Financial Incentives for Exercise: Effects of Different Incentive Structures


Brian C. Rider1,2 PhD
David R. Bassett1 PhD
Dixie L. Thompson1 PhD
Eugene C. Fitzhugh1 PhD
Hollie A. Raynor3 PhD, RD
Shannon M. Looney3 PhD, MPH, RD

1Department of Kinesiology, Recreation, and Sport Studies, University of Tennessee, Knoxville TN; 2Department of Kinesiology, Hope College, Holland MI; 3Department of Nutrition, University of Tennessee, Knoxville TN

Correspondence: Brian C. Rider, PhD
University of Tennessee–Knoxville,
1914 Andy Holt Avenue,
Knoxville, TN 37996.
Tel: 865-974-6040
Cell: 248-631-8444
Fax: 865-974-8981



Context: Financial incentives are being used more frequently to influence health behaviors.

Objective: The purpose of this study was to compare the effects of 2 different financial incentive structures—steps per day and body weight—on exercise patterns.

Design: The study was conducted within the context of a minimal contact lifestyle intervention, consisting of a 12-week pedometer-based walking program plus a low energy-density diet.

Participants: Fifty-seven adults (89% female; mean age 49.6 ± 9.0 years) were randomized into 1 of 2 financial incentive groups.

Intervention: Group 1 (G1, n = 29) participants received $1 for each day they met their step goal (money paid at the end of the program). Group 2 (G2, n = 28) participants received a fixed enrollment incentive of $70 at the beginning of the program. Participants carried an Omron HJ-720ITC pocket pedometer for 1 week to determine baseline steps, and they were instructed to increase daily steps by 1000, 2000, and 3000 steps per day over their individual baseline steps for week 1, week 2, and weeks 3 to 12, respectively. Pre- and post-anthropometric measurements (height, weight) were obtained.

Main Outcome Measure(s): A 2-way repeated measure ANOVA (group × time) was used to analyze anthropometric, measurements, physical activity, and dietary data.

Results: Intention-to-treat analysis revealed that the mean number of steps per day increased significantly for both groups from the beginning to the end of the program (G1 = 4549 ± 1366 to 6839 ± 2852 steps per day; G2 = 4524 ± 1171 to 6549 ± 2463 steps per day; P < 0.05); however, there was no significant difference between groups (P > 0.05). Both groups lost a significant amount of weight (G1 = 98.8 ± 14.1 to 95.0 ± 14.2 kg; G2 = 97.8 ± 13.9 to 94.8 ± 13.9 kg; P < 0.05); however, there were no significant between group differences.

Conclusions: This study found that the 2 incentive structures did not differ in terms of their impact on steps per day and body weight over the course of a 12-week lifestyle intervention. However, this minimal contact intervention consisting of a pedometer program and low energy-density diet was effective at increasing ambulatory physical activity and weight loss.

Trial Registration: #NCT02008071.


 physical activity; weight loss; intervention


Physical inactivity (PA) and obesity have been linked to a number of chronic diseases, including obesity, cardiovascular disease,1,2 type 2 diabetes,3 and certain cancers.4,5 A substantial body of literature has shown that lifestyle interventions that include a PA program along with a dietary prescription are effective at improving weight loss, PA levels, and a myriad of other conditions.6–8 However, a limitation of these types of interventions is an inability to demonstrate long-term adherence to the new levels of PA. A promising new approach to increasing individual levels of PA is through the use of financial incentives.

Financial incentives have long been used in behavior modification research, and they have been shown to be successful in smoking cessation,9 weight loss,10–12 and modifying other health behaviors.13 Despite their success, relatively few studies have examined the impact of incentives on increasing PA.14,15 Previous studies have found that using financial incentives to promote PA has resulted in varying success.14,15

Various amounts and types (gift cards, cash, gifts, etc.) of financial incentives have been used to motivate behavior change. In addition, different structures for administering the incentives have been employed.14 For example, incentives can either be guaranteed for performing a task or awarded using a lottery-style system. Guaranteed financial incentives offer a predetermined amount of money that a participant can earn for reaching established PA goals.15,16 A lottery-style system gives participants the chance to earn money for reaching goals, but they are not guaranteed that money. Four studies previously used a lottery system for incentivizing attendance at group exercise sessions. Three of these found no significant difference in attendance compared with a nonincentivized group,15–17 with the fourth study18 finding the lottery to be no more effective than a guaranteed incentivized group.

In addition to the incentive structure, the structure in which the guaranteed incentives are paid may influence the effectiveness of the program. For instance, Burns et al10 reviewed the effectiveness of financial incentives in weight-loss studies. The authors found differences among the different types of incentive structures but could not definitively recommend a specific approach. A systematic review by Strohacker et al14 examined the effectiveness of financial incentives to promote PA and was also unable to conclude which incentive structure was most effective. Thus, there is currently a gap in the literature regarding what incentive structure is most effective.

Previous research appears to support the use of financial incentives for increasing levels of PA.14,19 However, currently there is no consensus on the best structure for administering those incentives. Thus, the purpose of the study was to compare the effects of two 12-week financial incentive structures on steps per day and body weight, within the context of a minimal contact lifestyle intervention using a pedometer program and a low energy-density (ED) diet.

Materials and Methods


Between March and April 2013, individuals were recruited via e-mail, word of mouth, newspaper advertisement, and flyers placed on community bulletin boards. Eligibility was assessed via phone or e-mail screenings. Eligibility criteria included ages 30 to 65 years old, body mass index (BMI) between 25 and 45, access to a computer, and not currently walking more than 7000 steps per day. Individuals were excluded if they could not walk ¼-mile without difficulty or had medical contraindications to exercise (as determined by a health history questionnaire). The University of Tennessee’s institutional review board approved the study protocol. The trial was registered at #NCT02008071.

A total of 105 adults were screened via phone and e-mail for eligibility. Of those, 71 adults were recruited for the intervention and attended a one-on-one session with a study coordinator (Figure 1). Participants signed informed consent forms and provided basic demographic information. Height was measured to the nearest 0.1 cm at the initial visit using a stadiometer and weight was measured to the nearest 0.2 kg using a calibrated digital scale.


Participants were given an Omron HJ-720ITC pocket pedometer (Omron Healthcare, Inc., Bannockburn, IL). It weighs 35 g; it has a width of 47.6 mm, a height 73.0 mm, and a thickness of 15.9 mm. Participants were instructed to carry the pedometer for 1 week in order to determine baseline PA levels (steps per day). This pedometer has previously been shown to be accurate for step counting.19 The pedometer has 2 uniaxial piezoelectric acceleration sensors, oriented at 90 degrees to each other, allowing it to accurately count steps when attached to the belt/waistband or carried in a pocket.20 Participants were shown how to use the pedometer and their stride length was measured and entered into the pedometer. During the baseline period, the pedometer was covered with electrical tape to prevent participants from knowing how many steps they were accumulating each day, to reduce the possibility of their reacting to the pedometer.21 Participants were also shown how to set up their own account at This website enables participants to track their steps and weight, and provides them with the option of setting personal PA and weight loss goals.

Participants returned to the laboratory after 1 week and their pedometer data were downloaded to a computer with a USB cable. If the participant was averaging less than 7000 steps per day, they were included in the study. Each participant was required to have at least 4 days of data (with at least 10 hours of “wear time” per day) to be included in the analysis. Of those enrolled in the study, 57 individuals (89% female; mean age 49.6 ± 9.0 years) (Table 1) met the BMI and steps per day requirement, and they were randomized into 1 of the 2 conditions. Complete follow-up data were collected on 45 participants.

Table 1. Characteristics of Study Participants at Baseline Results

Group 1 Group 2
Age (years) 49.1 ± 9.3a 50.6 ± 8.6
Gender (% female) 89.7 89.3
Body weight (kg) 98.8 ± 14.1 97.8 ± 13.9
Body mass index 34.9 ± 4.2 35.1 ± 4.7
Waist-to-hip ratio 0.84 ± .07 0.86 ± .13
Marital status (%)
  Married 84 71
  Single 3.0 14
  Divorced 10 12
  Widowed 3.0 3.0
Education level (%)
  High school 28 8.0
  College 34 64
  Graduate school 38 28
Race (%)
  White 79 83
  African American 21 14
  Hispanic 0.0 0.0
  Other 0.0 3.0

aData are reported as mean ± standard deviation (SD).

Study Design

Participants were assigned to 1 of the 2 financial incentive groups—group 1 (G1) or group 2 (G2)—on a 1:1 basis ( Although participants were aware that there were 2 groups in the study, they were unaware of the study aims. The primary dependent variables were steps per day, body weight, and compliance with the PA goals. Secondary dependent variables included mean kcal intake, ED, percent kcal from fat, percent kcal from carbohydrate, percent kcal from protein, waist-to-hip ratio, BMI, and aerobic steps. Measures were taken at baseline (time 0) and at 12 weeks. At the conclusion of the 12-week intervention, participants were allowed to keep their pedometers and diet books, and encouraged to continue meeting their goals and tracking their steps online.


Participants in each condition attended a 60-minute group meeting. These meetings were held within the same week, and both groups had the option of attending either a noontime or evening meeting. This was done to accommodate all individuals’ schedules. During the meetings, participants were provided with diet instructions and were given a 30-minute orientation to the dietary program. A nutrition researcher (author S.M.L) led this part of the meeting. The second part of the meeting was led by an exercise physiologist (author B.C.R.), who discussed the ambulatory PA prescription, the use of the pedometer, and the pedometer website. Participants were provided with a detailed handout explaining the process of uploading their data to the central website.

Finally, participants were informed, as a group, as to what financial incentive they were receiving. Participants randomized to G1 (n = 29) were offered $1 for every day they met their step goal. For example, a participant who reached the personalized step goal every day of the 12 weeks would earn $84 (12 weeks × 7 days per week × $1 per day) at the completion of the study. Those randomized to G2 (n = 28) were given a fixed payment of $70 up front, for attending the 60-minute group meeting. Participants in each group were asked to not discuss their incentives with anyone they knew to be enrolled in the study, but who were not present in their meeting. Meetings were segregated so that participants in G1 and G2 did not attend the same meetings. This was done to help ensure that each group did not know about the other group’s payment structure. Both groups received their payment in the form of a VISA gift card.

Physical Activity

Participants in both groups received the same diet and PA prescription. They were instructed to increase daily steps by 1000, 2000, and 3000 steps per day over their individual baseline steps for week 1, week 2, and weeks 3 to 12, respectively. The participants uploaded pedometer data to the Omron Fitness website each week. This allowed the study coordinator to monitor each participant’s daily step counts remotely. Once the pedometer was connected to the computer and user account, it required only 1 click to upload pedometer data, which included total steps per day, aerobic steps per day, distance, and calories expended.


Participants received a copy of The Ultimate Volumetrics Diet,22 which outlines a 12-week diet plan that entails consuming foods that are low in ED, such as fruits and vegetables, soups, and low-fat yogurt, in order to promote satiety. Using a specific dietary prescription developed by Raynor et al,23 participants were instructed to consume at least 10 servings of low-energy density foods (< 1.0 kcal/g) and no more than 2 servings of high-ED foods (> 3.0 kcal/g) daily. This diet has been shown to yield greater weight loss than other diets.

Participants kept a complete 3-day food record in which they itemized all foods and beverages consumed, along with portion sizes, during the baseline period and week 12 of the study. For the purpose of tracking ED throughout the 12-week intervention, the participants recorded the foods and beverages they consumed and their corresponding ED (low ED = 1, moderate ED = 2, high ED = 3) in a pocket-sized record book. They also recorded their body weight each week. These dietary record books were submitted each week to the study coordinator via mail. Participants received biweekly feedback from the study coordinator (B.C.R.) via e-mail after reviewing their dietary record books and PA website.

Data Analysis

The outcome variables in this study were steps per day, aerobic steps per day, mean daily caloric (kcal) intake, ED, kcal from fat, kcal from protein, kcal from carbohydrates, and anthropometric variables (body weight, BMI, waist/hip circumferences). Ambulatory PA data were analyzed using intention-to-treat analysis in which a participant’s last data point was carried forward to the end of the 12 weeks. Differences in ambulatory PA, anthropometric, and dietary data over the duration of the intervention were analyzed using repeated measures ANOVAs, and compliance with recommendations were analyzed using unpaired t-tests.


Twelve participants (21% of those randomized) failed to complete the 12-week study. Intention-to-treat analysis revealed that the mean number of steps per day increased significantly for both groups from baseline to week 12 (G1 = 4516 ± 1390 to 7254 ± 2755 steps per day; G2 = 4524 ± 1171 to 6543 ± 2263 steps per day; P < 0.05) (Figure 2). The mean number of aerobic steps per day increased from baseline to week 12 for both groups (G1 = 244 ± 502 to 927 ± 1223 steps per day; G2 = 191 ± 484 to 1026 ± 1358 steps per day; P < 0.05). For both PA metrics, there were no significant between-group differences (P > 0.05). Participants in G1 met their daily step goal on 59% of the 84 days compared with participants in G2 (52%). The total amount paid out in gift cards was $1489 to G1 and $2030 to G2.


Both groups lost a significant amount of weight (G1 = 98.8 ± 14.1 to 95.0 ± 14.2 kg; G2 = 97.8 ± 13.9 to 94.8 ± 13.9 kg; P < 0.05), but there was no significant difference between groups (P > 0.05). Based on more detailed 3-day diet records collected during baseline and week 12, we observed that both groups had significant decreases in mean daily caloric (kcal) intake (G1 = 2181.6 ± 114.8 to 1579.9 ± 126.4 kcal per day; G2 = 1897 ± 129.2 to 1514 ± 142.3 kcal per day; P < 0.05) and percent of kcal from fat, over time (G1 = 37.7 ± 1.2 to 29.3 ± 1.6%; G2 = 39.9 ± 1.3 to 34.0 ± 1.9%; P < 0.05). Both groups had significant increases in percent of kcal from carbohydrates (G1 = 46.9 ± 1.5 to 52.8 ± 1.9%; G2 = 43.9 ± 1.7 to 45.6 ± 2.1%; P < 0.05) and percent kcal from protein (G1 = 15.3 ± 0.9 to 18.0 ± 1.2%; G2 = 16.0 ± 1.0 to 20.3 ± 1.3%; P < 0.05). However, there were no significant within- or between-group differences in ED (G1 = 1.0 ± 0.09 to 0.92 ± 0.1 kcal/g; G2 = 0.94 ± 0.01 to 0.93 ± 0.9 kcal/g; P > 0.05). Seventy-nine percent of participants in G1 and 67% of participants in G2 turned in all of their dietary record books.


The results of this study demonstrate that there were no significant differences in steps per day or weight loss, when using an up-front payment structure versus incentivizing steps per day. However, the use of a minimal contact lifestyle intervention design led to increases in ambulatory PA and weight loss.

Similar to other pedometer-based interventions,24 the participants in both groups increased their walking distance by approximately 2000 steps per day during the intervention, which is equivalent to approximately 1 mile of walking.25 A feature of the Omron HJ 720ITC is that it contains a 4.0-second filter, meaning that it discards steps that are taken in walking bouts shorter than 4.0 seconds. This filter is designed to screen out erroneous “steps” that could be the result of fidgeting. Although this feature means that the pedometer underestimates steps in intermittent household activities, it is still a valid instrument for measuring steps during continuous walking bouts.23,26

The low ED dietary program was successful at decreasing mean daily kcal intake. However, the overall ED of the participants’ diets was not significantly changed from baseline to week 12. Though an exact reason for this is unclear, one possibility is that participants reduced their consumption of high ED foods, which resulted in a decrease in total caloric intake. The significant decrease in percent kcals from fat (caloric content of 9 kcal/g), supports this explanation, as high ED food items have a greater fat content compared with low ED food items.

It is unclear whether a greater monetary incentive would have significantly increased exercise adherence in either group. Previous studies that offered larger incentives have shown improvements in PA,27,28 but so, too, have studies that offered lesser amounts.14 Research by Paul-Ebhohimhen and Avenell suggested that incentives exceeding 1.2% of a person’s disposable income are most effective for weight loss.29 Thus, future studies that tailor the incentive to each individual or uses a wide range of incentive amounts could help distinguish which amount is most effective at increasing PA among a diverse cohort.

Employers are experimenting with the use of financial incentives for health behaviors. There is a growing interest in using incentives and varying reinforcement models to improve employee health and ease the financial burden on employers and the health care system.26,30 Because of this, there have been many worksite wellness programs aimed at lowering employee cost and improving return on investment. Approximately 82% of employers used financial incentives for healthy behaviors in 2013.31 These behaviors include health risk assessments, smoking cessation, PA promotion, and weight management.

Financial incentives appear to be effective in getting employees to participate in programs. In fact, according to the Rand Workplace Wellness Program Study, employers who use incentives for screening activities report significantly higher participation rates than those who do not offer incentives.32 Specifically, when employees were provided with an incentive to complete a health risk assessment, 63% completed the assessment versus 29% of employees who were not provided with an incentive. Additionally, 57% of the employees underwent a clinical screening compared with 38% who did not.32 Despite these promising numbers, the long-term effect of these programs on improving employee health and decreasing absenteeism and presenteeism (workers’ being on the job but, because of illness or other medical conditions, not fully functioning)is still unclear.33

Strengths and Limitations

There are several strengths and limitations of this study. Strengths include the use of a valid, objective PA monitor, an easy-to-follow dietary prescription, and the use of a minimal contact intervention. A limitation was the lack of a non-incentivized control group. It is possible that the use of a non-incentivized group would have allowed significant differences in steps per day and possibly even weight loss to become evident, as demonstrated in previous studies.16,17


The present study found that the reinforcement structures for financial incentives had similar effects on steps per day or body weight, over the course of a 12-week lifestyle intervention. This minimal contact intervention consisting of a pedometer program combined with a low-ED diet was effective at increasing PA and decreasing caloric intake, and it led to weight loss. In the future, it would be important to test whether the increased activity and weight loss could be sustained for a longer period, as long as the financial incentives remained in place.


The authors thank Cary Springer for her statistical assistance, and Kara Mann and Caroline Holland for their enthusiastic assistance with data collection and entry.

Conflict of Interest Statement

Hollie A. A. Raynor, PhD, RD, has received research funding from Weight Watchers, International. David R. Bassett, PhD, received a research grant from Omron Healthcare, Inc. in 2009 to validate the Omron HJ-303 tri-axial pedometer. Brian C. Rider, PhD, Dixie L. Thompson, PhD, Eugene C. Fitzhugh, PhD, and Shannon M. Looney, PhD, MPH, RD, have no conflicts of interest to declare.


  1. Kohl HW 3rd, Craig CL, Lambert EV, et al. The pandemic of physical inactivity: global action for public health. Lancet. 2012;380(9838):294–305.
  2. Lee IM, Shiroma EJ, Lobelo F, Puska P, Blair SN, Katzmarzyk PT. Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy. Lancet. 2012;380(9838):219–229.
  3. Bassuk SS, Manson JE. Epidemiological evidence for the role of physical activity in reducing risk of type 2 diabetes and cardiovascular disease. J Appl Physiol (Bethesda, MD: 1985). 2005;99(3):1193–1204.
  4. Thune I, Furberg AS. Physical activity and cancer risk: dose-response and cancer, all sites and site-specific. Med Sci Sports Exerc. 2001;33(6 Suppl):S530–550; discussion S609–510.
  5. Pronk A, Ji BT, Shu XO, et al. Physical activity and breast cancer risk in Chinese women. Br J Cancer. 2011;105(9):1443–1450.
  6. Knowler WC, Barrett-Connor E, Fowler SE, et al. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med. 2002;346(6):393–403.
  7. Villareal DT, Banks M, Sinacore DR, Siener C, Klein S. Effect of weight loss and exercise on frailty in obese older adults. Arch Intern Med. 2006;166(8):860–866.
  8. Villareal DT, Miller BV 3rd, Banks M, Fontana L, Sinacore DR, Klein S. Effect of lifestyle intervention on metabolic coronary heart disease risk factors in obese older adults. Am J Clin Nutr. 2006;84(6):1317–1323.
  9. Kollins SH, McClernon FJ, Van Voorhees EE. Monetary incentives promote smoking abstinence in adults with attention deficit hyperactivity disorder (ADHD). Exp Clin Psychopharmacol. 2010;18(3):221–228.
  10. Burns RJ, Donovan AS, Ackermann RT, Finch EA, Rothman AJ, Jeffery RW. A theoretically grounded systematic review of material incentives for weight loss: implications for interventions. Ann Behav Med. 2012;44(3):375–388.
  11. Volpp KG, John LK, Troxel AB, Norton L, Fassbender J, Loewenstein G. Financial incentive-based approaches for weight loss: a randomized trial. JAMA. 2008;300(22):2631–2637.
  12. Wall J, Mhurchu CN, Blakely T, Rodgers A, Wilton J. Effectiveness of monetary incentives in modifying dietary behavior:a review of randomized, controlled trials. Nutr Rev. 2006;64(12):518–531.
  13. Sutherland K, Christianson JB, Leatherman S. Impact of targeted financial incentives on personal health behavior: a review of the literature. Med Care Res Rev. 2008;65(6 Suppl):36S–78S.
  14. Strohacker K, Galarraga O, Williams D. The impact of incentives on exercise behavior: a systematic review of randomized controlled trials. Ann Behav Med. 2014;48(1):92–99.
  15. Martin JE, Dubbert PM, Katell AD, et al. Behavioral control of exercise in sedentary adults: studies 1 through 6. J Consult Clin Psychol. 1984;52(5):795–811.
  16. Wing RR, Jeffery RW, Pronk N, Hellerstedt WL. Effects of a personal trainer and financial incentives on exercise adherence in overweight women in a behavioral weight loss program. Obes Res. 1996;4(5):457–462.
  17. Marcus BH, Stanton AL. Evaluation of relapse prevention and reinforcement interventions to promote exercise adherence in sedentary females. Res Q Exerc Sport. 1993;64(4):447–452.
  18. Epstein LH, Wing RR, Thompson JK, Griffin W. Attendance and fitness in aerobics exercise: the effects of contract and lottery procedures. Behav Mod. 1980;4(4):465–479.
  19. Silcott NA, Bassett DR Jr, Thompson DL, Fitzhugh EC, Steeves JA. Evaluation of the Omron HJ-720ITC pedometer under free-living conditions. Med Sci Sports Exerc. 2011;43(9):1791–1797.
  20. Holbrook EA, Barreira TV, Kang M. Validity and reliability of Omron pedometers for prescribed and self-paced walking. Med Sci Sports Exerc. 2009;41(3):670–674.
  21. Clemes SA, Parker RA. Increasing our understanding of reactivity to pedometers in adults. Med Sci Sports Exerc. 2009;41(3):674–680.
  22. Rolls B, Hermann M. The Ultimate Volumetrics Diet: Smart, Simple, Science-Based Strategies for Losing Weight and Keeping It Off. New York: William Morrow; 2012.
  23. Raynor HA, Looney SM, Steeves EA, Spence M, Gorin AA. The effects of an energy density prescription on diet quality and weight loss: a pilot randomized controlled trial. J Acad Nutr Dietet. 2012;112(9):1397–1402.
  24. Bravata DM, Smith-Spangler C, Sundaram V, et al. Using pedometers to increase physical activity and improve health: a systematic review. JAMA. 2007;298(19):2296–2304.
  25. Bassett DR, Jr., Mahar MT, Rowe DA, Morrow JR Jr. Walking and measurement. Med Sci Sports Exerc. 2008;40(7 Suppl):S529–536.
  26. Pratt M, Norris J, Lobelo F, Roux L, Wang G. The cost of physical inactivity: moving into the 21st century. Br J Sports Med. 2014;48(3):171–173.
  27. Finkelstein EA, Brown DS, Brown DR, Buchner DM. A randomized study of financial incentives to increase physical activity among sedentary older adults. Prev Med. 2008;47(2):182–187.
  28. Pope L, Harvey-Berino J. Burn and earn: a randomized controlled trial incentivizing exercise during fall semester for college first-year students. Prev Med. 2013;56(3–4):197–201.
  29. Paul-Ebhohimhen V, Avenell A. Systematic review of the use of financial incentives in treatments for obesity and overweight. Obes Rev. 2008;9(4):355–367.
  30. Oldridge NB. Economic burden of physical inactivity: healthcare costs associated with cardiovascular disease. Eur J Cardiovasc Prev Rehabil. 2008;15(2):130–139.
  31. Watson T. Towers Watson Survey on purchasing value in health care. 2013. Accessed March 28, 2014.
  32. Mattke S, Hangsheng Liu, John Caloyeras, Christina Y. Huang, Kristin R. Van Busum, Dmitry Khodyakov and Victoria Shier. Workplace Wellness Programs Study: Final Report. Santa Monica, CA: Rand Corp.; 2013.
  33. Osilla KC, Van Busum K, Schnyer C, Larkin JW, Eibner C, Mattke S. Systematic review of the impact of worksite wellness programs. Am J Managed Care. 2012;18(2):e68–81.

Figure 1. CONSORT (Consolidated Standards of Reporting Trials) 2010 flow diagram.

Figure 2. Mean number of steps per day over the course of the 12-week minimal contact lifestyle intervention.

Posted in Vol 1 Issue 1