifird for his assistance in performing post hoc power analyses. Dr Strohacker was supported by a T32 Training Fellowship (T32 HL076134) throughout the study design and implementation of cohort 1. Dr Galarraga is currently supported by an NIH Center Grant (R24 HD041020). This study was funded through pilot funds provided to Dr Williams by Brown University School of Public Health.
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romotion of regular physical activity is a priority. The American College of Sports Medicine recommends that adults accumulate a minimum of 150 minutes per week of moderate intensity aerobic activity, 75 minutes per week of vigorous intensity aerobic activity, or a combination of the 2, aiming to expend at least 1000 kcal each week.1 Substantial research has demonstrated that meeting the minimum recommendations reduces risk of all-cause mortality and major disease, such as cardiovascular disease and type 2 diabetes mellitus.2,3 Moreover, regular physical activity promotes important psychological benefits.4-6 Despite such benefits, only 20.6% of American adults report meeting the minimum aerobic guidelines.7 When measured objectively, the number of adults meet-
Kelley Strohacker, Assistant Professor, Department of Kinesiology, Recreation, and Sport Studies, The University of Tennessee, Knoxville, TN. Omar Galárraga, Assistant Professor, Jessica Emerson, Graduate Research Assistant, Samuel R. Fricchione, Graduate Research Assistant, Mariah Lohse, Undergraduate Research Assistant, and David M. Williams, Associate Professor, Brown University School of Public Health, Providence RI. Correspondence Dr Strohacker; [email protected]
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The sample size yielded 62% power. The repeated measures ANCOVA, controlling for body mass index, indicated a main effect of condition (F = 5.50, p =.03) with no significant interaction (F = 2.25, p = .06). Conclusions: This pilot study demonstrates initial feasibility in implementing small financial incentives to promote exercise behavior in previously inactive young adults. Due to the small sample size, results should be interpreted with caution and further research is warranted to improve and maintain exercise behavior in response to relatively smaller incentives. Key words: cash incentives; caloric expenditure; reinforcement Am J Health Behav. 2015;39(6):779-785 DOI: http://dx.doi.org/10.5993/AJHB.39.6.5
ing minimum recommendations is substantially worse, with less than 10% of adults meeting physical activity guidelines.7 Thus, it is important for researchers and practitioners to determine how to best promote regular physical activity. Providing financial incentives is a widely recognized method of promoting health behavior. According to operant conditioning theory, positive or negative reinforcements or punishments can be implemented to manipulate the frequency of a target behavior.8 Furthermore, “present bias,” a principle of behavioral economics, suggests that financial incentives represent a more tangible and immediate reward, compared to the health-related benefits that may not be realized for years or decades.9,10 Incentivizing behavior change has been shown to be effective in weight loss11.12 and smoking cessation programs.13,14 In addition, incentive-based programs are popular among worksite wellness programs15,16 and the use of incentivized programs for disease prevention has been recommended by the Patient Protection and Affordable Care Act (PPACA).17 Although less research has been conducted regarding incentives and exercise behavior, a review of the randomized controlled trials suggests that incentives tend to improve behavior during inter-
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Impact of Small Monetary Incentives on Exercise in University Students ventions.18 However, relatively few studies assessed behavior at follow-up. Those that did observed a regression to baseline behavior following the removal of incentives, a common limitation of incentivebased programs for health behavior change in general. This limitation could be addressed potentially by incorporating relatively smaller incentives that may be sustainable in promoting long-term behavior change. In 2013, Schumacher et al19 found that providing relatively minimal incentives (maximum of $0.20 per day) improved stair use relative to baseline levels within an employee-based health rewards program. However, no research to date has determined the impact of relatively smaller monetary incentives contingent upon measured exercise behavior relative to a non-incentivized control condition. To understand the potential impact, acceptability, and feasibility of using theoretically sustainable incentive structures to promote exercise behavior, a small-scale pilot study was conducted in a sample of inactive university students. The purpose was to test the effects of an incentive-based exercise promotion program that uses relatively small incentives ($0.01 earned per every 4 kilocalories expended through exercise, with a maximum amount of $5.00 per week [eg, 2000 kcal per week]). The maximum possible reward was determined based on 2 considerations: (1) $5.00 was estimated to be the largest amount of money that would be feasible for a funding organization to provide indefinitely (ie, maximum incentive of roughly $20/month/person or $260/year/person); and (2) 2000 kcal per week (ie, 300 minutes of moderateintensity aerobic exercise) is the upper limit of current public health recommendations for exercise. METHODS Participants A total of 22 students from Brown University (ages 18-24) were recruited in 2 cohorts to participate during spring and fall semesters of 2013. Participants were healthy, but sedentary or with low activity level (ie reporting less than 60 minutes of minutes of moderate-intensity exercise per week). Included participants reported no pre-existing conditions or symptoms indicative of increased risk of a cardiac event during exercise according to the Physical Activity Readiness Questionnaire (PARQ). Furthermore, all participants in both cohorts were required to have a current Brown University student identification card. In cohort 2, participants were required to have a smartphone capable of taking and emailing photos. Procedures Overview. The current study was designed as a small-scale, pilot randomized control trial occurring over 10 weeks during the fall (cohort 1, N = 10) and spring (cohort 2, N = 12) semesters of 2013. Eligible participants were randomized to incentive (N = 11) or non-incentive (N = 11) conditions, and then instructed to exercise at the main fitness
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center (Nelson Fitness Center) at Brown University and communicate with research staff on a weekly basis once randomized. Recruitment and randomization. Participants were recruited using flyer advertisements and word-of-mouth. Interested individuals were asked to respond through email to be assessed for eligibility and those interested were sent an email containing instructions on how to self-evaluate eligibility based on an attached PAR-Q and body mass index (BMI) chart. Those who had a BMI < 40, answered NO to all questions in the PAR-Q, and reported < 60 minutes per week of planned exercise were considered eligible and were contacted to schedule an orientation session. A research study email account was created so that participants could respond to posted advertisements, schedule an orientation session once eligible, and communicate with research staff on a weekly basis once randomized. Participants deemed eligible based on email screening procedures were invited to group-based orientation sessions to inform them of study eligibility requirements and expectations. Following informed consent procedures, eligible participants completed a questionnaire packet used to assess baseline physical activity behavior and basic demographic information. Following an objective measurement of height and weight, participants were randomized into one of 2 groups: no incentive (control) or monetary incentive. Participant contact and behavior tracking. Participants were given the goal to complete at least 30 minutes of moderate-intensity aerobic exercise on 5 days of the week (eg, moderate intensity walking or cycling). In Cohort 1, exercise had to be performed on Life Fitness® treadmills and cycle ergometers that had the capacity to download exercise data onto USB drives. Participants would then upload USB data to the LifeFitness website each week (https://www.virtualtrainer.lifefitness.com/ vt/), where data could be accessed by research staff with usernames and passwords provided by the participants. Anecdotal information collected during this cohort indicated that participants felt limited in exercise choice under this method. Therefore, participants in Cohort 2 were required to own a smartphone with capabilities of taking and emailing photos. Following the completion of each exercise bout, participants were told to take a photo of the equipment monitor to track caloric expenditure and immediately email the photo to research staff. Progress reports were sent in response to these materials each week, wherein incentivized participants were provided a running total of money earned in addition to accumulated caloric expenditure. Data verification. Several approaches were implemented to distinguish between true “zeroes” (ie participant performed no aerobic exercise at the fitness center on a given week) and missing data. First, any individual who did not report any exercise was sent an additional email asking for verification that no exercise had been performed over
Strohacker et al the previous week. Second, each participant was provided with a document outlining caloric expenditure across each week at the study completion visit. For each week, participants were asked to confirm their total caloric expenditure at the Nelson Fitness Center, including confirmation of zero calories expended. Finally, electronic swipe data from the Nelson Fitness Center were used to confirm the dates and times of exercise sessions according to participant records (USB data or smartphone photos). This process was implemented to prevent dishonest conduct, such as swiping 2 ID cards, swiping someone else’s ID card, or having friends take a picture of their workout summaries for study participants to use. Incentives structure. Participants in the incentive condition earned $0.01 for every 4 kilocalories expended through moderate-intensity treadmill or cycling exercise. As national guidelines recommend a weekly caloric expenditure of 1000-2000 kcal (eg, 5 bouts of moderate-intensity aerobic exercise per week of 30-60 minutes duration each), any caloric expenditure beyond 2000 kcal/week was not incentivized further. By meeting the maximum recommendation (2000 kcal), participants in the incentive group had the potential to earn $5.00 per week. Participants in the control condition were not given any monetary incentives based on weekly caloric expenditure, but were provided with weekly updates of exercise accumulation. All participants, regardless of condition, were compensated $10 for attending the post-intervention visit and completing their participation in the study. Measures. Participants completed a sociodemographic questionnaire at baseline asking them about their sex, age, ethnicity, current year of education, family household income, and employment status. Height and weight were measured using a calibrated digital scale with a stadiometer attachment (Detecto Pro Doc PD300) to record BMI objectively. All participants were asked to remove their shoes along with anything in their pockets (wallet, keys, cellphone). BMI was calculated by dividing weight in kilograms by height in meters squared. Weekly caloric expenditure was calculated by summing caloric expenditure reported each week via USB drive information uploaded to the LifeFitness website in Cohort 1 and via photographs of exercise equipment monitors sent to research study staff using smart phones in Cohort 2. Dates from exercise records provided by participants were verified by assessing Nelson Fitness Center attendance records. Statistical analysis. Statistical analyses were conducted using the Statistical Package for the Social Sciences (SPSS, version 21; SPSS Inc., Chicago, IL). All instances of weekly caloric expenditure included in the analyses were verified by NFC attendance data. Caloric expenditure reported that was not verified by NFC attendance was assigned a score of zero. Regarding accurate reporting, 93.6%
of reports (206 out of 220 weeks) was verified as accurate by the participants (for zero exercise) or by NFC attendance data (for reported exercise). Missing data (ie unconfirmed by participants) were replaced by carrying-over values from the most recent verified week (found to be zero in all cases). Prior to testing the primary outcomes, potential differences in baseline sociodemographic factors were assessed using separate independent t-tests (continuous variables) and chi-square analyses (categorical variables). Any continuous or categorical variable found to be different between conditions at baseline were tested further as predictors of exercise behavior using General Linear Models (GLM) Univariate procedures. Total caloric expenditure (summed across the 10-week intervention) was entered as a continuous dependent variable with other continuous variables entered as covariates and categorical variables (including condition) entered as fixed factors. Any variable found to significantly predict overall energy expenditure was entered as a covariate in a 2 (condition) x 10 (weeks) repeated measures ANCOVA to test the impact of condition on weekly caloric expenditure. Statistical significance was set at p < .05. Post hoc power analyses were conducted using GLIMMPSE software.20
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RESULTS Sociodemographic Characteristics Table 1 summarizes sociodemographic variables of participants. The majority of participants (86%) were undergraduate students. There were no significant differences between incentive and control conditions at baseline regarding age (t20 = .13, p = .89), the proportions of participants who were women (χ24, N=22 = .92, p = .34), non-Hispanic Whites (χ24, N=22 = 2.78, p = .60), or reported family income ≥$50K (χ24, N=22 = 3.07, p = .69). However, several important baseline differences between conditions were noted. Individuals in the incentive condition were more likely to be employed (χ24, N=22 = 4.55, p = .03). Although the baseline difference in BMI did not reach statistical significance (t20 = -1.80, p = .09), the average BMI of individuals in the incentive condition met the threshold for overweight status (BMI 25.0-29.9 kg/m2), whereas the average BMI of the individuals in the control group is categorized as normal weight. Given the inverse relationship between overweight and obese status with exercise behavior, BMI was subjected to further testing in order to determine whether it (and/ or employment status) should be included as a covariate in the statistical model. Predictors of Total Caloric Expenditure In order to provide a more conservative determination of potential covariates of weekly caloric expenditure (beyond baseline differences), employment status and BMI were assessed in regards to their ability to predict total caloric expenditure across the intervention. Both variables were entered in the GLM Univariate analysis to determine
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Impact of Small Monetary Incentives on Exercise in University Students
Table 1 Baseline Sociodemographic Characteristics Incentive (N = 11) Mean (SD) or %
Control (N = 11) Mean (SD) or %
Total (N = 22) Mean (SD) or %
Age (years)
20.09 (1.30)
20.18 (1.83)
20.14 (1.55)
BMI (kg/m2)*
25.43 (3.83)
22.89 (2.69)
24.16 (3.48)
Income (>50k)
63.64
72.73
68.18
Women
63.64
75
69.32
Non-Hispanic White
45.45
36.36
40.91
Employed
72.73
27.27
50.00
**
Incentive versus Control, * = p < .10, ** = p < .05
potential predictors of overall caloric expenditure. Despite the fact that the proportion of employed participants differed between conditions at baseline, employment status was not shown to predict overall energy expenditure, F(1,21) = 2.20, p = .16, or interact with condition, F (1,21) = 0.15, p = .70. However, BMI was found to be a significant predictor of overall energy expenditure, F(1,21) = 5.25, p = .04, and thus was included as a covariate in the subsequent repeated measures analysis. Caloric Expenditure over Time Repeated measures ANCOVA (controlling for BMI) was conducted comparing incentive and control conditions on exercise completed at the Nelson Fitness Center to examine treatment effects over 10 weeks (Figure 1) using weekly caloric expenditure provided by participants using their USB drives (Cohort 1) and cell phone camera and email functions (Cohort 2). There was a significant effect of condition on caloric expenditure after controlling for the effect of BMI, F(1, 19) = 5.50, p = .03. No significant effect of time was observed, F(1, 9) = 1.62, p = .17. The condition by time interaction did not reach statistical significance, F(1,171) = 2.25, p = .06, This represented a medium interaction effect (Cohen’s d = .69). Power Analysis Post hoc power analyses were conducted to determine power of the current study and to estimate the minimum sample size needed to examine interaction effects for caloric expenditure with at least 80% power and a Type I error rate of 5%. The effect of repeated observations over time was considered with a base correlation of 0.5 between adjacent measurement time points. With the sample size of 22, estimated power for the current study was 62%. Assuming that variances remain constant, a sample size of 30 is required to achieve 80% power. Conversely, a sample size of 46 is the minimum requirement if variances were to double.
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DISCUSSION The aim of this pilot study was to examine proofof-concept and initial feasibility of providing relatively small monetary incentives to improve exercise behavior in apparently healthy university students. Mean energy expenditure fell substantially below national recommendations (1000-2000 kcal per week) and appeared to diminish over time in both conditions. Further investigation is warranted regarding how to incorporate relatively small incentives for promoting consistent exercise behavior. Several methodological considerations for future research are noted. The greater exercise-related caloric expenditure observed in the incentive condition, relative to the control condition, is consistent with prior research. To date, few randomized controlled trials21-23 have incentivized exercise behavior using cash as positive reinforcement for each enrolled individual (ie, non-lottery). In the earliest of these studies21 participants earned cash in return for attending supervised walking sessions over 18 months ($1-3 per walk). In 2009, Charness et al22 paid university students $100 if they visited the campus recreation center 8 times within 4 weeks. In a more recent study by Pope et al,23 recreation center visits were rewarded with cash incentives ($5 per visit initially) and earned on an escalating scale (additional $0.25 per visit each week) during the 12-week intervention. In each study, providing incentives improved exercise behavior (attendance) relative to a control condition. Although incentives are typically effective in improving behavior, mean caloric expenditure appeared to diminish in both conditions over time in the present study. This pattern also emerged in the study conducted by Pope et al,23 wherein the percentage of university students meeting the fitness center visit goal diminished each week (83% in week 1 vs 47% in week 12) despite escalating rewards. Regression of exercise behavior is a common occurrence in intervention-based research.24 Because the current study sample was comprised
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Mean Kilocalories Expended
Figure 1 Weekly Caloric Expenditure 600 500 400 300 200 100 0
1
2
3
4
5
6
7
8
9
10
Weeks Note. Mean kilocalories expended each week and verified by fitness center attendance observed in control (white bars, N = 11) and incentive (gray bars, N = 11) conditions.
of university students, the observed declines may be attributable to an increase in academic demands as the semester progressed, with a concomitant reduction in time available for exercise. Indeed, perceived lack of time has been cited previously as the most important barrier for not participating in regular exercise in university students.25-27 Furthermore, given the size of the incentives, the decline may have been due to a loss of interest in obtaining them. Although research staff members provided a running tally of total earnings each week, providing cash-in-hand at the end of each week is likely a more salient approach compared to providing all incentives upon study completion,28 a consideration to be evaluated prior to future research. Although the current study utilized positive reinforcement to encourage exercise behavior, incentives may prove more reinforcing if deployed using a “buy-in” or “deposit-contract” approach, wherein participants provide cash to enroll in a program and stand to lose a portion each time they fail to exercise. This method relies on the concept of loss aversion29 and suggests that an individual’s fear of losing something they already have is more motivating that gaining something new and of equal value. One large-scale RCT demonstrated that when controlling for participant acceptance of treatment condition assignment, smoking cessation rates were 13% greater in deposit-based incentives relative to reward-based incentives.30 Deposit-based incentives are rarely employed in incentivized exercise interventions;18 thus, more research in this area is also warranted. It is also important to note that no structured
behavioral intervention was implemented in the current study. Like many health behaviors, maintaining newly adopted exercise behavior consistently has been shown to be difficult.31 The promotion of additional cognitive behavioral strategies and skills (eg, goal-setting, stimulus control, selfmonitoring, social support, problem-solving) purportedly enhance exercise behavior.32,33 Given that the minimal rewards in the current study yielded a significant condition effect behavior appeared to decrease in both conditions, it is likely that implementing relatively small incentives would be most appropriate within a behavioral exercise promotion program that also provides tools and strategies for initiating and sustaining behavior. Some researchers have criticized the use of external motivators on the basis of potentially undermining the development of intrinsic motivation.34-37 However, this issue remains controversial with contrary evidence suggesting that external motivation can enhance rather than undermine intrinsic motivation.38,39 Thus, more research is needed on this issue and it remains possible that smaller and indefinitely continued incentives can be effective. Additionally, the amount of time necessary for individuals to engage in exercise to make it habitual has yet to be established. If the duration is relatively long, the use of smaller incentives could be continued for a longer period of time, and thus, be more effective at establishing habitual exercise compared to the larger incentives that typically have been utilized. Whereas findings of the present pilot study are preliminary, the approach begins to address important limitations of previous incentive-based ex-
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Impact of Small Monetary Incentives on Exercise in University Students ercise interventions. Although the aforementioned randomized controlled trials21-23 found that providing cash incentives improved behavior, the metric assessed in each study was attendance (at a fitness center or supervised walking sessions). Attendance, particularly when assessed via a card swipe entry system at a fitness center, provides no information regarding exercise volume; exercise amount is, of course, a primary determinant of health improvement. Thus, whereas average energy expenditure fell below minimum health recommendations each week, the results provide meaningful information regarding estimated energy expenditure of previously inactive individuals in unsupervised settings when minimal incentives are promised with no further behavioral intervention. Although attendance at supervised group exercise session may be more indicative of a prescribed exercise volume, this approach introduces additional staffing costs and imposes time limitations on participants that may not be feasible in real-life (private-sector) settings. Designed with these limitations in mind, our study supports the feasibility of tracking exercise volume (caloric expenditure) within fitness centers using common devices (USB drives and smartphones) that can be verified using swipe card attendance data. In particular, the use of smartphone tracking to allow more choice in aerobic exercise mode may be more appropriate in future studies as variety has been found to be favorably associated with exercise behavior.40,41 The second limitation of prior research relates to relatively high cost of incentives. Adjusted for cost of inflation as of 2015 for comparison purposes, the maximum incentive costs per participant per week were $14,21 $30,22 and $26.23 Larger incentives within a program may necessitate a reduced enrollment capacity and/or shortened program duration. Our study aimed to determine the feasibility and impact of providing relatively small, potentially sustainable incentives ($5 maximum per participant per week). Given the encouraging results of this pilot study, further research into the impact of relatively smaller incentives is warranted. Limitations Finally, the current study has several important limitations to note. The sample size was relatively small, yielding 62% power. Using data from the current study, replication of these results within a fully powered trial (minimum of 30 participants) is necessary to support the use of small incentives to promote exercise behavior. Also, the use of university students as a convenience sample limits the generalizability of this study and it is possible that age as well as health, employment, and education would mediate the impact of relatively small incentives. Students attending Brown University, in particular, are likely to be above the mean for socioeconomic status, which may have biased the study to underestimate potential effects. It is also important to note that the accuracy of exercise-re-
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lated caloric expenditure displayed by commercial aerobic equipment is inherently limited. Whereas this approach was convenient in testing the feasibility of small incentives in the current study, future approaches may benefit from incentivizing more objective/accurate outcomes related to behavior. Finally, the relatively short duration of the intervention (10 weeks) limits extrapolation of these findings to longer periods of time. Thus, an important future aim would be to determine whether long-term, sustainable incentives result in long-term exercise behavior. Conclusion Our study provides preliminary evidence regarding feasibility of using relatively small monetary rewards to promote exercise-related caloric expenditure among university students. Strengths of the study include the relative ease of physical activity monitoring for participants and implementation of the intervention in an externally valid setting (ie, fitness center). This approach is important because there is currently a growing interest in using incentives to promote health behavior in research, commercial, and worksite settings.15,16,42 Given the magnitude of and time-based decline in caloric expenditure in both groups, further study into the efficacy and effectiveness of utilizing smaller, sustainable incentives is strongly warranted. To improve behavioral outcomes, it may be beneficial for future studies to provide cash incentives at more frequent intervals and implement other behavior change techniques within the intervention or program to supplement incentive-based effects. Human Subjects Statement All procedures were approved by the Institutional Review Board of Brown University (IRB#1210000722) and all participants provided written informed consent in the presence of a study staff member prior to enrollment and randomization. Conflict of Interest Statement All authors declare that they have no conflicts of interest. Acknowledgments The authors sincerely thank Mr Mathew Tsimkas and Ms Allyson Caudell for their support in the implementation of this pilot study and providing Nelson Fitness Center use records for enrolled participants. The authors also acknowledge Dr Tyler Stanifird for his assistance in performing post hoc power analyses. Dr Strohacker was supported by a T32 Training Fellowship (T32 HL076134) throughout the study design and implementation of cohort 1. Dr Galarraga is currently supported by an NIH Center Grant (R24 HD041020). This study was funded through pilot funds provided to Dr Williams by Brown University School of Public Health.
Strohacker et al References
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