[Skip to Content]
Sign In
Individual Sign In
Create an Account
Institutional Sign In
OpenAthens Shibboleth
[Skip to Content Landing]
Views 952
Invited Commentary
Health Policy
December 28, 2018

Incentivizing Healthy Behaviors at Scale: Closing the Gap Between Science and Population-Level Implementation

Author Affiliations
  • 1Department of Medicine, University of Washington School of Medicine, Seattle
  • 2The Leonard Davis Institute of Health Economics, The Wharton School, University of Pennsylvania, Philadelphia
  • 3Corporal Michael J. Cresencz VA Medical Center, Philadelphia, Pennsylvania
  • 4Department of Medical Ethics & Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia
JAMA Netw Open. 2018;1(8):e186173. doi:10.1001/jamanetworkopen.2018.6173

Section 1115 waivers are provisions through which the Centers for Medicare & Medicaid Services enables states to use federal funds and test promising new health care delivery approaches and improve the health of Medicaid beneficiaries. In particular, states are permitted to go beyond certain existing rules and regulations to implement demonstration projects that can meaningfully promote Medicaid program objectives. Under President Donald J. Trump’s administration, the Centers for Medicare & Medicaid Services has directed waiver provisions toward several focus areas, including access to care, Medicaid program inefficiencies, innovative payment reforms, and beneficiary health care decision making.1

Huf et al2 evaluated the association of Healthy Behavior Incentive Programs (HBIPs) implemented by states through the section 1115 waivers with smoking cessation, weight loss, and office visits. Using a difference-in-differences approach, longitudinal data from the Behavioral Risk Factor Surveillance Survey, and low income (<$25 000 annually) and education (high school or less) as proxies for Medicaid eligibility, the authors compared smoking, obesity, and use of preventive services among Medicaid-eligible individuals in the 4 states with HBIPs vs those in control states without HBIPs. In both low-income and low-education groups, smoking rates increased, rather than decreased, by small amounts during the HBIP implementation period, while obesity rates decreased by small, non–statistically significant amounts. There was not definitive evidence about an association between HBIPs and preventive health visit use.

The use of financial incentives to encourage health behaviors represents an important intersection between behavioral science and clinical medicine. An established body of literature has demonstrated that when tested in pragmatic trials, financial incentives can effectively reduce smoking and increase physical activity among overweight individuals.3,4 However, to date, robust data validating the effect of such interventions at the population level in large-scale, real-world program settings such as Medicaid programs have been lacking. Therefore, the study by Huf and colleagues2 provides important early evidence and several key insights that policy makers and health system leaders should consider in implementing behavior-specific incentives in such situations.

First, the study highlights the need to close the gap between the evidence-based interventions and their implementation in real-world settings. Huf et al2 suggest several factors that could explain the lack of association between HBIPs and changes in behavioral outcomes, including suboptimal incentive size, challenges disseminating program information to beneficiaries, and the impact of other concurrent Medicaid programs targeting similar health behaviors. Other factors, such as beneficiary churning (ie, frequent cycling in and out of Medicaid program eligibility), could have also decreased the effect of HBIP incentives.

As the authors note, intervention design is likely 1 key factor perpetuating the gap. While interventions in clinical trials incorporated principles from the field of behavioral economics beyond financial incentives, HBIP interventions did not necessarily do so systematically. For example, behavioral science indicates that incentives are more effective when provided immediately after achievement of the targeted behavior (immediacy), in discrete and separate means from regular income (mental accounting), and framed as losses rather than gains (loss aversion). The effectiveness of HBIP incentives may have been undercut by incentive designs that either omitted or counteracted these principles. This issue is compounded by the fact that HBIP incentives can vary significantly: they can be framed as rewards or penalties and provided via reduced premiums, health account funds, gift cards, or vouchers, among other forms.5

These dynamics also highlight the importance of infrastructure investments in promoting the connection between the science and implementation of financial incentives. For example, the HBIP in Michigan provided incentives through reducing premiums, but it did so in the year after individuals met behavioral goals rather than in the same year. Though same-year reductions would increase incentive salience and immediacy, operationalizing this approach would require substantial advancements or overhauls of program infrastructure.

Thus, it is important for policy makers to recognize that investing money in financial incentives is unlikely to increase healthy behaviors by itself. Implementation matters, and the effectiveness of incentives can be maximized through infrastructure that supports high-fidelity translation of insights from the scientific evidence base into implementation programs. Specific requirements can include technology (eg, wearable and phone-based step counters to track physical activity) and program support (eg, integrated approaches or platforms for providing feedback about performance and delivering incentives) infrastructure.4

Second, the study by Huf et al2 emphasizes the need for research that is designed with an explicit view toward implementation. Many successful healthy behavior interventions have been developed and tested within employers and employee benefit programs, which may limit their feasibility and generalizability, and large-scale experiments in other settings have not been uniformly successful. For example, an intervention waiving patients’ medication co-payments did not lead to changes in primary clinical outcomes among patients with prior myocardial infarction.6 Nonetheless, the pragmatic design allowed researchers to produce valuable insight for the private insurer as a primary stakeholder.

Similar insights are needed to guide large-scale implementation of financial incentives for healthy behaviors. For example, Huf and colleagues2 note that incentives tested in clinical trials have been much greater (up to $800) than those in some HBIPs. Given the strain that higher incentive amounts would place on Medicaid budgets, how clinically effective and cost-effective would smaller incentives be for encouraging healthy behaviors? What principles from behavioral science would most inform incentive design and maximize the patient benefit achieved through financial investments? Can incentives with specific implementation considerations in mind (eg, shorter-term incentives, given the high degree of Medicaid beneficiary churning) be feasibly tested? Research designed to answer such questions is sorely needed.

Ultimately, this is an important study that highlights the gap that can exist between interventions tested in research studies and those implemented at scale in real-world programs. Strides to close that gap depend on action from both policy makers (eg, attention to implementation and infrastructure reforms) and researchers (eg, studies designed and conducted to enable pragmatic implementation in specific settings). Together, these efforts can advance our ability to more effectively improve patient healthy behaviors with insights from behavioral science.

Back to top
Article Information

Published: December 28, 2018. doi:10.1001/jamanetworkopen.2018.6173

Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2018 Liao JM et al. JAMA Network Open.

Corresponding Author: Joshua M. Liao, MD, MSc, Department of Medicine, University of Washington, 1959 NE Pacific St, Seattle, WA 98115 (joshliao@uw.edu).

Conflict of Interest Disclosures: Dr Navathe reports grants from Hawaii Medical Service Association and Oscar Health, personal fees from Navvis and Company, Navigant Inc, Lynx Medical, Indegene Inc, Sutherland Global Services, and Agathos Inc, personal fees and equity from NavaHealth, and an honorarium from Elsevier Press outside the submitted work. No other disclosures were reported.

References
1.
Medicaid.gov. About section 1115 demonstrations. https://www.medicaid.gov/medicaid/section-1115-demo/about-1115/index.html. Accessed November 15, 2018.
2.
Huf  SW, Volpp  KG, Asch  DA, Bair  E, Venkataramani  A.  Association of Medicaid Healthy Behavior Incentive Programs with smoking cessation, weight loss, and annual preventive health visits.  JAMA Netw Open. 2018;1(8):e186185. doi:10.1001/jamanetworkopen.2018.6185Google Scholar
3.
Volpp  KG, Troxel  AB, Pauly  MV,  et al.  A randomized, controlled trial of financial incentives for smoking cessation.  N Engl J Med. 2009;360(7):699-709. doi:10.1056/NEJMsa0806819PubMedGoogle ScholarCrossref
4.
Patel  MS, Asch  DA, Rosin  R,  et al.  Framing financial incentives to increase physical activity among overweight and obese adults: a randomized, controlled trial.  Ann Intern Med. 2016;164(6):385-394. doi:10.7326/M15-1635PubMedGoogle ScholarCrossref
5.
Medicaid and CHIP Payment and Access Commission.  The use of healthy behavior incentives in Medicaid. https://www.macpac.gov/publication/the-use-of-healthy-behavior-incentives-in-medicaid/. Published August 2016. Accessed November 15, 2018.
6.
Choudhry  NK, Avorn  J, Glynn  RJ,  et al; Post-Myocardial Infarction Free Rx Event and Economic Evaluation (MI FREEE) Trial.  Full coverage for preventive medications after myocardial infarction.  N Engl J Med. 2011;365(22):2088-2097. doi:10.1056/NEJMsa1107913PubMedGoogle ScholarCrossref
Limit 200 characters
Limit 25 characters
Conflicts of Interest Disclosure

Identify all potential conflicts of interest that might be relevant to your comment.

Conflicts of interest comprise financial interests, activities, and relationships within the past 3 years including but not limited to employment, affiliation, grants or funding, consultancies, honoraria or payment, speaker's bureaus, stock ownership or options, expert testimony, royalties, donation of medical equipment, or patents planned, pending, or issued.

Err on the side of full disclosure.

If you have no conflicts of interest, check "No potential conflicts of interest" in the box below. The information will be posted with your response.

Not all submitted comments are published. Please see our commenting policy for details.

Limit 140 characters
Limit 3600 characters or approximately 600 words
    ×