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. 2017 Oct 23;171(12):1176–1183. doi: 10.1001/jamapediatrics.2017.3233

Effect of Financial Incentives on Glucose Monitoring Adherence and Glycemic Control Among Adolescents and Young Adults With Type 1 Diabetes

A Randomized Clinical Trial

Charlene A Wong 1,2,✉, Victoria A Miller 3, Kathryn Murphy 4, Dylan Small 2,5, Carol A Ford 3, Steven M Willi 6, Jordyn Feingold 7, Alexander Morris 8, Yoonhee P Ha 8, Jingsan Zhu 2,8, Wenli Wang 2,8, Mitesh S Patel 2,8,9,10
PMCID: PMC6583649  PMID: 29059263

Key Points

Question

Do daily financial incentives improve adherence to daily glucose monitoring goals and glycemic control among adolescents and young adults with type 1 diabetes during a 3-month intervention?

Findings

In a randomized clinical trial including 90 adolescents and young adults with poorly controlled type 1 diabetes, daily financial incentives improved glucose monitoring in the intervention group (50.0%) vs the control group (18.9%) but did not affect their glycemic control.

Meaning

Financial incentives showed promise for improving glucose monitoring behaviors among adolescents and young adults with type 1 diabetes.

Abstract

Importance

Glycemic control often deteriorates during adolescence and the transition to young adulthood for patients with type 1 diabetes. The inability to manage type 1 diabetes effectively during these years is associated with poor glycemic control and complications from diabetes in adult life.

Objective

To determine the effect of daily financial incentives on glucose monitoring adherence and glycemic control in adolescents and young adults with type 1 diabetes.

Design, Setting, and Participants

The Behavioral Economic Incentives to Improve Glycemic Control Among Adolescents and Young Adults With Type 1 Diabetes (BE IN CONTROL) study was an investigator-blinded, 6-month, 2-arm randomized clinical trial conducted between January 22 and November 2, 2016, with 3-month intervention and follow-up periods. Ninety participants (aged 14-20) with suboptimally controlled type 1 diabetes (hemoglobin A1c [HbA1c] >8.0%) were recruited from the Diabetes Center for Children at the Children’s Hospital of Philadelphia.

Interventions

All participants were given daily blood glucose monitoring goals of 4 or more checks per day with 1 or more level within the goal range (70-180 mg/dL) collected with a wireless glucometer. The 3-month intervention consisted of a $60 monthly incentive in a virtual account, from which $2 was subtracted for every day of nonadherence to the monitoring goals. During a 3-month follow-up period, the intervention was discontinued.

Main Outcomes and Measures

The primary outcome was change in HbA1c levels at 3 months. Secondary outcomes included adherence to glucose monitoring and change in HbA1c levels at 6 months. All analyses were by intention to treat.

Results

Of the 181 participants screened, 90 (52 [57.8%] girls) were randomized to the intervention (n = 45) or control (n = 45) arms. The mean (SD) age was 16.3 (1.9) years. The intervention group had significantly greater adherence to glucose monitoring goals in the incentive period (50.0% vs 18.9%; adjusted difference, 27.2%; 95% CI, 9.5% to 45.0%; P = .003) but not in the follow-up period (15.3% vs 8.7%; adjusted difference, 3.9%; 95% CI, −2.0% to 9.9%; P = .20). The change in HbA1c levels from baseline did not differ significantly between groups at 3 months (adjusted difference, −0.08%; 95% CI, −0.69% to 0.54%; P = .80) or 6 months (adjusted difference, 0.03%; 95% CI, −0.55% to 0.60%; P = .93).

Conclusions and Relevance

Among adolescents and young adults with type 1 diabetes, daily financial incentives improved glucose monitoring adherence during the incentive period but did not significantly improve glycemic control.

Trial Registration

clinicaltrials.gov Identifier: NCT02568501

This randomized clinical trial evaluates the effect of financial incentives on glucose monitoring adherence in adolescents and young adults with type 1 diabetes.

Introduction

Glycemic control in type 1 diabetes often deteriorates during adolescence and the transition to young adulthood with increased risks of acute and long-term complications.1,2 This poor control is attributed to difficulties with adhering to the prescribed medical regimen.2,3,4 These challenges occur in the context of decreasing parental involvement and developing psychosocial maturity.5,6,7 As young people prepare to transition to adult models of care that require independent self-management skills, interventions that empower them to successfully manage chronic disease can improve outcomes.6

Daily glucose monitoring is fundamental to achieving glycemic control in type 1 diabetes since glucose level awareness dictates insulin dose adjustment, eating behaviors, and physical activity. A number of interventions have targeted increased monitoring as a means of achieving better control.3,8,9 However, many interventions in adolescents and young adults that utilized family-related interventions, motivational interviews, text messages, and training in diabetes management skills have demonstrated relatively small effects (eg, mean effect of 0.5% drop in hemoglobin A1c [HbA1c]).3,10,11

Behavioral economics is a field of study that applies economic and psychological principles, such as immediate gratification and loss aversion, to overcome barriers to behavior change. Behavioral economic interventions utilizing financial incentives have been used to increase adherence to chronic disease management regimens in adult populations but have not yet been widely tested in adolescents and young adults.12,13,14,15,16 One feasibility trial with 51 adolescents and young adults with type 1 diabetes established acceptability, feasibility, and preliminary efficacy of increased monitoring with financial incentives that were contingent or noncontingent on meeting glucose monitoring goals.17 Two small studies (17 and 10 participants) without control groups used incentives (eg, $0.10 for each test with a bonus for 4 checks per day) for glucose testing in youth; both studies showed improved glucose monitoring adherence and HbA1c levels.18,19 Financial incentives have been proposed but have infrequently been tested in other adolescent health domains, such as exercise, nutrition, sexual health, and preventive behaviors for sun exposure and smoking.12,16,20,21,22,23,24,25 To our knowledge, no study has tested loss-aversion incentives among adolescents and young adults for self-monitoring behaviors.

In this randomized clinical trial, we sought to determine among adolescents and young adults with type 1 diabetes if daily financial incentives could improve adherence to daily glucose monitoring goals and glycemic control. To monitor adherence and provide incentives in real time, we used wireless glucometer devices, which are readily accepted by people in this age group.26 Such devices allow remote self-monitoring with minimal additional effort, especially when combined with enhanced engagement strategies, such as financial incentives.13,27 Home-based monitoring is particularly attractive for adolescents and young adults who tend to access outpatient care less frequently.28,29

Methods

Study Design and Participants

The Behavioral Economic Incentives to Improve Glycemic Control Among Adolescents and Young Adults With Type 1 Diabetes (BE IN CONTROL) study was a 6-month randomized clinical trial conducted between January 22 and November 2, 2016, with 3-month intervention and follow-up periods. We enrolled 90 participants who were randomly assigned to an incentive intervention or control arm. The protocol is reported in Supplement 1. The Children’s Hospital of Philadelphia and The University of Pennsylvania institutional review boards approved this study. All participants provided electronic informed consent (≥18 years) and/or assent (14-17 years) with parental permission. Participants received $20 for enrolling and $30 if they completed the HbA1c monitoring and surveys at the end of the intervention and follow-up periods.

Participants were aged 14 to 20 years and had received care for type 1 diabetes for at least 1 year at the Children’s Hospital of Philadelphia Diabetes Center for Children. Those with suboptimal glycemic control (defined as most recent HbA1c>8.0% [to convert to percentage of total Hb, multiply by 0.01]) were eligible if they had email access, had a smartphone, and were English speaking.

Participants were recruited between October 27, 2015, and May 6, 2016, by emailing and/or posting letters to all patients aged 14 to 20 years with type 1 diabetes who received care at the Children’s Hospital of Philadelphia (approximately 1160 people), as well as by contacting them by telephone or in person at their diabetes care visits. They were excluded if they were participating in another interventional trial. At enrollment, participants completed a survey assessing sociodemographic information and baseline type 1 diabetes characteristics. They also completed scales on diabetes-related self-efficacy, treatment adherence, and family and friend support.30,31,32,33

Randomization and Masking

Way to Health is an automated technology platform based at the University of Pennsylvania that was used for study enrollment, randomization, surveying, communication via text or email, and intervention delivery and data capture.34 Participants were randomly assigned to the control or incentive group using a block size of 6 and stratified by baseline HbA1c (8.0%-10.0% vs >10.0%). All investigators, statisticians, and data analysts were blinded to group assignments until the end of the study and all analyses. Participants and the study coordinator could not be blinded to arm assignment.

Procedures

All participants were given daily blood glucose monitoring goals of 4 glucose level checks per day (separated by ≥2 hours) with at least 1 reading within the target range (70-180 mg/dL per the American Diabetes Association [to convert to millimoles per liter, multiply by 0.0555]).35 We incentivized a within-range blood glucose reading to encourage monitoring and appropriate responses to high glucose level readings.

Each participant was given a glucometer (iHealth Smart Wireless Gluco-Monitoring System; iHealth) and test strips for the duration of the study. This glucometer syncs with a smartphone application via Bluetooth. Participants were enrolled only after they synced their first glucose level reading.

We designed the daily financial incentives as loss framed based on prior work on the motivating potential of loss aversion.15,36,37,38,39,40 Participants randomized to the intervention arm were credited $60 in a virtual account at the beginning of each month during the 3-month (13-week) incentive period; they lost $2 for each day of nonadherence to the glucose monitoring goals. Intervention arm participants received daily monitoring and incentive feedback during the incentive period by email or text message (per their preference). For example, “You met your glucose monitoring goals yesterday. Keep it up! You have $60 remaining in your account,” or “Sorry, you did not meet your glucose monitoring goal yesterday (at least 4 checks with 1 in target range). You lost $2 from your account. Remaining balance = $58.” Participants in the intervention arm received their remaining virtual account balance at the end of each incentive month on a reloadable debit card. The incentives were discontinued during the follow-up period.

Participants obtained an HbA1c level within 3 weeks of the end of the intervention (at 3 months) and follow-up (at 6 months) periods, most often during their usual diabetes clinic visits. Online surveys were administered at the end of the intervention and follow-up periods.

In addition, a sample of 20 intervention arm participants were interviewed via telephone after completing the study. Semistructured interviews included questions on financial incentives, connected glucometers, and study feedback. Interviews were recorded and transcribed. Responses to the semistructured interview questions were analyzed using thematic content analysis by 2 of us (C.A.W. and A.M.) independently.41

Outcomes

The primary outcome measure was the change in HbA1c levels at 3 months compared with baseline. Secondary outcome measures included adherence to daily glucose monitoring goals during the incentive and follow-up periods and change in HbA1c levels at 6 months vs baseline. Adherence to daily glucose monitoring goals was measured as the mean proportion of participant-days achieving the daily blood glucose monitoring goals. Themes from the interviews were identified.

Statistical Analysis

A priori sample size calculations were based on a clinically relevant HbA1c difference of 1.0 between the intervention and control groups. Assuming an HbA1c SD of 1.5, power of 0.80, 2-sided significance of P = .05, and 20% dropout rate, we estimated a sample of 90 participants (45 per arm).

For each participant on each day of the study (participant-day level), we obtained their glucose level readings. Data could be missing for any day if a participant did not use the study glucometer, although patients had the opportunity to manually enter readings from other glucometers (eg, at school). Manually entered glucose level readings (18% of all study readings) were tracked, and no suspicious patterns for cheating were detected. We dichotomized the data at the participant-day level to create a binary variable indicating that the participant achieved the daily blood glucose monitoring goals or did not. Using this binary variable, we estimated the mean proportion of participant-days achieving the goal for the group of participants in each study group during the intervention and follow-up periods and for each week during the study. All randomly assigned participants were included in the intention-to-treat analysis.

Mean change in HbA1c levels from baseline was calculated by arm at 3 and 6 months in separate unadjusted and adjusted models. In the adjusted model, we fit a prespecified generalized linear regression for the change in HbA1c level, controlling for the same baseline type 1 diabetes and demographic characteristics listed above plus calendar month fixed effects and interval from baseline to 3- and 6-month HbA1c levels (eTable 1 in Supplement 2). We used multiple imputation to generate values for participants missing 3-month (n = 5) and 6-month (n = 11) HbA1c level measurements with the following predictors in 5 imputations: study arm, baseline HbA1c level, 3- or 6-month HbA1c level, demographics (same as in adjusted analyses), baseline type 1 diabetes measures/scales (HbA1c level, insulin regimen, number of daily glucose checks, complications, hypoglycemic event frequency, self-efficacy scale, adherence to diabetes regimen scale, and diabetes support scale), and calendar month. Results were combined using the Rubin standard rules.42 The imputed analyses were qualitatively similar to the nonimputed analyses (eTable 2 in Supplement 2). The correlation between adherence quartiles and change in HbA1c levels was evaluated by Kruskal-Wallis tests by study arm.

We estimated the unadjusted proportion of participant-days adherent to the blood glucose monitoring goals for each arm and the difference between the means for the 2 arms for the incentive period, the follow-up period, and for each week during the study.

In adjusted analyses, the prespecified main generalized linear model for the differences between arms in adherence to blood glucose monitoring goals controlled for baseline diabetes (baseline HbA1c level, insulin regimen) and demographic (sex, age, race/ethnicity, living situation, insurance coverage, and household income) characteristics and calendar month fixed effects. Bootstrap methodology, which resampled participants within each arm 150 times, was used to construct the 95% CIs for the probability of achieving the goals.43

All statistical analyses were performed using R, version 3.2.3 (R Foundation). All hypothesis tests were 2-sided, and a significance level of P = .05 was used.

Results

We enrolled 90 participants (Figure 1). Among the 91 youths assessed for eligibility but not randomized, age and baseline HbA1c levels were available for 55 and 76 participants, respectively. The mean age of nonenrolled (16.5 years) and enrolled (16.3 years) participants were similar, and the mean HbA1c level of nonenrolled participants was lower (8.63%), in part because HbA1c ≤8.0% was the most common inclusion criteria not met.

Figure 1. Trial Profile.

Figure 1.

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All participants received smartphone-connected, wireless glucometers. The control arm received no further intervention. The intervention arm received daily financial incentives for the 3-month incentive period. These incentives were removed in the 3-month follow-up period.

The majority of participants identified as white non-Hispanic and were full-time students, covered by private insurance, and living with family (Table 1). The mean baseline HbA1c level was 9.88% in the control arm and 9.84% in the intervention arm.

Table 1. Baseline Participant Characteristicsa.

Characteristic Control
(n = 45)		 Intervention
(n = 45)
Demographics
Female, No. (%) 26 (58) 26 (58)
Age, mean (SD), y 16.5 (1.9) 16.0 (1.8)
Race/ethnicity, No. (%)
White non-Hispanic 32 (71) 32 (71)
Black non-Hispanic 7 (16) 3 (7)
Hispanic 5 (11) 6 (13)
Other non-Hispanic 1 (2) 4 (9)
Student status, No. (%)
Full time 42 (93) 44 (98)
Not a student 3 (7) 1 (2)
Employment status, No. (%)
Full time 2 (4) 0
Part time 11 (24) 16 (36)
Unemployed 32 (71) 29 (64)
Living situation, No. (%)
With parents or family 38 (84) 37 (82)
Independent/roommates 7 (16) 8 (18)
Insurance coverage
Private 33 (73) 31 (69)
Public 12 (27) 14 (31)
Estimated household income, $
<40 000 7 (16) 5 (11)
40 000-70 000 15 (33) 12 (27)
>70 000-100 000 20 (44) 18 (40)
>100 000 3 (7) 9 (20)
Baseline Diabetes Characteristics
Baseline HbA1c, mean (SD) 9.88 (1.7) 9.84 (1.6)
8%-10%, No. (%) 29 (64) 29 (64)
>10%, No. (%) 16 (36) 16 (36)
Age at type 1 diabetes diagnosis, mean (SD) 9.6 (4) 8.4 (4)
Insulin regimen, No. (%)
Injectable 19 (42) 18 (40)
Pump 26 (58) 27 (60)
Self-reported number of daily glucose checks, mean (SD) 3.91 (1.0) 3.96 (1.1)
Self-reported complications, No. (%)
DKA in last 12 mo 4 (9) 3 (7)
Self-reported hypoglycemic event frequency, No. (%)
≥7/wk 1 (2) 1 (2)
5-6/wk 2 (4) 2 (4)
3-4/wk 7 (16) 10 (22)
1-2/wk 15 (33) 14 (31)
0/wk 20 (44) 18 (40)
Baseline Diabetes Scalesb
Diabetes self-efficacy 10-point scale, mean (SD) 7.28 (1.8) 7.46 (1.8)
Adherence to diabetes regimen 5-point scale, mean (SD) 3.84 (0.7) 3.97 (0.8)
Diabetes support 15-point scale, mean (SD)
Family 7.21 (3.7) 6.93 (3.4)
Friends 4.29 (2.8) 4.71 (3.0)
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Abbreviations: DKA, diabetic ketoacidosis; HbA1c, hemoglobin A1c.

a

No statistically significant differences were found between control and intervention arm demographics or baseline diabetes characteristics.

b

A higher score indicates higher self-efficacy (diabetes self-efficacy scale), adherence (adherence to diabetes regimen scale), and support (diabetes support scale).

Glucose Monitoring Goals

The proportion of participant-days achieving the glucose monitoring goals during the 3-month incentive period was 18.9% in the control group vs 50.0% in the intervention group (adjusted difference, 27.2%; 95% CI, 9.5% to 45.0%; P = .003) (Table 2). Adherence to glucose monitoring goals decreased in both groups during the follow-up period (Figure 2). Rates of adherence were 8.7% and 15.3% in the follow-up period for the control and intervention groups, respectively (adjusted difference, 3.9%; 95% CI, −2.0% to 9.9%; P = .20) (Table 2). The percentage of days without any glucometer readings during the incentive period was 46.3% and 25.3% for the control and intervention arms, respectively, compared with 75.0% in the control arm and 65.8% in the intervention arm in the follow-up period. Most nonadherent days in both groups (>85%) were due to an insufficient number of glucose level checks; 10% to 15% of the days were nonadherent because none of at least 4 checks was within the goal range.

Table 2. Unadjusted and Adjusted Outcomes for Intervention and Follow-up Periods.

Characteristic Control Intervention Unadjusted Difference (95% CI) P Value Adjusted Difference (95% CI)a P Value
Incentive Period (mo 1-3)
Proportion of participant-days achieving glucose monitoring goals, mean (SD) 18.9
(23.7)		 50.0 (30.4) 31.1 (19.68 to 42.54) <.001 27.2 (9.5 to 45.0) .003
HbA1c change from baseline, mean (95% CI)b −0.24
(−0.66 to 0.17)		 −0.56 (−0.97 to −0.14) −0.31 (−0.91 to 0.28) .30 −0.08 (−0.69 to 0.54) .80
Follow-up Period (mo 4-6)
Proportion of participant-days achieving glucose monitoring goals, mean (SD) 8.7
(16.4)		 15.3 (19.3) 6.6 (−0.88 to 14.11) .08 3.9 (−2.0 to 9.9) .20
HbA1c change from baseline, mean (95% CI)b −0.17
(−0.51 to 0.17)		 −0.43 (−0.89 to 0.03) −0.26 (−0.82 to 0.30) .37 0.03 (−0.55 to 0.60) .93
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Abbreviation: HbA1c, hemoglobin A1c.

a

Adjusted difference uses data from the multivariable model with the following covariates: baseline HbA1c, insulin regimen, sex, age, race/ethnicity, living situation, insurance coverage, household income, and calendar month fixed effects; HbA1c interval from study start to 3 or 6 months was included for HbA1c only.

b

HbA1c change uses imputed data.

Figure 2. Proportion of Participant-days Adherent to Daily Glucose Monitoring Goals.

Figure 2.

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The solid vertical line represents the end of the incentive period and start of the follow-up period.

Change in HbA1c Levels

The HbA1c level decreased from 9.88% to 9.44% in the control group and from 9.84% to 9.27% for the intervention group at 3 months (eFigure 1 in Supplement 2). The change in the HbA1c level was not statistically significant when comparing the intervention and control groups from baseline to 3 months (adjusted difference, −0.08%; 95% CI, −0.69% to 0.54%; P = .80) or 6 months (adjusted difference, 0.03%; 95% CI, −0.55% to 0.60%; P = .93) (Table 2). We also found no significant correlation between quartiles of adherence to glucose monitoring and change in HbA1c levels (eFigure 2 in Supplement 2).

Participant Perspectives

Participants in the intervention group had positive feedback on the feasibility of daily financial incentives and mixed perspectives on the ideal incentive amount and structure (Table 3). Those who favored loss aversion incentives were motivated by the loss of money that they believed was already theirs and the cumulative money lost for repeated nonadherent days. While some believed gain incentives (ie, winning money for meeting the daily monitoring goals) would have been better, others proposed alternative incentive structures, such as bonuses (“If you had a week of not losing any money, maybe get an extra couple bucks just for a reward.”) or what another called “a multiplier effect” (“The first day you would lose $2.00 and the second day you could lose $4.00. So, if you continue to miss, it would rise at an exponential level.”). Others mentioned that the extrinsic financial rewards helped them to realize the intrinsic value of taking care of their type 1 diabetes.

Table 3. Intervention Participant Quotes.

Theme Illustrative Quotes
Financial Incentives
Gain vs loss framing of incentives “I don’t like to lose anything…something I would consider almost a possession. I already have $60.00 and then that’s the equivalent of having three $20.00 bills and every time you miss, somebody comes over and says ‘ha,’ you owe me $2.00.”
“I think [being able to win money] would be much better, because it was kind of stressful seeing all the money go away… it's probably easier to keep track of too, just adding the $2.00.”
Cumulative effect of loss incentives “If I had a bad day, I didn’t lose too much. But if I had a really bad week, then I would lose a lot of money, and it was really just when things started stacking up. Then that’s when I kind of realized that I need to get it together and start checking my blood sugar more or taking care of my diabetes better.”
Personal responsibility “The money gave some encouragement to test myself, but after a little, it was more about testing myself for me and not the money.”
Wireless, Smartphone-Connected Glucometers
Tracking and visualizing trends “The biggest thing that's helpful is that [the glucometer] translates data onto my phone, which lets me visually see trends.”
“I don’t really go back and look through my [old] meter to look at my numbers. So if you were to ask me, I wouldn’t know. But because I had the meter that hooked up to my phone, I had a list of all my numbers which was helpful.”
Ubiquitous phone use “I was just always on my phone and I saw the app, it would kind of remind myself oh, maybe I should check my blood sugar now even though I wouldn’t typically be thinking of something like that.”
Ability to easily share glucose data “I liked that I could connect my glucometer to other phones so my mother didn't have to call and ask me. She got updates sent to her phone, and it was up-to-date technology.”
Correlation Between Adherence to Glucose Monitoring and Change in HbA1c
Believed increased glucose monitoring led to improved glycemic control Participant with 2.2% HbA1c drop after incentive period: “I think that mostly it was the fact that I was checking my glucose more than I used to that really helped.”
Reasons for imperfect correlation between glucose monitoring and HbA1c “…because a lot of the times, I can just test my sugar and not do my insulin because it’s in another room or I’m busy doing something.”
Participant with 0.8% HbA1c increase after incentive period: “I started [the study] during the second semester of school last year…school kinda makes my blood sugars go all over the place, and there’s just a lot of stressfulness with my semester last year.”
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Abbreviation: HbA1c, hemoglobin A1c.

When asked about what study components were helpful, the most common response from both arms (36% of participants) was the connected glucometers and smartphone application. Participants liked tracking, interpreting, and sharing glucose readings on the application (Table 3).

Participants noted that glucose monitoring incentives did not always motivate other behaviors necessary for improved glycemic control, such as appropriate responses to high glucose level readings or accurate carbohydrate counting. Other HbA1c-relevant issues that participants identified were insulin regimen changes and life stressors (eg, busy school semester).

Discussion

In our study of adolescents and young adults with type 1 diabetes, financial incentives showed promise for improving diabetes self-monitoring behaviors. Daily financial incentives were effective in increasing adherence to blood glucose monitoring goals during the incentive period, but this adherence did not lead to an improvement in glycemic control. Those exposed to the daily incentives were more than twice as likely to meet their daily blood glucose monitoring goals. These improvements are noteworthy given that eligibility was limited to youth with poor control, who have historically been difficult to engage in treatment.44

This is one of the first studies to demonstrate that financial incentives for adolescents and young adults can motivate behavior change. Our participants identified the loss aversion financial incentives, particularly the cumulative losses, as important in motivating their increased daily blood glucose monitoring. While we based the loss aversion structure and amount of $2 per day on prior work in adults, further research is needed to determine how financial incentives might best be tailored to young people.15,27,36,37,38,39 For example, other financial incentive structures, such as those suggested by participants (eg, multiplier effect), may be more effective in sustaining the effect in the incentive and follow-up periods.

Unique opportunities exist for implementing financial incentives in youth, who are often financially dependent on others. For example, parents could allow a desired privilege (eg, later curfew) or purchase (eg, cell phone data plan) contingent on meeting type 1 diabetes care goals. Financial incentives may also be effective for behaviors critical for other chronic health conditions in youth, such as medication adherence in those who have received transplants or have asthma.

Increased daily blood glucose monitoring with the requirement that at least 1 level per day be within the target range did not translate to significant decreases in HbA1c levels. These monitoring goals may not have been sufficiently stringent to achieve an effect on HbA1c. In general, achieving substantial improvements in glycemic control has been challenging across multicomponent interventions promoting adherence in individuals with type 1 diabetes.10,11

In contrast to prior studies, we did not identify a correlation between blood glucose monitoring frequency and glycemic control.3,8,9 Participants identified several factors that they believed had a greater effect on HbA1c than adherence to glucose monitoring (eg, inadequate responses to high glucose levels, schedule predictability). The multifactorial influences on glycemic control suggest that future studies should test incentivizing both the process (eg, glucose monitoring with more readings within the goal range) and the outcome (eg, HbA1c level improvement). Adding social incentives (eg, social networks, relative social ranking) to the intervention could further improve outcomes; diabetes adherence promotion interventions that targeted behavioral (eg, blood glucose monitoring) and social processes were more potent in a meta-analysis.10

In our study, the increased level of blood glucose monitoring more rapidly declined after the incentives were removed. While longer-term habit formation is the ultimate goal, preventing serious health deterioration from chronic disease would be a valuable intermediate accomplishment for adolescents and young adults, who are in a developmentally critical transition period.2,45

Although this study did not test the effect of smartphone-connected glucometers, participants in both arms were generally positive about the devices. These glucometers with automated data entry and aggregation on an application may contribute to empowering and educating participants. Given ubiquitous smartphone use, connected glucometers could make self-management more convenient and engaging for young people with type 1 diabetes.26

Limitations

Our findings should be viewed in light of several limitations. The small sample size from a single site may limit generalizability and subgroup analyses, for example, by race/ethnicity or socioeconomic status.46 Participants were also required to have a smartphone, although more than three-quarters of adolescents and young adults have access to a smartphone.47 The differences in adherence to daily glucose monitoring goals may be explained in part by missing glucose level readings if participants used other glucometers. However, our qualitative data suggest that participants in both arms valued the study glucometer’s features and could manually enter glucose level readings if needed. Finally, the intervention included email or text notifications. Given the daily loss-framed incentive design, it was not possible to disentangle the effects of incentives and messaging. Nonetheless, many participants stated that the financial incentives, in and of themselves, were motivating.

Conclusions

The inability to manage type 1 diabetes effectively during the adolescent and young adult years is associated with poor glycemic control and complications in adult life.48 Identifying interventions that empower young people to manage their disease effectively is crucial. Financial incentives and smartphone-connected glucometers proved to be promising tools that deserve further exploration in adolescents and young adults with type 1 diabetes.

Supplement 1.

Protocol

Click here for additional data file. (223.7KB, pdf)
Supplement 2.

eTable 1. Study HbA1c Intervals in Weeks

eTable 2. Adjusted Difference in HbA1c for With and Without Imputed Data

eFigure 1. HbA1c by Arm at Baseline, 3 Months and 6 Months

eFigure 2. Change in HbA1c at 3 Months by Adherence to Daily Glucose Monitoring Goals

Click here for additional data file. (86.7KB, pdf)
Journal Club Slides
Click here for additional data file. (402.4KB, pptx)

References

  • 1.Miller KM, Foster NC, Beck RW, et al. ; T1D Exchange Clinic Network . Current state of type 1 diabetes treatment in the US: updated data from the T1D Exchange Clinic registry. Diabetes Care. 2015;38(6):971-978. [DOI] [PubMed] [Google Scholar]
  • 2.Borus JS, Laffel L. Adherence challenges in the management of type 1 diabetes in adolescents: prevention and intervention. Curr Opin Pediatr. 2010;22(4):405-411. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Hood KK, Peterson CM, Rohan JM, Drotar D. Association between adherence and glycemic control in pediatric type 1 diabetes: a meta-analysis. Pediatrics. 2009;124(6):e1171-e1179. [DOI] [PubMed] [Google Scholar]
  • 4.Standards of medical care in diabetes–2015: summary of revisions. Diabetes Care. 2015;38(suppl):S4. [DOI] [PubMed] [Google Scholar]
  • 5.Hilliard ME, Rohan JM, Rausch JR, Delamater A, Pendley JS, Drotar D. Patterns and predictors of paternal involvement in early adolescents’ type 1 diabetes management over 3 years. J Pediatr Psychol. 2014;39(1):74-83. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Sheehan AM, While AE, Coyne I. The experiences and impact of transition from child to adult healthcare services for young people with type 1 diabetes: a systematic review. Diabet Med. 2015;32(4):440-458. [DOI] [PubMed] [Google Scholar]
  • 7.King PS, Berg CA, Butner J, et al. . Longitudinal trajectories of metabolic control across adolescence: associations with parental involvement, adolescents’ psychosocial maturity, and health care utilization. J Adolesc Health. 2012;50(5):491-496. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Miller KM, Beck RW, Bergenstal RM, et al. ; T1D Exchange Clinic Network . Evidence of a strong association between frequency of self-monitoring of blood glucose and hemoglobin A1c levels in T1D exchange clinic registry participants. Diabetes Care. 2013;36(7):2009-2014. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Schütt M, Kern W, Krause U, et al. ; DPV Initiative . Is the frequency of self-monitoring of blood glucose related to long-term metabolic control? multicenter analysis including 24,500 patients from 191 centers in Germany and Austria. Exp Clin Endocrinol Diabetes. 2006;114(7):384-388. [DOI] [PubMed] [Google Scholar]
  • 10.Hood KK, Rohan JM, Peterson CM, Drotar D. Interventions with adherence-promoting components in pediatric type 1 diabetes: meta-analysis of their impact on glycemic control. Diabetes Care. 2010;33(7):1658-1664. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Hampson SE, Skinner TC, Hart J, et al. . Behavioral interventions for adolescents with type 1 diabetes: how effective are they? Diabetes Care. 2000;23(9):1416-1422. [DOI] [PubMed] [Google Scholar]
  • 12.Stevens J. Behavioral economics as a promising framework for promoting treatment adherence to pediatric regimens. J Pediatr Psychol. 2014;39(10):1097-1103. [DOI] [PubMed] [Google Scholar]
  • 13.Long JA, Jahnle EC, Richardson DM, Loewenstein G, Volpp KG. Peer mentoring and financial incentives to improve glucose control in African American veterans: a randomized trial. Ann Intern Med. 2012;156(6):416-424. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Kimmel SE, Troxel AB, Loewenstein G, et al. . Randomized trial of lottery-based incentives to improve warfarin adherence. Am Heart J. 2012;164(2):268-274. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.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] [PMC free article] [PubMed] [Google Scholar]
  • 16.Pope L, Harvey-Berino J. Burn and earn: a randomized controlled trial incentivizing exercise during fall semester for college first-year students. Prev Med. 2013;56(3-4):197-201. [DOI] [PubMed] [Google Scholar]
  • 17.Raiff BR, Barrry VB, Ridenour TA, Jitnarin N. Internet-based incentives increase blood glucose testing with a non-adherent, diverse sample of teens with type 1 diabetes mellitus: a randomized controlled trial. Transl Behav Med. 2016;6(2):179-188. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Stanger C, Ryan SR, Delhey LM, et al. . A multicomponent motivational intervention to improve adherence among adolescents with poorly controlled type 1 diabetes: a pilot study. J Pediatr Psychol. 2013;38(6):629-637. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Petry NM, Cengiz E, Wagner JA, Weyman K, Tichy E, Tamborlane WV. Testing for rewards: a pilot study to improve type 1 diabetes management in adolescents. Diabetes Care. 2015;38(10):1952-1954. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.García-Romero MT, Geller AC, Kawachi I. Using behavioral economics to promote healthy behavior toward sun exposure in adolescents and young adults. Prev Med. 2015;81:184-188. [DOI] [PubMed] [Google Scholar]
  • 21.Christian D, Todd C, Hill R, et al. . Active children through incentive vouchers—evaluation (ACTIVE): a mixed-method feasibility study. BMC Public Health. 2016;16:890. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Johnston V, Liberato S, Thomas D. Incentives for preventing smoking in children and adolescents. Cochrane Database Syst Rev. 2012;10:CD008645. [DOI] [PubMed] [Google Scholar]
  • 23.Minnis AM, vanDommelen-Gonzalez E, Luecke E, Dow W, Bautista-Arredondo S, Padian NS. Yo Puedo—a conditional cash transfer and life skills intervention to promote adolescent sexual health: results of a randomized feasibility study in San Francisco. J Adolesc Health. 2014;55(1):85-92. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Dolan P, Rudisill C. The effect of financial incentives on chlamydia testing rates: evidence from a randomized experiment. Soc Sci Med. 2014;105:140-148. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Loewenstein G, Price J, Volpp K. Habit formation in children: evidence from incentives for healthy eating. J Health Econ. 2016;45:47-54. [DOI] [PubMed] [Google Scholar]
  • 26.Center on Media and Human Development Teens, Health, and Technology: A National Survey. Chicago, IL: Northwestern University; 2015. [Google Scholar]
  • 27.Sen AP, Sewell TB, Riley EB, et al. . Financial incentives for home-based health monitoring: a randomized controlled trial. J Gen Intern Med. 2014;29(5):770-777. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Markowitz JT, Volkening LK, Laffel LM. Care utilization in a pediatric diabetes clinic: cancellations, parental attendance, and mental health appointments. J Pediatr. 2014;164(6):1384-1389. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Harris SK, Aalsma MC, Weitzman ER, et al. . Research on clinical preventive services for adolescents and young adults: where are we and where do we need to go? J Adolesc Health. 2017;60(3):249-260. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Iannotti RJ, Schneider S, Nansel TR, et al. . Self-efficacy, outcome expectations, and diabetes self-management in adolescents with type 1 diabetes. J Dev Behav Pediatr. 2006;27(2):98-105. [DOI] [PubMed] [Google Scholar]
  • 31.La Greca AM, Swales T, Klemp S, Madigan S. Adolescents with diabetes: gender differences in psychosocial functioning and glycemic control. Child Health Care. 1995;24:61-78. [Google Scholar]
  • 32.La Greca AM, Bearman KJ. The Diabetes Social Support Questionnaire–Family version: evaluating adolescents’ diabetes-specific support from family members. J Pediatr Psychol. 2002;27(8):665-676. [DOI] [PubMed] [Google Scholar]
  • 33.Bearman KJ, La Greca AM. Assessing friend support of adolescents’ diabetes care: the Diabetes Social Support Questionnaire–Friends version. J Pediatr Psychol. 2002;27(5):417-428. [DOI] [PubMed] [Google Scholar]
  • 34.Asch DA, Volpp KG. On the way to health. LDI Issue Brief. 2012;17(9):1-4. [PubMed] [Google Scholar]
  • 35.American Diabetes Association Standards of medical care in diabetes–2016: abridged for primary care providers. Clin Diabetes. 2016;34(1):3-21. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Kahneman D, Tversky A. Choices, Values, and Frames Cambridge, England: Cambridge University Press; 2000. [Google Scholar]
  • 37.Fryer RG Jr, Levitt SD, List J, Sadoff S Enhancing the efficacy of teacher incentives through loss aversion: a field experiment. NBER Working Paper No. 18237 2012. http://www.nber.org/papers/w18237. Published July 2012. Accessed August 26, 2017.
  • 38.Zeelenberg M, Pieters R. Consequences of regret aversion in real life: the case of the Dutch postcode lottery. Organ Behav Hum Decis Process. 2004;93:155-168. doi: 10.1016/j.obhdp.2003.10.001 [DOI] [Google Scholar]
  • 39.Camerer C. Three cheers—psychological, theoretical, empirical—for loss aversion. J Market Res. 2005;42(2):129-133. [Google Scholar]
  • 40.Halpern SD, French B, Small DS, et al. . Randomized trial of four financial-incentive programs for smoking cessation. N Engl J Med. 2015;372(22):2108-2117. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Corbin JM, Strauss AL. Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory. 3rd ed Los Angeles: Sage Publications, Inc; 2008. [Google Scholar]
  • 42.Rubin DB. Multiple Imputation for Nonresponse in Surveys. New York: Wiley; 1987. [Google Scholar]
  • 43.Efron B, Tibshirani R. An introduction to the Bootstrap. New York: Chapman & Hall; 1993. [Google Scholar]
  • 44.Ellis DA, Frey MA, Naar-King S, Templin T, Cunningham P, Cakan N. Use of multisystemic therapy to improve regimen adherence among adolescents with type 1 diabetes in chronic poor metabolic control: a randomized controlled trial. Diabetes Care. 2005;28(7):1604-1610. [DOI] [PubMed] [Google Scholar]
  • 45.Rosen DS, Blum RW, Britto M, Sawyer SM, Siegel DM; Society for Adolescent Medicine . Transition to adult health care for adolescents and young adults with chronic conditions: position paper of the Society for Adolescent Medicine. J Adolesc Health. 2003;33(4):309-311. [DOI] [PubMed] [Google Scholar]
  • 46.Willi SM, Miller KM, DiMeglio LA, et al. ; T1D Exchange Clinic Network . Racial-ethnic disparities in management and outcomes among children with type 1 diabetes. Pediatrics. 2015;135(3):424-434. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Lenhart A, Duggan M, Perrin A, Stepler R, Rainie L, Parker K Teens, Social media & technology overview 2015: smartphones facilitate shifts in communication landscape for teens. http://www.pewinternet.org/files/2015/04/PI_TeensandTech_Update2015_0409151.pdf. Published April 9, 2015. Accessed August 26, 2017.
  • 48.Wysocki T, Hough BS, Ward KM, Green LB. Diabetes mellitus in the transition to adulthood: adjustment, self-care, and health status. J Dev Behav Pediatr. 1992;13(3):194-201. [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplement 1.

Protocol

Click here for additional data file. (223.7KB, pdf)
Supplement 2.

eTable 1. Study HbA1c Intervals in Weeks

eTable 2. Adjusted Difference in HbA1c for With and Without Imputed Data

eFigure 1. HbA1c by Arm at Baseline, 3 Months and 6 Months

eFigure 2. Change in HbA1c at 3 Months by Adherence to Daily Glucose Monitoring Goals

Click here for additional data file. (86.7KB, pdf)
Journal Club Slides
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