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Framing Financial Incentives to Increase Physical Activity Among Overweight and Obese Adults: A Randomized, Controlled TrialFinancial Incentives for Physical Activity in Overweight and Obese Adults

Mitesh S. Patel, MD, MBA, MS; David A. Asch, MD MBA; Roy Rosin, MBA; Dylan S. Small, PhD; Scarlett L. Bellamy, ScD; Jack Heuer, EdD; Susan Sproat, MS; Chris Hyson, MEd; Nancy Haff, MD; Samantha M. Lee, MD; Lisa Wesby, MS; Karen Hoffer, BS; David Shuttleworth, MS; Devon H. Taylor, BS; Victoria Hilbert, MPH, RD; Jingsan Zhu, MBA, MS; Lin Yang, MS; Xingmei Wang, MS; and Kevin G. Volpp, MD, PhD
[+] Article, Author, and Disclosure Information

This article was published at www.annals.org on 16 February 2016.


From the Perelman School of Medicine at the University of Pennsylvania, Penn Medicine Center for Health Care Innovation, Wharton School of the University of Pennsylvania, Center for Health Incentives and Behavioral Economics at the Leonard Davis Institute of the University of Pennsylvania, and Philadelphia Veterans Affairs Medical Center, Philadelphia, Pennsylvania; Massachusetts General Hospital, Boston, Massachusetts; and Columbia University Medical Center, New York, New York.

Disclaimer: Dr. Patel had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Grant Support: By the National Institute on Aging (grant RC4 AG039114; Drs. Asch and Volpp) and in part by the Department of Veteran Affairs (Drs. Patel, Asch, and Volpp) and Robert Wood Johnson Foundation (Drs. Patel and Asch).

Disclosures: Dr. Asch reports grant support from the National Institutes of Health during the conduct of the study; further, he is a principal and part owner of the behavioral economics consulting firm VAL Health. Ms. Hilbert reports grant support from the National Institute of Aging during the conduct of the study. Dr. Volpp reports grant support from the National Institutes of Health during the conduct of the study. Further, he reports consulting income from CVS Health and VAL Health (principal and part owner) and grants (or grants pending) from CVS Health, Humana, Merck, Weight Watchers, Discovery (South Africa), and Hawaii Medical Services Association; and stock in VAL Health, all outside of the study. Authors not named here have disclosed no conflicts of interest. Disclosures can also be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M15-1635.

Editors' Disclosures: Christine Laine, MD, MPH, Editor in Chief, reports that she has no financial relationships or interests to disclose. Darren B. Taichman, MD, PhD, Executive Deputy Editor, reports that he has no financial relationships or interests to disclose. Cynthia D. Mulrow, MD, MSc, Senior Deputy Editor, reports that she has no relationships or interests to disclose. Deborah Cotton, MD, MPH, Deputy Editor, reports that she has no financial relationships or interest to disclose. Jaya K. Rao, MD, MHS, Deputy Editor, reports that she has stock holdings/options in Eli Lilly and Pfizer. Sankey V. Williams, MD, Deputy Editor, reports that he has no financial relationships or interests to disclose. Catharine B. Stack, PhD, MS, Deputy Editor for Statistics, reports that she has stock holdings in Pfizer.

Reproducible Research Statement:Study protocol: Available from Dr. Patel (e-mail, mpatel@upenn.edu). Statistical code and data set: Not available.

Requests for Single Reprints: Mitesh S. Patel, MD, MBA, MS, Division of General Internal Medicine, Perelman School of Medicine at the University of Pennsylvania, 423 Guardian Drive, 13th Floor Blockley Hall, Philadelphia, PA 19104; e-mail, mpatel@upenn.edu.

Current Author Addresses: Dr. Patel: Division of General Internal Medicine, Perelman School of Medicine at the University of Pennsylvania, 423 Guardian Drive, 13th Floor Blockley Hall, Philadelphia, PA 19104.

Drs. Asch and Rosin: Penn Medicine Center for Health Care Innovation, 13th Floor Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104.

Dr. Small: The Wharton School, University of Pennsylvania, 4th Floor Jon M. Huntsman Hall, 3730 Walnut Street, Philadelphia, PA 19104.

Ms. Bellamy: Division of General Internal Medicine, Perelman School of Medicine at the University of Pennsylvania, 423 Guardian Drive, 6th Floor Blockley Hall, Philadelphia, PA 19104.

Dr. Heuer, Ms. Sproat, and Mr. Hyson: Division of Human Resources, University of Pennsylvania, Suite 527A, 3401 Walnut Street, Philadelphia, PA 19104.

Dr. Haff: Massachusetts General Hospital, 730 Gray Bigelow, 55 Fruit Street, Boston, MA 02114.

Dr. Lee: Columbia University Medical Center, 622 West 168th Street, New York, NY 10032.

Ms. Wesby, Ms. Hoffer, Ms. Hilbert, Ms. Yang, Ms. Wang, Mr. Shuttleworth, Mr. Taylor, Mr. Zhu, and Dr. Volpp: Center for Health Incentives and Behavioral Economics at the Leonard Davis Institute, University of Pennsylvania, 11th Floor Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104.

Author Contributions: Conception and design: M.S. Patel, D.A. Asch, R. Rosin, N. Haff, D. Shuttleworth, K.G. Volpp.

Analysis and interpretation of the data: M.S. Patel, D.A. Asch, D.S. Small, S.L. Bellamy, J. Heuer, S.M. Lee, L. Wesby, D. Shuttleworth, J. Zhu, L. Yang, X. Wang, K.G. Volpp.

Drafting of the article: M.S. Patel, D.A. Asch, S.L. Bellamy, J. Heuer, D. Shuttleworth, V. Hilbert.

Critical revision of the article for important intellectual content: M.S. Patel, D.A. Asch, D.S. Small, S.L. Bellamy, L. Wesby, D. Shuttleworth, J. Zhu, K.G. Volpp.

Final approval of the article: M.S. Patel, D.A. Asch, R. Rosin, S.L. Bellamy, S.M. Lee, D. Shuttleworth, K.G. Volpp.

Provision of study materials or patients: D. Shuttleworth, D.H. Taylor.

Statistical expertise: D.S. Small, S.L. Bellamy, J. Zhu.

Obtaining of funding: D.A. Asch, K.G. Volpp.

Administrative, technical, or logistic support: M.S. Patel, R. Rosin, J. Heuer, S. Sproat, C. Hyson, N. Haff, L. Wesby, K. Hoffer, D. Shuttleworth, V. Hilbert, K.G. Volpp.

Collection and assembly of data: L. Wesby, K. Hoffer, D. Shuttleworth, V. Hilbert.


Ann Intern Med. 2016;164(6):385-394. doi:10.7326/M15-1635
© 2016 American College of Physicians
Text Size: A A A

Background: Financial incentive designs to increase physical activity have not been well-examined.

Objective: To test the effectiveness of 3 methods to frame financial incentives to increase physical activity among overweight and obese adults.

Design: Randomized, controlled trial. (ClinicalTrials.gov: NCT 02030119)

Setting: University of Pennsylvania.

Participants: 281 adult employees (body mass index ≥27 kg/m2).

Intervention: 13-week intervention. Participants had a goal of 7000 steps per day and were randomly assigned to a control group with daily feedback or 1 of 3 financial incentive programs with daily feedback: a gain incentive ($1.40 given each day the goal was achieved), lottery incentive (daily eligibility [expected value approximately $1.40] if goal was achieved), or loss incentive ($42 allocated monthly upfront and $1.40 removed each day the goal was not achieved). Participants were followed for another 13 weeks with daily performance feedback but no incentives.

Measurements: Primary outcome was the mean proportion of participant-days that the 7000-step goal was achieved during the intervention. Secondary outcomes included the mean proportion of participant-days achieving the goal during follow-up and the mean daily steps during intervention and follow-up.

Results: The mean proportion of participant-days achieving the goal was 0.30 (95% CI, 0.22 to 0.37) in the control group, 0.35 (CI, 0.28 to 0.42) in the gain-incentive group, 0.36 (CI, 0.29 to 0.43) in the lottery-incentive group, and 0.45 (CI, 0.38 to 0.52) in the loss-incentive group. In adjusted analyses, only the loss-incentive group had a significantly greater mean proportion of participant-days achieving the goal than control (adjusted difference, 0.16 [CI, 0.06 to 0.26]; P = 0.001), but the adjusted difference in mean daily steps was not significant (861 [CI, 24 to 1746]; P = 0.056). During follow-up, daily steps decreased for all incentive groups and were not different from control.

Limitation: Single employer.

Conclusion: Financial incentives framed as a loss were most effective for achieving physical activity goals.

Primary Funding Source: National Institute on Aging.

Figures

Grahic Jump Location
Figure 1.

Study flow diagram.

One participant randomly assigned to the gain-incentive group was later found to be ineligible due to previous enrollment in another physical activity study. One participant randomly assigned to the lottery-incentive group switched to a phone that was not eligible for use before the study began and therefore did not receive the intervention.

Grahic Jump Location
Grahic Jump Location
Figure 2.

Weekly unadjusted mean proportion of participant-days achieving the 7000-step goal.

Grahic Jump Location

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Were the “losers” really better off?
Posted on March 11, 2016
Marc S. Mitchell, PhD, Paul I. Oh, MD
University Health Network, Toronto Rehabilitation Institute
Conflict of Interest: Dr. Mitchell reports grant support from the Canadian Institutes of Health Research and the University Health Network as well as in-kind research support from Cookson James Loyalty Inc.; further he is president of the financial health incentive consulting company Incentive Avenue Inc. and reports consulting income from Social Change Rewards Inc. and Green Shield Canada Inc. and stock options in Social Change Rewards Inc., all outside of this commentary. Dr. Oh has disclosed no conflicts of interest.
Patel et al. (2016) found that the upfront allocation of financial incentives, followed by subsequent loss (“loss framed” incentives), stimulated physical activity to a greater extent than other incentive designs (“gain framed” and “lottery”). While consistent with behavioral economics, we believe this important finding should be interpreted with caution. There are a few reasons for this. First, without baseline step data, it is not clear if the generic 7,000-step target was even appropriate. It is possible that, for many, this target was unrealistically high, or conversely, far too easy. Tying incentives to a more tailored approach to goal setting (e.g., 2,000 steps above baseline) may have yielded different results.

Another concern was regarding the relatively large participation incentive ($100), and whether this amplified the so-called ‘Hawthorne effect’ amongst controls, where people perform better when they know they are being watched. Evidence of this is seen in the follow-up period (after all the participation incentives were given) where a relative decrease of 20% in the proportion of days step goal reached was observed amongst controls. Smaller participation incentives should be considered in the future to avoid unintentionally motivating controls.

One of the strengths of this study was the ability of the researchers to seamlessly track and reward steps using smartphone motion tracking technology. While this reduced participant burden, the validity and reliability of smartphone physical activity monitoring is questionable (1) despite the authors’ assertions of “accuracy”. Several issues cast doubt here, not the least of which has to do with smartphone placement (i.e. for the same person, step counts can vary depending on where the smartphone is carried – pocket, purse, etc.) (2,3). The authors concede that the continuous outcome (i.e. steps per day) had a wider distribution than expected which made it difficult to detect group differences. This is a serious limitation especially when considering the binary nature of the primary study outcome (above or below 7,000 steps). It is possible that many participant days were inaccurately categorized as goal met or not met, because of measurement issues rather than true physical activity pattern.

Finally, none of the experiment arms drove post-incentive physical activity. In future work, it may be appropriate to focus less on the magnitude of effect in the short-term, and more on the “quality” of the behavior change – that is, more on the impact on post-incentive behaviors and intrinsic motivation, a key predictor of sustained health behavior change.

References

1. Orr K, Howe HS, Omran J, Smith KA, Palmateer TM, Ma AE, Faulkner G.
Validity of smartphone pedometer applications. BMC Res Notes. 2015; 8(733). doi http://dx.doi.org/10.1186/s13104-015-1705-8.
2. Schrack, J, Zipunnikov, V, Crainiceanu, C. Electronic devices and applications to track physical activity. JAMA, 2015; 313(20).
3. De Cocker KA, De Meyer J, De Bourdeaudhuij IM, Cardon GM. Non-traditional wearing positions of pedometers: validity and reliability of the Omron HJ-203-ED pedometer under controlled and free-living conditions. J Sci Med Sport. 2012; 15(5). doi: http://dx.doi.org/10.1016/j.jsams.2012.02.002.
Reinforcing Behavior Change
Posted on April 12, 2016
Jeremiah Weinstock, PhD, Nancy M. Petry, PhD
Saint Louis University, University of Connecticut Health Center
Conflict of Interest: We report no potential conflicts or relevant financial interests. This comment was supported by the following grants from the National Institutes of Health R01-DA033411, R01-DA027615, P50-DA09241, P60-AA03510, R01-HD075630, R01-AA021446, T32-AA07290, DP3-DK097705, R01-AA023502. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Financial incentives can reinforce consumer behavior (e.g., cash back for using a specific credit card), and these procedures are gaining traction to modify health behaviors. Patel and colleagues’ study (1) examining the efficacy of incentives to reinforce walking 7,000 steps daily are commendable, and results underscore the complex nature of these interventions and the behaviors they are designed to influence. Reinforcement interventions are grounded not only in behavioral economics, but also in behavior analysis, which relies heavily on principles of operant conditioning. For example, reinforcing successive approximations toward the goal behavior (i.e., shaping) typically works best. Participants in this study (1) did not receive any reinforcement unless they met the criterion of 7,000 steps in a day; depending on baseline levels of activity, this criterion may have been relatively easy or difficult to achieve. When we reinforced persons to increase steps gradually over time, over 80% achieved the recommended goal of >10,000 steps per day (2). It also is unclear in their study (1) the extent to which participants in the gain incentive conditions ever experienced the contingencies, whereas in the loss conditions, most all experienced the adverse contingencies regularly. Magnitude of reinforcement is also important for impacting behavior change. Our study (2) provided about four times the magnitude as this one, and it also achieved much greater rates of success. In addition, many efficacious gain incentive interventions provide increasing magnitudes of reinforcement for sustained behavior change (3), but the present study did not include this feature. Thus, different results can be obtained depending on the nature of the reinforcement systems and the behavior being modified. In prior studies by this and other groups (3,4), probabilistic gain incentive systems generally yielded benefits relative to non-incentive control conditions. It is not clear if the lack of efficacy of the gain incentive conditions in this study (1) relates to the overall low magnitude of reinforcement or nature of the reinforcement system. Further, none of these reinforcement systems differed significantly from one another in terms of outcomes (1); however, differences in patient acceptability of gain versus loss contingencies likely exist, especially if patients are expected to contribute their own funds (5). As research and practice moves forward, it is important to recognize the complexity of behavior change and to consider a variety of behavioral and patient parameters in designing and implementing efficacious reinforcement interventions.


References

1. Patel MS, Asch DA, Rosin R, et al. Framing financial incentives to increase physical activity among overweight and obese adults a randomized, controlled trial. Annals of Internal Medicine. 2016;164(6):385-394.
2. Petry NM, Andrade LF, Barry D, Byrne S. A randomized study of reinforcing ambulatory exercise in older adults. Psychology and Aging. 2013;28(4):1164-1173.
3. Petry NM, Peirce JM, Stitzer ML, et al. Effect of prize-based incentives on outcomes in stimulant abusers in outpatient psychosocial treatment programs: A National Drug Abuse Treatment Clinical Trials Network study. Archives of General Psychiatry. 2005;62:1148-1156.
4. Volpp KG, John LK, Troxel AB, Norton L, Fassbender J, Lowenstein G. Financial incentive-based approach for weight loss: A randomized trial. Journal of the American Medical Association. 2008;300:2631-2637.
5. Halpern SD, French B, Small DS, et al. Randomized trial of four financial-incentive programs for smoking cessation. New England Journal of Medicine. 2015;372(22):2108-2117.
Author's Response
Posted on May 11, 2016
Mitesh S. Patel, MD, MBA, MS, David A. Asch, MD, MBA, Kevin G. Volpp, MD, PhD
University of Pennsylvania
Conflict of Interest: None Declared
Drs. Weinstock and Petry note that behavior change is complex and reinforcement is an important component of interventions to increase physical activity. While we agree with both of these comments, our study was focused on the impact of different ways to frame financial incentives. Our findings demonstrate that holding reinforcement constant, financial incentives framed as a loss were most effective. A prior study by Drs. Weinstock and Petry reveal important insights but comparisons to our study should be conducted with caution as theirs was smaller in size, did not target overweight and obese adults, used a different step goal, and had a different primary outcome measure (1).
Drs. Mitchell and Oh question the appropriateness of the same 7000 step goal for different participants. While we agree that individuals may vary in their baseline activity, we noted as a limitation that baseline data was not available. We also provided several reasons to support a 7000 step goal, including that it is endorsed by the American College of Sports Medicine (2). Many prior studies have used a 10,000 step goal, which has been shown to disengage the more sedentary who could benefit the most from these types of interventions (3). Mitchell and Oh state that step tracking will vary based on how a device is carried. We agree, but this is true for any device, not just smartphones. In addition, our prior work found smartphones had less variability in step tracking than wearable devices (4). Mitchell and Oh also comment that the control group may have increased their activity due to the participation incentive. While could have occurred, the same participation incentive was used for all study arms and therefore we were able to evaluate the differential effect of the intervention incentives, which was our primary goal. We agree with Mitchel and Oh that more study is needed to evaluate how to sustain behavior change for longer-term periods.
These letters highlight that behavior change is hard and driven by many factors. However, our findings demonstrated that insights from behavioral economics can improve the effectiveness of financial incentive-based interventions for physical activity.
Mitesh S. Patel, MD, MBA, MS
David A. Asch, MD, MBA
Kevin G. Volpp, MD, PhD

References
1. Petry NM, Andrade LF, Barry D, Byrne S. A randomized study of reinforcing ambulatory exercise in older adults. Psychology and Aging. 2013;28(4):1164-1173.
2. Garber CE, Blissmer B, Deschenes MR, Franklin BA, Lamonte MJ, Lee I, et al; on behalf of the American College of Sports Medicine. Quantity and quality of exercise for developing and maintaining cardiorespiratory, musculoskeletal, and neuromotor fitness in apparently health adults: Guidance for prescribing exercise. Medicine & Science in Sports & Exercise. 2011;43(7):1334-59.
3. Bravata DM, Smith-Spangler C, Sundaram V, Gienger AL, Lin N, Lewis R. Using pedometers to increase physical activity and improve health: A systematic review. JAMA. 2007;298(19):2296-2304.
4. Case MA, Burwick HA, Volpp KG, Patel MS. The accuracy of smartphone applications and wearable devices for tracking physical activity data. JAMA. 2015;313(6):625-626
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