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. Author manuscript; available in PMC: 2022 Oct 1.
Published in final edited form as: J Subst Abuse Treat. 2021 Apr 8;129:108383. doi: 10.1016/j.jsat.2021.108383

Beliefs related to health care incentives: Comparison of substance use disorder treatment providers, medical treatment providers, and a public sample

Kimberly C Kirby 1,2, Matthew J Dwyer 2, Connor Burrows 2, Dustin A Fife 2, Elena Bresani 1,2, Mary Tabit 1,3, Bethany R Raiff 2
PMCID: PMC8380654  NIHMSID: NIHMS1695080  PMID: 34080551

Abstract

This study surveyed substance use disorder (SUD) treatment providers, medical treatment providers, and a public sample about beliefs regarding health care incentives to explore differences among the groups and across health disorders for which research has demonstrated incentives improve outcomes. Six hundred participants (n = 200/group) completed the Provider Survey of Incentives. The study found between group differences for positive and negative beliefs. The public sample was highest on the positive beliefs subscale (M = 3.81), followed by SUD (M = 3.63) and medical treatment providers (M = 3.48; F(2, 597) = 20.09, p < .001). The medical treatment providers were highest on the negative beliefs subscale (M = 2.91), compared to the public sample (M = 2.77) and SUD treatment providers (M = 2.65; F(2, 597) = 7.521, p < 0.001). Endorsement of incentives to treat medical disorders was similar across the groups, with obesity the most endorsed disorder. In contrast, endorsement of incentives to treat SUDs differed across groups, except for smoking. The SUD treatment providers were almost twice as likely as the public sample (OR = 1.81, 95% CI = 1.27–2.59) and the public sample almost twice as likely as the medical treatment providers (OR = 1.74, 95% CI = 1.24–2.47) to endorse the use of incentives to treat more SUDs. Medical treatment providers were also the least likely to endorse incentives to treat both legal and illicit substance use. These findings suggest that incentive programs have good acceptability among SUD treatment providers and the public, but medical treatment providers are less accepting of incentive programs. This study provides evidence that incentive-based interventions are acceptable to the public and is the first to document specific objections that individuals disseminating incentive interventions will most likely face when introducing them in medical settings.

Keywords: Health care incentives, Financial incentives, Incentives, Contingency management, Social validity

1. Introduction

Strong empirical support exists for the use of health care incentives in treating a wide range of risky health behaviors. Researchers have developed various financial incentive interventions to promote preventive health-related behavior ranging from infancy to advanced age (Fairbrother, Siegel, Friedman, Kory & Butts, 2001; Briesacher, Andrade, Fouayzi & Chan, 2008). Federally run programs have developed conditional cash transfer systems where money is awarded to households when they meet health-related conditions (Ranganathan & Lagarde, 2012), and programs have used financial incentives on a national basis to address opioid use disorder and smoking cessation for pregnant women (Ballard & Radley, 2009; Pilling, Strang, Gerada, & NICE, 2007). However, most of these widely administered programs have been implemented outside of the United States, with the exception of one nationwide incentive program disseminated in substance use disorder (SUD) treatment programs in the Veterans Health Administration (Petry, DePhilippis, Rash, Drapkin, & McKay, 2014).

Decades of empirical research have demonstrated incentive-based interventions to be highly effective in SUD treatment (Dutra et al., 2008; Higgins et al., 1994; Lussier, Heil, Mongeon, Badger, & Higgins, 2006; Peirce et al., 2006; Petry et al., 2005). However, only about 5–33% of SUD treatment providers in America report using incentive-based interventions (Benishek, Kirby, Dugosh, & Padovano, 2010; Kirby, Benishek, Dugosh, & Kerwin, 2006; McGovern, Fox, Xie, & Drake, 2004), except in studies where the sample may have been biased toward empirically supported approaches or because of previous experience with incentive interventions (Kirby et al. 2012; Rash et al., 2012, 2020; Willenbring et al., 2004). Even among these potentially biased samples, utilization rates have been only as high as about 50% (Kirby et al. 2012; Rash et al., 2012). There have been several surveys assessing beliefs among SUD treatment providers (Benishek et al., 2010; Kirby et al., 2006; McGovern, Fox, Xie & Drake, 2004; Ritter & Cameron, 2007; Willenbring et al., 2004) and these suggest that, overall, more than half of SUD treatment providers view incentive interventions favorably, while expressing some concerns and acknowledging barriers to use.

Research on technology transfer suggests that attitudes regarding a treatment can influence how easily and quickly diffusion is accomplished and should not be ignored when encouraging providers to adopt certain treatments (Rogers, 2002; Simpson, 2002). For individuals introducing incentive-based interventions in new settings, knowing the specific objections that providers and patients are likely to express and the prevalence of those objections may assist in introducing the treatment and in making adjustments to address concerns where possible. However, few studies have surveyed beliefs about incentives for SUD treatments outside of mental health treatment provider samples (but see Murphy, Rhodes, & Taxman, 2012 and Raiff, Jarvis, Turturici & Dallery, 2013), so we know little about the acceptance of incentives for SUD treatment among other practitioners and in other settings. Given the movement toward integrated care models, and the need to coordinate care among an interdisciplinary team of providers (Mayo & Wooley, 2016; National Council for Behavioral Health, 2018), understanding the acceptability of incentive interventions among a diverse group of providers and for treatment of diverse conditions is critical. Previous research with SUD treatment providers has suggested that higher levels of education predicted better acceptance of incentive-based interventions (e.g., Kirby et al., 2006). As such, acceptability might be expected to be higher among medical treatment providers than SUD treatment providers, suggesting that disseminating incentive programs in medical settings that offer SUD treatment might be easier than in specialty treatment settings. Also, some of the hesitation professionals have in adopting incentive-based programs may be predicated on the belief that the approach is not well accepted by the public. As such, documenting and understanding the broader social acceptance of this approach, beyond that of medical professionals, are important.

Few studies have examined relative acceptance of incentive interventions across different SUDs and other types of disorders. One exception is research by Promberger, Brown, Ashcroft, and Marteau (2011), who compared the acceptance of incentives for weight loss, smoking cessation, drug addiction, serious mental illness, and physiotherapy after surgery among residents in the United States and United Kingdom. They found that incentive interventions were less acceptable than medication interventions across all five contexts. Also, participants favored rewards for use in serious mental illness, but not for the other conditions, and they were the least favored for drug addiction. In a second study (Promberger, Dolan & Marteau, 2012), incentives for weight loss were preferred relative to incentives for smoking cessation among UK residents. To the best of our knowledge, no comparisons exist among SUD treatment providers, medical treatment providers, and U.S. residents regarding the acceptability of incentive interventions to treat different disorders. As such, whether these findings would be replicated across all three groups is unclear. As new research demonstrating the utility of incentive-based interventions outside of specialty SUD treatment settings and for other medical conditions grows, it would be useful to better understand the level of acceptance and the concerns regarding them among medical treatment providers. Also, a review regarding the use of incentives for health behaviors noted that further research is needed to examine acceptability of incentives across a broader range of stakeholders (Giles et al., 2015; Hoskins, Ulrich, Shinnick, & Buttingheim, 2019). If incentives are more widely accepted for the treatment of some disorders, or by certain types of providers, one strategy might be to work to gain acceptance of the interventions in these areas, or with those providers, before attempting widespread dissemination in areas where they are less accepted.

Finally, some of the resistance to the use of incentives to treat SUDs may be related to their association with illicit activity. Comparing the acceptability of incentive-based interventions for treating SUDs for legal versus illegal substances across medical treatment providers, SUD treatment providers, and a public sample might suggest that this issue is worth further study and that targeted interventions addressing stigma associated with illegal substance is needed.

The current study seeks to explore the acceptability of and concerns regarding the use of incentive interventions for various health behaviors among SUD treatment providers, medical treatment providers, and a public sample consisting of individuals who are neither medical nor behavioral health treatment providers. Our specific purposes were to:

  1. Describe and compare differences in specific beliefs about incentive programs across medical treatment providers, SUD treatment providers, and a public sample.

  2. Compare differences in acceptance of incentive programs across participant groups.

  3. Compare the support of incentives for the treatment of medical disorders versus SUDs across subsamples.

  4. Compare support of incentives for the treatment of legal versus illegal substance use.

2. Method

2.1. Sample

This study recruited participants through CatalystMR Inc., a market research company with an extensive online panel sample made up of consumers, physicians, nurses, patients, and others from over 53 countries. The market research company pre-profiled each panelist on multiple characteristics so that they were able to extract samples to meet our specifications. We requested a national sample of medical treatment providers and of people living in the United States who were neither medical treatment providers nor behavioral health treatment providers. We recruited SUD treatment providers through the market research company’s online panel, but as their sample of SUD treatment providers was limited, we also recruited through the National Association for Alcoholism and Drug Abuse Counselors (NAADAC) list serve. Individuals who clicked the link to the survey read the consent statement then answered screening questions about their profession to determine their sample group (medical health provider, SUD treatment provider, public sample). The study excluded only mental health providers who did not treat substance use from the survey.

The sampling frame consisted of adults living in the United States with internet access through computer or mobile phone. The medical treatment providers included nurses (50%), physicians and surgeons (45%), nurse practitioners and physician assistants (5%). More than half of the sample (56%) identified their area of practice as general medicine (including primary care, internal medicine, and family practice) and the remainder (44%) were specialists. The study did not exclude any specialization, and none was predominant in the sample. Representation of any one specialty ranged from .1% (surgery) to 3.5% (endocrinology, gastroenterology, and obstetrics/gynecology) of the sample. The SUD treatment providers included addictions counselors (32%), social workers (26.5%), program directors/administrators (16%), mental health counselors and therapists (9%), physicians (6.5%), nurses (6%), and others (4%; e.g., recovery specialists, educators) who provided behavioral health services to patients with substance use disorders. The public sample consisted of participants who were neither medical nor behavioral health providers. These individuals were employed in unskilled or semi-skilled jobs (18%; Hollingshead, 1975 job classification categories 2–5), technical or semi-professional positions (23.5%; categories 6–7), mid to senior management or professional positions (11%, categories 8–9), or were retired (20%), homemakers (8%), or unemployed/disabled (14.5%).

Participants voluntarily signed up to complete the survey. Those who we recruited through the NAADAC list serve were compensated with a $25 gift card for their time while those who we recruited by the electronic survey company received varying rates of compensation consistent with the survey company’s agreements with the websites from which participants were recruited. Most received points that could be exchanged for gift cards or goods. All participants were aware of the type and amount of reimbursement they would receive prior to beginning the survey. The study did not link responses to individual identifiers. The Treatment Research Institute Institutional Review Board approved all procedures.

2.2. Measure

We used as our primary measure the Provider Survey of Incentives (PSI; Kirby, Benishek, Dugosh, & Kerwin, 2006). This measure assesses the beliefs of SUD treatment providers regarding incentive programs and contains 32 questions that assess beliefs regarding the use of incentives using a 5-point Likert scale (1 = Strongly disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly agree). For this study, we maintained the original 32-items assessing beliefs, but changed the wording to make it appropriate for a population broader than SUD treatment providers (e.g., client became patient, abstinence became healthy behavior, drug use became unhealthy behavior, and SUD examples were broadened to include other medical conditions). The study added a question to ask about six medical disorders and six SUDs that incentive programs have been used to address.

This study tested the construct validity of the modified PSI using confirmatory factor analysis, where we hypothesized 2 latent factors: 1) Positive Beliefs and 2) Negative Beliefs. This study re-specified the confirmatory factor model according to factor loadings and modification indices. We examined subscale reliability using Cronbach’s alpha. The modified model provided the following: CFI = 0.930, TLI = 0.924, SRMR = 0.049, RMSEA = 0.55, suggesting acceptable model fit. We examined subscale reliability using Cronbach’s alpha and found it to be excellent for the Positive Beliefs (α = 0.94) and the Negative Beliefs (α = 0.92) subscales.

2.3. Data analysis

2.3.1. Demographic characteristics.

The study compared the three participant groups (SUD treatment providers, medical treatment providers, and the public sample) on demographic characteristics to check for potential confounding differences. Because specialized education is required to practice medicine and treat SUDs, and because higher levels of pay are associated with greater education, this study anticipated differences between the groups in highest degree completed and annual income. This study expected medical treatment providers and SUD treatment providers to have more education and higher income than the public sample. The study also expected that medical treatment providers would have more education and higher income than the SUD treatment providers.

2.3.2. Specific beliefs and concerns.

To present descriptive data regarding specific beliefs and concerns about incentive programs, we summarize responses to the survey. The study calculated the proportion of respondents agreeing (Agree or Strongly Agree) with positive and negative beliefs for the total sample and separately for the SUD treatment providers, medical treatment providers, and public sample.

2.3.3. Incentives acceptability by group.

The research team calculated positive and negative subscale scores on the PSI by adding the 5-point Likert scale scores for the items on each subscale separately for the SUD treatment providers, medical treatment providers, and public sample, then dividing by the number of items. We compared subscale scores between groups using a general linear model, in which scale score (Positive Beliefs and Negative Beliefs) was predicted by provider group.

2.3.4. Support of incentives for the treatment of different disorders.

This study calculated and presented descriptively the proportion of the total sample and of the SUD treatment provider, medical treatment provider, and public sample participants endorsing the use of incentives in the treatment of six physical health disorders (asthma, diabetes, hypertension, cardiovascular disease, obesity, and chronic obstructive pulmonary disorder) and six SUDs (alcohol, smoking, prescription opioids, illicit opioids, marijuana, other illicit drugs). To estimate the generality of incentive program endorsement within physical health versus SUD domains, this treated the number of disorders endorsed as ordinal data (with responses varying from discrete values of 0 to 6 within each domain). The study used ordinal logistic regression (OLR) to analyze condition endorsement. OLR produces a single odds ratio (OR) summarizing the effect of the independent variable that applies across all levels of the dependent variable and indicates the expected change in odds ratio for every increase in number of conditions endorsed.

Similarly, within SUDs, the study compared the average percent of participants endorsing incentives to treat SUDs for legal (alcohol and smoking) versus illicit substances (illicit opioids and other illicit drugs). We omitted misuse of prescription opioids due to their ambiguous legal status (prescribed versus nonprescribed use). Similarly, we omitted marijuana because at the time of the survey, the legal status of the substance could differ by state and by reason for use (medical versus nonmedical). Again, we predicted the proportional odds ratios of varying levels of endorsement (0 to 2) as a function of group (i.e., SUD treatment providers, medical treatment providers, or public sample) via OLR.

3. Results

3.1. Sample description

Of the 764 participants who consented to the study, 101 terminated the survey during the demographic section and before answering any PSI questions. Of the 663 participants who began the PSI, 600 completed (91%). Completers consisted of a diverse sample of individuals who indicated that they were either a substance use treatment provider (n = 200), medical treatment provider (n = 200), or did not identify as a behavioral or medical health care provider and we, therefore, assigned them to the public sample (n = 200).

Table 1 presents sample characteristics for the SUD treatment providers, medical treatment providers, and public sample. Respondents were predominantly white, Non-Hispanic, and more likely to be male, especially in the SUD treatment provider and medical treatment provider groups. As expected, the groups differed in terms of education and income. The medical treatment providers were proportionally more likely to have a doctoral degree, the SUD treatment providers to have a master’s degree, and the public sample to have a high school diploma. Similarly, the medical treatment providers had the highest income, followed by the SUD treatment providers and public sample, respectively. Because these factors are functionally related to the grouping variable (SUD treatment providers, medical treatment providers, or public sample), we did not control for them. The study also found differences in race, with the medical treatment providers more likely to be white or Asian, and less likely to be Black/African American. The study conducted a comparison of models with and without race. As the study did not find race to contribute significantly to any of the models, we did not include it as a covariate in the results reported here.

Table 1.

Sample demographics.

Characteristic SUD Treatment Providers % (n) Medical T reatment Providers % (n) Public Sample % (n) p
Gender
 Male 67.0 (134) 64.5 (129) 55.8 (111) 0.06
 Female 33.0 (66) 35.5 (71) 44.2 (88)
Race
 Black/African American 15.9 (31) 2.5 (5) 11.6 (23) <.001
 White 76.4 (149) 86.4 (171) 79.3 (157)
 Asian American 4.6 (9) 9.6 (18) 5.5 (11)
 Other 5.6 (11) 2.5 (5) 6.6 (13)
Ethnicity
 Hispanic 7.0 (14) 6.0 (12) 8.5 (17) 0.62
 Non-Hispanic 93.0 (186) 94.0 (188) 91.5 (183)
Highest Degree Completed
 High School Diploma 7.0 (14) 10.5 (21) 55.0 (110) <.001
 Bachelor’s Degree 19.5 (39) 35.0 (70) 31.0 (62)
 Master’s Degree 61.5 (123) 8.5 (7) 8.5 (17)
 Doctoral Degree 12.0 (24) 46.0 (92) 2.5 (5)
Annual Income ($K)
 < 19 3.0 (6) 1.0 (2) 26.5 (53) <.001
 20 – 39 17.5 (35) 1.5 (3) 26.0 (52)
 40 – 59 25.5 (51) 12.5 (25) 20.0 (40)
 60 – 79 25.0 (50) 19.0 (38) 12.0 (24)
 80 – 99 11.5 (23) 13.5 (27) 7.0 (14)
 > 100 17.5 (35) 52.5 (105) 8.5 (17)
Previous Experience with incentive programs 38.5 (77) 9.0 (18) 21.5 (43) <.001
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To check the representativeness of our samples, the study compared medical treatment providers on gender, race, and ethnicity to a health workforce report (US DHHS, 2017) and found them to be similar to physicians. We compared the SUD treatment providers to national statistics from a national study (Mulvey, Hubbard & Hayashi, 2003) and found that our sample was more likely to be male (67.0% vs 49.5%) and less likely to be white (76.4% vs 84.5%), but was similar in ethnicity. We compared the public sample to the 2010 U.S. Census data (Howden & Meyer, 2011; Humes, Jones, & Ramirez, 2011) and found the samples to be similar in terms of gender and race, but under-represented Hispanic ethnicity (8.5% vs 16.3%), individuals with a high school degree (55.0% vs 72.8%), and individuals with an annual income of more than $100,000 (8.5% vs 20.4%) in our sample. Individuals with a bachelor’s degree (31.0% vs 18.0%) were over-represented. In general, research has reported web-based survey samples to over-represent non-Hispanic and college educated individuals relative to census data (Couper, 2000). Over-representation of >$100,000 annual incomes may also be related to the removal of medical treatment providers from the public sample group.

Overall, only 23% of the participants reported having had experience with incentive programs that targeted a specific behavior, with SUD treatment providers most likely to have experience (38.5%), followed by the public sample (21.5%). Medical treatment providers were substantially less likely to have experience with an incentive program (9.0%).

3.2. Specific beliefs and objections

3.2.1. Positive items.

Table 2 shows the percent of participants agreeing or strongly agreeing with each of the 16 positive PSI items. Items are ordered from the greatest to least proportion of total participants (N=600) agreeing with the statement. A substantial majority of participants (≥70% total) endorsed the first eight items listed; however, endorsement rates fell more quickly for the medical treatment providers compared to SUD treatment providers and the public sample. For the medical treatment providers, only the first three items were endorsed by ≥70% of the participants. These included the belief that many patients would be in favor of receiving rewards for health behavior, that incentives can be useful in building healthy behaviors, and that an advantage of incentive programs is that they focus on what is good in the patient’s behavior. More than two-thirds of the participants endorsed all but the last five items. The item “Overall, I would be in favor of patient incentive programs” was endorsed by 79% of the public sample and 69% of the SUD treatment providers, but only 57% of the medical treatment providers. Proportions of participants in each group who responded negatively to this item followed a consistent pattern with 20% of the medical treatment providers, 15% of the SUD treatment providers, and only 5% of the public sample disagreeing or strongly disagreeing (data not shown).

Table 2.

Percent of participants agreeing or strongly agreeing with 16 positive provider survey of incentives items.

Item Percent Agree or Strongly Agree
No. Positive Beliefs* SUD Treatment Providers (n=200) Medical Treatment Providers (n=200) Public Sample (n=200) Total (N=600)
Q21 Many patients would be in favor of receiving rewards for engaging in healthy behavior 87 80 81 83
Q29 Incentives can be useful in building healthy behaviors (e.g., exercise, healthy eating) 84 73 83 80
Q22 An advantage of incentive programs is that they focus on what is “good” in the patient’s behavior, not what is “wrong” 86 70 76 77
Q3 Incentives are worthwhile because they can get reluctant patients in the door for treatment 70 66 80 75
Q30 Incentives can be useful in reducing unhealthy behaviors (e.g., smoking, drug use, overeating) 77 67 80 74
Q24 Any source of motivation, not just internal motivation, is a good thing for the patient’s treatment 74 67 82 74
Q31 I would be in favor of using incentives to reduce unhealthy behaviors for patients 73 64 79 72
Q19 I would be in favor of using incentives to build healthy behaviors for patients 73 63 78 71
Q20 Incentives are useful if they reward fulfilling a health care goal (e.g., attending appointments, medication compliance) 72 68 77 71
Q9 Incentives are more likely to have positive effects on the patient than to have negative effects 66 65 79 70
Q4 Overall, I would be in favor of patient incentive programs 70 57 79 68
Q17 Incentives for biologically verified health behaviors will help the patient make positive changes 66 62 70 65
Q27 Giving incentives for verified medication compliance would be an acceptable practice 58 48 59 55
Q7 Overall, incentives are good for the patient-provider relationship 52 37 71 53
Q26 Incentives are most useful for short-term purposes 54 52 45 50
Q28 Incentives can be useful whether or not they address the underlying reasons for unhealthy behavior 40 34 49 41
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Note.

*

Wording of items has been truncated. Complete wording is available from first author.

3.2.2. Negative items.

In comparison to the positive beliefs items, rates of agreeing or strongly agreeing with negative PSI items were substantially lower overall. Table 3 shows that a majority of the participants did not endorse any of the items. Less than half of the total sample (N=600) agreed with any one of the items and this pattern was true when examining subgroups, with the exception that 50% of the medical treatment providers agreed with the item that was most endorsed (i.e., not right to give incentives for healthy behaviors when patients are not reaching other goals). More than a third of the participants in any subgroup endorsed only five of the negative items. The most prominent concerns regarding incentive treatments endorsed by more than one third of the participants included participants feeling uncomfortable rewarding a healthy behavior when other goals were not being met, that incentive programs were too labor intensive, and that patients would argue about incentives. One third of the participants overall agreed with the statement that giving tangible incentives on an ongoing basis was not consistent with their idea of acceptable treatment. This proportion was lower for the public sample (25%) and higher for the medical treatment providers (41%).

Table 3.

Percent of participants agreeing or strongly agreeing with 16 negative provider survey of incentives items.

Item Percent Agree or Strongly Agree
No. Negative Beliefs* SUD Treatment Providers (n=200) Medical Treatment Providers (n=200) Public Sample (n=200) Total (N=600)
Q23 It is not right to give incentives for healthy behaviors when patients are not reaching other goals 47 50 48 48
Q25 Incentive programs requiring close tracking of behavior are too labor intensive to be practical 32 51 33 39
Q12 Incentives for attending appointments should not be given if patients still have unhealthy behaviors 34 45 34 38
Q1 If you give incentives to patients who’ve earned them, but not to others, patients will argue 32 32 39 34
Q13 Giving tangible incentives on an ongoing basis is not consistent with my idea of appropriate treatment 33 41 25 33
Q11 Incentives will cause jealousy among patients who don’t get them 31 29 35 32
Q32 If the patient does the healthy behaviors just for incentives, it could have long-term negative effects on treatment 29 28 32 30
Q10 It is not useful to give patients incentives because behavior change will last only as long as the incentives are given 20 38 24 27
Q2 Most patients would sell any incentives they receive for cash, and then use it for unhealthy behaviors 27 21 20 23
Q6 It is not right to give incentives to patients for what they should be doing in the first place 13 30 21 22
Q15 Incentives are objectionable to me because they are a bribe 18 29 18 22
Q18 There are enough rewards in being healthy; incentives are not necessary 15 24 23 21
Q8 Incentives will stop the patient from realizing their internal motivation to engage in healthy behaviors 16 24 22 21
Q14 Overall, incentives have negative effects on the patient-clinician relationship 16 21 16 18
Q16 Incentives are more likely to have negative rather than positive effects on the patient 15 16 12 14
Q5 Many patients will be offended if you offer them rewards for engaging in healthy behaviors 5 11 21 12
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Note.

*

Wording of items has been truncated. Complete wording is available from first author.

3.3. Incentives acceptability by group

Lower acceptance of incentives by medical treatment providers was supported by the subscale analyses. On average, the public sample has the highest mean score on the positive beliefs subscale of the PSI (M = 3.81, SD = 0.50), followed by SUD treatment providers (M = 3.63, SD = 0.55), and last by medical treatment providers (M = 3.48, SD = 0.56). Using a general linear model, the study found a significant difference between the public sample, SUD treatment provider, and medical treatment provider mean positive belief subscale scores (F(2, 597) = 20.09, p < .001). Post-hoc analyses revealed that the public sample’s mean score was significantly higher than that for both the SUD (t(597) = 3.278, p = .001) and medical treatment providers (t (597) = 6.338, p < .001). The SUD treatment providers’ mean score was significantly higher than that of medical treatment providers (t (597) = 3.060, p = .002).

Consistent with their lower endorsement of positive beliefs, the medical treatment providers endorsed negative items in greater proportions compared to the SUD treatment providers and the public sample. On the negative beliefs subscale, the medical treatment providers had the highest mean subscale score (M = 2.91, SD = 0.66), followed by the public sample (M = 2.77, SD = 0.56) and SUD treatment providers (M = 2.65, SD = 0.66), respectively. These differences were statistically significant (F(2, 597) = 7.521, p < 0.001). Post-hoc analyses revealed that negative beliefs were significantly higher for the medical treatment providers than for both the public sample (t(597) = 2.170, p = .03) and SUD treatment providers (t(597) = 3.869, p < .001). The SUD treatment providers and public sample did not differ (t(597) = −1.115, p = .09).

An examination of the subscale scores for individual participants (data not shown) revealed that few of the medical treatment providers had a positive subscale score in the strongly agree range (i.e., 4–5), whereas a substantial number of participants in the public sample and SUD treatment provider groups had scores in this range. Also, none of the participants in the public sample had a positive subscale score in the strongly disagree range (i.e., 1–2). Differences between groups on the negative subscale were less striking, although fewer medical treatment providers had negative subscale scores in the highly disagree range compared to the other two groups.

3.4. Support of incentives for the treatment of different disorders

Figure 1 presents the proportion of participants in each group who endorsed incentives to treat different disorders. Rates of endorsing incentives to treat medical conditions were similar across SUD treatment providers, medical treatment providers, and the public sample, with obesity receiving the highest endorsement rates and asthma and chronic obstructive pulmonary disorder receiving the lowest. In contrast, rates of endorsing incentives to treat SUDs showed divergence between the groups in all areas except smoking, which was the most highly endorsed by all three groups (≥75%). The second highest endorsement was for treating alcohol use disorders; however, less than half of the medical treatment providers endorsed incentives for this purpose, although they were endorsed by about two-thirds of the SUD treatment providers and the public sample. For the remaining substances, a consistent pattern emerged with the highest endorsement among SUD treatment providers, followed by the public sample and medical treatment providers, respectively.

Figure 1.

Figure 1.

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Symbols show the proportion of the sample in each group that endorsed the use of incentives to treat each of 6 medical and 6 substance use disorders. The lines on either side of the symbols represent the top and bottom of the 95% confidence interval for the proportion.

3.4.1. Medical versus SUDs.

To estimate generality of endorsement for using incentives to address medical versus SUDs, the study calculated the number of disorders endorsed in each domain, producing ordinal values from 0 to 6. We present predicted probabilities of endorsement by provider type in Figure 2. Among medical health disorders (top panel), the odds of endorsing incentives for multiple medical disorders was similar across the three groups (SUD treatment providers vs. public sample: OR = 1.22, 95% CI = 0.86–1.74; medical treatment providers vs. public sample: OR = 1.25, 95% CI = 0.90–1.75; and SUD treatment providers vs. medical treatment providers: OR = 1.02, 95% CI = 0.72–1.15).

Figure 2.

Figure 2.

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Each bar shows the predicted probability that each group will endorse the use of incentives to treat 0 (none) through 6 (all) of six medical (top panel) or six substance use (bottom panel) disorders.

Among SUDs, the differences noted between the three groups in Figure 1 emerged again in Figure 2 and were statistically significant. The proportional odds of SUD treatment providers endorsing incentives for more disorders increased by a factor of 1.81 (OR = 1.81, 95% CI = 1.27–2.59) compared to the public sample, while the proportional odds of the public sample were increased by a factor of 1.74 compared to the medical treatment providers (OR = 1.74, 95% CI = 1.24–2.47). The difference was even larger when comparing the SUD treatment providers and medical treatment providers, with odds of the SUD treatment providers endorsing more substance use conditions increasing by a factor of 3.15 (OR = 3.15, 95% CI = 2.21–4.54).

In the current sample, the predicted probability of endorsement across all SUDs was highest for the SUD treatment providers (.43) compared to the medical treatment providers (.20) and the public sample (.30). Note also that divergence between groups occurs primarily at the ends of the scale, such that the differences are greatest in the endorsement of using incentives for 0–1 SUDs and for all SUDs (6). The medical treatment providers were most likely to endorse use of incentives for 0–1 SUDs, while the SUD treatment providers were most likely to endorse use for all six SUDs. Finally, when comparing across the upper and lower panels, it is apparent that probability of endorsing medical disorders versus SUDs appears similar for the medical treatment providers, ranging from .06 to .25 in both panels and between .20 and .25 for endorsing all six medical disorders and all six SUDs. However, both the SUD treatment providers and the public sample showed relatively higher probabilities of endorsing the use of incentives for all six SUDs than all six medical disorders. The difference is largest for the SUD treatment providers whose probability of endorsing the use of incentives for all six medical disorders was .22, while the probability of endorsing use of incentives for all six SUDs was .44.

3.4.2. Legal versus illicit substances.

As in the preceding analysis, the study assessed endorsement of incentives for treatment of legal (i.e., “alcohol” and “smoking”) and illicit substances (i.e., illicit opiates and other illicit drugs) via ordinal values from 0 to 2 in each domain. We present predicted probabilities for legal and illicit substances by provider type in Figure 3. Among legal substances (top panel), the proportional odds of participants increasing endorsement was similar for the SUD treatment providers and the public sample (OR = 1.19, 95% CI = 0.81–1.76). Proportional odds were higher for the public sample compared to the medical treatment providers by a factor of 1.71 (OR = 1.71, 95% CI = 1.18 – 2.49). The SUD treatment providers were about three and a half times more likely to increase endorsement of incentives (OR = 3.49, 95% CI = 2.36–5.20) compared to the medical treatment providers. Among illicit substances (bottom panel), SUD treatment providers were about twice as likely to increase endorsement compared to the public sample (OR = 2.14, 95% CI = 1.4–3.14), and the medical treatment providers (OR = 2.03, 95% CI = 1.40, 2.98). The proportional odds of the public sample increased by a factor of 1.63 (OR = 1.63, 95% CI = 1.11, 2.40) compared to the medical treatment providers. In both the top and bottom panels, the divergence occurs at the ends of the scale (0 and 2), with the medical treatment providers having a higher proportional probability of endorsing use of incentives for no SUDs and lower proportional probability of endorsing both SUDs. For all three groups, the probability of endorsing use of incentives for no SUDs was greater when considering illicit substances compared to legal ones. Both the medical treatment providers and the public sample had a lower probability of endorsing use of incentives for treating both SUDs when illicit substances were considered as opposed to legal substances. Only the SUD treatment providers were about equally likely to endorse use of incentives for both legal and illegal substances (.62 and .58, respectively).

Figure 3.

Figure 3.

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The bars shows the predicted probability that each group will endorse the use of incentives to treat 0 (none), 1, or 2 (all) of two legal (top panel) or two illicit (bottom panel) substance use disorders.

4. Discussion

Consistent with previous research, participants held more positive than negative beliefs regarding the use of incentives to treat disorders, suggesting that overall, opinions of incentive-based interventions are more positive than negative (Kirby et al., 2006, 2012; Rash et al., 2012). The majority of the participants believed that patients would be in favor of receiving rewards (83%) and that incentives could be useful in building healthy behaviors (80%). Just over two-thirds (68%) indicated that they would be in favor of using incentives.

4.1. Prominent beliefs and objections

Some of the most commonly held objections to incentives were similar to those noted in previous studies, such as rewarding some behaviors while other behaviors are unsatisfactory. Knowing that objections regarding interventions that target one behavior are prominent is important when trying to encourage adoption of interventions. Incentives could target multiple behaviors concurrently, but targeting multiple behaviors may also fail to produce improvements (Meredith, Jarvis, Raiff et al., 2014). In some cases, this may be because the targeted behaviors are too difficult for the patient to perform (e.g., targeting abstinence from all drugs; Griffiths et al., 2000), but monitoring multiple behaviors may make the incentive program more difficult for treatment providers to administer consistently. Also, sometimes choosing behavior targets carefully can produce increases in more than one behavior. For example, an incentive program that targets medication compliance may also increase attendance at appointments if providers deliver medication at each appointment. Discussing concerns and suggesting that behaviors be targeted sequentially can help stakeholders to accept interventions that target a limited number of behaviors.

Minimizing the number of behaviors targeted and the complexity of incentive programs are also important because another prominent concern is that incentive programs are too labor intensive. When disseminating incentive programs, stakeholders need to be assured that the incentive programs will minimize their workload and fit into their treatment setting as seamlessly as possible. Programs should continue to find ways to computerize aspects of incentive interventions (e.g., Budney et al., 2015, Dallery et al., 2019; McPherson et al., 2018) to help achieve this goal. It may also be easier to disseminate incentive programs that address behaviors that stakeholders perceive as problematic. An incentive program that eliminates an annoying problem for providers or makes their job easier to perform is more likely to be considered worth the labor required to execute it. Computer and mobile applications can make administration of incentives easier for providers and help in this respect.

Other prominent concerns included patients arguing about incentives or becoming jealous of others who are earning them when they are not. Stakeholders can be reassured that these problems do not occur frequently, and there are strategies for dealing with them if they do arise. Incentive programs have very clear rules that outline conditions for earning rewards and reminding patients of these rules is often sufficient. Doing this is easier when all patients are eligible to earn rewards, which is consistent with stakeholders’ preferences for universal programs, which other studies have reported (Hoskins et al., 2019).

In addition to knowing the most prominent concerns and objections of different stakeholder groups, those disseminating incentive interventions should know that the majority of stakeholders (≥80%) believe that incentive interventions will be acceptable to patients and that overall, more than half of stakeholders are likely to be in favor of using them. However, this study found important differences in acceptability of incentives when comparing the three groups of participants, the different medical conditions, and legal versus illegal treatment targets.

4.2. Acceptability by participant groups

Comparing SUD treatment providers, medical treatment providers, and a public sample revealed both similarities and differences in the acceptability of incentive programs. The groups were similar in that in all three, proportionally more participants endorsed positive than negative beliefs about the use of incentives. A majority of the participants in each group indicated that they believed patients would find incentive-based interventions acceptable and that incentives can be useful in building healthy behaviors.

Relatively lower subscale scores for the medical treatment providers suggest that contrary to expectation, despite greater education (which was a predictor of acceptability in a previous study; Kirby et al., 2006), medical treatment providers were less likely to be in favor of incentive programs than were SUD treatment providers or the public. Although the between-group differences in subscale scores were small, there is some research suggesting that small differences can be important in group decision-making (Brauer, Judd, & Gliner, 2006; Stangor, Jhangiani, & Tarry, 2014), and large between-group differences were noted for several items, including the summary item, “Overall, I would be in favor of patient incentives.” These larger differences indicated that 79% of the public sample would be in favor of patient incentive programs with only 5% objecting, compared to only 57% of the medical treatment providers favoring patient incentives and 20% objecting. The SUD treatment providers fell in the middle of these two groups, with 70% favoring incentives and 15% opposing. These data indicate that incentive programs are likely to be easier to market to the public and to SUD treatment providers than to medical treatment providers. Even so, more than half of the medical treatment providers indicated they would be in favor of patient incentive programs.

The reasons for these differences are unclear, but data from other studies provide some suggestions. For example, research suggests that individuals who have been exposed to incentive programs are more likely to rate them favorably (Kirby et al., 2012; Rash et al., 2020). Medical treatment providers had less experience with incentive interventions (9.0%) compared to SUD treatment providers (38.5%) and therefore rated them less favorably. However, the study found the most favorable ratings to be among the public sample, and while they reported greater experience with incentives (21.5%) than did medical treatment providers, they had less experience than did SUD treatment providers, so this factor alone is unlikely to adequately account for the variability among groups. Alternately, Promberger et al., (2011) found that when equally effective alternative treatments were available, incentive-based ones were less acceptable. Medical treatment providers were most familiar with alternate treatments for health behavior, the public sample was the least familiar, and the SUD treatment providers fell somewhere between the two groups, so this potential explanation is more consistent with the results regarding favorability of incentive programs.

4.3. Medical and substance use disorders

All three groups appeared to express similar support for using incentives to treat medical disorders, with the greatest support in the treatment of obesity and the least in the treatment of asthma and COPD. The reasons for differences in endorsement across medical conditions are not clear. One possibility is that the difference is related to the degree to which the behavior is perceived to be under the control of the patient. Promberger et al. (2011) found this relationship to be significant in participants from the UK, but not from the United States. However, their finding was in the opposite direction to what our data would predict. They found greater support for use of incentives for conditions where the patient was perceived to have less control over the contracting illness (e.g., mental illness versus obesity or smoking). We found better acceptance of incentive interventions for obesity and smoking where patients must make specific behavior changes to improve their condition compared to asthma and COPD where behavior changes may have less impact or occur too late to make much difference in the condition. Future research might explore if these differences are related to beliefs about the degree to which the disorder is modifiable by a patient’s behavior.

Differences among groups emerged with respect to the treatment of SUDs, with results suggesting that SUD treatment providers and the U.S. public may be more likely to support the use of health care incentive interventions to treat SUDs than were medical treatment providers. SUD treatment providers were the most favorable, being almost two times more likely to endorse the use of incentives to treat all SUDs than were members of the public sample. Medical treatment providers’ endorsement lagged significantly behind both of these groups. The only exception was in using incentives to treat smoking. Here the three groups converged, with three quarters or more in each group endorsing the use of incentives for smoking cessation. These findings suggest that overall, the differences among groups in acceptance of incentive interventions are more pronounced when the targeted disorder involves substance use than when other medical disorders are targeted. The wider acceptance of SUD treatment providers could be due to greater exposure to incentive interventions for treating SUDs, to better knowledge of their empirical support in treating SUDs, or to greater appreciation of the motivational problems common across SUDs. These reasons would not apply to the public sample, however, or explain their wider endorsement relative to the medical providers. Additional research should clarify the reasons for the large differences among these three groups in their support of incentives for treating different SUDs.

4.4. Legal versus illegal substance differences

Comparing the predicted probability of endorsing treatment of legal versus illegal substance use showed modest differences among groups with respect to treating problems using legal substances (i.e., smoking and alcohol). SUD treatment providers and individuals from the public sample were slightly more likely to support the use of incentives for both legal substances compared to medical treatment providers. Greater differences emerged regarding illicit substances, with substance use treatment providers twice as likely to support the use of incentives for treating both illicit SUDs compared to the public sample, and with medical treatment providers again the least likely to endorse the use of incentives. More than 50% of the SUD treatment providers endorsed using incentives for treating both illicit substance categories (illicit opioids and other illicit drugs), while less than 50% of the medical treatment providers endorsed their use for the treatment of either illicit SUD. In fact, the only substance for which more than 50% of the medical providers endorsed the use of incentives was smoking. This finding suggests that even within SUDs, there are differences in acceptance of incentives.

4.5. Strengths and limitations

One strength of our study is that it recruited participants nationally, but the sample was modest in size and as such, conclusions regarding beliefs related to health care incentives should be made cautiously. A larger study with a more representative sample might be warranted, especially to better understand how the U.S. public views incentive interventions. The largely positive views that the public sample participants expressed were unexpected and encouraging. However, while favorable beliefs regarding the use of health incentives might correlate with receptivity to using the intervention, programs must still address concerns regarding the time and resources needed to implement the intervention.

Another limitation of this study is that we were not able to explore possible reasons for some of the differences that we found among our three participant groups. For example, why did medical treatment providers have much lower endorsement of incentive interventions than the other two groups and what are the factors that might influence their support of health incentives? Use of incentives might be more acceptable to medical providers if incentive payments were conducted and/or financed by insurance companies. Studies of medical providers in the United States and the UK may be helpful in this respect because the UK has implemented health care incentives for patients for several health conditions. Research has shown that providing more education about the effectiveness of incentive programs for treating specific disorders increases SUD treatment providers’ acceptance of incentive-based interventions and may also positively influence medical treatment providers (e.g., Benishek et al., 2010).

Future research could explore the influence of previous experience with health care incentive interventions. Although previous experience is unlikely by itself to account for differences among groups, previous surveys have found that SUD treatment providers who have exposure to incentive-based interventions view them more positively than those who have no experience with such interventions (e.g., Kirby et al., 2012). We found significant differences in experience with incentives among the three groups, with more SUD treatment providers reporting previous experience with incentive programs, than individuals in the public sample, and with substantially fewer medical treatment providers reporting previous experience relative to the other two groups. Differences in experience may be more important than the grouping by type of provider, but it remains puzzling why few medical treatment providers report previous experience with incentive programs given the higher level of experience in the public sample. One possibility for this finding is that incentive-based wellness programs that employers and health insurers offer provide incentive experience to patients, without involving medical providers.

4.6. Implications for dissemination

Despite these limitations, this study adds to our knowledge about beliefs pertaining to incentive interventions. The high positive subscale scores for the public sample relative to SUD treatment and medical providers suggest that incentive programs may be viewed more positively by the U.S. public than providers recognize. Our study is also the first to ask about the acceptability of incentives for the treatment of a wide variety of substance use and medical disorders across these different populations. These comparisons have practical implications for those attempting to disseminate incentive programs.

First, these comparisons suggest that incentive programs may be more difficult to introduce into medical than SUD treatment settings. Also, support of incentive treatments differs depending on the disorder treated. Incentive programs were highly endorsed by all three groups (60–75%) for the treatment of obesity and smoking cessation and similarly supported by SUD treatment providers for treating all drug and alcohol disorders, suggesting that these areas may be better starting places for disseminating incentive programs. Medical treatment providers were less likely than the other groups to endorse the use of incentives to treat most of the disorders that we examined, particularly SUDs. Although most medical treatment providers do not specialize in SUD, they will encounter individuals with SUD in their practice, so medical providers should understand their patients’ acceptance of these treatments.

The second finding that has important practical implications was that it may be more difficult to get medical providers to support the use of incentives to treat illicit compared to legal SUDs. This difficulty could be particularly unfortunate in the context of the current opioid crisis, where medications for addiction treatment are frequently provided in medical settings and where incentive interventions could be useful in increasing patients’ medication compliance (Moore et al, 2015). Again, although the findings from our medical treatment provider sample may not generalize to providers of medications for addiction treatment, this group’s lack of support may have implications for patients with co-occurring disorders.

Third, although participants endorsed negative beliefs about incentives at lower rates compared to positive beliefs, substantial minorities in all three groups still endorsed them. Individuals disseminating health care incentive programs must be prepared to address these concerns. Some concerns may be more difficult to address than others. For example, about one third of the participants, overall, indicated that providing incentives on an ongoing basis was not consistent with their idea of appropriate treatment. Although difficult to address, knowing what concerns one is likely to encounter and being aware of their prevalence can be helpful in preparing dissemination efforts.

Our paper is not meant to imply that this survey identified and discussed all of the barriers and concerns hindering the dissemination of health care incentive programs. A variety of barriers, including knowledge of empirical support, training, regulatory concerns and cost of incentives, have been identified and discussed elsewhere (e.g., Benishek et al., 2010; Kirby et al., 1999; 2006; McGovern et al., 2004; McPherson et al., 2018; Petry & Simcic, 2002; Rash et al., 2012; Roll et al., 2009; Walker et al., 2010; Willingbring et al., 2004). The current research adds to this knowledge. Understanding the differences in beliefs about incentives, depending on the type of audience and the disorder requiring treatment, is helpful in promoting incentive-based interventions that support positive health behavior change.

Highlights.

  • Incentive programs have good acceptability among substance use disorder (SUD) treatment providers and the public, but medical treatment providers are less accepting.

  • Endorsement of incentives to treat different medical disorders was similar across the groups, with obesity the most endorsed.

  • Endorsement of incentives to treat different SUDs diverged across groups, except for smoking which was highly endorsed by all. SUD treatment providers were almost twice as likely as the public sample to endorse the use of incentives to treat multiple SUDs and three times as likely medical treatment provider group.

  • For all three groups, the probability of not endorsing the use of incentives to treat any SUD was greater when considering illicit substances compared to legal ones.

  • This provides evidence against objections that incentive-based interventions are not acceptable to the public, and also is the first to document specific objections that individuals disseminating incentive interventions will most likely face when introducing them in medical settings.

Acknowledgments

This work was supported by the National Institute on Drug Abuse of the National Institutes of Health (P50 DA027841 & R21 DA036818). Dr. Kirby conceptualized and designed the study, worked with Dr. Tabit to revise the original Provider Survey of Incentives, wrote large sections of the manuscript, and was primarily responsible for completing the manuscript. Mr. Dwyer developed the secondary purposes, provided a partial initial draft of the manuscript, summarized data, developed tables and figures, and reviewed and edited manuscript drafts. Mr. Burrows conducted statistical analyses, generated tables and figures, wrote parts of the results section, and reviewed and edited manuscript drafts. Dr. Fife provided statistical consultation and directed analyses. Ms. Bresani arranged and oversaw execution of the study and edited and wrote parts of the methods section. Dr. Tabit also conducted literature reviews on the use of health incentives for medical conditions and reviewed and edited drafts of the manuscript. Dr. Raiff edited the final manuscript and made substantive edits. All authors read and approved the final draft of the manuscript.

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

The authors have no competing interests to declare.

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