This randomized clinical trial investigates the effect of financial incentives added to meal replacement therapy for treatment of severe obesity among adolescents.
Key Points
Question
Do financial incentives improve treatment outcomes for adolescents with severe obesity?
Findings
In this randomized clinical trial among adolescents with severe obesity, those who received meal replacement therapy plus financial incentives had greater body mass index reduction and total body fat mass reduction compared with those who received meal replacement therapy alone. There was no increase in unhealthy weight-control behaviors with meal replacement therapy plus financial incentives compared with meal replacement therapy alone.
Meaning
This study found that financial incentives improved outcomes of a dietary intervention among adolescents with severe obesity.
Abstract
Importance
Adolescent severe obesity is usually not effectively treated with traditional lifestyle modification therapy. Meal replacement therapy (MRT) shows short-term efficacy for body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) reduction in adolescents, and financial incentives (FIs) may be an appropriate adjunct intervention to enhance long-term efficacy.
Objective
To evaluate the effect of MRT plus FIs vs MRT alone on BMI, body fat, and cardiometabolic risk factors in adolescents with severe obesity.
Design, Setting, and Participants
This was a randomized clinical trial of MRT plus FIs vs MRT alone at a large academic health center in the Midwest conducted from 2018 to 2022. Participants were adolescents (ages 13-17 y) with severe obesity (≥120% of the 95th BMI percentile based on sex and age or ≥35 BMI, whichever was lower) who were unaware of the FI component of the trial until they were randomized to MRT plus FIs or until the end of the trial. Study staff members collecting clinical measures were blinded to treatment condition. Data were analyzed from March 2022 to February 2024.
Interventions
MRT included provision of preportioned, calorie-controlled meals (~1200 kcals/d). In the MRT plus FI group, incentives were provided based on reduction in body weight from baseline.
Main Outcomes and Measures
The primary end point was mean BMI percentage change from randomization to 52 weeks. Secondary end points included total body fat and cardiometabolic risk factors: blood pressure, triglyceride to high-density lipoprotein ratio, heart rate variability, and arterial stiffness. Cost-effectiveness was additionally evaluated. Safety was assessed through monthly adverse event monitoring and frequent assessment of unhealthy weight-control behaviors.
Results
Among 126 adolescents with severe obesity (73 female [57.9%]; mean [SD] age, 15.3 [1.2] years), 63 participants received MRT plus FIs and 63 participants received only MRT. At 52 weeks, the mean BMI reduction was greater by −5.9 percentage points (95% CI, −9.9 to −1.9 percentage points; P = .004) in the MRT plus FI compared with the MRT group. The MRT plus FI group had a greater reduction in mean total body fat mass by −4.8 kg (95% CI, −9.1 to −0.6 kg; P = .03) and was cost-effective (incremental cost-effectiveness ratio, $39 178 per quality-adjusted life year) compared with MRT alone. There were no significant differences in cardiometabolic risk factors or unhealthy weight-control behaviors between groups.
Conclusions and Relevance
In this study, adding FIs to MRT resulted in greater reductions in BMI and total body fat in adolescents with severe obesity without increased unhealthy weight-control behaviors. FIs were cost-effective and possibly promoted adherence to health behaviors.
Trial Registration
ClinicalTrials.gov Identifier: NCT03137433
Introduction
Pediatric severe obesity, defined as a body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) at 120% of the 95th percentile or greater based on age and sex or a BMI of 35 or greater, whichever is lower, affects approximately 8% of US youth.1,2 Prevalence increases with age, with 9% to 14% of teenagers having severe obesity.2 Pediatric obesity is a factor associated with high rates of adult obesity3 and increases risk for cardiovascular disease,3 type 2 diabetes, and other comorbidities.4 Initiating effective weight reduction interventions before adulthood likely offers the best opportunity to alter disease trajectory and meaningfully improve long-term health.5,6 Unfortunately, traditional lifestyle modification therapies alone are often ineffective for providing meaningful and durable reduction of excess adiposity for adolescents with severe obesity.7,8
Meal replacement therapy (MRT) is a structured dietary plan traditionally including replacing standard meals with ready-to-eat meals or meal replacement shakes that include a prescribed nutrient and calorie content. In 1 trial9 of adolescents with severe obesity, MRT resulted in a mean 6% BMI reduction at 4 months; however, this was not maintained long term. Decreased adherence to MRT over time appears to contribute to decreased efficacy and durability.9 Therefore, incentivizing adolescents to remain engaged with MRT may facilitate sustained and improved BMI outcomes.
In adult obesity trials, financial incentive (FI) models have been successful in enhancing adherence to lifestyle modification therapy and improving weight-related outcomes.10,11,12,13,14,15,16,17,18,19 Although yet to be investigated as an obesity intervention among adolescents to our knowledge, FIs have been shown to improve many health-related behaviors in teenagers, including exercise duration,20 smoking cessation,21,22 and adherence to type 1 diabetes treatment23,24 and wearing physical activity accelerometers.25 Additionally, parents and teenagers have indicated that FIs are an acceptable behavior change strategy.26,27
The theoretical basis of FIs comes from operant conditioning and behavioral economics.12 Operant conditioning principles indicate that behavior followed by a reinforcer is more likely to occur in the future; relatedly, a stimulus is considered a reinforcer only if it increases the likelihood of a behavior. Money is a generalized reinforcer, meaning its reinforcing value comes from being paired with various other potential reinforcers.28 Taken together, providing money contingent on specific health behaviors is likely to increase engagement in health behaviors.12 Behavioral economics principles recognize that people do not consistently make completely rational decisions; instead, cognitive processes are nuanced and are influenced by contextual factors. FIs may provide a contextual factor that moves decision-making toward selecting positive health behaviors.29
Given that MRT showed initial effectiveness in adolescents and that FIs improve health behaviors and are acceptable to parents and teenagers, combining these approaches in adolescents with severe obesity may promote enhanced treatment outcomes. In this study, traditional MRT was modified to include provision of pre-prepared, well-balanced, and calorically portioned meals without using shakes or bars as meals. The primary aim of this randomized clinical trial was to evaluate the effect of MRT plus FIs vs MRT alone on BMI and cardiometabolic risk factors in adolescents with severe obesity. As a safety outcome, unhealthy weight-control behaviors were closely monitored.
Methods
The study protocol for this randomized clinical trial was approved by the University of Minnesota Institution Review Board and registered at ClinicalTrials.gov (NCT03137433) (see trial protocol in Supplement 1). We followed the Consolidated Standards of Reporting Trials (CONSORT) reporting guideline. Participants and parents or guardians provided written assent and consent, respectively.
Trial Design
This was a 52-week randomized clinical trial evaluating the effect of MRT plus FIs vs MRT alone on BMI reduction and cardiometabolic outcomes among adolescents with severe obesity. Participants were randomized 1:1 to MRT plus FIs or MRT alone using randomly permutated blocks of 2, 4, and 6 participants. The randomization sequence was generated by the study statistician and implemented using research electronic data capture.30 Randomization was stratified by health insurance status (public vs private). All participants were provided pre-prepared meals throughout the study. To blind participants to receipt of FIs, which may create undue influence or dropout in those randomized to MRT alone, all participants were informed that they were participating in a trial evaluating the effect of MRT on BMI reduction but were not told about the FI component at recruitment. After randomization, participants allocated to MRT plus FIs were informed of the FI protocol. Participants allocated to MRT alone remained unaware of the FI component throughout the trial. Study investigators and staff collecting clinical variables were blinded to treatment conditions. Notable changes to the protocol after trial commencement consisted of revising the sample size from 142 to 120 participants, removing a 78-week follow-up assessment, and removing metabolic rate and energy expenditure assessments. These changes were made due to trial disruptions from the COVID-19 pandemic.
Participants
Inclusion criteria were that participants be aged 13 to 17 years and have a BMI at 120% or greater of the 95th percentile based on sex and age or a BMI of 35 or greater, whichever was lower. Primary exclusion criteria included type 1 or 2 diabetes; previous (within 6 months) or current use of MRT; previous (within 6 months) or current use of a medication prescribed primarily for weight reduction; previous bariatric surgery; dose changes (within 6 months) in hypertension, dyslipidemia, or prediabetes medication; and endorsement of bulimia nervosa, binge eating disorder, or use of vomiting, laxatives, or diuretics for weight control. The first participant was screened in January 2018, and the final participant visit was in April 2022. Consistent with US National Institutes of Health policy on inclusion of women, minority racial and ethnic groups, and children, we report on participant self-reported race and ethnicity. Race options were listed as follows: American Indian, African American or Black, Asian, Caucasian or White, Native Hawaiian or Other Pacific Islander, or other. Participants could select more than 1 option, in which case, they were categorized under multiple races. Participants could provide additional details if they selected other. For ethnicity, choices included Latino or Hispanic or not Latino or Hispanic.
Intervention and Procedures
Meal Replacement Therapy
All participants were provided pre-prepared, well-balanced, calorically portioned (~1200 kcals/d) meals (Healthy For Life Meals) throughout the trial. Participants were instructed to eat only the meals provided. Meals were provided free of charge and delivered to participant homes. To support regular eating throughout the day and eating with others, study staff provided guidance regarding use of meals at school and encouraged participants to eat with family despite eating different foods. Participants met with study staff each month, with alternating months of in-person or phone visits.
Financial Incentives
To keep the process straightforward and understandable for participants in the MRT plus FI group, FIs were provided for body weight rather than BMI reductions. BMI reduction calculations involve interplay with height and weight, making effective communication about goals challenging; furthermore, body weight and BMI changes are highly correlated. For every 0.5% reduction in body weight from baseline, participants received a $20 gift card. Baseline body weight was used as the reference point throughout the study to avoid incentivizing weight cycling (ie, gain and subsequent loss). To make goals clear for participants, individual tables were created displaying the amount of weight reduction that corresponded to each half–percentage point reduction in body weight. FI payments were made to participants at 2, 4, 6, 8, 10, and 12 months. All participants received reimbursement for completing study visit assessments separately from FI payments regardless of group assignment. Reimbursement for study participation was up to $700 ($100 for each of 7 in-person visits).
Data Collection
Primary and Secondary End Points
The primary end point was mean BMI percentage change from randomization to 52 weeks. Height and weight were measured using a calibrated, wall-mounted stadiometer and electronic scale, respectively. Prespecified secondary end points included percentage body fat, blood pressure, triglyceride to high-density lipoprotein (HDL) ratio, heart rate variability, and arterial stiffness. Body composition was measured by dual energy x-ray absorptiometry (iDXA; GE HealthCare Lunar) and analyzed using Encore software version 16.2 (GE HealthCare). Dual energy x-ray absorptiometry provided measures of lean and fat masses and visceral adipose tissue, which was estimated using CoreScan (GE HealthCare), as described previously.31,32,33
Heart rate variability and arterial stiffness were measured using the SphygmoCor MM3 system (AtCor Medical). Using a 3-lead electrocardiogram (ECG) in a modified lead II configuration, heart rate was continuously recorded for 5 minutes. Time-domain variables, such as the mean R-R interval length, SD of R-R intervals, number of adjacent intervals over 50 milliseconds, and percentage of adjacent intervals over 50 milliseconds, were obtained from automated algorithms provided by the SphygmoCor software.34,35 Spectral analysis calculated frequency domains, including low frequency (LF), high frequency (HF), LF to HF ratio (LF:HF), and total power.34 Frequencies between 0.04 and 0.15 Hz and 0.15 and 0.40 Hz were defined as LF and HF, respectively.34 Right radial and carotid artery waveforms and carotid-radial pulse wave velocity (PWV) were recorded by applanation tonometry using SphygmoCor MM3 software version 8.0 (AtCor Medical). PWV was measured by the sequential acquisition of pressure waveforms from the carotid and radial artery by the same tonometer. Carotid-radial PWV was calculated from the transit time between the 2 arteries relative to the R-wave within the ECG complex, using the foot-to-foot method and the intersecting tangent algorithm.36,37,38
Quality of life was assessed as a tertiary outcome through participant report using the Impact of Weight on Quality of Life-Kids (IWQOL-Kids) questionnaire.39 This measure includes a total score and 4 subscale scores: physical comfort, body esteem, social life, and family relations. Total and subscale scores are ascertained by converting raw scores to transformed scores. Transformed scores range from 0 to 100, with higher scores representing better quality of life.
Safety Assessment
Adverse events were assessed at monthly study visits. Additionally incidence of unhealthy weight-control behaviors was evaluated at each in-person visit using 2 questions from the Eating Disorder Examination Questionnaire (EDE-Q). The 2 items from the EDE-Q asked participants how many times over the prior 28 days they had used laxatives or made themselves sick as a means to control their weight or shape. Any acknowledgment of laxative use or vomiting to control weight on the EDE-Q was followed up by a structured interview with a licensed psychologist to determine whether there was a new-onset eating disorder, for which participants would be removed from the study.
Sample Size
Based on adult clinical trials of FIs for weight reduction, we anticipated a 52-week, control-subtracted treatment effect on BMI reduction of 5%.13,14,40 We estimated 20% or greater attrition based on our previous clinical trial experience with this population.41,42 We did not expect differential attrition by treatment group based on adult FI trials.13,14,40 Using BMI variability estimates from a previous trial of MRT over 12 months suggesting an SD of approximately 8.8 and using a conservative correlation between baseline and follow-up scores of 0.6, we had more than 90% power for differences of 5% using a sample size of 60 participants per group (120 total participants).9
Statistical Analysis
The primary analysis was conducted using the intent-to-treat population. For BMI values collected more than 30 days after the 52-week target visit date, linear interpolation was used to estimate BMI at the target date using the latest observed value and the closest observed value prior to the 52-week target date. Missing data were addressed using 200 imputations via the mice package for R statistical software,43 where imputations for each outcome were based on the randomization assignment, insurance status (randomization stratification factor), and baseline value of the outcome. Linear regression models pooled across all imputed datasets were used to compare the mean BMI percentage change from randomization to 52 weeks between MRT plus FIs and MRT adjusted for BMI at randomization and insurance status. CIs and 2-sided P values were based on robust variance estimation. Statistical significance was considered as P < .05. Secondary end points were analyzed in a similar fashion to the primary outcome, wherein missing data were addressed using multiple imputations and analyses were adjusted for baseline values and the randomization stratification factor. No adjustment for multiple comparisons was made; as such, P values are not presented for secondary or exploratory outcomes. Safety assessments included all participants receiving any treatment according to the treatment received. These analyses were conducted in R statistical software version 4.2.1 (R Project for Statistical Computing).44 Data were analyzed from March 2022 to February 2024.
A Markov model was constructed to assess the cost-effectiveness of MRT and MRT plus FIs over 1 year. Model parameters are shown in eTable 1 in Supplement 2. Outcomes included quality-adjusted life-years (QALYs), costs, and incremental cost-effectiveness ratios (ICERs). A commonly used willingness-to-pay threshold of $100 000 per QALY determined cost-effectiveness.45,46 To assess the effect of parameter uncertainty on cost-effectiveness results, 1-way sensitivity analyses and probabilistic sensitivity analyses were performed. Further information on the model is in the eMethods in Supplement 2. These analyses were conducted in Python programming language version 3.8.8 (Python Software Foundation).47
Results
Among 126 participants (73 female [57.9%]; mean [SD] age, 15.3 [1.2] years; 3 American Indian [2.4%], 19 Black [15.4%]; 83 White [67.5%], and 15 with multiple races [12.2%] among 123 participants with race data; 14 Hispanic [11.2%] among 125 participants with ethnicity data), the mean (SD) BMI was 40.6 (5.1) and 89 participants completed the 52-week visit (70.6% retention) (Figure 1; Table 1). There were 63 participants in the MRT plus FI group and 63 participants in the MRT-only group. Baseline characteristics of individuals who completed and did not complete the 52-week visit are in eTable 2 in Supplement 2. After adjusting for insurance type (private vs public) and baseline BMI, the difference in the mean reduction in BMI was −5.9 percentage points (95% CI, −9.9 to −1.9 percentage points; P = .004) in the MRT plus FI compared with MRT group (Figure 2; Table 2). After adjusting for insurance type and baseline total fat mass, the reduction total body fat mass was greater by −4.8 kg (95% CI, −9.1 to −0.6 kg; P = .03) in the MRT plus FI compared with MRT group at week 52 (Table 2). No significant differences were observed between groups in the change in cardiometabolic risk factors, including blood pressure, triglyceride to HDL ratio, heart rate variability, or arterial stiffness. Additionally, there was no significant difference between groups in quality of life at 52 weeks. Week 26 outcomes are shown in eTable 3 in Supplement 2.
Figure 1. Study Flowchart.
PI indicates principal investigator.
Table 1. Participant Characteristics.
| Characteristic | Randomized participants, No (%) | ||
|---|---|---|---|
| Total (N = 126) | MRT + FIs (n = 63) | MRT (n = 63) | |
| Sex | |||
| Female | 73 (57.9) | 38 (60.3) | 35 (56.6) |
| Male | 53 (42.1) | 25 (39.7) | 28 (44.4) |
| Age, mean (SD), y | 15.3 (1.2) | 15.4 (1.2) | 15.2 (1.1) |
| Tanner stage | |||
| 1 | 1 (0.8) | 1 (1.6) | 0 |
| 2 | 3 (2.4) | 1 (1.6) | 2 (3.2) |
| 3 | 8 (6.3) | 2 (3.2) | 6 (9.5) |
| 4 | 45 (35.7) | 22 (34.9) | 23 (36.5) |
| 5 | 69 (54.8) | 37 (58.7) | 32 (50.8) |
| Insurance | |||
| Private | 81 (64.3) | 40 (63.5) | 41 (65.1) |
| Public | 45 (35.7) | 23 (36.5) | 22 (34.9) |
| Race | |||
| No. with data | 123 | 62 | 61 |
| African American or Black | 19 (15.4) | 10 (16.1) | 9 (14.8) |
| American Indian | 3 (2.4) | 3 (4.8) | 0 |
| Asian | 1 (0.8) | 0 | 1 (1.6) |
| White | 83 (67.5) | 40 (64.5) | 43 (70.5) |
| Multiplea | 15 (12.2) | 8 (12.9) | 7 (11.5) |
| African American or Black and White | 12 | 5 | 7 |
| Asian and White | 1 | 1 | 0 |
| American Indian and White | 2 | 2 | 0 |
| Other | 2 (1.6) | 1 (1.6) | 1 (1.6) |
| Missing, No. | 3 | 1 | 2 |
| Ethnicity | |||
| No. with data | 125 | 63 | 62 |
| Latino or Hispanic | 14 (11.2) | 8 (12.7) | 6 (9.7) |
| Not Latino or Hispanic | 111 (88.8) | 55 (87.3) | 56 (90.3) |
| Missing, No. | 1 | 0 | 1 |
| Anthropometrics, mean (SD) | |||
| Height, cm | 170.0 (8.7) | 170.4 (8.2) | 169.7 (9.1) |
| Weight, kg | 117.7 (18.4) | 118.6 (17.6) | 116.8 (19.3) |
| Body composition, mean (SD) | |||
| BMI | 40.6 (5.1) | 40.7 (4.8) | 40.5 (5.3) |
| BMI percentage of the 95th percentile | 146.8 (19.4) | 146.7 (19.2) | 146.9 (19.6) |
| Body fat mass | |||
| Total, kg | 58.0 (11.7) | 58.2 (10.7) | 57.8 (12.7) |
| % | 49.2 (4.8) | 49.0 (4.5) | 49.3 (5.0) |
| Visceral fat mass, kg | 1.5 (0.6) | 1.6 (0.6) | 1.5 (0.7) |
| Cardiometabolic factors, mean (SD) | |||
| SBP, mm Hg | 119.9 (11.4) | 119.5 (11.3) | 120.2 (11.6) |
| DBP, mm Hg | 66.6 (8.7) | 67.1 (8.9) | 66.1 (8.5) |
| Heart rate, bpm | 77.4 (10.3) | 77.6 (10.6) | 77.3 (10.1) |
| Total cholesterol, mg/dL | 156.3 (31.2) | 158.6 (32.4) | 153.9 (30.1) |
| LDL, mg/dL | 89.2 (29.3) | 89.3 (30.3) | 89.1 (28.5) |
| No. with data | 124 | 62 | 62 |
| Missing | 2 | 1 | 1 |
| HDL, mg/dL | 43.8 (9.3) | 44.0 (9.9) | 43.6 (8.7) |
| Triglycerides, mg/dL | 122.2 (72.0) | 129.3 (77.2) | 115.0 (66.2) |
| Triglycerides/HDL cholesterol ratio | 3.1 (2.5) | 3.3 (2.6) | 2.9 (2.3) |
| Glucose, mg/dL | 87.3 (7.6) | 87.4 (6.4) | 87.3 (8.6) |
| Insulin, μIU/mL | 31.3 (20.8) | 34.2 (24.6) | 28.3 (15.7) |
| No. with data | 125 | 63 | 62 |
| Missing | 1 | 0 | 1 |
| Hemoglobin A1c, % | 5.4 (0.3) | 5.3 (0.4) | 5.4 (0.3) |
| IWQOL-Kids total transformed score, mean (SD) | 74.8 (19.5) | 75.7 (19.9) | 73.9 (18.9) |
| No. with data | 118 | 62 | 56 |
| Missing | 8 | 1 | 7 |
Abbreviations: BMI, body mass index (calculated as weight in kilograms divided by height in meters squared); DBP, diastolic blood pressure; FI, financial incentive; HDL, high-density lipoprotein; IWQOL, Impact of Weight on Quality of Life; LDL, low-density lipoprotein; MRT, meal replacement therapy; SBP, systolic blood pressure.
SI conversion factors: To convert glucose from milligrams per deciliter to millimoles per liter, multiply by 0.0555; HDL, LDL, and total cholesterol from milligrams per deciliter to millimoles per liter, multiply by 0.0259; hemoglobin A1c from percentage of total hemoglobin to proportion of total hemoglobin, multiply by 0.01; insulin from micro-international units per milliliter to picomoles per liter, multiply by 6.945; triglycerides from milligrams per deciliter to millimoles per liter, multiply by 0.0113.
Participants could select more than 1 race option.
Figure 2. Percentage Change in Body Mass Index (BMI) From Baseline to 52 Weeks.
Error bars indicate 95% CIs; FI, financial incentive; MRT, meal replacement therapy. BMI is calculated as weight in kilograms divided by height in meters squared.
Table 2. Estimated Treatment Effect of FIsa.
| Outcome | Mean (SD) change from randomization to week 52 | Estimated treatment difference (95% CI)a | |
|---|---|---|---|
| MRT + FIs | MRT | MRT + Fls vs MRT | |
| Primary outcome | |||
| % Change in BMI | −6.6 (1.6) | −0.9 (1.3) | −5.9 (−9.9 to −1.9)b |
| Secondary outcomes | |||
| Change in BMI | −2.6 (0.6) | −0.3 (0.5) | −2.4 (−3.9 to −0.8) |
| % Change in weight | −5.7 (1.7) | 0.5 (1.4) | −6.3 (−10.8 to −1.7) |
| Change in weight, kg | −6.9 (2) | 0.8 (1.6) | −7.7 (−12.9 to −2.6) |
| Change in % of 95th percentile BMI | −12.5 (2.3) | −5.1 (1.9) | −7.5 (−13.3 to −1.7) |
| Total fat mass, kg | −6.1 (1.5) | −1.3 (1.5) | −4.8 (−9.1 to −0.6) |
| SBP, mm Hg | −2.1 (1.7) | −3.1 (1.7) | 0.6 (−3.8 to 5.1) |
| DBP, mm Hg | 0.1 (1.5) | −0.3 (1.4) | 0.9 (−2.7 to 4.6) |
| Triglycerides/HDL cholesterol ratio | −0.5 (0.3) | −0.2 (0.3) | −0.1 (−0.7 to 0.5) |
| LF/HF ratio | −0.3 (0.2) | −0.1 (0.1) | 0.0 (−0.3 to 0.2) |
| RMS SDD | 16.3 (5.8) | 14.7 (5.7) | 1.7 (−13.5 to 16.8) |
| PWV, m/s | 0.1 (0.2) | −0.1 (0.1) | 0.1 (−0.3 to 0.5) |
| Exploratory IWQOL-Kids transformed score outcomes | |||
| Physical comfort | 3.9 (3.4) | 0.3 (4.4) | 6.7 (−3.4 to 16.8) |
| Body esteem | 12.1 (4.2) | 10.3 (3.8) | 1.5 (−8.5 to 11.5) |
| Social life | 2.2 (3.2) | 0 (2.8) | 2.2 (−5.6 to 10.1) |
| Family relations | −0.9 (3.4) | −0.2 (3.1) | −0.9 (−9.5 to 7.8) |
| Total | 5.2 (3.1) | 3.2 (2.5) | 2.5 (−4.9 to 9.9) |
Abbreviations: BMI, body mass index (calculated as weight in kilograms divided by height in meters squared); DBP, diastolic blood pressure; FI, financial incentive; HDL, high-density lipoprotein; IWQOL, Impact of Weight on Quality of Life; LF/HF, low frequency/high frequency; MRT, meal replacement therapy; PWV, pulse wave velocity; RMS SDD, root mean square of the square difference between adjacent normal R-R intervals; SBP, systolic blood pressure.
Treatment effect was estimated from randomization to week 52 in the intention to-treat population using linear interpolation for late visits and multiple imputations for missing data and adjusted for baseline BMI and the randomization stratification variable insurance type (private vs public).
P value = .004.
There was no significant difference between MRT plus FI and MRT groups endorsing unhealthy weight-control behaviors of laxative use (9 events among 6 participants [9.5%] vs 7 events among 7 participants [11.1%]) or vomiting (10 events among 6 participants [9.5%] vs 10 events among 9 participants [14.3%]) to control weight (Table 3). Per protocol, any participant acknowledging these behaviors was individually interviewed to assess for a new-onset eating disorder. No individual interviews for participants in either group resulted in study removal.
Table 3. AEs and EDE-Q Safety Indicators in Randomized Participants.
| Event | MRT + Fl (n = 63) | MRT (n = 63) | ||
|---|---|---|---|---|
| Participants, No. (%) | Events, No.a | Participants, No. (%) | Events, No. | |
| Any AE | 29 (46.0) | 52 | 31 (49.2) | 58 |
| Serious AE | 1 (1.6) | 1 | 3 (4.8) | 3 |
| EDE-Q16: made yourself sick | 6 (9.5) | 10 | 9 (14.3) | 10 |
| EDE-Q17: taken laxatives | 6 (9.5) | 9 | 7 (11.1) | 7 |
Abbreviations: AE, adverse event; EDE-Q, Eating Disorder Examination Questionnaire; FI, financial incentive; MRT, meal replacement therapy.
Events for EDE-Q are the number of occasions the question was answered with any number greater than zero.
We recorded 1 serious adverse event in the MRT plus FI group and 3 serious adverse events in the MRT group. The participant in the MRT plus FI group had a hospitalization related to asthma (a preexisting condition) and remained in the study after the event. In the MRT group, 3 hospitalizations occurred: 1 for a suicide attempt, 1 for overdose after being overwhelmed with bullying at school, and 1 for acquired pneumonia and e-cigarette or vaping use–associated lung injury. The first 2 participants withdrew after hospitalization; the latter participant remained in the study. All severe adverse events were determined to be unrelated to study participation.
The mean (SD) total FI payment over 52 weeks in the MRT plus FI group was $330.77 ($252.64). MRT plus FIs was cost-effective over 1 year, with an ICER of $39 178 per QALY compared with MRT (eTable 4 in Supplement 2). MRT plus FIs remained the cost-effective strategy across most 1-way sensitivity analyses. The only circumstance in which MRT plus FIs was not considered the optimal strategy over MRT was when the utility of BMI reduction approached zero (ie, BMI reduction did not improve quality of life) (eFigure 1 in Supplement 2). In our probabilistic sensitivity analyses, MRT plus FIs was cost-effective in 740 of 1000 iterations (74.0%) (eFigure 2 in Supplement 2).
Discussion
This randomized clinical trial found that the use of FIs combined with MRT led to greater reductions in BMI and body fat in adolescents with severe obesity compared with MRT alone. These results are congruent with obesity trials in adults18 and other studies using FIs to promote health behavior change among adolescents.21,24 Notably, BMI reduction in this study was statistically significant and potentially clinically meaningful at a nearly 6–percentage point greater BMI reduction in the MRT plus FI than MRT group. A systematic review and meta-analysis48 identified a BMI reduction of 1.6 as a threshold that may indicate clinical significance, and the mean control-subtracted BMI reduction in our trial was 2.4. The clinically meaningful impact of this 6% BMI reduction is further supported by the associated significant decrease in body fat in the trial. Even with the expense of incentive payments, MRT plus FIs was cost-effective compared with MRT.
Importantly, adding FIs did not increase the incidence of unhealthy weight-control behaviors compared with MRT alone. Regarding the possibility of weight management treatment promoting unhealthy weight-control behavior, available literature shows that participating in supervised, behavioral weight management has not been associated with increased eating disorder risk. In aggregate, participation in these interventions has been associated with reduced eating disorder risk.49,50
In a prior study of MRT in adolescents,12 weight reduction occurred early in treatment but was not maintained. Treatment adherence over time was a barrier to long-term efficacy. In our study, FIs may have allowed adolescents to better adhere to MRT over the year compared with those not receiving FIs. We attempted to track treatment adherence by encouraging participants to maintain a journal of meal adherence; however, substantial data were missing, precluding a meaningful analysis. Therefore, we cannot say with certainty that FIs promoted treatment adherence. Nonetheless, the trajectory of weight change shows that most weight reduction occurred during the initial months of treatment and was maintained to a greater degree among participants receiving FIs.
A pattern of initial BMI reduction followed by rebound has been observed in almost all obesity interventions51 and was seen in the MRT group in our study. We speculate that the addition of FIs allowed for better maintenance of BMI reduction through improved and sustained adherence to MRT. Additionally, participants in the MRT plus FI group were incentivized to achieve weight reduction goals and did not have increased unhealthy weight-control behaviors compared with those in the MRT group, suggesting that participants achieved these goals via appropriately adhering to MRT. Finally, based on principles of operant conditioning, individuals or outcomes are not reinforced; instead, it is behavior that is reinforced.28 Therefore, health behaviors were theoretically maintained through reinforcement from FIs or other shorter-term reinforcers (eg, positive self-talk) that bridged the time between specific health behaviors and longer-term reinforcers of FIs.
In this study, adding FIs to MRT was effective for achieving significant BMI reduction in adolescents with severe obesity and was cost-effective compared with MRT alone. These findings are based on setting individualized goals with adolescents; thus, evaluations of the application of FI interventions in clinical or community settings are necessary to determine scalability. Similar strategies are used in health care plans that incentivize adult health behaviors,52 but there may be more opportunities with adolescents or families. In addition to scalability, such applications may also address the need for interventions that extend beyond a year. While FIs plus MRT appears to be a longer-term strategy than MRT alone, treatment withdrawal will likely result in BMI increase.53 As such, research is needed to identify strategies that are scalable and feasible in the long term given the chronic nature of obesity.
Strengths and Limitations
This study has several strengths and limitations. Participants in the MRT group were blind to the FI component, thus decreasing the likelihood of treatment disengagement due to any possible negative perceptions of not receiving the FI opportunity. While study procedures kept participants in the MRT group blinded to the FI component, we cannot know for certain that participants did not learn about FIs through communication with nonstudy personnel. All participants were reimbursed for their time and provided with meals, thus removing additional financial burden from study participation. While the intent of reimbursement was to mitigate potential burden, this could have led to an underestimation of the FI effect. The primary weakness of the study was the aforementioned lack of adherence data. Additionally, this study was conducted during the height of the COVID-19 pandemic, which we believe led to higher than expected attrition (nearly 30%) and some missing data (in-person research visits were suspended at our institution for months) and may have influenced BMI trajectories.54 Additionally, we focused the cost-effectiveness analysis on MRT vs MRT plus FI groups but did not include other interventions, such as antiobesity medications or metabolic or bariatric surgery.
Conclusions
In this randomized clinical trial, FIs combined with MRT significantly improved BMI and body fat in adolescents with severe obesity compared with MRT alone. It is possible that incentives allowed participants to better adhere to the meal plan over the course of the year, resulting in meaningful BMI and body fat reduction.
Trial Protocol and Statistical Analysis Plan
eMethods. Cost-Effectiveness Modeling
eTable 1. Model Inputs.
eTable 2. Participant Demographics, Anthropometrics, Body Composition, Cardiometabolic Risk Factors, and Quality of Life
eTable 3. Estimated Treatment Effect of Financial Incentive From Randomization to 26 wk
eTable 4. Cost-Effectiveness Results
eFigure 1. 1-Way Sensitivity Analysis
eFigure 2. Cost-Effectiveness Acceptability Curves
eReferences.
Data Sharing Statement
References
- 1.Kelly AS, Barlow SE, Rao G, et al. ; American Heart Association Atherosclerosis, Hypertension, and Obesity in the Young Committee of the Council on Cardiovascular Disease in the Young, Council on Nutrition, Physical Activity and Metabolism, and Council on Clinical Cardiology . Severe obesity in children and adolescents: identification, associated health risks, and treatment approaches: a scientific statement from the American Heart Association. Circulation. 2013;128(15):1689-1712. doi: 10.1161/CIR.0b013e3182a5cfb3 [DOI] [PubMed] [Google Scholar]
- 2.Skinner AC, Ravanbakht SN, Skelton JA, Perrin EM, Armstrong SC. Prevalence of obesity and severe obesity in US children, 1999-2016. Pediatrics. 2018;141(3):e20173459. doi: 10.1542/peds.2017-3459 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Freedman DSP, Mei Z, Srinivasan SRP, Berenson GSMD, Dietz WHMD. Cardiovascular risk factors and excess adiposity among overweight children and adolescents: the Bogalusa Heart Study. J Pediatr. 2007;150(1):12-17.e2. doi: 10.1016/j.jpeds.2006.08.042 [DOI] [PubMed] [Google Scholar]
- 4.Weiss R, Taksali SE, Tamborlane WV, Burgert TS, Savoye M, Caprio S. Predictors of changes in glucose tolerance status in obese youth. Diabetes Care. 2005;28(4):902-909. doi: 10.2337/diacare.28.4.902 [DOI] [PubMed] [Google Scholar]
- 5.Epstein LH, Valoski AM, Kalarchian MA, McCurley J. Do children lose and maintain weight easier than adults: a comparison of child and parent weight changes from six months to ten years. Obes Res. 1995;3(5):411-417. doi: 10.1002/j.1550-8528.1995.tb00170.x [DOI] [PubMed] [Google Scholar]
- 6.Juonala M, Magnussen CG, Berenson GS, et al. Childhood adiposity, adult adiposity, and cardiovascular risk factors. N Engl J Med. 2011;365(20):1876-1885. doi: 10.1056/NEJMoa1010112 [DOI] [PubMed] [Google Scholar]
- 7.Danielsson P, Kowalski J, Ekblom Ö, Marcus C. Response of severely obese children and adolescents to behavioral treatment. Arch Pediatr Adolesc Med. 2012;166(12):1103-1108. doi: 10.1001/2013.jamapediatrics.319 [DOI] [PubMed] [Google Scholar]
- 8.Knop C, Singer V, Uysal Y, Schaefer A, Wolters B, Reinehr T. Extremely obese children respond better than extremely obese adolescents to lifestyle interventions. Pediatr Obes. 2015;10(1):7-14. doi: 10.1111/j.2047-6310.2013.00212.x [DOI] [PubMed] [Google Scholar]
- 9.Berkowitz RI, Wadden TA, Gehrman CA, et al. Meal replacements in the treatment of adolescent obesity: a randomized controlled trial. Obesity (Silver Spring). 2011;19(6):1193-1199. doi: 10.1038/oby.2010.288 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Arterburn D, Westbrook EO, Wiese CJ, et al. Insurance coverage and incentives for weight loss among adults with metabolic syndrome. Obesity (Silver Spring). 2008;16(1):70-76. doi: 10.1038/oby.2007.18 [DOI] [PubMed] [Google Scholar]
- 11.Finkelstein EA, Linnan LA, Tate DF, Birken BE. A pilot study testing the effect of different levels of financial incentives on weight loss among overweight employees. J Occup Environ Med. 2007;49(9):981-989. doi: 10.1097/JOM.0b013e31813c6dcb [DOI] [PubMed] [Google Scholar]
- 12.Jeffery RW. Financial incentives and weight control. Prev Med. 2012;55(Suppl)(suppl):S61-S67. doi: 10.1016/j.ypmed.2011.12.024 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Kullgren JT, Troxel AB, Loewenstein G, et al. Individual- versus group-based financial incentives for weight loss: a randomized, controlled trial. Ann Intern Med. 2013;158(7):505-514. doi: 10.7326/0003-4819-158-7-201304020-00002 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Volpp KG, John LK, Troxel AB, Norton L, Fassbender J, Loewenstein G. Financial incentive-based approaches for weight loss: a randomized trial. JAMA. 2008;300(22):2631-2637. doi: 10.1001/jama.2008.804 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Finkelstein EA, Tham KW, Haaland BA, Sahasranaman A. Applying economic incentives to increase effectiveness of an outpatient weight loss program (TRIO)–a randomized controlled trial. Soc Sci Med. 2017;185(185):63-70. doi: 10.1016/j.socscimed.2017.05.030 [DOI] [PubMed] [Google Scholar]
- 16.Glanz K, Shaw PA, Kwong PL, et al. Effect of financial incentives and environmental strategies on weight loss in the healthy weigh study: a randomized clinical trial. JAMA Netw Open. 2021;4(9):e2124132-e2124132. doi: 10.1001/jamanetworkopen.2021.24132 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Ladapo JA, Orstad SL, Wali S, et al. Effectiveness of goal-directed and outcome-based financial incentives for weight loss in primary care patients with obesity living in socioeconomically disadvantaged neighborhoods: a randomized clinical trial. JAMA Intern Med. 2023;183(1):61-69. doi: 10.1001/jamainternmed.2022.5618 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.West DS, Krukowski RA, Monroe CM, et al. Randomized controlled trial of financial incentives during weight-loss induction and maintenance in online group weight control. Obesity (Silver Spring). 2022;30(1):106-116. doi: 10.1002/oby.23322 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Yancy WS Jr, Shaw PA, Reale C, et al. Effect of escalating financial incentive rewards on maintenance of weight loss: a randomized clinical trial. JAMA Netw Open. 2019;2(11):e1914393-e1914393. doi: 10.1001/jamanetworkopen.2019.14393 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Short KR, Chadwick JQ, Cannady TK, et al. Using financial incentives to promote physical activity in American Indian adolescents: a randomized controlled trial. PLoS One. 2018;13(6):e0198390-e0198390. doi: 10.1371/journal.pone.0198390 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Krishnan-Sarin S, Cavallo DA, Cooney JL, et al. An exploratory randomized controlled trial of a novel high-school-based smoking cessation intervention for adolescent smokers using abstinence-contingent incentives and cognitive behavioral therapy. Drug Alcohol Depend. 2013;132(1-2):346-351. doi: 10.1016/j.drugalcdep.2013.03.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Sigmon SC, Patrick ME. The use of financial incentives in promoting smoking cessation. Prev Med. 2012;55(Suppl)(suppl):S24-S32. doi: 10.1016/j.ypmed.2012.04.007 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.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: 10.1093/jpepsy/jst032 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Wong CA, Miller VA, Murphy K, et al. Effect of financial incentives on glucose monitoring adherence and glycemic control among adolescents and young adults with type 1 diabetes: a randomized clinical trial. JAMA Pediatr. 2017;171(12):1176-1183. doi: 10.1001/jamapediatrics.2017.3233 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Sirard JR, Slater ME. Compliance with wearing physical activity accelerometers in high school students. J Phys Act Health. 2009;6(Suppl 1)(suppl 1):S148-S155. doi: 10.1123/jpah.6.s1.s148 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Malik FS, Senturia KD, Lind CD, et al. Adolescent and parent perspectives on the acceptability of financial incentives to promote self-care in adolescents with type 1 diabetes. Pediatr Diabetes. 2020;21(3):533-551. doi: 10.1111/pedi.12970 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Beskin KM, Caskey R. Parental perspectives on financial incentives for adolescents: findings from qualitative interviews. Glob Pediatr Health. 2019;6:X19845926. doi: 10.1177/2333794X19845926 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Skinner BF. Science and Human Behavior. Free Press; 1965. [Google Scholar]
- 29.McGill B, O’Hara BJ, Bauman A, Grunseit AC, Phongsavan P. Are financial incentives for lifestyle behavior change informed or inspired by behavioral economics: a mapping review. Am J Health Promot. 2019;33(1):131-141. doi: 10.1177/0890117118770837 [DOI] [PubMed] [Google Scholar]
- 30.Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)–a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42(2):377-381. doi: 10.1016/j.jbi.2008.08.010 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Bosch TA, Steinberger J, Sinaiko AR, et al. Identification of sex-specific thresholds for accumulation of visceral adipose tissue in adults. Obesity (Silver Spring). 2015;23(2):375-382. doi: 10.1002/oby.20961 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Kaul S, Rothney MP, Peters DM, et al. Dual-energy X-ray absorptiometry for quantification of visceral fat. Obesity (Silver Spring). 2012;20(6):1313-1318. doi: 10.1038/oby.2011.393 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Bosch TA, Burruss TP, Weir NL, et al. Abdominal body composition differences in NFL football players. J Strength Cond Res. 2014;28(12):3313-3319. doi: 10.1519/JSC.0000000000000650 [DOI] [PubMed] [Google Scholar]
- 34.Ryder JR, O’Connell M, Bosch TA, et al. Impaired cardiac autonomic nervous system function is associated with pediatric hypertension independent of adiposity. Pediatr Res. 2016;79(1-1):49-54. doi: 10.1038/pr.2015.188 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Farah BQMS, Barros MVGP, Balagopal B, Ritti-Dias RMP. Heart rate variability and cardiovascular risk factors in adolescent boys. J Pediatr. 2014;165(5):945-950. doi: 10.1016/j.jpeds.2014.06.065 [DOI] [PubMed] [Google Scholar]
- 36.Kelly RP, Millasseau SC, Ritter JM, Chowienczyk PJ. Vasoactive drugs influence aortic augmentation index independently of pulse-wave velocity in healthy men. Hypertension. 2001;37(6):1429-1433. doi: 10.1161/01.hyp.37.6.1429 [DOI] [PubMed] [Google Scholar]
- 37.Millasseau SC, Stewart AD, Patel SJ, Redwood SR, Chowienczyk PJ. Evaluation of carotid-femoral pulse wave velocity: influence of timing algorithm and heart rate. Hypertension. 2005;45(2):222-226. doi: 10.1161/01.HYP.0000154229.97341.d2 [DOI] [PubMed] [Google Scholar]
- 38.Rajzer MW, Wojciechowska W, Klocek M, Palka I, Brzozowska-Kiszka M, Kawecka-Jaszcz K. Comparison of aortic pulse wave velocity measured by three techniques: Complior, SphygmoCor and arteriograph. J Hypertens. 2008;26(10):2001-2007. doi: 10.1097/HJH.0b013e32830a4a25 [DOI] [PubMed] [Google Scholar]
- 39.Kolotkin RL, Zeller M, Modi AC, et al. Assessing weight-related quality of life in adolescents. Obesity (Silver Spring). 2006;14(3):448-457. doi: 10.1038/oby.2006.59 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.John LK, Loewenstein G, Troxel AB, Norton L, Fassbender JE, Volpp KG. Financial incentives for extended weight loss: a randomized, controlled trial. J Gen Intern Med. 2011;26(6):621-626. doi: 10.1007/s11606-010-1628-y [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Kelly AS, Rudser KD, Nathan BM, et al. The effect of glucagon-like peptide-1 receptor agonist therapy on body mass index in adolescents with severe obesity: a randomized, placebo-controlled, clinical trial. JAMA Pediatr. 2013;167(4):355-360. doi: 10.1001/jamapediatrics.2013.1045 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Kelly AS, Metzig AM, Rudser KD, et al. Exenatide as a weight-loss therapy in extreme pediatric obesity: a randomized, controlled pilot study. Obesity (Silver Spring). 2012;20(2):364-370. doi: 10.1038/oby.2011.337 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.van Buuren S, Groothuis-Oudshoorn K. Mice: multivariate imputation by chained equations in R. J Stat Softw. 2011;45(3):1-67. doi: 10.18637/jss.v045.i03 [DOI] [Google Scholar]
- 44.The R project for statistical computing. Accessed May 9, 2024. https://www.R-project.org/
- 45.Vanness DJ, Lomas J, Ahn H. A health opportunity cost threshold for cost-effectiveness analysis in the United States. Ann Intern Med. 2021;174(1):25-32. doi: 10.7326/M20-1392 [DOI] [PubMed] [Google Scholar]
- 46.Neumann PJ, Kim DD. Cost-effectiveness thresholds used by study authors, 1990-2021. JAMA. 2023;329(15):1312-1314. doi: 10.1001/jama.2023.1792 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Python Software Foundation. Python 3.8.8. Accessed May 9, 2024. https://www.python.org/downloads/release/python-388/
- 48.Rajjo T, Almasri J, Al Nofal A, et al. The association of weight loss and cardiometabolic outcomes in obese children: systematic review and meta-regression. J Clin Endocrinol Metab. 2017;102(3):758-762. doi: 10.1210/jc.2016-2575 [DOI] [PubMed] [Google Scholar]
- 49.Jebeile H, Gow ML, Baur LA, Garnett SP, Paxton SJ, Lister NB. Treatment of obesity, with a dietary component, and eating disorder risk in children and adolescents: a systematic review with meta-analysis. Obes Rev. 2019;20(9):1287-1298. doi: 10.1111/obr.12866 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50.Jebeile H, Libesman S, Melville H, et al. Eating disorder risk during behavioral weight management in adults with overweight or obesity: a systematic review with meta-analysis. Obes Rev. 2023;24(6):e13561. doi: 10.1111/obr.13561 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51.Hall KD, Kahan S. Maintenance of lost weight and long-term management of obesity. Med Clin North Am. 2018;102(1):183-197. doi: 10.1016/j.mcna.2017.08.012 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 52.Ananthapavan J, Peterson A, Sacks G. Paying people to lose weight: the effectiveness of financial incentives provided by health insurers for the prevention and management of overweight and obesity—a systematic review. Obes Rev. 2018;19(5):605-613. doi: 10.1111/obr.12657 [DOI] [PubMed] [Google Scholar]
- 53.Kelly AS, Auerbach P, Barrientos-Perez M, et al. ; NN8022-4180 Trial Investigators . A randomized, controlled trial of liraglutide for adolescents with obesity. N Engl J Med. 2020;382(22):2117-2128. doi: 10.1056/NEJMoa1916038 [DOI] [PubMed] [Google Scholar]
- 54.Appelhans BM, French SA, Martin MA, Lui K, Janssen I. Attenuated efficacy of pediatric obesity treatment during the COVID-19 pandemic. Obesity (Silver Spring). 2022;30(1):45-49. doi: 10.1002/oby.23313 [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Trial Protocol and Statistical Analysis Plan
eMethods. Cost-Effectiveness Modeling
eTable 1. Model Inputs.
eTable 2. Participant Demographics, Anthropometrics, Body Composition, Cardiometabolic Risk Factors, and Quality of Life
eTable 3. Estimated Treatment Effect of Financial Incentive From Randomization to 26 wk
eTable 4. Cost-Effectiveness Results
eFigure 1. 1-Way Sensitivity Analysis
eFigure 2. Cost-Effectiveness Acceptability Curves
eReferences.
Data Sharing Statement