A Secondary Data Analysis Examining Young Adults’ Performance in an Internet Weight Loss Program with Financial Incentives

Abstract

Objectives:

In traditional behavioral weight loss (BWL) programs, young adults (YA) fare worse than older adults (OA) with respect to engagement, retention and weight loss, but money and use of technology have been cited as program factors that might improve outcomes for this population. We evaluated YA performance in internet-based BWL offering financial incentives for self-monitoring and weight loss.

Methods:

Participants (N=180; BMI=33.2±6.0) were randomized to 12-week internet-based BWL (IBWL) or IBWL+incentives (IBWL+$). In this secondary data analysis, we compared YA (ages 18–35) in IBWL (n=16) to YA in IBWL+$ (n=12) on percent weight loss, engagement, and retention. YA (n=28) were also compared to OA (ages 36–70; n=152) on these outcomes.

Results:

YA weight loss was −2.8±5.2% in IBWL and −5.4±5.7% in IBWL+$ (p=.23, partial eta² .06). A greater proportion of YA in IBWL+$ achieved a 10% weight loss compared to IBWL (42% vs. 6%, p=.02). Compared to OA, YA were less engaged, but there were no differences for retention or weight loss (p’s>.05).

Conclusions:

Findings suggest that technology-based BWL has the potential to eliminate weight loss disparities observed between YA and OA in in-person BWL trials. Moreover, adding financial incentives holds promise for promoting clinically meaningful weight losses for YA.

Introduction

The problem of obesity in the United States results in significant medical and financial burden.¹ Though prevention and treatment across the lifespan is a priority, young adulthood in particular represents a high-risk period for weight gain and the development of obesity due to unhealthy weight-related behaviors such as increased consumption of fast food, alcohol, and sugar-sweetened beverages, in addition to sharp declines in physical activity.^2–6 Moreover, over half of the young adult population already meets criteria for overweight or obesity.⁷

Evidence suggests that modest weight losses of 5–10% of body weight combined with increases in physical activity can decrease cardiometabolic risks.^8–12 Although in-person behavioral weight loss programs consistently produce average weight losses in this range,^13–14 such programs do not appear to be meeting the needs of young adults, as a secondary analysis of NIH-funded randomized controlled trials revealed that those aged 18–35 were markedly underrepresented in these trials, totaling just 7% of participants.¹⁵ Moreover, compared with their older counterparts, the few young adults who did enroll fared much worse in terms of weight loss (−4.3kg vs. −7.7kg). Of note, these differences appear to be driven by differential engagement, as young adults attended fewer sessions (52% vs. 74%) and were less likely to be retained at 6-months (67% vs. 95%).¹⁵ Thus, not only is work needed to improve recruitment efforts with this population, but adaptations are likely needed to promote better engagement and retention in order to effectively optimize weight loss outcomes during this high-risk developmental period.

The need to develop targeted weight control programs for young adults has been acknowledged by the NIH,^16–17 and formative work conducted with young adults corroborates the importance of such efforts. Across both focus groups and surveys, young adults overwhelmingly report that treatment duration, mode of delivery, and content found in traditional adult behavioural weight loss programs are unappealing and insufficient to meet their needs.^18–19 Instead, findings from this body of work suggest that young adults interested in weight management prefer an individual-level treatment program delivered using technology in order to minimize the need for frequent in-person contact and maximize their ability to access the program when it is convenient for them given tremendous time demands and life transitions. Further, lack of motivation and time were identified as central barriers among young adults, both to participating in a program and to maintaining healthy lifestyle behaviors. Importantly, young adults have also cited money as the most powerful and desirable method of promoting engagement, retention, and weight loss.^18,20

Based on these data, a reduced-intensity, technology-mediated program would likely appeal to this population, given that this would allow for individual-level delivery and addresses the time demands typical of this age group. However, recent efforts within the EARLY Trials, a consortium of studies focused specifically on weight control during young adulthood, inspire some caution with respect to using technology alone to promote clinically significant weight losses in this population. In fact, both of the EARLY Trials that used technology platforms alone to deliver weight loss programs—CITY and SMART—yielded very modest outcomes and the authors themselves cautioned against overreliance on technology.^21–22 The intervention in the SMART Trial was delivered using a social and mobile technology platform and was associated with small reductions in weight relative to an information only control group at 6 months (−1.1kg vs. +0.3lg, p=.011).²¹ CITY investigators compared a human coaching arm to a smartphone-based mode and a control arm; within the smartphone condition they observed similarly modest reductions in weight at 6-months (−.87kg), which was not significantly different from control (−1.14lg)—the personal coaching arm (−3.07kg) outperformed both the control (p=.003) and smartphone (p<.001) conditions.²² Thus, an important question remains how to reach this population and deliver a scalable weight loss intervention that produces clinically meaningful outcomes.

Incorporating small financial incentives within the context of a technology-driven platform may be one promising avenue to pursue to achieve both reach and scalability without sacrificing clinical impact. Financial incentives programs have been shown to produce significant weight losses, but many incentives paradigms use large pay-outs and are plagued by disproportionate rates of regain.^23–24 Recent data suggest that an incentive schedule that is more modest and linked to process behaviors (i.e., self-monitoring of diet, weight, activity) as well as weight loss outcomes, can promote clinically meaningful weight losses using an Internet-based platform.²⁵ Of note, this incentives paradigm also appeared to mitigate differential regain compared with an identical treatment program without incentives²⁵ and has been shown to be more cost-effective than a web only arm and interventions involving in-person treatment.²⁵ Given data indicating that money is a central motivator for young adults, and their preference for a technology delivered program,^18,19 it is of interest how young adults might perform in this type of program. To that end, we conducted a secondary analysis of young adults’ performance in a trial wherein participants were assigned to an internet-based behavioral weight loss program or the same program plus the provision of small financial incentives linked to self-monitoring behaviors as well as weight loss. Given prior research showing that younger adults tend to fare far worse in weight management interventions relative to older adults we also compared young adults’ performance in these interventions to that of older adults. Our overall goal was to explore whether this type of technology-driven program which incorporates modest financial incentives might have utility to bolster engagement, weight loss and retention among high-risk young adults.

Methods

Participants

Eligible participants had to be between 18–70 years of age, with a body mass index ≥25 kg/m² and willing to be randomly assigned to any of the study arms. Exclusion criteria included physical or mental health conditions that pose safety concerns (e.g., uncontrolled heart condition, eating disorder), current or planned pregnancy (within study period), non-English speaking, and unreliable Internet access. Recruitment took place via a community-based campaign and did not target young adults specifically. Additional details and a participant flow diagram were published in the main outcomes paper for the trial.²⁵

Intervention Conditions

Participants were randomized to one of the 12-week weight loss interventions; each including personalized calorie and fat goals based on starting weight (1200–1800 kcals and <30% calories from fat), as well as gradually increasing physical activity goals based on starting level, with an end goal of achieving at least 250 minutes per week of moderate-to-vigorous intensity physical activity. Those randomly assigned to the Internet BWL (IBWL) condition received a single in-person group “weight loss 101” session wherein participants are introduced to the concept of energy balance and received an overview of evidence-based behavioral strategies (e.g., self-monitoring, goal setting) to help promote adherence to diet and physical activity goals, as well as an orientation to the study website. During the 12-week online program, participants received weekly multimedia lessons based on the Diabetes Prevention Program,²⁶ a self-monitoring platform to submit their weight, calorie, and physical activity information, and weekly automated personalized feedback based on their progress.

Participants assigned to the Internet + Incentives (IBWL+$) condition received the above-described program in addition to an incentives paradigm rooted in learning theory²⁷ and principles of behavioral economics.²⁸ Specifically, participants received small financial incentives ranging from $1 to $10 for each week that they reported at least 5 days of weight, calorie, and physical activity information into the study website. The incentive payout schedule was prespecified by investigators and designed to promote early engagement and shape behavior; payouts were higher in earlier weeks and then varied thereafter. The schedule was unknown to participants, and the maximum amount that they could earn during the entire 12-week program was $45. Loss and regret aversion messaging was incorporated into the automated feedback messages to amplify the effects of the incentives. Participants were also able to view their “bank” on the study homepage, which displayed both the previous week’s earnings as well as total earnings on the study website. In addition to incentivizing the use of the self-monitoring platform, those who lost 5–10% of their initial body weight at program end were entered into a $50 raffle; those who lost ≥10% were entered into a $100 raffle. Ten winners were chosen from each raffle. All payouts were delivered immediately following the post-treatment assessment.

Assessments

Assessments were conducted in person at baseline and post-treatment (13 weeks) unless otherwise noted. All visits were conducted by trained research staff masked to study assignment.

Height and weight.

Height was measured at baseline only to the nearest millimeter using a wall-mounted stadiometer. Weight was measured to the nearest 0.1kg in light clothes without shoes using a calibrated digital scale at baseline and post-treatment. Percent weight loss was calculated as: ((post-treatment weight-baseline weight)/baseline weight) * 100.

Engagement:

Program engagement was tracked throughout the 12-week intervention using the following metrics: weekly website log-ins, days of self-weighing, days of self-monitoring calories, days of self-monitoring fat grams, and days of self-monitoring physical activity.

Retention:

Retention was dichotomized (yes/no) based on attendance at the post-treatment assessment visit.

Statistical Analyses

Statistical analyses were conducted using IBM SPSS Statistics for Windows, version 24.0.²⁹ We first conducted descriptive analyses to assess demographic characteristics of the sample at baseline and test for potential group differences between young and older adults. In order to test between-group differences with respect to continuous variables (weight change and engagement metrics), ANOVAs were employed. Chi-squared analyses were used to assess between-group differences with respect to categorical outcomes (retention). Primary weight change analyses were conducted using intent-to-treat methodology with baseline weight carried forward (BLCF) for participants who were lost to follow-up. BLCF was used instead of multiple imputation given this was a short-term study with only a single follow-up time point. Secondary weight change analyses were conducted using only the subsample of participants retained for post-treatment follow-up assessment (i.e., completers). Percent weight loss was the outcome variable of interest, which is calculated as: ((post-treatment weight-baseline weight)/baseline weight) * 100.

Results

Participant Characteristics

The overall sample (N=180) was 18.4% male and 88.3% non-Hispanic White, with mean age = 45.7±10.2, and mean BMI = 33.2±6.0. Young adult enrollment in the current trial was higher than that seen in typical adult behavioral weight loss trials, with those aged 18–35 representing 15.6% of participants. However, the number of young adults enrolled was still quite small (n=28). Demographic characteristics are displayed in Table 1. There were no significant differences with respect to gender or race between young and older adults, but there were significant differences for BMI such that young adults had higher BMI at baseline relative to older adults. There were no differences with respect to demographic characteristics or baseline BMI for those young adults assigned to IBWL vs. IBWL+$ (p=.72 for gender; p=.40 for percent non-Hispanic White; p=.73 for baseline BMI).

Table 1.

Demographic Characteristics of Young vs. Older Adults

	IBWL (N=91)			IBWL + $ (N=89)			Overall
	Young Adults (N=16)	Older Adults (N=75)	p value	Young Adults (N=12)	Older Adults (N=77)	p value	Young Adults (n=28)	Older Adults (n=152)	p value
Gender (% Female)	83%	88%	.64	92%	78%	.27	89%	80%	.42
Race/Ethnicity (% non-Hispanic White)	88%	91%	.67	75%	88%	.20	82%	89%	.33
Baseline Mean BMI (SD)	35.1 (6.9)	32.5 (5.0)	.08	36.1 (8.6)	33.1 (6.0)	.14	35.5 (7.5)	32.8 (5.6)	.03*

Young Adults’ Performance in IBWL vs. IBWL + Incentives

Retention of young adults randomized to IBWL vs. IBWL+$ did not differ significantly (see Table 2). Similarly, no significant differences were observed for engagement, although all metrics favored IBWL+$ with small-to-medium effect sizes (see Table 2). Intent-to-Treat (ITT) analyses revealed that young adults in the IBWL+$ condition lost −5.4±5.7% and those in IBWL alone lost −2.8±5.2% (p=.23, partial eta² = .06). A significantly higher proportion of young adult participants in the IBWL+$ condition achieved a 10% weight loss (42% vs. 6%, p=.02). Parallel analyses of weight outcomes in completers only were consistent (data not shown).

Table 2.

Engagement and Weight Loss (ITT)

	IBWL (n=91)			IBWL+Incentives (n=89)			p valueb	Partial eta2b
	Young Adults (n=16)	Older Adults (n=75)	p valuea	Young Adults (n=12)	Older Adults (n=77)	p valuea
Total Login Weeks	8.3 (3.4)	9.2 (3.3)	.32	9.9 (2.4)	10.7 (2.8)	.41	.21	.06
Total Lesson Views	4.1 (2.9)	6.1 (3.5)	.04*	6.0 (3.7)	7.3 (4.1)	.31	.13	.09
Total Days Weight Reported	49.1 (28.4)	60.2 (26.2)	.14	59.3 (25.0)	73.6 (20.3)	.03*	.34	.04
Total Days Calories Reported	46.8 (27.3)	59.1 (26.7)	.10	55.6 (24.6)	72.3 (20.8)	.01*	.39	.03
Total Days Fat Grams Reported	46.1 (27.1)	58.8 (26.7)	.09	55.4 (24.6)	72.1 (21.0)	.01*	.36	.03
Total Days Exercise Reported	25.5 (23.4)	42.5 (24.8)	.02*	40.6 (25.1)	54.6 (21.7)	.045*	.14	.08
Retention at 12-weeks	88%	−4.2+4.8%	.29	92%	−6.3+4.8%	.57	.72
ITT Weight Loss	−2.8±5.2%	9.2 (3.3)	.32	−5.4±5.7%	10.7 (2.8)	.41	.23	.06
Proportion Achieving 10% Weight Loss	6%	12%	.50	42%	21%	.113	.02*

^aComparison between young adults and older adults within condition

^bComparison between young adults between conditions

Comparing Young Adults vs. Older Adults Performance

Of note, retention of both young adults and older adults was excellent in this trial and did not differ by age (92% vs. 96%). Within the IBWL+$ condition, young adults demonstrated less frequent self-monitoring of weight, calories, fat grams and physical activity (see Table 2). However, there were no significant differences between young adults and older adults on lesson views or log-in frequency. Moreover, within the IBWL+$ arm, ITT analyses reveal clinically significant weight losses for both young and older adults, with no significant differences between age groups (5.4+5.7% vs. 6.3+4.8%, p=.57, see Table 2 and Figure 1). A higher proportion of young adults achieved a 10% weight loss, but this did not reach statistical significance (42% vs. 21%, p=.11). Of note, this is in contrast to weight loss outcomes observed within the IBWL arm, wherein young adults did not on average achieve clinically significant weight losses (2.8±5.2%), although differences between young adults and their older counterparts were not statistically significant in this condition either (2.8±5.2% vs. 4.2+4.8%, p=.29; see Table 2).

Figure 1.

Percent Weight Loss Outcomes (ITT) by Age Group within IBWL and IBWL+$

Discussion

Enrollment of young adults in this internet-based weight loss program was better than in traditional in-person treatment programs, as evidenced by 15.6% of the current sample consisting of young adults, which is double documented enrollment rates within in-person behavioral weight loss trials.¹⁵ Of note, recruitment materials for the present trial stated explicitly that the program was Web-based and that only occasional in-person visits to the research center would be required, which likely contributed to improved enrollment. Additionally, the observed disparities between young adults and older adults on engagement, retention and weight loss¹⁵ appear to be at least partially mitigated by the use of a primarily individual-level, technology-delivered behavioral weight loss program. This is consistent with formative data in young adults^18,19,30 which suggests a strong preference for a program that is asynchronous and available when it is convenient for them given life stressors and time demands. While young adult enrollment was improved by offering a technology-driven program, and disparities in weight losses between young and older participants were less than observed in in-person programs, it is still important to note that the standard IBWL condition yielded worse outcomes for young adults relative to the IBWL+$ arm. Indeed, weight losses achieved by young adults in the IBWL+$ arm were nearly twice those achieved in the IBWL alone condition. Thus, while technology is an important tool and can improve reach with this high-risk population, these data suggest that modest financial incentives have the potential to greatly improve outcomes in a scalable program for this age group.

There is a strong precedent for employer-funded incentive-based health behavior change initiatives in the United States. Approximately 33% of large employers in the use financial incentives to increase employee participation in wellness programs,³¹ and nearly 50% of surveyed medium-to-large employers reported that they plan to implement behavioral economic techniques in order to assist employees in improving health and reduce healthcare costs.³² However, many companies use large monetary rewards (in excess of $300 per employee)³³ and require in-person visits for their weight loss programs.^34–36 Given that the incentive paradigm described in the current analysis has been found to be more cost-effective than IBWL alone,²⁵ there is potential for workplaces to optimize their financial investment while broadening the reach of their wellness programs and improving the health of its youngest employees.

Of note, contrary to findings in standard in-person adult programs, those young adults enrolled in the IBWL+$ condition fared just as well as older adults with respect to retention, many engagement metrics, as well as weight loss. In fact, retention rates were comparable for young adults and older adults, as were weight losses. Program engagement was also much better for young adults in this program, although self-monitoring of weight, calories, fat and physical activity data was still lower for young adults relative to older adults. This is perhaps not unexpected, given the well-documented challenge of promoting engagement and self-monitoring among young adults.^37–38 It is of interest that self-monitoring of weight and diet in IBWL was not significantly different between young adults and older adults—data suggest that the older adults experienced more of a “bump” in self-monitoring behaviors if they were assigned to IBWL+$ as opposed to IBWL, whereas the improvements in self-monitoring were more modest for young adults. It is possible that the weight loss raffle was more motivating for young adults than the self-monitoring incentives, thereby driving the larger proportion of YAs who achieved a 10% weight loss in this arm. This underscores the need to test this paradigm over longer-term follow up to determine what happens to both self-monitoring behaviors and weight when the incentives are removed. Moreover, future work is clearly needed in order to identify ways to further enhance engagement in self-monitoring behaviors among young adults given the well-established association between self-monitoring and weight loss.^14,39

These findings are in stark contrast to previous findings in which disparities were observed between young and older adults’ outcomes in BWL¹⁵ and underscore the potential for this type of incentives paradigm coupled with a technology-driven program to improve outcomes for this population. When considering possible reasons for the improved performance of young adults in this program, it is important to note the aspects of the program that aligned with key formative findings – namely, the primarily individual level delivery, use of technology with minimal in-person commitment, and using money to facilitate engagement and weight loss.¹⁸ Of note, young adults in the IBWL alone arm experienced better outcomes than in recent reports of technology-driven programs among young adults,^21,22 which may be due to the initial in-person session offered in this trial. The use of evidence-based content adapted from DPP coupled with self-monitoring and automated personalized feedback on progress might also account for these better weight losses. Future studies might seek to isolate and understand the benefit of a the in-person session, given this does limit the potential for scaling the intervention—it might also be interesting to consider delivering this initial session via video which would still allow for the same content and a more interactive format, but would lend itself to remote delivery.

Despite the relatively promising outcomes observed in IBWL alone relative to previous technology-driven trials in young adults, IBWL still did not produce clinically meaningful outcomes for young adults. Thus, the inclusion of financial incentives coupled with technology-driven delivery might be critical—this approach has potential to promote better weight loss outcomes in this vulnerable population. Longer-term follow up should clearly be a priority in future studies, however, given the tendency for steep rates of weight regain when financial incentives are removed.^23–24 Of note, the incentive structure used in this trial has been shown to produce comparable rates of weight loss maintenance over 12 months relative to IBWL alone, ²⁵ but whether or not similar findings would be observed among young adults once incentives are removed remains unknown. Indeed, it will be important to consider how to enhance long-term effects of this paradigm in young adults specifically.

Future studies should also seek to determine the impact of this type of a paradigm on intrinsic and extrinsic motivation processes, particularly among young adults, for whom motivation was identified as a central barrier to program participation in formative work.¹⁸ For example, participants were reminded of the weight loss raffles periodically throughout the trial, which could have increased motivation for weight loss, but could also undermine motivation for the process / self-monitoring behaviors and / or contribute to weight regain once the incentives are removed. Future studies could potentially amplify observed effects by having “weight loss challenges” periodically throughout the intervention, which could be tied to additional incentives. Although these might improve weight loss outcomes in the short-term, the long-term impact on weight and motivation would be critical considerations in future work. Additionally, an understanding of the potential moderators of treatment response among young adults would also be important to explore in future work, particularly given the tremendous heterogeneity that exists in this population in terms of financial strain, mood symptoms and stress, as well as baseline weight and health behaviors. However, based on these preliminary findings, it appears that technology-delivered behavioral weight loss programs that incorporate modest financial incentives has the potential to improve weight loss outcomes among high-risk young adults.

The present findings should be considered in light of some limitations. Perhaps most importantly, given this is a secondary data analysis, the numbers of young adults are quite small and as such, we did not have power for a 2×2 analytical approach and the effects observed are preliminary in nature—thus, findings should be interpreted with caution. Future work should test this paradigm in a fully powered trial specifically targeting young adults and include longer-term follow up to examine maintenance effects. Further, this sample was rather homogeneous with respect to race, ethnicity and education and as such it is unclear whether findings will generalize to more diverse samples of young adults. This is a critical question to address in future trials given the disproportionate rates of overweight and obesity among young adults from racial / ethnic minority backgrounds.⁴⁰ The approach to handling of missing data was not ideal, and alternative approaches such as multiple imputation or maximum likelihood would be preferable in future trials with longer-term follow up. This concern is somewhat lessened by the strong retention rates observed, which did not differ for young adults and older adults. Limitations withstanding, there are some notable strengths. To our knowledge, this is the first study to examine young adults’ performance in a technology-driven behavioral weight loss trial using small financial incentives. The randomized controlled design, coupled with objective measures of weight and engagement data pulled directly from the web platform are additional strengths. In sum, findings suggest that technology-delivered behavioral weight loss programs that incorporate modest financial incentives hold promise for improving engagement and weight loss in young adults.

What is already known about the subject?:

• Young adults ages 18–35 enrolled in traditional behavioral weight loss programs fare worse than their older adult counterparts with respect to program engagement, retention, and weight loss outcomes.

• Formative work with this population has identified money and technology as important considerations when designing a weight loss intervention that would appeal to young adults, but previous technology-driven trials in young adults have reported modest outcomes.

• Incentive paradigms have demonstrated promise with respect to bolstering weight loss outcomes in adults, with those linked to both process and outcome faring better in terms of weight loss maintenance.

What does this study add?:

• An incentives paradigm has never been tested within a technology-driven program targeting young adults.

• In order to determine the potential viability of this approach, we evaluate the performance of young adults enrolled in an Internet-based behavioral weight loss program wherein participants in one arm received modest financial incentives linked to self-monitoring and weight loss while the other arm received no financial incentives.

• In addition to comparing young adults’ performance between arms, we also compare young adults to older adults with respect to engagement, retention, and weight loss in order to inform future efforts with this vulnerable population.

How might your results change the direction of research or the focus of clinical practice?:

These findings suggest that technology-driven behavioral weight loss programs are capable of producing clinically meaningful weight losses among young adults, exceeding previously published reports. Moreover, data suggest that adding financial incentives holds promise for improving weight losses among this population. Future trials should test this paradigm in a larger trial over longer follow up and among a more diverse sample of young adults.