Adding Financial Incentives to Online Group-Based Behavioral Weight Control: An RCT

West, Delia Smith; Krukowski, Rebecca A.; Finkelstein, Eric A.; Stansbury, Melissa; Ogden, Doris; Monroe, Courtney M.; Carpenter, Chelsea A; Naud, Shelly; Harvey, Jean

doi:10.1016/j.amepre.2020.03.015

Cited by 25 publications

(50 citation statements)

References 43 publications

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“…This parallel randomized controlled trial allocated participants from two clinical sites (South Carolina and Vermont) in a 1:1 ratio to either of the following groups: 1) an 18‐month, online, group‐based behavioral lifestyle program with synchronous group text‐based chat sessions (Internet‐Only); or 2) the same 18‐month, online, group‐based program with financial incentives available contingent on key weight‐control behaviors and achieving target weight‐loss outcomes (Months 1‐6) and weight stability goals (Months 7‐12; Internet+Incentives). The initial 6‐month outcomes of the weight‐loss induction period have been reported previously (17); the current report extends this earlier work, focusing on weight‐loss outcomes across the full incentive scheme, spanning the weight‐loss induction phase (Months 1‐6) and weight‐maintenance phase (Months 7‐18), and including a 6‐month period without financial incentives (Months 13‐18).…”

Section: Methodssupporting

confidence: 67%

Randomized controlled trial of financial incentives during weight‐loss induction and maintenance in online group weight control

West

Krukowski

Monroe

et al. 2021

Obesity

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Objective: This study examined the impact of a financial incentive scheme integrating process and outcome incentives across weight-loss induction and weight maintenance on 18-month weight outcomes. Methods: This was a randomized controlled trial. Participants with overweight or obesity (n = 418; 91% female; 28% racial/ethnic minority) were randomized to an 18-month, online, group-based behavioral weight-control program (Internet-Only) or the same program with financial incentives provided for 12 months, contingent on self-regulatory weight-control behaviors (self-weighing, dietary self-monitoring, and physical activity) and weight-outcome benchmarks (Internet+Incentives). No financial incentives were provided from Months 13 to 18 to examine the durability of weightcontrol behaviors and outcomes without incentives.Results: Weight-loss induction at Month 6 was significantly greater for Internet+Incentives than Internet-Only (6.8% vs. 4.9%, respectively, p = 0.01). Individuals receiving incentives were significantly more likely to maintain weight loss ≥ 5% at Month 12 (45% in Internet+Incentives vs. 32% in Internet-Only, p < 0.02) and remain weight stable (39% vs. 27%, respectively, p < 0.01). Internet+Incentives participants also reported significantly greater behavioral engagement through Month 12. However, once incentives ceased, there were no differences in sustained weight outcomes (Month 18), and engagement declined dramatically.Conclusions: Despite promoting greater treatment engagement and initial weight loss, financial incentives as offered in this study did not promote better extended weight control.

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Section: Methodssupporting

confidence: 67%

Randomized controlled trial of financial incentives during weight‐loss induction and maintenance in online group weight control

West

Krukowski

Monroe

et al. 2021

Obesity

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“…This is in contrast to previous research, which found that older adults (ie, aged 60-85 years) were more successful in losing weight in the Diabetes Prevention Program's weight management intervention [25], perhaps because older individuals have fewer competing obligations. Finally, although Black respondents indicated that they would be more likely to self-monitor in the future, previous research has not found significant race-based differences in self-monitoring engagement [17,21]. Nonetheless, a meta-analysis that examined the impact of tailored messages for health behavioral change in general also found a minimal impact of sociodemographic factors on the effect of the messages [5].…”

Section: Discussionmentioning

confidence: 87%

“…Archived email feedback on diet and exercise self-monitoring that had been previously written for participants in the iREACH3 behavioral weight control study [17] was used in this study. A pool of emails was classified into points that noted positive behaviors and those that suggested an area of change to consider.…”

Section: Emailsmentioning

confidence: 99%

Importance of Multiple Reinforcing Comments and Areas for Change in Optimizing Dietary and Exercise Self-Monitoring Feedback in Behavioral Weight Loss Programs: Factorial Design (Preprint)

Krukowski¹,

Kim²,

Stansbury³

et al. 2020

Preprint

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BACKGROUND Individualized dietary and physical activity self-monitoring feedback is a core element of behavioral weight loss interventions and is associated with clinically significant weight loss. To our knowledge, no studies have evaluated individuals’ perspectives on the composition of feedback messages or the effect of feedback composition on the motivation to self-monitor. OBJECTIVE This study aims to assess the perceptions of feedback emails as a function of the number of comments that reinforce healthy behavior and the number of areas for change (ie, behavioral changes that the individual might make to have an impact on weight) identified. METHODS Emailed feedback followed a factorial design with 2 factors (ie, reinforcing comments and areas for change), each with 3 levels (ie, 1, 4, or 8 comments). A total of 250 adults with overweight or obesity who were interested in weight loss were recruited from the Qualtrics research panel. Participants read 9 emails presented in a random order. For each email, respondents answered 8 questions about the likelihood to self-monitor in the future, motivation for behavioral change, and perceptions of the counselor and the email. A mixed effects ordinal logistic model was used to compute conditional odds ratios and predictive margins (ie, average predicted probability) on a 5-point Likert response scale to investigate the optimal combination level of the 2 factors. RESULTS Emails with more reinforcing comments or areas for change were better received, with small incremental benefits for 8 reinforcing comments or areas for change versus 4 reinforcing comments or areas for change. Interactions indicated that the best combination for 3 of 8 outcomes assessed (ie, motivation to make behavioral changes, counselor’s concern for their welfare, and the perception that the counselor likes them) was the email with 8 reinforcing comments and 4 areas for change. Emails with 4 reinforcing comments and 4 areas for change resulted in the highest average probability of individuals who reported being very likely to self-monitor in the future. CONCLUSIONS The study findings suggest how feedback might be optimized for efficacy. Future studies should explore whether the composition of feedback email affects actual self-monitoring performance.

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“…All participants randomized to the control group (N=212) in the larger RCT [ 17 ] were included in this study ( Table 2 ). On average, participants in the current analyses were 47.9 years old, with a BMI of 35.8 kg/m 2 .…”

Section: Resultsmentioning

confidence: 99%

“…This study was a secondary analysis of a randomized controlled trial (RCT) that evaluated the impact of financial incentives on weight loss in a group-based behavioral weight control program delivered online [ 17 ]. Briefly, participants received either (1) the online program augmented with financial incentives for achieving behavioral and weight loss goals or (2) the online program alone (ie, control group).…”

Section: Methodsmentioning

confidence: 99%

Describing Transitions in Adherence to Physical Activity Self-monitoring and Goal Attainment in an Online Behavioral Weight Loss Program: Secondary Analysis of a Randomized Controlled Trial

Stansbury¹,

Harvey²,

Krukowski³

et al. 2022

J Med Internet Res

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Background Standard behavioral weight loss interventions often set uniform physical activity (PA) goals and promote PA self-monitoring; however, adherence remains a challenge, and recommendations may not accommodate all individuals. Identifying patterns of PA goal attainment and self-monitoring behavior will offer a deeper understanding of how individuals adhere to different types of commonly prescribed PA recommendations (ie, minutes of moderate-to-vigorous physical activity [MVPA] and daily steps) and guide future recommendations for improved intervention effectiveness. Objective This study examined weekly patterns of adherence to step-based and minute-based PA goals and self-monitoring behavior during a 6-month online behavioral weight loss intervention. Methods Participants were prescribed weekly PA goals for steps (7000-10,000 steps/day) and minutes of MVPA (50-200 minutes/week) as part of a lifestyle program. Goals gradually increased during the initial 2 months, followed by 4 months of fixed goals. PA was self-reported daily on the study website. For each week, participants were categorized as adherent if they self-monitored their PA and met the program PA goal, suboptimally adherent if they self-monitored but did not meet the program goal, or nonadherent if they did not self-monitor. The probability of transitioning into a less adherent status was examined using multinomial logistic regression. Results Participants (N=212) were predominantly middle-aged females with obesity, and 67 (31.6%) self-identified as a racial/ethnic minority. Initially, 73 (34.4%) participants were categorized as adherent to step-based goals, with 110 [51.9%] suboptimally adherent and 29 [13.7%] nonadherent, and there was a high probability of either remaining suboptimally adherent from week to week or transitioning to a nonadherent status. However, 149 (70.3%) participants started out adherent to minute-based goals (34 [16%] suboptimally adherent and 29 [13.7%] nonadherent), with suboptimally adherent seen as the most variable status. During the graded goal phase, participants were more likely to transition to a less adherent status for minute-based goals (odds ratio [OR] 1.39, 95% CI 1.31-1.48) compared to step-based goals (OR 1.24, 95% CI 1.17-1.30); however, no differences were seen during the fixed goal phase (minute-based goals: OR 1.06, 95% CI 1.05-1.08; step-based goals: OR 1.07, 95% CI 1.05-1.08). Conclusions States of vulnerability to poor PA adherence can emerge rapidly and early in obesity treatment. There is a window of opportunity within the initial 2 months to bring more people toward adherent behavior, especially those who fail to meet the prescribed goals but engage in self-monitoring. Although this study describes the probability of adhering to step- and minute-based targets, it will be prudent to determine how individual characteristics and contextual states relate to these behavioral patterns, which can inform how best to adapt interventions. Trial Registration ClinicalTrials.gov NCT02688621; https://clinicaltrials.gov/ct2/show/NCT02688621

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Adding Financial Incentives to Online Group-Based Behavioral Weight Control: An RCT

Cited by 25 publications

References 43 publications

Randomized controlled trial of financial incentives during weight‐loss induction and maintenance in online group weight control

Randomized controlled trial of financial incentives during weight‐loss induction and maintenance in online group weight control

Importance of Multiple Reinforcing Comments and Areas for Change in Optimizing Dietary and Exercise Self-Monitoring Feedback in Behavioral Weight Loss Programs: Factorial Design (Preprint)

Describing Transitions in Adherence to Physical Activity Self-monitoring and Goal Attainment in an Online Behavioral Weight Loss Program: Secondary Analysis of a Randomized Controlled Trial

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